text:
Editors

Ralph Bangs and Larry E. Davis

Race and Social ProblemsRestructuring Inequality

Editors

Ralph Bangs

Center on Race and Social Problems, University of Pittsburgh, Pittsburgh, PA, USA

Larry E. Davis

Center on Race and Social Problems, University of Pittsburgh, Pittsburgh, PA, USA

ISBN 978-1-4939-0862-2e-ISBN 978-1-4939-0863-9

DOI 10.1007/978-1-4939-0863-9

Springer New York Heidelberg Dordrecht London

Library of Congress Control Number: 2014947272

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Contents

Part I Introduction

1 America's Racial Realities 3

Ralph Bangs and Larry E. Davis

2 Do Current Race and Ethnicity Concepts Reflect a Changing America? 15

Karen Humes and Howard Hogan

3 Intergroup Dialogue: Race Still Matters 39

Patricia Gurin, Nicholas Sorensen, Gretchen E. Lopez and Biren (Ratnesh) A. Nagda

Part II Economics

4 Wealth Building in Communities of Color 63

Trina R. Williams Shanks, Stephanie Clintonia Boddie and Robert Wynn

5 Recent Employment Trends Among Black Men and Their Policy Implications 79

Harry J. Holzer

6 Challenging the Conventional Understanding of American Poverty 93

Mark R. Rank

Part III Education

7 Interventions to Address Racial/Ethnic Disparities in School Discipline: Can Systems Reform Be Race-Neutral? 107

Russell J. Skiba

8 Exploring, Understanding, and Closing the Achievement Gap(s): Efforts from Harlem to Homewood 125

John M. Wallace

9 High School Quality and Race Differences in College Achievement 137

Jason M. Fletcher and Marta Tienda

Part IV Special Topics

10 Family Matters: Strengthening the Fabric of Minority Families 163

Ruth G. McRoy and Amy J. Griffin

11 Aging and Health in Mexican Americans: Selected Findings from the Hispanic EPESE 171

Kyriakos S. Markides, Raphael Samper-Ternent and Soham Al Snih

12 Racial Disproportionality in Prison 187

Alfred Blumstein

13 Resetting Race 195

David Kennedy

Part V Health and Mental Health

14 Disparities in Health Care for Minorities: An Ecological Perspective 209

Jeannette E. South-Paul

15 Considering the Role of Social Determinants of Health in Black–White Breast Cancer Disparities 227

Darrell L. Hudson and Sarah Gehlert

16 Understanding and Addressing Racial/Ethnic Disparities in Obesity 247

Monica L. Baskin

17 Mental Health Indicators for African American Males 267

Oscar A. Barbarin and Emma Sterrett

Index279

Contributors

Ralph Bangs

Center on Race and Social Problems, University of Pittsburgh, Pittsburgh, PA, USA

Oscar A. Barbarin

Department of Psychology, Tulane University, New Orleans, LA, USA

Monica L. Baskin

University of Alabama at Birmingham, Birmingham, AL, USA

Alfred Blumstein

H. John Heinz III School of Public Policy and Management, Carnegie-Mellon University, Pittsburgh, PA, USA

Stephanie Clintonia Boddie

University of Pittsburgh, Pittsburgh, PA, USA

Larry E. Davis

Center on Race and Social Problems, University of Pittsburgh, Pittsburgh, PA, USA

Jason M. Fletcher

Yale University, New Haven, CT, USA

Sarah Gehlert

Brown School of Social Work, Washington University in St. Louis, St. Louis, MO, USA

Amy J. Griffin

Boston College, Chestnut Hill, MA, USA

Patricia Gurin

University of Michigan, Ann Arbor, MI, USA

Howard Hogan

US Census Bureau, Washington, DC, USA

Harry J. Holzer

McCourt School of Public Policy, Georgetown University, Washington, DC, USA

Darrell L. Hudson

Washington University in St. Louis, St. Louis, MO, USA

Karen Humes

US Census Bureau, Washington, DC, USA

David Kennedy

John Jay College of Criminal Justice, New York, NY, USA

Gretchen Lopez

Syracuse University, Syracuse, NY, USA

Kyriakos S. Markides

Department of Preventive Medicine & Community Health, University of Texas Medical Branch, Galveston, TX, USA

Ruth G. McRoy

Graduate School of Social Work, Boston College, Chestnut Hill, MA, USA

Biren (Ratnesh) A. Nagda

University of Washington, Seattle, WA, USA

Mark R. Rank

George Warren Brown School of Social Work, Washington University, St. Louis, MO, USA

Raphael Samper-Ternent

Pontificia Universidad Javeriana, Bogota, Colombia

Trina R. Williams Shanks

School of Social Work, University of Michigan, Ann Arbor, MI, USA

Russell J. Skiba

Center for Evaluation and Education Policy, Indiana University, Bloomington, IN, USA

Soham Al Snih

Department of Preventive Medicine & Community Health, University of Texas Medical Branch, Galveston, TX, USA

Nicholas Sorensen

American Institutes for Research, Washington, DC, USA

Jeannette E. South-Paul

Department of Family Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

Emma Sterrett

University of Louisville, Louisville, KY, USA

Marta Tienda

Office of Population Research, Princeton University, Princeton, NJ, USA

John M. Wallace

School of Social Work, University of Pittsburgh, Pittsburgh, PA, USA

Robert Wynn

Akamai LLC, Madison, WI, USA

# Part I
Introduction
© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_1

# 1. America's Racial Realities

Ralph Bangs1  and Larry E. Davis1

(1)

Center on Race and Social Problems, University of Pittsburgh, Pittsburgh, PA 15260, USA

Ralph Bangs (Corresponding author)

Email: rbangs@pitt.edu

Larry E. Davis

Email: ledavis@pitt.edu

## Introduction

There is no doubt that America was a racist society in the past. Whites enslaved Blacks from the early 1600s to the 1860s. Jim Crow laws were used for another 100 years to oppress Blacks. Violence against Blacks was widespread until at least the mid-1900s. Whites took the land and other property of Native Americans over several centuries, Mexicans in the early 1800s, and Japanese Americans during World War II. Major government programs from the 1930s through the 1950s, such as social security, unemployment insurance, low-cost loans for new suburban housing, and college aid for GIs, benefitted Whites but largely excluded non-Whites (Katznelson 2005). Most Whites believed that Blacks, Asians, Hispanics, and many other groups were inferior races in relation to intelligence, work ethic, morals, and culture (Feagin 2010).

There is also no doubt that racial progress has been made. Today, slavery and racial discrimination are no longer legal. Explicit racist statements are usually not acceptable in public. Lynchings, race riots, mob attacks, and other forms of racial violence have been stopped or greatly reduced. Civil rights laws and government programs helped to expand the middle class among non-Whites. A Black person has been elected president of the United States.

Many White Americans have interpreted this progress to mean that racial disparities and racial discrimination have been eliminated (Bonilla-Silva 2013; Bobo 2011). They also believe that they are not racist and that racism is limited to isolated acts by a few racist individuals. In fact, Whites now often believe that the nation's only race problems are excessive government help for non-Whites and non-Whites falsely blaming White racism for problems (Bonilla-Silva 2013). On the other hand, many Blacks report in surveys that they continue to experience unfair treatment by Whites in nearly all aspects of life, such as housing, employment, education, and the criminal justice system (Feagin 2010).

Besides some improvement in racial conditions, another major trend is the rapidly growing minority population. The nation was 12 % minority in 1940, 17 % in 1970, and 37 % in 2012. By 2043 minorities are expected to be the majority population (U.S. Census Bureau 2012).

Given the great differences in views about racial conditions and given the growing minority population, the Center on Race and Social Problems at the University of Pittsburgh held a national conference in 2010 to examine racial problems, causes, and solutions. Many of the nation's experts gave presentations and about 1,200 people participated in discussions on racial issues. This Handbook contains updated information for 16 of the presentations at the conference.

This chapter provides an overview to the current state of race in America. First we identify racial disparities in six fundamental areas (health, family structure, residential segregation, economics, education, and criminal justice) for the four largest racial/ethnic groups (Whites, Blacks, Hispanics, and Asians, where data are available). Second, we discuss major explanations for the continuing pervasiveness of racial disparities. This chapter ends with a description of the substantive chapters in this book.

## Current Racial Disparities

### Health

Large racial disparities exist in health conditions. For people born in 2010, life expectancy is 86.5 years for Asian Americans, 82.8 for Latinos, 78.9 for Whites, and 74.6 for Blacks (Kaiser Family Foundation 2013). Differences in life expectancy are even greater when taking into account both race and gender. Murray et al. (2006) found that Asian females born in the United States in 2001 had a life expectancy that was 20.7 years longer than Black males living in counties with a large number of Blacks and high homicide rates.

In addition, the infant mortality rate in 2009 was 12.4 per 1,000 live births for Blacks, 5.3 for Whites, 5.3 for Hispanics, and 4.4 for Asians (CDC 2013b). In 2009–2010 Black women (58 %) and Mexican American women (45 %) had higher rates of obesity than that of White women (32 %) (Fryar et al. 2012). In 2007–2009 12.6 % of Black, 11.8 % of Hispanic, 8.4 % of Asian American, and 7.1 % of White adults had diabetes (CDC 2012). Blacks were 12 % of population but accounted for 47 % of new HIV cases in 2011 (CDC 2013a).

Health disparities are caused by: (1) social factors, especially low education, racial segregation, low social support, and poverty (Galea et al. 2011); (2) individual behaviors, especially smoking, poor diet and physical inactivity, and alcohol consumption (Mokdad et al. 2004); and (3) healthcare factors, especially lack of access to primary care and poor quality of care (AHRQ 2013). Health disparities lead to shorter and poorer quality lives, less employment and income, and lower education levels.

### Family Structure

Large racial differences in family structure exist in America. In 2013, 85 % of Asian, 77 % of White, 65 % of Hispanic, and 39 % of Black children age 0–17 lived with two parents. Further, 51 % of Black, 28 % of Hispanic, 15 % of White, and 11 % of Asian children lived with their mother only (U.S. Census Bureau 2013b). Family structure can be greatly affected by job opportunities, incarceration, and education.

Family structure is important because it can have large effects on poverty and child development. Only 13 % of children living with two parents are in poverty compared to 45 % of children in single-mother families (U.S. Census Bureau 2013a). Further, "children living with two married adults (biological or adoptive parents) have, in general, better health, greater access to health care, and fewer emotional or behavioral problems than children living in other types of families" (Child Trends 2013).

### Residential Segregation

The dissimilarity index has often been used to measure residential segregation. The index represents the percent of each racial group that would have to move so that each census track or block has the same percentage of a group as the whole city or metro area. A measure of 60 or more is considered highly segregated.

The Black-White dissimilarity index for the United States declined from 64 in 2000 to 59 in 2010 (Logan and Stults 2011). Milwaukee (81.0) had the highest level among central cities, and Chicago (83.8) had the highest level among metro areas (McDade and Turner 2012).

Another valuable measure is to compare the racial composition of the typical neighborhood for one race to that race's share of the US population. The Brookings Institution (Frey 2010) found that in 2005–2009 the typical White person lived in a census tract (5,000–10,000 people) that was 79 % White, the typical Black person lived in a tract that was 46 % Black, and the typical Hispanic lived in a tract that was 45 % Hispanic. In 2010 the US population was 64 % White, 12 % Black, and 16 % Hispanic.

The problem is not just racial segregation in housing. Rather, this segregation for Blacks is accompanied by poverty concentration and the economic, criminal, and educational problems that come with it. Racial segregation is caused by discriminatory housing practices, income disparities, and unfriendly and exclusionary treatment of minorities by police and community residents.

### Economics

Large differences in economic conditions by race and ethnicity continue to exist. Some of the current disparities are:

  * The labor force participation rate for men in 2012 was 74 % for Hispanics, 71 % for Asians, 67 % for Whites, and 62 % for Blacks (USBLS 2013)

  * The unemployment rate in 2013 was 13 % for Blacks, 9 % for Hispanics, and 7 % for Whites (EPI 2013b)

  * The underemployment1 rate in 2013 was 22 % for Blacks, 18 % for Hispanics, and 11 % for Whites (EPI 2013a)

  * Annual household income in 2012 was $68,636 for Asians, $57,009 for Whites, $39,005 for Hispanics, and $33,321 for Blacks (Fry 2013)

  * Black male earnings for ages 18–64 declined from 52 % of White male earnings in 1980 to 28 % in 2008 (Pettit 2012)

  * Poverty rates in 2012 were 26 % for Blacks, 23 % for Hispanics, 12 % for Whites, and 12 % for Asians (Macartney et al. 2013)

  * 65 % of Black, 65 % of Hispanic, 32 % of Asian, and 31 % of White children live in low-income families (<200 % of poverty2) (National Center for Children in Poverty 2013)

  * 66 % of Black and 6 % of White children grow up in neighborhoods with 20 % or more poverty (Sharkey 2013)

  * The share of workers earning poverty wages in 2011 was 43 % for Hispanics, 36 % for Blacks, and 23 % for Whites (EPI 2012)

  * Homeownership rates in 2011 were 74 % for Whites, 58 % for Asians, 47 % for Hispanics, and 45 % for Blacks (McArdel et al. 2012)

  * Median household net worth in 2011 was $100,500 for Whites, $89,339 for Asians, $7,683 for Hispanics, and $6,314 for Blacks (Ishimatsu 2013)

  * Among single women age 18–64 in 2007, Whites had median wealth of $41,500, Hispanics had $120, and Blacks had $100, and roughly 50 % of Black and Hispanic women had zero or negative net worth (Insight Center for Community Economic Development 2010)

Racial disparities in economic conditions are caused by many factors, including differential access to quality education and transportation, employment discrimination, residential segregation, home buying discrimination, and declining home values in inner cities. A recent study found that wealth disparities are primarily caused by years of home ownership, income, unemployment, college education, and financial transfers from families and friends (Shapiro et al. 2013). Economic conditions greatly affect family structure, health, housing, crime, education, and other important aspects of life.

### Education

Large racial and ethnic differences in education continue to exist. According to the U.S. Department of Education (2014):

  * 82 % of Black, 80 % of Hispanic, 54 % of White, and 47 % of Asian fourth grade students were not proficient in reading

  * 82 % of Black, 74 % of Hispanic, 46 % of White, and 34 % of Asian fourth grade students were not proficient in math

  * 83 % of Black, 78 % of Hispanic, 54 % of White, and 46 % of Asian eighth grade students were not proficient in reading

  * 86 % of Black, 79 % of Hispanic, 55 % of White, and 37 % of Asian eighth grade students were not proficient in math

Further, Blacks are 16 % of students in grades 6–8 but are 42 % of those held back a year in those grades (U.S. Department of Education 2012). Black students make up 15 % of enrollment in schools but are 35 % of students who get suspended, 44 % of those suspended twice, and 36 % of students expelled (U.S. Department of Education 2014).

High school graduation in 2009–2010 for students entering ninth grade 4 years earlier was 94 % for Asians, 83 % for Whites, 71 % for Hispanics, and 66 % for Blacks (Stillwell and Sable 2013). In relation to males, 78 % of Whites, 58 % of Hispanics, and 52 % of Blacks graduated 4 years after entering ninth grade (Schott Foundation for Public Education 2012). Among males age 20–34 in 2008, 21 % of Blacks and 10 % of Whites were high school dropouts (Pettit 2012).

Regarding college enrollment, 65 % of Asian, 50 % of White, 44 % of Hispanic, and 39 % of Black 18–19 year-olds were postsecondary students in 2012 (U.S. Census Bureau 2013c). Among 25–29 year-olds in 2012, 43 % of White women, 35 % of White men, 24 % of Black women, 16 % of Black men, 17 % of Hispanic women, and 11 % of Hispanic men had a bachelor's degree or higher (Baum et al. 2013).

Racial education gaps are caused by disadvantaged home environments, lack of access to quality early education, and the concentration of minority students in high-poverty schools with poorer quality teachers, lower quality curriculum, and fewer resources. For example, in 2008–2009 in the 100 largest metro areas 99 % of Black, 96 % of Hispanic, 6 % of Asian, and 3 % of White elementary school students attended schools that had 50 % or more low-income students (McArdel et al. 2010). Racial education gaps result in lower employment, income and wealth; poorer health; and higher levels of crime and incarceration.

### Criminal Justice

Immense racial disparities exist in the criminal justice system. For example, Blacks are 12 % of population and 14 % of monthly drug users but are 37 % of those arrested on drug charges, 59 % of those convicted, and 74 % of those sentenced to prison for drugs (Webb 2009). White, Black, and Hispanic drivers are stopped at similar rates nationwide, but Black drivers are three times as likely to be searched during a stop as White drivers and twice as likely as Hispanic drivers (Sentencing Project 2013).

Presently, there are 2.3 million people incarcerated in America. Of these, 39 % are Black, 34 % are White, 21 % are Hispanic, and 6 % are other races (U.S. Bureau of Justice Statistics 2010). In 2010 the Black male incarceration rate was about seven times and the Hispanic rate was about three times the White rate (U.S. Bureau of Justice Statistics 2011).

Further, the lifetime likelihood of imprisonment is 1 in 3 for Black men, 1 in 6 for Latino men, and 1 in 17 for White men (Sentencing Project 2013). In the state of Wisconsin it is predicted that 50 % of the Black men will have been incarcerated by the time they reach age 30 (Causey 2013).

Criminal justice disparities are caused by poor education, lack of employment, and discrimination in criminal justice policies and practices. The high incarceration rates of Black men have destroyed many Black communities since few men are available to be husbands, breadwinners, parents to their kids, and role models.

## Explaining the Continuing Pervasiveness of Racial Disparities

The above overview showed that racial disparities are large and widespread and identified some specific causes of these problems. This section describes the general factors that produce these problems, particularly the Black-White disparities.

We identified five major causes of current disparities. First, past racist practices, such as slavery, Jim Crow, and New Deal policies, provided great financial benefits to Whites and largely excluded Blacks. Past benefits led to increased education, employment, and business opportunities as well as wealth for subsequent generations of Whites (Feagin 2010).

Second, past discrimination and violence by Whites caused many Blacks since at least the 1940s to live in racially segregated, urban neighborhoods in both the North and the South. These neighborhoods became very poor as manufacturing jobs were lost and Blacks lacked access to the growing numbers of jobs in services and in suburban areas. This led to increased welfare dependency, single-mother families, and crime as well as lower quality education (Massey 2008b). These disadvantages were passed on to future generations (Sharkey 2013).

Third, discrimination is presently common for Black home-buyers in "virtually all phases of a housing market transaction: when they contact agents by phone, when they meet with agents in person, when they are shown units in different neighborhoods, when they apply for loans, when they are evaluated for credit, when they receive private mortgage insurance, and when their interest rate and repayments periods are set" (Massey 2008a). This discrimination keeps many Blacks at different income levels from escaping Black ghettos (Pattillo 2013), which tend to have high crime, poor schools, and declining housing values (Massey 2008a).

Fourth, current discrimination in low and high-skill employment markets occurs at high rates. Across the stages of the hiring process Black job-seekers receive less consideration than equally qualified Whites about half of the time (Massey 2008a). This results in higher unemployment and lower earnings. Indirectly, this increases poverty, single-female headed families, and crime.

Finally, the mass arrest and incarceration of Black males is continuing (Brame et al. 2014; Alexander 2010). This is due to unfair criminal justice practices, especially targeting Black males in relation to who gets stopped, searched and arrested for drug crimes. The unfair processes also involve lack of legal representation, excessive use of plea bargaining, and mandatory sentencing (Alexander 2010). The high rates of arrest and incarceration of Black males result in greater school failure, unemployment and underemployment, single-mother families, poverty, HIV infection, and disadvantaged children (Massey 2008a; Alexander 2010; Brame et al. 2014; Western 2006).

## The Remaining Chapters

The remaining 16 chapters of this book are grouped into five subject areas: introduction, economics, education, special topics, and health/mental health. The first section contains this chapter plus two others. In Chap.  Humes and Hogan show that current race categories do not capture very well the identity of many Hispanics and can be problematic for Middle Eastern people, Afro-Caribbeans, and people who identify as more than one race. The Census Bureau has been testing alternate racial categories to address these issues.

In Chap.  Gurin et al. discuss the different views among White and non-White youth about the causes of racial/ethnic inequality and report on an experiment that uses race dialogue courses in college to reduce these differences. They found that these courses increase the number of Whites who recognize structural causes of inequality.

The section on economics contains three chapters. In Chap.  Shanks et al. discuss racial gaps in wealth and their causes. They also provide a blueprint for individuals and communities of color to build wealth. The individual strategies include investing EITC refunds, Individual Development Accounts, retirement savings, real estate, and estate planning. The collective strategies include shared-risk insurance pools, housing cooperatives, investment clubs, worker-owned and microenterprise cooperatives, community development corporations, venture capital entities, and foundations.

In Chap.  Holzer reports that Black male labor market participation is 25–30 % below that of White males. The causes include low postsecondary educational attainment and high rates of incarceration for Black males along with discrimination in the labor market and declining numbers of low-skill jobs. Black male labor market participation could be expanded with programs that link high school-aged youth with postsecondary education and through increased assistance for ex-offenders and noncustodial parents.

In Chap.  Rank argues that poverty rates are high in the United States because American society views poverty as the fault and responsibility of the individual and not of society. The typical American view on poverty could be challenged in three ways: recognizing that poverty affects all Americans, recognizing that poverty is largely the result of failings of the economic and political system and not the individual, and focusing on moral arguments that poverty is an injustice and requires social change.

The section on education contains three chapters. In Chap.  Skiba shows that Black students are greatly overrepresented in suspensions and expulsions. These disciplinary disparities are not due to poverty or different rates of behavior. Few interventions have been proven to reduce these disparities. Addressing issues relating to race and culture will be needed to increase equity in school discipline.

In Chap.  Wallace reports on trends in racial/ethnic and social class achievement gaps. He examines the social and economic impacts of these gaps on the nation and describes two current approaches to reducing the gaps: the Harlem Children Zone and the Homewood Children's Village.

In Chap.  Fletcher and Tienda use enrollment data at public universities in Texas to determine in what ways and to what extent high school attended affects college achievement. They found that minority college students from poor high schools outperform their White college classmates from the same high schools in the first semester but not beyond. They argue that minority students perform more poorly after the first semester because they are less academically prepared and because college courses become more difficult. They suggest that increasing poor minority student access to college is worthwhile but more support mechanisms are needed.

The section on special topics contains four chapters. In Chap.  McRoy and Griffin note that many children are growing up in stressful and disadvantaged situations such as poverty, single-parent households, and parental incarceration. In addition, a high number of African American children are removed from their birth families and placed in foster care. The authors identify promising practices for overcoming barriers to finding permanent family connections for these children.

In Chap.  Markides et al. point out that Mexican Americans, despite having low socioeconomic status, tend to live longer than non-Hispanic Whites but that older Mexican Americans have relatively poor health and high disability rates. Using longitudinal data on Hispanic elderly, the authors determine trends in obesity, disability, depression, and frailty. They highlight the need for public health programs that can address these growing problems among Mexican American adults over age 65.

In Chap.  Blumstein discusses the overrepresentation of Blacks in jails and prisons. He notes that the growth in incarceration has been due in part to changes in drug policies and drug laws, such as mandatory sentences for drug offenses. Because of detrimental effects on the Black community and on state budgets, he argues that it is important for political and criminal justice systems to find innovative ways to reduce prison rates and do so without increasing crime.

In Chap.  Kennedy argues that most police are not racist. However, most Blacks in urban communities believe that most police are racist. So long as people think the police are racist, police-community cooperation to reduce violence and crime is not likely to occur. The view that the police are racist is not likely to change until the police change their behavior. Many police are realizing that their standard practices don't stop drug use, violence, or crime in the most troubled neighborhoods. Some police departments are finding success with an alternative approach which is designed to minimize criminal justice intrusion into the community and to get offenders help rather than incarcerate them.

The final section has three chapters on health and one on mental health. In Chap.  South-Paul argues that disparities in health and health care for minorities result from institutional, patient-level, and provider-level factors. Examples of institutional factors are lack of access to quality healthcare facilities and variations in insurance status. Patient behaviors include tobacco use, diet, and exercise patterns, among others. Provider factors include actions such as treating patients with the same conditions differently. In general, providers need to match patients with a patient-centered medical home where the patient is known, the patient can name his/her provider, and first-contact, continuous, compassionate, collaborative, and comprehensive care is available. That care should address physical, behavioral, oral, and preventive health components of the patient's needs.

In Chap.  Hudson and Gehlert focus on the fact that White women are more likely to develop breast cancer but Black women are more likely to die from the disease. The chapter illustrates how knowledge of multiple levels of causes, from the microbiological to the societal, can be used to develop interventions. The authors present a model to identify social determinants of breast cancer in Black women. Addressing social isolation, improving early detection, including social factors in addition to clinical information, and developing community partners can help to reduce the disparities.

In Chap.  Baskin notes that three-quarters of African American adults are overweight or obese and African Americans are more likely to be overweight or obese than Whites. Individual and family factors, culture (beliefs, traditions), neighborhood conditions, and policies may explain the higher Black rates of obesity. The author offers multi-level solutions to improve minority health and eliminate obesity disparities.

In Chap.  Barbarin and Sterrett examine a variety of data on the mental health status and psychosocial functioning of African American boys. The authors argue that strain and trauma in the lives of boys of color lead to coping mechanisms that are often interpreted as behavioral disorders. The resilience of Black boys in the face of economic disadvantage, discrimination, and other sources of psychosocial stress can be enhanced by promoting a positive ethnic identity and involving a father or father-figure.

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Massey, D. (2008a). Categorically unequal: The American stratification system. New York: Russell Sage.

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Footnotes

1

Underemployment refers to skilled workers in low-wage or low-skill jobs, and persons working part-time but unable to find full-time work.

2

200 % of the poverty threshold for a family of four in 2013 was $47,100.
© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_2

# 2. Do Current Race and Ethnicity Concepts Reflect a Changing America?

Karen Humes1  and Howard Hogan1

(1)

US Census Bureau, Washington, DC, 20233, USA

Karen Humes

Email: Karen.humes@census.gov

Howard Hogan (Corresponding author)

Email: howard.r.hogan@census.gov

Email: hhogan@census.gov

This chapter reports the results of research and analysis undertaken by the Census Bureau staff. It has undergone a Census Bureau review more limited in scope than that given to official Census Bureau publications. This is released to inform interested parties of ongoing research and to encourage discussion of work in progress.

## Introduction

Race and ethnicity concepts, which have evolved continually in the United States, must be periodically examined in order to ensure that the nation is keeping pace with its changing composition. Following each census, it is appropriate to examine the current set of race and ethnicity concepts to assess their adequacy in describing the population. Results from the 2010 Census reveal that more than 15 million people, the majority of whom were of Hispanic origin, did not report any of the standard race categories utilized by the US federal government. Another 14 million people did not answer the race question at all. Over the past few censuses, the number not reporting a standard race category has increased, while the race and ethnicity concepts have not adapted enough to address factors that may be fueling this trend. Policymakers in federal agencies, local governments, business, universities, and think tanks need data based on race and ethnicity concepts that accommodate the continued rapid growth of the Hispanic population, and of groups who trace their ancestry to, or identify with, an increasingly diverse set of world population groups.

The classification of race and ethnicity has a long history of change in the United States—as evidenced by the measurement of these concepts in every decennial census since 1790. These concepts have evolved from census to census, influenced by political, social, and demographic phenomena in American society such as slavery, the civil rights movement, and immigration patterns, as well as by scientific and pseudo-scientific concepts of race and ethnicity. We argued previously that in order for constructs of race and ethnicity to be socially, or at least statistically, useful they would have three properties: (1) be recognized by society and the individual, (2) categorize individuals into the same groups over a long period of time, and (3) be predictive of social and economic opportunity (Humes and Hogan 2009). To the extent that the current or any categorizations of race do not meet these criteria, the data collected based upon them will not provide an adequate picture of society and its needs.

This chapter examines current race and ethnicity concepts used by the US federal government and comments on their applicability to the current US population. We provide an overview of race and ethnicity in the United States. We also examine segments of the population where the application of the current race and ethnicity concepts may be most problematic and investigate additional social dimensions that may impact individual's acceptance or rejection of current race and ethnicity standards. Additionally, we discuss possible future research directions regarding race and ethnicity concepts. Throughout this chapter, data from the decennial census and the American Community Survey are used to provide insights into the race and ethnicity concepts utilized by the US federal government.

## Current Race and Ethnicity Concepts

In 1997, the US Office of Management and Budget (OMB) issued the current standards for the collection and tabulation of federal data on race and ethnicity.1 OMB standards state four key principles: (1) there are five minimum categories for data on race: White, Black or African American, American Indian or Alaska Native, Asian, and Native Hawaiian and Other Pacific Islander; (2) the reporting of more than one race is accepted; (3) there are two minimum categories for data on ethnicity: Hispanic or Latino and not Hispanic or Latino; and (4) race and ethnicity are two separate and distinct concepts—therefore, people of Hispanic or Latino origin may be any race. The US Census Bureau, along with other federal agencies, must adhere to OMB standards. The general purpose of the OMB standards is to provide relatively consistent statistics on race and ethnicity across the federal statistical agencies for such uses as the enforcement of civil rights laws and monitoring equal access to education, employment, housing, etc.

The 1997 OMB standards differ from the first set of government-wide race and ethnicity standards issued in 1977. The two biggest changes were: (1) the Asian and Pacific Islander category was split into two groups and (2) multiple-race reporting was introduced. These are the only two major changes that have occurred in the race and ethnicity concepts used by the federal government in more than 30 years. Are these changes enough to adequately reflect the race and ethnicity composition of America as it has taken shape over the past 30 years? While it is desirable for change in race and ethnicity concepts to occur slowly over time, in order to minimize the disruption of the historical time series, static race and ethnicity standards may not capture sufficient data about evolving major population groups.

While only two major changes were introduced in the 1997 OMB revision, other significant race and ethnicity conceptual issues were being discussed among demographers. Most notable were the consideration of an additional race category for people of Middle Eastern and North African heritage, as well as the combining of the separate concepts of race and ethnicity into one. Although these issues were tabled as needing further research, they show that population group identification issues were changing in substantial ways.

## Race and Ethnicity Distribution Patterns

Data from the decennial census and from the American Community Survey show how the American public responds to the current concepts of race and ethnicity used by the federal government and suggest whether these concepts adequately describe the US population.

### 2010 Census Race and Ethnicity Overview

According to the 2010 Census, 308.7 million people resided in the United States on April 1, 2010. This total included 50.5 million Hispanics, who composed 16 % of the total population (see Table 2.1). Additionally, 97 % of the total population (299.7 million) were classified into one of the single race groups. Individuals who were one race are referred to as the race-alone population.2 The largest group reported White alone (223.6 million), accounting for 72 % of all people living in the United States.3 The Black or African American alone population was 38.9 million and represented 13 % of the total population.4 There were 2.9 million people categorized as American Indian and Alaska Native alone (0.9 %). Approximately 14.7 million (about 5 % of the total population) were Asian alone. The smallest race group was Native Hawaiian and Other Pacific Islander alone (0.5 million) and represented 0.2 % of the total population. The remainder of the population who were one race—19.1 million (6 % of the total population)—were classified as Some Other Race alone. People who were more than one race numbered 9.0 million in the 2010 Census and made up about 3 % of the total population.5

Table 2.1

Population by Hispanic or Latino origin and by race for the United States, 2010 | 2010

---|---

No. | Total population (%)

Hispanic or Latino origin and race

Total population | 308,745,538 | 100.0

Hispanic or Latino | 50,477,594 | 16.3

Not Hispanic or Latino | 258,267,944 | 83.7

White alone | 196,817,552 | 63.7

Race

Total population | 308,745,538 | 100.0

One Race | 299,736,465 | 97.1

White | 223,553,265 | 72.4

Black or African American | 38,929,319 | 12.6

American Indian and Alaska Native | 2,932,248 | 0.9

Asian | 14,674,252 | 4.8

Native Hawaiian and Other Pacific Islander | 540,013 | 0.2

Some Other Race | 19,107,368 | 6.2

Two or More Races | 9,009,073 | 2.9

Source: U.S. Census Bureau, 2010 Census Redistricting Data (Public Law 94-171) Summary File

Figure 2.1 shows the size in 2010 and the percentage change between 2000 and 2010 for selected race and ethnic groups.6 In the United States, all race and ethnic groups increased in population size between 2000 and 2010, but they grew at different rates. Between 2000 and 2010, the Hispanic population grew relatively fast, increasing by 43 %. The non-Hispanic Asian-alone population experienced fast growth and also increased by 43 % between 2000 and 2010, more than any other race group. The non-Hispanic Native Hawaiian and Other Pacific Islander-alone population, the smallest race group, also grew substantially between 2000 and 2010, increasing by more than one-third. The non-Hispanic Two or More Races population was also one of the fastest-growing groups over the decade. This population increased almost one-third between 2000 and 2010.7

Fig. 2.1

Numeric size in 2010 and percentage change between 2000 and 2010 by selected race and Hispanic origin group (For information on confidentiality protection, non-sampling error, and definitions, see http://www.census.gov/prod/cen2010/sf1.pdf)

Within the non-Hispanic population, several groups grew relatively slowly. The non-Hispanic Black-alone population grew 11 % between 2000 and 2010, far slower than Hispanics, Asians, Native Hawaiians and Other Pacific Islanders, and the Two or More Races population. Additionally, 9 % growth in the non-Hispanic American Indian and Alaska Native-alone population occurred over the decade. The non-Hispanic White-alone population grew at the slowest rate between 2000 and 2010 (1 %). Further, while the non-Hispanic White-alone population increased numerically from 194.6 to 196.8 million over the 10-year period, its proportion of the total population declined from 69 to 64 %.

### Racial Distribution Among Hispanics in the 2010 Census

An examination of the pattern of responses provided to the 2010 Census questions on race and Hispanic origin provides important insights. Table 2.2 presents information on the type of response (or no response) given to the 2010 Census race question cross-tabulated by the type of response (or no response) given to the 2010 Census question on Hispanic origin—prior to the application of any data editing or allocation procedures.8 In terms of reporting race, the majority of the total population (90 %) provided one or more OMB race categories only. Another 5 % of the total population provided a response to the race question that could not be racially categorized, which resulted in their responses being classified as Some Other Race only. Less than 1 % of the total population reported a mixture of OMB race categories and responses that were classified as Some Other Race. Those who did not respond at all to the race question represented 4.5 % of the total population.

Table 2.2

Responses to the Hispanic origin question and the race question, 2010

Response to the question on race | Response to the Hispanic origin question

---|---

Total | Reported "No" not of Hispanic origin | Reported "Yes" of Hispanic origin | Did not responda

No. (in thousands) | (%) | No. (in thousands) | (%) | No. (in thousands) | (%) | No. (in thousands) | (%)

Total | 302,975 | 100.0 | 240,036 | 100.0 | 47,557 | 100.0 | 15,381 | 100.0

Reported OMB race(s) only | 271,797 | 89.7 | 237,303 | 98.9 | 25,069 | 52.7 | 9,424 | 61.3

Reported Some Other Race only | 15,187 | 5.0 | 495 | 0.2 | 14,501 | 30.5 | 191 | 1.2

Reported OMB race(s) and Some Other Race | 2,250 | 0.7 | 369 | 0.2 | 1,807 | 3.8 | 74 | 0.5

Did not responda | 13,742 | 4.5 | 1,869 | 0.8 | 6,181 | 13.0 | 5,692 | 37.0

Note: Counts may not add to total due to rounding

Source: U.S. Census Bureau, 2010 Census, special tabulation

aIncludes the population who had a race or Hispanic origin value assigned during data editing and allocation procedures. This table does not include people in the 2010 Census who were missing race and Hispanic origin values and had them assigned through the "whole house" substitution procedure. Households where data were missing for all variables for all individuals had values assigned through the "whole house" substitution procedure

The pattern of response to the race question changes dramatically when taking into account the type of response to the Hispanic-origin question (prior to the application of any data editing or allocation procedures). Among those who reported not being Hispanic, virtually all (99 %) provided OMB race responses. Among those who reported being Hispanic, 53 % reported OMB race categories only, 31 % provided responses classified as Some Other Race only, 4 % provided a mixture of OMB race(s) and Some Other Race responses, and 13 % did not respond to the race question at all. It is striking that almost half of all those who reported being Hispanic either did not provide a race response that was classifiable, did not respond to the race question at all, or provided a mixture of OMB race categories along with responses that could not be racially classified. This is significant given that the Hispanic population is the largest and one of the fastest-growing minority groups in the United States.

Recall that OMB standards mandate that people of Hispanic origin may be any race. This reflects the diverse populations (especially European, African, and indigenous American) that constitute the Spanish speaking world. For the 2010 Census, a new instruction was added immediately preceding the questions on Hispanic origin and race that was not used in Census 2000 (see Fig. 2.2). The instruction stated that "For this census, Hispanic origins are not races." However, this did not preclude individuals from self-identifying their race as "Latino," "Mexican," "Puerto Rican," "Salvadoran," or other national origins or ethnicities; in fact, many did so. If the response provided to the race question could not be classified in one or more of the five OMB race groups, it was generally classified in the category Some Other Race. Therefore, responses to the question on race that reflect a Hispanic origin were classified in the Some Other Race category.

Fig. 2.2

Reproduction of the questions on Hispanic origin and race from the 2010 census. Source: U.S. Census Bureau, 2010 Census questionnaire

Table 2.3 and Fig. 2.3 examine the racial distribution among the Hispanic population by origin, utilizing final 2010 Census data that have undergone data editing and imputation procedures. Just over half of the total Hispanic population was classified (either directly from their responses, or via editing or imputation) as White and no other race, while about one-third were classified as Some Other Race alone. Much smaller proportions of Hispanics were other race groups alone: Black alone (3 %), American Indian and Alaska Native alone (1 %), and Asian alone or Native Hawaiian and Other Pacific Islander alone (0.5 %).9

Table 2.3

Hispanic or Latino population by type of Hispanic or Latino origin and race, 2010

Origin | Total Hispanic or Latino population | One Race | Two or More Races

---|---|---|---

Total | White | Black or African American | American Indian and Alaska Native | Asian and Native Hawaiian and Other Pacific Islander | Some Other Race

Total Hispanic | 50,477,594 | 47,435,002 | 26,735,713 | 1,243,471 | 685,150 | 267,565 | 18,503,103 | 3,042,592

Central American

Mexican | 31,798,258 | 30,221,886 | 16,794,111 | 296,778 | 460,098 | 126,254 | 12,544,645 | 1,576,372

Guatemalan | 1,044,209 | 969,462 | 401,763 | 11,471 | 31,197 | 9,637 | 515,394 | 74,747

Salvadoran | 1,648,968 | 1,535,703 | 663,224 | 16,150 | 17,682 | 5,842 | 832,805 | 113,265

Costa Rican | 126,418 | 115,942 | 80,608 | 7,725 | 520 | 576 | 26,513 | 10,476

Honduran | 633,401 | 583,117 | 273,389 | 28,378 | 9,377 | 1,724 | 270,249 | 50,284

Nicaraguan | 348,202 | 323,758 | 216,116 | 8,211 | 2,632 | 1,979 | 94,820 | 24,444

Panamanian | 165,456 | 142,094 | 53,905 | 55,197 | 816 | 1,629 | 30,547 | 23,362

South American

Argentinean | 224,952 | 216,329 | 193,129 | 959 | 427 | 956 | 20,858 | 8,623

Bolivian | 99,210 | 91,565 | 63,065 | 470 | 1,346 | 368 | 26,316 | 7,645

Chilean | 126,810 | 118,368 | 96,403 | 706 | 476 | 520 | 20,263 | 8,442

Colombian | 908,734 | 857,517 | 655,735 | 18,218 | 3,971 | 2,684 | 176,909 | 51,217

Ecuadorian | 564,631 | 522,234 | 293,679 | 5,611 | 5,922 | 1,916 | 215,106 | 42,397

Paraguayan | 20,023 | 18,666 | 14,192 | 119 | 328 | 90 | 3,937 | 1,357

Peruvian | 531,358 | 487,834 | 290,871 | 5,141 | 7,406 | 5,298 | 179,118 | 43,524

Uruguayan | 56,884 | 54,793 | 48,417 | 382 | 78 | 75 | 5,841 | 2,091

Venezuelan | 215,023 | 202,025 | 162,100 | 5,514 | 602 | 986 | 32,823 | 12,998

Caribbean

Cuban | 1,785,547 | 1,719,585 | 1,525,521 | 82,398 | 3,002 | 5,165 | 103,499 | 65,962

Dominican | 1,414,703 | 1,276,878 | 419,016 | 182,005 | 19,183 | 5,335 | 651,339 | 137,825

Puerto Rican | 4,623,716 | 4,220,908 | 2,455,534 | 403,372 | 42,504 | 35,073 | 1,284,425 | 402,808

Note: Counts of individual origin groups do not add to the total Hispanic population count. Counts of people who responded to the 2010 Census question on Hispanic origin with general terms such as "Hispanic" or "Central American" are not shown separately in this table

Source: U.S. Census Bureau, 2010 Census special tabulation

Fig. 2.3

Hispanic or Latino population by origin and race: 2010 (For information on confidentiality protection, non-sampling error, and definitions, see http://www.census.gov/prod/cen2010/sf1.pdf)

When examining the racial distribution by origin, we see several trends. In general, large proportions of Hispanics of Central American origin were White alone or were classified as Some Other Race alone. At least half of Hispanics of Mexican origin and at least 60 % of Hispanics of Costa Rican and Nicaraguan origins were White alone. Additionally, about one-fifth of Costa Ricans and one-quarter Nicaraguans were classified as Some Other Race alone. A larger proportion of Mexicans were categorized as Some Other Race alone (39 %).

A different pattern was observed for Hispanics of Guatemalan, Salvadoran, and Honduran origins, where less than half of these populations were White alone (38 %, 40 %, and 43 %, respectively). Forty-three percent of Hondurans were classified as Some Other Race alone, while half of Salvadorans and nearly half of Guatemalans were Some Other Race alone. The proportions of most Central American origin groups who were other races were relatively small.

Origin groups with larger proportions classified as Some Other Race alone could reflect the more complex racial makeup of their countries of origin or a conceptualization of racial and group identity different from that of the OMB. Those with origins in countries that have significant Mestizo populations, as well as Central American indigenous populations, may find the federal government racial classification system not relevant to them. The Panamanian population, however, had a different pattern than those of other Central American origins. Panamanians were the least likely among those of Central American origin to be White alone (33 %) or Some Other Race alone (19 %), and they were the only Central American origin to significantly identify as Black or African American (33 %).

Hispanics of South American origin had a different racial distribution than those of Central American origin. In general, very large proportions of those of South American origin were White alone and small proportions were categorized as Some Other Race alone. At least 70 % of those of Argentinean, Chilean, Colombian, Paraguayan, Uruguayan, and Venezuelan origins were White alone. The proportion classified as Some Other Race alone was 20 % or less for each of these origin groups. The majority of those of Bolivian, Ecuadorian, and Peruvian origins also were White alone, but to a lesser extent than others of South American origin (64, 52, and 55, respectively). Conversely, those of Bolivian, Ecuadorian, and Peruvian origins were the most likely among those of South American origin to be classified as Some Other Race (27 %, 38 %, and 34 %, respectively). Again, this pattern may reflect the significance of Mestizo and the presence of South American indigenous populations in these countries, which may make it difficult for those of these origins to identify with OMB race categories.

Hispanics of Caribbean origin did not exhibit a consistent racial distribution, perhaps reflecting both the racial makeup of the countries of origin and the particular histories of immigration to the United States. About half of those of Puerto Rican origin were White alone, with 28 % classified as Some Other Race alone. A very large proportion of those of Cuban origin (85 %) were White alone, with 6 % classified as Some Other Race alone. Of all Hispanic origin groups, Cubans had the lowest proportion categorized as Some Other Race alone. In contrast, 46 % of those of Dominican origin were categorized as Some Other Race alone, and 30 % were White alone. Of all Hispanic origins, Dominicans had the lowest proportion of White alone.

The different racial distributions among Hispanic-origin groups reveal that, while just over half of all Hispanics identify with one or more OMB race groups, large proportions did not identify with any of them. This is particularly true for those with origins in countries with significant Mestizo populations and indigenous populations. Individuals with origins in Central American, South American, and Hispanic Caribbean countries whose concepts of race and ethnicity are distinct from those used in the United States may determine that the OMB race categories do not apply to them. Other dimensions can also be examined to explore factors that may indirectly impact identification with the current federal government race categories.

### Examining Citizenship Status, Year of Entry, and Racial Distribution Among Hispanics

If lack of identification with OMB standards occurs because these race concepts are incongruent with those in Hispanic-origin countries, citizenship status and year of entry data may provide evidence to support or disprove this theory. Perhaps those who have had more exposure to and immersion in US culture have also had more opportunity to understand the racial constructs used by the federal government. They would be more likely to identify with OMB race categories than newer arrivals. Data from the 2005–2009 American Community Survey offer information on the intersection of citizenship status, year of entry, and racial distribution among Hispanics.

Table 2.4 shows the percentage of the Hispanic population 20 years and older who were classified as Some Other Race alone by citizenship status and selected origin.10 When taking citizenship status into account, Hispanics who were not citizens (41 %) were more likely than Hispanic citizens (34 %) to be classified as Some Other Race alone.11 Conversely, Fig. 2.4 shows that among Hispanics 20 years and over, citizens were slightly more likely to be classified as White alone or other OMB race groups than non-citizens. This racial distribution could suggest that non-citizens are less likely than citizens to understand the race categories delineated by OMB.

Table 2.4

Percentage of the Hispanic or Latino population 20 years and over classified as some other race alone by citizenship and selected origin, 2005–2009 (in percent)

Origin | Classified as Some Other Race alone

---|---

Citizen | Not a citizen

Total Hispanic 20 years and over | 33.7 | 40.7

Mexican | 34.7 | 41.5

Guatemalan | 45.0 | 48.2

Salvadoran | 48.8 | 46.7

Cuban | 7.8 | 9.3

Dominican | 53.0 | 59.9

Source: U.S. Census Bureau, 2005–2009 American community survey

Fig. 2.4

Hispanic or Latino population 20 years and over by citizenship and race: 2005–2009 (Data based on sample. For information on confidentiality protection, sampling error, non-sampling error, and definitions, see www.census.gov/acs/www)

Additionally, when examining the classification of Some Other Race alone among Hispanics 20 years and over by citizenship status and selected origin, the above pattern holds for those of Mexican origin and of Dominican origin. However, about half of citizens and half of non-citizens of Guatemalan and Salvadoran origin were classified as Some Other Race alone. Therefore, for some origins, controlling for citizenship does not appear to impact the racial distribution. Perhaps an additional dimension contributes to this distribution.

Figure 2.5 shows the racial categorization for foreign-born Hispanics 20 years and over by year of entry. The recency of immigration impacts the racial distribution. Foreign-born Hispanics who entered the United States after 1970 were more likely to be classified as Some Other Race alone than those who entered before the 1970s. Conversely, Hispanics who entered the United States before the 1970s were more likely to be White alone than those who entered after 1970. It is notable that the proportion of foreign-born Hispanics who identified as any of the other race groups was about 4 or 5 % across all categories of year of entry.12 The Immigration and Nationality Act of 1965 significantly changed the way the immigration quotas were allocated to different parts of the world, including the nations of the Western Hemisphere, as well as the criteria for admission. Thus, the racial composition of the immigration flow changed. Additionally, the effects of the civil rights movement may have made the United States more attractive to immigrants who were not White.

Fig. 2.5

Foreign-born Hispanic or Latino population 20 years and over by year of entry and race: 2005–2009 (Data based on sample. For information on confidentiality protection, sampling error, non-sampling error, and definitions, see www.census.gov/acs/www)

Similar patterns emerge when examining the proportion of foreign-born Hispanics 20 years and over who were classified as Some Other Race alone by year of entry and origin. Figure 2.6 shows that the proportions of those of Mexican and Dominican origins who were classified as Some Other Race alone were higher for those who entered the United States after 1970 than for those who entered prior to the 1970s. For those of Salvadoran origin, the proportions classified as Some Other Race alone were higher for those who entered the United States after 1970 than for those who entered before 1960. People of Guatemalan origin who entered the United States from 1970 to 1999 were more likely to be classified as Some Other Race alone than those who entered prior to the 1970s. However, this pattern did not hold for those of Cuban origin. The proportion of Cubans categorized as Some Other Race alone was below 10 % for all year of entry categories, except for the 1980s (10 %). This could reflect the increased racial diversity that existed among the wave of refugees that arrived in the United States via the Mariel, Cuba boatlift in 1980.

Fig. 2.6

Foreign-born Hispanic or Latino population 20 years and over who identified as some other race alone by origin and year of entry: 2005–2009 (Data based on sample. For information on confidentiality protection, sampling error, non-sampling error, and definitions, see www.census.gov/acs/www)

Thus, the 2010 Census and the 2005–2009 American Community Survey show that large proportions of the Hispanic population do not identify with OMB race categories. Responses to the race question that were classified as Some Other Race alone varied by Hispanic origin, citizenship status, and year of entry. This finding could reflect the importance of the racial makeup of an individual's, or their preceding generation's, country of origin. It could also reflect the social climate encountered by individuals when arriving in the United States, as well as the acculturation of those individuals over time.

### 2010 Census Race Responses Not Classified or Seeking Group Recognition

A number of people enumerated during the 2010 Census indicated that the OMB race categories did not apply to them and/or that they were seeking recognition for population groups that are not typically tabulated from the decennial census. Table 2.5 displays the most frequently reported responses to the 2010 Census race question that could not be racially or ethnically classified.13 By far, the term most commonly reported was simply "American" (nearly two million responses). Other responses, such as "Human," "Human race," "No," "None," and "N/A" (not applicable), could be interpreted as representing a rejection of the race question itself. Additionally, responses that could not be racially or ethnically classified included religious responses, the most commonly reported being "Jewish" and "Muslim." Also, some respondents appeared not to know how to identify their race and reported "Unknown" or "Other." The counts of people reporting a particular term that could not be racially or ethnically classified drops significantly beyond "American," reflecting the myriad of ways this segment of the population struggled to answer the question on race.

Table 2.5

Top ten uncodable responses to the 2010 Census question on race

Write-In Responsea | Total

---|---

Rank | No. (in thousands)

American | 1 | 1,956

Human | 2 | 158

Jewish | 3 | 57

Unknown | 4 | 18

Other | 5 | 18

Human race | 6 | 18

None | 7 | 11

No | 8 | 8

N/A | 9 | 8

Muslim | 10 | 8

For information on confidentiality protection, non-sampling error, and definitions, see http://www.census.gov/prod/cen2010/pl94-171.pdf

Source: U.S. Census Bureau, 2010 Census, Custom Tabulation

a2010 Census count reflects unedited responses provided alone without other codable race information

As discussed earlier, a number of people reported a Hispanic origin as their race. Table 2.6 shows the most frequently reported responses to the race question that were Hispanic origins. There were 7.5 million responses of "Mexican," the most reported Hispanic origin in response to the question on race. Still, it should be noted that many (53 %) of people of Mexican origin were White alone. Other specific Hispanic-origin groups reported by several hundred thousand people were "Puerto Rican," "Dominican," "Salvadoran," and "Guatemalan." After reports of "Mexican," the second and third most frequently reported Hispanic-origin responses to the race question were general terms. Just over four million people reported their race as "Hispanic" and almost two million reported "Latin American." Thus, the wide range of Hispanic-origin responses to the question on race indicate a broad inability or unwillingness to identify with OMB race categories.

Table 2.6

Top ten Hispanic origin responses to the 2010 Census question on race

Write-In Responsea | Total

---|---

Rank | No. (in thousands)

Mexican | 1 | 7,519

Hispanic | 2 | 4,339

Latin American | 3 | 1,972

Puerto Rican | 4 | 738

Spanish | 5 | 470

Salvadoran | 6 | 318

Mestizo | 7 | 317

Dominican | 8 | 277

Guatemalan | 9 | 248

Chicano | 10 | 185

For information on confidentiality protection, non-sampling error, and definitions, see http://www.census.gov/prod/cen2010/pl94-171.pdf

Source: U.S. Census Bureau, 2010 Census, Custom Tabulation

a2010 Census counts reflect unedited responses provided to the 2010 Census question on race

Leading up to the 2010 Census, a number of organizations launched campaigns instructing their communities how to complete the question on race. A basic goal was to obtain an official count of their community from the 2010 Census, which was not included in the standard census data products, nor was the race question designed to elicit their community-specific responses for tabulation in standard data products. Most notable were the efforts of the Arab and Iranian communities. OMB standards define those with origins in the original peoples of Europe, the Middle East, and North Africa as "White." Therefore, reports of "Iranian" or Arab nationalities to the 2010 Census question on race were classified and tabulated in the category "White." Historically, the Census Bureau has not separately tabulated White ethnic groups, thus, counts of White ethnic groups are not included in standard census data products. However, a number of advocates for this community lobbied the Census Bureau to collect ancestry data on the 2010 Census, in order to obtain counts of Arab groups and Iranians, which are traditionally tabulated from the ancestry question formerly asked on the long form of the census and currently asked on the American Community Survey. The Arab American Institute garnered the support of the Democratic National Committee, which passed a resolution calling for the collection of ancestry data in the census; however, this was passed too late to impact the 2010 Census questionnaire design. This community urged its members to write in their group affiliation in the race question, expecting to obtain group counts through a custom tabulation after the 2010 Census.

Additionally, members of the Afro-Caribbean community launched campaigns regarding the reporting of race prior to the 2010 Census. OMB standards define those with origins in the Black racial groups of Africa as "Black or African American." Therefore, reports of Afro-Caribbean and African nationalities are classified and tabulated in the category "Black or African American." Also, historically, the Census Bureau has not separately tabulated Black ethnic groups, thus, counts of Black ethnic groups are not included in standard census data products. White House briefings on the 2010 Census collection and tabulation of data on race were organized by the Afro-Caribbean community. This community also influenced the introduction of a bill in Congress that required the addition of a "Caribbean" checkbox to the 2010 Census question on race. Again, because of the timing, changes to the 2010 Census race question could not be entertained. This community used social media, among other methods, to reach out to their members, instructing them to write-in their group affiliation in the race question, with the expectation that a custom tabulation of group counts would be obtained after the 2010 Census.

Table 2.7 shows the most common specific write-ins classified as "White" or "Black or African American" responses to the 2010 Census question on race. It is expected that reports of Italian, German, Irish, and Polish would be commonly reported, as these ethnic groups are among the largest in the United States. However, it is less expected to see large numbers reporting Iranian, Arab, and Middle East in the list of the top ten, as these groups represent a relatively small segment of the US population. Additionally, reports of Haitian and Jamaican are included in the list of the top ten. We argue that reports of these relatively small population groups indicate seeking group recognition, as well as a rejection of OMB categories as the sole representation of their racial identity. Unfortunately, the communities discussed above that advocated their members write-in their group affiliation in the race question expected that data from the 2010 Census could provide official counts of their communities. Since the question on race was not designed to collect data on White or Black ethnic groups, these counts do not reflect the size of these communities. The data in Table 2.7 represent only the number of people who went out of their way to report these responses in the 2010 Census race question and should not be construed as accurate counts of these populations in the United States.

Table 2.7

Top ten specific Write-Ins classified as White or Black responses to the 2010 Census question on race

Write-In Responsea | Total

---|---

Rank | No. (in thousands)

Italian | 1 | 328

Iranian | 2 | 310

Arab | 3 | 261

Haitian | 4 | 231

Armenian | 5 | 199

German | 6 | 197

Irish | 7 | 192

Polish | 8 | 127

Middle East | 9 | 122

Jamaican | 10 | 111

The 2010 Census question on race was not designed to collect data on White or Black ethnic groups. Therefore, these counts only represent the people who went out of their way to report these responses and should not be construed as accurate counts of these populations in the United States

For information on confidentiality protection, non-sampling error, and definitions, see http://www.census.gov/prod/cen2010/pl94-171.pdf

Source: U.S. Census Bureau, 2010 Census, Custom Tabulation

a2010 Census counts reflect specific unedited responses provided to the 2010 Census question on race for which unique codes are available

### Multiple-Race Reporting Over Time by Age Cohort

Examining age cohorts of selected multiple-race combinations provides interesting insights about race reporting over time. The 1997 OMB standards permitted respondents to report more than one race for the first time. However, it is debatable whether or not the reporting of more than one race meets the fundamental requirements for a useful classification system. The individual may indeed recognize his or her background, but it is unclear whether these groups are recognized by their social networks. Evidence also indicated that the classification into multiple-race groups is not stable over time (Bentley et al. 2003). Finally, it is too soon to determine whether these multiple-race categories are predictive of social or economic opportunity. This analysis examines race reporting by age cohort for the two largest multiple-race populations among the OMB race categories.

In terms of race reporting over time, Table 2.8 shows interesting data for age cohorts for the White and Black population. The data shown are the White and Black population age distribution in 2000 and the counts of these age cohorts in the 2010 Census, by 10-year age groups. For example, among those White and Black, 231,361 people were reported as under 5 years old in 2000 and 296,497 were reported as 10–14 years old in 2010. Therefore, we see that the Census 2000 under 5 years old age cohort grew by 28 % in the past 10 years. While the overall population counts for each age cohort are relatively small, virtually all of the age cohorts experienced substantial growth over the past 10 years. In total, these White and Black age cohorts increased by nearly one-third in the past 10 years.

Table 2.8

White and Black population by age cohort: 2000 and 2010

Age in 2000 | 2000 | 2010a | Change, 2000–2010

---|---|---|---

No. | (%) | No. | (%) | No. | (%)

Total | 784,764 | 100.0 | 1,030,451 | 100.0 | 245,687 | 31.3

Under 5 years | 231,361 | 29.5 | 296,497 | 28.8 | 65,136 | 28.2

5–9 years | 170,669 | 21.7 | 218,024 | 21.2 | 47,355 | 27.7

10–14 years | 112,544 | 14.3 | 137,104 | 13.3 | 24,560 | 21.8

15–19 years | 75,956 | 9.7 | 98,600 | 9.6 | 22,644 | 29.8

20–24 years | 51,448 | 6.6 | 76,291 | 7.4 | 24,843 | 48.3

25–29 years | 37,174 | 4.7 | 55,607 | 5.4 | 18,433 | 49.6

30–34 years | 27,015 | 3.4 | 40,196 | 3.9 | 13,181 | 48.8

35–39 years | 20,261 | 2.6 | 30,950 | 3.0 | 10,689 | 52.8

40–44 years | 15,536 | 2.0 | 23,607 | 2.3 | 8,071 | 52.0

45–49 years | 11,258 | 1.4 | 16,711 | 1.6 | 5,453 | 48.4

50–54 years | 8,768 | 1.1 | 11,775 | 1.1 | 3,007 | 34.3

55 years and over | 22,774 | 2.9 | 25,089 | 2.4 | 2,315 | 10.2

For information on confidentiality protection, non-sampling error, and definitions, see http://www.cenus.gov/prod/cen2010/sf1.pdf

Source: U.S. Census Bureau, Census 2000 Summary File 2 and 2010 Census, special tabulation

aThe 2010 Census counts reflect the size of the Census 2000 age cohorts 10 years later. For example, the 5–9 years old age cohort numbered 170,669 in Census 2000. In the 2010 Census, this population group (aged by 10 years) numbered 218,024

Table 2.9 presents similar statistics for the White and Asian population. The trend in the growth of White and Asian population age cohorts was similar to the growth seen in the White and Black population age cohorts. For example, among those White and Asian, 149,628 people were reported as under 5 years old in 2000 and 196,692 were reported as 10–14 years old in 2010. This age cohort increased by nearly one-third in the past 10 years. Significant growth was exhibited in most of the White and Asian age cohorts, particularly up through the cohort that was 30–34 years old in 2000. Overall, the White and Asian age cohorts increased just over one-quarter in the past 10 years.

Table 2.9

White and Asian population by age cohort: 2000 and 2010

Age in 2000 | 2000 | 2010a | Change, 2000–2010

---|---|---|---

No. | (%) | No. | (%) | No. | (%)

Total | 868,395 | 100.0 | 1,107,012 | 100.0 | 238,617 | 27.5

Under 5 years | 149,628 | 17.2 | 196,692 | 17.8 | 47,064 | 31.5

5–9 years | 127,064 | 14.6 | 172,729 | 15.6 | 45,665 | 35.9

10–14 years | 110,348 | 12.7 | 142,289 | 12.9 | 31,941 | 28.9

15–19 years | 94,632 | 10.9 | 121,109 | 10.9 | 26,477 | 28.0

20–24 years | 74,456 | 8.6 | 100,917 | 9.1 | 26,461 | 35.5

25–29 years | 64,812 | 7.5 | 86,674 | 7.8 | 21,862 | 33.7

30–34 years | 53,374 | 6.1 | 68,889 | 6.2 | 15,515 | 29.1

35–39 years | 48,843 | 5.6 | 60,277 | 5.4 | 11,434 | 23.4

40–44 years | 41,850 | 4.8 | 50,713 | 4.6 | 8,863 | 21.2

45–49 years | 29,701 | 3.4 | 35,368 | 3.2 | 5,667 | 19.1

50–54 years | 20,844 | 2.4 | 23,753 | 2.1 | 2,909 | 14.0

55 years and over | 52,843 | 6.1 | 47,602 | 4.3 | −5,241 | −9.9

For information on confidentiality protection, non-sampling error, and definitions, see http://www.census.gov/prod/cen2010/sf1.pdf

Source: U.S. Census Bureau, Census 2000 Summary File 2 and 2010 Census, special tabulation

aThe 2010 Census counts reflect the size of the Census 2000 age cohorts 10 years later. For example, the 5–9 years old age cohort numbered 127,064 in Census 2000. In the 2010 Census, this population group (aged by 10 years) numbered 172,729

The growth in the age cohorts among the White and Black population and the White and Asian population between decennial censuses is not easily explained. Population change over time is attributed to three basic phenomena: births, deaths, and immigration. Since we examined age cohorts over time, births are not a factor. Mortality is low for most of these age groups and in any case deaths would reduce the size of the cohort. We know from the 2007–2009 American Community Survey that about 7 % of the White and Asian population and about 5 % of the White and Black population are foreign born. Therefore, immigration probably had a minimal impact on the growth of these age cohorts.

Shifts in reporting race remains the most likely explanation for the growth of these age cohorts. Much of the significant increase in age cohorts for both the White and Black population and the White and Asian population occurred for those who were young adults in 2010. This could represent those who did not report their own race in Census 2000, as they were children or had just entered adulthood. However, for the 2010 Census, perhaps they reported their own race and chose to report more than one. Increased awareness and acceptance of reporting multiple races since Census 2000 could have significantly impacted the growth of the age cohorts between the decennial censuses. This possibility, that race reporting can vary significantly over time among a population group, questions the fundamental usefulness of multiple-race reporting in the current racial classification system used by the federal government. It is too early to determine whether this is a secular trend (increased reporting over time) or the effect of age (increased reporting as one grows older).

## New Directions for Race and Ethnicity Data Collection

The Census Bureau and the federal statistical system face many challenges, including a growing list of groups who find the current race and ethnic classification system confusing, if not irrelevant, or who wish to see their own specific group as a category on the US decennial census questionnaire. The research objectives of the Census Bureau are to design questionnaire items that will increase reporting in the standard race and ethnic categories established by OMB, elicit the reporting of detailed race and ethnic groups, lower item nonresponse, and increase accuracy and reliability (Humes 2009). The Census Bureau engaged in such research most recently in the 2010 Census Alternative Questionnaire Experiment (AQE).

The first and primary component of the AQE was mailout/mailback questionnaires focusing on three areas of research. The first research area includes several features: (1) testing the use of modified examples in the race and Hispanic-origin questions; (2) testing the removal of the term "Negro" from the "Black, African American, or Negro" checkbox response category; and (3) testing the use of a modified instruction that permits multiple responses to the Hispanic-origin question. The second research area focuses on several exploratory approaches to combining the race and Hispanic-origin questions into one item. The third research area focuses on (1) ways to clarify that the detailed Asian checkbox categories and the detailed Native Hawaiian and Other Pacific Islander checkbox categories are part of the two broader OMB race groups, and (2) ways to limit the use of the term "race" in the race question. Additionally, two features from the first research area, testing the removal of the term "Negro" and testing modified examples in the race question, are also tested in this research area.

The second component of the AQE was a telephone reinterview study conducted with a sample of AQE mail respondents. This research assessed the accuracy and the reliability of both the control and the alternative race and Hispanic-origin questionnaires by exploring responses to a number of probing questions.

The third component of the AQE was a series of focus groups that were conducted to complement the quantitative analyses. The focus groups sought to identify the source of response anomalies that emerged from the AQE mailout/mailback questionnaires, as well as to identify trends in race and Hispanic origin reporting, giving us a better understanding of response patterns. Preliminary results from the mailout/mailback, the reinterview, and focus groups are being assessed to determine successful strategies to use during the 2020 Census research program.

We will focus here on the area of research that involves combining the race and ethnicity questions into one, as well as the focus group research, as they best illustrate how the process of adapting federal statistics to a changing and multi-cultural society will continue into the next decade.

A primary purpose of the AQE is to test alternative approaches to combining the Hispanic-origin and race questions into one item. As discussed earlier, although the OMB race classification system works well for many respondents, there are others, particularly those of Hispanic origin, who do not identify with OMB race categories. Thus, the Census Bureau is forced to statistically allocate an increasing number of people to a specific OMB race category when preparing special tabulations for the administration of federal programs. With the projected steady growth of the Hispanic population, the "Some Other Race alone" population is expected to continue increasing since a large proportion of Hispanic-origin ethnicity responses correlated with Some Other Race racial identification. Therefore, in light of the growing "Some Other Race" population in consecutive decennial censuses, new approaches to combining the race and Hispanic-origin questions into one item were tested in the AQE. In all of the combined-question experimental panels, respondents were allowed to mark all responses that applied to them.

For example, the design of one panel represents a "detailed" approach to the combined question. This panel combines the two questions, lists examples for all OMB groups, has write-in areas for each OMB group and "Other," and retains all of the checkbox response categories on the 2010 Census control panel. A simple instruction is used that directs respondents to mark one or more boxes and to write in a specific race or origin. The use of both of the terms "race" and "origin" was included to represent both of the existing OMB concepts. This version provides an opportunity for all OMB race and ethnic groups to report detailed ethnic information in their own write-in areas—for which many groups have recently lobbied the Census Bureau and Congress.

Another design panel represents a "streamlined" approach to the combined question (see Fig. 2.7). This version also provides examples for all OMB groups, as well as an opportunity for members of all OMB race and ethnic groups to report detailed ethnic information in their own specified write-in areas. This approach removes all national origin and ethnic checkboxes, which simplifies and streamlines the presentation of the combined question. All groups that are national origin checkboxes on the 2010 Census control panel have been added as examples to their respective checkbox response categories. This permits the removal of the individual checkboxes, yet still allows the groups to be listed on the questionnaire in the form of additional examples.

Fig. 2.7

2010 Census alternative questionnaire experiment streamlined question

Yet another panel represents a "very streamlined" approach to the combined question. This approach also removes all national origin checkboxes, which simplifies and streamlines the question. This panel also brings equity to all OMB race and ethnic groups by providing one shared write-in area for reporting all detailed race and ethnic responses.

Preliminary results from the AQE focus group research support further testing of the combined question strategy and the special design features associated with these experimental panels. Focus groups with members of the Afro-Caribbean community and the Middle Eastern and North African community about the experimental race and Hispanic-origin question panels confirmed that their racial identity is not always consistent with the OMB standards. Many Middle Eastern and North African participants did not identify as "White." It is interesting to note that many members of most other focus groups representing different races and ethnicities also did not view the Middle Eastern and North African population as "White" and wondered why they were classified as such. Similarly, Afro-Caribbean members of the focus groups confirmed that terminology does matter: many do not identify as "African American." Focus group participants also questioned why other race/ethnic groups could provide detailed responses (e.g., "Chinese" or "Mexican") while the Black population and the White population were not given the opportunity to identify their specific heritage. These are some of the racial identification issues that need to be addressed as research plans are developed for the 2020 Census.

The AQE represents the beginning of the 2020 Census content testing. The AQE was designed to assess strategies for improving race and Hispanic-origin reporting (e.g., combined question, multiple response option to the Hispanic-origin question, modified example strategies, etc.), rather than to identify specific question panels to place in the 2020 Census content testing (Hill 2008).

## Conclusion

Evidence from the decennial census and the American Community Survey shows that applying the 1997 OMB standards to data collection efforts is becoming increasingly problematic. Since a significant proportion of Hispanic respondents do not identify with any of the five OMB race groups, the "Some Other Race" population is expected to swell for future data collection efforts. Further, the greater the proportion of the US population who do not identify with the OMB race groups in the decennial census, the greater the impact on other federal statistical programs that rely on census data. For example, most federal statistical programs do not include a "Some Other Race" category. In order to meet the requirements of those programs, the Census Bureau must allocate those classified as "Some Other Race alone" to one of the five OMB race groups.

Additionally, a number of population groups dispute the way the OMB standards categorize populations from their area of geographic origin. A number of Middle Eastern and North African community groups are protesting their OMB racial categorization as "White," while other groups such as the Afro-Caribbean community are seeking recognition on the census questionnaire as a distinct group separate from African Americans. Further, the fluidity of race itself is very evident among those who identify as more than one race, even as race reporting shifts significantly over time.

The results of the AQE provide a basis for future research, analysis, and discussion of the race and Hispanic-origin questions for the 2020 Census. AQE experimental panels produced promising results that could initiate a dialog about the future standards and measurement of race and ethnicity. Any request to open the 1997 OMB standards for review would need to be well rooted in statistical evidence and stakeholder support. Further, any change to the OMB race and ethnicity standards would impact the entire federal statistical system.

All statistical classification systems must by their nature impose a simplification on a more complex reality. Throughout its history, the federal government's concepts of "race" have always been a mixture of race, color, national origin, and other elements. Chinese, Mexican, and Hindu have all been used as racial concepts, as have "Mulatto" and "Part Hawaiian" (Humes and Hogan 2009). Although one might argue that many of these are not "races," they largely reflected both society's and the individual's concepts of group identity. Group identity is increasingly complex, with society's view of the individual perhaps differing from his or her own. Does American society treat a Black Spanish-speaking immigrant from the Dominican Republic as Black or Hispanic or both? When this person reports his race as Dominican, is he attempting to identify with a larger Hispanic group, or is his response no different from a French-speaking Haitian who reports his national origin rather than Black? Will their grandchildren be treated by society as Dominican or Haitian or as Black? Will, in a few generations, the distinction between Vietnamese and Korean, in terms of racial classification, be no different than that between Swede and Italian? The authors of this chapter cannot answer these questions, but we know that these are the right questions to ask.

References

Bentley, M., Mattingly, T., Hough, C., & Bennett, C. (2003). Census quality survey to evaluate responses to the Census 2000 question on race: An introduction to the data. Washington, DC: U.S. Census Bureau.

Hill, J. (2008). The 2010 Census alternative questionnaire experiment—Introduction to the 2020 Census life-cycle content testing. Paper presented to the Census Advisory Committee of Professional Associations, Suitland, MD.

Humes, K. (2009). 2010 Census alternative questionnaire experiment: Race and Hispanic origin treatments. Paper presented to the Joint Census Race and Ethnic Advisory Committees, Suitland, MD.

Humes, K., & Hogan, H. (2009). Measurement of race and ethnicity in a changing, multicultural America. Race and Social Problems, 1(1), 111–131.CrossRef

Footnotes

1

The 1997 revised standards for the classification of federal data on race and ethnicity are available at this URL: www.whitehouse.gov/omb/fedreg/1997standards.html.

2

Six categories make up this population: White alone, Black or African American alone, American Indian and Alaska Native alone, Asian alone, Native Hawaiian and Other Pacific Islander alone, and Some Other Race alone. Individuals who were more than one of the six race categories are referred to as the Two or More Races population. The Two or More Races category, combined with the six race-alone categories, yield seven mutually exclusive and exhaustive categories. Thus, the six race-alone categories and the Two or More Races category sum to the total population.

3

As a matter of policy, the US Census Bureau does not advocate the use of the alone population over the alone-or-in-combination population or vice versa. The use of the alone population in sections of this paper does not imply that it is a preferred method of presenting or analyzing data. Data on race can be presented and discussed in a variety of ways.

4

The terms "Black or African American" and "Black" are used interchangeably in this chapter.

5

For more information on race and Hispanic-origin data from the 2010 Census, please see Humes, K., Jones, N., & Ramirez, R. (2011). Overview of Race and Hispanic Origin: 2010. Census brief at www.census.gov/prod/cen2010/briefs/c2010br-02.pdf.

6

This figure compares race groups with the Hispanic population. Therefore, mutually exclusive categories are presented, where Hispanics are excluded from the race categories.

7

In Census 2000, an error in data processing resulted in an overstatement of the Two or More Races population by about one million people (about 15 %) nationally, which almost entirely affected race combinations involving Some Other Race. Therefore, data users should assess observed changes in the Two or More Races population and race combinations involving Some Other Race between Census 2000 and the 2010 Census with caution. Changes in specific multiple-race combinations not involving Some Other Race, such as White and Black or White and Asian, generally, should be more comparable.

8

This does not include people in the 2010 Census who were missing a race value and had it assigned through the "whole house" substitution procedure. Households where data were missing for all variables for all individuals had values assigned through the "whole house" substitution procedure.

9

For more information on the Hispanic population from the 2010 Census, please see Ennis, S., Rios-Vargas, M., & Albert, N. (2011). The Hispanic Population: 2010. Census brief at www.census.gov/prod/cen2010/briefs/c2010br-04.pdf.

10

The universe of 20 years and over was chosen to limit cases to adults who were able to self-identify their race.

11

All comparative statements in this report involving data from the American Community Survey have undergone statistical testing, and, unless otherwise noted, all comparisons are statistically significant at the 10 % significance level.

12

For foreign-born Hispanics categorized as all other races, there is no statistically significant difference between the proportions who entered the United States from 1990 to 1999 and since 2000 or between the proportions who entered from 1970 to 1979 and before 1960.

13

The 2010 Census counts in Table 2.5 reflect unedited responses to the race question that were provided alone without other information that could be racially or ethnically classified.
© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_3

# 3. Intergroup Dialogue: Race Still Matters

Patricia Gurin1  , Nicholas Sorensen2  , Gretchen E. Lopez3  and Biren (Ratnesh) A. Nagda4

(1)

University of Michigan, Ann Arbor, MI 48109, USA

(2)

American Institutes for Research, Washington, DC, 20007, USA

(3)

Syracuse University, Syracuse, NY 13210, USA

(4)

University of Washington, Seattle, WA, USA

Patricia Gurin (Corresponding author)

Email: pgurin@umich.edu

Nicholas Sorensen

Email: nsorensen@air.org

Gretchen E. Lopez

Email: gelopez@syr.edu

Biren (Ratnesh) A. Nagda

Email: ratnesh@u.washington.edu

## Introduction

In 2003, the US Supreme Court in a five to four decision ruled in the University of Michigan's law school affirmative action case, Grutter v. Bollinger et al. (123 S.Ct.2325, 2337-41), that:

>...student body diversity is a compelling state interest that can justify using race in university admissions.... The Law School's claim is further bolstered by numerous expert studies and reports showing that such diversity promotes learning outcomes and better prepares students for an increasingly diverse workforce, for society, and for the legal profession. (p. 3)

But what kind of education actually leverages diversity to foster these outcomes both within law and in higher education generally? Intergroup dialogue courses are one way of using diversity in an intentional way by bringing together in a credit-bearing course a balanced number of students from two social identity groups (e.g., White students and students of color; men and women) to dialogue with each other. The intent of intergroup dialogue courses is to promote greater intergroup understanding of group-based inequalities, improve and deepen intergroup communication and relationships, and create greater commitment to intergroup collaboration (Nagda and Gurin 2007; Zúñiga et al. 2007).

In 2005, colleagues from nine universities initiated a multi-year, multi-university research evaluation to assess if intergroup dialogue courses have these three intended effects and if so, through what processes the effects occur. The nine universities included Arizona State University, Occidental College, Syracuse University, and the Universities of California (San Diego), Maryland, Massachusetts, Michigan, Texas (Austin) and Washington. The research project focused on race/ethnicity1 and gender dialogue courses as two examples of dialogue course offerings across these nine universities. The project used an experimental design in which students who applied to enroll in either a race/ethnicity or a gender course were randomly assigned either to an intergroup dialogue course or to a waitlist-control group. In total, the project included a race experiment, comprised of 26 race/ethnicity dialogues and 26 race/ethnicity control groups, and a gender experiment, comprised of 26 gender dialogues and 26 control groups. The results, based on multiple measures of understanding structural causes of racial/ethnic and gender inequalities, group identity, intergroup empathy, motivation to bridge differences, and both efficacy and frequency of action, provide an impressive picture of positive effects of intergroup dialogue. See Gurin et al. (2013) for a book-length presentation of the results from the Multi-University Intergroup Dialogue Research Project.

In this chapter, we focus on the race experiment to examine three questions:

  * Did White students and students of color differ in their attributions of inequality when they initially applied to enroll in a race/ethnicity dialogue?

  * Did participation in a race/ethnicity dialogue course affect students' attributions for inequality?

  * Did the race/ethnicity dialogues reduce the initial differences in attributions that students brought with them before the dialogue courses began?

We begin by looking at the national context of racial/ethnic inequality and of attributions for such inequalities in the United States. We turn first to this context, and then to describing what intergroup dialogue is and how the larger study was carried out. Then we present results from the race/ethnicity dialogues that address these three research questions. Finally, we discuss the results within a much heralded depiction of young people as a new post-racial generation, and discuss the implications of the results for what intergroup dialogue courses can and should be expected to accomplish educationally.

## The Context of Racial/Ethnic Inequalities and Their Explanations

Major reductions in racially and ethnically based inequalities occurred over the course of the twentieth century and especially as a consequence of the civil rights movement and of federal action producing both the 1964 Civil Rights Act and the 1965 Voting Rights Act. Still, sizeable racial/ethnic inequalities continue to exist and have widened in the economic arena. Sociologist Lawrence Bobo (2011) stresses that despite the expansion in size, security, and salience of the Black middle class, the official poverty rate for Blacks, which declined during the boom years of the 1990s, has increased since 2000 and remains substantially higher than for White Americans. The National Urban League's 2009 report, The State of Black America, shows that Blacks are twice as likely to be unemployed, three times as likely to live in poverty, and more than six times as likely to be incarcerated as Whites. Moreover, although the unemployment rate rose for all groups since the beginning of the 2008 recession, it has increased much faster for racial/ethnic minorities than for Whites. This was true for both Blacks and Latinos.2 Family incomes in 2007 for Whites were about 30 % greater than for Latinos and 36 % greater than for Blacks. The poverty rate among Latinos in 2007 was also nearly three times that of Whites (Logan and Weller 2009). We should not lose sight of a very large increase in income and wealth inequality for all racial/ethnic groups, including for White Americans. The widening of economic inequality has affected White Americans but just not as much as both Blacks and Latinos.

Despite large racial/ethnic differences in economic and other kinds of inequality, there is far less attention to the racial/ethnic dimension of rising economic inequality in the United States than to the rise in general. Desmond King and Rogers Smith, writing in the New York Times (September 2, 2011), emphasize the bipartisan nature of the silence that exists about the racial economic crisis. They write that:

>... leaders in both political parties—for different reasons—continue to act as though race were anachronistic and irrelevant in a country where an African American is the president.... The two parties, which openly clashed over race from the late 1970s through the mid-1990s, have for the last decade pretty much agreed not to talk about race—a silence that impedes progress toward racial equality.

Thus, there is talk of a post-racial America in which inequality no longer exists, or that, as some would argue, even if some inequality persists, it would have little to do with structural and institutional causes.

## Explanations for Inequality

Research conducted over many years on how people explain group-based disparities has generally shown that White Americans more than others have favored individual-centered causes for inequality (lack of motivation, less ability, values and behaviors that impede achievement (see Bobo and Fox 2003; Bobo et al. 1997; Feagin 1975) for earlier research demonstrating racial/ethnic differences in attributions for inequality). Although some of the studies of attributions for inequality have included Latinos and Asian Americans, a greater focus on Blacks and Whites have produced the most consistent research picture.

In polls taken over the years, Whites have always de-emphasized structural factors (discrimination and lack of a chance for education) more than others. Hunt (2007) shows, in 2004, nearly twice as many African Americans (61 %) as Whites (31 %) endorsed the statement that Blacks on average have worse jobs, income and housing than Whites because of discrimination. Latinos (41 %) fell between these two groups in endorsing discrimination as a cause of Black/White disparities. With respect to lower access to education, Whites and Latinos converged and differed from Blacks. Moreover, in 2004, 10 % more Blacks than both Whites and Latinos endorsed lack of chance for education as a reason for Black/White inequality (see also Krysan and Faison 2008, for over time trends up to 2005 in these explanations). It is true that racial/ethnic differences in explanations for Black/White inequality have been narrowing over time. The narrowing is seen especially in the growing convergence in the percentages of groups that endorse "lack of motivation and willpower" as an explanation for disparities between Blacks and Whites in jobs, income, and housing. Whites endorsed the motivational explanation somewhat less in 2004 than in 1977, while more Blacks endorsed the explanation somewhat more in 2004 than in 1977 (Hunt 2007). Latinos' endorsement of motivation for the Black/White inequality remained fairly constant throughout this period and was identical to the views of Whites in 2004.

Bobo (2011) updated these comparisons through 2008, showing that while the closer convergence between Blacks and Whites about the role of motivation persists, sizeable differences continue to exist between these two groups with respect to discrimination and lack of access to education as explanations for Black-White disparities. In 2008, only 30 % of Whites but 59 % of Blacks endorsed discrimination as a cause of racial inequality, and 9 % more Blacks than Whites attributed it to less of a chance for Blacks to receive an education. Moreover, the relative stress on motivation and discrimination differs greatly across the two groups. Among Blacks, 15 % more endorsed discrimination than low motivation, while Whites show the opposite pattern—10 % more endorsed low motivation than discrimination (Bobo 2011).

Differences between Blacks and Whites are also evident in responses to a series of questions asked in the Gallup Poll from 1997 to 2004 about how fairly Blacks are treated in a range of venues, including on the job, in neighborhood shops, downtown, in restaurants and by the police. The percentage of Whites who acknowledged discrimination across these venues ranged from 10 to 15, while the percentage of Blacks ranged from 37 to 70 (Krysan and Faison 2008). Wide discrepancies also emerged in the extent to which Blacks and Whites believed that racism was a cause of people's treatment in various parts of New Orleans following Hurricane Katrina (Adams et al. 2006).

Bobo (2011) highlights other differences that challenge the notion that the United States is a post-racial society. Racial resentment is widespread among the White population, with nearly three-fourths of White Americans agreeing that "Irish, Italian, Jewish and many other minorities overcame prejudice and worked their way up, and Blacks should do the same without special favors." Further, on a question directly about post-racialism, nearly two-thirds of White Americans but less than one-fifth of Blacks said in a 2009 survey that Blacks have already achieved racial equality. Moreover, negative stereotyping of Blacks by White Americans still remains quite commonplace despite a slight decrease over the past two decades. Although stereotypes by Whites are less prevalent now than in 1990 when stereotype trait ratings were first included in the General Social Survey, Bobo shows that even in 2008 approximately 40 % of Whites expressed an industrious stereotype whereby Whites are believed to be more hard-working than Blacks, and approximately 30 % of Whites expressed an intelligence stereotype whereby Whites are viewed as more intelligent than Blacks.

In an earlier study in Los Angeles, Bobo and colleagues (Bobo 2001; Bobo and Johnson 2000; Bobo and Massagli 2001) showed a stereotype pattern where Latinos and Blacks were viewed similarly, and Whites and Asian Americans were viewed similarly. Four groups of respondents in this Los Angeles survey (Blacks, Latinos/as, Asian Americans, and Whites) rated their own and other groups on six qualities that often define racial and ethnic stereotypes: wealth, intelligence, preference for self-support (versus living off welfare), English language ability, involvement in drugs and gangs, and tendencies to discriminate against other groups (versus treat members of other groups equally). On a stereotype index that summarized the six qualities, all groups considered Whites and Asian Americans to be reasonably close to each other and different from Latinos/as and Blacks, who in turn were considered fairly similar on this index. These results supported what has been called the racial/ethnic fault line in American society, one in which Blacks and Latinos share perspectives and are viewed similarly, while Whites and Asian Americans share perspectives and are also viewed similarly. That fault line may be shifting, however, to what Bobo (2011) and others call a new racial scheme defined as a Black–non-Black divide.

Results from a recent on-line survey confirm wide discrepancies between the views of White and Black respondents about how much Blacks (and Whites) were/are the victims of discrimination in six decades beginning in 1950 and ending in the 2000s (Norton and Sommers 2011). This survey shows overall that both groups of respondents perceived greater anti-Black than anti-White discrimination and that both groups believed that anti-Black discrimination had declined over time. There were also several important differences between the two groups. First, Blacks perceived relatively nonexistent anti-White bias across all these decades, while Whites perceived increasing anti-White bias, which in their views became more prevalent than anti-Black bias in the 2000s. Moreover, Norton and Sommers (2011) provide evidence showing that Whites, but not Blacks, see racism as a zero-sum game. They write: "Both within each decade and across time, White respondents were more likely to see decreases in bias against Blacks as related to increases in bias against Whites—consistent with a zero-sum view of racism among Whites—whereas Blacks were less likely to see the two as linked" (p. 217).

In summary, studies of the nation at large reveal continued racial/ethnic differences in how Americans view racial inequality. An important question remains about the extent to which these differences also continue to exist among younger Americans. Do the attributions and attitudes of younger Americans support the idea of a post-racial generation?

## A Post-Racial Youth Generation?

Companion essays in Daedalus, one by political scientists Jennifer Hochschild, Vesla Weaver and Traci Burch (2011), and one by political scientist Cathy Cohen (2011), reveal different perspectives on post-racialism among the young.3 Jennifer Hochschild and colleagues find much to support post-racialism in a youth cohort. The young increasingly think of themselves as multiracial, with about half of the seven million Americans who marked more than one racial census category in 2000 falling under 18 years of age. Young adults are more likely to marry across racial/ethnic lines. Hochschild et al. (2011) also highlight the more favorable opinions that young people 18–29, compared to their elders 60 years or older, hold of racial/ethnic groups other than their own. This appears to be true, however, only of Latino respondents' opinions of all other groups and White respondents' opinions of Asian Americans. The opinions held by younger and older Whites of Blacks and Latinos differ very little, as do the opinions held of the other three groups by younger and older Blacks. Finally, these writers emphasize the convergence among 18- to 29-year-old Whites and Blacks, like the racial/ethnic convergence evident in other age groups well, on the motivation attribution that Americans make to explain the Black/White gap in income, jobs, and housing. It is interesting that Hochschild and colleagues (2011) do not provide information about how young Whites and Blacks view the discrimination attribution for racial/ethnic inequality. However, Bobo (2011) does provide such information, giving age breaks on the endorsement of discrimination as a cause for inequality. He shows a sizeable difference between White and Black youth among those ages 18–33. Only 31 % of White youth compared to 52 % of Black youth endorsed this explanation in 2008. Moreover, Bobo shows no evidence across the age breaks of a convergence between the views of Whites and Blacks about discrimination as a cause of racial inequality. The evidence about anti-Black and anti-White discrimination presented by Norton and Sommers (2011) also challenges a post-racial depiction of the young in that they found no evidence that age was a factor in the views of Blacks and Whites.

Cathy Cohen (2011) presents a picture of the racial views of the youth generation showing that race still matters among young people. Based on data from two surveys,4 Cohen shows evidence of some similarities but mostly differences in the views of young people depending on their racial background. Black and Latino youth agreed with each other, for example, and differed from White youth in the percentages who believed that racism is still a major problem since the election of President Obama. They also agreed with each other and differed from Whites in the percentages who claimed that Latinos have achieved racial equality. On other questions, Black youth stood apart from both Whites and Latino youth. Between 15 and 20 % more Blacks than either of the other two groups of young Americans agreed that "it is hard for young Black people to get ahead because they face so much discrimination," and "that on average, Black youth receive a poorer education than White youth." On two specific post-racial questions, Blacks and Latinos were considerably more negative than Whites. Sixty-eight percent of the Blacks and 58 % of the Latinos but only 33 % of the White youth agreed that "racism is still a problem." Moreover, Black youth viewed the achievement of equality among Blacks and Latinos as much the same, while both White youth and Latino youth thought Blacks have outstripped Latinos in how much equality has been achieved. Thus, there is mixed support for the level of convergence in perspectives across racial/ethnic groups that would be expected if the young are a post-racial generation.

## Perspective Differences Among College Students

The picture of racial differences among the young covered thus far pertains to young people in the nation at large. Is there more evidence of a post-racial generation among young people attending college? A large longitudinal study of students attending the University of Michigan, following one cohort from 1990 to 1994 and a second cohort one decade later from 2000 to 2004, shows very similar academic motivations and goals but widely different perspectives about the causes of racial inequalities and poverty held by White, Black, Latino, and Asian American students (Matlock et al. 2007). A companion study that followed the first cohort who graduated in 1994 nine years later into their adult lives in 2003 revealed much the same picture. White, Black, Latino, and Asian American alumni shared similar values and personal goals but still held significantly different views of racial inequalities and poverty. Because these Michigan studies were longitudinal over such a long period of time, some detail in these results seems useful as a context for our examination of racial/ethnic differences among students applying for a race/ethnicity dialogue across the nine universities, one of which was the University of Michigan.

The responses of the seniors of 2004 illustrated sizeable differences in views about racial inequality expressed by the different racial/ethnic groups, especially between Black seniors and seniors from other racial/ethnic groups. While a majority of all Michigan students felt that some racial discrimination still exists in our society, there was a difference between the racial/ethnic groups in strength and urgency of their convictions about this. Seventy-one percent of Black seniors, in contrast to only 25 % of White seniors, strongly disagreed with the statement that: "Most people of color are no longer discriminated against in this country." The responses of Asian American (34 %) and Latino (33 %) seniors were closer to those of the White seniors. On another question usually thought of as measuring racial resentment, striking differences also occurred. Fifty-four percent of the Black seniors, in contrast to only 14 % of the White seniors, strongly disagreed with the statement that: "In the generation since the Civil Rights Movement, our society has done enough to promote the advancement of people of color." Again, the Asian American (17 %) and Latino (16 %) responses were closer to those of Whites than those of Blacks.

These group differences in perceptions of racial inequality extend more generally to differences in views of poverty. A set of questions, adapted from a scale developed by the sociologist Joe Feagin (1975) and used widely in studies of poverty to judge the importance of reasons why there are poor people in the United States, reveal significant differences between Black and other racial/ethnic groups of Michigan seniors. Black seniors much more often than others attributed poverty to systemic causes. "Failure of society to provide good schools for many Americans" was chosen by 77 % of Blacks and 54 % of Whites as a "very important" reason for poverty in the United States. Endorsement of this reason among both Latino and Asian American seniors was 59 %. The second systemic reason, "Failure of industry to provide enough jobs," was chosen by 42 % of Blacks but only 22 % of Whites. The other two groups fell between Blacks and Whites, with 28 % of Latinos and 31 % of Asian Americans endorsing job inadequacy (see Gurin et al. 2011).

In light of these persisting differences about the role of racial discrimination and other structural factors held by different racial/ethnic groups—in the nation at large, among the young in the nation at large, and among college students at the University of Michigan—we expected that White students and students of color who applied to take a race/ethnicity dialogue course would also differ in their causal explanations for inequalities. After all, they grew up in a United States in which causal explanations for inequality have varied considerably across racial/ethnic groups. Before examining their views and how intergroup dialogue might have affected them, we first describe what intergroup dialogue is and how the multi-university study was designed and conducted.

## What Is Intergroup Dialogue?

Today, intergroup dialogues are in place at numerous colleges and universities in the United States, usually offered as credit-bearing courses led by trained facilitators (Dessel et al. 2006). In the 5 years that the University of Michigan has conducted summer institutes for faculty and staff interested in developing dialogue courses or programs, 73 institutions have participated. In addition, institutes for faculty and staff have been conducted on both the east coast and the west coast led by collaborators of the Multi-University Intergroup Dialogue Project.

Intergroup dialogue is a facilitated educational approach that brings an equal number of students from two social identity groups. In race/ethnicity dialogue courses, the two groups are White students and students of color.5 Since their initiation in the late 1980s, intergroup dialogue courses have sought to educate students in pro-active ways to understand and work with intergroup differences conflicts that are not only historical and structural but persistent and present in their daily college lives together (for details, see Zúñiga et al. 2007). The pedagogy of intergroup dialogue courses include academic content reflected in assigned readings, reflection papers and a final paper, as well as in-class structured learning activities and facilitative guidance. In each race/ethnicity dialogue two facilitators, one who self-identifies as White and one who identifies as a person of color, intensively trained in dialogue facilitation processes guide the weekly sessions of 2–3 h over the course of an academic term.

The intergroup dialogue curriculum model is organized to cover four stages: group beginnings, understanding identities and inequalities within a context of power and privilege, exploring controversial issues, and taking action. In the beginning, participants in intergroup dialogue courses co-create guidelines for engagement (e.g., respect different perspectives, challenge ideas not individuals) and explore differences between dialogue, discussion and debate (Ellinor and Gerard 1998; Nagda and Gurin 2007). Through role playing these different modes of communication, participants learn the importance of careful listening, asking each other questions, probing each other's ideas, and seeking to understand rather than trying to convince each other. In the second stage, readings and in-class exercises stimulate participants to explore the role of racial/ethnic identity and socialization in shaping their social and political perspectives, identifying both commonalities and differences within and between the two racial groups. Those differences and commonalities are analyzed critically with respect to how they connect to systems of power, privilege, and inequality. In the third stage, participants use their growing understanding of identity, privilege, and inequality to address controversial topics involving race/ethnicity (e.g., affirmative action, immigration, quality of urban and suburban schools). The participants themselves select these topics and are encouraged to challenge their own assumptions as they explore issues that are often avoided or sometimes approached warily or argumentatively. In the final stage, small groups of participants, two from each of the two identity groups, are assigned to an intergroup collaboration project in which they conceive and implement some kind of action, and then present it to the whole dialogue with a critical analysis of the intergroup dynamics that occurred as they worked together.

Intergroup dialogue fosters distinctive communication processes. Central to the practice and theory of intergroup dialogue is the role that communication processes play in fostering a learning climate that engages all participants. Nagda (2006) articulated a critical-dialogic model of intergroup dialogue in which communication processes play a central theoretical role. Dialogic communication processes focus on self and other-oriented exchanges: engaging self (sharing one's perspectives and experiences) and appreciating difference (actively listening to others, asking questions, and exploring the experiences and perspectives of students in the two identity groups). In dialogic communication, the goal is not just to present one's own perspective or to simply hear the perspectives of others (something that characterizes discussion) nor is it a venue to defend one's own beliefs about what is right or wrong (something that characterizes debate). Instead, the goal is to better understand both one's own and others' perspectives and experiences. A critical-dialogic model integrates these dialogic processes with critical processes: critical reflection (examining the assumptions and biases held by various members of the dialogue and how such assumptions and biases are influenced by systems of power and inequality) and alliance building (working through disagreements together and finding common ground and alliances within differences). Critical processes depict communication where participants apply a critical analysis, seeking to understand self and group dynamics in the context of broader social systems; it does not mean that participants are critical of one another.

## The Multi-University Intergroup Dialogue Research Project

As described earlier, the Multi-University Intergroup Dialogue Research Project focused on race/ethnicity and gender dialogues. In this chapter, we focus on the race/ethnicity dialogues that were conducted and on the associated race/ethnicity waitlist control groups because they are most relevant to the controversies about the racial views of young people. Students in both the race/ethnicity dialogue courses and the race/ethnicity control groups were evenly distributed across four demographic groups: White men, men of color, White women, and women of color. Participants in the race/ethnicity dialogue and control groups included 730 students (393 female, 337 male; 362 White students, 368 students of color including 158 African Americans, 116 Asian/Asian-Americans, 77 Latinos/as, 4 Native-Americans, 3 Arab/Arab-American, and 10 other-identified students). In illustrating responses to specific attribution questions, we provide information for student applicants from the four separate groups that are most frequently compared in the literature on attributions for inequality—the 362 White, 158 African American, 116 Asian/Asian-American, and 77 Latino applicants. Otherwise, the results that we discuss here are based on analyses of White students and students of color.

The Multi-University Intergroup Dialogue Project's design produced a genuine experiment in that students who applied to a race/ethnicity (or gender) dialogue were randomly assigned either a dialogue course or a control group. This is important because any effects that were revealed by comparing pre- and post-survey scores taken at the beginning and end of the semester from students in the dialogue and control groups could reasonably be attributed to dialogue participation rather than simply to being in college (or to other experiences) over the academic term. It could have been argued without randomly assigned control groups that students who are motivated to take a dialogue course would have changed even without having participated in the intergroup dialogue course. It is also important that careful tracking led to high retention at posttest (95 %), which did not vary by dialogue/control condition, χ 2(2) = 0.32, p = 0.85.

## Measures of Attributions for Inequality

Students in both the dialogue and control groups completed surveys at the beginning and end of the academic term in which the intergroup dialogue courses were offered at the nine universities. These surveys included specific measures of attributions for inequality based on earlier studies. Students' beliefs about the causes of racial and gender inequalities were measured by survey items developed for the National Election Study at the University of Michigan in 1972 (Gurin et al. 1980) and subsequently used in national surveys at Michigan's Institute for Social Research. Students were asked how much they agreed, on a seven-point scale ranging from "strongly disagree" to "strongly agree," with four statements attributing racial/ethnic inequality to structural causes. Another four statements concerned structural causes of gender inequality. An example on the race measure was "Unfair hiring and promotion practices help keep many people of color from gaining positions of power"; an example on the gender measure was: "Discrimination in the workplace still limits the success of many women." Students in both race/ethnicity and gender dialogues were asked to respond to both the race/ethnicity and gender attribution statements. Students were also asked how much they agreed with two statements attributing race/ethnicity (and gender) inequality to individual causes. An example on the race/ethnicity measure was "People of color aren't as successful in the workplace as Whites because they do not have the same work ethic." An example on the gender measure was "Women are less willing to make the personal sacrifices needed to make it in American society." Measures for attributions for why poverty exists were identical to those described above for the Michigan studies, all a modification of the scale developed originally by Feagin (1975) that gave two structural reasons (poor schools and poor labor markets) and two individual ones (lack of effort and lack of thrift/poor money management).

## Racial/Ethnic Differences Among Applicants to Race/Ethnicity Dialogues

First, we address the question of whether or not White students and students of color differed in their attributions of inequality when they initially applied to enroll in a race/ethnicity dialogue course. We further consider if such differences mirrored differences reported from national studies. We studied all applicants to the race/ethnicity dialogues together to see if White students and students of color who were motivated to dialogue about race and ethnicity across race and ethnicity differed before any of them even had a dialogue experience.

The racial/ethnic group differences presented in Fig. 3.1 are effect sizes, specifically standardized mean differences (d).6 Positive effect sizes indicate higher values for students of color than White students and negative effect sizes indicate higher values for White students than students of color. Interpreting the size of an effect is not straightforward and depends largely on conventions within each discipline. For the behavioral sciences as a whole, Cohen (1988) suggests that effects greater than 0.2 could be considered small, greater than 0.5 could be considered medium and greater than 0.8 could be considered large. Because effects in education tend to be small, using these classifications may be misleading (Valentine and Cooper 2003).

Fig. 3.1

Differences between students of color and White students in understanding of inequality at pretest, expressed as standardized mean differences (d). Effect sizes are presented as standardized mean differences by dividing the difference in means between students of color and White students by their pooled standard deviation. Positive effect sizes indicate higher values for students of color than White students; negative effect sizes indicate higher values for White students than students of color. ***Difference is significant, p < 0.001; †difference is marginally significant, p < 0.10

Figure 3.1 shows that a significant group difference was found among the applicants to the race/ethnicity dialogue courses across nine universities. Applicants of color, compared to White applicants, more frequently attributed racial/ethnic and gender inequality, as well as poverty, to structural causes. White applicants not only less frequently endorsed structural attributions for inequality but also more frequently attributed racial and gender inequality (but not poverty) to individual deficiencies. The race difference on structural attributions for racial/ethnic inequality, gender inequality, and poverty were all small to moderate. The racial/ethnic group differences on individual attributions were not statistically significant and did not meet Cohen's criteria for small effects, supporting the evidence from other studies showing either small differences or actual convergence between racial/ethnic groups about the role of individual factors (motivation) in explaining racial/ethnic inequality.

Overall, these differences mirror the differences that have been found in national studies and in the study of the University of Michigan students.7 Across all these studies consistent racial/ethnic differences were found in how much Whites and various groups of non-Whites hold structural causal factors responsible for racial/ethnic inequality.

Another similarity exists between results from these applicants to race/ethnicity dialogue courses and results from groups in national studies. Many of the national studies of attributions for racial/ethnic inequality and of stereotypes of racial/ethnic groups, as well as the studies of the attributions and attitudes of students attending the University of Michigan, revealed a division in which the views of Blacks and Latinos were relatively close and differed notably from those of Whites and Asian Americans, whose views also were relatively close.

Such a division also characterized the attributions for racial inequality and poverty that were expressed by the applicants to the race/ethnicity dialogue courses. Results based on the single items that comprised the composite measure of structural attribution of racial/ethnic inequality showed support for such a division on three of the four items. Fifty-nine percent of African Americans and 56 % of Latinos, but 43 % of Asian Americans and 36 % of Whites agreed somewhat or strongly that "prejudice and discrimination in the educational system limit the success of people of color." Eighty-seven percent of African Americans and 84 % of Latinos but 60 % of both Asian Americans and Whites disagreed somewhat or strongly that "most people of color are no longer discriminated against in this country." The one item on which Latinos endorsed a structural explanation less frequently than African Americans was "unfair hiring and promotion practices help to keep many people of color from gaining positions of power." On this item, 61 % of African Americans, 48 % of Latinos, 39 % Asian Americans and 35 % Whites agreed somewhat or strongly.

The same division existed on the single items measuring attributions for poverty. Eighty-three percent of Latino applicants and 78 % of African American applicants agreed strongly or somewhat that the poverty results from "failure of society to provide good schools for many American," while 59 % of both Asian Americans and Whites concurred. Seventy-five percent of African Americans and 69 % of Latinos, but 52 % of Asian Americans and Whites also agreed strongly or somewhat that "prejudice and discrimination against the poor" explains poverty. Seventy-seven percent of both African Americans and Latinos, but 55 % of Asian Americans and Whites agreed strongly or somewhat that "inadequate opportunities for the poor" was an influence leading to poverty. And the same division emerged in the percentages of Latinos (71 %), African Americans (68 %), Asian American (46 %) and Whites (46 %) agreeing strongly or somewhat that poverty is explained by the "failure of private industry to provide enough jobs that pay more than poverty wages." The one item on which Asian Americans differed not only from African Americans and Latinos but also from Whites stated that "what one can achieve in life is still limited by one's race or ethnicity." On this item, 56 % of both African American and Latino applicants, 43 % of Asian American, but only 32 % of White applicants agreed somewhat or strongly.

No such division existed in the percentages of these groups of applicants with respect to the individualistic explanations for racial inequality and poverty. Very few (less than 10 %) of all the groups agreed strongly or somewhat that racial inequality derives from individual responsibility or deficiencies, that "people of color are responsible for their lack of accomplishments in society," and that "people of color aren't as successful in the workplace ad Whites because they don't have the same work ethic." The four groups also generally agreed with each other about the importance of individualistic factors in explaining poverty. Less than 10 % of each group said that "lack of ability and talent among the poor" was extremely or very important. The percentages across the four groups endorsing that level of importance to "lack of thrift and proper money management by poor people" ranged from 21 to 26 %, and the percentages endorsing "lack of effort by the poor themselves" ranged from 13 to 20 %.

The picture drawn from these data on applicants to the race/ethnicity dialogues reflects the picture that the various national studies have portrayed. As in the national studies:

  * Very few of these applicants attributed racial/ethnic inequality and poverty to inadequacies of individual members of racial/ethnic minority groups and the poor.

  * More applicants attributed them to various kinds of structural factors than to individual factors.

  * Racial/ethnic groups differed significantly in their attributions to structural explanations, with African Americans and Latinos generally converging and reporting more structural attributions than Asian Americans and Whites.

  * Racial/ethnic groups differed very little in their attributions to individualistic explanations.

## Overall Effects of Race/Ethnicity Intergroup Dialogues

In the overall Multi-University Intergroup Dialogue Research Project, the effect of intergroup dialogue was demonstrated when pre–post change among the dialogue course participants was statistically different than among the control group students (see Gurin et al. 2013). Lopez et al. (2011) report that dialogue increased structural explanations not only for racial inequality but for gender inequality. Furthermore, participation in intergroup dialogue courses decreased individual explanations for inequality as well.

This chapter, focusing on the effects specifically in the race/ethnicity dialogues, showed increased structural attributions for inequality. Such increases occurred for both White students and students of color in explaining the causes of gender inequalities and poverty. However, the effect of dialogue on structural attributions for racial/ethnic inequality was statistically significant only among White students. This conclusion is based on three-way interactions involving time (pre vs. post surveys), condition (dialogue vs. control), and race/ethnicity (students of color vs. White students). See Lopez et al. (2011) for fuller detail and tabular presentation of statistical interaction analyses. Thus, in race/ethnicity dialogues both groups of students became more structural and less individualistic in their thinking about gender inequality and poverty. Both also became less individualistic in their thinking about racial inequality.

What might explain significant effects of dialogue for racial/ethnic inequality for White students but not for students of color? The pre and post data for the two groups within the dialogues and control groups suggest a possible explanation. White students in the race/ethnicity dialogue courses became considerably more structural in their thinking, whereas White students in the control groups did not demonstrate any change in this regard. In contrast, the data for students of color show an increase in structural attributions about racial/ethnic inequality over the academic term in both the dialogue and control groups. Thus, part of the reason the effect of dialogue was not statistically significant, though in the predicted direction, for students of color is because students of color in the control groups became more structural just by being in college or by having other experiences that term. This did not happen among the White students in the control groups. See Lopez et al. (2011) for the means and standard deviations demonstrating this phenomenon.

It is important to put the question of differential impact of race/ethnicity dialogues on White students and students of color in the broader perspective of other measures of possible outcomes of intergroup dialogue. Altogether 24 multi-item measures of possible positive impact of intergroup dialogue were included in the Multi-University Intergroup Dialogue Research Project (see Gurin et al. 2013). The first finding of interest here is that students of color had higher scores than White students even before the dialogue courses began not only on attributions for inequality but on all other measures as well. That included multi-item measures of identity involvement, consideration of multiple perspectives, preference for complex thinking, liking to think about society, positive emotions experienced during intergroup interaction, positive intergroup interactions, attitudes toward diversity initiatives in higher education, intergroup empathy, motivation to bridge differences, and both efficacy and frequency of action. Second, the analyses just on the race/ethnicity dialogues showed a significant effect of dialogue for both White students and students of color on 20 of these 24 measures (see Gurin et al. 2013). One of the exceptions, as noted above, was structural understanding of racial inequality. Another exception was positive intergroup interactions, which followed the same pattern of results as for structural understanding of inequality. White dialogue students increased considerably over the course of the dialogue while White control group students did not change. Students of color in the race/ethnicity dialogues also increased, but so too did the students of color in the control groups.

Thus, on both structural understanding of racial/ethnic inequality and positive interactions across race/ethnicity, the lack of a significant dialogue effect reflects both a greater increase among White students than students of color in the dialogue courses, and importantly an increase as well in the control groups among students of color but not White students. A somewhat different pattern was revealed in regard to the other two other exceptions: identity involvement and motivation to bridge differences. On these measures, there was a significant effect of dialogue for both groups of students, but the effect for White students was larger than for students of color. Together, this evidence shows broad effects of participation in race/ethnicity dialogues that, with only a few exceptions, occurred for both White students and students of color.

The third research question in this chapter is whether or not the race/ethnicity dialogue experience reduced the initial differences in attributions that students brought with them when the dialogue courses began. Because the effect of participation in the race/ethnicity dialogue course on structural attributions for racial/ethnic inequality was statistically reliable only for White students, the impact of the dialogue courses was to narrow the gap in the initial attributions expressed by White students and students of color. This narrowing is clear in Fig. 3.2. Two phenomena are important. The racial/ethnic group difference in structural attributions among the dialogue students was notably smaller at posttest than at pretest. And, the group difference among the control group students was larger at posttest than at pretest because students of color in the control group had, as indicated above, increased in their structural thinking about racial inequality.

Fig. 3.2

Differences between students of color and White students in structural attributions for racial inequality at pretest and posttest by condition, expressed as standardized mean differences (d). Effect sizes are presented as standardized mean differences by dividing the difference in means between students of color and White students by their pooled standard deviation. Positive effect sizes indicate higher values for students of color than White students. ***Difference is significant, p < 0.001

The narrowing on structural attributions in the race/ethnicity dialogues was unique to attributions for racial/ethnic inequality. On the other two attribution measures—structural attributions for gender inequality and poverty—the race/ethnicity dialogues did not narrow the initial group differences in the students' thinking. Although both White students and students of color became more structural in their attributions for gender inequality and poverty over the course of the academic term, they still differed from each other at the end of the term approximately as much as they had earlier.

## Summary and Discussion

### Important Findings

The results from the analyses reported here and in Lopez et al. (2011) show:

1.

Sizeable differences in attributions for inequality existed between White students and students of color when they initially applied to enroll in a race/ethnicity dialogue course.

2.

Participation in a race/ethnicity dialogue course had a significant effect on increasing structural attributions and decreasing individualistic attributions for gender inequality and poverty, and these effects occurred for both White students and students of color.

3.

Participation in a race/ethnicity dialogue course also increased structural attributions for racial/ethnic inequality but this effect was statistically reliable only for White students.

4.

Participation in race/ethnicity dialogue courses narrowed the initial racial/ethnic group differences on structural attributions for racial/ethnic inequality but had no effect on the size of the initial group differences on other measures of explanations for inequality.

5.

Overall that there was little in the results that supports the idea, suggested by some critics of intergroup dialogue (see Gorski 2008), that dialogue is somehow "good" only for students from more privileged backgrounds. The effects of participation in the race/ethnicity dialogues were statistically significant for both students of color and White students on 20 of the 24 measures of outcomes in this large experimental study of intergroup dialogue.

### Narrowing the Gap in Structural Attributions for Racial/Ethnic Inequality

Narrowing the racial/ethnic differences in how students initially thought about structural causes of racial inequality is an important finding because it shows what an educational program can potentially accomplish with White youth. All of the studies reviewed in this chapter show that Whites less often endorse structural attributions for racial/ethnic inequality. While it is true that there was still a significant difference between White students and students of color at the end of the academic term in which the race/ethnicity dialogues were conducted, these two groups of students were much closer together in how much they held structural factors responsible for racial/ethnic inequality. This narrowing augurs well for possible coalitions of White youth and youth of color in attempting to reduce racial inequality by at least sharing a structural analysis of the problem.

### A Post-Racial Generation?

Scholars and writers in the popular media have depicted a supposedly post-racial youth cohort in the United States. Young people have been described as constructing the meaning of race differently from their elders, seeing themselves both nonracially as well as racially depending upon the particular situation, holding similar views across race about how much race affects their lives, and identifying as multi-racial more frequently than earlier generations of youth (Bai 2010; Hochschild et al. 2011). As we have noted, some evidence from national studies supports aspects of a post-racial description of young people in the United States.

However, most of the evidence reviewed from surveys of young people, as well as the results from this study, counter the idea that young people are now post-racial. That racial/ethnic differences existed even among students who were motivated to dialogue about race and ethnicity across race and ethnicity challenges the idea that the young—in this case college students—are somehow now post-racial. The present results conform to the conclusion drawn by Bobo (2011) that there are wide differences between young people from various racial/ethnic groups when it comes to understanding that racial discrimination still plays a role in the continuing racial disparities in income, employment, and wealth. They also conform to the results presented by Cohen (2011) showing racial/ethnic differences among young people on many other measures of how race operates in the United States.

The Applied Research Center, a national public policy think tank that supports research and policy analysis on race, has suggested that "too many journalists, political commentators, and even researchers have taken the established fact of increased racial tolerance among today's youth and hastily labeled them "post-racial" (Apollon 2011). That Center conducted a dozen focus groups about race with young people ages 18–25 in Los Angeles, and found the majority of the focus group participants reporting that race still mattered in their lives. Converging with survey evidence we have reviewed, Apollon (2011) concludes there were real differences in how young people of different races and ethnicities think and talk about this subject. The focus groups also revealed a difference between White participants and participants of color in how they discussed structural causes of racism. White participants tended to believe "that there were simply some racist individuals within those (political and economic) systems," while participants of color had no trouble labeling "criminal justice and employment systems themselves" as institutional sources of racial inequality.

Differences have repeatedly appeared in how much young White people and people of color explain racial inequality. That was true of the young cohort in the nation at large, the Michigan students in the longitudinal studies that were conducted between 1994 and 2003, and the students who applied to take race/ethnicity dialogues at the nine campuses that were part of the Multi-University Intergroup Dialogue Research Project. While race may be less central in other ways in which young people think about race, particularly in contrast to their elders, race differences in structural attributions for inequality still persist today.

### What Can/Should Intergroup Dialogue Accomplish Regarding Race?

Intergroup dialogue is an educational approach that intentionally utilizes diversity, an active learning pedagogy, and distinctive communication processes to help students become able to talk about race in honest and personally meaningful ways. Intergroup dialogue aims for students to learn how race, gender, and other social divisions affect members of these various social categories. It specifically aims to foster intergroup understanding, not necessarily intergroup agreement. It is intended to educate how inequality is created and maintained through structural and institutional influences, without discounting that individuals also have responsibilities for their outcomes in life as well considerable agency in altering those outcomes (Nagda et al. 2009). Because inequality in many arenas—economic, health, criminal justice, education—remains a major social issue in the United States and around the world, and because racial/ethnic inequality has increased in some arenas in recent decades, intergroup dialogues have an important educational role. So, too, do other social science courses that teach about inequality. Yet, in intergroup dialogue students are expected to learn not only from readings and instructor information but also from dialoguing about the personal experiences of students who have grown up in different environments. The larger Multi-University Intergroup Dialogue Research Project conducted supplementary analyses comparing students in 26 intergroup dialogue courses with students in 26 social science courses and found that intergroup dialogue participants changed significantly more on all but 7 of the 24 outcome measures in that study (Gurin et al. 2013). With respect to attributions for inequality, dialogue students changed significantly more in structural attributions for racial inequality and gender inequality, but not in structural attributions for poverty. What differs between the knowledge gained in intergroup dialogue courses and other social science courses that focus on race, ethnicity, and gender is that the existence of inequality is understood both statistically and personally in intergroup dialogue courses. In contrast to many traditional lecture/discussion courses on inequality, intergroup dialogue courses combine academic sources of learning with relational sources of learning that result from examining how inequality has operated in the lives of the dialogue participants.

Race/ethnicity dialogues will have accomplished their educational goal when students learn how to share their own perspectives about race and ethnicity, listen to and learn from the perspectives of others, critically analyze how race operates in various arenas in the United States and in the lives of the dialogue participants, and see that talk about race can be embraced instead of avoided. Race/ethnicity dialogues that are guided by the critical-dialogic model of intergroup dialogue can go a long way in overcoming what political scientist Katherine Walsh (2007) casts as huge problems in talking about race. Writing about the behavior of ordinary citizens taking part in interracial face-to-face conversations, Walsh stresses how atypical it was for them to engage in interracial discussion and to do so in talking about race, especially when talk about race is "generally treated as a potential for disaster by politicians and ordinary citizens alike" (p. 3). English professor Paula Moya and psychologist Hazel Markus (2010) stress that:

> (E)ven though race and ethnicity pervade every aspect of our daily lives, many of us become deeply uncomfortable whenever the conversation turns to those topics. The discomfort takes a variety of forms and affects people differently. Some people believe that the United States has successfully moved beyond what were painful racially conflicted chapters in its national history; others think that race and ethnicity are unrelated to their own lives... Some... avoid talking about (race and ethnicity) for fear of being thought racist. Yet others think that even noticing race and ethnicity is wrong... Still others believe that U.S. Americans have not begun to talk seriously about these topics and that no one can understand society without analyzing how race and ethnicity are linked and deeply intertwined with wealth, status, life chances and well-being in general. (p. 4)

Critical-dialogic race/ethnicity dialogues, such as those in this chapter, make it possible for students to have sustained discussion about race and ethnicity, however difficult that may be in the ordinary discourses of campus life and the broader society. That is an important educational benefit for all students.

Acknowledgment

This study was supported by the grants from the W. T. Grant Foundation and the Ford Foundation.

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Footnotes

1

We use the term "race/ethnicity" as an inclusive term, comprising members of racially privileged and racially disadvantaged groups in the United States. We acknowledge that race and ethnicity have distinct meanings, and also that both are social constructions denoting experiences of groups defined in some places and at some times as races and in some places and at some times as ethnicities. Given the history of the United States, race and racism are sometimes seen only in terms of Black–White relations. Race/ethnicity as an inclusive term includes Arab and Arab Americans, Asian and Asian/Pacific Islander Americans, Black/African and African Americans, Latino(a)s, Native Americans/First Nations people, Whites/European Americans and multiracial/ethnic people.

2

Studies vary in terminology used for the US population of Latin American/Spanish speaking country background, some using Latino and others using Hispanic. In this chapter, we use Latino regardless of the term a specific study may have used.

3

Studies cited here vary in how youth are defined, some using 15–25, others 18–29, and still others 18–33.

4

One survey was of youth 15–25 conducted by the National Opinion Research Center, with an oversampling of Black youth, and the other was random sample of the population of households in the United States with an oversampling of Blacks, Latinos, Asians (as well as Whites) ages 18–35.

5

The effect of dialogue was assessed using the categories of White students and students of color because at no university were there enough students from various groups of color to conduct dialogue courses involving White students with separate groups of color (Whites and African Americans, Whites and Latinos, Whites and Asian Americans) or involving two different groups of color.

6

Standardized mean differences are calculated by dividing the difference in means between students of color and White students by their pooled standard deviation.

7

The exact percentages cannot be compared because in our study multiple questions about inequality were used, whereas in the national studies two single items (discrimination, lack of access to education) were consistently used to measure structural attributions for inequality, and two single items (lack of motivation and willpower, less inborn ability) were used to measure individual attributions for inequality.

# Part II
Economics
© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_4

# 4. Wealth Building in Communities of Color

Trina R. Williams Shanks1  , Stephanie Clintonia Boddie2  and Robert Wynn3

(1)

School of Social Work, University of Michigan, Ann Arbor, MI 48109, USA

(2)

University of Pittsburgh, Pittsburgh, PA 15260, USA

(3)

Akamai LLC, Madison, WI, USA

Trina R. Williams Shanks (Corresponding author)

Email: trwilli@umich.edu

Stephanie Clintonia Boddie

Email: scb58@pitt.edu

Robert Wynn

Email: akamaillc@gmail.com

## Still on the Bottom of the Economic Ladder with the Wealth Gap Widening

Racial disparities exist in many aspects of social and economic life in the United States—including education, employment, income, health, and incarceration—and, empirical data reveal that communities of color typically fare worse than White communities in these areas. One of the more persistent and potentially damaging disparities is the racial gap in wealth. Wealth is synonymous with power and influence—putting those without it at a lower status—and even modest amounts of assets can lead to greater economic security and provide more options for one's future and children. These facts invite further exploration of asset building in communities of color. In this chapter, we (a) explore why some communities remain behind in asset accumulation, (b) introduce blueprints for individual and collective strategies for building wealth in communities of color, and (c) provide three brief case studies.

In recent decades, communities of color have made noticeable gains in income and education, expanding the ranks of the middle class. Although unemployment still tends to be high, Blacks have gone from working in primarily lower class occupations with much lower mean overall income before 1940 to becoming solidly working class and earning close to the mean income (Horton et al. 2000). This change in economic status can be attributed partly to gains in education. For example, the percentage of Blacks aged 25–29 who had completed high school or higher in 1970 was 58.4 %, with only 10 % of this group having completed a Bachelor's degree or higher. These percentages had risen to 80.5 % and 11.6 % in 1985 and 88.1 % and 20.1 % in 2011 (Snyder and Dillow 2012). A similar story can be told for Hispanics. The percentage of 25–29-year-olds with a high school diploma increased from 53.1 % in 1975 to 71.5 % in 2011, and the percentage with a Bachelor's degree rose from 8.8 to 12.8 % (Snyder and Dillow 2012). For Asians, the percentage of 25–29-year-olds who had completed high school increased from 89.7 % in 1989 to 95.3 % in 2011, and the percentage with a Bachelor's degree rose from 45.1 to 57.2 % (Snyder and Dillow 2012). Despite minor gains in some areas, Native Americans, Blacks, Hispanics, many Asian Americans (e.g., Koreans, Filipinos, Vietnamese), and Native Hawaiians/Pacific Islanders still lag behind Whites in net worth (Lui et al. 2006).

This persistent wealth gap is the most striking indicator of economic disparity (Conley 1999; Oliver and Shapiro 1995, 2006; Shapiro 2004; Sherraden 1991) and widened with the onset of the recent economic crisis. In the wake of the Great Recession, the median net worth of White households is 20 times that of Black households and 18 times that of Hispanic households, according to a Pew Research Center (2011) report based on data from the 2009 Survey of Income and Program Participation. To be more specific, the typical Black household had just $5,677 in net worth1 (assets minus debts) in 2009, and the typical Hispanic household had only $6,325, while the typical White household had $113,149 (Kochhar et al. 2011). This is the largest gap recorded in the last 25 years, when researchers began collecting such data. Declining housing values largely account for this increasing wealth disparity. Black, Hispanic, and even some Asian American households are more likely to be caught in the double bind of predatory loans and homes with inflated values and low equity.

## No Head Start

So how did communities of color get so far behind in wealth accumulation? According to Shapiro (2004), the answer is simply that they had no head start. When wealth has been in a family for generations, it is easier to encourage and replicate transformative experiences that allow children to be more successful, starting with a quality education and homeownership. In addition, some groups have systematic advantages for building wealth, while others face barriers. Government policies, structural racism, and historical discrimination have stunted asset accumulation among communities of color, most notably Native Americans, Hispanics, Blacks, and Asian Americans, to the advantage of Whites (Kijakazi 1997; Lui et al. 2006). As the demographics shift toward greater numbers of people of color, addressing racial disparity in wealth becomes even more important as the nation seeks to retain a position of financial strength.

Two governmental policies in particular have encouraged wealth building and asset accumulation among White Americans: the 1862 Homestead Act and the Servicemen's Readjustment Act of 1944. Between the years of 1867 and 1940, nearly 1.5 million people were awarded homesteads, typically of 160 acres each. Despite discussions by politicians about offering 40-acre plots to the four million newly freed slaves, beneficiaries of this transfer of public lands overwhelmingly were European immigrants (Williams Shanks 2005).

The Servicemen's Readjustment Act of 1944, commonly known as the GI Bill, was intended to help World War II veterans successfully transition back to civilian life. The GI Bill had a profound effect because it offered opportunities—previously available only to very well-off families—to more than 15 million veterans, including 7.8 million who received educational benefits (i.e., college, graduate school, or other education/training); four million who received loans guaranteed by the U.S. Department of Veterans Affairs to finance a home, farm, or business; and 8.4 million who received unemployment benefits or "readjustment allowances." At least 12.4 million (78 % of veterans) received at least one of these benefits, while many requested more than one (Altschuler and Blumin 2009). Although the benefits of the GI Bill were available to all returning veterans, minority recipients were underrepresented. In addition, during a time of residential redlining2 and racially biased college admissions, few people of color were able to attend top universities or felt welcome in the newly built suburban homes (Quadagno 1996). Today, individuals serving in the military can receive some money for college, but the benefits are not as far-reaching or generous as those mandated in the original bill.

Without the advantages of family wealth or beneficial policies that helped large numbers of White recipients, people in communities of color find themselves in a precarious economic position. According to an Urban Institute study, the racial gap grows with age (McKernan et al. 2013). Whites in a cohort started out as adults in their thirties with three and a half times more wealth than Blacks of the same age. These same whites had seven times more wealth in their sixties. In the case of immigrant families, language and cultural barriers often hinder their use of standard financial services. Many rely on informal methods of asset building, such as resorting to stashing money under their beds or establishing informal savings groups. Without a head start, these households have little or no wealth and very little economic security.

In the next section, we offer a list of strategies for building assets and closing this wealth gap. The suggestions are not offered as a magic bullet and definitely are not intended to minimize the very real struggles that communities of color have faced in light of the recent recession and subsequent foreclosure crisis and drop in retirement savings. Acknowledging that many have seen a decline in net worth and even lost their homes in the past several years, it is a wise strategy to preserve assets and return to building wealth as quickly as is feasible. This is the one predictable way to attain greater economic security.

## A Blueprint for Building Wealth in Communities of Color

In Fig. 4.1, we offer a range of asset-building activities that can help people in communities of color accumulate wealth. The activities noted are not meant to be an exhaustive list, but they do offer a range of both individual and collective strategies for building wealth across an array of household incomes. We present the case in this way for several reasons. First, racial wealth gaps are not caused by lower incomes and higher levels of poverty only. At every point across the income distribution, Blacks and Hispanics have less net worth than their White counterparts (Blau and Graham 1990; Keister 2000; Oliver and Shapiro 1995, 2006; Wolff 2000). Thus, we note strategies that might be relevant across the income continuum. Second, in some circumstances fiscal prudence at the household level may not be enough for families to accumulate wealth. Collective strategies are important for creating and sustaining economic security, particularly in communities where low wealth and low income have been the norm for generations. Thus, we note ways that institutional anchors—including Historically Black Colleges and Universities (HBCUs), civic organizations, neighborhood associations, and faith-based institutions—can work to help build community resources and help reduce racial wealth gaps. We summarize these strategies below and provide short case studies of how they have been implemented.

Fig. 4.1

Working model for a continuum of asset-building activity

Before discussing the asset-building activities listed, a few general principles are warranted. First, income and assets are different. Income is the inflow of money to an individual on a weekly, biweekly, monthly, or annual basis. Regardless of how much income a person earns, it is possible to spend money as quickly as it comes. In contrast, assets are the stock of resources that people hold over time for security and socioeconomic development, including financial assets that can be liquidated into cash easily (e.g., bank accounts, mutual funds, stocks, and bonds) and tangible assets (e.g., vehicles, real estate/home equity, business equity, and other items of marketable value) (McKernan and Sherraden 2008). When it comes to measuring assets, net worth—the total value of all assets minus all debt—is a common indicator, but some suggest measuring asset poverty and asset thresholds, a point above which a higher level of economic functioning and status becomes possible (Nam et al. 2008).

If a person (or household) has a regular income, the first step in building and preserving assets is to budget available resources wisely. Most financial experts recommend saving as much as 15–25 % of one's income for emergency funds and retirement (e.g., Nichols 1998; Ramsey 2009). At a minimum, it is advised not to live paycheck to paycheck or run up unsecured debt because this creates a deficit that reduces overall net worth. If an individual is able to capture even 1–3 % of income as savings, this provides a base upon which long-term asset building becomes possible. In fact, once an emergency fund is established (suggested as 3–6 months of living expenses), additional savings can go directly to investment opportunities.

A second principle is that the current racial wealth gap in the United States has taken generations to develop, and it will not go away quickly. Even if racial wealth equity is not achieved in the lifetime of anyone reading this book, reducing the number of people living in asset poverty and improving economic security among communities of color are still worthy goals on the path to equality and can improve drastically the lives of children and families.

## Asset-Building Strategies

### Individual/Household Level

Saving or investing a portion of the tax refund. Specific asset-building strategies that target low-income households include providing advice about and incentives for saving and investing Earned Income Tax Credit (EITC) refunds. For some households, the only time they receive a significant lump sum of money or any surplus at all is when they receive their tax refunds. Several studies and experiments, including the SaveUSA program in New York City, have explored ways to reach low-income families and encourage them save at least a portion of this money (Beverly and Dailey 2003; Duflo et al. 2006; New York City Department of Consumer Affairs 2009).3

Opening an Individual Development Account. Individual Development Accounts (IDAs) are another method of asset-building targeted to low-income households. The idea for this approach came about in the 1990s as researchers noted that assessing asset-building tax and credit programs typically did not include the poor (Ackerman and Alstott 1999; Oliver and Shapiro 1995; Sherraden 1991). IDAs, first proposed by Michael Sherraden (1991), are matched savings accounts for low-income families with a particular purpose, typically homeownership, education, or microenterprise. The match rate can vary, but on average is about 2:1 (i.e., $2 are deposited for every $1 saved by the participant). By encouraging saving, offering financial education, providing recommended targets and feedback, establishing a habit of making regular deposits, promoting future-focused planning, offering asset-specific education, and setting rules on allowed asset purchases and matched withdrawals, something more than a simple transfer of resources occurs. Helping low-income families build assets can improve current and future well-being because asset owners think about themselves differently and are treated differently by others (Schreiner and Sherraden 2007). IDAs are a good example of a market-driven strategy for poverty alleviation (Cooney and Shanks 2010).

Purchasing a home. Home equity traditionally has been the largest portion of net worth for most households in the United States. Although Fig. 4.1 lists homeownership as a low-income strategy, anyone that qualifies for a mortgage or can afford to pay the sale price outright can purchase a home. However, in an era of subprime lending, high home foreclosure rates, and underwater property values, making wise choices about homeownership and mortgage terms is essential. In particular, low-income households can benefit from homebuyer counseling programs to obtain an honest determination of whether owning or renting is the better option.

Saving and investing for long-term goals. Once a person or household has arranged a budget in which some portion of income goes toward saving, many tax-protected ways to invest toward designated purposes are available. Although Fig. 4.1 lists these options as middle-income strategies, low-income households that establish an emergency fund and have extra money to save also can implement them. If an employer matches retirement savings through a 401(k) or 403(b) program, this should be the first investment choice because it uses pretax dollars and compounds money saved. Individual Retirement Accounts (IRAs) are another good retirement planning option. State college savings plans (i.e., 529 s) allow invested money to grow tax free if withdrawals are used for educational expenses for the saver, a child, or a family member. These are all ways to accumulate wealth for necessary future expenses and reduce potential tax burdens.

Many people with money to invest choose to purchase real estate to use as rental property or sell for a profit after rehabilitating it. In an uncertain housing market, making wise choices is necessary, but this path works well for those who have mastered finding housing sales at good prices and making home repairs.

Another set of strategies targeted to those with higher incomes or those who already have accumulated a significant amount of assets usually begins with estate planning to decide how to dispose of one's assets upon death. This can be done through a will with the assistance of a lawyer or accountant. In some instances, assets are put into a trust with specific instructions rather than left to an individual or children. This can ensure that the wishes of the owner are met and provide beneficiaries with a minimal gift tax by freezing asset values as of the date of transfer. Assets held in trust generally are protected from the claims of a beneficiary's creditors, thus providing some liability protection for those assets. A family endowment serves a similar purpose but allows for family assets to be turned over to an institution for specific purposes with tax-exempt status. A board of trustees or professional manager might oversee the funds to ensure that they are used as the donor intended.

### Community/Collective Level

Asset-building approaches primarily are focused on individuals and typically are implemented outside of more traditional community-level or collective approaches, which does not consider the importance of the neighborhood and organizational context for people of color. We document promising examples of initiatives that expand the range of stakeholders and potential asset owners. Rather than shifting or concentrating wealth among a few, these strategies seek to share it among those often from middle and lower socioeconomic groups (Gordon-Nembhard 2013; McCulloch and Robinson 2001; Williams Shanks et al. 2010). While these collective asset-building strategies are not overly complicated, people should not attempt to use most of them without the help of an experienced adviser and legal consultation. These "group-centered, neighborhood-centered and needs-based" asset-building strategies have particular value for stabilizing communities by providing affordable goods and services, creating living wage jobs, promoting civic engagement, and increasing local business development (Gordon-Nembhard 2013).

Shared risk pools. Shared risk pools distribute the risk and cost for specific benefits (e.g., health care). This idea grew out of the self-help tradition of providing health care and death benefits and can be useful for individuals from any socioeconomic status (Butler and Herring 1991; Butler 2005). North Carolina Mutual Life Insurance Company (NCM), the largest Black-owned insurance company, operates in 24 states and the District of Columbia and offers a model for private shared risk pools (North Carolina Mutual Life Insurance Company n.d., 1940). Established in 1898, at last count NCM had assets of $210 million, a surplus of $26.2 million, and insurance valuing $14.1 billion (African American Registry n.d.).

Housing cooperatives. Housing cooperatives allow residents to share ownership of real estate used as the primary residence. New housing cooperatives are created from vacant buildings or through new construction, sweat equity cooperatives use members' labor as investment in new construction or rehabilitation, and leasing cooperatives lease a building from the owner. While participating in a housing cooperative limits the individual's potential return from resale, it creates an opportunity for real estate ownership among those with low levels of assets. There are more than 400,000 housing cooperative units in the United States, and 30 % of all housing in New York City is part of a cooperative. In their fight against racism and discriminatory practices in housing and lending during the Harlem Renaissance, A. Philip Randolph and Adam Clayton Powell advocated this type of group ownership (Limited Equity Housing Co-op 2001).

Investment clubs. A proven method of collective wealth building is an investment club. Investment clubs consist of a group of people who commit to working together to save and invest. Clubs usually are organized as partnerships of around 15 people. Members update and review their portfolios, present new stock ideas, and make joint decisions on stock buying and selling decisions. By investing small amounts consistently over a long period of time, investment club members often are able to amass sizable portfolios within the club on their own.

Worker-owned cooperatives. Another collective strategy slowly emerging is the worker-owned cooperative, a democratically organized business in which workers invest in shares to generate capital. While this type of collective strategy is not as popular as others, it has considerable value when small businesses and job creation are necessary to revive the sluggish economy. Childspace Cooperative Development, Inc., a Philadelphia-based organization, also provides quality childcare, excellent training opportunities, and an IDA program (Gordon-Nembhard 2013).

Farm cooperatives. Black farmers have lost over ten million acres of land in the last 200 years. Farm cooperatives support farmers with technical assistance, legal assistance, business development, financial training, and resource development. The Federation of Southern Cooperatives/Land Assistance Fund (FSC/LAF), one such organization, formed in 1967 as an outgrowth of the Civil Rights Movement to help African Americans save their farmland and their way of life (The Federation of Southern Cooperatives/Land Assistance Fund n.d., 2007). Such organizations help identify causes of African American land loss and organize campaigns to help prevent it (Thomas et al. 2004; Wood and Gilbert 2000). FSC/LAF has a demonstration farm and training center. Their constituency represents approximately 25,000 low-income families organized into over 100 cooperatives in rural communities across the South.4

Microenterprise cooperatives. Microenterprise cooperatives and related associations often form to support groups of microentrepreneurs. This collective strategy supports small businesses with five or fewer employees with investments of less than $35,000. There are about two million microentrepreneurs in the United States that offer services and products ranging from home-based day care to specialty foods. Dr. Muhammad Yunus established this concept through the Grameen Bank in Bangladesh in 1976. Some microenterprises focus on serving particular target groups, such as formerly incarcerated individuals (Burrus 2006; Rogers 2010).

Community Development Corporations. Community Development Corporations (CDCs) add value to collective strategies as nonprofit organizations that can attract public and private dollars to promote a range of asset-building activities. The most common reinvestment activities include community building, developing affordable housing, initiating business development and job creation, expanding commercial and industrial development, and developing social services, particularly family and youth services. According to an industry census published in 2006, there are about 4,600 CDCs in the United States with a significant share that focus on either housing development or local services (http://www.community-wealth.org/strategic/panel/cdcs/index.html [The Democracy collaborative n.d.]). For example, Lawrence Community Works has more than 25 years of investment experience in a largely immigrant community and boasts 4,000 members and a range of family and community asset programs (About LCW n.d). Similar ongoing community economic development work has been done in Native American communities (Fiddler 2012).

Intermediaries. Intermediaries are typically regarded as brokers that use their networks and skills to leverage resources to help nonprofits build and preserve assets. The Korean Churches for Community Development (KCCD) has established successful relationships with public- and private-sector organizations to save more than $82 million in mortgages (Boddie 2005; Boddie et al. 2011; KCCD n.d.; Boddie and Thirupathy 2005).

Limited liability companies. Limited liability companies (LLCs) are legal entities that combine the personal asset-protecting aspects of a corporation with the flexibility of a partnership or sole proprietorship. In an LLC, no contributing owner is liable for the debt of the entire organization, while any profits can be shared or reinvested however the participating owners decide.

Venture capital entities. Venture capital entities are another viable approach for collectively aggregating capital to support the early-stage development of high-potential start-up companies. Venture investors make money by establishing an equity (i.e., investment) position in innovative companies in information technology, bioscience, distribution, and other sectors. In exchange, the venture fund assumes part ownership of the company and a right to participate in its decision making. One such example is The Obsidian Society, organized in 1995 to financially support people involved in the arts, education, and social services and generate profits through its subsidiary, Obsidian Films (The Obsidian Society n.d.).

Foundations. Often overlooked, foundations also can combine collective assets to support charitable activities for the common good. Foundations typically engage in grantmaking and/or operate programs with income earned from investing endowments. Independent or private foundations (e.g., the National Black United Fund [NBUF]) are the most common kind of foundation. NBUF and its 15 affiliates serve as the only national network of Black American philanthropic organizations and a corporate sponsor of the 27 organizations participating in the National Black Federation of Charities (NBFOC). NBUF and NBFOC raise millions each year primarily by workplace fundraising efforts that previously had been the domain of groups like the United Way (Carson 1993; National Black United Fund n.d.).

## Practical Gap-Closing Case Studies

Although the principles in this chapter could help any community of color build wealth, most of the following examples are from African American communities. Investment clubs are a gap-closing method that requires energy and individual effort, but the long-term impact of studious and methodical investing can be just what communities of color have needed.

## Now Is the Time... To Start Your CLIMB

Although individuals of color did not compose the Mutual Investment Club of Detroit, it is one of the best and most-inspiring examples of the long-term benefits that investment clubs can offer. As chronicled in the book, From Little Acorns Grow: Main Street Millionaires, Thomas O'Hara, George Nicholson, and others started the Mutual Investment Club of Detroit in 1941 with a commitment from 25 persons to contribute $10 per month to the club. The idea was to pool funds to buy shares of stock in publicly traded companies. They increased the monthly contribution amounts to $25 in the 1960s and accumulated a portfolio valued at $4.013 million in 1999 and $7.5 million in 2005. In 1951, members created a national nonprofit to assist investment club start-ups, formerly called the National Association of Investors Corporation (NAIC) and now known as BetterInvesting (Better Investing n.d.).

Many eager novice investors followed the lead of the Mutual Investment Club of Detroit, including Robert Wynn, founder and president of Asset Builders of America, Inc. Through Asset Builders of America, Inc. n.d., Wynn initiated the Communities Learning to Invest and Mobilize for Business (CLIMB) project through which he has facilitated the start-up of 35 investment clubs. Wynn's first club (which included a merger with a companion club) peaked at a value of $365,000.

The investment club model also can be used to start family clubs, which Wynn did with his family in 1999. They selected a name for the club, decided on the monthly investment amount, and elected officers. Wynn's newly retired sister agreed to be treasurer—a club must have a competent and dedicated treasurer—and the group agreed to meet monthly by conference call. There are 11 members of the club, including two infants who had accounts created for them when they were born. Club members contribute $25 as their monthly dues, and the club has accumulated a portfolio valued at $65,000 so far.

## There Is Strength to Be Gained in Numbers: A Faith-Based CDC and Asset Building

The Collective Banking Group (CBG), an incorporated CDC, operates as a membership-based, service-driven organization. Its founder, Rev. Jonathan Weaver, formed this organization in response to the then 700-member Greater Mt. Nebo African Methodist Episcopal Church's struggle to get a loan to finance renovations for their church facility in 1992 (Johnson 2009; Personal communication 2011). If a large church and loyal banking customer that had even paid off a 30-year, $250,000 church mortgage in 7 years had such difficulty securing a loan, what must be the experience of other churches and church members? Rev. Weaver estimated that the 40,000+ Black churches in the US deposit at least $50 million in banks each Monday morning.

If Black churches could use their clout to get politicians elected, this pastor envisioned using the collective economic power of churches to institute a new paradigm for working with financial institutions and businesses. Concerned about the economic vitality of disadvantaged communities, Rev. Weaver founded The CBG with 25 churches representing 20,000 members. In short order, banks were contacted to solicit their interest and screened as candidates for entering a partnership with these churches. In December 1995, the first agreement was publically signed by Riggs Bank, Industrial Bank, Enterprise Federal Savings Bank, and Harbor Bank of Maryland.

As an incorporated CDC, CBG expanded its vision and changed its name to the Collective Empowerment Group, Inc. (n.d.) (Personal communication 2011). As of 2011, the Collective Empowerment Group (CEG) has chapters in six other cities—Baltimore, Charlotte, Miami, Austin, Newark, and Cincinnati. All chapters embrace the vision to address the needs of the whole person and community by providing services in the following areas: health, criminal justice, employment, economics/financial education, and public policy. The national chapter and affiliates operate much like AAA or AARP. To join this membership-based organization, churches pay a fee based on their membership size and their church members become card-carrying members of the CEG entitled to special benefits and services. Banking partners are assessed a fee based on their asset-base (fees range from $13,000 to $33,000), are responsible to support CEG community events and fundraisers and are encouraged to provide pro bono services. Both banking partners and strategic business partners receive business from participating churches and their members. The CEG now has several flagship programs and has expanded its strategies to include the following:

  * Financial education classes and counseling

  * Distribution of financial education materials (e.g., MONEY Smart, Crown Financial)

  * CEG Sunday for sharing banking and financial information

  * Collective Institutes, monthly workshops

  * Programs to save money on utilities

  * Annual community outreach events

  * Power in one fundraising to establish a CEG financial institution

With the collective bargaining power of over 200,000 members, The CEG has helped secure over $350 million in loans to churches, church members, and businesses owned by members. The sentiment of these faith leaders is: as long as we keep ourselves collected, this can work.

## Not Just One Organization's Efforts: Urban Innovation 21

Urban Innovation 21 (formerly known as the "Pittsburgh Central Keystone Innovation Zone") links technology and other high growth business clusters to the needs of underserved communities in Pittsburgh. A consortium of higher education, government, local corporations, regional economic development organizations, financial lenders, and charitable foundations governs and financially support this work. Urban Innovation 21 has targeted Pittsburgh neighborhoods like the Hill District and Homewood to promote individual and cooperative business ownership as well as support existing business owners. Mr. William Generett, President and CEO of Urban Innovation 21, views entrepreneurship as an economic engine as well as a catalyst to change the world view of residents by allowing them to see new opportunities (Personal communication 2014). To date, 20 % of the businesses supported are African-American owned.

Since 2007, participating entrepreneurs have benefitted from services to help them eliminate barriers to business growth. These services primarily include access to tax credits, business capital, paid interns, networking opportunities, and pro bono legal services. Urban Innovation 21's flagship programs are:

  * Homewood Innovation Zone Project

  * Promise Scholars and Community Internship Programs

  * The Southwestern Pennsylvania Urban Revitalization Program (Federal Jobs and Innovation Acceleration Grant Implementation Project)

  * The Pittsburgh Wealth Building Initiative

Urban Innovation 21 prioritizes engaging local residents in community revitalization and entrepreneurship at all levels. Its wealth-building strategy links local business development to the supply chain of anchor institutions to meet their corporate needs and infuse capital into disinvested neighborhoods. One local resident started a security firm contracting with the local grocery store and other neighborhood organizations.

## Dedicating Funds for Big Investments

Astute business people understand the need for private equity and resourcefully develop pools of private equity capital by aggregating assets from multiple people or entities (i.e., investors) in a fungible, liquid form (i.e., cash). Facilitators can deploy this capital as investments in various growth-oriented start-ups or early-stage businesses. These entities are designed to attract very large sums of equity capital from $5 million to $500 million and have not been a prolific tool for wealth building within communities of color until recently.

Michael Porter of Harvard University, the Kauffman Foundation, the U.S. Small Business Administration's Minority Enterprise Small Business Investment Corporations, and others pioneered double bottom line venture investing by calling for hybrid Community Development Venture Funds (CDVFs) to invest in growth-oriented companies owned by persons of color or that benefit communities of color. These entities, encouraged and nurtured by the Community Development Venture Capital Association, began to take root in urban areas with leaders who understand the inextricable connection between economic development and capital formation.

One example is Generations Growth Capital located in Milwaukee, Wisconsin, which began in March 2007 to provide growth capital to lower middle market growth companies. As a CDVF, they believe that underserved businesses have the potential to grow and prosper with appropriate financial and management support. The firm, led by Cory Nettles, J.D., is committed to working with low- and moderate-income communities and minority entrepreneurs. Generations Growth Capital currently has investments in construction firms, a window and overhead door distributor, and an electromechanical component manufacturer (Generation Growth Capital n.d.).

Private equity has been the missing link in the panoply of strategies and tactics necessary for closing the racial wealth gap. Private equity entities that serve minorities and disadvantaged communities have the potential to achieve the double bottom line of wealth creation and community development. These community development-oriented angel capital networks (i.e., equity invested at the earliest stage of a business's development) and venture capital funds can create jobs and stimulate economic growth in communities of color while creating wealth for the owners and investors. Both objectives are laudable and necessary to close the racial wealth gap.

## Conclusion

Although governmental policy and local investments potentially could address long-standing racial wealth gaps, such approaches are unlikely with limited available resources and lack of political will. The asset-building strategies described in this chapter are a useful blueprint for building wealth, but economic insecurity and limited participation in the larger financial and economic system in communities of color suggest that these strategies are not enough. To make meaningful progress, "gap closers"—energetic, strategic, and collectively effective campaigns on a large and protracted scale—and institutional outreach are necessary.

Gap-closing methods could begin with community-based and faith-based organizations providing information and access (e.g., investment clubs in urban communities and on college campuses). Established groups, such as the Urban League, National Association for the Advancement of Colored People, National Council of La Raza, Nueva Esperanza, and Korean Churches for Community Development, might launch a national campaign to provide wealth-building education and outreach through their constituent groups. Based on the examples above, gap closers could develop or work through existing networks of congregations and philanthropic organizations to collaborate with organizations such as BetterInvesting and Asset Builders of America, Inc. This network could be further enhanced and empowered by the leadership and participation of HBCUs, community- and faith-based organizations, and successful athletes, entertainers, and other professionals of color. With strategic outreach and mobilization of interested partners, a broad-based audience will be established to offer webinars and meetings to provide examples, answer questions, and link people to local resources.

As mentioned earlier, it has taken generations to create the current racial wealth gaps, and they will persist until the problem of racial economic inequality is recognized in wider and wider circles. With a combination of individual effort and collective strategies, gap closers and gap-closing methods of reducing asset poverty in communities of color eventually will reduce racial wealth disparity, and increase financial security and create a more inclusive economy.

Acknowledgment

We gratefully acknowledge the assistance of Tiffany Trautwein and Breanna F. Goff.

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Footnotes

1

Wealth is measured at total assets minus total liabilities/debt. Assets are the sum of financial assets (such as bank accounts, stocks bonds, and 401 ks/IRAs) and nonfinancial tangible assets (such as homes and real estate, businesses, and vehicles). Liabilities include both unsecured debt (such as credit card balances) and secured debt (such as mortgages and vehicle loans).

2

In the late 1960s, community groups in Chicago's Austin neighborhood coined the term redlining referring to the redlines lenders and insurance Companies drew around areas they would not offer loans and other services, especially inner city neighborhoods likely to attract people of color (see Hillier 2003).

3

See http://www.nyc.gov/html/ofe/html/policy_and_programs/saveusa.shtml for more details.

4

Theses agricultural cooperatives are primarily based in South Carolina, Mississippi, Georgia, and Alabama.
© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_5

# 5. Recent Employment Trends Among Black Men and Their Policy Implications

Harry J. Holzer1

(1)

McCourt School of Public Policy, Georgetown University, Washington, DC, 20057, USA

Harry J. Holzer

Email: hjh4@georgetown.edu

## Introduction

In recent decades, employment rates among African American women, especially for the less educated, have improved markedly. Despite the recent economic downturn, labor force activity among Black women has trended upward over the past two decades, with very notable increases during the 1990s.

In contrast, employment and labor force activity among less-educated Black men, especially among the young, has declined for several decades now. Even during the 1990s, and despite a booming job market, the long-term trend towards lower employment for Black men continued. These worsened substantially during the Great Recession that began after 2007, but it is the secular (rather than cyclical) decline in such activity that is most troubling.

And declining relative outcomes for Black men are not limited to the labor market. Along several other dimensions—including educational achievement and attainment, as well as incarceration—the outcomes of young Black men have worsened relative to those of Black women and other racial/ethnic groups, including native-born Hispanics.

In this chapter I review some evidence on the deteriorating education and employment outcomes that we observe among less-educated Black men. I consider a variety of causes of this situation, and then turn to their implications for policy.

I argue that young Black men have seen their economic opportunities steadily diminish, as they have for less-educated men in the USA more generally. As a result, many of these young men often "disconnect" from both school and work at a relatively early age. More broadly, they disconnect from an entire set of mainstream social institutions, including marriage and staying within the law. As a result, high rates of unwed fatherhood and incarceration tend to accompany the poor educational and employment outcomes for this group.

Like Daniel Patrick Moynihan, I believe that improving the relative social and economic status of young Black men will require us to improve their perceived employment opportunities and their incentives to participate in the mainstream worlds of schooling and work. A range of policy options should be undertaken to achieve those goals, as I note below.

## Deteriorating Outcomes: Employment, Education, Incarceration

Figure 5.1 presents data on the employment and labor force participation rates of young men since 1979, separately by race. The samples include those aged 16–24 who are not enrolled in school and who have a high school diploma or less.1

Fig. 5.1

(a) Employment rates of 16–24-year-olds, 1979–2005. (b) Labor force participation rates of 16–24-year-olds, 1979–2005. Samples are limited to non enrolled men, high school or less education

The results show very modest declines in such activity for young White and Hispanic men, but more pronounced declines for Blacks. Indeed, while Black men in 1979 had employment rates that lagged behind those of Whites and Hispanics by 15–20 % points, by 2005 they lag behind by nearly 30 % points.2 The growing gaps appear in relative rates of labor force participation, defined as working or looking for work in the previous month, as well as in employment. The widening of the gaps occurred even during the 1990s, when young Black women were pouring into the labor market as a result of welfare reform, a strong economy, and the expansions of work supports for low-income single parents (Blank 2002).

And, if anything, these diagrams understate the relative employment declines among Black men, as they are based only on the civilian noninstitutional populations of each group. The growing relative incarceration rates among Black men reduce such populations, since the incarcerated are not included in these samples; if they were, the downward trends among Black men would no doubt be more severe.

But the deteriorating status of Black men is not limited only to employment rates. Table 5.1 presents data on a range of educational and other outcomes for young men and women by race/ethnicity. These are drawn from the 1997 cohort of the National Longitudinal Survey of Youth (NLSY97).3 Data are presented here on educational achievement, as measured by high school grade point average (GPA) and percentile rank on a national test (the Armed Services Vocational Aptitude Battery, or ASVAB); educational attainment, as measured by the fractions of young people who have either dropped out of high school or attained a bachelor's degree, as well as those enrolled in 2-year or 4-year college; and the percentages who are unmarried parents or who have ever been incarcerated.4

Table 5.1

Educational and behavioral outcomes of youth | WM | BM | HM | WF | BF | HF

---|---|---|---|---|---|---

High school GPA (mean) | 2.5 | 1.9 | 2.1 | 2.7 | 2.2 | 2.3

Armed Services Vocational Aptitude Battery (percentile rank) | 57.3 | 28.1 | 39.4 | 58.2 | 32.0 | 38.8

HS dropouts (%) | 13.4 | 27.6 | 20.8 | 12.0 | 19.0 | 20.6

B.A. degree (%) | 12.8 | 5.6 | 3.6 | 18.2 | 6.9 | 5.5

Enrolled in college (%) | 17.2 | 9.7 | 10.1 | 19.0 | 14.4 | 13.2

Unmarried parent (%) | 9.9 | 30.8 | 17.9 | 17.3 | 47.5 | 47.5

Ever incarcerated (%) | 7.6 | 14.8 | 9.6 | 2.7 | 3.1 | 2.4

Source: NLSY97

The results show that young Black men lag behind Black women, as well as Whites and Hispanics, on virtually every outcome. Of course, women tend to outperform men in educational attainment and achievement more broadly (Jacob 2002); and it has become well-known Blacks lag behind Whites and Hispanics in educational achievement, beginning at very early ages (Fryer and Levitt 2004).

But the lagging behind of young Black men relative to all other groups remains striking. Young Black men have the lowest GPAs, the worse test scores, the highest rates of high school dropping out, and the lowest rates of college enrollments; only in rate of BA attainment do they exceed those of Hispanic men. Furthermore, Black men are also the most likely to report being unmarried parents (except for Black women) and show much higher rates of incarceration by their early 20s than any other group.5

## Causes of Lagging Outcomes Among Young Black Men

What might explain the ongoing gaps in outcomes between young Black men and all other groups, including their female counterparts, and why might their status have deteriorated over time in relative terms?

While many social and economic forces could contribute to this range of outcomes, I focus primarily on labor market forces, which might encourage or be reinforced by a range of other developments. As Fig. 5.2 indicates, I believe that much can be explained by two important economic phenomena: (1) the ongoing decline in labor demand (or employer hiring behavior) facing less-educated men in general and Black men in particular; and (2) an "elastic" labor supply function among these men, who withdraw from the labor market as their opportunities diminish.6 This combination of shifting labor market demand away from less-educated Black men and their elastic labor supply responses to these shifts would result in mildly lower wages but significantly lower employment activity, as the figure indicates and as we have observed in reality.

Fig. 5.2

Labor demand shifts and labor supply response

What has caused the demand for the labor of young Black men to deteriorate over time? In general, the demand for less-educated men in the labor market has worsened substantially in the past few decades, and more for less-educated men than for women (Autor 2010). No doubt, this reflects a relatively greater deterioration of employment in manufacturing and other highly paid blue-collar work while low-wage service sector employment has grown. And, in response to such changes, less-educated men of all racial groups have tended to withdraw from the labor market to some extent (Juhn 1992).

But the deterioration in market opportunities for less-educated Black men seems to have been worse than for other groups of men. At least in the industrial Midwest, Black men had been very dependent on durable manufacturing for good-paying jobs, and they have been hurt more by its decline there and elsewhere in the country than have other groups (Bound and Freeman 1992; Bound and Holzer 1993). As employer demand for workers with strong basic skills has grown, the groups that lag behind the most in such skills will be most hurt (Holzer 1998)—and clearly Black men are that group. Ongoing labor market disadvantages associated with racial discrimination and "spatial mismatch" between inner-city Blacks and suburban jobs no doubt make it harder for Black men to adjust to structural changes in the labor market and find new areas of strong employment (Holzer 2001). And, as earlier generations of Black men have withdrawn from the labor market, the ability of younger cohorts to gain labor market information and contacts that might lead to good job opportunities has deteriorated as well.

Furthermore, as the opportunities for young Black men have diminished in the formal labor market, they have increasingly withdrawn their "labor supply" from that market. The evidence that the relative willingness of young Black men to accept low wages has diminished first appeared in the 1980s (Holzer 1986); and their reluctance seems to have grown during that decade as opportunities in the illegal sector grew more rapidly than in the legal one, and their participation in the drug trade grew dramatically (Freeman 1992; Fryer et al. 2005).

As young men withdrew from the labor market, and their perceptions of incentives to participate in this market diminished, their attachment to other forms of related mainstream activity and institutions—such as schooling and marriage—likely diminished as well. Values and norms around school, working and lifestyles began to change, and often at early ages. The tendency of low-income young men to disconnect from school in the adolescent or teen years seems related to their perceptions of declining opportunity (Edelman et al. 2006); indeed, the growth of an "oppositional culture" among such young men that some conservatives have stressed (Patterson 2006; Mead 2007) no doubt reflects and also reinforces these labor market trends. And, as they become less "marriageable," rates of unwed childbearing have grown while marriage has declined as well (Wilson 1996; Edin and Kefalas 2005).7

But an emphasis on illegal activity as a primary alternative to labor market participation raises an obvious question: since crime rates have been dropping quite dramatically since the mid-1990s, why did the labor force activity of young Black men not improve? Indeed, during the employment boom of the late 1990s, during which young Black men poured into the labor market, why did the relative rates of such activity for young Black men continue to decline?

I have argued (Holzer et al. 2005) that two countervailing forces have occurred during and since the 1990s that further impeded labor market activity for young Black men: (1) Rising rates of incarceration; and (2) Rising enforcement of child support obligations.

Though crime rates have fallen, incarceration rates in the USA rose continuously throughout the 1990s and well into the 2000s, especially among Black men. Thus, the fraction of young Black men who have been incarcerated at some point rose to unprecedented heights during this time period—indeed, one in three of every Black men by age 35 has been incarcerated, while two out of every three high school dropouts has been as well (Raphael 2006). And at least half of all young Black men have become unwed fathers by this time as well (Holzer et al. 2005), with even larger fractions among the less educated.

What impacts do previous incarceration and unwed fatherhood have on the employment of young men? Previous incarceration tends to limit employer willingness to hire them, either because of explicit legal prohibitions on such activity in key employment sectors or employer fears of legal liability (if a coworker or customer gets hurt by a former offender) or conflict more generally (Pager 2004; Holzer et al. 2004). Incarceration also likely damages the attitudes and social networks of young men that might otherwise contribute to employment, and certainly reduces their early labor market experience in ways that restrict future earnings potential (Raphael and Stoll 2009; Lerman 2009; Holzer 2009a).

As for child support, one might imagine that child support orders could increase the incentives of noncustodial fathers to work. As enforcement of such orders dramatically increased during the 1980s and 1990s, many more young men were subject to their effects. But many such orders have been applied to low-income young men with very poor labor market opportunities (Mincy and Sorensen 1998; Sorensen 2010). If these young men fall behind in their payments—which they certainly do when they are incarcerated—they fall into "arrears," after which the rate of taxation on their meager earnings associated with child support becomes very high. And if much of this money fails to be "passed through" to their families, as often is the case when the family has been on public assistance, the incentives of the fathers to pay diminish even more. Thus, the high orders imposed on young Black men and the resulting arrears for many likely reduce the incentives of low-income Black men.

Indeed, we observe the following irony: during the 1990s, we not only pushed low-income women into the workforce through welfare reform, but we also subsidized their labor market entry with the EITC, child care assistance, and the like. But, while we were helping low-income mothers enter the labor market, we were imposing more barriers and restrictions on the fathers of their children—through incarceration and stringent child support enforcement.

Perhaps we should not be too surprised, then, that the low-income mothers entered the labor force in great numbers while the fathers continued to drop out. If we want to reverse the latter trend, a comprehensive policy aimed at encouraging low-income youth and men to improve their skills and enter the workforce is needed.

## Policy Implications

Based on the above analysis, any policy efforts to improve employment outcomes among young Black men should address several issues. First, efforts must be made to improve educational and employment opportunities facing young minority men, by improving the skills they obtain and their access to decent jobs. These would include efforts to prevent their disconnection from school and the labor market, as well as those to reconnect young men who have already disengaged. Second, the incentives of men facing only low-wage job opportunities to accept employment and remain attached to the labor market must be improved, as we have done for low-income single mothers through the EITC and other work supports. Third, the specific issues of high incarceration rates and child support orders and arrears for noncustodial fathers must be addressed as well.

Efforts to improve educational attainment, and to narrow gaps in achievement between young Black men and other groups, must begin early through pre-kindergarten programs and be sustained through reform in the elementary and secondary schools. These efforts have been discussed elsewhere at great length (e.g., Magnuson and Waldfogel 2008; Jacob and Ludwig 2009).

But efforts at remediating test score gaps must be supplemented by broader attempts to prevent disconnection and improve postsecondary educational and employment opportunities in the middle and high school years. These efforts might include youth development programs, like Big Brothers/Big Sisters; efforts in high schools to promote both postsecondary access and employment opportunities; and efforts to reconnect those who have already dropped out of school and the labor market (Heinrich and Holzer 2011).

During the high school years, dropout rates must be reduced—either through individual case management and other intensive efforts to identify those at most risk or through broader structural changes in schools, such as the Small Schools of Choice in New York (Kemple and Rouse 2009; Bloom et al. 2010a, b). Students also need to face a range of "multiple pathways" to success, which include both direct access to higher education and to successful labor market outcomes.

Improving access to postsecondary education, and improving program completion rates for those who attend, requires improving both the academic preparation and the information that students have in the high school years (Haskins et al. 2009). A range of support services at 2-year and 4-year colleges, including performance-based financial aid and requiring counseling, could be strengthened (Brock 2010), and remedial efforts could be much better integrated with occupational training programs at these schools (Jenkins et al. 2009).

But high-quality career and technical education (CTE) should also be available to high school students. For too long, legitimate fears of "tracking" minority students into vocational education and away from college have prevented us from developing strong CTE options. These needn't shut off options for higher education; indeed, high-quality CTE could ensure that students get strong academics as well as occupational training and employment experience, thus keeping open both postsecondary and employment options for young high school graduates.

Our most successful efforts to date at providing high-quality CTE involve Career Academies in high schools. The academies, which are often found within larger comprehensive high schools, often target a particular sector of the labor market (like health care, information technology, or financial services). Students get specific occupational training and work experience in these fields, in addition to their academic classes. Rigorous evaluation shows that the earnings of at-risk young men who attend these academies are nearly 20 % higher than comparable students who did not, and that these earnings premia persist for as much as 8 years afterwards (Kemple and Willner 2008). And, despite concerns about tracking, academy enrollees attended postsecondary education at rates similar to those of those who did not attend them. More broadly, high-quality CTE options could also include apprenticeship programs and other efforts to provide both occupational training and/or paid work experience in high school (Lerman 2007; Symonds et al. 2011) without diminishing their prospects for postsecondary education.

And, for those who have already dropped out and disconnected, efforts to reengage them in education and/or employment must be pursued as well. Efforts to return these individuals to school include the National Guard Challenge program, which has been rigorously evaluated and shows strong positive impacts on GED or high school completion rates (Millenky et al. 2010); others include the Gateways programs on community college campuses and programs for dropouts developed by the Office for Multiple Pathways to Graduation in New York City (Bloom et al. 2010a, b). Alternatively, programs that prepare young men for the labor market include the Job Corps (Schochet et al. 2008) and service employment approaches such as YouthBuild. Recent evidence also shows the strong potential for "sectoral" training programs, which target key sectors of the economy for workforce activities and heavily involve employers in those efforts, to raise earnings among disadvantaged youth with at least a GED or high school diploma (Roder and Elliott 2011).

Finally, efforts to develop more systemic approaches to the needs of youth, as opposed to fragmented individual programs, include the Philadelphia Youth Network and the Youth Opportunities programs created by the Clinton Administration. The latter, which operated in over 30 low-income neighborhoods during the early 2000s, showed positive impacts on both educational and employment outcomes of youth residing there (Decision Information Research 2008).

Despite all of these efforts to improve educational and employment opportunities, many young men will still enter the labor market with poor skills and very limited work histories. For them, incentives to remain attached to the low-wage labor market must be strengthened. Low-income parents with custody of children, usually mothers, can effectively get earnings subsidies of up to 40 % of their low earnings from the federal EITC, plus more from various state programs. But childless adults, including noncustodial fathers paying child support, get very little such encouragement.

Thus, it makes sense to expand the EITC facing childless adults. Several such expansions have been proposed (Edelman et al. 2009), though none has yet been enacted. Any such proposal would have to limit the marriage penalties associated with having separate EITCs for men and women for which they would no longer qualify if they became a joint household, though a variety of means are available for doing so.8 And EITC expansions would also have to address issues of child support enforcement and arrears management, since those who are in arrears or have difficulty meeting current orders would usually not be eligible to receive any such payments; this appears to have limited the impact of the EITC for noncustodial fathers implemented under Governor Pataki in the state of New York (Sorensen 2009).

Finally, the specific labor market problems of ex-offenders and noncustodial fathers in arrears must be directly addressed. My first choice would be to simply incarcerate fewer young men; as such incarceration inevitably leads to long-term "scarring" with collateral consequences for themselves and their families (Holzer 2009a). Generating alternatives to incarceration for nonviolent youthful offenders, and limiting the numbers of parolees who recidivate for technical violations, would certainly help (Western 2006).

But employment barriers facing those with criminal records should also be reduced (Holzer et al. 2004). States should reconsider the legal prohibitions they have put in place against hiring those with nonviolent felony convictions, including drug offenses, in certain sectors, and enforcement of antidiscrimination provisions against offenders should be strengthened. Expungement or sealing of records should be considered after some number of years (perhaps five or seven) during which an offender does not recidivate or reoffend. And programmatic efforts to help offenders reenter the labor market, such as transitional jobs programs, should continue to be explored.9

And reforms in the child support process might help raise employment rates among low-income noncustodial fathers. Employment and fatherhood services should accompany efforts to strengthen enforcement of child support orders (Sorensen 2010; Mead 2010). Arrears management or even forgiveness might be part of such efforts, as well as assuring greater "passthrough" of collections to the families and children of noncustodial parents rather than the state (Cancian et al. 2011).

## Conclusion

As Daniel Patrick Moynihan pointed out over 45 years ago, the decline in employment opportunities facing young Black men is likely responsible for a wide range of negative outcomes we observe for them—including relatively low academic achievement and attainment, declining labor force activity, rising incarceration, and high rates of unwed fatherhood (Holzer 2009b). While all less-educated young men have struggled with these issues in the USA in recent decades, the negative shifts in labor demand facing young Black men, and their withdrawal from the labor market and other mainstream institutions in response, has been the most pronounced. Even relative to young Black women, or to native-born Hispanics, the education and employment outcomes of less-educated young Black men have been declining. The Great Recession has worsened these outcomes in the past few years, though it is the long-term decline in their prospects that is most troubling.

To reverse these negative developments, we must put a high priority on improving educational and employment opportunities for these young men, and preventing their "disconnection" from school and work at early ages and striving to reconnect those who do. A wide range of policies to improve schooling and employment outcomes are potentially available. These should not focus exclusively on raising test scores and enhancing college attendance, though these are both important goals. Rather, we must improve a range of opportunities facing young people, including high-quality CTE and other youth or workforce development efforts. Improving the incentives of young men to remain in the labor market, even when they can only obtain low-wage jobs, is important; while reducing the costs and disincentives associated with high rates of incarceration and child support enforcement are important too.

All of these efforts will require some public resources in a time of extreme fiscal distress, and some political will among politicians who have exploited public fear of young Black men for too long. While the costs of implementing effective policies might be high, the costs associated with our collective failure to address these issues are even higher. Indeed, many states are now beginning to realize the enormous fiscal costs of incarcerating so many young men, and are starting to take steps to limit these rates. Perhaps a more sensible and compassionate approach to the circumstances of young Black men can still be devised and implemented.

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Footnotes

1

The data are drawn from the Outgoing Rotation Groups of the Current Populations Surveys (CPS). See Holzer and Offner (2002) for more discussion of samples and their characteristics.

2

If anything, the recession since 2008 has exacerbated these gaps, though here we focus primarily on longer-term trends.

3

These tabulations are drawn from Hill et al. (2009) and reflect survey outcomes for young people in their early 20s that are drawn from round eight of the panel or earlier.

4

The measure of incarceration is drawn not only on self-reports, which are notoriously downward biased, but also on the extent to which some youth were interviewed while in prison. Short spells of incarceration, in between annual survey rounds or among those who attrite from the sample, would still be missed here.

5

The higher rates of unmarried parenthood reported among young Black women than men might represent a greater reluctance of the men to self-report this status in the survey, or the possibility that the fathers of the children of young Black women might be older than the women themselves. Racial gaps in incarceration rates among men grow wider as they age, with roughly one out of three young Black men being incarcerated by age 35 (Raphael 2006).

6

For economists, "elasticity" measures the extent to which economic activity responds to changes in the price of such activity. Labor supply is elastic if potential workers withdraw from the labor market as their wages deteriorate; it is more inelastic if they remain in the labor force and continue to work at these lower wages.

7

While empirical support for the notion that declining labor market opportunities for men has been somewhat limited, some evidence appears in Blau et al. (2000) and Moffitt (2001). It is clear that the improvement in labor market opportunities of women relative to men tends to reduce marriage rates, and this relative improvement for women has been greatest in the Black community.

8

For instance, only part of the earnings of the second earner might be counted in determining overall income and therefore EITC eligibility. The earnings levels at which EITC benefits begin to phase out might also be higher for two-parent then one-parent households.

9

The evidence to date on postprogram impacts of transitional jobs programs for ex-offenders is mixed. While none seems to have lasting impacts on employment or earnings, The Center for Employment Opportunity in New York City was successful at reducing recidivism rates for those who entered right after release (Zweig et al. 2010). In contrast, the more recent Transitional Jobs Reentry Demonstration showed less positive impacts on this outcome (Redcross et al. 2010).
© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_6

# 6. Challenging the Conventional Understanding of American Poverty

Mark R. Rank1

(1)

George Warren Brown School of Social Work, Washington University, St. Louis, MO 63130, USA

Mark R. Rank

Email: markr@wustl.edu

## Introduction

Let us begin with a fundamental puzzle and paradox—why is it that in America, the wealthiest country in the world, one also finds the highest rates of poverty in the developed world? Whether one examines children's rates of poverty, poverty among working age adults, poverty within single parent families, or overall rates of poverty, the story is much the same. The US is at the very high end of the poverty scale among the industrialized nations (Alesina and Glaeser 2004; Smeeding 2006; Brady 2009). Although there are several possible explanations for why this might be the case, the argument developed in this chapter is that a major reason has to do with how we as a nation have tended to conceptualize this issue, and based upon this thinking how we have acted, or better put, failed to act toward the issue.

The traditional manner of thinking about poverty in the United States has been one of viewing impoverishment as largely due to individual inadequacies and failings (Schwartz 2000). Whether it be that those in poverty have not worked hard enough, have failed to acquire sufficient skills, or have made bad decisions in their lives, the problem of poverty is generally seen through the lens of individual pathology (Sawhill 2003). Since individuals are perceived as having brought poverty onto themselves, our collective and societal obligations have been viewed as limited.

The age old distinction between the deserving versus the undeserving poor reflects this perspective—unless the working age poor have very good grounds for their poverty, they are deemed as largely undeserving of help from others. Poverty is therefore understood as primarily affecting those who choose not to play by the rules of the game. Ultimately, this perspective reflects and reinforces the myths and ideals of American society—that there are economic opportunities for all, that individualism and self-reliance are paramount, and that hard work is rewarded.

In addition, the issue of race has been inextricably connected to the perceptions of American poverty. Poverty has often been viewed and framed as a nonWhite issue, rather than as an American issue. The result has been to intensify feelings of undeservedness, and a lack of will to address the issue (Gilens 1999; Feagin 2010). Indeed, research has indicated that the greater the racial heterogeneity in a society, the less generous and effective is its overall social safety net (Alesina and Glaeser 2004). This has been particularly true in the United States.

This overall mind set has long influenced both the general public's attitudes toward the poor, as well as much of the policy and academic work analyzing poverty (O'Connor 2001). Yet it has seriously misconstrued the nature of poverty and, as a result, helped to foster a lack of political and social will to address the problem itself.

In this chapter I focus on three important changes in thinking that are fundamental for reframing the issue of poverty toward a more realistic and proactive perspective. First, there is a need to shift our understanding of poverty from seeing it as something that happens to others, to understanding it as something that affects us all. Second, it is essential to recognize that a major cause behind why US rates of poverty are so high is not to be found in individual failings, but primarily in weaknesses at the economic and political levels. And third, the moral ground on which poverty should be understood is one of injustice and the need for social change, rather than the all too common viewpoint of looking at poverty through the lens of individual blame, which in turn leads to inaction and a continued acceptance of high levels of poverty. The argument made in this chapter is that for us as a country to make substantial progress in reducing the extent of poverty, an essential step is to change our fundamental understanding of the issue.

## Poverty Affects Us All

A first major change is the recognition that poverty affects us all (Rank 2004). All too often the viewpoint has been that poverty affects someone other than myself. That the problem of poverty is confined to certain areas and neighborhoods such as inner cities or remote rural areas, and that one can largely avoid such areas and therefore simply not confront the issue. The notion is often one of out of sight and out of mind.

The argument made here is that this perspective is clearly incorrect, and that in one way or another, poverty affects us all. There are at least two ways of thinking about this. The first is that whether we realize it or not, we pay a steep price for our high rates of poverty in the United States. As mentioned earlier, the extent and depth of poverty in the US is by far greater than in any other Western industrialized country, as is the extent of our economic inequality and our lack of intergenerational mobility from the bottom up. Various statistical analyses using the Luxembourg Income Study and other datasets have consistently shown these patterns over a number of years (Brady 2009; Ermisch et al. 2012).

As a result, we spend a considerable amount of money on social problems that poverty is associated with. These include greater health problems, family problems, a less able work force, and so on down a long list. A report by the Children's Defense Fund on the costs of child poverty made this strikingly clear.

> The children who suffer poverty's effects are not its only victims. When children do not succeed as adults, all of society pays the price: businesses are able to find fewer good workers; consumers pay more for their goods; hospital and health insurers spend more treating preventable illnesses; teachers spend more time on remediation and special education; private citizens feel less safe on the streets; governors hire more prison guards; mayors must pay to shelter homeless families; judges must hear more criminal, domestic, and other cases; taxpayers pay for problems that could have been prevented; fire and medical workers must respond to emergencies that never should have happened; and funeral directors must bury children who never should have died (Sherman 1994, p. 99).

When we speak about homeland security, these are the issues that undermine us and our security as a nation. In short, each of us pays a tremendous price for allowing so many of our citizens and communities to be mired in poverty.

An empirical analysis of this can be found in work by Holzer and colleagues (2007) who estimated the annual monetary cost of childhood poverty in the United States. They calculated the economic costs that children growing up in poverty had upon their future earnings, increased risk of crime, and health quality in later life. Their estimate was that the overall cost of childhood poverty was 500 billion dollars per year, or nearly 4 % of the country's annual GDP.

The result is that the US spends much of its tax dollars and resources on the back end of the problem of poverty, which is assuredly a more expensive approach to take in the long run, than prevention on the front end. For example, it generally requires considerably more money in the long run to build prisons and incarcerate people for years at a time than to alleviate the conditions that lead to crime in the first place. It is no coincidence that the United States has both the highest rates of economic inequality and poverty in the Western world and that we also have the highest rates of incarceration in the entire world (Wilkinson and Pickett 2010; Pettit 2012). In short, each of us pays dearly in a number of ways for letting poverty exist at such high levels, although unfortunately, we too often fail to see this connection.

However, there is also a second way of thinking about poverty as affecting us all. And that is, what are the chances that an average American will directly encounter poverty at some point during his/her lifetime? As it turns out, the number of Americans who are touched by poverty during their adulthood is exceedingly high. Between the ages of 20 and 75, nearly 60 % of Americans will experience at least 1 year below the poverty line, and three quarters of Americans will experience a year either in poverty or near poverty (Rank 2004). Perhaps even more surprising is the fact that two thirds of Americans between the ages of 20 and 65 will wind up using a social welfare program such as Food Stamps or Medicaid, and 40 % will use such a program in at least 5 years scattered throughout their working age adulthood (Rank 2004).

A further life course study demonstrates that half of all American children at some point during their childhood will reside in a household that uses food stamps for some period of time (Rank and Hirschl 2009). Consequently, although those in poverty and on welfare assistance are routinely vilified and portrayed as members of marginalized groups (Gilens 1999), in fact, most Americans will find themselves below the poverty line and using a safety net program at some point during their lives.

It is also the case that nonWhites are at a particularly high risk of life course poverty. For example, 90 % of African Americans can expect to spend at least 1 year below the poverty line between the ages of 20 and 75 (Rank 2009). Likewise, although 40 % of White children will experience at least 1 year in which their family receives food stamps, 90 % of Black children will do so (Rank and Hirschl 2009).

The reason these overall percentages are so high is that during the course of a lifetime, any number of things can happen to people, many of which are unexpected and detrimental—losing a job, a family splitting up, or developing a health problem. Rather than a risk that affects a few on the fringes of society, it turns out that poverty and the use of a safety net program are events that will actually strike the vast majority of American citizens.

Furthermore, recent research has shown that this life course risk of poverty and economic instability has been rising during the 1990s and the 2000s (Hacker 2006; Gosselin 2008). Greater numbers of families, including middle class families, are experiencing increased volatility and downward swings in their income as a result of greater instability in the labor market and the lack of benefits such as health and unemployment insurance. Jobs are no longer as stable as they once were, healthcare benefits are harder to get, the safety net has been weakened over time, and so on.

In short, there has been a shift in economic risk occurring. The political scientist Jacob Hacker (2006) has written extensively about this. This shift in economic risk has been transferred from government and businesses that once shouldered a good portion of the risk, onto the backs of families and individuals. Consequently, job stability is much less, people have fewer benefits, home ownership is less stable, the social safety net has been weakened, and so on (Jacobsen and Mather 2010). In fact, a number of commentators have pointed out that for many Americans, their individual social safety net has been to increasingly accumulate debt on their credit cards and/or to tap into their home equity (Vyse 2008).

Recent life course work has also shown that the risk of experiencing poverty in the 1990s was much greater than it was in the 1970s or 1980s (Sandoval et al. 2009). For example, in the 1970s for those who were in their 30s, the chance of experiencing poverty was 18 %. By the 1990s if you were in your 30s, the likelihood of experiencing a year in poverty increased to 27 %.

Consequently, more Americans at some point in their lives will be directly affected by poverty or near poverty. This is a fundamentally different understanding of the issue, and one that implies we all have a direct self-interest in addressing the issue of poverty.

A first shift in thinking therefore asks the question who is at risk of poverty and the fallout from poverty? And the answer is, virtually all of us are. As a result, each of us has a vested interest and imperative for reducing poverty in this country.

## Poverty Is the Result of Structural Failings

A second fundamental shift in thinking is the recognition that American poverty is largely the result of failings at the economic and the political levels, rather than at the individual level. In the past, individual inadequacies have been emphasized as the major reason for poverty. That is, people are not motivated enough, they are not working hard enough, they have failed to acquire enough skills and education, or they have made bad decisions in their lives. These are the behaviors and attributes that are often seen as leading people into poverty as well as keeping them in poverty (Schwartz 2000; Sawhill 2003).

And in fact, this is the manner in which we have tended to confront most social problems in this country, that is, as individual pathology. In contrast to this perspective, the argument made here is that the fundamental problem lies in the fact that there simply are not enough viable opportunities for all Americans.

While it is certainly true that particular individual shortcomings, such as the lack of education or skills, helps to explain who is more likely to be left out in the competition to locate and secure good opportunities, it cannot explain why there is a shortage of such opportunities in the first place. In order to answer that question we must turn to the inability of the economic and political structures to provide the supports and opportunities necessary to lift all Americans out of poverty.

The most obvious example of this is the mismatch between the number of decent paying jobs, versus the pool of labor in search of such jobs. Over the past 40 years, the US economy has been producing more low-paying jobs, part-time jobs, and jobs that are lacking in benefits (Fligstein and Shin 2004). It is estimated that approximately one third of all jobs in the United States are low-paying, that is, less than approximately $11.50 an hour (Bourshey et al. 2007). In addition, male median wages reached a peak in 1973, failing to surpass that level for the past 40 years (U.S. Bureau of the Census 2013).

And of course, beyond these low-paying jobs, there are millions of Americans who are unemployed at any point in time. Following the economic collapse in 2008, rates of unemployment rose to over 10 % and continue to remain high. For example, at the end of 2011, the percentage of the population unemployed was 8.5 %, or 13 million Americans (U.S. Bureau of Labor Statistics 2012). Of those Americans unemployed, 42.5 % have been looking for jobs for more than 6 months. These figures do not include nearly one million Americans who have given up looking for work because they feel that there simply are not jobs available for them, eight million Americans who are working part-time but want to be working full-time, or the roughly two million Americans who are currently in prison.

The US has also failed to offer the types of universal coverage for child care, health care, and affordable housing that most other developed countries routinely provide (Alesina and Glaeser 2004; Esping-Andersen 2007). The result has been an increasing number of families at risk of economic vulnerability and poverty.

One way to illustrate this situation is through the analogy of musical chairs (Rank 1994, 2004). Picture a game of musical chairs in which there are ten players but only eight chairs available at any point in time. Who is more likely to lose out at this game? Those more likely to lose out will tend to have characteristics that put them at a disadvantage in terms of competing for the available chairs (such as less agility, not as much speed, a bad position when the music stops, and so on). We can point to these reasons for why the two individuals lost out in the game.

However, given that the game is structured in a way such that two players are bound to lose, these individual attributes only explain who in particular loses out, not why there are losers in the first place. Ultimately, those two people have lost out because there were not enough chairs for everyone who was playing the game.

The critical mistake that has been made in the past is that we have equated the question of who loses out at the game, with the question of why the game produces losers in the first place. They are, in fact, distinct and separate questions. While characteristics such as deficiencies in skills or education, or being in a single parent family, help to explain who in the population is at a heightened risk of encountering poverty, the fact that poverty exists in the first place results not from these characteristics, but rather from a failure of the economic and political structures to provide enough decent opportunities and supports in society.

By focusing solely upon individual characteristics, such as education, we can shuffle people up or down in terms of their being more likely to land a job with good earnings, but we are still going to have somebody lose out if there are not enough decent paying jobs to go around. In short, we are playing a large scale version of musical chairs in which there are many more players than there are chairs.

The recognition of this dynamic represents a fundamental shift in thinking from the past. It helps to explain why the social policies of the last four decades have largely been ineffective in reducing the rates of poverty. We have focused our attention and resources on either altering the incentives and disincentives for those playing the game through various welfare reform measures, or in a very limited way, upgrading their skills and ability to compete in the game through various job training programs, while at the same time we have left the structure of the game untouched.

When the overall rates of poverty do in fact go up or down, they do so primarily as a result of changes on the structural level that increase or decrease the number of available chairs. In particular, the performance of the economy has been historically important (Hoynes et al. 2006). Why? Because when the economy is expanding, more opportunities (or chairs in this analogy) are available for the competing pool of labor and their families. The reverse occurs when the economy slows down and contracts, as we have seen in the most recent recession beginning in 2008.

Likewise, changes in various social supports and the social safety net will make a difference in terms of how well families are able to avoid poverty or near poverty. When such supports were increased through the War on Poverty initiatives in the 1960s, along with the strong economy, poverty rates declined. Similarly, when Social Security benefits were expanded during the 1960s and 1970s, the elderly's poverty rates sharply declined (U.S. Bureau of the Census 2013). Conversely, when social supports have been weakened and eroded, as in the case of children's programs over the past 30 years, their rates of poverty have gone up (Cancian and Danziger 2009).

The recognition of poverty as a structural failing also makes it quite clear why the United States has such high rates of poverty compared to other Western countries. These rates have little to do with Americans being less motivated or less skilled than those in other countries, but rather with the fact that our economy has been producing millions of low-wage jobs in the face of global competition and that our social policies have done relatively little to economically support families compared to other industrialized countries (Smeeding 2006).

A structural perspective also helps to explain why poverty is so much higher for nonWhites, particularly African Americans, Hispanics, and Native Americans. Long-term patterns of occupational discrimination, racial residential segregation, and exposure to inferior education have combined to put racial minority groups at a significant disadvantage vis-a-vis the labor market (Wilson 2009; Feagin 2010). In addition, these patterns have resulted in vast wealth differences between Whites and nonWhites. A study by the Pew Foundation (2011) found that in 2009, the median wealth of White households was 20 times that of Black households, and 18 times that of Hispanic households.

From this perspective then, one of the keys to addressing poverty is to increase the labor market opportunities and social supports available to American households. A second important shift in thinking is to recognize the fundamental distinction between understanding who loses out at the game, versus understanding how and why the game produces losers in the first place.

## The Moral Ground to View Poverty is Injustice

Let us now turn to a third shift in thinking that needs to occur in order to begin to successfully confront poverty. And that is, the moral ground on which we view poverty in America must change. In the past, our moral perspective has been largely rooted in the ethos of individual blame. That is, poverty is often seen as the fault of the individual, that individuals are largely to blame for their situation, and consequently, that the rest of society bears little responsibility for their plight. The result has been a general acceptance of the status quo of high poverty, and a lack of initiative to address it. In other words, it is somebody else's problem and responsibility, not mine.

This perspective has often been tinged with a racial component as well. Politicians have long played the race card when discussing issues of poverty and welfare in order to foster resentment and undeservedness, and to score political points among the White majority (Soss et al. 2012). Poverty and the use of the social safety net have frequently been portrayed as a nonWhite issue, rather than as an American issue. For example, Ronald Reagan's popular story of the inner city welfare queen pulling up to the grocery store in a Cadillac and using her food stamps to purchase sirloin steaks and alcohol was assuredly a racial image designed to instill feelings of anger and undeservedness among Whites. More recently, former Speaker of the House Newt Gingrich repeatedly referred to President Obama as the best "Food Stamp President" the country has ever had during the 2012 Republican presidential nomination campaign. Gingrich also railed against the lack of a proper work ethic among Black children living in poverty, and the need for them to take on janitorial work in order to help instill such an ethic (Blow 2011).

The issue of race has also had a long history of being used to divide poor Whites and Blacks so that they fail to see their common interests. Perhaps this was most starkly laid out by President Lyndon Johnson when he explained to an aide in 1960, "I'll tell you what's at the bottom of it. If you can convince the lowest White man that he's better than the best colored man, he won't notice you picking his pocket. Hell, give him somebody to look down on, and he'll empty his pockets for you" (Dallek 1991, p. 584).

This racial strategy, combined with the strong emphasis in America upon individualism, has resulted in an ethical lens of blame being the dominant perspective for viewing the morality of poverty. This moral ground must change in order for us to effectively deal with the issue. It must shift from viewing poverty as an individual failing worthy of blame, to seeing poverty as a condition of widespread injustice.

Poverty represents an injustice of a substantial magnitude. It constitutes severe deprivation and hardship. This has been documented in countless studies not to mention millions of human lives. And as argued earlier, a large portion of this poverty is the result of failings at the structural level, which places much of the responsibility for poverty beyond that of the individual.

However, what makes this injustice particularly grievous, is the stark contrast between the wealth, abundance, and resources of America on the one hand, and its levels of destitution on the other. Something is seriously wrong when we find that in a country with the most abundant resources in the world, there are children without enough to eat, families who cannot afford health care, and people who are sleeping on the streets for lack of shelter. Or that the life expectancy in Harlem is less than it is in Bangladesh (McCord and Freeman 1990).

It should also be noted that the gap between prosperity and poverty has never been wider. The noted economist, Paul Samuelson (1948), writing in the first edition of his introductory economics textbook in 1948, observed that if we were to make an income pyramid out of a child's play blocks, with each layer representing $1,000 of income, the peak would be somewhat higher than the Eiffel Tower, but almost all of us would be within a yard or so of the ground. By the time of Samuelson's 2001 edition of the textbook, most of us would still be within a yard or two of the ground, but the Eiffel Tower would now have to be replaced with Mount Everest in order to represent those at the top (Samuelson and Nordhaus 2001).

Or take what has happened with respect to the distance between the average worker's salary and the average CEO's salary. In 1980, the average CEO of a major corporation earned around 42 times that of the average worker's pay. Today it's well over 300 times (Anderson et al. 2009; Krugman 2007). Adding insult to injury, during the past 30 years, an increasing number of companies have demanded concessions from their workers, including pay cuts and the elimination of health benefits in order to keep their labor costs down, while those at the top have prospered beyond any sense of decency.

Patterns of wealth accumulation have become even more skewed. Today in America, we find that the top one percent of the US population currently own 42 % of the entire financial wealth in the country, while the bottom 60 % of Americans are in possession of less than 1 % of the country's financial wealth (Wolff 2007). And while all of these trends have been happening, our social policies have continued to give more to the well to do and less to the economically vulnerable, with the argument that these policies have been helping all Americans.

A new way of thinking recognizes this as a moral outrage. Injustice, rather than blame, becomes the moral compass on which to view poverty amidst abundance. This type of injustice constitutes a strong impetus for change. It signals that a wrong is being committed that cries out for a remedy. Indeed, the Occupy Wall Street protest beginning in the fall of 2011 in New York City, along with the myriad of occupy protests that have grown across the country, have been emblematic of this moral outrage.

In addition, it is a moral injustice that particular racial and ethnic groups are exposed to significantly higher levels of poverty as a result of skin color. For example, using data from the 1990 and 2000 US Census, Drake and Rank (2009) demonstrated that Black children were up to 14 times more likely to live in a high childhood poverty neighborhood compared to their White counterparts. Findings for Hispanic children were similar to those of Black children.

As a result, nonWhite children are routinely exposed to significantly higher levels of neighborhood poverty when growing up compared to their White counterparts. Exposure to such levels of poverty can have a profound impact upon one's life chances. For example, children growing up in neighborhoods marked by high poverty are much more likely to encounter a variety of environmental health and social hazards. These include elevated exposure to various toxic pollutants, greater likelihood of being victimized by crime and violence, increased probability of dropping out of high school, higher arrest rates, and increased risk of substance abuse (Evans 2004). All of these can detrimentally affect a child's health and economic well-being as an adult (Case and Paxson 2006).

Furthermore, mobility out of such neighborhoods, particularly for racial minorities, is often limited. For example, Quillian (2003) has shown that for Black residents living in high poverty census tracts (40 % or more in poverty), nearly 50 % will still be residing in a high poverty census tract 10 years later. In addition, Sharkey (2008) has found that 72 % of Black children who grew up in the poorest quarter of American neighborhoods remained in the poorest quarter of neighborhoods as adults. Consequently, the effects of neighborhood poverty upon children of color are typically prolonged and long lasting. It is a moral and economic injustice that some children are exposed to such conditions because of the color of their skin, while other children are not.

A shift in thinking recognizes this and is premised upon the idea that social change is essential in addressing the injustices of poverty. This is in sharp contrast with the old way of thinking, in which the moral focus is upon individual blame. This has had the effect of simply reinforcing the status quo of doing little, resulting in continued rates of elevated poverty. The perspective of injustice allows us to actively engage and confront poverty, rather than comfortably settling for the status quo of widespread impoverishment.

## Summary

To summarize, three key changes with respect to understanding the problem of poverty from a more heuristic perspective are: (1) recognizing that poverty affects us all; (2) poverty is largely the result of weaknesses at the economic and policy levels; and (3) poverty is a moral injustice in a country with the resources that the United States has. These changes are fundamental in terms of creating a more conducive and constructive environment for addressing the levels and depths of impoverishment in this country.

Of course, the question arises, can we move our thinking as a society toward such a framework? It would be foolish not to acknowledge that accomplishing the changes in thinking discussed in this chapter will be an uphill battle. But we also should not let this immobilize us. Social change can and does occur.

To take but one example, there has been a dramatic change in how we have come to view the physical environment over a relatively short period of time, and as a result, significant legislation (beginning in the early 70s) has been put in place to deal with environmental protections. Our thinking has shifted from one of environmental damage and pollution being someone else's problem, to recognizing it as being a problem that affects us all, that must be addressed on a structural level, and in which we have a moral obligation to address. It is a profound change in thinking which has led to a profound change in acting. Such a shift in thinking has spurred other social movements as well, including the civil rights and women's movements.

Such a change in thinking is now needed with respect to American poverty in order to effectively tackle the problem. A new framework is needed in which to understand poverty in America, and how to go about addressing it.

Martin Luther King summed this up well with the following passage from his last book, Where Do We Go from Here: Chaos or Community? He wrote:

> A true revolution of value will soon cause us to question the fairness and justice of many of our past and present policies. We are called to play the Good Samaritan on life's roadside; but that will be only an initial act. One day the whole Jericho road must be transformed so that men and women will not be beaten and robbed as they make their journey through life. True compassion is more than flinging a coin to a beggar; it understands that an edifice which produces beggars needs restructuring. A true revolution of values will soon look uneasily on the glaring contrast of poverty and wealth. (1967, pp. 187–188)

This revolution of values must begin with a fundamental shift in how American society understands and addresses the poverty in which so many of our citizens live. Poverty ultimately affects us all, is primarily the result of structural failings, and is both a moral and economic injustice. These are the building blocks on which to challenge and confront the paradox of poverty amidst plenty.

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# Part III
Education
© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_7

# 7. Interventions to Address Racial/Ethnic Disparities in School Discipline: Can Systems Reform Be Race-Neutral?

Russell J. Skiba1

(1)

Center for Evaluation and Education Policy, Indiana University, Bloomington, IN 47401, USA

Russell J. Skiba

Email: skiba@indiana.edu

## Introduction

Racial and ethnic disparities remain consistent and widespread in American education. Significant gaps continue to be manifested in achievement (Ladson-Billings 2006), special education (National Research Council 2002), dropout and graduation rates (Wald and Losen 2007), and eligibility for gifted/talented programs (Milner and Ford 2007). In reviewing both the scientific literature and case law on disproportionality, Skiba et al. (2010) concluded that, while Brown v. Board of Education and the civil rights statutes that followed have guaranteed students of color access to public education, they have in no way guaranteed equal educational opportunity.

Of particular concern are severe and continuing racial disparities in exclusionary school discipline, out-of-school suspension and expulsion (Skiba and Rausch 2006). Among the most consistently documented of educational inequities, disproportionate representation in school discipline places students of color—in particular, African American students—at-risk for a wide range of negative outcomes. This chapter will review what we know about racial and ethnic disparities in school discipline and in particular examine the status of intervention research. Given the predisposition in America's current political landscape towards color-blind or race-neutral intervention, a significant part of this chapter will be devoted to considering the extent to which universal, race-neutral interventions could be expected to be sufficient to successfully address disproportionality in school discipline.

## What Do We Know About Disciplinary Disproportionality

A review of the literature on African American disproportionality leads to four conclusions:

1.

Disparities in school discipline are consistent and severe for African American students, and found less consistently for other groups.

2.

Racial and ethnic disparities in discipline often confound our expectations about the locus of disproportionality.

3.

In particular, African American over-representation in exclusionary school discipline cannot be explained by poverty status or differential rates of behavior.

4.

Over-representation in punitive and exclusionary school discipline in turn yields increased risk for a host of other negative outcomes.

Each of these propositions is explored in turn in the sections that follow.

## African American Disproportionality Has Been Consistent and Severe

### African American Disproportionality

Since the Children's Defense Fund (1975) brought the issue of racial disparities in discipline to national attention, the over-representation of African American students in a variety of school punishments has been consistently documented across time, location, and type of punishment. African American disproportionality has been reported in studies across the nation for office disciplinary referrals (Bradshaw et al. 2010; Rocque 2010; Skiba et al. 2011), suspension and expulsion (Eitle and Eitle 2004; Gregory and Weinstein 2008; Hinojosa 2008), school arrests (Theriot 2009), and corporal punishment (Gregory 1995; Owen 2005; Shaw and Braden 1990). Others have reported that Black students receive fewer mild disciplinary sanctions when referred for an infraction (McFadden et al. 1992; Payne and Welch 2010). While it might be expected that the "one-size-fits-all" approach of zero tolerance would increase consistency in the application of discipline across groups, African Americans have also been found to be over-represented in punishments for zero-tolerance-related disciplinary outcomes (Tailor and Detch 1998), and to be more likely to attend schools with greater use of both school security measures and police presence (Payne and Welch 2010). The over-representation of Black students in out-of-school suspension and expulsion appears to be increasing over the last 30 years (Losen and Skiba 2010; Noltemeyer and Mcloughlin 2010a; Wallace et al. 2008).

### Other Racial/Ethnic Groups

Disproportionality in school discipline for other racial/ethnic categories has been less thoroughly studied, and the results have been less consistent. While Peguero and Shekarkhar (2011) found disparities in discipline for first and third generation Latino students, others have reported rates of out-of-school suspension for Latino students not significantly different from White students (Horner et al. 2010; McFadden et al. 1992; Skiba et al. 1997). In a national examination of self-reported data concerning discipline outcomes, Wallace et al. (2008) reported that American Indian students were over-represented and Asian students under-represented in school discipline in general and suspension in particular.

#### Data on Disciplinary Disproportionality Counters Expectations

Discussions on the topic of disproportionality often revolve around a storyline that highlights the challenges of urban education with concentrations of high poverty, focusing in particular on issues facing African American males. Yet data on disciplinary disparities more often than not yields conclusions that run counter to these expectations. For example, while rates of both suspension and expulsion increase with grade level, differences between Black and White rates of suspension have been found to be as great or greater at the elementary as at the secondary school level (Rausch and Skiba 2006; Wallace et al. 2008). Similarly complex findings appear to hold with respect to gender differences in school discipline. Wallace et al. (2008) reported that, although boys of all races and ethnicities were more likely than girls to be disciplined, disparities between Black and White rates of discipline were actually greater among female students.

Such findings raise questions about our typical understanding of racial disparities, and nowhere is this more evident than with respect to the issue of urbanicity. Schools in poor urban districts have been consistently found to have higher rates of suspension and expulsion than schools in suburban, town, or rural settings (Losen and Skiba 2010; Nicholson-Crotty et al. 2009; Noltemeyer and Mcloughlin 2010b). Yet the degree of disparity between Black and White suspension rates appears to be as great or even greater in higher resourced suburban districts (Eitle and Eitle 2004; Rausch and Skiba 2006; Wallace et al. 2008). Together these findings suggest a picture far different than our typical understanding of racial disparities. Clearly, boys, secondary schools, and under-resourced schools in urban areas all experience higher rates of suspension and expulsion. Yet Black–White disparities do not necessarily follow the same patterns. Rather, racial disparities are as high or higher for girls, in elementary schools, and in suburban settings. Simply put, racial disproportionality in school discipline is ubiquitous.

#### Disproportionality Cannot Be Explained by Poverty or Differential Rates of Behavior

##### Poverty and Racial Disparities

It is true that students from poverty backgrounds are significantly more likely to experience suspension and expulsion (Brantlinger 1991; Noltemeyer and Mcloughlin 2010a; Skiba et al. 1997; Wu et al. 1982). Hinojosa (2008), for example, found that a number of demographic variables, including presence of mother or father in the home, number of siblings, and quality of home resources were all predictors of the likelihood of suspension.

Yet it is also true that poverty and its effects are not sufficient to account for the over-representation of students of color in school suspension and expulsion. Even after controlling for socioeconomic effects (e.g., percent of parents unemployed and percentage of students enrolled in free lunch programs), Wu et al. (1982) reported that nonWhite students still reported significantly higher rates of suspension than White students in almost all locales. Wallace et al. (2008) found that disproportionality in office referrals and suspension and expulsion for Black, Latino, and American Indian tenth graders remained significant even after controlling for family structure and parental education. Finding that urban schools consistently suspended a higher proportion of students out-of-school even after controlling for poverty, Noltemeyer and Mcloughlin (2010b, p. 33) concluded that "there is something above and beyond poverty that explains disciplinary differences between school types."

##### Different Rates of Disruptive Behavior?

Since school consequences such as suspension and expulsion are at least in part a response to student behavior, it is possible that differential rates of exclusionary discipline for Black and White students could be due to different rates or types of school misbehavior. Although there are no recent studies that have directly observed rates of Black and White student behavior in classrooms, a fairly substantial number of studies address the issue of whether disparities in suspension and expulsion are due to differences in rates of behavior.

Results from a variety of sources, using a variety of research designs, have not supported the hypothesis that the Black–White discipline gap is due to different rates of disruption. First, research has not found that students of color engage in more seriously disruptive behavior warranting higher rates of school punishment. Wallace et al. (2008) found Black, Latino, and Native American students to be more likely to receive out-of-school suspensions, despite few racial differences in actual zero-tolerance policy violations (e.g., drugs, alcohol, weapons). Where racial/ethnic differences are found in reasons for disciplinary referral, those differences tend to be for less serious infractions such as defiance (Gregory and Weinstein 2008), or disrespect and excessive noise (Skiba et al. 2002). Second, race persists as a determinant of school punishment, regardless of the characteristics or seriousness of student behavior. Multivariate studies introducing statistical controls for more or less seriously disruptive office referrals (Eitle and Eitle 2004; Peguero and Shekarkhar 2011; Skiba et al. 2011) have reported that race remains a significant predictor of school punishment regardless of the type or severity of behavioral infraction. Racial differences in discipline appear to be independent even of teacher perceptions of the seriousness of behavior. A number of studies have controlled for teacher and peer ratings of aggressive or externalizing behavior (Bradshaw et al. 2010; Horner et al. 2010; Rocque 2010); all have reported that racial differences in office referrals or serious disciplinary actions remain significant regardless of the teachers' own ratings of behavioral severity. In sum, a fairly extensive body of research has failed to find any evidence to support that notion that students of color earn higher rates of exclusionary discipline through higher rates of disruptive behavior.

##### What Does Predict Disciplinary Disproportionality?

In contrast to poverty or differential behavior, there appear to be a number of variables that have been found to show at least some relationship to rates of racial disparity in school punishment. Gregory et al. (2010) reviewed literature on the achievement gap and the discipline gap and found sufficient evidence to suggest a relationship between the two. The representativeness or diversity of school faculty has been explored; results thus far suggest that schools with a more diverse and representative teaching force have lower rates of racial disparity in school discipline (Rocha and Hawes 2009), although race of the school administrator has not been found to be a significant contributor to disproportionality (Roch et al. 2010). There is evidence that racial disparities in school discipline begin with classroom referral and classroom management (Gregory et al. 2010), but that there is also a contribution at the level of administrative decision-making (Skiba et al. 2011). Finally, both the school climate in general (Gregory et al. 2011) and perceptions of the racial climate at school (Mattison and Aber 2007) have been found to relate to levels of disproportionality in school discipline.

#### Association of Disciplinary Disproportionality with Negative Outcomes

Regardless of the reasons for racial and ethnic disparities in school discipline, over-representation in suspension and expulsion places students of color at increased risk for a number of negative outcomes. Although exclusionary and punitive approaches to discipline are intended to improve school safety and student behavior, a number of negative effects have been documented associated with suspension and expulsion or increased police presence, including (a) negative impact on school climate, (b) reduced academic achievement and lost educational opportunity, (c) a moderate negative relationship with school dropout, and (d) increased risk for involvement in the juvenile justice system. Together these outcomes appear to provide support for a school-to-prison pipeline (Kim et al. 2010), that punitive and exclusionary approaches to school discipline represent the first step in a process that ultimately increases juvenile risk for involvement in the juvenile justice system.

##### Negative Relationship with School Climate

One of the fundamental assumptions of zero tolerance and disciplinary exclusion is that removal of troublemakers from the school will improve school climate, reducing disruption, and improving the learning environment for those who remain (Ewing 2000). Extensive review of the research (e.g., American Psychological Association 2008) has, however, failed to support that contention. Schools with higher rates of suspension have been reported to have higher student–teacher ratios and a lower level of academic quality (Hellman and Beaton 1986), spend more time on discipline-related matters (Davis and Jordan 1994) and pay significantly less attention to issues of school climate (Bickel and Qualls 1980). A survey of students and teachers in Chicago Public Schools found that schools with harsh discipline policies and higher rates of suspensions were perceived as less safe by students and teachers (Steinberg et al. 2011). Such relationships may be even more salient for students of color. Mattison and Aber (2007) compared self-reported rates of detention and suspension with ratings of racial school climate and found that African American students reported more experiences of racism and lower ratings of racial fairness at school, and that both of these ratings were associated with higher rates of detentions and suspensions.

##### School Engagement/Achievement

Educational research has consistently documented a relationship between time engaged in academic learning and student achievement (Brophy 1988; Greenwood et al. 2002; Wang et al. 1997). Time lost to suspension and expulsion may thus have a negative impact on school connectedness and student engagement, and ultimately on student achievement. McNeely et al. (2002) found school connectedness to be lower in schools that expel students for relatively minor infractions, while Davis and Jordan (1994) reported that the number of suspensions that African American males received was negatively related to achievement in eighth grade and to school engagement in tenth grade. Emerging data have revealed a negative relationship between the use of school suspension and expulsion and academic achievement. In a multivariate analysis of the relationship between school discipline and achievement, Rausch and Skiba (2005) reported that higher school rates of out-of-school suspension were associated with lower school passing rates on the state accountability test, regardless of the demographic, economic, or racial makeup of the school.

##### Relationship to School Dropout

In the long term, school suspension has been found to be a moderate to strong predictor of dropout or not graduating on time (Ekstrom et al. 1986; Raffaele Mendez and Knoff 2003; Wehlage and Rutter 1986). Suh and Suh (2007) found that being suspended at least once increased the likelihood of dropping out of school by 77.5 % and that suspensions are a stronger predictor of dropout than either grade point average or SES. In a 5-year longitudinal study of all students in the state of Texas through their high school years, the Council of State Governments (2011) found that suspended/expelled students were five times as likely to drop out compared to students with no disciplinary action.

##### Increased Risk of Juvenile Justice Contact

Recent studies appear to support the contention that out-of-school suspension and expulsion increase youth risk for contact with the juvenile justice system. Racial disproportionality in out-of-school suspensions has been found to be a strong predictor of similar levels of racial disparity in juvenile court referrals, even when controlling for levels of delinquent behavior, poverty, and other demographic variables (Nicholson-Crotty et al. 2009). Multivariate analyses of longitudinal data have indicated that suspended or expelled students had a greater likelihood of contact with the juvenile justice system in subsequent years, even after controlling for demographic data; the relationship was even stronger for African American students (Council of State Governments 2011).

## Interventions to Address Disproportionality

A number of universal, school-wide interventions have been shown to be effective in improving school discipline or school climate and have thus been suggested as having potential for reducing disproportionality (Osher et al. 2010). School-wide Positive Behavior Supports (SWPBS) (Bradshaw et al. 2009; Horner et al. 2009) is a framework that is intended to restructure school disciplinary practices through a school-wide team-based approach to restructuring school discipline. Social–emotional learning (SEL) programs are implemented as preventative curriculums and/or through the creation of supportive learning environments aimed at reducing problem behaviors by teaching students needed social or life skills (Durlak et al. 2010; Ialongo et al. 2001; Payton et al. 2008). Finally, restorative justice programs aim to restore relationships and repair the harm caused by misbehavior through such strategies as: (a) collaborative decision-making and restitution to victims, (b) holding offenders accountable, (c) conferences and community meetings, and (d) preventing similar actions in the future by changing behavior and the conditions that caused that behavior (Jennings et al. 2008; International Institute for Restorative Practices 2009; Strang and Braithwaite 2001; Stinchcomb et al. 2006).

To what extent could such universal interventions be expected to be successful in reducing racial and ethnic disparities in school discipline? Certainly universal interventions without specific attention to issues of race or culture could be expected to be more acceptable to the courts in a political context that favors color-blindness and race-neutrality in intervention (see e.g., Skiba et al. 2010). Yet there is as yet no evidence that race-neutral interventions are sufficient for addressing issues of disproportionality and, in the area of desegregation, some evidence that race-neutral strategies are not as effective as race-conscious approaches for addressing racial and ethnic disparities (Mickelson 2003; Reardon et al. 2006).

There is some data that could inform the extent to which universal, race-neutral methods could successfully address racial and ethnic disparities in discipline. The following sections evaluate the possibility of a race-neutral approach to disciplinary disproportionality, in the areas of data disaggregation, interpretation, culturally responsive intervention, and evaluation.

## Data Disaggregation

One can assume that a universal or race-neutral intervention approach could be effective in addressing issues of racial disparity under certain conditions. For example, if there were no qualitative differences in how the procedures were experienced by race, the problem might well be one of simply reducing rates of exclusion for all groups. Yet the data have consistently documented that students of different races are treated differentially at the level of classroom referral (Skiba et al. 2011), more likely to be disciplined for more interactive or subjective behaviors (Skiba et al. 2002), and in particular for defiance (Gregory and Weinstein 2008). Such differences are not lost on students of color, who perceive differences in both the administration of the school discipline system (Sheets 1996) and school climate in general (Mattison and Aber 2007). This difference in baseline rates of discipline makes it difficult to conceptualize how race-neutral intervention could be effective in addressing racial disparities. In order to create equal outcomes beginning from an initial state of inequality, such an approach would have to affect groups differentially (e.g., create larger improvements for African American students) without consciously intending to do so.

Limited data on the differential effects of universal interventions suggest that such skepticism is justified. In a nationally representative sample of elementary and middle schools implementing school-wide PBS for at least one year, Skiba et al. (2011) found that PBS schools in general use an efficient, graduated system of discipline; that is, minor infractions receive less severe punishments and more severe consequences are reserved for more serious infractions. A dramatically different pattern was found, however, when the data were disaggregated—African American and Latino students were significantly more likely than White students to receive suspension and expulsion for minor infractions. Similar findings have been reported by Vincent and Tobin (2010).

Together these data strongly suggest that it is not sufficient to review schoolwide data in the aggregate and assume effects on specific groups. Rather, understanding current levels of disparity and monitoring the effects of intervention on those disparities require disaggregation of disciplinary data by race and ethnicity.

## Data Interpretation

The availability of data, assessing the current status of educational processes and outcomes, is a critical first step underlying most current school reform models. Yet the accessibility of data do not in and of themselves guarantee that data will be used in a way to ensure an effective reform process (Earl and Fullan 2003). In the area of racial/ethnic disparity, the ability to use available data to fuel a change process is especially threatened by one particular barrier—the difficulty that educators and other professionals have in openly discussing issues of race and culture.

The difficulty that educators, especially White educators, have in openly talking about race and racism has been well documented (Henze et al. 1998; King 1991; Singleton and Linton 2006). To gain insight into practitioners' perspectives on racial and ethnic disproportionality, Skiba and Rausch (2006) interviewed teachers, administrators, and related services personnel in diverse urban and near-urban school districts. In general, Skiba and colleagues found that, particularly for White respondents, race proved a difficult topic to approach:

> When you say minorities, are you, what are you speaking of?... INTERVIEWER: Ethnic and racial minorities... Oh.... OK... Alright... We have like... I guess we have about half and half. I don't know that I've ever really paid attention to it. (Skiba and Rausch 2006, p. 1445)

In contrast, African American teachers seemed much more aware of, and willing to talk about, the diversity in their classes.

The inability to bring the topic of race to the table covers over a different kind of racial gap—that is, differential perception concerning the seriousness of the issue. Consideration of nationally publicized events concerning race and racism highlights a fundamental difference in how White and Black Americans perceive and talk about the topic of race. In a survey conducted by Time Magazine in February of 1997, 68 % of African Americans but only 38 % of Whites agreed that racism is a significant problem in America (Lafferty 1997). Table 7.1 presents a series of salient events over the last 20 years pertaining to race, along with public polls disaggregating the substantial and at times dramatic African American and European American responses. Across every national event involving race across a twenty year span, there is a striking difference in the perspectives of Black and White respondents.

Table 7.1

Differential reactions of Black and White respondents to national polls on incidents involving race

Incident and question | African American respondents (%) | White respondents (%)

---|---|---

Reactions to Pastor Jeremiah Wright comments: | 16 | 46

Should Barack Obama leave Rev. Wright's church in the wake of Wright's comments about race? (Rasmussen Reports 2008)

Jena 6 case: | 79 | 33

The six Black teenagers in the Jena, Mississippi case were treated unfairly by the legal system. (CNN/Opinion Research Corp. Poll, DiversityInc. 2007)

Hurricane Katrina: | 71 | 28

Did poverty and race affect hurricane protection? (ABC News/Washington Post 2005)

O.J. Simpson verdict: | 29 | 87

From what you've heard, do you think O. J. Simpson murdered Nicole Brown Simpson and Ronald Goldman? (Dateline NBC 2004)

Trial of Policemen in Rodney King case: | 55 | 21

The guilty verdicts in the Rodney King Trial were not enough (USA Today/CNN/Gallup Poll Jet 1993)

Regardless of policy preferences, it is quite clear that neither American society nor American education have become race-neutral; rather, the experience of race depending on the color of one's skin differs on a daily basis. Authors on White privilege note that Whites are in the position where it is possible to avoid considering race and how it has affected the opportunity structure in America (McIntosh 1990; Wise 2002). Howard (2008) uses the stories of African American high school students, such as this Black male, to describe the incidents of microaggression they face in their day-to-day schooling:

> I play football, so you know they expect you to be good in sports. But when you are on the ASB (Associated Student Body) council, like I am, and being a school leader, have good grades, and talking about going to college on an academic scholarship, then they look at you like Whoa!! I didn't think that they (Black males) were into those kinds of things. One teacher even told me once, "You're not like the rest of them." I didn't ask her what that meant, but believe me, I knew what that meant. (Howard 2008, p. 907)

Thus, silence about the topic of race and ethnicity in no way signals unanimity among Americans and indeed, that silence may make it more difficult to uncover and explore important cultural differences in the personal experience of race.

Systems change is a difficult undertaking in any organization, even when it does not involve an emotionally-laden issue. Attempts to create systemic reform that can address racial and ethnic disparities are likely further compounded by emotional reactions that limit the ability of school personnel to directly address the issue of race. School practitioners, for example, may well resist attempts to identify racial disparity in their school if they fear that information will reflect poorly on them or their institution. It is unlikely that schools that are unwilling to broach the topic of race will be able to formulate solutions that are responsive to racial, ethnic, or cultural differences, much less accept the need for extensive reform of policies or procedures. Thus, attempts to address inequity in special education service may need to attend not only to the data and the recommendations that flow from those data, but also to the way in which dysconscious beliefs (King 1991) may short-circuit full consideration of race-based data (Singleton and Linton 2006). In order to address racial issues, we must be able to talk about race.

## Culturally Responsive Intervention

For almost 40 years, the database documenting disproportionality has been consistent and extensive; yet there is little research that has specifically targeted reductions in racial/ethnic disparities in discipline. As noted, a number of universal, school-wide interventions that might be regarded as race-neutral have been used to address issues of school discipline or school climate, and thus may have potential for reducing disproportionality (Osher et al. 2010), including SWPBS (Bradshaw et al. 2009; Horner et al. 2009), SEL (Durlak et al. 2010; Ialongo et al. 2001; Payton et al. 2008), and restorative justice (Jennings et al. 2008; International Institute for Restorative Practices 2009).

To date, however, the study of interventions specifically intended to reduce disciplinary disparities is isolated to a few case studies. In a case study conducted on a Dine reservation in New Mexico, Jones et al. (2006) reported that both the fidelity and effectiveness of implementation of School-Wide Positive Behavior Interventions and Supports (SWPBIS) was dramatically increased by embedding the culture, language, and history of the Dine people into the implementation of SWPBIS. Similarly, Wearmouth et al. (2007) describe the application of restorative justice in a New Zealand school with a predominantly indigenous Maori student population in order to illustrate how understanding and embedding students' and families' cultural values and worldviews into disciplinary systems can facilitate the development of culturally safe and responsive schools. While such case studies demonstrate the promise of adapting universal approaches to address racial and cultural issues, more research will be necessary to assess the effectiveness of universal interventions for the specific issue of racial and ethnic disparities in discipline.

Our nation's population is composed of a number of cultural subgroups; the complex and differing history of those groups calls into question the assumption that educational strategies and interventions will operate in the same way for all groups. The need to be responsive to the needs of diverse students has led to calls for culturally responsive pedagogy (Gay 2000; Ladson-Billings 2001) and more recently for culturally responsive classroom management (Brown 2004; Weinstein et al. 2004). While Kauffman et al. (2008) have argued that there is no evidence that behavioral interventions operate differently based on ethnicity, gender, or religion, they also note that differential effects based on race and ethnicity have been under-studied in the behavioral literature. Until a sufficient database on interventions for reducing disciplinary disproportionality has accumulated, it seems logical that implementations of interventions designed to affect student behavior in school should explicitly explore the extent to which those interventions work equally well for all groups.

## Outcomes

As noted, few studies have used racial and ethnic disparities as a dependent measure in studying interventions to affect school discipline outcomes. As a result, it is impossible at this point to definitively answer the question framed by Kauffman et al. (2008) regarding the necessity of culturally responsive variants of universal programs for creating change in different racial/ethnic groups. Early indications, however, raise questions about whether the universal application of PBIS is sufficient to reduce measured disparities in office referrals and out-of-school suspension.

Indeed, if interventions addressing disciplinary and management practices address only the needs of a school's White students, it is possible they will increase the racial/ethnic disciplinary gap, even while appearing to reduce overall rates of referral, suspension, and expulsion. Table 7.2 represents the results in terms of changes in Office Disciplinary Referrals (ODR's) in two middle schools in a statewide PBIS network as a result of their implementation of PBIS. Clearly, the overall effects appear promising, in that implementation over a 3-year period appeared to lead to very substantial drops in rates of ODRs for both schools over that period (Columns 1 and 2). In both schools there are clear racial discrepancies between the percent of students (risk index) referred to the office for White and Black students (Columns 3 and 4). In particular, while White risk indices decrease in both middle schools, Black rates of referral drop significantly only in Middle School #2. As a result, while disproportionality as measured by the risk ratio (Column 5) decreases in Middle School #2, racial disparities in ODRs increase in Middle School #1, even as the overall rate of referrals decreased.

Table 7.2

SWPBIS implementation in two middle schools: effects on rate and disproportionality of office disciplinary referrals (ODRs) | Total ODRs | ODRs per 100 students | Risk index AA | Risk index White | RR AA

---|---|---|---|---|---

Middle school #1

2004–2005 | 1,738 | 204.47 | 75.90 | 41.18 | 1.84

2006–2007 | 1,080 | 124.00 | 71.43 | 31.51 | 2.27

Middle school #2

2004–2005 | 2,150 | 318.52 | 85.88 | 32.39 | 2.65

2006–2007 | 805 | 115.83 | 54.93 | 26.88 | 2.04

Data Source: SWIS Ethnicity Reports

Note: ODR = Office Disciplinary Referrals; Risk index represents the percentage of a given racial/ethnic group in the school receiving ODRs; RR AA is the risk ratio comparing the rate of African American ODRs to the rate of White ODRs. Thus African American students are 1.84 times more likely to be referred to the office in Middle School #1 for the 2004–2005 school year, but 2.27 times more likely to receive an ODR in the 2006–2007 Academic Year. The author is grateful to Dr. Lucille Eber and her colleagues at the Illinois PBIS Network for sharing these data

Nor do recent tests of implementation across a national sample provide encouragement for a race-neutral approach to disciplinary intervention. Vincent and Tobin (2010) studied the effects of PBIS implementation on disciplinary outcomes in 77 elementary, middle, and high schools drawn from a national sample of schools implementing that approach for at least 2 years. Results suggested that more complete implementation of PBIS procedures in classroom settings was associated with reductions in rates of out-of-school suspension (OSS) in elementary schools, whereas fuller implementation of PBIS in non-classroom settings was associated with OSS decreases at the high school level. When the data were disaggregated, however, there was no effect of PBIS implementation on the disparity between number of days lost for OSS between African American and White students, prompting the authors to conclude, "These rather sobering outcomes suggest that SWPBS implementation might have little effect on the pervasive disproportionate exclusion of African American students" (Vincent and Tobin 2010, p. 12).

It is important to be clear that there is nothing in any of this data to suggest that universal interventions could not be adapted in order to effectively address racial and ethnic disparities in school discipline. Tobin and Vincent (2011) examined eight schools that had reduced racial disproportionality over time while implementing PBIS and found that those schools had made significant improvement in one of the core competencies of PBIS, Expected student behaviors are acknowledged regularly (reinforced). The institutional change process inherent in PBS (Lewis et al. 2006), using a team-based examination and re-engineering of school practices, policies, and procedures around school discipline (Sugai and Horner 2006), may be well-suited for addressing issues of reproductive behavior within schools as institutions. The processes of self- and systemic reflection inherent in restorative practices (Stinchcomb et al. 2006) or the instruction in social and emotional skills that is part of the Social Emotional Learning model (Payton et al. 2008) also address key skills that could be promising in addressing disciplinary disparities. Data to this point make the case, however, that in implementing any intervention to reduce disproportionality, changes in rates of racial/ethnic disparity in school disciplinary outcomes should be explicitly tested, not assumed.

The need to address culture directly in any intervention should come as no surprise, given the central importance of directly instructing the targeted skill in previous education research. Across outcomes ranging from academic achievement (Greenwood et al. 2002; Wang et al. 1997) to reading intervention (Foorman et al. 2003) to classroom behavior (Harvey et al. 2009), research has been highly consistent in finding that chances of successful outcomes are significantly improved by providing direct instruction or intervention designed to specifically address the identified problem. It is reasonable to believe that creating a change in outcomes in which race and culture are implicated will likewise require targeted attention to those specific issues of concern.

## Conclusions

The problem of racial and ethnic disparity in school discipline in America's schools remains unresolved, and some ways unaddressed, in America's schools. After nearly forty years of attention, research has succeeded in better describing the problem, but there remain few if any interventions proven to address the issues.

The data reviewed herein often contradict intuitive assumptions. Although boys are suspended more frequently than girls in general, some data suggests that racial disparities are greater among girls. Similarly, while suspensions occur more frequently in poor urban schools and districts, disproportionality is as great or greater in suburban locales with lower rates of poverty. Most importantly, the data contradict the widely-held perception that disproportionality in discipline is mostly a matter of poor kids behaving badly. Race remains a potent factor predicting disciplinary outcomes regardless of the level of poverty; accumulating data has consistently failed to find any evidence that Black students engage in more serious behaviors, hence earning a higher rate of suspension and expulsion.

There remains a pressing need for research-validated interventions specifically addressing racial disparities in discipline. Although several universal and race-neutral intervention approaches have reported success in changing disciplinary outcomes, there have been insufficient tests to know whether or not such approaches can reduce disparities. Indeed initial tests raise some concerns in this regard.

Racial and ethnic disparities in educational outcomes have both an empirical and a moral/ethical element. Data have demonstrated repeatedly the existence of racial and ethnic disparities in discipline and have begun to more clearly elucidate the causal variables that maintain disproportionality. Those data can also be used to track the effectiveness of interventions to reduce disparities. The commitment to a new course of action, however, is in part a moral decision, one that has informed every leap forward in civil rights, from the Emancipation Proclamation to Brown v. Board of Education. Data regarding racial and ethnic disparities are becoming increasingly clear over time. It remains to be seen when the attendant national commitment necessary to bring an end to such disparities will emerge.

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© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_8

# 8. Exploring, Understanding, and Closing the Achievement Gap(s): Efforts from Harlem to Homewood

John M. Wallace1

(1)

School of Social Work, University of Pittsburgh, Pittsburgh, PA 15260, USA

John M. Wallace

Email: johnw@pitt.edu

## The Changing Demographics of America's Children

According to the 2010 Census, the United States has 74.2 million children (ages 0–17) (Federal Interagency Forum on Child and Family Statistics 2011). The percent of the US population that is children peaked in 1960 at 36 % and declined to 24 % by 2010. Although the percent of the US population that is comprised of children is expected to remain relatively constant through 2050, its racial/ethnic composition has changed significantly over time and is expected to continue to do so. For example, in 1980, 74 % of US children were non-Hispanic White, 15 % were African American, 9 % were Hispanic, 2 % were Asian or Pacific Islander, and 1 % were American Indian or Alaskan Native. In 2010, the proportion of the nation's children who were White had declined to 54 %, 14 % were African American, 23 % were Hispanic, 4 % were Asian, and 5 % were all other races.1 By 2050, it is projected that only 38 % of US children will be White, 13 % will be African American, 39 % will be Hispanic, 6 % will be Asian, and 8 % will be all other races (Federal Interagency Forum on Child and Family Statistics 2011).

In addition to becoming increasingly diverse, a growing proportion of children in the United States are poor. In fact, in 2010 more that 20 % (15.75 million) of the nation's 74 million young people lived below the federal poverty line (Federal Interagency Forum on Child and Family Statistics 2011). Further, although White children comprise the numerical majority of America's poor children, poverty is concentrated disproportionately among young people of color. Specifically, 38 % of African American children, 32 % of Hispanic children, 36 % of children of "some other" race, and 23 % of children of "two or more races" are poor compared to only 17 % of White children and 13 % of Asian children (Federal Interagency Forum on Child and Family Statistics 2011).

A key measure of poverty used by educational researchers is whether or not a student is eligible, based upon their parents' income, to receive free or reduced priced lunch. Nationally, approximately half of Black and Hispanic elementary school students (i.e., fourth graders), and more than a third of American Indian students attend high poverty schools (i.e., 75 % or more free/reduced lunch eligible) compared to 13 % of Asian students and fewer than 10 % of White students (Aud et al. 2010). Put another way, relative to White students, Asian Americans are nearly twice as likely, American Indians are more than five times as likely, and Black and Hispanic students are roughly seven times as likely to attend a high poverty school. And so, while race/ethnicity and poverty are not synonymous, children of color are significantly more likely than White children to be poor and to attend schools populated by other poor children.

## The Demographics of America's Children and the Achievement Gap(s)

America's growing racial and ethnic diversity, coupled with the growth in the number of children who are poor, has drawn increased attention to the "achievement gap"—the disparity in educational outcomes that exists between White children and children of color, and between children of means and those who are economically disadvantaged. Although past research typically focuses on disparities in standardized test scores, the data reveal that there are, in fact, numerous educational achievement gaps. These gaps include not just test scores, but also other important educational outcomes such as graduation rates, dropping out of school, being held back and being suspended. Below, data are presented on some of these gaps.

### Test Scores

Racial/ethnic disparities in standardized educational tests begin early in the United States. For example, research on letter, number, and shape recognition assessments among 4 year olds finds that proficiency rates are highest among Asian children, somewhat lower among White children, lower still among Black and Hispanic children, and lowest among American Indian/Alaskan Native children (Aud et al. 2010).

Longitudinal analyses of the National Assessment of Educational Progress (NAEP) test scores, the "national report card of student achievement" reveal that "during the 1970s and the first half of the 1980s NAEP showed substantial academic improvement of Black and Hispanic students and a significant narrowing of the Black-White and Hispanic-White achievement gaps. However, since then this progress slowed down and even showed signs of a setback during the 1990s" (Lee 2004, p. 3). Analysis of the most recently published NAEP data (2009) suggests that although the size of the achievement gaps between White and Black students and between White and Hispanic students have not grown, they have not declined. As a result, substantial gaps continue to exist between the reading, math, and science test scores of White students and Black and Hispanic students (Aud et al. 2011).

Analysis of NAEP data by poverty status reveals that schools in which 75 % or more of students were eligible for free or reduced priced lunch scored significantly lower on the NAEP than did their counterparts in which only 25 % or fewer of the students were eligible for free or reduced lunch (Aud et al. 2011). Further, trend data reveal that the achievement test score gaps between poor children and their more advantaged peers have grown over time (Reardon 2011).

Results from the Program for International Student Assessment (PISA), a standardized test coordinated by the Organization for Economic Cooperation and Development (OECD), compare 15 year olds' math, science, and reading literacy for 60 nations and 5 other education systems (e.g., Shanghai, China) around the world. In the most recently published PISA data (2009), focused on international differences in reading scores, the United States ranked 17th among the 65 locations (score of 500 versus the international average of 493). Interestingly, when disaggregated by race, American Asian students ranked second internationally (score 541), American White students ranked seventh (score 525), and Hispanic American (score 466) and Black American (score 441) students ranked 41st and 46th, respectively (Fleischman et al. 2010). Not surprisingly, disaggregating the data by free and reduced price lunch status also reveals substantial subgroup differences in test scores. For example, the PISA reading literacy score for schools in which less than 10 % of students were free or reduced lunch eligible was 551 compared to a score of only 446 for schools in which 75 % or more of students were free/reduced lunch eligible (Fleischman et al. 2010). These data suggest that the relatively low PISA performance of poor students and Hispanic and Black students have a significant impact on the overall US PISA score and thus, its international ranking. Given recent increases in poverty, and the facts that young people of color currently represent approximately half of America's population of children, and in the future will represent at least half of the nation's total population, their educational success, or lack thereof, has serious implications, not just for their families and communities, but also for the well-being and international standing of the nation.

### Graduation and Dropping Out

On-time graduation is an important measure of educational success. Like test scores, it is also a measure on which there are substantial racial and ethnic disparities. Nationally, three-quarters (74 %) of students who began high school in 2003–2004 graduated within 4 years but the percentages varied widely across groups. Specifically, 91 % of Asian students, 80 % of White students, 62 % of Hispanic students, 61 % of American Indian/Alaskan Native students, and only 60 % of Black students graduated on time (Aud et al. 2010).

Dropping out of school is another important indicator of educational success. Perhaps the most widely used measure of dropping out is the status dropout rate—the percentage of 16–24 year olds who are not enrolled in school and who have not earned a high school diploma (or its equivalent). In 2007, 9 % of the nation's 16–24 year olds had dropped out. The racial/ethnic status dropout rates in 2007 were 21 % for Hispanic students, 19 % for American Indian/Alaskan Natives, 8 % for Blacks, 6 % for Asian/Pacific Islanders, and 5 % for Whites.

### Retention, Suspension, Expulsion

Data on racial and ethnic differences in being retained and suspended or expelled largely are consistent with expectations, based upon racial/ethnic disparities in test scores; rates generally are lowest for Asian students, somewhat higher among White students, and highest among Hispanic, Black, and other students of color. For example, among students in kindergarten through 12th grade, 4 % of Asian students, 9 % of White students, 12 % of Hispanic students, 13 % of American Indian/Alaska Native students, 15 % of two or more race students, and 21 % of Black students have repeated a grade in school (Aud et al. 2010). Similarly, among students in grades 6–12, 11 % of Asian students, 14 % of American Indian/Alaska Native, students, 16 % of White students, 22 % of Hispanic students, 25 % of two or more race students, and 43 % of Black students have ever been suspended (Aud et al. 2010). Data on expulsion reveal large disparities as well with 13 % of Black students having been expelled compared to 4 % of students of two or more races, 3 % of Hispanic students, and only 1 % of White students (Aud et al. 2010).

### Opportunity Gaps

Beyond the racial/ethnic disparities in educational outcomes, there are also racial and ethnic disparities in important correlates, if not predictors, of student outcomes that relate to the opportunities that schools and school systems provide to equip students for educational success. Opportunity gaps identified by past research include disparities in access to experienced and qualified teachers, disparities in teachers' expectations for achievement, and disparities in per pupil funding.

Research on teacher qualifications finds that compared to White students and students who are more economically privileged, Black and Hispanic students and poor students are twice as likely to be taught by inexperienced teachers (i.e., those with 3 years of experience or less) and are significantly more likely to be taught by teachers who lack full credentials in their subject matter and who teach subjects outside of their field of expertise (i.e., they do not have at least a minor in the subject area).

Teacher's perceptions of students and expectations of their performance have consistently been found to predict academic outcomes, with those students for whom teachers perceive as being more capable and for whom they hold high expectations experiencing the greatest academic success (Ferguson 2003). In light of the fact that the vast majority of teachers in America (88 %) are White, despite the fact that a growing proportion of the nation's young people are non-White, White teachers' expectations are a predictor of students' of color's academic success. Past research finds that teachers in predominantly Black and Hispanic classrooms, often expect less of these students than of White students, and according to one study, were far more likely to use "multiple-choice testing and other means of assessing low-level cognitive objectives than teachers who had a majority of White students in their classrooms". Similarly, there is evidence that suggests that compared to their White and Asian counterparts, Black and Latino students are more likely to be placed in low academic tracks, even when their test scores and other measures do not justify it, are less likely to be placed in gifted and accelerated programs, and have less access to advanced placement courses in their schools.

An area in which racial/ethnic and socioeconomic opportunity gaps are particularly visible is the amount of money that school districts spend per pupil. Disparities in per-pupil allocations emerge largely as a result of the nation's high level of race and class-based residential segregation and the fact that school funding is based upon property taxes. Since Black and Hispanic students are more likely than White students to be poor, to attend high poverty schools and live in poor neighborhoods in poor school districts, less money is allocated, on average, to educate them. Recent salary data from the National Center on Educational Statistics reveals substantial differences in both teacher and administrator salaries and other benefits, based upon the percentage of students who are reduced and free lunch eligible. More specifically, the average base salary for the highest paid teachers in low poverty schools (0–34 % free and reduced priced lunch) is 15 % higher (i.e., $67,000 versus $56,700) than that for the highest paid teachers in high poverty (i.e., 75 % or more of students receive reduced/free lunch) districts (Aritomi and Coopersmith 2009). Similarly, the average lowest and highest base salaries for principals of low poverty schools are greater than those of principals of high poverty schools, among those who are paid the least and those that are paid the most (i.e., $79,300 versus $66,300 among the lowest paid and $96,300 versus $79,900 among the highest paid). In addition to higher salaries, low poverty districts offer greater job security (i.e., tenure) for principals than do high poverty districts (31.5 % versus 17.7 %) (Aritomi and Coopersmith 2009). Beyond the economic disparities that often translate into lower teacher and administrator salaries, high poverty school districts are more likely to house students in older buildings, provide less current educational materials, and offer fewer academic enrichment opportunities (e.g., advanced placement classes) and extracurricular activities.

Interestingly, because of seniority policies in which more experienced teachers get to choose the schools in which they work, financial inequities often exist even within school districts. Because of these policies, experienced teachers (i.e., those with higher salaries) are often concentrated in higher performing schools, while teachers with less experience and lower salaries are concentrated in high poverty and high minority schools.

A recent summary of past research on these and related inequities notes that relative to White students, Black and Hispanic students, "are less likely to have access to qualified and experienced teachers, are more likely to face low expectations, and are less likely to receive equitable per student funding". Based upon this conclusion, and the findings of past research, an important question that future research needs to address is, "to what extent is the achievement gap largely a reflection of the opportunity gap?"

## Why Achievement Gaps Matter

The fact that there are significant and persistent gaps in academic achievement across race/ethnicity and social class has been well established. The question is, "why do achievement gaps matter?" Much of the reason for the focus on racial/ethnic and class disparities in educational outcomes is related to the fact that educational attainment is one of, if not the strongest predictor of virtually every measure of life chances and present and future well-being. Some of the outcomes with which educational success strongly correlates include income, wealth, employment status, marital quality, mental and physical health, and mortality (McKinsey & Company 2009; CITES). Put another way, compared to their less educated counterparts, people with higher levels of education have higher incomes, more wealth, higher employment rates, more stable marriages, better mental and physical health and live longer. Further, school failure and limited educational attainment extract a tremendous toll on society in terms of lost tax revenue, health and social welfare costs, the cost of incarceration, and the loss of productivity.

A recent study by McKinsey & Company discussed four achievement gaps and attempted to quantify their adverse economic impact on the total value of goods and services produced in the United States (i.e., the Gross Domestic Product), via an underutilization of human potential. The four achievement gaps and their estimated associated impact on the GDP are as follows: (1) the gap between the United States and higher performing nations ($1.3–2.3 trillion, 9–16 % of GDP); the gap between Black and Hispanic/Latino students and White students ($310–525 billion, 2–4 % of GDP); the gap between low income students and higher income students ($400–670 billion, 3–5 % of GDP); and the gap between low performing states and higher performing ones ($425–700 billion, 3–5 % of GDP) (McKinsey & Company 2009). The report concludes that "these educational gaps impose on the United States an economic equivalent of a permanent national recession" (McKinsey & Company 2009, p. 6).

The substantial, and potentially growing adverse economic impact of the various achievement gaps that have been discussed thus far have a tremendous negative impact on individuals, families, communities, cities, and the entire nation. Accordingly, efforts to close and eventually eliminate these gaps should be a central concern for researchers, practitioners, policy makers, and every citizen of the United States.

## Efforts to Close the Gap

### Harlem Children's Zone

A variety of programmatic and policy efforts have been made to close the achievement gaps. These efforts include, among other things, early childhood interventions (e.g., Head Start, Nurse Family Partnership), after-school programs, school choice, busing, small schools and classrooms, charter schools, merit pay for school faculty, and numerous other strategies (Dobbie and Fryer 2009). To date, most attempts to close the racial/ethnic and social class achievement gaps have failed. One potentially important reason for the failure of past efforts is that they tend to operate in a piece-meal fashion and attempt to intervene on a single academic risk factor while ignoring the myriad mental, physical, economic, social, and environmental factors that disproportionately impact poor children and many children of color.

One effort to close the achievement gap, that attempts to simultaneously reduce risk factors for educational failure and to promote those factors known to enhance young people's educational outcomes, is Geoffrey Canada's internationally acclaimed Harlem Children's Zone (HCZ). The HCZ is a large multi-service organization in Harlem with a nearly $100 million per year annual budget, focused on more than 10,000 poor Black and Hispanic children who live in a 97 block area of central Harlem (i.e., the "Zone"). The HCZ's theory of change is that in order for poor and minority young people to have the kinds of academic outcomes achieved by White and middle class students, poor children and children of color need to have the kinds of experiences, exposure, and access to resources that White and middle class children have. Accordingly, the HCZ model seeks to provide children who live in the "Zone" everything that their White and middle class counterparts receive, in an integrated "pipeline" of cradle to college programs. These programs include prenatal care for pregnant mothers, parent education programs, high quality early childhood education, dental, vision, hearing, health and mental health services, other family wrap-around programs, fresh organic fruits and vegetables, rigorous charter schools that include extended days, Saturday classes, an extended school year, high quality teachers, and academically enriched arts-based and civic engagement focused out-of-school time activities.

A recent evaluation of the HCZ found that it successfully closed the achievement gap in English language arts and mathematics among elementary school students, and among eighth graders, the HCZ substantially reduced the gap in English language arts and actually reversed the Black-White math achievement gap (i.e., Black students scored significantly higher than the typical White student in New York City) (Dobbie and Fryer 2009). Although the data did not allow researchers to determine the specific "ingredients" of the HCZ model (e.g., the charter school curriculum, parent engagement, health and mental health wrap-around programs) that accounted for its ability to close the achievement gap, the results of the evaluation garnered national attention and significantly bolstered the HCZ as the prototype for President Obama's "Promise Neighborhoods" initiative. Below, we describe a collaboration designed to create a Promise Neighborhood in an economically disadvantaged predominantly Black neighborhood in Pittsburgh, PA.

### Homewood Children's Village

The Homewood Children's Village (HCV) is a place-based, child-centered, comprehensive community initiative inspired by the HCZ. The mission of the HCV is "to simultaneously improve the lives of Homewood's children and to reweave the fabric of the community in which they live." The HCV does not attempt to replicate the HCZ's role as the primary provider of all of the services that comprise its "pipeline" of services. Rather, the HCV uses a collaborative approach to service delivery in which its role is threefold: (1) to convene existing service providers; (2) to coordinate their services along the developmental birth to college and career pipeline; and (3) to build the capacity of the existing service providers to deliver high quality evidence-based services to every child and family in Homewood that desires to participate.

Homewood is a one square mile neighborhood located in the east end of Pittsburgh. According to the 2010 census, the neighborhood is comprised of 2,787 households and 6,442 residents, of whom 1,798 are children under 18 years of age. Demographically, Homewood is racially and economically segregated. Specifically, Homewood is more than 94 % African American; 32 % of all residents live below the federal poverty level; 26 % of adults have not earned a high school diploma; more than 50 % of the population lacks transportation; Temporary Assistance for Needy Families (TANF) cash assistance and food stamp eligibility rates are more than double those of the rest of the city; only 39 % of Homewood's residents are in the workforce; 72 % of Homewood's children are being raised by one parent; and more than 80 % of students are eligible for free or reduced lunch (Allegheny County Department of Human Services 2010; A+ Schools 2011). The average home in Homewood was built around 1920; 28.2 % of its residential properties are vacant; 57.1 % of the 4,364 taxable properties are tax delinquent; and the average non-vacant residential home sales price in 2009 was $9,152 (compared to $90,491 for the rest of the city) (University Center on Social and Urban Research 2011).

Despite its social, economic, and physical challenges, Homewood, like many poor urban neighborhoods, remains a community of hope with significant human, physical, and social assets. Key assets upon which the HCV is being built include the presence of many long-time residents, numerous faith- and community-based based organizations, a neighborhood health center, a new elementary school, relatively new YMCA and YWCA buildings along with their associated youth-serving programs, a recently renovated public library, a public swimming pool, a branch of the Community College of Allegheny County, and a key transportation hub—the Martin Luther King East Busway—that provide riders from the east end of the city direct and immediate access to Pittsburgh's largest employment and commercial centers—Oakland (i.e., the location of several colleges, universities and the hospitals that comprise the University of Pittsburgh Medical Center) and Downtown Pittsburgh.

The vision to bring the HCZ model to Homewood was born in the spring of 2007. The idea emerged out of a search to identify a replicable, evidence-based strategy to address the myriad problems that confront the children and families who live and learn in Homewood. The HCV was initially conceived as a community-based participatory demonstration project partnership between the University of Pittsburgh's School of Social Work and a Homewood community organizing focused non-profit named Operation Better Block (OBB). In light of the centrality of education, social services, health, and neighborhood development to the success of the HCZ, the leaders of OBB systematically reached out to key stakeholders in each of these domains, both inside and outside of Homewood. The results of these outreach efforts were the formation of the HCV steering committee, a group of Homewood residents, directors of Homewood non-profits, and leaders from various sectors of the greater Pittsburgh community (e.g., local universities, the City of Pittsburgh, the Allegheny County Department of Human Services, the Pittsburgh Public Schools, various local foundations). The work of the HCV has been recognized nationally and its leaders were invited by Geoffrey Canada to present at the HCZ's national conference in November, 2009.

A fundamental focus of the work of the HCV is to eliminate school failure and promote academic achievement among Homewood's children. Academic performance (e.g., grades, standardized test scores, school dropout) in Homewood is among the lowest in Pittsburgh (A+ Schools 2011) and in the state of Pennsylvania. In fact, according to 2009 data from www.greatschools.com, students in 99 % of Pennsylvania's schools, scored higher on the standardized Pennsylvania System of School Assessment (PSSA) tests than students in Homewood. For example, in 2009 only 25 % of 11th graders in Homewood's high school scored proficient in reading (versus 65 % for PA) and only about a tenth (13 %) scored proficient in math (versus 56 % for PA). Further, the school's low graduation rate of 53 % (Engberg and Gill 2006) caused it to be identified as one of nation's "dropout factories" (i.e., a high school in which 60 % or fewer students graduate).

In the spring of 2008, the leaders of the HCV drafted a four-phase "doing while planning, planning while doing" strategy to design, implement, and evaluate the cradle-to-college-to-career continuum of evidence-based academic programs, undergirded by an extensive array of family and community supports. The basic goals of these four distinct, yet interrelated and overlapping phases were as follows:

  * Phase 1-Exploration: Thoroughly research the HCZ model and attend the HCZ Practitioner's Institute to assess the viability of the HCZ model and the feasibility of adapting it to Homewood.

  * Phase 2-Planning, Assessment and Piloting: Acquire resources with which to plan the implementation of the HCZ model for Homewood; build the infrastructure needed to establish the HCV as viable 501c3 non-profit; conduct a comprehensive assessment of the "State of the Village" (i.e., collect and analyze existing data on the on social, economic, and educational condition of Homewood's children and families and situations in the environment that impact their lives); engage the Homewood community in the creation of a Children, Youth and Family Master Plan that defines its vision for its children and the strategies to achieve it; work with existing HCV partner organizations to develop and pilot the HCV community organizing strategy; pilot and refine a strategy and programs to get Homewood's high school students eligible to receive up to $40,000 for college through the Pittsburgh Promise scholarship program; and develop the—HCV business plan, i.e., the "blueprint" to guide the implementation of the HCV over the next 3–5 years.

  * Phase 3-Implementation: Implement, rigorously evaluate, and continuously improve the service delivery model described in Phase 2.

  * Phase 4-Expansion: Use the lessons learned in Phase 3 to take the HCV to scale in Homewood and expand the model to other distressed communities in the Pittsburgh region.

To date, the exploration phase of the plan has been completed, and many goals of the planning, assessment and piloting phase are actively being pursued. Key goals that have been accomplished include the following: the research team has collected and analyzed a significant amount of data on current social, economic, educational, and property conditions in Homewood; the HCV has become a 501c(3); a president/CEO and core leadership team have been hired; numerous community engagement projects have been implemented, a full-service community school, designed to provide health, mental health and social and academic enrichment programs has been launched with over 20 student interns, AmeriCorps members and staff; an arts-based after-school program in partnership with the YMCA and a "Bridge to College" program to prepare high school students for the Pittsburgh Promise have been started; a parenting program for high school students with children has been created; dangerous vacant and abandoned properties around the schools have begun to be demolished through a partnership with the Mayor's Office and the Pittsburgh Urban Redevelopment Authority; bus transportation is being provided to students who live more than a mile away from the school, and who would be required to walk to school because of recent budget-driven school consolidation; and the HCV business plan has been completed. Over the next 2 years, the HCV will work to build the capacity of the HCV and its partner organizations to fully design and implement a seamless continuum of evidence-based cradle-to-college-to-career academic, family and community support programs to effectively serve every child and family that live and learn in Homewood and that desire to be a part of its work.

## Conclusion

As the nation's population becomes increasingly diverse, the social and economic future of the nation will increasingly depend upon the educational preparation and success of its soon to be majority population—children, youth, and adults of color. Currently, there are substantial gaps in academic achievement, that are due, at least in part, to substantial gaps in social, economic, and educational opportunity. If the United States is to remain among the world's leading nations it is imperative that the education of all of our children be a national priority and that we be willing to do "whatever it takes" to ensure that race, ethnicity nor the social class into which young people are born be the key determining factors in the quality of the education that we as a society afford them. Unlike traditional piece-meal approaches to education reform that just focus on schools and test scores, holistic approaches, like that of the HCZ, are beginning to demonstrate that all children can learn if provided the opportunity and resources. In sum, the data described above suggest that the issue of educational success of poor children and children of color is an issue about which all Americans should be concerned. The future of the nation depends upon it.

References

A+ Schools. (2011). Report to the community on public schools progress in Pittsburgh. Retrieved June 25, 2012 from http://www.aplusschools.org/pdf/cspr11/A+2011-front.pdf

Allegheny County Department of Human Services. (2010). Homewood: A community profile. Retrieved June 25, 2012 from http://www.alleghenycounty.us/uploadedFiles/DHS/About_DHS/Report_and_Evaluation/Homewood-Childrens-Village-Profile.pdf

Aritomi, P., & Coopersmith, J. (2009). Characteristics of public school districts in the United States: Results from the 2007–08 schools and staffing survey (NCES 2009-320) (National Center for Education Statistics, Institute of Education Sciences, U.S.). Washington, DC: Department of Education.

Aud, S., Fox, M., & Kewal Ramani, A. (2010). Status and trends in the education of racial and ethnic groups (NCES 2010-015). U.S. Department of Education, National Center for Education Statistics. Washington, DC: U.S. Government Printing Office.

Aud, S., Hussar, W., Kena, G., Bianco, K., Frohlich, L., Kemp, J., & Tahan, K. (2011). The condition of education 2011 (NCES 2011-033). U.S. Department of Education, National Center for Education Statistics. Washington, DC: U.S. Government Printing Office.

Dobbie, W. & Fryer, R. G. (2009). Are high-quality schools enough to close the achievement gap? Evidence from a bold social experiment in Harlem. Retrieved from http://www.economics.harvard.edu/faculty/fryer/files/hcz%204.15.2009.pdf

Engberg, J. & Gill, B. (2006). Estimating graduation and dropout rates with longitudinal data: A case study in the Pittsburgh Public Schools. RAND Working Paper.

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Ferguson, R. F. (2003). Teacher's perceptions and expectations and the black-white test score gap. Urban Education, 38(4), 460–507.CrossRef

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McKinsey & Company. (2009). The economic impact of the achievement gap in America's schools. Retrieved from http://mckinseyonsociety.com/downloads/reports/Education/achievement_gap_report.pdf

Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In R. Murnane & G. Duncan (Eds.), Whither opportunity? Rising inequality and the uncertain life chances of low-income children. New York: Russell Sage.

University Center on Social and Urban Research. (2011). Anatomy of a neighborhood in distress: Homewood in the 21st century. Pittsburgh, PA: University of Pittsburgh.

Footnotes

1

Includes American Indian, Eskimo and Aleut, Native Hawaiian and Other Pacific Islander and all multiple race ("two or more races") individuals.
© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_9

# 9. High School Quality and Race Differences in College Achievement

Jason M. Fletcher1 and Marta Tienda2

(1)

University of Wisconsin-Madison, Madison, WI 53715, USA

(2)

Office of Population Research, Princeton University, Princeton, NJ 08544, USA

Marta Tienda

Email: tienda@princeton.edu

## Introduction

A longstanding controversy about the sources of racial and ethnic differences in academic achievement revolves around the relative importance of individual and institutional factors. This debate occupied center stage following the release of the landmark Coleman Report (1990), which argued that family background, rather than features of educational institutions, is the primary determinant of low student achievement. Another prominent study by Jencks and associates (1972) supports Coleman's claim that school influences on achievement gaps are substantively trivial, although they conceded that average school traits conceal more than they reveal under conditions of high socioeconomic inequality. Despite his sweeping generalization about the salience of family compared to school effects on achievement, even Coleman acknowledged that Black students attending integrated schools outperformed their race counterparts who attended segregated schools.

In light of the counterintuitive conclusion that school quality is responsible for a negligible share of the K-12 achievement gap, these two influential studies spawned numerous investigations that sought to identify the features of schools where economically disadvantaged students reach high levels of academic achievement. Researchers searched for sources of variation in academic outcomes that may be correlated with family background, but either intensify or attenuate the direct influences of family background on student achievement. Murnane's (1981) review of the school effectiveness literature concludes that schools can and do influence student learning. Based on a wide-ranging review of the empirical literature about school effects, Rutter (1983) explains that the mixed evidence reflects extensive variation both in the criteria used to characterize school effectiveness and the outcome measures of student achievement. Likewise, a meta-analysis of school effects on student performance by Hedges and associates (1994) concludes that the median association is both large and substantively important, but they did not consider links between high school attributes and postsecondary outcomes.

In fact, with few exceptions studies of school effects on student performance, largely ignore postsecondary achievement. Furthermore, much of the available evidence about links between high school economic mix and postsecondary outcomes is not only dated, but also focused on college intentions rather than actual performance outcomes (Meyer 1970; Alwin and Otto 1977). That minority students are disproportionately concentrated in low-performing, under-resourced high schools complicates the task of teasing out unique influences of family background and school quality on academic outcomes, including postsecondary performance. Although higher education draws from the upper half of the K-12 achievement distribution, minority students who attend postsecondary institutions perform at lower levels, on average.

For example, Vars and Bowen (1998) show that White students achieve higher college grades than Black students across five SAT strata, even after controlling for family background and high school achievement. Yet, Light and Strayer (2000) find that Blacks and Hispanics are more, not less, likely to graduate from college than their White counterparts of comparable ability. Kane (1998) also concludes that among Black students, those who attend more selective colleges have higher graduation rates compared with those who attend less selective colleges. Using two nationally representative longitudinal surveys and a retrospective survey of students who were enrolled at selective and highly selective institutions, Alon and Tienda (2005) affirm that both minority and White students who attended selective institutions were more likely to graduate than their statistical counterparts attending less selective colleges, even after modeling the selection regime into the most competitive institutions. Because selectivity of college attended is correlated with persistence and the likelihood of completion, high school influences on performance likely operate via institutional selectivity (Velez 1985; Braxton et al. 1997; Bowen and Bok 1998; Light and Strayer 2000; Alon and Tienda 2005; Schneider et al. 2006).

Building on research that links high school quality to collegiate academic achievement, we investigate whether race differences in college grades depend on the quality of high school attended, and if so, whether the performance gaps also differ according to institutional selectivity. Both issues are policy relevant inasmuch as social class variation remains a salient concern for selective institutions seeking to broaden postsecondary access to underrepresented groups (Finder 2008; Kahlenberg 2004, 2010; Nichol 2008) and they undergird persisting controversies about the fairness and wisdom of race preferences in college admissions (Bowen and Bok 1998).

The next section summarizes prior studies that link pre-collegiate achievement and postsecondary outcomes, focusing on variation in high school quality and ethno-racial gaps in grades, persistence and graduation. Section "Texas College Admissions and the School Quality Debate" justifies the value of Texas as a case study to address theoretical debates about individual and institutional determinants of postsecondary achievement gaps. Specifically, we capitalize on a statewide change in college admission criteria that shifted emphasis from ascribed attributes of individual students to attributes of enrollees' high schools. Following a description of the data and estimation strategy in section "Data and Methods," we present empirical results in section "Multivariate Results." We find that attending an affluent high school does not insulate minority students from achievement disparities vis-à-vis their same school classmates beyond the first semester. Furthermore, high school influences on academic achievement carry over through the college career at least through 4-year graduation, but only at selective institutions. The "Conclusion" section summarizes key findings and considers policy implications.

## Prior Studies

Early studies seeking to establish links between high school attributes and collegiate outcomes focus on postsecondary intentions (or aspirations) rather than actual behavior. As one of the first studies to consider how the economic mix of a high school influences college behaviors, Meyer's (1970) highly influential study of college intentions shows that average ability students attending high-income schools exhibit lower college intentions than their status counterparts attending low-income schools. Social comparison is the alleged mechanism, but school climate, peer influences, and curriculum placement (e.g., tracking) are also viable contenders (Rutter 1983).

Comparatively fewer studies consider whether and to what extent high school effects persist beyond the enrollment decision, and in particular whether race and ethnic variation in the quality of high school attended contributes to college performance. Nevertheless, several studies suggest these influences exist (Summers and Wolfe 1977; Massey 2006; Schneider et al. 2006; Niu et al. 2006). Manski and Wise (1983) contend that students who attended "better" high schools are more likely to persist in college than students of comparable SES and academic achievement who attended high lower quality schools. They represent school quality using the share of the high school class that enrolled in college.

Hill (2008) explains how high schools' commitment to link students with postsecondary institutions fosters race and ethnic variation in college enrollment. Specifically, if high schools operate as clearinghouses rather than brokers, college enrollment rates are low and presumably so also are academic achievements. Her analyses demonstrate that lower performing schools, which also have high shares of low-income students, use the minimalist clearinghouse approach. Yet, these are precisely the students who would most benefit from both school administrator and parental involvement in the college preparation process. Hill's analysis suggests that high school economic status is a reasonable proxy for the college orientation of the school.

Because postsecondary attendance is not compulsory and draws from the upper half of the achievement distribution, one might expect smaller race and ethnic differences in academic achievement in college compared to high school. At selective institutions in particular, admission officers seek applicants that are likely to succeed, yet racial and ethnic gaps in performance and graduation persist (Vars and Bowen 1998; Sacerdote 2001). That minority students average lower standardized test scores than nonminority students is used to argue against race-sensitive criteria that give Black and Hispanic students an admission advantage (Alon and Tienda 2007). This rationale not only presumes that standardized test scores are reliable predictors of college success, but also ignores the role of schools in producing inequities in college preparedness.

Despite the relative neglect of empirical research establishing links between high school quality and postsecondary performance, there are compelling reasons to expect an association. First, high schools differ appreciably in their student mix, their college-going traditions, and their curricula, all of which likely carry over to postsecondary choices and academic success (Manski and Wise 1983; Meyer 1970; Jencks and Mayer 1990). For example, McDonough (1997) posits that high school attended dictates whether selective postsecondary institutions are even envisioned as possible options, which is consistent with findings by Niu and associates (2008) showing that both the number and the selectivity of institutions named by seniors interested in attending college differed vastly according to the socioeconomic mix of high schools. They also show that minority students attending poor or highly segregated public high schools are less likely than similarly situated Whites to enroll at a selective institution, even if they qualify for college admission.

Massey (2006) argues that minority students who attend selective universities attend lower quality high schools that differ on a variety of difficult-to-measure dimensions, such as levels of violence and college orientation, which in turn carry over to their college experiences. In another study based on enrollees at seven selective colleges and universities in the late 1990s, Espenshade and Radford (2009) find an association between high school quality and college performance that is independent of individual attributes and family background. Specifically, they show that students who attended elite high schools are significantly more likely to graduate within 6 years compared with statistically equivalent students who attended non-elite high schools.

Several recent studies capitalized on changes in admission regimes in Texas to identify possible links between high school economic mix and various aspects of college behavior, including application behavior and college choices. For example, Koffman and Tienda (2010) show that students from affluent high schools who qualify for automatic admission are significantly more likely to apply compared with comparably ranked students from poor high schools. Based on a representative longitudinal survey of Texas public school graduates, Niu and Tienda (2008) find a strong association between both the number and selectivity of institutions included in students' college choice sets and the socioeconomic mix of their high school. Using administrative data from four public Texas institutions, Fletcher and Tienda (2010) implement a school-fixed-effects methodology to consider whether race and ethnic gaps in college academic performance can be traced to group differences in the types of high schools attended. They show that Black-White and Hispanic-White college performance gaps are mostly eliminated, and often reversed, when students compared attend the same high school. Their results strongly implicate variation in high school quality in producing postsecondary achievement gaps, but they stop short of directly examining high school attributes that might explain race and ethnic variation in postsecondary academic achievement.

Building on this work, we hypothesize that racial and ethnic collegiate performance gaps will differ according to high school quality, but it is unclear whether minority students from the poorest high schools outperform their White classmates. On the one hand minority students from disadvantaged backgrounds may be highly motivated to succeed, especially if they are first or second generation students. On the other hand, White students attending poor high schools may be less economically disadvantaged than their minority counterparts. Because collegiate performance of minority students also depends on institutional selectivity (Bowen and Bok 1998; Alon and Tienda 2005), we compare academic outcomes at three universities whose admission regimes range from highly selective to noncompetitive. Before turning to the data and methods, we discuss the circumstances from our Texas case study that refocused attention on the significance of high school quality for college performance.

## Texas College Admissions and the School Quality Debate

During the late 1990s, the state of Texas assumed center stage in the higher education drama about race-sensitive admission preferences. In a bold response to court-ordered ban on use of race in college admissions (see Hopwood v. University of Texas), the 75th Texas legislature passed H.B. 588, which guarantees automatic admission to any Texas public college or university for all students who graduate in the top 10 % of their class. Initial political support for H.B. 588, popularly known as the top 10 % law, derived from its race-neutral admission criteria that were applied uniformly to all high schools, irrespective of size, wealth, or location. Both political and public support dissipated, however, as the demand for access to the flagships surged and students qualified for the admission guarantee swamped the UT—Austin campus, taxing both its carrying capacity and the ability of admission officers to balance the freshman classes along multiple dimensions (Tienda and Sullivan 2009).

Texas ranks 43rd out of 50 in high school graduation rates (O'Rourke 2010), but the statewide average rate of 60–65 % (depending on how the rate is calculated) conceals large disparities across districts and among demographic groups. For example, the Alliance for Excellent Education (2009) reports that just over half of Black (52 %) and Hispanic (56 %) students graduated in the 2005–2006 academic year, compared with three-fourths of White and 85 % of Asian students. Equally large variation in graduation rates is evident across districts, which range from 100 % to a low of 40 % (Texas Education Agency 2010).

The vast economic heterogeneity of Texas public high schools suggests the testable hypothesis that differences in the quality of Texas public high schools are partly responsible for race and ethnic differences in college performance gaps. Furthermore, the top 10 % admission regime likely increased salience of high school quality for academic achievement for two reasons. First, because eligibility for automatic admission is determined on a high school-specific basis rather than standardized statewide criteria, top ranked students from every high school—rich or poor—qualify for the guarantee. Second, the two public flagships—the University of Texas at Austin (UT) and Texas A&M University (TAMU)–each implemented a scholarship program designed to ensure that rank-eligible students from low-income high schools could enroll (Domina 2007).

In fact, the number of high schools represented among applicants and matriculants to UT increased under the top 10 % regime (Montejano 2001; Long et al. 2010), including many with high shares of low-income students. Because the Texas percent plan guarantees admission to a segment of the top-performing students of each high school in the state, it theoretically leveled the playing field by diversifying the high school feeding patterns to the State's 4-year public institutions (Long et al. 2010). It is conceivable, as critics of the percent plan allege, that students from high schools with low college-going traditions will underperform academically compared with their classmates who graduated from high schools with a strong college orientation.

Although graduates from high schools that largely serve economically disadvantaged students are less likely to attend college even if they qualify for admission (Niu et al. 2008), those who decide to attend are largely drawn from the very top ranks of the high school achievement distribution, particularly at the most selective institutions. The recruitment of students from poor high schools has direct implications for racial and ethnic gaps in college performance because minority students are disproportionately represented at resource-poor schools and because students who attend poor schools tend to be low-performing relative their peers who graduate from affluent schools.

Just as critics of affirmative action alleged that relaxing the test score threshold for minority students is responsible for the race gap in collegiate achievement, critics of the top 10 % law claim that it privileges graduates from low-quality high schools over lower-ranked graduates from highly competitive schools who presumably are better prepared academically even if they do not qualify for the admission guarantee. In effect, the change in admission regime shifted the focus of criticism about admission preferences from individual ascription (i.e., race and ethnic status) to organizations, namely high schools of differing quality. The Texas admissions and merit debate offers a propitious opportunity to re-engage discussions about the relative importance of individual and institutional factors in achievement by asking whether there are links between high school quality and collegiate performance.

### Data and Methods

We use 10 years of administrative data for three Texas public universities that differ in the selectivity of their admissions. These include the two public flagships—the University of Texas at Austin (UT), Texas A&M University (TAMU)–and the University of Texas–San Antonio (UTSA), which has relatively open admissions.1 Administrative records for the three institutions include information about enrollment status along with students' class rank, senior class size, SAT scores, and an identifier indicating the high school attended.2 Publicly available data from the Texas Education Agency (TEA) are used to stratify regular Texas public high schools for the years 1994–2003 according to the socioeconomic status of the students they serve.3 For each regular public high school, TEA data indicate the total number of graduates in each year, as well as mean school SAT scores and the school-specific share of students ever classified as economically disadvantaged.4 These data are appended to student records using the high school identifier available in both the administrative and the TEA files.

Jencks and Mayer (1990) claim that high school economic mix is a reasonable proxy for school environment and college orientation. Therefore, to portray high school quality, we derive a tripartite measure of economic disadvantage based on percent of students ever economically disadvantaged.5 Because high school students eligible for free or reduced lunch may be disinclined to request the benefit in order to avoid public stigma, the TEA measure based on receipt of lunch subsidy over the full academic career is a better proxy for low-income students than a current year measure. For each year in the observation period, we classify regular Texas public high schools into three broad socioeconomic strata based on the share of students that were ever economically disadvantaged. High schools in the lowest quartile of students are designated affluent; those in the highest quartile are designated poor; and high schools in the middle quartiles are classified as average (or typical). We make no presumption that school socioeconomic strata represent the status of individual students, but expect that academic preparation and college-linking activities will vary accordingly.

Table 9.1 shows how the three high school economic strata differ in their ethno-racial composition and three key indicators of college attendance, namely the percent that pass algebra; the share that take a college entrance exam; and conditional on doing so, the average test score. Consistent with prior studies, minority students are disproportionately represented in the poor schools, where Hispanics accounted for three-fourths of Texas high school students in 2002 but only 15 % of affluent high schools. Statewide, Hispanics comprised over one-third of Texas high school students.6 Blacks also are underrepresented at affluent high schools; they comprise about 13 % of Texas public high school students, but approximately 9 % of the student body at affluent high schools. The ethno-racial composition of high schools in the middle quartiles roughly approximates the Texas public high school student population except that African American students are slightly overrepresented relative to their statewide share and Whites are slightly under represented at these schools. Tienda and Niu (2006) show that minority students' chances of qualifying for the admission guarantee based on class rank are higher at schools where they constitute a larger share of the population, but their enrollment likelihood is lower owing to financial and information about college options (Niu et al. 2008).

Table 9.1

High school characteristics by economic strata, 2002 (means or percent)a | School economic strata

---|---

Affluent | Average | Poor

Composition

% Black | 8.8 | 16.7 | 14.3

% Hispanic | 15.0 | 31.8 | 74.7

% Asian | 5.2 | 2.6 | 0.8

% White | 71.0 | 48.9 | 10.2

% Pass H.S. Algebra (SD) | 58.8 | 45.7 | 34.4

(18.7) | (18.7) | (18.8)

% Take SAT (SD) | 75.2 | 60.5 | 53.0

(12.7) | (14.3) | (16.6)

![
$$ \\overline{x} $$
](A216298_1_En_9_Chapter_IEq1.gif) SAT/Taking (SD) | 1037.8 | 976.4 | 863.0

(53.3) | (68.6) | (73.9)

Ratio ![
$$ \\frac{\\mathrm{Graduates}}{10\\mathrm{th}\\kern0.1em \\mathrm{Graders}} $$
](A216298_1_En_9_Chapter_IEq2.gif) (SD) | 0.818 | 0.744 | 0.719

(.089) | (.129) | (.231)

N | 268 | 537 | 268

Source: Texas Education Agency, Special Tabulation

aWeighted by size of high school

Arguments about differential college readiness based on high school socioeconomic status find support in the gradients of algebra completion and test taking behavior. Close to 60 % of students attending affluent high schools pass algebra compared with just over one-third of their counterparts from poor high schools. A similar pattern obtains for taking standardized tests, which range from 75 % of students from affluent high schools versus just over half of their counterparts attending poor schools. And, not surprisingly, the average test scores vary monotonically with the income composition of the student body, confirming Rothstein's (2004) claim that the test scores are a rough proxy for the high school's economic status. The last entry in Table 9.1 presents the ratio of graduates in 2002 to the number of tenth graders in that year, which is a rough proxy for graduation rates. Not surprisingly, poor high schools feature the lowest graduation rates, but there is considerable heterogeneity within strata, as indicated by the standard deviations.

Table 9.2 provides a distribution of the high school composition of freshmen enrolled at the three public universities of interest. For this tabulation, we have disaggregated affluent and poor high schools further by separating "feeder" and "Longhorn/Century" high schools. The former are a subset of 28 high schools out of over 1,400 public high schools that accounted for between 20 and 25 % of admittees to the two public flagships as of 2000 (Niu and Tienda 2010). Virtually all of the Longhorn/Century high schools fall into the lowest economic quartile; hence they sent relatively few if any students to the public flagships. The Longhorn and Century scholarship programs implemented by UT and TAMU respectively, not only boost the number of students from low-income schools, but they also target the highest ranked graduates from these schools.7

Table 9.2

Distribution of first time freshmen by type of public high school attended and period: three Texas public universities (percent)

H.S. Strata | UT | TAMU | UTSA

---|---|---|---

Pre-1997 | Post-1997 | Pre-1997 | Post-1997 | Pre-1997 | Post-1997

Feeder | 29.9 | 28.1 | 19.2 | 19.2 | 6.4 | 9.8

Affluent | 36.2 | 33.0 | 38.6 | 37.4 | 37.6 | 33.0

Typical | 22.8 | 26.8 | 29.2 | 33.7 | 26.4 | 28.0

Poor | 7.0 | 6.7 | 8.1 | 6.0 | 19.3 | 19.9

Longhorn/Century | 4.1 | 5.1 | 4.9 | 3.7 | 10.4 | 9.2

Total | 100 | 100 | 100 | 100 | 100 | 100

N | 15,231 | 36,212 | 15,298 | 28,774 | 4,141 | 15,171

Source: THEOP Administrative Data

There is evidence that the high school composition of enrollees' changed after the uniform admission regime was implemented, but only slightly and not uniformly across institutions. At UT the share of enrollees from affluent schools, including the historically dominant feeder high schools, dropped nearly 2 percentage points and enrollees from other affluent high schools fell an additional three points, mainly as a result of the institutional saturation with rank-eligible applicants from a growing number of high schools (Tienda and Sullivan 2009). Enrollees from high schools classified in the second and third quartiles of the socioeconomic distribution, designated "typical" in Table 9.2, accounted for the largest increases in UT's freshman classes after the top 10 % law went into effect. There was a modest change also in the representation of students from the Longhorn/Century high schools, which serve large numbers of low-income students.

At TAMU the changes in the socioeconomic composition of sending high schools also favored the typical schools. By contrast to UT, where most of the increase in student enrollment from typical schools came at the expense of affluent high schools, at TAMU increased representation in students from average income high schools came at the expense of students from poor high schools, including the Longhorn/Century schools. Despite the intensive outreach to rank-eligible students from Century high schools, TAMU was less successful than UT attracting students from poor and minority high schools—at least through 2003, when our data series ended.

The saturation of UT with top 10 % admits benefited UTSA in that its share of students from feeder high school students rose from 3 to 10 %. Nevertheless, by comparison to the public flagships, UTSA enrolls a much larger share of students from poor high schools—roughly 30 % both before and after the top 10 % law went into effect. The main change is the slight dip in the share of students from Longhorn/Century high schools, possibly as the highest ranked graduates were lured away to the flagship campuses.

Given the observed changes in the socioeconomic composition of enrollees at the public flagships, it is conceivable that the Black-White and Hispanic-White performance gaps will be impacted, particularly as larger numbers of students from low-income schools enroll. Table 9.3, which reports sample characteristics of first-time freshman for each university and the three high school strata, confirms that nearly two-thirds of UT enrollees from poor high schools are Hispanic. At TAMU, which draws its students from a broader geographic and socioeconomic spectrum owing partly to its land grant mission (Long et al. 2010), nearly half of enrollees from poor high schools are White. Less than 10 % of enrollees at both UT and TAMU graduate from poor high schools, compared with UTSA, where one-fourth of the student body do so. Moreover, 80 % of UTSA students who graduated from poor high schools are Hispanic, but among UTSA enrollees who attended affluent high schools, nearly two-thirds are White.

Table 9.3

Characteristics of first time freshmen by type of public high school attended: three Texas public universities | UT—Austin Texas A&M | UT—Austin Texas A&M | UT-San Antonio

---|---|---|---
|

Affluent | Average | Poor | Affluent | Average | Poor | Affluent | Average | Poor

Composition (proportions) Hispanic | 0.08 | 0.17 | 0.63 | 0.06 | 0.09 | 0.44 | 0.26 | 0.40 | 0.80

Black | 0.03 | 0.07 | 0.06 | 0.02 | 0.05 | 0.05 | 0.05 | 0.07 | 0.04

White | 0.69 | 0.64 | 0.25 | 0.86 | 0.82 | 0.48 | 0.62 | 0.48 | 0.14

Asian | 0.21 | 0.11 | 0.06 | 0.04 | 0.03 | 0.01 | 0.07 | 0.04 | 0.02

First semester GPA (SD) | 2.99

(0.84) | 2.76

(0.90) | 2.62

(0.93) | 2.80

(0.77) | 2.64

(0.80) | 2.44

(0.83) | 2.23

(1.08) | 2.08

(1.07) | 1.85

(1.07)

Sixth semester GPA (SD) | 3.06

(0.56) | 2.94

(0.57) | 2.79

(0.58) | 2.99

(0.53) | 2.91

(0.53) | 2.78

(0.52) | 2.54

(0.66) | 2.51

(0.65) | 2.43

(0.62)

Four-year graduation rate (SD) | 0.38

(0.49) | 0.29

(0.45) | 0.22

(0.41) | 0.33

(0.47) | 0.30

(0.46) | 0.22

(0.41) | 0.06

(0.23) | 0.05

(0.22) | 0.04

(0.20)

Test score (SD) | 1222.56 | 1166.89 | 1081.85 | 1176.65 | 1130.16 | 1070.64 | 1021.37 | 982.68 | 915.68

(133.92) | (141.39) | (145.30) | (131.99) | (138.19) | (136.69) | (136.94) | (135.31) | (126.94)

Rank (SD) | 83.93 | 89.11 | 91.91 | 84.20 | 89.20 | 90.96 | 59.56 | 67.66 | 77.26

(14.02) | (10.63) | (8.65) | (12.92) | (9.95) | (8.98) | (21.34) | (20.81) | (18.55)

No. of students | 46078 | 17920 | 6482 | 33108 | 18893 | 4804 | 9301 | 5257 | 5436

Proportion of university total | 0.60 | 0.23 | 0.08 | 0.54 | 0.31 | 0.08 | 0.41 | 0.23 | 0.24

Source: THEOP Administrative Data

These differences in the socioeconomic composition of high school feeding patterns likely influence minority achievement gaps at the postsecondary level. In fact, Table 9.3 shows monotonic variation in average first and sixth semester grade point averages according to high school strata. Grade point levels tend to be higher at UT, the most selective institution, and lowest at UTSA, the least selective, where students from poor high schools did not earn a C-average during their first semester. Similar differentials obtain for sixth semester GPA, except that the grade point averages are higher, most likely due to selective attrition of the weaker students and potentially differential course portfolios from selection into chosen majors. At UT and TAMU, 4-year graduation rates vary directly with the quality of high school attended, but not at UTSA, where the likelihood of graduating in 4 years averages 5 %, irrespective of high school quality. This measure combines three different groups of students: those who transferred to other institutions; those who have withdrawn; and those who are still pursuing their studies.8 Transfers and delayed completion rates are particularly problematic for UTSA, both because a higher share of students attend part time and because transfers to more selective institutions may be considered a positive outcome. Both transfers and part-time attendance make interpretation of UTSA graduation rate problematic; therefore analyses of 4-year completion rates focus on UT and TAMU.

#### Modeling Strategy

To address whether the types of high schools that minority students attend contribute to college achievement disparities, we evaluate three measures of academic performance, namely first and sixth semester grades and 4-year graduation rates for students who attended affluent, average, and poor high schools. In particular, we estimate a standard production function that specifies college achievement outcomes as determined by individual, family, and school-level inputs:

![
$$ {\\mathrm{outcome}}_{iut}=\\beta {X}_{ist}+\\alpha U+{\\tau}_t+{\\varepsilon}_{iut} $$
](A216298_1_En_9_Chapter_Equ1.gif)

(9.1)

where an educational outcome for student i at university u at time t is determined by the student's demographic and background characteristics (X), university characteristics (U), and an idiosyncratic error term. In order to control for secular trends in the freshman class, university grading standards, etc., we also control for year fixed effects, τ t . Institution-specific estimates obviate the need to control for institutional characteristics, U. For all specifications, the estimated β coefficients for student racial background represent institution-specific racial disparities in college achievement:

![
$$ {\\mathrm{outcome}}_{iut}={\\beta}_u{X}_{ist}+{\\tau}_t+{\\varepsilon}_{iut} $$
](A216298_1_En_9_Chapter_Equ2.gif)

(9.2)

Furthermore, we estimate variants of (9.3) to assess whether high schools attended influence race and ethnic differences in college achievement:

![
$$ {\\mathrm{outcome}}_{ist}=\\beta {X}_{ist}+{S}_s+{\\tau}_t+{\\varepsilon}_{ist} $$
](A216298_1_En_9_Chapter_Equ3.gif)

(9.3)

This specification models all time-invariant characteristics of each student's high school, denoted by (s), to control for school-specific differences ("fixed effects"). Results for (9.3) indicate whether racial disparities in college achievement exist for students who attended the same high school. That is, we use a within-high-school-of-origin estimator for racial gaps in college achievement, where the coefficient of interest is only identified by within-high school disparities in college performance between individuals of different race/ethnicity who attended the same high school.9

The high school fixed effects models compare students from the same high school, but do not reveal whether differentials are similar among rich and poor high schools. The considerable ethno-racial heterogeneity within socioeconomic strata shown in Table 9.3, warrants further refinement to determine whether and in what ways school quality contributes to racial and ethnic disparities in collegiate achievement. Therefore, we stratify the sample into three types of high schools based on the level of school resources and re-estimate (9.3). This allows the coefficients of interest, β, to vary by high school quality; substantively this refinement addresses whether the estimated ethno-racial gaps among students who attend the same high schools differ according to the resource level of their high schools.

### Multivariate Results

Tables 9.4, 9.5 and 9.6 report estimates of race and ethnic differences in first semester grades, sixth semester grades, and 4-year graduation, respectively, based on (9.3) for students enrolled at each university. All specifications include standardized test scores and class rank, as well as year fixed effects to monitor annual variation in grading and freshmen class attributes, such as those produced by changes in admission criteria.10 The point estimates compare Blacks and Hispanics who were freshmen in a particular year with White students from the same high school. Thus, the fixed effects specification captures variation in curricula, college orientation of the school, sports activities, and physical resources across schools, but not individual experiences with college guidance or sports activities.11

Table 9.4

Determinants of first semester GPA with high school fixed effects: three Texas public universities, stratified by high school poverty

Institution and years | UT—Austin 1990–2001 | TAMU 1992–2002 | UTSA 1995–2003

---|---|---|---

H.S. Strata | Affluent | Average | Poor | Affluent | Average | Poor | Affluent | Average | Poor

Male | −0.110*** | −0.112*** | −0.145*** | −0.038*** | −0.039*** | −0.033 | −0.054** | −0.023 | −0.086***

(0.007) | (0.012) | (0.022) | (0.008) | (0.011) | (0.022) | (0.022) | (0.031) | (0.030)

Black | 0.053** | 0.060** | −0.056 | 0.076*** | 0.109*** | 0.047 | 0.048 | 0.030 | 0.164*

(0.021) | (0.026) | (0.066) | (0.026) | (0.029) | (0.068) | (0.049) | (0.062) | (0.089)

Hispanic | 0.030** | 0.026 | 0.083*** | −0.094*** | −0.093*** | −0.069** | 0.038 | 0.046 | 0.095**

(0.013) | (0.018) | (0.032) | (0.015) | (0.020) | (0.029) | (0.025) | (0.035) | (0.048)

Asian | 0.002 | 0.070*** | 0.111** | −0.008 | 0.002 | 0.230** | 0.050 | 0.040 | 0.119

(0.009) | (0.020) | (0.054) | (0.018) | (0.032) | (0.095) | (0.043) | (0.074) | (0.123)

Class rank | 0.029*** | 0.035*** | 0.035*** | 0.026*** | 0.030*** | 0.027*** | 0.024*** | 0.022*** | 0.023***

(0.000) | (0.001) | (0.001) | (0.000) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)

Test score (SAT/ACT) | 0.105*** | 0.124*** | 0.137*** | 0.098*** | 0.114*** | 0.132*** | 0.076*** | 0.109*** | 0.146***

(0.003) | (0.005) | (0.009) | (0.003) | (0.004) | (0.009) | (0.009) | (0.013) | (0.013)

Constant | −0.723*** | −1.754*** | −1.976*** | −0.669*** | −1.310*** | −1.289*** | −0.061 | −0.518*** | −1.417***

(0.037) | (0.073) | (0.151) | (0.038) | (0.061) | (0.136) | (0.090) | (0.131) | (0.136)

Observations | 45,576 | 18,479 | 6,082 | 33,103 | 18,891 | 4,804 | 7,667 | 4,194 | 4,478

R 2 | 0.33 | 0.26 | 0.20 | 0.27 | 0.23 | 0.19 | 0.26 | 0.20 | 0.20

Number of high schools | 254 | 406 | 176 | 269 | 504 | 235 | 169 | 207 | 163

Source: THEOP Administrative Data

***1 %, **5 %, *10 %, all models include year fixed effects

Table 9.5

Determinants of sixth semester GPA with high school fixed effects: three Texas public universities, stratified by high school poverty

Institution and years | UT—Austin 1991–2000 | TAMU 1992–2002 | UTSA 1995–2001

---|---|---|---

H.S. Strata | Affluent | Average | Poor | Affluent | Average | Poor | Affluent | Average | Poor

Male | −0.135*** | −0.157*** | −0.145*** | −0.109*** | −0.113*** | −0.095*** | −0.142*** | −0.079** | −0.079**

(0.005) | (0.010) | (0.018) | (0.006) | (0.008) | (0.016) | (0.027) | (0.035) | (0.034)

Black | −0.122*** | −0.102*** | −0.117** | −0.046** | −0.058*** | −0.086* | −0.036 | −0.125* | −0.018

(0.017) | (0.022) | (0.051) | (0.020) | (0.021) | (0.051) | (0.060) | (0.072) | (0.098)

Hispanic | −0.030*** | −0.034** | −0.074*** | −0.092*** | −0.072*** | −0.092*** | −0.024 | 0.005 | −0.035

(0.010) | (0.015) | (0.025) | (0.011) | (0.015) | (0.021) | (0.031) | (0.039) | (0.055)

Asian | −0.056*** | −0.001 | −0.019 | −0.034** | −0.016 | −0.005 | −0.025 | −0.084 | −0.110

(0.007) | (0.016) | (0.039) | (0.013) | (0.024) | (0.065) | (0.055) | (0.076) | (0.135)

Class rank | 0.019*** | 0.020*** | 0.020*** | 0.019*** | 0.021*** | 0.020*** | 0.015*** | 0.013*** | 0.014***

(0.000) | (0.001) | (0.001) | (0.000) | (0.000) | (0.001) | (0.001) | (0.001) | (0.001)

Test score (SAT/ACT) | 0.070*** | 0.096*** | 0.103*** | 0.073*** | 0.093*** | 0.100*** | 0.043*** | 0.100*** | 0.083***

(0.002) | (0.004) | (0.007) | (0.002) | (0.003) | (0.007) | (0.011) | (0.014) | (0.014)

Constant | 0.541*** | −0.098* | −0.191 | 0.480*** | −0.088* | −0.035 | 1.210*** | 0.733*** | 0.611***

(0.030) | (0.059) | (0.121) | (0.028) | (0.045) | (0.106) | (0.108) | (0.145) | (0.161)

Observations | 32659 | 11516 | 3745 | 27778 | 15088 | 3500 | 2082 | 1279 | 1386

R 2 | 0.33 | 0.28 | 0.23 | 0.31 | 0.28 | 0.24 | 0.25 | 0.23 | 0.18

Number of high schools | 258 | 387 | 176 | 266 | 500 | 232 | 128 | 144 | 130

Source: THEOP Administrative Data

***1 %, **5 %, *10 %, all models include year fixed effects

Table 9.6

Determinants of 4-year graduation with high school fixed effects: three Texas public universities, stratified by high school poverty

Institution and years | UT—Austin 1990–2000 | TAMU 1992–2000 | UTSA 1996–2000

---|---|---|---

H.S. Strata | Affluent | Average | Poor | Affluent | Average | Poor | Affluent | Average | Poor

Male | −0.127*** | −0.123*** | −0.108*** | −0.205*** | −0.208*** | −0.180*** | −0.030*** | −0.028*** | −0.010

(0.005) | (0.007) | (0.012) | (0.006) | (0.007) | (0.013) | (0.008) | (0.010) | (0.008)

Black | −0.078*** | −0.053*** | −0.026 | −0.049** | −0.008 | −0.017 | 0.005 | −0.008 | 0.024

(0.015) | (0.016) | (0.033) | (0.020) | (0.019) | (0.041) | (0.017) | (0.019) | (0.023)

Hispanic | −0.026*** | −0.039*** | −0.016 | −0.034*** | −0.063*** | −0.027 | −0.007 | 0.001 | 0.003

(0.009) | (0.011) | (0.016) | (0.012) | (0.013) | (0.017) | (0.009) | (0.010) | (0.012)

Asian | −0.049*** | −0.002 | 0.037 | −0.007 | 0.005 | 0.070 | 0.032* | −0.011 | −0.020

(0.007) | (0.013) | (0.027) | (0.014) | (0.022) | (0.054) | (0.017) | (0.024) | (0.034)

Class rank | 0.007*** | 0.007*** | 0.006*** | 0.006*** | 0.007*** | 0.006*** | 0.001*** | 0.001*** | 0.001***

(0.000) | (0.000) | (0.001) | (0.000) | (0.000) | (0.001) | (0.000) | (0.000) | (0.000)

Test score (SAT/ACT) | 0.011*** | 0.016*** | 0.028*** | 0.013*** | 0.020*** | 0.023*** | 0.004 | 0.012*** | 0.007**

(0.002) | (0.003) | (0.005) | (0.002) | (0.003) | (0.005) | (0.003) | (0.004) | (0.003)

Constant | −0.417*** | −0.544*** | −0.585*** | −0.291*** | −0.465*** | −0.550*** | −0.063** | −0.136*** | −0.113***

(0.026) | (0.043) | (0.071) | (0.029) | (0.041) | (0.079) | (0.028) | (0.037) | (0.033)

Observations | 37641 | 14425 | 5046 | 26843 | 15084 | 4011 | 4066 | 2562 | 3013

R 2 | 0.10 | 0.09 | 0.07 | 0.10 | 0.11 | 0.09 | 0.03 | 0.03 | 0.02

Number of high schools | 263 | 403 | 186 | 262 | 498 | 229 | 162 | 204 | 163

Source: THEOP Administrative Data

***1 %, **5 %, *10 %, all models include year fixed effects

The fixed effects specifications presented in Table 9.4 concur with Fletcher and Tienda's (2010) finding that minority students at UT outperform their White counterparts who graduated from the same high school, but also reveal that race and ethnic gaps in freshman grades differ by school quality. Both Asian and Hispanic freshmen graduates from poor high schools average higher first semester GPAs compared with their White high school classmates; however, no comparable advantage obtains for Black students who attend poor high schools, most likely because the low-quality schools Blacks attend have few Whites. On average, students from poor high schools represent less than 10 % of UT's freshman classes, and nearly two-thirds of these are Hispanic (Table 9.3). Among graduates from affluent high schools, Hispanic and Black UT freshmen also outperform their White same-school classmates, but Asian freshmen achieve grade point averages comparable to their White classmates. These findings not only support claims that high school quality contributes to postsecondary achievement gaps, but also suggest that the minority students from poor schools are highly selective on unobservable attributes like motivation. Asian and African American graduates from typical Texas high schools also outperform their White same-school classmates. Thus, at UT it appears that based on first semester grades students from poor high schools do not underperform academically.

Results for TAMU parallel those of UT, with several notable differences. Like UT, grade point gaps for TAMU's Asian and Hispanic freshmen from poor high schools are smaller than those of their counterparts who attended affluent high schools. The important difference is that Asians outperform their White high school classmates, but Hispanics achieve lower grades than their White classmates.12 Still, among graduates from poor high schools, the Hispanic-White grade point gap is smaller than that of their ethnic counterparts who attended affluent high schools. Surprisingly, African American TAMU freshmen from both affluent and typical high schools outperform their same-school classmates during their first semester by .08–.11 grade points, respectively. Only 2 % of Black TAMU freshmen hail from affluent high schools, but they appear to be highly motivated to succeed academically.13

UTSA provides a stark comparison to the public flagships both in its nonselective admissions and the socioeconomic composition of its student body. The fixed-effects estimates reveal very little evidence of minority achievement gaps, but there is evidence that both Hispanic and to a lesser extent Black freshmen from poor high schools achieve higher first semester grades than their same-school White classmates. Fletcher and Tienda's (2010) pooled estimates showed no race gap in first semester grades once school-fixed-effects were modeled, and a .05 Hispanic advantage across all schools. Our strata-specific analyses reveal that the average Hispanic and Black freshman achievement advantages derive mainly from the superior performance of students from poor high schools, who comprise nearly one quarter of the student body.

Sustaining the achievement advantages through the college career is essential for changing the ethnic composition of college graduates. To the extent that attrition is driven by withdrawal of academically weaker students, those who persist through the third year of study are presumably adequately equipped to complete their course of study. A sensitivity analysis confirmed that students with higher grade point averages are less likely to withdraw before their sixth semester, but attrition is not uniform across demographic groups and neither is academic performance in more advanced courses, as Table 9.5 shows.

Apparently drive and motivation cannot compensate for weak academic preparation as courses become more advanced, which is evident in the erosion of the priority freshman grade advantages. The magnitude of the resulting grade gap differs by group and high school quality. By their third year of college, UT Hispanic students from poor high schools achieve lower grades than their White classmates as a result of an average grade point erosion of .15 points (−.07 to .08) over the next five semesters. Hispanic students from affluent schools also witness a reversal of academic fortunes vis-à-vis their same high school White classmates, but the average change is much smaller—only .06 points. Black UT students also experience grade erosion over the next five semesters, but the magnitude of their achievement gap relative to their White high school classmates is similar for graduates of both poor and affluent high schools. Even Asian students lost their grade point advantages relative to their White high school classmates by the end of their sixth college semester; moreover, Asians who graduated from affluent schools averaged grades .06 points below their same-school classmates.

At TAMU, Black students also earned GPAs between .05 and .09 points below their White high school classmates after 3 years of study, irrespective of the quality of their high school. The grade erosion for Blacks was most pronounced for graduates from affluent and typical schools, where they initially outperformed their White classmates. However, TAMU's Hispanic-White achievement gap does not widen by the third year of study, and students from average schools manage to narrow the gap modestly. GPAs of Asian students from affluent high schools are below their same-school White classmates by the sixth semester, but graduates from poor schools lost their grade-point advantage over their White classmates. The latter result likely reflects selective attrition of the weakest students from both groups.

At UTSA there are no discernible achievement gaps by the end of the sixth semester. As a nonselective commuter institution, UTSA has some of the highest attrition rates in the UT system. The number of high schools represented in the first semester (Table 9.4) and sixth semester (Table 9.5) reveals how attrition differs according to high school quality. Not only did the number of high schools represented among the junior class drop by 24 %, 30 %, and 20 %, respectively, for students who attended affluent, average, and poor schools, but shrinkage in the cohort sizes was a whopping 69–72 % over the next five semesters. Even though many students at UT and TAMU had withdrawn by their sixth semester, attrition was not disproportionate by high school quality. At least one student from each poor high school that sent students to UT remained enrolled through their sixth semester, and only three of the poor schools that sent students to TAMU were no longer represented by the sixth semester. This indicates greater power to hold students from poor high schools at the more selective institutions, which is consistent with findings from several studies showing that persistence and graduation rates are higher at the more selective institutions (Alon and Tienda 2005; Bowen and Bok 1998).14

Differential attrition by institutional selectivity and demographic group is also evident in the 4-year graduation rates, which range from 22 to 38 %, depending on high school economic status (see Table 9.3).15 Despite persisting minority-White sixth semester grade gaps among UT and TAMU juniors who attended poor high schools, Table 9.6 reveals that, these disparities do not carry over to 4-year graduation rates at either flagship. One plausible explanation is that graduation rates are equally low for all groups, but an alternative is that minority students who graduate in 4 years earn lower grade point averages. Without additional information based on cohort graduation rates, which are not available due to right censoring, we cannot evaluate this possibility.

By contrast, among students who attended affluent and average high schools, 4-year graduation rates differ along race and ethnic lines at both flagships. Black students who attended affluent high schools are 5 (TAMU) to 8 (UT) percentage points less likely than their White high school classmates to graduate in 4 years. Hispanic students from affluent high schools also are less likely to graduate in 4 years compared with their White classmates, but the differential is lower—approximately 3 % points at both flagships. The Asian-White graduation disparity is intermediate between Hispanic and Black students who attended affluent high schools, but only reaches statistical significance at UT. In part this reflects the large heterogeneity of UT's Asian students, who comprise 21 % of first-time freshmen from affluent high schools. Only 4 % of TAMU's freshmen from affluent high schools are Asian.

Four-year graduation disparities between minority and White students from typical high schools also emerge at both flagships. Black and Hispanic students from average high schools are between 4 and 5 % points less likely than their White high school classmates to graduate from UT in 4 years, and at TAMU Hispanics are 6 % points less likely to do so. Many of these students will eventually graduate but some will not. It remains to be seen whether high school quality also influences the likelihood of ever graduating.

## Conclusions

Using a fixed-effects modeling strategy, we examine how class stratification of secondary schools reproduces academic disparities at the postsecondary level. Our analyses generated a plethora of specific results, but three generalizations capture the main story. First, attending an affluent high school does not insulate minority students from achievement disparities vis-à-vis their same school minority classmates beyond the first semester; however, this generalization only obtains for selective institutions. Furthermore, during the first semester, students who attended poor high schools often outperform their White classmates by a larger grade point margin than their race counterparts who attended affluent or poor high schools.

Second, high school influences on academic achievement carry over through the college career at least through 4-year graduation, but only at selective institutions. Our results show that the Hispanic-White and Black-White performance advantages evident in first semester grades dissipate by their sixth semester. With one exception, the modest minority-White grade point disparities indicate that graduates from poor high schools who enroll in college are not necessarily ill prepared for postsecondary training relative to their race and ethnic counterparts who attended affluent or typical high schools. However, the sizable Black-White sixth semester achievement disparities at UT warrant concern, both because they obtain across the three high school strata and because they may undermine eventual graduation prospects. What is unclear, however, is whether the absence of graduation achievement gaps among students from poor high schools simply means that both minority and nonminority students have very low prospects of graduating or that selective attrition equalizes by eliminating the weak students.

Third, the character of "typical" high schools warrants further examination. By definition, these schools are more heterogeneous along economic lines and other dimensions, such as size, geographic location, and ethno-racial composition. These three traits are likely related to curriculum and hence college performance in ways that the fixed-effect estimation strategy cannot disclose. That the two public flagships draw unequally from this pool of students may partly explain why race and ethnic disparities are somewhat difficult to characterize—sometimes intermediate between affluent and poor schools, but often not.

Although our tripartite typology of school quality is crude, it resonates with the college-linking strategies outlined by Hill that differentiated among traditional, clearinghouse, and brokering approaches. The institutional policy question centers around the value of broadening economic diversity, which is becoming more difficult as the sticker price of college continues to soar. Our analyses address this question through the lens of ethnic and racial achievement disparities, which remain tightly coupled with economic resources. If the social policy goal is weakening the reproduction of class inequality through postsecondary educational opportunities, it is necessary also to provide support mechanisms to narrow achievement gaps.

Acknowledgments

This research was supported by grants from the Ford, Mellon, and Hewlett Foundations and NSF (GRANT # SES-0350990). We gratefully acknowledge institutional support from Princeton University's Office of Population Research (NICHD Grant # R24 H0047879). An earlier version was presented at the 2009 annual meetings of the Association for Public Policy and Management, Washington DC and at the Race in America Conference, Center on Race and Social Problems, University of Pittsburgh, Pittsburgh, PA, June 5, 2010.

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Footnotes

1

The Texas Higher Education Opportunity Project (THEOP) collected these data (see http://www.texastop10.princeton.edu.) Files are available at the University of Michigan's Institute for Social Research.

2

Applicant percentile rank is calculated using the actual class rank and senior class size. For UT—Austin, 2.8 % of applicants' records lack precise class rank measures, but instead include an indicator of class rank within ranges. We smoothed these applicants into appropriate class rank ranges and would like to thank Mark Long (University of Washington) for generously sharing his Stata code to facilitate the interpolation.

3

We use publicly available data from the National Center for Education Statistics (NCES) to identify special and alternative high schools, which are excluded from the analysis.

4

The measure of students ever economically disadvantaged was provided in response to a specific request to the Texas Education Agency.

5

This approach is consistent with Rutter's (1983) recommendation to focus on relative differences among schools based on their placement in a distribution rather mean differences that can obscure inequities within and between schools.

6

According to the Texas Public School Statistics, Pocket Editions 2004–2005 and 2005–2006, Hispanics comprised 35 % of public high school graduates, African Americans 13 %, and Asian and other groups about 4 %. Just under half of Texas public high school graduates were white (48 %) in 2004, down from 56 % a decade earlier.

7

Domina (2007) provides a detailed account of the Longhorn and Century Scholarship program. Classification of high schools is relatively stable over time, but owing to the rapid growth of the high school population during the observation period, some schools shifted between categories. The Longhorn/Century high schools do not change their designation, however, even if the dates of entry into the program differ.

8

Because many students take time off, or are required to extend their studies for additional semesters when they change majors or to fulfill specific requirements, most institutions reports use the 6-year graduation rate.

9

A complementary approach to the method of using high school fixed effects would be to measure and examine the predictors of school-specific race gaps (Stiefel et al. 2006).

10

Even before the top 10 % law was passed, over 90 percent of students who graduated in the top decile of their class were admitted to UT and TAMU. The law converted a de facto standard to a de jure criterion, but also changed the high school sending patterns. Although standardized test scores were not considered in the admission decision of top 10 % graduates after 1997, all students were required to submit the scores for an application to be considered complete. Schools could establish criteria for ranking students, but not the cut-points. To avoid gaming, schools were required to submit the number of students and the exact ranking, which we used in deriving the class rank distribution.

11

One caveat is that the coefficients are only identified using high schools that send multiple students to a particular institution and where the race and ethnic background of the students differs. Fletcher and Tienda conducted a sensitivity analysis restricting the sample to high schools that send students from multiple race groups and concluded that the results were robust. However, we will conduct the robustness test for the strata-specific estimates in the future.

12

The large point estimate for Asians warrants caution because it is based on a relatively small number of students—less than 1 % of all graduates from poor high schools attending TAMU are Asian.

13

We have no way of knowing whether any of the students or their parents are foreign-born, which in the case of African Americans often involves students with highly educated parents rather than underrepresented minorities. Most Caribbean populations settle in the northeast or southeast, so this potential bias is likely to be small.

14

Fletcher and Tienda (2010) examined choice of major as a potential avenue through grade point gaps widen after the freshman year. They detected little evidence that Black and Hispanic students sort into majors in ways that accentuate achievement gaps at the public flagships, but there is suggestive evidence that major choices accentuate race and ethnic grade gaps at UTSA.

15

Data censoring precludes analysis of 6-year graduation rates for all but a few cohorts; therefore we analyze 4-year graduation rates mainly to illustrate the large variation by institutional selectivity. We exclude UTSA from the graduation analyses.

# Part IV
Special Topics
© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_10

# 10. Family Matters: Strengthening the Fabric of Minority Families

Ruth G. McRoy1  and Amy J. Griffin1

(1)

Graduate School of Social Work, Boston College, Chestnut Hill, MA 02467, USA

Ruth G. McRoy

Email: ruth.mcroy@bc.edu

## Introduction

Families are the backbone of society. The family represents a vital institution within American society and often serves as the major source of support for individuals and is critical to children's development (Lunkheimeir et al. 2007; Ross et al. 1999). A close family unit is important as it helps youth develop a sense of self (Peterson 2005) and impacts levels of anxiety, self-esteem, and aggression for children (Lopez et al. 2008; McKinney and Renk 2011). The Children's Defense Fund (2007) in their annual report noted:

> All children need mothers and fathers and strong positive male and female role models and mentors of all colors and backgrounds in their homes, schools, child serving institutions and public life. They need family connections. They need to see sound examples of who and what they can become from the adults they see in daily life and at important stages in their development (p. 93).

Families are able to provide love, safety, stability, and hope for the future. Research has suggested that cohesive family environments help children thrive and are associated with psychological well-being (Henderson et al. 2003; Uruk et al. 2007). Family values, roles, and rituals all provide a form of consistency and guidance for developing children. This type of consistency plays a role in helping academic and personal achievement, overcoming adversity, and discipline. Families, for example, that spend time together during the holidays, vacation together, and go to church together, build cohesion, buffer against stress, build sense of comfort, stability and ability to cope (APA 2008; Triple P America 2012). The structure of families has changed over time. Cosby and Poussaint (2007, pp. 2–3) noted "in 1950, we still feared our parents and respected them. Back then, we were more likely to have a mother and father present. A house without a father is a challenge. A neighborhood without fathers is a catastrophe, and that's just about what we have today!" In the 1950s only one in 20 children was born to an unmarried mother, now the rate is more than one in three. Single women who are economically poor are three times as likely to have children as affluent single women (Edin and Kalifas 2005). Moreover, according to Edin and Kalifas (2005) half of poor women who give birth while unmarried, have no high school diploma at the time and nearly a third have not worked at all in the last year. Another important factor impacting families today is that up to 30 % of children born to married couples can expect to see their parents' part before they reach adulthood (Edin and Kalifas 2005).

## Implications for Minority Families

As with many other aspects of society, racial inequities exist in the makeup of families today. African Americans are less likely to marry than Hispanics, who are in turn are less likely to marry than Whites, even when income and other demographic characteristics are taken into account. Black children live with a sole parent more often than Hispanic children or children of other races. Approximately, 66 % of Black children, 41 % of Hispanic children, 24 % of White children, 52 % of American Indian children, and 16 % of Asian children live in single parent households (Annie E. Casey 2011). In 2007, there were 83 million family groups and 73 % of these family groups were married couples and 44 % had children under the age of 18 years old. Approximately 66 % of married couples with children under the age of 18 years old had both spouses in the labor force. Single Black women outnumber their Black male counterparts, for every 100 Black single women there are 70 Black men (U.S. Census Bureau 2007). Also, African American divorce rates are higher than those for Whites or Hispanics (U.S. Department of Commerce 2009). From a historical perspective, there are factors that have negatively impacted the structure of many African American families. Issues such as prohibition of marriage during slavery, the loss of communal institutions during northern migration, welfare policies, declining job opportunities for Black men, segregation and isolation of neighborhoods, and concentrated poverty (Franklin 1997) all have weakened marital and familial bonds. Also, Lawrence-Webb (1997) suggests that changes in social policies such as the restructuring of Aid to Families with Dependent Children (AFDC) in the 1950s and 1960s led to families being viewed as "unworthy" of financial assistance if they had children out of wedlock or lived with their partners without being married. This policy shift kept many African American children from receiving financial assistance. Lack of resources in many cases resulted in many African American families being charged with neglect which led often to their children being removed from their families and entering foster care.

Being poor in America also leads to unique challenges for family life. Poor families tend to live in impoverished communities that are high in crime and lack safety, and many tend to have undertreated physical and mental health issues. They also may suffer from hopelessness, poor education, and diminished life opportunities (Duke-Lucio et al. 2010). The high correlation between poverty, poor child outcomes, and the rise of single motherhood has led some to wonder if "marriage" is the missing link. Should there be more focus on developing social policies that promote and sustain marriages?

Another factor impacting families is parental incarceration rates. The high incarceration rate of not only poor men in America, but minority men as well, plays a role in the makeup of families. Black children are more than seven times as likely and Hispanic children are more than two and a half times as likely as White children to have a parent in prison (Children's Defense Fund 2011). In 2007, 1.7 million children had a parent in prison and about 45 % of these children were Black. The number of children with incarcerated parents has nearly doubled since 1991. While imprisoned parents are mostly males, the rate of imprisoned mothers of minority children has increased to 65,000 in 2007 (Children's Defense Fund 2011). Lack of parent–child contact during incarceration jeopardizes chances of families reuniting upon release.

Family disruption can negatively impact the development of the child. American children suffer from more family disruption than children anywhere else in the industrialized world. The US divorce rates among couples with children, while lower than for couples without children, are much higher than those of other Western industrialized countries. By age 15, only half of American children live with both biological parents, whereas roughly two thirds of Swedish, Austrian, German and French children do, as do nearly nine in ten in Spain and Italy. Many believe that lack of marriage, rather than the lack of skills or living wage jobs, is at the root of the disadvantages faced by so many American children (Edin and Kalifas 2005).

Lack of proper educational and employment opportunities also play a factor in family life in the United States. The Center for Market Studies (2009) found that the rate of high school dropouts is becoming a crisis. In 2007, nearly three in ten Latino students, including recent immigrants, were dropouts (27.5 %) and more than one in five Black students dropped out of school (21 %), whereas the dropout rate for White students was 12.2 % (Center for Market Studies 2009). Being a high school dropout makes it almost impossible to earn an adequate income to be able to support and sustain a family. In March 2012, the Joint Economic Committee reported that 8.2 % of African Americans with college degrees were unemployed, whereas only 4.5 % of college educated Whites were unemployed. In 2011, the US employment rate was at 8.9 %. Approximately 13 % of African Americans were unemployed (15.2 % of adult African American men and 11.9 % of adult African American women), 10.3 % of Latinos were unemployed (9.5 % Latino men, 10.2 % of Latino women), and 7.4 % of Whites were unemployed (7.1 % White men and 6.5 % White women) (U.S. Department of Labor 2012). It is important to note that official unemployment rates can be misleading because they do not include those not seeking work or those who are incarcerated (Mincy 2006).

## Family Disruptions and Placement in Out-of-Home Care

Poverty is often a predictor of child neglect, inadequate housing, and parental substance abuse, which together often lead to family disruptions and children needing to be removed from their birth families and placed in foster care. In 2010, there were 408,000 children in the foster care system and on average these children were 9.4 years old and approximately 52 % of the children were male. The average amount of time children spent in care was 25.3 months and 43,083 had been in care for 5 years or more. The case goals for children in care varied: 51 % had the goal of reunification with their birth families, 25 % had a goal of adoption, 6 % had the goal of long-term foster care, 6 % had a goal of emancipation from the system, 4 % had the goal of guardianship, 4 % had the goal of living with another relative, and 5 % of children in care (18,102) did not have an established case plan (U.S. DHHS 2011).

Disproportionately high numbers of children in care are ethnic minorities. In 2010, they represented 59 % of those children in care. Specifically, White children that are under the age of 18 years old represent approximately 55.3 % of the US population, but 41 % of those in care. Hispanic children represent 22.5 % of the US population, but 21 % of those children in care. African American children represent 15.1 % of the US population, but 29 % of those in care. Asian American children represent 4.4 % of the US population but 1 % of those in care. Finally, American Indian/Alaskan Natives represent 1.3 % of the US population but 2 % of those in care (U.S. Census Bureau 2012). As of September 30, 2010, 107,011 children were awaiting adoption. Forty-eight percent of children in care are placed in non-relative foster homes. This percentage of children placed in foster care is composed of 39 % White children, 29 % Black children, 22 % Hispanic children, 6 % two or more non-Hispanic children, 2 % American Indian/Alaskan Native children, and 1 % unknown/unable to determine racial/ethnic background children. Of those children who are adopted from foster care in 2010, 15 % are by non-relatives/non-foster parents, 53 % are foster parent adopters, and 32 % are relative adopters (U.S. DHHS 2011). Race and income are two significant factors that play a role in child welfare services provided to families. Rivaux and colleagues (2011) found that African American families are more likely to have their cases remain open and have their children removed from home.

While the total number of children in foster care nationally has decreased every year for more than a decade, the number of youth aging out of foster care has continued to grow. Aging out of care without a permanent placement or adoptive family is still an issue that plagues the child welfare system. More than 230,000 young people have aged out of care since 1999, ranging from 19,000 young people in 1999 to nearly 28,000 in 2010 (U.S. DHHS 2011). Research shows that, when compared with their peers, young people aging out of care are, on average, less likely to have a high school diploma, less likely to pursue higher education, more likely to experience economic hardships, less likely to earn a living wage, more likely to have a child out of wedlock, and more likely to become involved with the criminal justice system (Jim Casey Youth Opportunities Initiatives n.d.). It is critical for child welfare agencies to develop strategies to find families for adolescents in care, before they age out. Many over the years have established connections with others including previous foster parents, coaches, mentors, birth family members, and teachers. Agencies must acknowledge and recognize the importance of family to all children and adolescents, and therefore explore all of these possible options for permanent connections.

## Promising Practices

Strategies are needed to prevent child removal and provide supports for families. It is important to pursue alternative responses to removals such as the provision of community-based networks of formal and informal support and services for children and families with multiple inconclusive child abuse and neglect referrals. These options could help divert families from further disruptions and entering the child protective system. In order to accomplish this, possible solutions could include early intervention with families; teaching families to encourage children, set limits, and provide consistency; system support of parents and children; innovative educational programs (e.g., Knowledge is Power Program; Urban prep academy; job training in high school); and economic policies to establish more jobs that pay enough to offer way out of poverty. Interventions are needed to help foster these visions such as, community-based family service agencies, government investments in communities, and self-help efforts in which more affluent families establish and maintain links with poorest families.

Casey Family Programs has also put forth many innovative programs aiming at reinvesting in the family and decreasing the overrepresentation of children in care. Their Breakthrough Series Collaborative (BSC) selected certain child welfare jurisdictions to initiate strategies and systems of change that target institutional and practice biases with the aim to improve outcomes for children and minority families. Thirteen jurisdictions currently aim to "engage with a group of other jurisdictions in critical change activities; create environments in which strategies can be developed and tested; develop a cadre of leaders across the country who are working towards solutions; create and sustain partnerships to the work; and disseminate lessons learned" (Casey Family Programs 2012; Miller 2009). It is hoped that the BSC initiative will lead eventually to child welfare systems that are free of structural racism and work in the benefit of the children, families, and communities they serve.

Family group decision making (FGDM) has been recognized as a possible strategy to address racial disproportionality in the child welfare system (U.S. GAO 2007). This term is used to describe family center-focused approaches to ensure the safety of the child. These approaches include the family, child, extended family, kin, community, and other networks that may be important to the family in the decision-making process (e.g., church). This group will create a plan to address well-being and safety concerns. Research suggests that when plans are developed in collaboration with families, their support networks, and the child welfare system, opposed to a plan mandated solely by the child welfare system, they are more likely to benefit children, result in permanency, decrease the rate of removal of the children from home, maintain family bonds, and promote family well-being (Merkel-Holguin 2005). FGDM aims to incorporate this method by acknowledging different cultures, spirituality, and the rights and abilities of the targeted family in order to develop the best plans and decisions for families and their communities. FGDM has the "potential to energize hope, guide change and foster healing" (American Humane Association 2010) as it has been shown to provide child safety, increase kinship care, create stability for children, provide timely decisions and results, and increase family supports and family functioning (Merkel-Holugin et al. 2003).

Another way to support families is to establish collaborations with community and cultural resources. The National Survey of Black Americans (1979–1980) found that religion is "central" to their lives, which is related to the fact they have higher rates of religiosity, belong to church, and read the Bible more frequently than Whites (Taylor et al. 1996). Based on these findings, incorporating community faith groups and churches as resources for family development, foster care, and adoption is important. Singleton and Roseman (2004) report in their study of 51 Black ministers in Florida that 83 % of the ministers had never included the topic of foster care or adoption in sermons and most had no experience with adoption or foster care. Better plans and initiatives are needed to reach out to African American churches and ministers in order to enlist support and family resources for children in the system.

## Increasing Permanency Through Adoptions

When families cannot be preserved, there are innovative programs that have been found to be successful in increasing the likelihood of finding adoptive families for children in care.

One African American church that has been very successful in finding African American families for children in rural areas is Bennett Chapel located in East Texas. Through the leadership of Reverend Martin and his wife, the Bennett Chapel "Saving a Generation" ministry was formed to help find foster and adoptive families for African American children. Also, One Church One Child, founded by Father George Clements, has been actively involved since the early 1980s in promoting adoptions of waiting African American children in care.

A number of federal initiatives are also in place to promote adoptions of children from foster care. AdoptUSKids is one such program that aims to change public perception about adopting children from the foster care system; increase the number of potential adoptive families inquiring about adoption; and ultimately have more children aged 8 years and older adopted from foster care (AdoptUSKids 2012). Other selected Federal initiatives that are having an impact on addressing the issue of disproportionality for African American adoptions include the following:

  * The National Resource Center for Permanency and Family Connections, which is funded through the U.S. Children's Bureau, focuses on safety-focused, family-centered, and community-based approaches to address the needs of children and families.

  * National Resource Center for Adoption, funded through the U.S. Children's Bureau, specializes in the provision of technical assistance and training in order to help states, tribes, and other federally funded child welfare agencies to increase their capacity to serve abused and neglected children through adoption and adoption preservation services.

  * Children's Bureau Grants for Diligent Recruitment have been awarded to programs throughout the United States for the purpose of improving permanency outcomes for public child welfare agencies by implementing comprehensive diligent recruitment programs for kinship, foster, concurrent, and adoptive families (McRoy 2011).

## Conclusion

It is up to social workers and advocates of the child welfare community to remind society that "family matters." It is time for fostering partnerships to address disparate outcomes especially for African American children, enhance service delivery to reduce and prevent child removals, and develop policies and practices that support and preserve all families in this society. We must ask ourselves, what can we do together on behalf of all of today's vulnerable children and families? Family truly matters.

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© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_11

# 11. Aging and Health in Mexican Americans: Selected Findings from the Hispanic EPESE

Kyriakos S. Markides1  , Raphael Samper-Ternent2 and Soham Al Snih1

(1)

Department of Preventive Medicine & Community Health, University of Texas Medical Branch, Galveston, TX 77555, USA

(2)

Pontificia Universidad Javeriana, Bogota, Colombia

Kyriakos S. Markides

Email: kmarkide@utmb.edu

## Introduction

The population of the United States has continued to grow in recent decades fueled to a large extent by high rates of immigration from Latin America, especially Mexico. The US population surpassed 310 million in 2010 with Hispanic/Latinos now amounting to over 50 million people and constituting by far the largest minority population (Jacobsen et al. 2011; Saenz 2010).

By most projections the US population will continue to grow and will reach approximately 440 million by 2050. While the non-Hispanic White population will grow very slowly and reach 210 million in 2050 the Hispanic/Latino population is expected to grow from just over 50 million today to approximately 120 million or around 30 % of the total population. With moderate growth the African American population is expected to reach 61 million while, with fairly rapid growth, the Asian/Pacific Islander population is expected to grow to around 30 million. The US minority population is expected to become the majority by 2050 (Passel and Cohn 2008).

In this chapter we focus primarily on the health status and health characteristics of older Mexican Americans, by far the largest component of the Hispanic population of the United States. The vast majority of older Mexican Americans continue to live in the Southwestern United States. After a brief overview of recent evidence regarding mortality and life expectancy of the population discussed in the context of the "Hispanic Paradox" literature, we present and discuss selected findings from the Hispanic Established Population for the Epidemiological Study of the Elderly, referred to here as the Hispanic EPESE. The Hispanic EPESE is a large, on-going study of older Mexican Americans residing in the Southwestern United States funded by the National Institute on Aging since 1992. We focus on a few major health outcomes including obesity, diabetes, disability, depression, and frailty. Both predictors and consequences of these health conditions are examined using longitudinal data. A major conclusion to be discussed later is that while the Mexican origin population of the United States is long living by the older years, it is characterized by significantly high rates of disability owing perhaps to high rates of poverty, obesity, disability, as well as a lifetime of substandard medical care.

## The Hispanic Paradox and Recent Evidence of a Mortality Advantage

Over 25 years ago Markides and Coreil (1986) used the term "epidemiologic paradox" to describe the health of Southwestern Hispanics, the vast majority of whom were of Mexican origin. The evidence appeared paradoxical because the health status of Hispanics was much closer to the health status of non-Hispanic Whites than that of African Americans with whom they were more similar socioeconomically. The paradox was attributed to selective migration, strong families, certain cultural practices, and superior health behaviors (Markides and Coreil 1986).

Before long, national mortality statistics began suggesting superior mortality profiles of Southwestern Hispanics and the "Hispanic Paradox" quickly became the main theme in Hispanic health (Abraido-Lanza et al. 1999; Crimmins et al. 2007; Franzini et al. 2001; Hayes-Bautista 1992; Markides and Eschbach 2005; Palloni and Morenoff 2001). A major challenge to the paradox was the manuscript by Palloni and Arias (2004) which suggested that the Hispanic mortality advantage can be explained by a "salmon bias," or return of older Mexican origin people who are in poor health to Mexico whose deaths in Mexico result in lower mortality rates of older Mexican Americans living in the United States.

Perhaps the most rigorous test of the "salmon bias" was performed by Turra and Elo (2008) who used data from the Master Beneficiary Record and NUDIMENT data files of the Social Security Administration. They found evidence that indeed a "salmon bias" existed but it was far too small to explain the Hispanic mortality advantage. A quite different test of the "salmon bias" was performed by Hummer et al. (2007), who found that the lower infant mortality of foreign-born Mexican-origin women than that of the US-born non-Hispanic White women was unlikely to be the result of migration to Mexico of mothers and infants. This was the case because the rates were lower even in the first few hours of life when out-migration would be highly unlikely.

A recent review concluded that immigrant health selection continues to be the most viable explanation of a mortality or health advantage of the Mexican-origin population coupled with superior health behaviors and strong family support systems (Markides and Eschbach 2011). The Hispanic mortality advantage received new support in October 2010 when the National Center for Health Statistics issued its first official life tables for the Hispanic Origin Population (Arias 2010). The overall Hispanic population had a life expectancy at birth of 80.6 years in 2006, which was 2.5 years higher than that of the non-Hispanic White population and 7.7 years higher than that of the African American population (Table 11.1). The table also shows that the advantage is present at all ages and for both genders. The non-Hispanic Black life expectancy only exceeds the non-Hispanic White life expectancy at age 85 and above, which is consistent with the long-observed racial mortality crossover phenomenon (Manton et al. 1991).

Table 11.1

Life expectancy by age and gender for the Hispanic, Non-Hispanic White, and Non-Hispanic Black Populations: United States 2006 | Hispanic | Non-Hispanic White | Non-Hispanic Black

---|---|---|---

Age | Total | Male | Female | Total | Male | Female | Total | Male | Female

0 | 80.6 | 77.9 | 83.1 | 78.1 | 75.6 | 80.4 | 72.9 | 69.2 | 76.2

30 | 51.9 | 49.6 | 54.0 | 49.4 | 47.2 | 51.4 | 45.2 | 42.0 | 48.0

50 | 33.2 | 31.2 | 34.9 | 30.9 | 29.0 | 32.6 | 27.7 | 24.9 | 30.0

65 | 20.6 | 19.0 | 21.7 | 18.5 | 17.1 | 19.7 | 17.0 | 15.0 | 18.4

85 | 7.6 | 6.8 | 8.0 | 6.3 | 5.6 | 6.7 | 6.7 | 5.9 | 7.1

Adapted from Arias (2010)

Despite the Mexican American population's greater longevity, the literature has provided consistent evidence that by the time Mexican Americans reach old age they experience rates of disability that are higher than those of the general population possibly because of high rates of poverty, obesity, and diabetes as well as a lifetime of substandard medical care. We focus on these issues by presenting selected findings based on data from the Hispanic EPESE.

## The Hispanic EPESE

The Hispanic Established Population for the Epidemiological Study of the Elderly (Hispanic EPESE) was funded by the National Institute on Aging in 1992. It was modeled after the previous EPESE conducted in East Boston, New Haven, North Carolina, and rural Iowa (Cornoni-Huntley et al. 1986). The primary objectives of the Hispanic EPESE were to examine the prevalence of key medical conditions and disabilities, to examine their correlates, to examine predictors of mortality as well as changes in health characteristics over time. Unlike the previous EPESE, which were conducted in restricted geographic areas, the Hispanic EPESE aimed at obtaining a representative sample of older Mexican Americans residing in the five Southwestern states: Texas, New Mexico, Colorado, Arizona, and California, where the overwhelming majority of older Mexican Americans lived.

The baseline sample of 3,050 Mexican Americans aged 65 and over was drawn using area probability sampling procedures to assure representativeness of the older Mexican American population of the region. In-home interviews and limited medical assessments were conducted in 1993–1994 with a response rate of 83 %. The baseline subjects were followed up in 1995–1996 (N = 2,439), 1998–1999 (N = 1,981), 2000–2001 (N = 1,682), and 2004–2005 (Wave 5), when 1,167 of the original subjects then aged 75 and over were re-interviewed in person or via proxy.

An additional sample of 902 subjects also aged 75 and over was drawn using similar procedures to the baseline sample giving a combined cohort of 2,069 subjects in 2004–2005. These subjects were followed up approximately 2.5–3 years later during 2007 when 1,542 of them were interviewed in person or via proxy. Table 11.2 shows the six waves of data collection, numbers of subjects, those lost to follow-up, and those who refused to be re-interviewed.

Table 11.2

Hispanic EPESE summary: baseline and Waves 2–6 | Total | Proxy+ | Deceased | Refused | Not located | Age

---|---|---|---|---|---|---

1993–1994 | 3,050 | 177

|  |  |  |

65+

1995–1996 | 2,439 | 143 | 241 | 109 | 261 | 67+

1998–1999 | 1,981 | 101 | 290 | 133 | 272 | 72+

2004–2005 | 1,167 | 93 | 504 | 139 | 277 | 75+

Added sample

2004–2005 | 902 | 49 | – | – | – | 75+

2004–2005 | 2,069 | – | – | – | 75+

|

Combined

2007 | 1,542 | 159 | 418 | 157 | 368 | 78+

Cumulative deceased = 1,885 at end of sixth wave (2007) + Included in total

The study has produced numerous publications on various aspects of health and quality of life. Below we review selected findings relevant to obesity, diabetes, disability, and emotional well-being. Existing and new longitudinal findings are presented.

## Trends in the Prevalence of Diabetes and Disability

It has been well established for some time now that older Mexican Americans experience high rates of disability. Comparison of Hispanic EPESE data from baseline in 1993–1994 with national data on non-Hispanic Whites and African Americans suggested that older Mexican Americans reported significantly higher rates of disability in Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) than did non-Hispanic Whites. Rates were similar to older African Americans (Rudkin et al. 1997). More recent evidence continues to support high disability rates of older Mexican Americans despite favorable mortality profiles (Markides and Eschbach 2005, 2011; Tovar et al. 2007).

We mentioned earlier that the population's high disability rates are partly the result of high rates of diabetes (Miech et al. 2009; Papon-Nau et al. 2010). Since the Hispanic EPESE added a new cohort of subjects aged 75 and over at Wave 5 in 2004–2005, it was possible to examine trends in the prevalence of diabetes by comparing the new cohort with the baseline cohort of the same age in 1993–1994. Beard et al. (2009) found that the prevalence of self-reported diabetes in Mexican Americans aged 75 and over nearly doubled during this period going from around 20 % to approximately 37 %. However, there was no improvement in the prevalence of diabetic complications, which has been found to be the case in the general population (Chaturvedi 2007).

The increase in the prevalence of diabetes in older Mexican Americans may partly reflect improvement in diagnosis. Yet no doubt it is also the result of increases in obesity as well as increases in life expectancy. To some extent this is good news in that Mexican Americans are living longer with their diabetes, suggesting improvements in the management of the disease. At the same time, a significant increase in the prevalence of diabetes is likely to lead to increases in disability and other negative health outcomes (Beard et al. 2009).

After increases in disability observed in the US older population in the 1970s and early 1980s, there appear to have been improvements in the health of older Americans as well as of older people in other western countries beginning in the mid-1980s (Crimmins et al. 1997; Manton 2008; Manton and Gu 2001; Freedman et al. 2002; Waidmann and Liu 2000; Zunzunegui et al. 2006). This recent trend represents a reversal from the 1970s and early 1980s when increases in life expectancy were accompanied by increases in morbidity and disability. There is some recent evidence that the recent decline in disability among older Americans may have come to a halt and may have reversed itself at least among the young old, the latter being attributed primarily to increases in obesity (Seeman et al. 2010).

We have argued elsewhere that the Mexican American population is at a stage in the epidemiologic transition where the general United States and other western populations were in the 1970s and early 1980s (Markides and Eschbach 2011; Markides et al. 2010), a period of rising life expectancy accompanied by increases in morbidity and disability. A comparison of our new cohort of subjects aged 75 and over in 2004–2005 with earlier data collected during 1993–1994 enables the investigation of trends in disability and other health indicators for this age group since 1993–1994. Results by gender are presented in Table 11.3.

Table 11.3

Sociodemographic characteristics and prevalence of medical conditions in older Mexican American men and women in 1993–1994 and 2004–2005 | Men | Women

---|---|---

1993–1994 (N = 470) | 2004–2005 (N = 371) | 1993–1994 (N = 662) | 2004–2005 (N = 531)

Age (mean ± SD) | 80.9 ± 5.2 | 81.3 ± 4.7 | 81.0 ± 5.0 | 81.5 ± 5.4

Years of education (mean ± SD)

|

4.4 ± 3.9 | 4.9 ± 4.4 | 4.4 ± 3.7 | 5.1 ± 4.2

Married | 67.50 | 66.00 | 25.10 | 29.80

Chronic diseases

Hypertension | 57.00* | 66.00 | 66.80* | 72.60

Self-reported diabetes | 21.30** | 32.70 | 21.50** | 38.10

Self-report heart attack | 14.60 | 13.40 | 13.40 | 10.10

Self-reported stroke | 9.60 | 9.80 | 10.00 | 8.30

Self-reported cancer | 6.60 | 8.40 | 6.80 | 7.30

Self-reported hip fracture | 3.60 | 4.30 | 7.30 | 7.20

Any ADL limitation | 19.90** | 28.80 | 26.60** | 42.30

Obesity (BMI ≥30 kg/m2) | 17.90 | 22.40 | 26.70 | 28.90

*p<0.05, **p < 0.01

Table 11.3 shows bivariate comparisons for reporting any ADL disability (toileting, bathing, dressing, eating, walking across a room, and transferring from bed to chair) between the 1993–1994 and 2004–2005 cohorts by gender. Percent reporting any ADL disability increased from 20.2 to 29.7 % among men and from 21.5 to 41.2 % among women. The table also shows significant increases in the prevalence of diabetes (see also Beard et al 2009) and hypertension (see Al Ghatrif et al. 2011). The increases in disability can be partly attributed to increases in survival to advanced ages and increases in frailty among very old Mexican Americans (Palloni 2007). We also observed increases in the prevalence of obesity, which may be another contributor to rising levels of disability.

## Obesity, Disability, and Mortality

Numerous studies have documented an increase in obesity in the United States and elsewhere, which has often been labeled an "epidemic" (Arterburn et al. 2004; Hedley et al. 2004; Galuska et al. 1996). A number of studies have found evidence that underweight and obesity are associated with mortality (Flegal et al. 2005; Stevens et al. 1998; Calle et al. 1999; Bender et al. 1999). Yet inconsistent evidence has been accumulated about the impact of obesity on mortality in old age (Stevens et al. 1998). Al Snih et al. (2007) analyzed the combined EPESE samples, including the Hispanic EPESE, from five sites over a 7-year period and found that underweight and extreme obesity of BMIs of 35 or over were associated with excess mortality. Subjects with BMIs between 25 and 35 experienced the lowest mortality. In contrast, low BMI of less than 18.5 and BMI in the obese range of 30 and over were associated with the development of disability over the 7-year period.

We conducted analyses similar to the above using only the Hispanic EPESE sample over a 14-year period from 1993–1994 to 2007. Table 11.4 shows that even with twice the follow-up period as the above, overweight (BMI of 25 < 30) and obesity (BMI 30 < 35) were associated with lower mortality than in subjects with baseline BMIs of 18.5 < 25. There was no association with mortality at the extreme BMI level of 35 and over which was found in the analyses reported above. All analyses controlled for age, gender, education, marital status, smoking, and major medical conditions.

Table 11.4

Cox proportional hazard model predicting hazard ration of ADL disability and mortality as a function of BMI among non-disabled subjects at baseline over 14-year period (N = 2,362), Hispanic EPESE: 1993–1994–2007

BMI (kg/m2) | N | ADL disability N (%) | Model 1 HR (95% CI) ADL disability | Deaths N (%) | Model 2 HR (95% CI) Mortality

---|---|---|---|---|---

<18.5 | 38 | 15 (1.6) | 1.20 (0.71–2.02) | 32 (2.6) | 1.58 (1.09–2.28)

18.5 < 25 | 664 | 240 (25.5) | 1.00 | 384 (31.5) | 1.00

25 < 30 | 945 | 363 (38.5) | 1.01 (0.86–1.19) | 471 (38.6) | 0.79 (0.69–0.91)

30 < 35 | 511 | 229 (24.3) | 1.21 (1.01–1.45) | 233 (19.1) | 0.72 (0.61–0.85)

≥ 35 | 204 | 95 (10.1) | 1.23 (0.96–1.58) | 100 (8.2) | 0.98 (−0.78 to 1.23)

Note: controlling for age (continuous, gender, education, marital status, smoking, diabetes, heart attack, stroke, hypertension, hip fracture, arthritis, and cancer

BMI body mass index, HR hazard ratio, CI confidence interval

The table also shows that unlike in analyses reported earlier (Al Snih et al. 2007), obesity (BMI of 30 and above) was only marginally associated with disability in older Mexican Americans over such a long period of time. One advantage of the Hispanic EPESE analysis over the other EPESE is that it is based on measured height and weight which was not the case in the other four sites. In any case, the protective effect of obesity on mortality and its weak association with disability over such a long period of time raise questions about the influence of obesity on health outcomes in older Mexican Americans compared with other populations. It is also possible that BMI is a poor measure of adiposity in older Mexican Americans as has been suggested for the general population (Vischer et al. 2001). Clearly, further research that assesses the influence of alternative measures of obesity, such as waist-circumference, on health outcomes in older Mexican Americans and other populations is needed, especially given the rising rates of obesity among older people in recent years.

## Depressive Symptoms, Diabetes, and Health Outcomes

Research both in the community and in the clinical setting has shown that people with diabetes are more likely than people without diabetes to experience high depressive symptomatology and high rates of clinical depression (Eaton 2002; Anderson et al. 2001; Katon et al. 2005; Black et al. 2003; Lin et al. 2009). Moreover it has been found that depression exacerbates the influence of diabetes on mortality because it often leads to poor management of the disease in terms of diet, exercise, and medications compliance, which can lead to poor glycemic control and high risk of complications (Lin et al. 2004, 2009; Black et al. 2003). The result is often higher mortality among diabetics from ischemic heart disease. Other contributing factors identified in the literature include lack of social support, physical inactivity, and certain inflammatory factors (Skala et al. 2006).

In addition to its influence on the association of diabetes with mortality, there has been some interest in examining whether depression leads to more functional disability in diabetics based on similar mechanisms mentioned above with respect to mortality. For example, data from the 1999 National Health Interview Survey on older adults have shown that depression and diabetes have a synergistic effect on the development of functional disability, meaning that the comorbid effect is greater than the sum of the independent effects of diabetes and depression (Egede 2004).

We previously showed synergistic effects of diabetes and high depressive symptomatology on both mortality and functional disability in Mexican Americans aged 65 and over using data from the Hispanic Established Population for the Epidemiological Study of the Elderly. Baseline data in 1993–1994 predicted outcomes over a 7-year period. As we showed above, the prevalence of both diabetes and functional disability in Mexican Americans aged 75 and over increased significantly from 1993–1994 to 2004–2005.

Given the substantial increase in the prevalence of diabetes from approximately 20–37 % at ages 75 over, we thought it would be important to revisit the influence of high depressive symptomatology on health outcomes of diabetes in the older sample over the short term, from 2004 to 2005 to 2007. We examined the independent as well as the potential comorbid or synergistic effects of depressive symptoms and diabetes on functional disability. Older Mexican Americans are characterized by very high and rising rates of diabetes. Examining the role of depressive symptoms on increasing rates of diabetes and its consequences on functional disability and mortality will have significant clinical and policy implications.

Diabetes was assessed by asking the respondent if a doctor had ever told them that they had diabetes. Depressive symptoms were measured by the Center for Epidemiologic Studies Depression (CES-D) scale (Radloff 1977), which consists of 20 items asking how often respondents experienced specific symptoms in the week prior to the interview, coded 0–3. Potential scores range from 0 to 60 and a score of 16+ is typically used to indicate a clinically significant level of depressive symptomatology. Disability was assessed with seven items measuring ADL from the modified Katz scale (Branch et al. 1984; Katz et al. 1963): walking across a small room, bathing, dressing, grooming, eating, using the toilet, and transferring from bed to chair. Subjects were asked if they needed help or were unable to perform each task. Subjects needing help or unable to perform one or more tasks were considered ADL disabled.

Table 11.5 shows that among non-disabled at baseline (2004–2005) the risk of becoming disabled at 3-years of follow-up was higher among those who had diabetes and depression (reference for CES-D) (OR = 4.05, 95 % CI 2.00–8.23), as compared with subjects who had diabetes alone, depression alone or none of these conditions after controlling for sociodemographics, arthritis, hypertension, stroke, heart attack, cancer, and hip fracture. Similarly, subjects who had diabetes and depression were at higher risk for 3-year mortality (HR = 2.94, 95 % CI 1.96–4.41), as compared with subjects who had diabetes alone, depression alone, or none of these conditions after controlling for all covariates. Older Mexican Americans are characterized by very high and rising rates of diabetes. Examining the role of depressive symptoms on increasing rates of diabetes and its consequences on functional disability and mortality will have significant clinical and policy implications.

Table 11.5

Odds ratio of disability and hazard ratio of death at 3-year of follow-up as a function of diabetes and depression in Mexican Americans aged 75 and over (N = 1,674)

BMI (kg/m2) | N | ADL disabilitya N (%) | ADL disabilitya OR (95 % CI) | Deaths N (%) | Mortality HR (95 % CI)

---|---|---|---|---|---

No Diabetes/No Depression | 925 | 184 (61.3) | 1.00 | 119 (47.0) | 1.00

Depression Alone | 188 | 11 (3.7) | 0.51 (0.25–1.04) | 33 (13.0) | 1.37 (0.92–2.04)

Diabetes Alone | 449 | 79 (26.3) | 1.14 (0.81–1.60) | 66 (14.7) | 1.24 (0.91–1.69)

Diabetes and depression | 112 | 26 (8.7) | 4.05 (2.00–8.23) | 35 (13.8) | 2.94 (1.96–4.41)

aAnalyses for disability were conducted among non-disabled subjects at baseline (N-879 in 2004–2005)

All analyses (disability and mortality) were controlled for age, gender, education, nativity, marital status, arthritis, hypertension, stroke, heart attack, cancer, and hip fracture

## Frailty in Older Mexican Americans

In recent years frailty has been established as a concept that helps identify older adults at risk of adverse events (Fried et al. 2004). The concept of frailty serves as an important marker that differentiates between healthy and pathologic aging (Walston et al. 2006). Subjective identification of frail individuals is relatively easy. However, health care professionals face some difficulties when applying a standardized definition. Different sets of criteria have been used to define frailty (Fried et al. 2004; Rockwood et al. 1999). Despite their limitations, these criteria have provided a good and structured starting point for researchers to study older adults at risk while using a common language. Alterations in physical function have been the main focus of widely used constructs that define frailty (Fried et al. 2004; Abellan et al. 2008a). Today, frailty is a highly relevant clinical entity with a well-defined phenotype (Fried et al. 2004). Frailty is also a clear predictor of adverse outcomes including mortality (Cawthon et al. 2007; Ensrud et al. 2007; Klein et al. 2005).

Mexican Americans have a unique disease and mortality profile (Markides et al. 1999; Markides and Coreil 1986). Concepts, such as frailty, that help understand and predict adverse events in vulnerable populations like older Mexican Americans are not only important to analyze but may also provide useful information to inform public policy, develop preventive measures, and design interventions to help this group of older adults.

Using data from the Hispanic Established Populations for the Epidemiological Study of the Elderly (EPESE), different aspects of frailty have been analyzed. To evaluate frailty in older Mexican Americans, the original index derived from the Cardiovascular Health Study (CHS) was modified. The index includes five components: exhaustion (measured using two questions from the CES-D scale), weight loss (defined as an unintentional loss of 10 or more pounds), low activity (defined as low kcal of physical activity per week calculated from the Minnesota Leisure Time Activity Questionnaire (MLTA), slow walk (derived from 15-foot timed walk and stratified by gender and height), and hand grip muscle strength (measured with a dynamometer and stratified by gender and body mass index (BMI) (Fried et al. 2004). The index used in the Hispanic EPESE included the same components. However, physical activity was measured using the Physical Activity Scale for the Elderly instead of the MLTA (Al Snih et al. 2009; Ottenbacher et al. 2005; Peek et al. 2003). Additionally, we did not use the actual cut-off point used by Fried et al. (2004) since the sample in their original study was younger than our baseline sample, and anthropometric values (weight and height) used to adjust for hand grip muscle strength and walking speed were different in our Mexican American sample than in the predominantly non-Hispanic White sample included in the frailty study by Fried and colleagues (2004) and Ottenbacher et al. (2005).

With the modified frailty index the prevalence of frailty among older Mexican Americans was reported at 20 % compared to 6.9 % reported from the CHS cohort (Fried et al. 2004; Ottenbacher et al. 2005; Ottenbacher et al. 2009). The percentage of individuals with a positive score for the different frailty components was higher for all components in the Mexican American cohort compared to the CHS cohort (Fried et al. 2004; Ostir et al. 2004; Ottenbacher et al. 2009).

Several risk factors have been identified to predict transition into frailty among older Mexican Americans. Low acculturation, poor cognitive function, higher number of comorbidities, in addition to the presence of specific chronic conditions such as diabetes and arthritis, smoking history, obesity, low cognitive function and negative affect have been identified as significant risk factors for frailty in this population group (Masel et al. 2011; Ottenbacher et al. 2009; Raji et al. 2010). Conversely, positive affect was reported to decrease the risk of becoming frail in this population group. A one-point increase in positive affect was associated with a 3 % decrease in the risk of becoming frail over time (Ostir et al. 2004).

Moreover, frailty is a risk factor for many adverse events among older Mexican Americans. Supporting the results from the CHS, pre-frail and frail older Mexican Americans have higher rates of mortality compared to their non-frail counterparts (Berges et al. 2009; Graham et al. 2009). In addition, frail older Mexican Americans are at increased risk of reporting poor health-related quality of life (HRQoL) (Masel et al. 2010). This is important given that poor HRQoL is associated to poor outcomes in older populations (Grundy and Bowling 1999). Furthermore, frailty is associated with increased risk of poor cognition in this group, as well as increased risk of falls (Samper-Ternent et al. 2008; Samper-Ternent et al. 2011). Finally, pre-frail and frail older Mexican Americans are at increased risk for disability in ADL (Al Snih et al. 2009). This is very important because poor cognition, falls, and disability are common causes of institutionalization and death in older populations (Abellan et al. 2008b; Bergman et al. 2008; Fried et al. 2004; Inouye et al. 2007). Table 11.6 shows the hazard risk and confidence intervals for some adverse events stratified by frailty status over a 10-year period.

Table 11.6

Risk of some adverse events for pre-frail and frail individuals compared to non-frail individuals form the Hispanic EPESE over a 10-year period | Cognitive impairmenta | Disabilityb | Mortalityc

---|---|---|---

N = 1,370d | N = 1,645 | N = 1,996

HRe | 95 % CI | HR | 95 % CI | HR | 95 % CI

Non-frail | 1.00

|  |

1.00

|  |

1.00

|

Pre-frail | 1.02 | 0.95–1.08 | 1.32 | 1.10–1.58 | 1.25 | 1.07–1.46

Frail | 1.27 | 1.07–1.52 | 2.42 | 1.70–3.46 | 1.81 | 1.41–2.31

aCognitive impairment was defined as a score in the Minimental State Examination less than or equal to 21

bDisability was defined as difficulty performing one or more of seven items from the Katz Scale

cMortality was calculated over a period of 10 years and stratified by frailty status at baseline

dEach study was performed separately and the number of subjects reported was obtained after applying inclusion criteria to obtain a comprehensive sample with complete information needed for each analysis

e HR hazard ratio, 95 % CI 95 % confidence interval

Apart from these findings, research has also shown that there are differences in the frailty phenotype of older Mexican American men and women. Mexican American men are at higher risk of dying compared to women (HR = 3.04, 95 % CI 2.16–4.28 for men compared to HR = 1.92, 95 % CI 1.39–2.65 for women) (Berges et al. 2009). Additionally, altered upper extremity strength, higher number of comorbidities and poor cognitive function are risk factors predicting frailty only among men while lower extremity strength and BMI are risk factors predicting frailty only among women (Ottenbacher et al. 2005). This is important for the diagnosis of frailty and the development of clinical interventions. As a final point, as suggested in other population groups, frailty is a dynamic process for older Mexican Americans. Individuals move in and out of the three frailty categories (non-frail, pre-frail, and frail) (Ottenbacher et al. 2009), indicating that there are characteristics and interventions that help individuals recover as well as characteristics and conditions that affect individuals and accelerate their transition into frailty (Aranda et al. 2011). Unique cultural, socioeconomic, health, and demographic characteristics of older Mexican Americans modify the frailty phenotype and warrant further analysis of this condition (Aranda et al. 2011).

In conclusion, frailty is a useful concept in aging because it helps identify older adults at risk of adverse events. Similar to reports in other population groups, risk factors associated with older Mexican Americans becoming frail have been identified. Additionally, frailty has been linked to risk of adverse events that increase the risk of institutionalization and ultimately death in this group. However, there are important differences in the phenotype of frailty that need further analysis to develop targeted interventions for this rapidly growing older adult group.

## Conclusion

Given the rapidly growing numbers of older Mexican Americans in the United States, understanding the different factors influencing their health status and quality of life is very important. The "Hispanic paradox" continues to represent a challenge for researchers to better understand how minority populations and how, despite socioeconomic adversities, are able to survive to advanced age. The growing trends in diabetes, obesity, disability, and frailty among older Mexican Americans are pressing issues that must be addressed in the public health arena. Interventions to prevent, better identify, and better manage such conditions are urgently needed. However, these interventions must take into account the cultural differences that Mexican Americans have with respect to other populations. Although some progress has been made to improve coverage and quality of health care among older Mexican Americans as evidenced in better management of diabetes and other chronic conditions, more aggressive programs are required as well as more research dedicated to understanding the behaviors and unique features that characterize older Mexican Americans.

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Tovar, J. J., Angel, R. J., Eschbach, K., Espino, D. V., & Markides, K. S. (2007). Hispanic established populations for the epidemiologic studies of the elderly: Selected longitudinal findings. Aging Health, 3(3), 225–331.CrossRef

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© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_12

# 12. Racial Disproportionality in Prison

Alfred Blumstein1

(1)

H. John Heinz III School of Public Policy and Management, Carnegie-Mellon University, Pittsburgh, PA 15213, USA

Alfred Blumstein

Email: ab0q@andrew.cmu.edu

## The Problem

The US incarceration rate was impressively flat for 50 years from 1925 to 1975 at a rate of 110 per 100,000 population, a rate that is quite comparable to many of the industrial nations to which we compare ourselves. Indeed, that observation of the flat rate gave rise to a paper entitled "A Theory of the Stability of Punishment" intended to explain this apparently homeostatic process. The argument was that when prisons get filled, then space is made available by releasing prisoners early on parole; when prisons have more capacity, then the system could crack down on marginal offenses like pornography. The key here was that the system was under the control of the functionaries within the criminal justice system, prosecutors, judges, parole boards, and base understood and responded to each other's needs.

That stable process lasted until the late 1970s, when the rate began to increase increasing by 6–8 % per year until about 2000, when the states, in the aggregate, leveled off at a rate of about 450 per 100,000 and the federal prisons continued to increase, until very recently. Now, the total US incarceration rate has climbed by a factor of about 4.5 to a level of about 500 per 100,000 in prisons, representing about 1.6 million sentenced prisoners. Local jails contain another 250 per 100,000, so that the total incarceration rate is about 750 per 100,000 population, which makes us by far the world's leader in incarceration, trailed by Russia, whose rate is about 550. If one substitutes the adult population for the total population (a substitution that is quite reasonable since the great majority of prisoners are adults) the rate is a full 1 % of the adult population.

African Americans have been seriously affected by the growth in incarceration. At the present time, 3 % of all US Black males are in prison, which is 6.3 times the White rate. What is particularly striking is the estimated prevalence of a prison experience to a black male. A BJS report has estimated that 32 % of black males born in 2001 can expect to find themselves in prison sometime in their lives if the 2001 incarceration rates were to continue. This is a striking contrast with 5.8 % of whites and 17 % of Hispanics. When a full third of any particular population group finds itself in prison, that certainly diminishes any stigma associated with that sanction and must significantly diminish its deterrent effect. Also, however, legitimate might be the process that led to such high rates of incarceration, is bound to be seen as discriminatory and thereby diminished the sense of legitimacy of that process along that disadvantaged group.

When one looks at the age-crime curve, which grants the number of arrests of a particular age divided by the total population of that age, one sees a sharp rise to a peak at about age 18, and then a dropping off which happens more quickly for property crimes (where the drop to half the peak value occurs at about age 24) and more slowly for violent crimes and for murder (where the half-peak point occurs in the early 30s). By no means are all arrestees imprisoned; it takes a particularly heinous crime or an accumulation of less serious crimes before an individual is sent to prison. About 29 % of prisoners are in their 20s and an additional 30 % are in their 30s. The rates are only somewhat higher for Black males. As we look beyond incarceration, however, we find that on any single day a full 32 % of Black males in their 20s are under control of the criminal justice system, which includes federal prison, county jail, county parole, and county probation.

## Drug Markets

Drug crimes represent the single largest crime type in prison, comprising over 20 % of state prisoners and over 50 % of federal prisoners. The incarceration rate for drug offenses grew by a factor of 10 between 1980 and 2000, by far the single crime type with the greatest growth rate. Drug crimes are the ones where the representation of African Americans in prison is most disproportionate compared to their representation at arrest. And incarceration for drug selling is inherently limited in its ability to reduce drug transactions.

From the viewpoint of incapacitation, incarceration of drug dealers is very weak. In contrast to incarcerating a rapist, which results in taking his rapes off the street, locking up a drug seller is much more likely to result in the recruitment of a replacement as long as the demand remains. Indeed, there is clear evidence that the major effort of incarcerating drug dealers in the 1980s led to recruitment of young people as replacements. Those young people were far less restrained than their predecessors in the use of the guns they had to carry to protect themselves against street robbers, and so we saw a major growth in homicides by young people in the late 1980s as a result.

Since the major target of incarceration was the markets in crack cocaine, and since those markets were operated primarily by African Americans, the major growth in drug prisoners, in the young people arrested for homicide, and in their victims was all predominantly among African Americans.

From the perspective of deterrence, the threat of the criminal justice system must be much weaker than the intense desire to satisfy addiction and much more ambiguous than the threat in the street of a rival drug dealer or a disgruntled customer.

## Black–White Incarceration Rate Ratios Across the States

Understandably, different states can display very different incarceration rates for blacks and for whites, and so could display quite different ratios of those incarceration rates. Using data on the incarceration rate of blacks and whites in the different states in 2011. The ratio for the United States as a whole is 5.8. We have calculated the incarceration rate ratio for each individual state and list below the ten states with the highest ratio and the ten states with the lowest ratio:

Ten highest | Ten lowest

---|---

Wisconsin | 14.8 | W. VA | 4.3

Iowa | 12.6 | Tennessee | 4.3

New Jersey | 12.6 | Nevada | 4.2

Minnesota | 12.5 | Texas | 4.2

Connecticut | 10.7 | Arkansas | 4.1

Utah | 10.2 | Florida | 4.0

Pennsylvania | 9.9 | Kentucky | 3.9

Illinois | 8.9 | Georgia | 3.7

New York | 8.7 | Alabama | 3.6

Kansas | 8.7 | Mississippi | 3.2

What may be particularly surprising in this tabulation is that the states with the high ratios are in the Northeast and Midwest, and are states that are generally seen as progressive. On the other hand, the states with the low ratios are predominantly in the South. To the extent that one considers the racial disproportionality in prisons to be predominantly a consequence of racial discrimination, a practice that might be thought of as much more common in the South, this tabulation could well raise important questions about that perspective.

It then raises the question of what in the South is keeping their ratios low and increasing it in the North. At this point one can only speculate on the factors. Undoubtedly, socialization of people who have been there for a long time has been an important factor in the South. Their long residence ensures that they know the social mores and are more likely to obey the rules. On the other hand, African Americans in the North have been much more mobile and are concentrated in cities with much weaker social control and where crime rates are highest and with much greater socioeconomic differences. It would certainly appear useful to explore this issue of the factors contributing to these North–South differences in incarceration rate ratios. That would certainly be worn to generate insights into the factors contributing to the racial disproportionality in prisons.

The high incarceration rate of African Americans is a problem not just for the people incarcerated. It is also a broader problem because:

  * Communities and families are disrupted as people move in and out of prison.

  * Prison mores and culture are brought into communities through community–prison networks.

  * People with a criminal record, and especially ex-prisoners, find it difficult to reenter the labor force and community.

  * The community-level social stigma of having been to prison is reduced as more young people are incarcerated, and this reduces effect of the threat of incarceration as a crime deterrent.

  * The large racial disproportionality in prison raises concern in the Black community that all the differences are attributable to discrimination, thereby diminishing the credibility and perceived legitimacy of the criminal justice system.

## Causes

Incarceration rates began to increase rapidly four decades ago as control over prison populations shifted from the officials in the criminal justice system to political officials, especially legislators. This politicization of criminal justice policy was initiated by Barry Goldwater, the Republican candidate for President in 1964 by blaming his opponent, Lyndon Johnson, for what he called "the crime in the streets." In fact, neither Lyndon Johnson nor the national administration had very much to do with the rising crime rates of the 1960s. Rather, the surge of teenagers in the US population at that time was a reflection of the early stages of the "baby-boom" generation that began in 1946 after World War II coming into the high-crime ages as reflected by the peak at age 18 of the crime curve. But it did establish the principle that the political arena was an appropriate place for the public to raise concerns about crime.

That was followed by public demands to "do something" about the crime problem, and especially about the drug problem when so many young people were becoming involved with marijuana as part of the youth culture that began in the 1960s. Those in the political system are constrained by a very limited repertoire of possible responses to this demand. The easy solution was to pass laws to increase prison terms for criminal offenses and especially to mandate prison terms of some minimum duration. These mandatory-minimum sentencing laws were a particularly important feature of the response to the drug concerns, since judges were often sentencing minor drug dealers to probation. The initial response might have been a 2-year mandatory-minimum sentence; when they saw that wasn't doing much good, they would crank the sentence up to 5 years, and then even to 10 years, hoping that at some point the behavior would be deterred. Unfortunately, the increased sentences were found to be of diminishing effectiveness at reducing crime.

The notorious 100:1 crack-powder-cocaine disparity is an example of this response and certainly one reason for the overrepresentation of Blacks in prison on drug charges. In the early 1980s, crack was an important technological innovation that made the pleasures of cocaine available to poor people at a low price, and the newly established crack markets used violence as an important means of competition. In an attempt to suppress the violence, Congress passed the Anti-Drug Abuse Act of 1986, which imposed a mandatory-minimum sentence of 5 years for possessing five or more grams of crack cocaine and the same sentence for 500 or more grams of powder cocaine. This disparity in the sentencing laws was a response to concern about the high level of violence then prevalent in crack markets. It also reflected information subsequently established as erroneous about the disparate effect of crack and powder on babies. This resulted in many street-level crack dealers being sent to federal prisons for extended terms, and many states followed suit.

The crack-powder disparity also contributed greatly to the racial disparities in prison since 85 % of the people convicted for crack cocaine are Black, whereas only 30 % of those convicted for powder-cocaine offenses are Black. People convicted of crack offenses serve about 50 % more time than those convicted of powder cocaine. Since 1986, the crack markets have largely stabilized and the violence diminished, and so the disparity looked more and more as racial discrimination, but it took the Congress 24 years to reduce the disparity, and then only to a crack-powder ratio of 18:1 under the Fair Sentencing Act of 2010.

Trends in the prison population also reflect actions by prosecutors, judges, and parole boards. They must be responsive to changes in legislation, and those who run for office—most prosecutors and many judges—are motivated by the same political influences that affected legislators. The public is not very sophisticated about what works and how well in controlling crime, and most do seem to respond to actions that seem to be "tough on crime." That was certainly the case when crime rates were high, but now that crime rates are lower than they have been since the 1960s, the public is likely to respond similarly but probably not with the intensity that they would during periods of high crime The actions of these political actors include deciding what offense to charge; most crimes with a mandatory-minimum sentence have a non-mandatory variant, usually depending on the amount of drugs when targeted at a drug offense or the nature of the use of a gun. They also decide on the length of the sentence imposed, when to permit parole release, and on what basis to return a parole violator to prison. Another occasionally important participant in sentencing policy are the correctional-officer unions that have an economic stake in keeping prisons full in order to secure the jobs and increase the wages for their members, and so they can become politically active and pressure on legislatures to enact laws that increase incarceration.

In 1998, 70 % of the Black–White differences in incarceration rates were due to corresponding differences in arrest rates for the crimes that are likely to lead to prison. The other 30 % can be accounted for by differences in socioeconomic situations, prior arrest records, as well a possible discrimination by prosecutors, judges, or parole authorities. Thus, the Black–White ratio at arrest is fairly close to that in prison, and that relationship will differ by crime type.

There is one crime type in which Blacks can be somewhat underrepresented in prison, and that is mostly associated with the crime of murder. This is probably a consequence of what has come to be known as "victim discounting," punishing more severely for murder against Whites than against Blacks. This has been explained as attributable to a phenomenon known as "victim discounting," another aspect of racial discrimination where those who murder whites are punished more severely than those who murder blacks. Since most murder is intra-racial, then blacks convicted of murder could be the beneficiary of this discrimination against black murder victims. This issue was raised in the McCleskey case as a "disproportionate impact," but the Supreme Court refused to act on it.

The extreme difference at the other end is associated with drug offenses, where blacks are most significantly overrepresented in prison compared to arrest. This could be attributable to the emphasis on punishing crack offenders. It could also be associated with the observation that drug markets operated by blacks are more often run as street markets, whereas those run by whites are more likely to be indoors, thereby making arrest and conviction easier.

## Solutions

Although it is hard to attribute the 6:1 disproportionate representation of Blacks in prison as attributable entirely to racism in the presence of their differential involvement in the crimes that lead to prison, it is hard to argue that racial discrimination plays no role. There are many opportunities for discrimination to appear, and it is important to root out discrimination wherever it exists.

Part of the solution will be to view the drug epidemic in America as a public health problem rather than a crime problem, and deal with that through the public health system accordingly. In addition, policy makers need to recognize the futility of averting drug transactions through deterrence or incapacitation when replacements for drug sellers are available. Incarcerating a rapist removes his rapes from the community, but incarcerating a drug dealer opens the door for a replacement to serve the demand for drugs. Also, it is possible that the replacements represent a greater threat to public safety than the people they replaced; in the crack experience, the replacements were younger and less restrained in using the guns they had to carry to protect themselves from street robbers, and so there was a major rise in the homicide with guns by young people as a result. Locking up the dealer does not solve the issue of drug use in society, and could well make matters worse.

In addition, we need to facilitate redemption by informing employers when a criminal record is stale; the risk of a new crime drops below that of the general population when the former offender has stayed clean for a reasonable amount of time. Employers who follow such a policy could be protected against due-diligence liability by statute. State criminal-record repositories can choose not to disseminate such stale criminal-record information.

There is also the need to reduce incarceration by dramatically shortening the long sentences and increasing certainty of punishment and celerity or immediacy of response. This need is particularly important for individuals who are on parole or probation, where a common requirement is avoiding drug use. It is widely recognized that many prisoners have drug problems, and so it is not surprising that, when they return to the community, they initiate drug use, and that puts them at high risk of being sent back to prison as parole violators. The HOPE program initiated in Hawaii is a good model for avoiding that revolving-door process. HOPE tests drug-using probationers weekly on a randomly chosen day. Those who fail the test are subject to immediate incarceration for several days. The certainty of the response and its immediacy has been shown to be an effective means of reducing the probationers' drug abuse, and thereby avoids the much greater cost of sending them back to regular incarceration as probation violators.

Part of the solution also involves reducing crime by reducing disadvantage, by facilitating employment opportunities through education, job skills, and reentry services. This also warrants a focus on the next generation. For example, home visitation by nurses has been shown to be effective in giving young mothers the knowledge and support for raising their children.

The primary challenge involves a willingness to pursue rational and evidence-based policies and avoid the ideological and discriminatory policies that have driven too much of our actions regarding crime over the past 40 years. The pressure on state governments' budgets created by the Great Recession represents an important opportunity for convergence in an otherwise highly polarized political environment.

Suggested Readings

Blumstein, A. (1993). Making rationality relevant—The American Society of Criminology Presidential Address. Criminology, 31(1), 1–16.CrossRef

Blumstein, A. (1995). Youth violence, guns, and the illicit-drug industry. Journal of Criminal Law and Criminology, 86(4), 10–36.CrossRef

Blumstein, A., & Cohen, J. (1973). A theory of the stability of punishment. The Journal of Criminal Law and Criminality, 64(2), 198–207.CrossRef

Blumstein, A., & Nakamura, K. (2009). Redemption in the presence of widespread criminal background checks. Criminology, 47(2), 327–359.CrossRef

Bonczar, T. P. (2003). Prevalence of imprisonment in the US population, 1974–2001 (Bureau of Justice Statistics Report No. NCJ 197976).

Carson, E. A., & William, J. S. (2012, December). Prisoners in 2011 (Bureau of Justice Statistics Report No. NCJ 239808).
© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_13

# 13. Resetting Race

David Kennedy1

(1)

John Jay College of Criminal Justice, New York, NY 10019, USA

David Kennedy

Email: dakennedy@jjay.cuny.edu

As my part of the 2010 University of Pittsburgh Center on Race and Social Problems conference "Race in America: Restructuring Inequality," I gave a talk on reconciliation between the police and the largely Black citizens of America's most troubled and violent communities. It was a fundamentally optimistic talk: there are proven approaches, I said, in which law enforcement and communities can work together to reduce violence and other public safety problems dramatically, while also reducing arrest and incarceration and improving relationships between communities and police. I offered a simple but powerful analysis of shared interests between police, neighborhoods, and even street offenders: all involved, I said, want safety and security, help for those who will take it, as little exercise of state authority as possible, strong communities and community standards, and that the very few truly dangerous be controlled. I gave concrete examples of where and how this had worked, including strong formal social science evaluations (Braga and Weisburd 2012). This is, I said, demonstrably within our reach.

Then came a question I have come to expect as inevitable, in one form or another. How can you do this kind of work, a White man asked, with the racist police? The police aren't racist, I said, that's not the problem. He was not convinced; he was evidently, richly, skeptical. I have come to expect this, too, as inevitable.

This essay is a meditation on that exchange, what—writ large—it means, and what can be done about it.

The idea that the police—and law enforcement as a whole and the criminal justice system as a whole—are racist is an enormously powerful theme in many quarters, many communities, and in many analyses and narratives about crime, the criminal justice system, criminal justice policy, and the impact of them all. It is often taken as a given, as a starting point. It is the explanation for a host of ills: for the lack of safety in minority, especially Black, communities; for intrusive and abusive policing; for mass incarceration; for the disparate treatment of minorities, especially Blacks, as they move through the system; for abusive practices such as profiling and abusive incidents such as police killings of minority men. One hears it on the street, where minority residents experience and articulate what they take as personal animus: that, as ethnographer Rod Brunson captured it, "the police don't like Black people" (Brunson 2007). One hears it from Black citizens of every class; at my home institution, John Jay College in New York City, I have a running conversation on the subject with my friend and colleague Delores Jones-Brown, a Black professor and until recently director of our Center on Race, Crime, and Justice, who believes that there is a racially motivated conspiracy underlying American criminal justice and its many toxic outcomes for, especially, Blacks. One reads it in formal scholarship such as that of Loic Waquant (2002), who argues that America's history of slavery is seamlessly linked with today's mass incarceration. It has recently had a powerful and deeply influential presentation in Michelle Alexander's breakthrough book The New Jim Crow: Mass Incarceration in the Age of Colorblindness, which argues that "criminal justice in the United States had...emerged as a stunningly comprehensive and well-designed system of racialized social control..." (Alexander 2010).

It is a point of view against which I regularly argue. My issue here is narrow. I am as outspoken as anybody—more so than most—about the awful consequences of American criminal justice for, especially, Blacks. A recent opportunity to address the subject outside the conventions of scholarly discourse produced the following:

> We have taken America's most vulnerable, most historically damaged, most economically deprived, most poorly educated, most stressed, most neglected, and most alienated neighborhoods and imposed on them an epidemic of imprisonment. We have given America's poor Black communities an iatrogenic condition. They cannot stand against it. It has become an independent source of terrible damage, like racism, or terrible schools, or official neglect, or vanishing jobs. It is the one thing that will prevent anything else from working, make meaningless all of our aspirations for better schools and economic development and community uplift. Nothing else will work until we fix this (Kennedy 2011).

My issue is with that of motive: I do not believe that these ills are driven by racism or racial animus, or that they are deliberate. I belong, in so not believing, to a distinct group that is fervent about the damage being done while rejecting the now-classic understanding of its causality. Yale University law professor James Forman, for example, argues that while "the New Jim Crow writers and I agree more often than we disagree, I also believe that we who seek to counter mass incarceration will be hobbled in our efforts if we misunderstand its causes and consequences in the ways that the Jim Crow analogy invites us to do" (Forman 2012).

Those of us who take this position have our reasons: we have different analyses of what is going on, including different historical and causal accounts. (I particularly commend to the interested reader Forman's article, which is both a thorough and a sympathetic critique of the "New Jim Crow" argument. My recent book Don't Shoot (Kennedy 2011) is in large part an exploration of my own views.) I will not address them here, for the simple and important reason that for the most part they change nobody's mind. In the arenas I care about the most, which are hands-on engagements with people committed to these issues, living with them themselves and committed to making a difference, and willing to do real work, the arguments fail nearly entirely even to register, much less to influence or persuade. The strength of feeling and depth of conviction are simply too powerful. I spoke fairly recently with a Black lawyer, committed to national progress on criminal justice issues, about what I regard as the ready-to-hand opportunities for working in a fundamentally different way with the police and other criminal justice authorities. "Why should we work with them," she said, "when they can kill us whenever they want and nothing is ever done about it?" My own work has been cast in this light. Bay Area lawyers arguing that alleged gang members should not be subject to an injunction had this to say about the "Ceasefire" approach I have been part of developing (the alleged gang members had been called into a Ceasefire meeting in Oakland):

> In point of fact, the whole "Cease Fire" (sic) program was and is arbitrary, opportunistic, and unconscionable, and in truth, only one more mindless component of the giant, morally degenerate "prison-industrial complex," which operates on a $9 billion-dollar annual basis in this state, and requires a steady supply of Black and brown bodies at the gates of the R and R Centers, each month, to feed its gruesome process. At bottom, it is for doing this "work," of gathering flesh to feed the monster gulag, that the police want the Court here to supply them with "another tool" (Bulwa 2012).

It has even been mobilized to make the same point—that the system is racist and deliberately damaging—from exactly the other direction. A Black social worker with whom I was having an energetic exchange on the subject argued that my work in gang violence and drug markets was demonstrably successful—it had been shown to save lives and keep people out of prison—but was not being broadly embraced in criminal justice circles, which proved that such circles in fact desired the current and disastrous status quo. In the face of such logic, I found myself—and in general find myself—helpless.

This is not, emphatically, to diminish the importance of a continued, rigorous, and public debate on the core issues. That should and will continue, and we can I think not only hope but expect that it will over time advance our common understanding. That will be of both intellectual and practical significance. It is to say that we are facing crises—as a people, as a nation, and particularly in specific neighborhoods all across the country—of violence, disorder, incarceration, intrusive and abusive law enforcement, and poisoned relationships between minority communities and the police. As a way to address those crises, our arguments, and in fact the debate as it stands about the role of racism in driving these crises, are getting us nowhere.

Most fundamentally, in terms of the way this discourse actually proceeds, those of us who believe more or less as I believe find ourselves in the position of having to prove a negative. It is an absolute conviction in many circles that the police are racist, that racism is at the root of these criminal justice ills. As with my interlocutor at the Pittsburgh Race in America conference, it is the starting point for any further exchange. I do not know why he so believed, what evidence and analysis and experience had brought him to that point. I do know that that is what he did believe—as do many—and that that put me in the position of proving otherwise. It may not be impossible, but is in practice exceedingly difficult, to prove that negative. His view, which I believe to be fundamentally mistaken, is also fundamentally plausible. In the communities I work in, experience with the police and the rest of the criminal justice system is widespread and dire. It is normal experience for people to be, or to know people who have been, stopped, arrested, treated badly, treated illegally, and jailed and imprisoned. It is community and historical experience that for much of the American history this was true and was part of an outright and legally formalized racism. In the communities I work in, which are driven by violence and drug problems, the current practices of criminal justice are demonstrably not making the community safe. A conclusion of "racism" is reasonable, well grounded in history, and supported by well-constructed and evidenced arguments. Any arguments to the contrary must overcome that experience, that historical grounding, that evidence, and those arguments. In practice they rarely do.

This—the conviction that racism and in particular racist police and policing is driving criminal justice policy and practice—has tremendous practical significance. It goes fundamentally to the idea of the legitimacy of the police and undercuts that legitimacy. As we now know, and seem steadily to be understanding is even more profound and powerful than we thought, the belief on the part of the public that the law and the police are legitimate has direct, immediate impact on crime and violence (Tyler 2006; Meares 2009). If legitimacy is perceived, people are more likely to obey the law, cooperate with police, and undertake informal social control. Telling the police who shot your friend is facilitated by legitimacy. Not "snitching," getting a gun and your friends, and shooting him yourself is facilitated by a lack of legitimacy. Seeing the police as racist enemies puts them out of bounds as potential partners in important common enterprises. Police, in turn, are less likely to see any possibility of working with an angry and withdrawn community. In extremely concrete and immediate terms, these beliefs have consequences.

Less immediately, they shape what we think we need to do to create change. If we are dealing with a racist institution, and even a racist society, and we believe that that racism is causal in creating these awful outcomes, then we must address that institutional and social racism. Alexander argues that the roots of racism, and thus of mass incarceration, are so deep in American society and core public attitudes that any effective response must "meaningfully address the racial divisions and resentments that gave rise to mass incarceration, and [must]...cultivate an ethic of genuine care, compassion, and concern for every human being—of every class, race, and nationality—within our nation's borders, including poor Whites, who are often pitted against poor people of color..." (Alexander 2010). If this is so then it is so, but we might note that our society is nowhere near this now, and that it is a standard that has probably never been met in any society, anywhere, historically. A social movement that would produce that outcome is nowhere in evidence, nor is it clear how it might be created. If this is what we must do to create meaningful safety and address mass incarceration in our most needful communities, they will be in trouble for a long time to come. Implied in my Pittsburgh interlocutor's question was a middle-ground goal: how do we eradicate racism within policing? This is itself a tall order, also with no clear and effective pathway in evidence. If, on the other hand, racism is not the core issue, than we need not assume that we must proceed along these pathways: others, possibly simpler and faster, might be available.

In thinking about these issues, I have found my memory turning to another moment in our criminal justice history in which we faced a critically important issue, one also viewed through a racial lens, and one we got very badly wrong and which in fact was instrumental in producing some of the worst ills we face today. In 1996, William Bennett, John DiIulio, and John P. Waters published Body Count: Moral Poverty—and How to Win America's War Against Crime and Drugs. Looking at the wave of minority, and especially Black, violence and neighborhood disorder that had come with the crack epidemic and was spiking across America's urban centers, Body Count argued that a new breed of "super-predators"—"the youngest, biggest and baddest generation any society has ever known"—was taking over the country. Their portrayal was one of an alien, animalistic other, "radically impulsive, brutally remorseless youngsters" unmoved by "the stigma of arrest, the pains of imprisonment, [nor] the pangs of conscience...." Their causal analysis was a collapse of culture, community, and character, a "moral poverty" of "fatherlessness and godlessness." Their prediction was Armageddon—the absolute number of such minority youth would increase, as would their share of the overall population, as would the steady worsening of their behavior.

The only problem, from an intellectual point of view, was that none of it was true. Aside from the basic facts—serious and violent crime had increased dramatically amongst especially young Black men, and there were more or less predictable demographic trends that had been and would be at work—Body Count's argument was less an argument than it was a quilt of horrific insinuation rooted in the country's worst preconceptions about race, minority communities, and minority men. Its imagery about those minority men was at one remove from "Birth of a Nation," the subprimitive Black male to whom "nothing else matters" but "sex, drugs, money." Its arguments about causality were either mistaken—there simply is no historical pattern or inevitability, as it argued, in which each generation of young men is more criminal and violent than the next—or entirely unsupported, as with the proposition that "fatherlessness and godlessness" were driving the violence. In the language of logic and social science, correlation was being presented as causation (when no actual evidence of, for example, godlessness was ever presented).

So, intellectually, Body Count was a mess. In the simplest and most important sense, it was just wrong: as careful scholarship would later show, the actual movement of the cohort of young men Body Count was mostly about did not fit the Body Count story; far from being somehow inevitably depraved, that cohort had notably lower-crime than preceding cohorts before suddenly making an about-face over the crack years (Cook and Laub 2002). Where it was not wrong it was unsupported, sloppy, and often hateful. None of that diminished in any way its public power and impact: we should be honest, in fact, that its public power and impact was in considerable measure due to the way in which it puts an apparently grounded and objective face on what was really prejudice and cant. But that public power and impact was enormous. The term and notion "super-predator" took over the public imagination. It drove the crime debate, got almost immediate media and political traction, and led to a wave of draconian federal and state legislation and widespread and dramatic changes to juvenile justice at the state level, especially widespread and sometimes mandatory waiving of juvenile offenders to adult courts and corrections.

But—and here things get interesting, both with respect to the Body Count story and with respect to its resonance for the current attention to racism in criminal justice and "The New Jim Crow"—the idea of the "super-predator" had a very short shelf life. Its fall from grace, which came very quickly, did not rest on any effective and widely understood challenge to the Body Count arguments. Politicians, reporters, judges, and citizens did not, and probably mostly still do not, understand the ways in which Body Count was factually and logically flawed. Many still fundamentally believe the individual planks. They believe that young minority men would not be on the corner if they cared about their families and communities, and if their families and communities cared about them; they believe that there is something distinctive and fundamentally toxic about the criminality of young minority male offenders; and all the rest. Argue against these things today, as then, and one again finds oneself in the position of trying to prove a negative. Most people who look at these issues from any remove do not need to be persuaded that high-crime Black communities have family, culture, and normative issues. They start there, and it is up to you to convince them otherwise, which on most days you will not do. So it is not because those notions went away, and people came to see how they had shored up Body Count's otherwise unsupportable conclusions, that the super-predator idea went away.

The super-predator idea went away because the young men and communities in question changed their behavior. The killing did not get worse—it got better, rapidly and dramatically. The next wave of young men was not even wilder and more violent than their fathers and older brothers; they were calmer and quieter. The country did not descend into a new barbarism; it entered into a 15-year-and-counting crime decline. Looking back, it is clear that even as Body Count was published, the phenomena it described had already begun to ease: the national crime decline began in 1992, some 4 years before Body Count hit the streets. There were many, many people who understood how fundamentally mistaken Body Count was. Had they had to win the arguments as to why—had they had to persuade the country that, for example, young Black murderers were mostly caught up in street dynamics not of their choosing or liking, and that they loved their mothers and their mothers loved them—they would have gotten nowhere. They did not have to win the arguments. New facts spoke for themselves. They have not changed the common view of everything connected to this issue, but the worst of it—the country, and especially Black neighborhoods, are doomed to fear and fight their own children—collapsed of its own accord.

I, and many others, believe that the idea that the police and the rest of the criminal justice world are fundamentally racist and actively or implicitly seeking to harm, especially, Blacks and their communities is wrong. This is not to say that there is not racism in policing and elsewhere in criminal justice, or racists, or disparate treatment and disparate impact. There is. But the strongly held belief expressed to me at the Pittsburgh conference goes beyond those more careful parsings. That core belief, whether expressed in simple or in elaborate terms, is that the police are racist, are acting out of racism, and that the awful harms being visited through criminal justice on America's minorities and minority neighborhoods are an expression of that racism. Many, many believe this. That is, I am convinced, false.

I do not believe that many who believe otherwise will be convinced by argument. I believe that they will be convinced only through the same mechanism by which America's young Black men, and fraught Black neighborhoods, convinced the rest of the country: by the changing of behavior. They will be convinced when they see the police and others in criminal justice—but always, in this context, especially the police—changing what they are doing. Angry Black communities, and many others, do not feel that the police protect them, and they want protection. They feel that the police cannot tell the difference between the small numbers of active, serious offenders and the rest of the community. They feel disrespected and poorly treated in their encounters with police. They feel profiled—stopped, pulled over, searched. They feel damaged by rampant arrest, prosecution, and incarceration. They attribute these practices and their outcomes to racism. Arguing about racism will get us nowhere where these facts—and they are facts, all too often—remain facts. Changing these facts will change the understanding about racism.

The good news is that police and others in law enforcement are increasingly ready to change, and to change these facts. Many are there already; many more are not far behind. The closest I usually get, in practice, to arguing the racism question with those who most believe it is to say, if that were true, they wouldn't listen, and they are in fact listening. Nobody could have gone to Jim Clark or Bull Connor and said, the way you're conducting yourself is doing objective harm and beyond that feeding into the community's worst racialized preconceptions, and you should stop it. They were racists and they did what they did exactly in order to control and oppress. You can go to many of today's equivalents and say exactly that, and they not only hear it, they embrace it. Police chiefs, prosecutors, and many others in criminal justice have been saying for years that they "can't arrest their way" out of the crime problem; it has become practically a mantra. They are fully aware that their standard practices are not addressing the most important problems in the most troubled neighborhoods. They are increasingly open to the idea that there are dire, if unintended, consequences that flow from those standard practices. Jim Fealy, then chief in High Point, North Carolina (after a career in Austin, Texas) could not have been more explicit when he told the High Point Police Department that he was committing it to a new drug market strategy in several historically deeply troubled Black neighborhoods:

> I've been doing this my whole life, he said. It doesn't work. We move into these neighborhoods like an army. We occupy them. We stop everything that moves, turn them upside down, shake them, see if crack falls out of their pockets. If it does, we cuff them and take them away. If it doesn't, we send them along, be on your way. We never ask the community what it wants from us, we say, we're the police, we know how to do our job, get out of the way and let us do it. We scorch the earth and roll back on out and think we've done a good job. We say, look at all the arrests we've made. Look at all the warrants we've served. We stand tall next to the tables full of coke and cash and guns. It doesn't work. It doesn't stop the drugs. It doesn't stop the violence. It all comes back as soon as we leave. And we go home and the people we're supposed to serve, the people who need our help the most, are no better off, and that much more alienated than before (Kennedy 2011).

When Chicago Police Superintendent Garry McCarthy was police director in Newark, New Jersey, he was part of a series of discussions begun by the National Network for Safe Communities (NNSC), and supported by the Office of Community Oriented Policing Services at the Department of Justice and the MacArthur Foundation. The discussions were on what the NNSC calls "reconciliation and truthtelling": the idea that broken relationships between needy minority communities and the police need to be addressed in order to produce public safety effectively, and that honesty about history and current practice is part of the route to fixing those relationships and practices. McCarthy had difficulty with the idea that the Black communities he policed would believe that law enforcement was part of a deliberate conspiracy to do them harm until he saw Michelle Alexander make the "New Jim Crow" argument in front of a largely middle-class Black audience at an event in New Jersey. She got a 5-min standing ovation. McCarthy was convinced: not that the argument was right, or that he was a racist, but that much of the public to which he was committed did believe it. He has committed himself to addressing that belief and changing police practice to honor the history and perceptions of such angry communities. "I understand the historical divide between police and communities of color—it's rooted in the history of this country," he said in an interview in Chicago. He added:

> The most visible arm of government is a police force, and the institutionalized governmental programs that promoted racist policies that were enforced by police departments in this country are part of the African American history in this country. And we have to recognize it because recognition is the first step towards finding a cure towards what is ailing us.
>
> Over the years we've actually done a lot of things wrong and I'm willing to admit that. A lot of police executives are defensive. We've done a lot wrong (Wildeboer 2011).

Similarly, Salt Lake City Chief Phil Burbank, writing with academic Phillip Atiba Goff and division chief of the Denver Police Department Tracie L. Keesee, said—as part of a discussion of why many local police departments want no part of enforcing heavy-handed immigration policies—

> Given law enforcement's history as an effective tool of social oppression, it should not be surprising that many law enforcement officials across the nation are troubled at the proposition of mandatory immigration enforcement practices that appear motivated by prejudice—a point the report also supports—and are likely to result in increased crime. The profession of law enforcement has long struggled to regain the trust it lost when it was directed to detain runaway slaves, patrol Japanese internment camps, and enforce laws that kept water fountains and schools racially segregated (Burbank et al. 2010).

Police chiefs and others in law enforcement who feel this way in fact have enormous latitude to take direct action to undercut charges of and appearances of racism, undo policies harming minority communities, and address the damaging narratives that can dominate both communities and law enforcement agencies. Many of the arguments that support diagnoses of racism in criminal justice, and make the case that it is producing outcomes like the "New Jim Crow" posit a kind of inexorably, deeply determined, structurally monolithic system. There is truth to that—there is law, there are criminal justice agencies, there are practices and tendencies by which the latter apply the former, and there are the outcomes thus produced—but there is both more and less to it than that. New York State, to take one notable example, more or less decriminalized low-level private marijuana possession in 1977, making it a non-arrestable violation. Only "public view" possession of small quantities of marijuana remained subject to arrest. Such arrests stayed under, mostly well under, 5,000 a year from 1977 until the mid-1990s. They then soared—not because of any change in the law, but because of changes in policy and practice in the New York Police Department. By 2000 they had hit almost 50,000 arrests a year, overwhelmingly of Black and Latino youth (Thrasher 2012). This was not a matter of law, or of overall "system" behavior, but of discretion, as exercised by the NYPD.

But such discretion can be exercised in both directions. The drug market strategy to which Jim Fealy was committing his department was a deliberately framed low-arrest approach, explicitly designed to minimize criminal justice intrusion into the community in both the short and the long term, and to get offenders help rather than incarcerate them. It was presented as such to the neighborhoods involved. The police were direct with the community about the facts that what they had been doing in their standard approaches had not worked, had visited serious—if unintended—harms on the community, had alienated the community in ways the police now understood, and that the new approach was intended to reverse all that. The community welcomed both the overture and the new policy. The underlying approach, now called the "drug market intervention," or DMI, has been widely—not universally, but widely—embraced elsewhere, including by the United States Department of Justice. It has been implemented in multiple jurisdictions. Formal evaluations attest not only to its crime control impact but impact on underlying community attitudes: neighborhood residents in the original High Point neighborhood, for example, "reported high levels of satisfaction with the police and an appreciation for police efforts to address the drug dealing in the neighborhood" (McGarrell et al. 2010).

There is mounting evidence that this kind of work—in which police go directly to angry and suspicious neighborhoods, admit the realities of both history and current practice, treat the community with respect, and frame operational approaches that are serious not only about crime control but also about strengthening communities and minimizing the collateral damage from enforcement—can have transformative impact on the views that such neighborhoods have of police. A drug market operation patterned after DMI was implemented in Hempstead, Long Island. Eddison Bramble, then president of a grassroots leadership group in the city, 100 Black Men, was blunt about what he thought before the operation. "People were thinking that the police and the DA were just waiting for their kids to reach a certain age so that they could lock them up," he said. "We're Black folk. We see overseers. That's what we see" (Kennedy 2011). Seeing those same police and prosecutors go out of their way not to lock up even drug dealers in the area, and work hard to get them the help they needed, upended those views. It produced, crucially, the sense that the police and prosecutors were not illegitimate oppressors but legitimate agents of, and even partners with, the community.

Yale Law School Professor Tracey Meares, who has done similar work in both the research and operational realms, including ground-breaking interventions in high-crime neighborhoods in Chicago, focuses on this core element of legitimacy and writes that:

> Whether in Chicago or High Point, Cincinnati, or the host of other cities that have seen this approach succeed, those who lead this new wave of law enforcement and community safety projects take them seriously. They understand that attempting to sustain neighborhood safety through a continuing commitment to carpetbombing and locking up the next generation of young African-American men is doomed to failure. They understand that, despite an often crippling alienation between law enforcement and communities, police, community members, and offenders alike want the streets to be safe, residents to succeed, and for jail and prison to be a rare last resort. They are discovering—in practice, not just in theory—that a normative commitment to compliance is a sustainable and realistic approach to bringing crime down. When it does not work, law enforcement is still there, but it is used far less often and is seen as legitimate by the affected community.
>
> Legitimacy in law enforcement is not just a nascent strategy. It is a movement. It is movement with the potential to transform the way this nation does law enforcement, achieves community safety, and heals longstanding rifts between police and minority communities. It is, in short, about nothing less than ensuring domestic tranquility (Meares 2009).

Police departments and other criminal justice agencies can do this work in this way if they choose to. Increasingly, they are choosing to. These views are approaching the mainstream in law enforcement. In January 2011 the Department of Justice's Office of Community Oriented Policing Services, in cooperation with the NNSC, hosted a conference on "racial reconciliation, truthtelling, and police legitimacy." COPS Office director Bernard Melekian wrote, in a report resulting from the conference:

> As a society, we address other systemic, social problems—educational, economic, and health disparities, for example—with social programs and community-based solutions. However, the cycle of violence that disproportionately affects our Black youth remains a social problem that we treat first and foremost with a criminal justice system response. This is not justice, neither for the victims of this problem nor the police who are charged with the primary responsibility for solving it (Mentel 2012).

This is the police playing against type—saying and doing things that are directly in opposition to the image so many hold, and for so many plausible reasons, that the police are racist. As with Black neighborhoods and young Black men in the aftermath of the crack epidemic and the toxic and mistaken "super-predator" narratives, we can hope that such changed behavior will start to tell a story, and describe a new reality, that will undercut the toxic and mistaken narrative that the police are—today—fundamentally racist. I began this essay with the observation that the idea of a racist police is a common and plausible explanation for a host of ills: for the lack of safety in minority, especially Black, communities; for intrusive and abusive policing; for mass incarceration; for the disparate treatment of minorities, especially Blacks, as they move through the system; for abusive practices such as profiling and abusive incidents such as police killings of minority men. These are real, and appalling, outcomes of how we currently police and otherwise practice criminal justice. I, and many others whose views I share, differ from certain other critics only on the question of motive. It turns out that in order to produce better outcomes in America's most vulnerable and damaged neighborhoods—to produce what they need and are due, domestic tranquility—it is necessary to change not only how we police and practice criminal justice, but to understand, face, and address how we view one another. If it is historically and currently plausible for angry minority communities to understand the police as racist and their concrete conditions as the result of racism—and it is plausible—then the police must change how they act and relate to those communities. Increasingly they are doing so. Only those actions can reset the narratives, and more importantly the relationships and their outcomes, with which we are now burdened.

References

Alexander, M. (2010). The New Jim Crow: Mass incarceration in the age of colorblindness. New York: The New Press.

Bennett, W., DiIulio, J., & Waters, J. (1996). Body count: Moral poverty—And how to win America's war against crime and drugs. New York: Simon and Schuster.

Braga, A., & Weisburd, D. (2012, April). The effects of 'pulling levers' focused deterrence strategies on crime. Campbell Systematic Reviews, The Campbell Collaboration.

Brunson, R. (2007). Police don't like black people: African-American young men's accumulated police experiences. Criminology & Public Policy, 6(1), 71–101.CrossRef

Bulwa, D. (2012). Oakland gang case's over-the-top argument, SFGate, April 27. Retrieved September 15, 2012, from http://blog.sfgate.com/crime/2011/04/27/oakland-gang-cases-over-the-top-argument

Burbank, P., Goff, P., & Keesee, T. (2010, June 7). Policing immigration. A job we do not want. Retrieved September 15, 2012, from http://www.huffingtonpost.com/chief-chris-burbank/policing-immigration-a-jo_b_602439.html

Cook, P., & Laub, J. (2002). After the epidemic: recent trends in youth violence in the United States. In M. Tonry (Ed.), Crime and justice: A review of research (Vol. 29). Chicago: University of Chicago Press.

Forman, J. (2012). Racial critiques of mass incarceration: Beyond the New Jim Crow. New York University Law Review, 87, 21–69.

Kennedy, D. (2011). Don't shoot: One man, a street fellowship, and the end of violence in inner-city America. New York: Bloomsbury.

McGarrell, E., Corsaro, N., & Brunson, R. (2010). The drug market intervention approach to overt drug markets. Journal of Criminal Justice and Security, 12(4), 397–407.

Meares, T. (2009). The legitimacy of police among young African-American men. Marquette Law Review, 92(4), 651–666.

Mentel, Z. (2012, August). Racial reconciliation, truthtelling, and police legitimacy. Office of Community Oriented Policing Services, US Department of Justice.

Thrasher, S. (2012, February 2). NYPD arrests for marijuana soar in 2011, second highest on record. Retrieved September 15, 2012, from http://blogs.villagevoice.com/runninscared/2012/02/nypd_arrests_fo.php

Tyler, T. (2006). Why people obey the law. Princeton: Princeton University Press.

Waquant, L. (2002). From slavery to mass incarceration. New Left Review, 13, 42–60.

Wildeboer, R. (2011, August 15). Interview: Garry McCarthy on the future of the Chicago Police Department. Retrieved September 15, 2012, from http://www.wbez.org/story/interview-garry-mccarthy-future-chicago-police-department-90445#

# Part V
Health and Mental Health
© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_14

# 14. Disparities in Health Care for Minorities: An Ecological Perspective

Jeannette E. South-Paul1

(1)

Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA

Jeannette E. South-Paul

Email: southpaulj@upmc.edu

## Introduction

Disparities in health and health care for minorities have existed in the United States for decades, occur across the lifespan, encompass physical and behavioral health, involve a variety of clinical and scientific disciplines, and demand cultural proficiency to manage. Media coverage of health disparities has been extensive during the past 10–15 years—often trying to characterize factors contributing to the disparities. A recent review assessed relationships between genetic, behavioral, health system, and/or societal responsibilities and racial/ethnic disparities (Kim et al. 2010). The discussions of health disparities focused mainly on African Americans and prioritized HIV/AIDS, cardiovascular disease, and cancer. However, only 30 % of the articles addressed causal theories or provided solutions. Articles written by academicians tend to theorize why disparities occur and those originating from advocacy groups primarily suggest solutions. Academicians and community leaders are now being challenged to work together to be responsive to elements of the Patient Protection and Affordable Care Act (ACA), to address these persistent disparities through attention to areas of research (for example through the new Patient-Centered Outcomes Research Institute), and to improve quality and the patient experience when receiving care.

Yet collaboration between all entities involved in the delivery of care and better definition of why disparities occur are needed in order to eliminate these disparities. Although the specific contributions of different factors to disparities are not known, what is appreciated is that the causes are multifactorial. During my Family Medicine residency, we often spoke of "the problem patients" in the practice. Everyone knew who these were—the ones who did not follow directions and made the lives of dedicated—but overworked and underappreciated—young residents difficult! They came to appointments late; they brought up new problems that they had not declared when making their appointments; they brought children or family members with them who were not on the schedule and then asked you to answer questions about or examine these interlopers; they came back with repeat complaints even though they had not followed therapeutic recommendations for the same complaints raised at earlier visits; they did not respond rapidly and positively even when they followed directions! Furthermore, they dared to raise social problems in the course of the encounter that you had no hope of solving and that you found baffling at best and shocking and overwhelming at worst!

We learned how to evaluate patients in medical school, the structure of an organized medical history, how to organize our physical exam, and how to order appropriate tests in order to create an evidence-based differential diagnosis. This approach was founded on the biomedical model of disease and was guaranteed to ensure our development as excellent diagnosticians. Several years after I completed residency and while teaching medical students and residents, I learned of the paradigm-shifting work of George L. Engel—an internist whose work in consultation-liaison psychiatry stimulated him to suggest moving from the biomedical to the biopsychosocial model. This was the first formal recognition I recalled of how complex an issue caring for the whole person was and seemed an appropriate starting point for addressing health disparities! He noted that the dyad of physician and patient formed the substrate whereby meaningful data could be observed and used to make connections between the patient's life history and current clinical problems (Engel 1960). New elements needed to be included in the evaluation of the patient to ensure establishment of a complete differential diagnosis. Only when health professionals are aware of the psychosocial elements influencing the encounter and the possibility of disparities can they screen for and be prepared to manage them. Consider the following case that presented to me while covering for a colleague.

### Case Study

> A patient presented in labor to a large, urban, women's hospital and the staff notified the continuity physician that the patient had been admitted in labor. As the physician exited the doctor's lounge, the labor nurse caring for the patient cautioned her to do something about the large group of family and friends in the labor room and the associated waiting room asking too many questions and getting in the way! Upon entering the labor room, the physician was immediately surrounded by multiple family members demanding something for pain for the patient! The teen who was laboring was crying in pain with each contraction. As the physician began to describe the modalities available for pain control, the patient's father asserted – 'you're not sticking a needle in my daughter's back! I know what happens when you do that – they become paralyzed and can't walk anymore!' The physician started by reassuring the family and explained how epidurals actually work and asked to speak personally with the patient, although the family was welcome to listen as long as the patient wanted them there. She elected to have an epidural because she wanted the most complete pain control. The physician left to notify anesthesia and to change into scrubs. Upon returning to the patient room 45 minutes later, she noticed that all but four family members had left. When she asked those remaining what had happened to everyone, they responded – they decided they could go home because they were reassured you (the physician) cared about their loved one and would take good care of her!

When examining potential explanations for this clinical scenario in order to best address the care of this patient, it is useful to consider the medical ecology framework. What issues relate to individual patient characteristics? What role does the family play and how do they impact the events both in the hospital and before and after the inpatient encounter? What environmental factors have influenced the patient's complaints and presentation—in her home, in her neighborhood, and in the community? What issues relating to the hospital and the overall healthcare system are affecting care? Are there any significant overarching issues beyond the hospital or community—e.g., recent political or national events, policies, or laws—that may be influencing this particular clinical encounter?

Recent endeavors to expand the biopsychosocial approach to clinical encounters have focused on clinical relationships and communication patterns—addressing cultural proficiency and/or focusing on patient-centeredness. I would suggest that health and healthcare disparities among minorities can only be addressed through a lens that allows attention to be paid to multiple factors simultaneously and at multiple levels—factors that influence both patient and clinical provider. This can be described as the cultural model—an extension of George Engel's model where both patient and clinician bring history and perceptions to the encounter. The healthcare system typically must identify one or more vulnerable aspects of each patient's condition in order to triage care and intervene effectively. Consider Fig. 14.1, a model that identifies aspects of the environmental and healthcare systems and specific areas of vulnerability in order for clinicians to appropriately address each level of care.

Fig. 14.1

Interconnectedness of factors influencing population health

The earlier case illustrates a number of issues directly relating to communication between the patient, the family, and the health professionals providing her care. In addition to the obvious communication problems, other factors influence this encounter, such as misconceptions regarding risks of therapy and limited health literacy. Such factors are often subsumed by institutional decision-making related to aspects of prenatal care and labor that are deemed more important or more urgent and are inadvertently forgotten. The challenges are (1) knowing the scope of issues that must be considered; (2) having a framework to identify and prioritize those issues; (3) establishing a reasonable differential diagnosis; (4) selecting and implementing an appropriate style of communication and decision-making that the patient can embrace—e.g., oral, written, visual, involving other family members and/or health professionals; and (5) implementing an intervention.

One accepted framework for organizing potential sources of disparities in health care divides the disparities into three categories—(1) Institutional or healthcare system level: lack of interpretation and translation services, time pressures on physicians, availability and mix of health providers, fragmentation in systems of financing and delivery of care, general access issues; (2) Patient level: patient preferences, treatment refusal, care-seeking behaviors and attitudes, and differences in clinical presentation of symptoms; and (3) Provider level: bias, clinical uncertainty, and beliefs or stereotypes about behavior or health of patients. This framework is not new, but I would suggest it can be more useful with the modifications described in the diagram. The desirable diversity now existing among patient and provider populations guarantees the presence of multiple cultural determinants in the healthcare workforce. Rather than explicating and/or trying to minimize these multiple factors, we must reorganize/reorient the lens through which academic health centers approach patients—irrespective of recognized diversity.

One must first understand the organizational framework in which clinical encounters such as this occur. Much scholarship has focused on individual elements of this framework and has explicated encounters centered in one area or another. In reality, each component of the framework embodies its own culture as does the overall environment and community in which these elements are located. Essential to understanding the patient experience and that of all health professionals involved must be the recognition that not only is the culture of the individual components important, but also the culture of the overall environment. When these different cultures interact, modified responses from each element can result. Much of the earlier scenario is anchored in elements of the healthcare setting in which it takes place.

## Institutional–Healthcare System Factors

The institutional factors influencing health care have been the subject of much research in recent years. Structural characteristics of healthcare delivery influence care and subsequently disease outcome—even when considering the most common chronic diseases. Figure 14.1, introduced earlier, is a pictorial representation of the interconnectedness of multiple factors. Structural characteristics can be availability of healthcare facilities, access to health insurance, transportation, and similar factors.

African Americans and Hispanic Americans generally have less access to standard health care—that is, they use more urgent care and access services more through Medicaid—or utilize free clinics or alternative healthcare providers, as more likely occurs among Hispanic Americans. The question has been raised as to whether patients who are largely cared for by safety net providers can receive the necessary scope of services to achieve health (Shields 2007). Families of uninsured children face nonfinancial access barriers to care such as a lack of continuity with a primary care clinician and inadequate visit time (Fry-Johnson et al. 2005). These issues are compounded when the patient has special needs.

Patients informally gather data regarding which healthcare institutions are more or less supportive of patients that look like them. They can use such information to inform their choice of places to seek care. A desire to receive equitable care often drives families to bring multiple family members and friends with them to the hospital in order to have a team of advocates available for their loved one. Furthermore, this custom of bringing family advocates is more the rule than the exception in communities globally.

One effort to better characterize the institutional factors influencing healthcare disparities was a study of patient-level data among 4,450 non-federal hospitals in the US who provided care for acute myocardial infarction, congestive heart failure or pneumonia in 2005 (Hasnain-Wynia et al. 2010). Disparities were found in 37 of 95 findings—11 of which were explained entirely by where minorities received care. The magnitude of 25 others was substantially reduced after adjusting for site of care. These results revealed that where disparities exist, the primary cause may be that minorities are more likely to receive care in lower-performing hospitals. The investigators suggested that policies to reduce disparities should target facilities serving a high percentage of minority patients. A related observational study of 123 hospitals reporting to the University Health System Consortium assessing patient-level data found consistent differences between minority and nonminority patients in quality of care received across 8 of 13 quality measures during hospitalizations for acute myocardial infarction, congestive heart failure, and pneumonia (Hasnain-Wynia et al. 2007). Disparities were consistent in services requiring counseling even when controlling for site of care. These data raise the question of what specific factors account for the low performance in these hospitals. Are they understaffed? Are they underfunded? Is the staff less competent or inadequately trained? Are there issues influencing their performance that can be easily addressed?

Disparities occur with respect to having a usual source of care and have persisted for the past 20 years. Latinos are most likely to not have a usual source of care as compared to Asians, African Americans, and non-Hispanic White Americans, respectively 31 %, 19 %, 17 %, and 14 % (National Center for Health Statistics 2007). These findings suggest that disparities occurring in the ambulatory environment could be better addressed if each patient were seen in a patient-centered medical home that provided continuity of care in a team-based setting. The availability of patient-centered medical homes will become even more limited as legislation increases access to insurance because of the growing shortage of primary care providers.

### Impact of Health Insurance on Health Disparities

Access to health care—the oft-quoted distinguishing factor for achieving health—encompasses not only a place of care and quality of care as discussed above, but also the wherewithal to pay for that care. The influence of healthcare payment options cannot be minimized. Private health insurance developed in the United States to help consumers manage the increasing effectiveness and rising costs of hospital care in the first half of the twentieth century. A few hospitals provided plans for certain employee groups in their own communities in the late 1920s and led to the establishment of statewide Blue Cross hospital insurance policies by the American Hospital Association allowing free choice of hospitals by the late 1930s. Because of the limitations the Great Depression imposed on what individuals could pay out of pocket, the California Medical Association established the first Blue Shield plan that extended healthcare coverage beyond the hospital to cover physician services in 1939 (Starr 1982; Bodenheimer and Grumbach 1995).

Public programs (municipal hospitals, dispensaries, state-supported hospitals) became the primary sources of care for the poor. The passage of legislation to create Medicare for the elderly and Medicaid for the poor in 1965 transformed health care by providing public insurance payments for privately operated health services in the United States (US Census Bureau 2000). In 1997, the federal government created the State Children's Health Insurance Program (SCHIP) to cover uninsured children in families with incomes at or below 200 % of the federal poverty level, but above the traditional Medicaid income eligibility level. This 1997 legislation was the first substantial increase in coverage that had occurred since 1965—although increasing the responsibility of individual states to craft effective programs.

Health insurance has become a significant factor in understanding health disparities among different population groups. Those without health insurance are divided into two major categories—the unemployed uninsured and the employed uninsured, which is about three quarters of the uninsured (US Census Bureau 2000). Health status is substantially less for the medically uninsured than for their insured counterparts. Higher rates of hypertension and cervical cancer and lower survival rates for breast cancer are found among the uninsured than among those with health insurance. Furthermore, the uninsured have less frequent blood pressure screenings, Pap smears, and clinical breast examinations (Ayanian et al. 2000; National Center for Health Statistics 2009).

There is no guarantee of access to care for the poor, however, because Medicaid pays physicians far less than does Medicare or private insurance (Iglehart 1999). Therefore, between 25 and 50 % of private practice physicians do not accept Medicaid, varying by state and specialty. Furthermore, private health insurance does not guarantee financial access to care because of limitations in services covered (Bodenheimer 1992). More than 30 million people have private health insurance that leaves major expenses uncovered in the event of serious illness (Shearer 1998).

The higher mortality experienced by older African American adults when compared to White older adults has been attributed to health behaviors, socioeconomic status (SES), health status, and health insurance. SES has been found to account for a substantial proportion, but not all, of the racial differences in mortality. Thorpe et al. (2012) have demonstrated racial differences in self-related health—probably reflecting severity of chronic conditions and overall disease burden. They also showed that fewer African Americans than Whites (men 61.1 % vs. 91.2 %; women 66.8 % vs. 92.6 %) had supplemental health insurance, which likely contributed to the excess risk of cancer mortality for African Americans. Thorpe's and previous work showed that cancer survival is higher and all-cause mortality lower in people with private insurance or comprehensive health care (McDavid et al. 2003; Roetzheim et al. 2000).

Although Whites have a higher prevalence of cancer than African Americans, they have lower cancer-related mortality. In a study of adherence to guideline-directed treatments for breast cancer, significant predictors of non-guideline chemotherapy included lack of insurance, Medicaid insurance, high poverty areas, and treatment in hospitals not certified by the Commission on Cancer (Wu et al. 2012). When evaluating outcomes of breast cancer treatment relative to the type of hospital in which care was received, substantial differences are evident. The time between diagnosis and surgery was longer in safety net hospitals for all patients regardless of insurance source. Medicaid insured and uninsured women were approximately 20 % less likely to receive reconstruction than White women (Bradley et al. 2012).

Disparities in outcomes of many chronic diseases are seen relative to health insurance status. Outcomes of neurosurgical procedures vary according to insurance status, such that postoperative complications among Medicare and uninsured patients were higher than what was seen among privately insured patients (El-Sayed et al. 2011). African Americans have undergone urgent/emergent surgery more often than Whites. When retrospectively analyzing inhospital mortality, African Americans demonstrated significantly increased mortality risk after controlling for age, sex, and comorbidities (Schneider et al. 2011). These findings suggest that factors beyond insurance status and medical comorbidities result in racial disparities in health.

In one study of newly diagnosed breast and colorectal cancer patients, the most commonly identified barrier to care included a lack of social support, insurance/financial concerns, and problems communicating with healthcare providers (Hendren et al. 2011). The care of these populations has been found to respond positively to patient navigators—clinicians or clinically trained lay patient partners who facilitate access to care for patients with chronic illnesses.

One issue that needs to be explained further is the potential overlap of factors relative to insurance status and those related to provider decision-making. How do the structural limitations physicians face influence their decision-making? The literature is replete with evaluations of how use of the electronic medical record influences decision-making and the physician–patient relationship, but less is known about the extent of the overlap and ways to change how insurance status influences care. This question may be moot or require reframing as the Patient Protection and Affordable Care Act of 2011 is implemented.

### Infant Mortality

Disparities in outcomes of pregnancy and neonatal health and well-being have been well known for some time—often involving factors extant long before the mother begins the labor process. Infant mortality rates (IMRs) vary dramatically according to racial and ethnic groups and age of the mother. The federal Department of Health and Human Services set a goal of an IMR of less than 7 per 1,000 births more than 30 years ago. The IMR for non-Hispanic Whites was 6 per 1,000 whereas that of Latinos overall was 7.6 and 8.9 for Puerto Ricans, 9 for American Indians, and 14.2 for African Americans. It has become more clear over time that race alone does not explain these disparities. Characterization of the institutional factors influencing maternal and birth outcomes suggest the need for more focused interventions.

In Allegheny County, Pennsylvania, neonatal, postnatal, and IMRs have been consistently three or more times higher in African Americans than in non-Hispanic Whites. Potentially the differences were thought to relate to lack of prenatal care. The rates of mothers receiving prenatal have been increasing during the past 20 years, yet still persistently lag behind for African American and Latina mothers. There are variations in and among racial and ethnic groups. Latinas from Puerto Rico tend to have poorer maternal and neonatal outcomes than those from Cuba. Japanese mothers do better than do mothers from Southeast Asia.

Recognizing the neonatal and maternal disparities that are present nationally and the increased burden upon mothers and their children, a model prenatal support program was launched in several states—to include Pennsylvania in 1991—the Healthy Start Program. This program was designed to (1) increase the numbers of women receiving prenatal care, (2) reduce behavioral risk factors, (3) improve family and community support for pregnant women and women with infants, and (4) increase public awareness of devastating effects of infant mortality and its contributing factors. Over the next 10 years, the program demonstrated a 30 % decrease in IMR in the six communities initially targeted in Pittsburgh. When mothers were case-managed by the Healthy Start Program, the decrease in IMR was 40 %. The elements of this program addressed areas often under-resourced because of socioeconomic factors.

Health disparities are consistently associated with the lowest income and least educated segments of the population, as well as with racial and ethnic minorities (Braveman et al. 2010). The Healthy Start Program demonstrated the importance of addressing multiple aspects of care in vulnerable populations and the resulting benefits in improving maternal health and decreasing infant mortality.

Much discussion has centered on whether maternal–child health disparities potentially reflect gaps in the education of the mothers. However, a study comparing White, Black, Hispanic, and American Indian mothers according to educational achievement—i.e., completion of 12 or fewer years of education vs. completion of 16 or more years of education—found that the IMR of African American mothers with a college degree was still higher than that of non-Hispanic White mothers with less than a high school diploma (Mathews et al. 2000). So, educational achievement alone does not guarantee health. If institutional or provider factors exist, their impact can supersede the benefits of maternal education. Many, but not all, of the aspects of care that need to be addressed come within the scope of the healthcare system. Often the factors impacting health are socioeconomic and/or environmental.

## Patient-Level Factors

Patient behaviors (such as tobacco use, diet, and exercise patterns) impact risk for many chronic diseases for which there are significant disparities (such as cardiovascular disease, diabetes, and hypertension). Patients also have the power to choose one clinician over another and make that decision based upon a variety of perceptions. A survey of a diverse group of participants regarding their perceptions of their physicians' participatory decision-making style revealed distinctly different perceptions (Cooper-Patrick et al. 1999). African Americans rated their visits with physicians as less participatory than did non-Hispanic White participants. They noted that patients who saw physicians of their own race rated those physicians' decision-making styles as more participatory than racially discordant physician–patient relationships. Thus, improving cross-cultural communication between primary care doctors and patients and providing patients with access to a diverse group of doctors may improve adherence, satisfaction, and health outcomes. Other surveys of communication characteristics between patients and physicians corroborate that minorities face a greater difficulty in communicating with physicians with 33 % of Hispanic patients noting these problems, but 27 % of Asians and 23 % of African Americans also noting communication difficulties (Fund 2012).

Patients' perceptions drive their choices for clinical care. Recognition of the existence of health disparities was revealed in a Henry J. Kaiser Foundation focus group survey. Perceptions of factors that erode patients' trust were: (1) Lack of time and attention given by healthcare professionals; (2) Perceived lack of concern and empathy; (3) Perceptions that desire for profits drive medical decision-making; (4) Perceptions that managed care plans aren't designed to protect patient interests; and (5) Perceptions that many healthcare providers hold negative stereotypes of minority patients (Henry J. Kaiser Family Foundation 1999). These factors describe the level of patient satisfaction or dissatisfaction.

However, having supplemental clinical services available does not guarantee these services will be embraced by the target population. Recent studies have addressed the dilemma of patient nonadherence to recommended therapy. When diabetics were studied (n = 1,823), one-quarter endorsed intrapersonal adherence barriers and 23 % restricted care due to cost (Mackey et al. 2012). The movement for "patient-centered" care has stimulated the relabeling of this problem from "noncompliance" to "nonadherence" and discussion of "concordance" and "shared decision-making" with patients. Policy experts have described the importance of embracing the patient perspective to achieve better health outcomes as "patient empowerment." This concept includes patient self-reliance and often also reflects a mistrust of medications and medical practice (Bezreh et al. 2012). Furthermore, an assessment of articles published between 2005 and 2010 showed a significant link between treatment satisfaction and adherence, compliance, or persistence. Greater treatment satisfaction resulted in better compliance and improved persistence, and with lower regimen complexity and treatment burden (Barbosa et al. 2012).

The earlier case study suggested that the teen patient wanted many family members with her in labor not only for support, but also for protection. These additional family/friend/advocates could stimulate/demand a higher level of attention from the medical and nursing staff. Barr and Wanat (2005) studied low-income, ethnic minority patients to identify cultural and linguistic characteristics that impede access to care. Patients from all groups saw non-physician staff as frequently impeding access—sometimes even exhibiting hostility at physicians' efforts at prevention and patient education. When the physician in the case study returned to the labor room following insertion of the epidural, many of the friends and family were gone. The response was that they now felt comfortable going home and leaving just the young mother's parents because they were convinced that the physician on duty was genuinely concerned about the health of their loved one. They had not felt that assurance from their initial contacts with the clinical staff.

## Provider-Level Factors

There is a growing body of evidence that providers treat diverse patients differently. In the early and mid-1990s, studies of pain management of patients presenting to emergency departments with long bone fractures showed that African American and Latino patients received less analgesia even when clear diagnoses of acute fractures existed (Todd and Hoffman 1993; Todd et al. 1993, 2000; Green et al. 2003). Later work confirmed these disparities in pain management. There were racial and ethnic disparities in pain perception, assessment, and treatment in all settings (e.g., the emergency department, postoperatively) and across all types of pain (Green et al. 2003).

The now classic study by Schulman et al. (1999) demonstrated that primary care physicians asked to diagnose and treat patients presenting with cardiac symptoms were more likely to have a comprehensive work-up and definitive treatment if they were male and non-Hispanic White. When the diagnosis of coronary artery disease was made in another study, minorities were less likely than majority participants to be counseled regarding secondary prevention measures (Leape et al. 1999; Kravitz 1999).

A similar lack of counseling on secondary prevention was demonstrated in a population of women diagnosed with osteoporotic fractures in an emergency department. It is not surprising to note that mortality rates across the lifespan reveal higher mortality rates for all minority groups in comparison to non-Hispanic Whites except for Asian Americans (Henry J. Kaiser Family Foundation 2003; Miniño et al. 2002).

In addition to variation in provider decision-making related to the race/ethnicity of the patient, there is evidence that the SES of the patient drives how physicians make management decisions. Physicians note they face personal and financial strains when caring for low SES patients (Bernheim et al. 2008). As a result, some areas that appear to have adequate numbers of physicians are limited in those willing to care for low-income patients.

## Interpersonal-Level Factors

Increasing evidence of personal decision-making impacting health and health care exists with respect to patient decision-making as well as physician/clinician decision-making. Patients note a different relationship with their physicians depending on whether there is racial and/or ethnic and gender discordance between them and their physicians. When physician–patient racial concordance existed, patients noted physicians exhibiting more participatory decision-making style with patients (Cooper-Patrick et al. 1999). Presumably improved cross-cultural communication in the doctor–patient relationship could positively influence healthcare decision-making and ultimately result in improved health status. Minorities, especially Hispanic Americans, Asian Americans, and African Americans, note greater difficulty in communicating with physicians (The Commonwealth Fund 2001). Similar findings have been documented during focus groups held with consumer groups (Henry J. Kaiser Family Foundation 1999).

## Environmental Factors

In addition to being influenced by socioeconomic factors, certain segments of the population are overrepresented in their exposure to environmental factors. Minority populations are more likely to be exposed to higher levels of commercial waste. Furthermore, minority status is more predictive of being exposed to commercial hazardous waste than income, house value, or number of waste sites. Three of every five African Americans and Hispanic Americans live in communities with uncontrolled toxic waste sites (15 million African Americans and eight million Hispanic Americans) (Johnson and Coulberson 1993). More than 17 % of American children suffer from lead toxicity. Of those suffering, 40 % are African American, 15 % are Mexican American, and 20 % are Puerto Rican. Consequences of this toxicity are delayed cognitive development, reduced intelligence, impaired hearing, impaired Vitamin D and calcium levels, and retarded growth (Johnson and Coulberson 1993).

Racial disparities in health cannot be explained solely on the basis of poverty, access to health care, behavior, or environmental factors. Genetics are gradually being recognized as influential in health status and to vary by race. Most common variants in genes exist in all human populations, but their frequency can vary substantially so that individuals or groups can be more or less susceptible to particular environmental exposures (Olden and White 2005). These findings have resulted in a highly publicized analogy, "genetics loads the gun, but the environment pulls the trigger."

Environmental toxicities are known to directly influence asthma morbidity and mortality. Of the approximately 4,000 deaths per year in the US from asthma, African Americans are disproportionately represented. African Americans are generally undertreated when compared to non-Hispanic Caucasian Americans, have more comorbid conditions than do Caucasian Americans, and are more obese although having lower birth weight.

Mexican American children have the lowest asthma rates among Latinos—except in central California where one in five have had an asthma attack. Many Mexican immigrant farm workers have relocated to the San Joaquin Valley—an area known for agricultural produce, extreme poverty, and poor air quality (Schwartz and Pepper 2009).

Much work needs to be done with respect to clarifying whether there is a genetic component to the disparities seen in asthma. There has been an increase in the number of genetic population studies of Caucasian and Asian patients in the past 10 years, but few studies of African American patients. There are low asthma rates in general in Latino populations, but there is variability in prevalence. Asthma prevalence is 5 % in Mexican Americans and 17 % in Puerto Ricans (Reibman and Liu 2010). More research is needed in teasing among the complex interactions between environmental exposures, socioeconomic factors, and genetic variations.

### Neighborhood Poverty

Two studies recently unveiled by the Joint Center for Political and Economic Studies showed that 1 in 11 residents of metropolitan areas now live in communities where at least 30 % of their neighbors are poor (Pendall et al. 2011; LaVeist et al. 2011). The studies underscore the linkage between poverty, racial segregation in metropolitan neighborhoods, and the health of the people who live in them. Other authors have used five measures of healthcare use and the racial composition of the zip code to assess the impact of residential segregation. The findings suggest that disparities in healthcare utilization are related to both individuals' racial and ethnic identity and the racial composition of their communities (Gaskin et al. 2012). These studies emphasize how powerful quality of education, health care, available food, public infrastructure, and exercise options are in shaping opportunities to achieve good health.

These conclusions built on the earlier work of Williams and Collins (2001) that described racial residential segregation as a fundamental cause of racial disparities in health. Segregation can directly influence SES by determining access to education and employment opportunities. Furthermore, segregation creates conditions hostile to health in the social and physical environment.

### Impact of Healthcare Reform on Disparities

Following the implementation of the Patient Protection and ACA—originally passed in March 2010—disparities may worsen if the burden of care in safety net hospitals increases without commensurate increases in reimbursement and staffing levels (Bradley et al. 2012). Of potentially greater significance, however, are the current workforce assessments revealing that there will be insufficient numbers and locations of primary care clinicians to accommodate the increased coverage of the formerly uninsured with the new legislation (Phillips and Bazemore 2010).

Many questions arise—(1) Who will care for the increased population of insured? (2) Will use of advanced practice non-physician professionals create another two-tiered system that exacerbates the factors influencing health disparities? (3) How will the cost savings be determined and who will be the "haves" and the "have nots?" (4) Could the potential of a tiered system be avoided by incentivizing clinicians to care for a diverse group of patients and not one socioeconomic group (Petterson et al. 2011)? (5) What other public service changes will be needed to ensure improved population health—e.g., linked transportation availability?

## A Way Forward

To begin to reduce health disparities as we implement healthcare reform, we must address who will provide the care, where the care will be provided, and what constitutes essential, recommended, and optional types of care.

### Who

Physicians must remain fully integrated into the delivery of primary and preventive care. The excellent clinician is one most experienced in distinguishing sick from well. There is no substitute for experience and it cannot be replaced by abbreviated, short courses or on the job training. Those with less training and experience are valuable partners in care delivery as long as their efforts are integrated and managed by experienced, in depth clinical leadership. This translates into integrated team-based care—not just those who share the same electronic record, but rather those who meet, collaborate, and make decisions together over time. The proportions of which team members fall into which category are not clear, but physicians, dentists, nurses, social workers, pharmacists, nutritionists, and health educators are all necessary. Incentives should be tied to how well the team addresses the complete healthcare needs of a defined population and how closely they engage and partner with that population (visits, phone contact, group encounters, etc.).

### What

Care must be based upon matching patients with a patient-centered medical home where the patient is known, the patient can name his/her provider, and first-contact, continuous, compassionate, collaborative, and comprehensive care is available. That care should address physical, behavioral, oral, and preventive health components of the patient's needs. Secondary services such as radiologic, lab, and other diagnostic services should be accessible—either within the primary care environment or regionally located to minimize additional barriers to adherence. One model of such an approach is the federally qualified health center—originally designed to provide comprehensive primary care for those whose access to care was limited by low socioeconomic resources. But a comprehensive, interdisciplinary care management approach is valuable to all patients—not just the financially disadvantaged patient.

### Where

To achieve such a comprehensive suite of services, the patient should not be shuttled to multiple, disconnected sites. There must be seamless interface between the primary care services and tertiary care services when they become necessary. Hospitalizations should not constitute an adventure into an unknown realm where there is no communication between the clinicians providing the primary care and those engaged to address acute, more urgent, or serious complaints.

There is no better summary of the requirements for achieving a healthy population than Terris's words that not only health care—preventive care and medical treatment—but also

> Full employment and adequate family income; improved working conditions; decent housing, including the elimination of urban and rural slums and the grim spectacle of homeless Americans; effective protection from environmental discomforts such as excessive heat and cold, smog, noise, and noxious odors; good nutrition that will foster optimal physical and mental development; increased financial support to public education and elimination of financial barriers to higher education; improved opportunities for rest, recreation, and cultural development; greater participation in community activities and decision-making; an end to discrimination...based on race, gender, age, social class, religious belief, national background or sexual preferences; and freedom from the pervasive fear of violence, war, and nuclear annihilation. (Bodenheimer and Grumbach 1995).

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Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_15

# 15. Considering the Role of Social Determinants of Health in Black–White Breast Cancer Disparities

Darrell L. Hudson1  and Sarah Gehlert1

(1)

Washington University in St. Louis, Brown School of Social Work, St. Louis, MO 63130, USA

Darrell L. Hudson

Email: dhudson@wustl.edu

Sarah Gehlert (Corresponding author)

Email: sgehlert@wustl.edu

## Introduction

Health disparities in the United States occur in screening, diagnosis, incidence, mortality, and treatment across a variety of diseases and conditions. Disparities occur by race and ethnicity, socioeconomic position (SEP), geography (rural versus urban), and other factors. By far, the most attention has been paid to health disparities based on race and ethnicity.

In response to growing evidence of group differences in health made possible by refinements in the 1990 census, President Clinton launched his Racial and Ethnic Health Disparities Initiative in 1998 (Satcher 1999). Although we have become adept at characterizing health disparities since that time and have made progress in characterizing and understanding the determinants of disparities, we have made very little progress in eliminating them at the population level. The gap between White and certain racial/ethnic minority populations actually has grown for a number of conditions, including a number of different cancer types.

In this chapter we will use well-characterized disparities in breast cancer mortality between Black and White women to illustrate how knowledge of the determinants of racial and ethnic breast cancer disparities that occur at multiple levels of influence—from the microbiological to the societal—might be used to develop interventions to eliminate those disparities. In doing so we draw upon the work of the Center for Interdisciplinary Health Disparities Research (CIHDR) at the University of Chicago, which was one of the eight Centers of Population Health and Health Disparities (CPHHD) during CPHHD's first phase of funding, to illustrate our points. We emphasize the social determinants of health disparities because they have received the least attention to date and because growing evidence suggests that their contribution may be more significant than previously thought.

## Black and White Disparities in Incidence and Mortality

Although White women are more likely to develop breast cancer, Black women are more likely to die from the disease. Data from the National Cancer Institute's Surveillance Epidemiology and End Results (SEER) demonstrate that 126.5 White women per 100,000 are diagnosed with breast cancer every year versus 118.3 per 100,000 Black women. However, an examination of breast cancer mortality reveals that 23.4 per 100,000 White women die per year versus 32.4 per 100,000 Black women (Alterkruse et al. 2010). Likewise, the 5-year relative survival rate for White women is 90 %, compared with 77 % for Black women (American Cancer Society 2009).

Black and White women in the United States also differ with regard to the age at which they develop breast cancer. Black women are more likely than White women to develop breast cancer before menopause. This mirrors the pattern of premenopausal development of breast cancer seen in West Africa and other parts of the developing world (Bhikoo et al. 2011; Gukas et al. 2006).

According to Lisa Newman (2005), 30–40 % of Black breast cancer patients in the United States are younger than 50 years, compared with 10 % of White breast cancer patients. Anderson et al. (2008) refer to this as a crossover effect, in which breast cancer incidence is higher among Black women younger than 40 years of age than White women in the same age group but lower among Black women 40 years of age or older. As might be expected from the Black–White disparity in breast cancer mortality outlined above, Black women are twice as likely to die from breast cancer diagnosed before menopause than White women (Lund et al. 2008; McClintock et al. 2005).

## Determinants of Black–White Cancer Disparities

Although a clear understanding of the determinants of health disparities has not been developed, a scheme developed by McGinnis et al. (2002), based on a review of hundreds of empirical articles about early deaths in the United States, serves as a useful framework. In this scheme about 40 % of early deaths can be accounted for by behavioral patterns, 30 % by genetic predisposition, 15 % by social circumstances, 10 % by environmental exposures, and 5 % by shortfalls in medical care.

This distribution of determinants likely holds true to some extent for disparities related to cancer diagnosis. A number of behavioral patterns are linked to breast cancer incidence, including smoking (Terry and Rohan 2002), physical inactivity (McTiernan et al. 2003), failure to use screening mammography (Kalager et al. 2010), and obesity (Morimoto et al. 2002). Differences between Black women and White women in some of these behaviors have been posited as contributors to the Black–White disparity in breast cancer mortality. Cui and colleagues (2002) concluded that a higher rate of obesity in Black women compared with White women helps explain more advanced stage of breast cancer at diagnosis among Black women, which is associated with increased mortality. Although they are not linked directly to breast cancer, differences in rates of smoking and physical activity between Black women and White women have also been implicated in this breast cancer disparity (Crespo et al. 2000; Davis et al. 2007). Conversely, according to the Centers for Disease Control and Prevention (CDC), the rates of screening mammography for Black and White women do not differ significantly from one another (CDC 2010).

Breast cancer also has been linked to social circumstances. For example, later stage breast cancer at diagnosis is more common among women living in census tracts with higher percentages of residents below the federal poverty line (Campbell et al. 2009) and a higher concentration of immigrants (Cho et al. 2011). Likewise, Gehlert et al. (2011) found features of the built environment to be associated with tumor characteristics linked to more aggressive breast cancers among Black women. Whitman et al. (2011) found higher mortality among Black women living in Chicago to be associated with poorer quality of mammography in medically underserved areas.

Genetic propensity also plays a role in Black–White disparity in breast cancer mortality. Although most cancers, including breast cancer, do not occur due to inherited mutations of cancer genes (Grönberg 2003; Lynch and de la Chapelle 2003; Olopade et al. 2003; Schwartz and Ruckdeschel 2006), sporadic mutations that occur across the life course, perhaps occurring epigenetically, are thought to make a significant contribution. Epigenetic mutations are associated with long-term changes in DNA that do not involve changes in the DNA sequence and may develop because of exposure to adverse physical and social conditions (Esteller 2008). Gehlert and colleagues (2011) found changes in the expression of estrogen and progesterone receptors among Black women living in unsafe housing in areas with high rates of crime. Other studies have linked higher rates of breast cancer to exposure to chemicals such as bisphenol-A (Doherty et al. 2010).

Black–White disparities in breast cancer mortality also have been attributed to stage of breast cancer at the time of diagnosis (Sassi et al. 2006), whether women obtain follow-up after diagnosis (Yabroff et al. 2004), ability to access screening and treatment (Peek and Han 2004), tumor biology (Carey et al. 2006), and response to treatment (Chavez-MacGregor et al. 2010; Dawood et al. 2009), as well as interactions among these factors (Gehlert et al. 2008; Masi and Olopade 2005; McClintock et al. 2005). A clear understanding of exactly how these factors interact to explain differences in mortality remains elusive, however.

## Social Determinants of Health

Public health researchers increasingly encourage the use and consideration of social determinants of health in understanding population health and racial/ethnic health disparities. The social determinants of health most frequently examined are race/ethnic status, social class or SEP, and gender. Each of these determinants plays a critical role in the perpetuation of Black–White breast cancer mortality disparities, and the complex interaction between these factors is manifest in the everyday lives of Black women. Next, we outline how factors such as race/ethnic status, social class or SEP, and gender may intertwine to produce the alarming disparities in breast cancer mortality that we witness in epidemiologic surveys.

### Racial/Ethnic Status

The general consensus among public health researchers in the United States is that race, as typically measured in research studies, is a social construct. As of yet no specific genetic markers explain the existence of large scale Black–White breast cancer mortality disparities, and although we don't exactly know what race means from a genetic perspective (Kittles et al. 2007), women who are identified as Black suffer a disproportionate burden of breast cancer mortality.

What is it about race that could contribute to such disparities? Advantage and adversity in the United States are patterned by race/ethnicity. An examination of factors responsible for social and economic disparities likely is essential in the elimination of Black–White health disparities, as is the development of policies to eliminate those factors. A social determinants framework compels researchers and interventionists to explore the social and environmental stressors that have been patterned by race/ethnicity.

Evidence from studies that examine the relationship between race and health indicate that perceived racial discrimination is a unique stressor that Black individuals encounter (Clark et al. 2002), and researchers have found empirical associations between perceived racial discrimination and impaired psychological well-being, depression, and decreased self-esteem (Karlsen and Nazroo 2002; Williams et al. 1992, 1997). There also is ample evidence to support a relationship between experiences of discrimination and heightened blood pressure and hypertension (Din-Dziethan et al. 2004; Krieger and Sidney 1996). Kessler et al. (1999) argue that the experience of racial discrimination is highly stressful, ranking in significance with other major stressful life events such as job loss, divorce, and death of a loved one.

Racial residential segregation has been described as the "structural lynchpin" of racial relations in the United States (Bobo 1989; Farley and Frey 1994; Massey and Denton 1993). Racial residential segregation is historically rooted in practices such as redlining and residential steering but also in insidious restrictive residential covenants that barred Black families from buying homes in predominantly White neighborhoods (Gordon 2008; Massey and Denton 1993). In an exploration of the implications of residential segregation for health outcomes among Black people, Williams and Collins (1995) in a review of studies of SES, race, and health outcomes, concluded that residential segregation contributes to health inequalities by shaping socioeconomic mobility and socioeconomic conditions across multiple levels including individual, household, neighborhood, and community.

Racial residential segregation is largely responsible for many Black individuals and families residing in neighborhoods with reduced access to equitable services and institutions, ranging from full-service grocery stores to high-quality public schools and libraries (Massey and Denton 1993; Williams and Collins 1995, 2001), the lack of which likely contributes to the perpetuation of Black–White health disparities. Importantly, researchers have noted neighborhood effects on health independent of individual SEP markers. Balfour and Kaplan (2002), for example, found that negative neighborhood attributes such as excessive noise, inadequate lighting, and heavy traffic were associated with loss of physical functioning in later life.

Although home ownership accounts for about 60 % of the total wealth of the average US family, Black homeowners actually are financially penalized for owning a home because their homes are more likely to be located in segregated, less affluent neighborhoods (Charles 2003; Oliver and Shapiro 2008; Shapiro 2004). Yet even Black individuals who can afford homes in integrated neighborhoods may encounter residential steering. Despite efforts by the Federal Housing Administration (FHA) to outlaw residential steering and discriminatory loan practices, audit studies (i.e., studies that use a matched-pair technique to compare the treatment of equally qualified Black and White homebuyers) still find that Black homebuyers are more likely to experience steering and are more likely to be denied home mortgages (Ondrich et al. 2003; Yinger 1986, 1995). Williams (1999) says that Black individuals tend to receive poorer educations, work in more hazardous jobs, are paid lower salaries despite equivalent levels of education and work experience, and experience less wealth and purchasing power at equivalent income levels compared with their White counterparts. At every level of education, Black workers tend to earn lower levels of income compared with White workers. The discrepancy in returns on human capital investment of Black workers, compared with their White counterparts, also may be a unique source of stress and alienation for Black Americans. Persistent levels of racial residential segregation have important implications in the perpetuation of inequalities in wealth as well as in health disparities between Black and White individuals.

### Social Class/Socioeconomic Position

Williams and Collins (1995, p. 350) wrote, "class has proven to be remarkably robust in elucidating the complexities of social and historical processes and in predicting variations within and between social groups in living conditions and life chances, skill levels and material resources, relative power and privilege." Health status is one area in which the effects of class appear. As previously discussed, the consideration of wealth may be particularly important when examining the relationship between SEP and health among Black Americans. Substantial, historically rooted disparities in wealth exist between Black and White Americans. The net worth of typical White families is $81,000 but just $8,000 for a typical Black family (Shapiro 2004). This equates to Black families possessing only 10 cents for every dollar of wealth held by White families. In addition, Oliver and Shapiro (1995) report that 63 % of Black households retain zero or negative net financial assets compared with the 28 % of White households that report they have no financial assets or that they are in debt. Black–White wealth disparities in the United States may imply a differential manifestation of socioeconomic status between the two races (Krieger et al. 1997; Shapiro 2004; Williams and Collins 1995). White workers earn more pure income than Black workers even when holding levels of education and experience constant (Shapiro 2004). Subsequently, income inequalities are also related to the perpetuation of wealth disparities, because Black families do not have as much disposable income to invest into mechanisms that could generate wealth and may not be able to afford homes that would yield greater returns on their investments.

When researchers fail to account for wealth, it is possible that important information about SEP is omitted from studies. Williams and Collins (1995) argue that SEP indicators are not equivalent across race and that this inequality could be one potential explanation for the persistence of racial differences in health. More specifically, the most commonly used indicators of SES, which are education, income, and occupational status, do not adequately capture economic status differences between households of different races. Thus, many researchers prefer the term SEP when making Black–White comparisons. Racial differences in wealth are much larger than those for income (Krieger et al. 1997; Shapiro 2004; Williams and Collins 1995). So although some Black individuals have levels of education, income, and even occupational status indicative of a stable middle-class status, there likely are underlying differences in wealth that ultimately affect overall levels of SEP. Wealth disparities between Black and White people are large, pervasive, and widely documented, yet very few studies have thoroughly investigated the relationship between wealth and SEP and health among Black people.

Black–White wealth disparities alter the quantity and quality of social and cultural capital among Black people, even those of seemingly higher SES. For instance, Williams and Collins (1995) argue that there are profound differences in the quality of elementary and high school education for Black versus White students, such that Black individuals bring fewer basic skills to the labor market than do their White counterparts. Cultural capital is another form of inheritance that typically affords White families with greater financial assets unique advantages that can be used in school, business, and social settings (Shapiro 2004). Annette Lareau's (2003) ethnographic work also addresses the cultural inheritance that families with higher SES pass along to their children. As described in her book Unequal Childhoods, Lareau reports that middle-class children, Black and White, have a greater sense of entitlement, have much wider vocabularies, and are more likely to be encouraged to engage in conversations with authority figures such as doctors and teachers. Conversely, poor and working class children are given substantially more directives, are less often encouraged to engage in decision making, and participate in far fewer organized activities than middle-class children. Furthermore, the poor and working class parents discussed in Lareau's work are often less empowered and engaged in critically important settings, such as education and health care, thus limiting their participation in interactions with doctors and teachers as well as their ability to advocate for their children. It is possible that these attitudes and behaviors permeate across generations.

Healthcare access as part of one's socioeconomic resources is another factor that may contribute to Black–White health disparities, especially breast cancer mortality. The United States primarily has an employer-driven healthcare system, and Black workers are more likely to be employed in positions that do not include healthcare benefits as part of compensation. These two factors together may help explain why breast cancer is discovered in Black women at later stages of disease. It is possible that lack of healthcare coverage prevents Black women from seeking breast cancer screening or paying for necessary treatment and follow-up examinations. Although mammography services are provided through the Breast and Cervical Cancer Program, there is evidence that the quality of mammography is lower in medically underserved areas (Whitman et al. 2011).

The consideration of social capital is important in linking social and economic disparities to Black–White health disparities. Kawachi and Berkman (2001) argue that social capital is external to the individual and inheres in the structure of social relationships and social capital at both the individual and community levels. They argue that could help explain disparities in health across race and class. One is capable of determining the level of trust and communication within communities based on ecological characteristics or features of the built environment (Kawachi and Berkman 2001)—people who live in more impoverished neighborhoods express more concerns about their own personal safety and that of their loved ones (Williams and Collins 2001). In her ethnographic work in Chicago, Mary Patillo (1998) highlights some of the day-to-day struggles of Black residents of a seemingly stable, middle-class neighborhood. Even in one of the more affluent and organized Black neighborhoods in Chicago, Patillo found that parents were concerned about finding adequate schooling for their children, and she chronicled their strategies to buffer their children from prevalent negative environmental stimuli. In several cases, despite the presence of parents who had provided excellent educational opportunities and the trappings of a "middle-class lifestyle," she found that a substantial portion of Black youth in the community had lost their lives to drug-related violence or had been incarcerated. Numerous respondents in Patillo's ethnographic study noted the declining levels of trust and cohesion among the community's residents due to concerns about safety, drug trafficking, and street gangs within their community. This lack of social capital among urban Black residents may have a tremendous impact not only on the employment and financial outlook of Black Americans but also on their exposure to stressors based on declining levels of trust, lack of social cohesion, and neighborhood disorganization.

Other critical access issues should be considered in addition to the affordability of health care. Black women are often employed in occupations that do not provide fringe benefits, such as paid sick leave, or fail to allow them to take days or hours from their workdays for personal needs without penalty (Angel et al. 2009). In light of current levels of unemployment and economic instability, especially within the Black community, Black women who may be the primary or sole breadwinners within their households may not have the opportunity to seek care simply because clinic hours conflict with their work hours and other responsibilities, such as child care, during the day. Many Black women hold multiple roles within their family structures because they are the primary or the sole breadwinners and caregivers of children and elders (Higginbotham and Weber 1992; Mullings 2002, 2005; Schulz and Mullings 2006). If healthcare systems fail to adapt—for example, by providing childcare or allowing family members to visit the doctor along with the patient—it is possible the demands for Black women's time and responsibilities will preclude them from seeking screening for cancer or getting treatment.

A related access issue is the location of medical facilities, which dictates to some extent the ease with which inner-city residents can travel to clinics for screening and treatment. The ability of some women to be screened for breast cancer may, unfortunately, depend largely on how easily accessible the location of clinics are to where they live or work.

Another important factor to consider is the care and consideration that Black women receive within the healthcare system. Results from numerous studies and reports indicate that Black patients are less likely than White patients to receive timely treatment (Van Houtven et al. 2005) and less likely to be given pain medication for the same injury or disease (Bonham 2001). Discriminatory biases held by clinicians may result in differential treatment or even misdiagnosis (Institute of Medicine 2003). Gaps in understanding between providers and patients may prevent Black women from following medical instructions. This may occur because instructions are misunderstood or because women perceive that they are not respected in the medical setting, resulting in their being less willing to pursue treatment.

### Gender

The role of gender and specific gender roles assumed by Black women likely contribute to differences in breast cancer type and disparities in breast cancer mortality. The intersection of race, gender, and class plays a significant role in the development of health problems for Black women (Mullings 2002). Mullings and Wali (2001) describe the various struggles that pregnant Black women face, including perceptions of neighborhood safety, erratic work schedules, lower quality housing, and job strain. Black women in their study in Central Harlem reported negative neighborhood characteristics across class levels and researchers observed that participants had to battle with hostile landlords and intimidating legal procedures in order to maintain adequate living quarters. With a current marriage rate of only 30 % in Black communities (Western and Wildeman 2009), it is possible that there is a greater burden on Black women for economic and familial stability, compared with White women.

The multiplicative effect of caregiving along with occupational stress, experiences of discrimination and racism, and addressing the financial needs of their households is likely to play a role in the "weathering" of Black women's health over time. The weathering hypothesis, developed by Arline Geronimus et al. (2006), contends that Black women experience premature health deterioration because of the cumulative impact of material hardships, exposure to environmental hazards, stress from leadership roles, frustration with structural level, racial inequalities, and pressure to adopt unhealthy behaviors such as smoking and unhealthy eating. Geronimus also asserts that weathering is apparent in examinations of Black–White health disparities across gender and class designation, and upward mobility of Black individuals does not necessarily translate into improved health status (Colen et al. 2006; Geronimus 2003).

Overall, women have increased likelihood to experience certain stressors, such as being a victim of sexual abuse, than men, which could increase Black women's risk of depression and psychological distress (Nolen-Hoeksema 2001). Black women, in particular, face chronic strains such as making less money than men, increased risk of living in poverty, and increased risk of sexual harassment, all of which may be related to depression (Nolen-Hoeksema 2001).

## The Center for Interdisciplinary Health Disparities Research Model of Health Disparities in Breast Cancer

The CIHDR illustrates a novel model to identify social determinants of breast cancer in Black women, examining each link in the chain of causation in a downward, iterative manner from the population (social) to the disease (genetic) level (see Fig. 15.1). This model uses a social determinants framework rather than solely a disease-specific model. The CIHDR model is unique, however, in its ability to demonstrate important links among determinants along the chain of causation. That chain, vertically oriented, starts at the top with race-related determinants of health. It then covers issues such as concentrated poverty, neighborhood disruption, and neighborhood crime. It then considers isolation, acquired vigilance, and depression. Finally, it moves to stress-hormone dynamics and cell survival and tumor development.

Fig. 15.1

Center for interdisciplinary health disparities research model for health disparities in breast cancer

### Deterioration of Neighborhoods

Exclusion from material resources due to racial residential segregation could result in poorer health outcomes for Black individuals compared with White individuals. For example, in The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy, William Julius Wilson (1987) argues that a significant reason for the development of urban "ghettos" throughout the country is the migration of middle-class Black people from inner-city communities, leaving only poorly skilled, socially isolated, and marginally employed Black residents who do not possess the required social capital to gain legal employment. Szreter and Woolcock (2004) argue that persistent and growing economic disparities have contributed to social isolation, increased anxiety, and diminished health among poorer, inner-city communities. Because Black individuals have lower levels of social capital, they may have fewer resources to cope with stressful situations, which in turn could increase their vulnerability to depression and other psychological impairments. These resources might include access to knowledge, prevention, and treatment of mental health problems. Wilson argues, too, that the exodus from middle-class Black neighborhoods may also mean that fewer well-functioning role models remain.

### Social Isolation

As mentioned earlier in this chapter, social forces related to race, such as discrimination, segregation, and urban inequality, have a direct impact on neighborhoods and how they are used by residents, such as if and how they use public spaces. They further affect social interactions by shaping how people perceive their circumstances, influencing where and with whom they live, and by shaping available resources. These conditions can engender social isolation by limiting the number and types of relationships people hold as well as the frequency of their interactions. Derelict buildings, vacant lots, litter, and excessive traffic interfere with residents' ability to establish and maintain social relationships (Taylor 2001). Understanding social isolation is important for understanding health disparities because of its links to numerous health outcomes.

The quality and content of the built environment of neighborhoods, defined as the buildings, spaces, and products created by people, have a profound effect on health outcomes. For example, deteriorated infrastructures and overall design of buildings affect the formation of relationships and maintenance of collective efficacy (Sampson et al. 1997). Neighborhoods with fewer signs of occupation, fences, and higher speed limits are more likely than others to be burglarized. These issues may cause residents to retreat into their homes, thus limiting interaction and increasing feelings of loneliness. In addition, attempts to deal with threats may deplete people's physical or psychological resources over time.

Evidence from CIHDR animal experiments supports the notion that social isolation affects both social relationships and biology. Using both Sprague-Dawley rat and SV40 Tag transgenic mouse models, CIHDR investigators manipulated the social conditions of animals at various stages of the life cycle and examined the effects on biology. Hermes et al. (2006) found that normally highly social rats that were socially isolated from the time of weaning became hypervigilant to novel phenomena in their environments and developed larger spontaneous mammary gland tumors at a much earlier age than their nonisolated peers. These socially isolated rats also developed a dysregulated stress hormone response compared with their group-housed peers (Hermes et al. 2009), characterized by higher levels of glucocorticoids (stress hormones) after a stressor and slower recovery to baseline. Other work by the group linked glucocorticoid response to mammary tumor development (Cavigelli et al. 2006). Work by the Conzen laboratory with SV40 Tag transgenic mice supported McClintock's findings (Williams et al. 2009).

Disrupted social connections may hamper a person's ability to cope with social and environmental stressors, which in turn can dysregulate physiological processes and affect disease outcomes. Social and environmental conditions can influence psychological states and subsequent physiological stress reactions through the social isolation and the loneliness it engenders. Loneliness has been linked to various cardiac activations, decreased cellular immune function, and increased release of stress hormones (Cacioppo and Hawkley 2003).

### Embodiment and Effects of Accumulated Disadvantage

People vary in their ability to cope with environmental challenges based on genetic, developmental, and experiential factors, including the long-term effect of early-life stress that may predispose people to overreact physiologically and behaviorally. Arline Geronimus and colleagues (2006) found that Black people of all ages have higher allostatic load scores than White people, and these differences were not entirely explained by individual SES, which suggests that the cumulative impact of social and economic adversity can profoundly affect health. For example, Black youth aged 18–24 years were almost 50 % more likely to have higher allostatic load scores than White youth in the same age range. By age 55–64 years, the Black–White relative odds ratio rose to 2.31, indicating that Black adults were more than twice as likely as White adults to show the physiological effects of high-effort coping. The work of CIHDR helps us to understand how this might lead to higher breast cancer mortality among Black women. The group found an association between neighborhood variables and stress hormone response through cluster analysis of the diurnal salivary cortisol measures of the women in their study of Black women newly diagnosed with breast cancer living on the south side of Chicago (Gehlert et al. 2011). Two distinct clusters emerged, one with a typical pattern showing circadian fluctuations and another that was flat. The latter pattern, which was exhibited by 67 % of the women in the study, is analogous to endocrine burnout like that seen in animals that have experienced severe and chronic stressors.

Minority groups, including those of moderate and upper income, often face multiple environmental and social risks. Geronimus et al. (2006) highlights the need to contextualize the experiences of minorities to include more nuanced measures of socioeconomic status as well as other disease risk factors. Given that the social environment shapes a person's stress response, interventions to address racial disparities in health should consider the interaction between individual susceptibility to stress and the environmental conditions that may lead to disease expression.

## Multilevel Modeling of the Determinants of Cancer Disparities

It is clear that the determinants of health disparities occur at multiple levels of influence, from the microbiological to the societal (Crimmins and Seeman 2004; LaVeist 2005; Marmot et al. 1991). Many of the factors outlined in the preceding paragraphs can be assigned to these levels. A model of cancer disparities developed by investigators working across a range of racial and ethnic groups and cancer types during the first phase of funding of the Centers for Population Health and Health Disparities initiative (CPHHD; Warnecke et al. 2008) assigned determinants to proximal, intermediate, and distal levels of influence. Proximal determinants include biological and genetic factors and individual-level factors such as race/ethnicity, gender, and health behaviors. Intermediate-level determinants include the immediate social environments and social relationships in which proximal effects are experienced, such as family and neighborhood. The social context includes access to local healthcare resources and quality of those services and features of the built environment, such as shared public places that promote or impede social interaction. Distal determinants include population-level social conditions like variation in rates of poverty and state and national policies on health care, such as provisions of the Patient Protections and Affordable Care Act that was enacted in March 2010.

## Areas of Intervention

### Decreasing Social Isolation

Black women have markedly higher breast cancer mortality rates than White women. They also are more likely to experience the so-called triple-negative cancers (those lacking receptors for three hormones—namely estrogen, progesterone, and HER2/neu) that develop at a younger age, often before menopause, and are more lethal and aggressive than other breast cancers (Carey et al. 2006). We hypothesize that a number of the so-called upstream factors ultimately produce or at least increase the risk for triple-negative cancers. Identifying these upstream factors will help to target interventions.

In CIHDR investigations, neighborhood characteristics that discourage social interaction may be linked to hormone profiles that have the potential to produce triple-negative tumors. If so, interventions to increase collective efficacy and improve neighborhood safety should reduce breast cancer mortality by disrupting the link between isolation and loneliness and gene expression changes. In her discussion of the effects of urban renewal on inner-city neighborhoods, Mindy Fullilove (2004) suggests offering places for exchange in neighborhoods, not unlike the settlement houses of the early 1900s.

Another avenue for decreasing social isolation in neighborhoods targets the vacant buildings that foster crime and negatively affect the formation and maintenance of social relationships. A number of municipalities have begun efforts to ensure that landlords maintain properties by levying fines and financial disincentives. Examples include Cleveland's Housing Court and Chicago's Troubled Building Initiative. The latter placed 20 properties in receivership during its first 3 years of operation (Keating 2006).

### Early Detection

Factors that interact with cancer type also are potential points of intervention. Because triple-negative cancers grow fast, detecting them at an early stage is key to survival. Although the gap between White and Black women in rates of breast cancer screening has narrowed, it remains that facilities in predominantly Black communities are (a) less likely to offer timely breast cancer screening using state-of-the-art techniques and (b) more likely to misread mammograms than are clinics in more affluent areas (Elmore et al. 2005). Ensuring that inner-city health facilities have up-to-date, well-maintained equipment and that mammographers have access to continuing training and opportunities for consultation should help reduce breast cancer mortality among Black women.

### Including Social Indicators with Clinical Information

Individual risk profiles that capture multiple components of people's social circumstances as well as the subclinical indicators of the physiological effects that flow from these circumstances may serve as effective primary and secondary prevention strategies and help improve individual and population health outcomes. The cross-level effects of individual and neighborhood SES, for example, are significantly related to mortality from a number of diseases (Winkleby et al. 2006). Including this type of cross-level information with clinical information may help providers to design individualized prevention programs and clinicians to choose treatment strategies targeted to patients' specific needs.

### Community Partnerships

Understanding the nature of upstream determinants is best achieved through partnerships with community stakeholders. Amy Schulz and colleagues (2005), for example, worked with community practitioners and stakeholders in Detroit to develop a model of the pathways through which the social and physical environments influence racial and socioeconomic disparities in cardiovascular disease.

Community-based participatory research approaches, which combine research and social change, have also proven useful in devising and testing the effectiveness of interventions in areas such as church-based diabetes prevention (Schultz et al. 2005). Involving stakeholders likewise helps ensure that interventions are "owned" by community members and thus more likely to be sustained through time. CIHDR investigations were facilitated greatly by increasing input from the community (Gehlert and Coleman 2010). In addition, early CIDHR work and that of the CPHHD at the University of Illinois–Chicago helped nurture the development of the Metropolitan Chicago Breast Cancer Mortality Task Force that was the impetus for the enactment of the 2009 Illinois Breast Cancer Disparities Act (PL95-1045).

## Conclusion

Social determinants of health are integral to the access individuals have to resources and affect multiple disease outcomes through different mechanisms (Link and Phelan 1995). Although social aspects are arguably the least understood and studied aspects of breast cancer disparities, they are very complex and play a critical role in the development of breast cancer, and they are manifest in the disparities we witness between Black and White women. We have highlighted the importance of considering the roles of racial/ethnic status, social class, and gender as fundamental determinants of these disparities.

Racial residential segregation is integral in determining neighborhood quality, including safety and social cohesion. Critical resources such as quality of education, widely considered to be the primary vehicle of upward social mobility and a key determinant of individuals' social class and life chances, are distributed at the neighborhood level. In addition, racial/ethnic status is an important factor in the determination of individuals' social class/SEP, which in turn is fundamental to the type and quality of neighborhoods in which individuals reside. Social class can provide or restrict access to resources that may protect against stress and also determines the access individuals are afforded to medical care, including the quality of medical care and even the quality of provider–patient communication. Similarly, the role of gender is important to understand and account for in interventions targeted at elimination of breast cancer disparities. Black women face unique stressors, including balancing roles such as provider and caregiver, contending with issues such as discrimination based on race and sex, and neighborhood safety, in addition to maintaining their own personal health.

We have demonstrated how interventions aimed at addressing social determinants must incorporate proximal, intermediate, and distal levels of influence. In our own work we have found several key levels of intervention that should be considered in addressing breast cancer mortality disparities. Early detection is a key factor in addressing the earlier onset of breast cancer experienced by Black women. To this end, we recommend that health facilities that serve inner-city communities have state-of-the-art, well-maintained equipment and that mammographers have access to continuing training and opportunities for consultation to achieve the most accurate mammography for women who are at risk. In addition, providers must be cognizant of the social and environmental factors that Black patients experience. These factors should be documented in clinical settings as seriously as standard medical risk factors for breast cancer. Social factors play an important yet underestimated role in the development of breast cancer and increased likelihood of mortality among Black women. Social isolation related to Black women's psychological state of felt loneliness, the social resources that they have access to, and the neighborhood factors they must contend may be a formidable determinant of breast cancer disparities. One strategy that could be helpful in increasing rates of early detection, documenting aspects of patients' social environment that may contribute to increased risk, and reducing levels of social isolation among Black women is the development of community partnerships. These partnerships can be beneficial in enlisting support among community members who have a stake in the reduction of breast cancer mortality among Black women as well as factors that could improve neighborhood quality, thus reducing levels of social isolation. Consulting community members can also be important in learning about the barriers that preclude women from engaging in and continuing treatment. It also can help to identify aspects of their social environment that could affect the stressors that Black women face that could increase their vulnerability to developing breast cancer as well as other deleterious health conditions.

To address breast cancer disparities we must simultaneously develop strategies to encourage women at risk of developing breast cancer to get screened and find ways to improve the neighborhoods in which women reside all while seeking policy-level changes geared toward addressing socioeconomic and healthcare disparities. These tasks are no doubt challenging and complex but also necessary if we endeavor to see meaningful changes in breast cancer mortality disparities as well as other health inequities.

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Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_16

# 16. Understanding and Addressing Racial/Ethnic Disparities in Obesity

Monica L. Baskin1

(1)

University of Alabama at Birmingham, Birmingham, AL 35294, USA

Monica L. Baskin

Email: mbaskin@uab.edu

Of all forms of inequality, injustice in health is the most shocking and the most inhumane.

Dr. Martin Luther King, Jr.

## Obesity: A Public Health Problem

The terms overweight and obesity reflect a range of weights that are believed to be heavier than is considered healthy for a particular height and may increase the risk of certain diseases and health conditions (Centers for Disease Control and Prevention 2004). Weight status is typically evaluated using the body mass index (BMI), a calculation comparing one's weight and height that is highly correlated with measures of body fatness for most people (NHLBI 1998; Pietrobelli et al. 1998). The National Health and Nutrition Examination Survey (NHANES) (Centers for Disease Control and Prevention 2004), a database of measured heights and weights from a representative sample of the US population, tracks the national prevalence of overweight and obesity. A review of this data indicates that over the past half-century, overweight and obesity have become a major public health issue in the United States. Rates of obesity (BMI ≥ 30 kg/m2) among adults doubled between 1980 and 2006 (Flegal et al. 2002; Ogden et al. 2006; National Center for Health Statistics 2009). During the same period, rates of youth obesity (≥95th percentile of sex-specific, BMI-for-age growth charts) tripled (Troiano et al. 1995; Hedley et al. 2004; Ogden et al. 2008; National Center for Health Statistics 2009). Currently, about one in three adults and one in five children and adolescents are obese (National Center for Health Statistics 2009).

There are multiple consequences of the apparent national increase in obesity. Health consequences include increased risk of certain cancers, cardiovascular disease (e.g., high blood pressure, heart attack, high cholesterol, other lipid disorders), problems of the endocrine system (e.g., type 2 diabetes, insulin resistance, impaired glucose tolerance, menstrual irregularities), pulmonary complications (e.g., asthma, sleep apnea), orthopedic complications (e.g., bowed legs, hip disorders), gastrointestinal complications (e.g., liver disease), problems with mental health and social interaction (e.g., depression, low self-esteem, discrimination, teasing) (Harris et al. 1998; Mensah et al. 2005; Robinson 2006; Young-Hyman et al. 2006; Jemal et al. 2008; Puhl et al. 2008). Further, adult obesity is associated with excess mortality and a myriad of health problems and overweight children are at greater risk of becoming overweight adults (US Department of Health and Human Services 2001).

In addition to increased health risk, overweight and obesity have negative economic consequences as well. Medical costs associated with overweight and obesity are estimated at over 90 billion dollars annually, with about half of these expenditures paid by Medicare and Medicaid (Finkelstein et al. 2009). Thus, each tax payer contributes about $180/year toward obesity-related medical costs for these public sector health plans. Across all payers, per capita medical spending for the obese is $1,429 more per year (42 %) than for someone of normal weight (Finkelstein et al. 2009). In the absence of obesity, it is estimated that annual medical expenditures could be reduced by 7–11 % (Trogdon et al. 2012). Worker obesity has also been linked with increased costs due to worker absenteeism and reduced productivity (also known as presenteeism). An earlier study using data from 2001 to 2003 estimated that obese workers compared to workers of normal weight, cost US employers an additional $11.7 billion beyond medical expenditures due to absenteeism and presenteeism (Ricci and Chee 2005). However, a more recent investigation using data from 2008, estimated this amount at $30.0 billion (Finkelstein et al. 2010).

## Disparities in Obesity

### Race/Ethnicity

While rates of obesity have increased, in general, across the US population, disparities in rates by certain demographic characteristics exist (Figs. 16.1, 16.2, 16.3, and 16.4). Non-Hispanic Blacks and other populations of color are disproportionately affected (Ogden et al. 2006). Over 80 % of non-Hispanic Black women are either overweight or obese (BMI ≥ 25 kg/m2), compared to 58 % of non-Hispanic White women, and 54 % of non-Hispanic Black women are classified as obese (BMI ≥ 30 kg/m2; about 35 lb of excess weight), compared to 30 % of non-Hispanic White women (Ogden et al. 2006). Further, 15 % of non-Hispanic Black women are considered "extremely obese" (BMI ≥ 40 kg/m2; about 100 lb of excess weight) compared to about 6 % of their non-Hispanic White counterparts (Ogden 2009; Ogden and Carroll 2010). Non-Hispanic Black men are as likely as non-Hispanic White men to be overweight or obese (69 % and 70 %, respectively) (Ogden et al. 2006). However, non-Hispanic Black men are nearly twice as likely to be classified as "extremely obese" (5.4 % vs. 2.8 %) compared to non-Hispanic White men (Ogden and Carroll 2010).

Fig. 16.1

Prevalence of obesity among women > 20 years† (1976–2008). *BMI > 30; †age-adjusted. Source: Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Health Examination Survey (NHES), National Health and Nutrition Examination Survey (NHANES)

Fig. 16.2

Prevalence of extreme obesity** among women > 20 years† (1988–2008). **BMI > 40; †age-adjusted. Source: Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Health Examination Survey (NHES), National Health and Nutrition Examination Survey (NHANES)

Fig. 16.3

Prevalence of obesity* among men > 20 years† (1976–2008). *BMI > 30; †age-adjusted. Source: Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Health Examination Survey (NHES), National Health and Nutrition Examination Survey (NHANES)

Fig. 16.4

Prevalence of extreme obesity** among men > 20 years† (1988–2008). **BMI > 40; †age-adjusted. Source: Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Health Examination Survey (NHES), National Health and Nutrition Examination Survey (NHANES)

Racial/ethnic disparities in the prevalence of overweight and obesity among youth mirror adult trends (Figs. 16.5, 16.6, 16.7, 16.8, 16.9, 16.10, 16.11, and 16.12). Differences between non-Hispanic Black and non-Hispanic White children emerge as early as preschool years and generally widen with age (Ogden et al. 2006). Non-Hispanic Black girls have a higher prevalence of overweight or obesity (BMI for age ≥ 85th percentile) than non-Hispanic White girls. About 16 % of non-Hispanic Black girls age 2–5, 27 % age 6–11, and 25 % age 12–19 are obese (BMI for age ≥ 95th percentile) compared to non-Hispanic White girls, whose prevalence is 10 %, 17 %, and 15 %, respectively (Ogden et al. 2006). Non-Hispanic Black girls are also disproportionately represented among youth with BMI-for-age ≥ 97th percentile (Ogden et al. 2008, 2010; Flegal et al. 2010). Among 2–19-year-old non-Hispanic Black girls, 18 % are estimated to have the higher BMI classification compared to 9 % of non-Hispanic White girls this age (Ogden et al. 2008). For boys, overweight or obesity prevalence among non-Hispanic Blacks and non-Hispanic Whites is similar. An estimated 30 % of non-Hispanic Black boys fall into this category compared to 35 % of non-Hispanic White boys. About 10 % of non-Hispanic Black boys age 2–5, 18 % age 6–11, and 19 % age 12–19 are obese (BMI for age ≥ 95th percentile) compared to non-Hispanic White boys, whose prevalence is 13 %, 19 %, and 19 %, respectively (Ogden et al. 2006). Non-Hispanic Black boys are slightly more likely to have a BMI for age at or about the 97th percentile, with 14 % of youth falling into this category compared with 11 % of non-Hispanic White boys (Ogden et al. 2008, 2010; Flegal et al. 2010).

Fig. 16.5

Prevalence of overweight* among girls age 6–11 years (1976–2008). *BMI > 95th percentile for age and sex based on CDC growth charts. Source: Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Health Examination Survey (NHES), National Health and Nutrition Examination Survey (NHANES)

Fig. 16.6

Prevalence of High BMI* among girls age 6–11 years (2003–2008). *BMI > 97th percentile for age and sex based on CDC growth charts. Sources: Ogden, C. L., Carroll, M. D., & Flegal, K. M. (2008). High body mass index for age among US children and adolescents, 2003-2006, JAMA, 299 (20), 2401-2405; Ogden, C. L., Carroll, M. D., Curtin, L. R., Lamb, M. M. & Flegal, K. M. (2010). Prevalence of high body mass index in US children and adolescents, 2007-2008, JAMA, 303 (3), 242-249

Fig. 16.7

Prevalence of overweight* among girls age 12–19 years (1976–2008). *BMI > 95th percentile for age and sex based on CDC growth charts. Source: Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Health Examination Survey (NHES), National Health and Nutrition Examination Survey (NHANES)

Fig. 16.8

Prevalence of High BMI* among girls age 12–19 years (2003–2008). *BMI > 97th percentile for age and sex based on CDC growth charts. Sources: Ogden, C. L., Carroll, M. D., & Flegal, K. M. (2008). High body mass index for age among US children and adolescents, 2003-2006, JAMA, 299 (20), 2401-2405; Ogden, C. L., Carroll, M. D., Curtin, L. R., Lamb, M. M. & Flegal, K. M. (2010). Prevalence of high body mass index in US children and adolescents, 2007-2008, JAMA, 303 (3), 242-249

Fig. 16.9

Prevalence of overweight* among boys age 6–11 years (1976–2008). *BMI > 95th percentile for age and sex based on CDC growth charts. Source: Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Health Examination Survey (NHES), National Health and Nutrition Examination Survey (NHANES)

Fig. 16.10

Prevalence of High BMI* among boys age 6–11 years (2003–2008). *BMI > 97th percentile for age and sex based on CDC growth charts. Sources: Ogden, C. L., Carroll, M. D., & Flegal, K. M. (2008). High body mass index for age among US children and adolescents, 2003-2006, JAMA, 299 (20), 2401-2405; Ogden, C. L., Carroll, M. D., Curtin, L. R., Lamb, M. M. & Flegal, K. M. (2010). Prevalence of high body mass index in US children and adolescents, 2007-2008, JAMA, 303 (3), 242-249

Fig. 16.11

Prevalence of overweight* among boys age 12–19 years (1976–2008). *BMI > 95th percentile for age and sex based on CDC growth charts. Source: Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Health Examination Survey (NHES), National Health and Nutrition Examination Survey (NHANES)

Fig. 16.12

Prevalence of High BMI* among boys age 12–19 years (2003–2008). *BMI > 97th percentile for age and sex based on CDC growth charts. Sources: Ogden, C. L., Carroll, M. D., & Flegal, K. M. (2008). High body mass index for age among US children and adolescents, 2003-2006, JAMA, 299 (20), 2401-2405; Ogden, C. L., Carroll, M. D., Curtin, L. R., Lamb, M. M. & Flegal, K. M. (2010). Prevalence of high body mass index in US children and adolescents, 2007-2008, JAMA, 303 (3), 242-249

### Geographic Location

Rates of obesity also vary by geographic residence. Data from the 2010 Behavioral Risk Factor Surveillance System (BRFSS), a state-specific database of a cross-sectional telephone survey of US adults, show a range of obesity rates from 21.0 (Colorado) to 34.0 (Mississippi) (Centers for Disease Control and Prevention 2010). The five states making up the Deep South (Alabama, Georgia, Louisiana, Mississippi, South Carolina) are among the heaviest in the nation with obesity prevalence ranging from 29.6 (Georgia) to 34 (Mississippi) (Centers for Disease Control and Prevention 2010). These same states have non-Hispanic Black populations ranging from 27.5 % (Georgia) to 36 % (Mississippi) (US Census Bureau 2010). Note that these state-specific figures from BRFSS rely on self-reported heights and weights which tend to underestimate obesity rates, as such, actual rates of obesity may be even higher. Similar regional patterns are seen among children. Measured heights and weights on 8,270 children (4–12 years) in the National Longitudinal Survey of Youth (1986–1998), indicate that in 1986 the prevalence of overweight children living in the western and southern US was similar (7.6 % vs. 9.4 %, p = 0.39), however, in 1998 these regions differed significantly with western youth at 10.8 % and southern youth at 17.1 % (p < 0.005) (Strauss and Pollack 2001). Differences in obesity rates also vary at the local level. Residing in neighborhoods with a high percentage of racial/ethnic minorities and in non-metropolitan areas have been associated with increased obesity prevalence (Margellos-Anast et al. 2008; Singh et al. 2008, 2010; Dunton et al. 2009).

## Potential Causes of Obesity Disparities

Though there are myriad factors considered to contribute to overweight and obesity, energy imbalance, in which too many calories are consumed (diet) and not enough calories are burned (energy expenditure), is commonly considered the root cause for most Americans (Centers for Disease Control and Prevention 2004). Similarly, there are multiple influences that may explain higher prevalence of energy imbalance (i.e., obesity) among racial/ethnic minorities. As such, efforts to understand, and subsequently address disparities in obesity, necessitate comprehensive models. An ecological approach (Bronfenbrenner 1979) assumes that health behavior is a function of multiple levels of influence including individual, family, culture (beliefs, traditions), neighborhood, and policy. Discussion of potential factors that may contribute to racial/ethnic and geographic disparities is offered below.

### Individual Influences

Genetic and biological factors can influence diet and weight status (Fernández et al. 2008; Galgani and Ravussin 2008). Blacks have a higher preference for high-fat and high-calorie foods (Troiano et al. 2000), a greater predilection for sweets (Bacon et al. 1994; Schiffman et al. 2000; Pepino and Mennella 2005), lower preference for vegetables (Granner et al. 2004), and greater soda consumption (Giammattei et al. 2003) than their White counterparts. These patterns may predispose Blacks to overconsume calories (at least one part of the energy balance equation). Further, there is evidence that Blacks tend to have lower levels of physical activity (Crespo et al. 2001), the other part of the energy balance equation.

### Family Factors

The family environment is the primary context by which we learn about food and eating (Birch and Fisher 1988). Food preferences are learned through early and repeated exposures to foods (Birch 1999; Hill 2002). In addition, we learn norms about meal size and frequency of eating from our experiences growing up (Campbell et al. 2006). Black mothers are more likely than White mothers to monitor child food intake and pressure the child to eat (Spruijt-Metz et al. 2002, 2006). This parental feeding style may be linked, in part, to high levels of food insecurity (i.e., fear of not having access to enough food to eat) among Black households. Rates of food insecurity among Blacks compared to Whites are 22.2 % vs. 7.9 %, respectively (Nord and Andrews 2008). Black households are also more likely to contain foods high in fat (Befort et al. 2006) and these families are more likely to eat fast food (Schmidt et al. 2005) and food from all-you-can eat or buffet-type restaurants (Befort et al. 2006), which are associated with higher calorie foods and oversized portions. Finally, evidence suggests that overweight children are more likely to have at least one parent that is also overweight (Whitaker et al. 1997). Whether biologically linked or learned from exposure, there are clear reasons to suspect familial influences are at least part of the explanation of higher rates of obesity among families of color.

### Cultural Factors

Shared beliefs and values among groups of people can have profound influence over behaviors associated with excessive weight. For example, Black adults and adolescents report a higher ideal body weight, and are more likely to be satisfied with their weight, even when they are statistically overweight, than White women (Gipson et al. 2005). In addition, for many ethnic cultures, food preparation is considered a labor of love, with generous portions offered to friends and family as a symbol of affection (Liburd 2003). Consuming these foods in large portions can easily contribute to excess caloric intake. With respect to physical activity, cultural influences may also play a role in lived behavior. There is some evidence that Blacks may perceive physical activity as "work" and thus competing with desires for rest and relaxation (Airhihenbuwa et al. 1995). In addition, Blacks may prefer activities different than that of Whites (Resnicow et al. 2002; Hooker et al. 2005). Water sports and activities that generally exert more energy may be seen as less favorable for Black girls and women, who often express hairstyle-related barriers to participation in physical activity (Baskin et al. 2001a, b; Barnes et al. 2007).

### Neighborhood Influences

Growing evidence suggests that where you live impacts your overall health. Neighborhood influences on obesity are also apparent. Predominantly Black neighborhoods have 2.4 fast-food restaurants per square mile compared to 1.5 restaurants in predominantly White neighborhoods (Block et al. 2004). Supermarkets, noted for greater variety of foods at lower prices, are less prevalent in low-income and predominately Black communities (Morland et al. 2002). Similarly, predominately African American neighborhoods are often characterized by the absence of or dilapidated parks and recreation facilities (Powell et al. 2006).

### Public Policy

There are also factors beyond the individual and his/her immediate physical environment that may influence aspects of the energy balance equation. For example, advertising and other forms of targeted marketing to Blacks are more likely to predispose them to excess calorie consumption and poor dietary quality (Grier and Kumanyika 2008). In fact, there is evidence that more advertisements of unhealthy foods (e.g., desserts, soda, candy, fast food) can be found in commercials aired during shows largely watched by African American audience (Outley and Taddese 2006). Other potential influences include access to healthcare to either prevent or treat obesity. Fewer African American families have adequate health insurance and access to health care facilities (Kaiser Commission 2006).

## Potential Solutions to Reduced Obesity Disparities

Efforts to reduce rising obesity rates, particularly among racial/ethnic minorities must focus on the pursuit of health equity (i.e., the elimination of health disparities/inequalities) (Braveman 2006) and should be pursued at all levels of influence described earlier. For most Americans, issues with excess weight are largely a function of energy imbalance. The main components in this balance equation are two health behaviors (diet, physical activity). As such, it is imperative to focus some programs and interventions at the individual and family level. An effective approach to ensuring cultural appropriate programs is including community members as partners in developing, implementing, and evaluating programs. For these programs to be most meaningful to the target audience, they must include culturally appropriate literature on prevention and weight control. With the help of members of the target group, programs that build on existing traditions with respect to food, music, and types of activities are more likely to resonate with racial/ethnic minorities than attempts to impose a one-size-fits-all program. Program content (e.g., terminology, language, symbolism, role models, choice of incentives) should respect and match the target population. Collecting formative data (e.g., focus groups, interviews) from community members to identify important cultural variables is important to better understand culturally-driven concerns (e.g., body image; parenting styles) that may serve as barriers to behavioral change. Careful adaptation and tailoring of program materials to address these issues will be needed.

Specific program content should include a review of the benefits of healthy eating and regular physical activity, particularly as a means to reduce risk for chronic diseases where racial/ethnic minorities are disproportionately impacted. Individuals and families should be referred to community education resources, particularly if they have no access to an individual healthcare provider. Programs offered or supported by local health departments, cooperative extension agencies, academic institutions and/or health systems may be available at a range of costs. Community-based organizations may also partner with health providers to organize exercise and cooking classes in community settings (e.g., civic clubs, neighborhood association meetings). Often local vendors (e.g., food stores, farmers, food pantries, sports stores) may donate food/activity products and/or provide discounts to participating members. Organizing focus groups meeting with community members and stakeholders may be helpful to generate other strategies.

Beyond individuals and families, there is a need to organize various institutions to bring greater awareness and resources to reducing obesity-related disparities among racial/ethnic minorities. Such efforts might include organizing a local coalition to develop a strategic plan for promoting healthy eating and physical activity in the community. Enlisting members from recreation departments, school nurses, school administrators, exercise facility owners, grocers, local government, local public health agencies, and other leaders in the community to work together can be helpful to document the program and identify local solutions that are sustainable. Potential strategies at this level could include increased awareness of the problem by increasing local media attention (e.g., newspaper article, interview on local TV/radio station), working with local schools to develop programs for youth (e.g., walk to school day), working with local businesses to display health education material/brochures in their establishments, working with local health clinics to distribute information about community programs, or providing literature to local community-based groups (e.g., civic clubs, neighborhood associations, sororities/fraternities) to help them form walking clubs and cooking classes.

Further, obesity prevention and treatment programs may have increased participation if they are embedded within an existing minority community institution or organization. For example, Black churches have historically worked to improve the health status of African Americans (Baskin et al. 2001a, b). As early as the 1920s, Black churches participated in outreach programs providing community members access to free health clinics (Mays and Nicholson 1933). In recent years, a number of programs focusing on healthier eating and physical activity have been successfully implemented (Resnicow et al. 2002, 2004, 2005a, b; Wilcox et al. 2008). Faith-based organizations are often among the most visible, respected, and credible agencies in minority communities (Baskin et al. 2001a, b; Campbell et al. 2007) and may be particularly a key to reducing health disparities (Braithwaite and Taylor 2001).

Finally, there is need for interventions that may impact environments and policies that support higher rates of obesity in minority populations. Possible strategies may include bringing attention to community leaders and local government officials the local obesity data and information about the availability of healthy food and recreational options in the community. As described above, the issue of high rates of obesity goes beyond health impacts, but has economic consequences as well. Framing the discussion to include both public health and economic implications may be more powerful. A number of policies to support reduced rates of obesity are being implemented and evaluated throughout the country. Among these are efforts by local governments to build and/or improve recreational options (Kahn et al. 2002; Wiggs et al. 2008), efforts to implement and evaluate national school nutrition guidelines (Pitt Barnes et al. 2011; Taber et al. 2011), zoning regulations to limit new fast-food development (Sturm and Cohen 2009), and joint use agreements to allow community members to access public school facilities (e.g., gym, track) for physical activity during after-school hours (Spengler et al. 2007).

While not specific to Black communities, additional national strategies to help reduce obesity may have particular benefits to those disproportionately impacted by obesity (e.g., racial/ethnic minorities). First, the recent Patient Protection and Affordable Care Act (2010) (Pub.L. 111-148) includes legislation that restaurants with 20 or more locations will be required to display calories on menus and have additional nutrition information (i.e., calories from fat, total fat, saturated fat, cholesterol, sodium, carbohydrates, sugars, dietary fiber and protein) available for consumers. Even vending machines will fall under these new rules. The law also stipulates that insurance companies will be required to provide coverage for preventive-health services including: obesity screening (i.e., doctor's physical exam combined with a measurement of BMI) and nutritional counseling. Second, recent funding from the Department of Health and Human Services (DHHS) has focused on grants to local communities to fight obesity (http://www.hhs.gov/recovery/programs/cppw/granteedescriptions.html#communities; Community Transformation grants). Finally, there are multiple national partnerships that have been developed to address issues of obesity. One example is the Let's Move! program (www.letsmove.gov/index.html) spearheaded by First Lady Michelle Obama. The program includes a partnership between the White House, United States Department of Agriculture, Unites States Department of Interior, the United States Department of Education, and DHHS. It is a comprehensive initiative whose mission is to solve the problem of obesity within a generation. Another important national partnership is National Collaborative on Childhood Obesity Research (www.nccor.org). This partnership includes four leading national research funders (Centers for Disease Control and Prevention, National Institutes of Health, Robert Wood Johnson Foundation, United States Department of Agriculture) to focus on reducing childhood obesity among youth with the highest rates of obesity, including African Americans, Hispanics, Native Americans, Asian/Pacific Islanders, and children living in low-income communities.

## Summary and Conclusion

Obesity is a major public health problem in America with tremendous health and economic consequences for the entire nation. However, as described in this chapter, there is particular cause for alarm concerning the racial/ethnic disparities in overweight and obesity. Non-Hispanic Blacks are disproportionately impacted by obesity and the myriad of short- and long-term health consequences. The cause of this disparity is likely complex and involves the interaction of multiple factors (biological/genetic, learned behaviors, interpersonal relationship, cultural beliefs/values, neighborhood environment, policies). While there are some recent and ongoing strategies to address the problem at many of these levels, more is needed to reach and maintain success in ameliorating disproportionately high rates of obesity in the Black community. Additional research, clinical and community-based approaches, and policies will be needed (Baskin et al. 2009). Further, there is a tremendous need to better understand the variation in obesity-related attitudes and practices within subgroups of Black Americans (e.g., geographic residence, country of origin, socioeconomic status) (Baskin et al. 2009). While information discussed in this chapter ascribed characteristics to Blacks/African Americans and/or the Black community, Blacks in the United States represent a diverse group with significant cultural variation. Future research and program development should consider this variation in the design and execution of work with this population.

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© Springer Science+Business Media New York 2015

Ralph Bangs and Larry E. Davis (eds.)Race and Social Problems10.1007/978-1-4939-0863-9_17

# 17. Mental Health Indicators for African American Males

Oscar A. Barbarin1  and Emma Sterrett2

(1)

Department of Psychology, Tulane University, New Orleans, LA 70118, USA

(2)

University of Louisville, 310 N. Whittington Pkwy., Louisville, KY 40222, USA

Oscar A. Barbarin (Corresponding author)

Email: barbarin@tulane.edu

Emma Sterrett

Email: e0ster01@louisville.edu

## Introduction

National estimates of the prevalence of mental disorders among children range from 11 to 25 % (Roberts et al. 1998). Conservative estimates set the average rate of disorders among preschoolers at 10.2 %, at 13.2 % among preadolescents, and at 16.5 % among adolescents. Given the multiple strains experienced by African American boys, the prevalence rates for childhood disorders might reasonably be predicted to fall at the higher end of the range estimated for a general population of children. Such is the case with data obtained on primary school-age African American boys using the ABLE mental health screening (Barbarin 2007). ABLE (which is an acronym for Attention, Behavior, Language, and Emotions) is used to gather teacher reports of the mental health problems they observe in their students. These data provide an informative window into the mental health challenges facing young boys in schools. Teachers completed the ABLE online for all of their students. They reported potential behavioral and emotional problems and rated the severity of those problems. ABLE data on African American boys have been remarkably consistent across large and small, urban and rural school districts. Typical of the screening data are the results obtained from an urban school district in the South. Its student body is almost entirely African American (99 %) and the overwhelming majority (92 %) come from families poor enough to qualify for free or reduced price lunch. This particular school serves almost 500 children from pre-Kindergarten to eighth grade. Based on the screening, 21 % of the children were judged to be at risk of a severe psychological disorder; 14 % were at very high risk; and another 7 % were at moderately high risk of problems severe enough to impair functioning at school. The results of the screening revealed that the three most common mental health concerns cited by teachers fall into the categories of conduct, mood, and hyperactivity attention deficit disorders. Of the children found to be at greatest risk of behavior and emotional difficulties, 65 % were boys. Moreover, one in four African American boys was at risk because of conduct problems, and one in five was at risk for aggression and hyperactivity. These boys were much less likely to be identified as having an emotional difficulty. Anxiety was a source of concern in only 6 % of African American boys in the school and sadness in only 8 %. Thus, symptoms of emotional problems were observed at rates close to the prevalence found for other ethnic groups.

## Emotional Distress in African American Males

Although emotional difficulties are not reported with very high frequency by teachers or parents, they may still be a source of difficulty for males as they move through adolescence. For example, in our school sample, the words angry, suspicious, hot-tempered, and emotionally reactive often accompany descriptions of African American males where the principal concern is aggressive and oppositional behavior. Accordingly, such disturbances of mood intimated by anger, suspicion, and emotional volatility may be very important to understanding the etiology and development of the behaviors which seem so troubling at school and sometimes at home. For example, the work of Shepard Kellam and his associates at the Johns Hopkins University Prevention Research Center has demonstrated a strikingly high comorbidity of affective disturbances with conduct problems and academic underachievement, difficulties of concentration, and affective disturbances. The concurrence of emotional mood affective and behavioral disorders were initially greeted with suspicion and treated as an artifact of measurement error. However, the consistency in these findings suggests that these problems often occur together in children. Data gathered at the Hopkins Prevention Research Center with inner city children have noted depressed mood in nonclinical, non-referred African American children and adolescents in poor communities that were on average equivalent to levels for children and adolescents hospitalized for clinical depression. These mood disturbances are particularly prevalent among young African American males in elementary and middle school and in adolescent females. Cross-sectional data reviewed by Barbarin and Soler (1993) show that depression peaks for boys at about ages 9–10 (Grades 4–5), then drops to normal levels. For girls, depression is only moderately elevated up through age 10, but rises and peaks around ages 15–16. Consequently, it is possible that adverse emotions provide the motivational undercurrent for aggression and opposition. If this is true, efforts to address the problem behavior without recognition of the emotional components integrated into or driving it are likely doomed to failure.

## DSM Disorders

The DSM's (Diagnostic and Statistical Manual) rubrics (American Psychiatric Association 2013) for the diagnosis of mental disorders do not offer a comprehensive set of criteria for psychological well-being and in fairness nor were they designed to do so. Nevertheless, a DSM diagnosis stands as an indicator of severe mental health problems that are widely used and generally accepted. When we move from symptoms such as those reported on a mental health screener and raise the bar to the level of a DSM diagnosis, we find somewhat smaller differences in the mental health status of African American boys. Contrary to widely held views, African American boys did not for the most part experience fundamentally higher rates of diagnosable psychological disorders that met DSM criteria than other groups of children although there are a few differences. Striking differences are still observed in the screening of symptoms of behavior problems but this may be attributable largely to ratings of disorders that are biased due to SES and race.

### Severe Mental Illness

Psychotic disorders, such as schizophrenia, are not usually diagnosed until around or after age 18, and thus empirical studies are just now beginning to examine psychosis among children and adolescents. However, there do seem to be some racial and gender differences in terms of diagnosis. African American male adolescents have been found to be more likely to be diagnosed with schizophrenia than African American females and Caucasian males and females (Delbello et al. 2001).

### Depressive Disorders

In general, conflicting evidence exists regarding differences in depression rates between African American and Caucasian youth, with some studies finding that Caucasians report higher rates of depression (Brooks et al. 2002) and others finding that African Americans report higher rates of depression (Roberts et al. 1997). However, studies examining associations between gender, ethnicity, and depression have found that African American boys meet diagnostic criteria for depression at a lower rate than their female and Caucasian counterparts. For example, an epidemiological study of urban African American youth has found the lifetime prevalence of depression to be 6.9 % among African American males as opposed to 11.4 % among African American females (Ialongo et al. 2004). While African American males appear to exhibit lower rates of depression, one important caveat must be highlighted. Some empirical and theoretical work with African American adults has introduced questions as to whether current diagnostic criteria and methods are sufficient for detecting depression among African Americans. For example, after interviewing African American adults, Baker (2001) found three alternative presentations of depression that differ from DSM IV criteria, including those that refuse to admit sadness in favor of relying on faith, those that exhibit extreme irritability or a significant change in personality, and those who prioritize taking on multiple tasks over their health. In addition, among depressed adults, African Americans report poorer physical health and more stressful life events than Caucasians (Brown et al. 1996). These studies suggest that presentations of depression, including irritability, high levels of productivity, and somatic symptoms, need to be included when assessing African Americans for depression.

### Conduct Disorders and Delinquency

There is considerable information about the disproportionately high rates of behavioral problems from early childhood through adolescence. Consistent with the ABLE data presented above, studies of the socio-emotional development of African American children, have typically observed differential rates of externalizing disorders and problems related to social and academic maladjustment. Teachers, specifically Caucasian and Hispanic teachers, have been found to rate African American youth as higher on externalizing behavior problems than Caucasian and Hispanic children. However, because much of these data are based on teacher and school staff reports, it is possible to raise questions about whether the elevated levels of behavioral problems reported for African American males may be due in part to biased reporting.

Differential rates of conduct problems are also evident in data on juvenile delinquency. African Americans are almost twice as likely as Whites to be incarcerated in state prisons (Ditton and Wilson 1999). Although African American youth accounted for only 15 % of the adolescent population, they accounted for 50 % of arrests for murder, 63 % of arrests for robberies, and 34 % of arrests of youth for forcible rape in 2004 (National Council on Crime and Delinquency 2007). Furthermore, at every level of the juvenile justice system, they are overrepresented in the number that is sent on to more severe levels of adjudication. For example, while 33 % of youth in juvenile court were African American, 44 % of youth who were then detained were African American. Similarly, for youth with drug offenses, African Americans made up 39 % of the cases petitioned but 63 % of cases transferred to adult court (Poe-Yamagata and Jones 2000). As in the case of teacher rating of behavior problems and referrals to the principal for disciplinary reasons, racial bias may be an important factor accounting for the disproportionately high representation of African American males ensnared by the criminal justice system.

### Substance Abuse

Racial differences also exist in the abuse of alcohol and cigarettes but in a direction that is counter to what many would predict. In general, African American youth tend to exhibit lower levels of alcohol abuse and cigarette use than Caucasian youth (Table 17.1). For example, the National Institute of Drug Abuse (NIDA) has found that among youth aged 12–17 reporting about substance use in the previous month, 13.1 % of African Americans as compared to 20.9 % of Caucasians had drunk alcohol, and 13.7 % of African Americans as compared to 20.5 % of Caucasians had smoked cigarettes. African American youth used marijuana at a slightly lower rate than Caucasian youth, 8.3 % as compared to 8.7 % (NIDA 2003). In addition, recently a new drug, codeine cough syrup, has become popular among African American youth living in the southern United States. Although the use of codeine syrup is just beginning to be examined, one study found that 25 % of at-risk youth in Houston have abused codeine syrup (Peters et al. 2003).

Table 17.1

Drug use by race | African Americans | Whites

---|---|---

Drink alcohol | 13.1 | 20.9

Tobacco use | 13.7 | 20.5

Marijuana use | 8.3 | 8.7

Source: NIDA (2003)

Although, for the most part, African American youth engage in substance abuse at a lower level than their Caucasian counterparts, when they do abuse drugs, the social consequences tend to be worse. For example, onset of alcohol use before sixth grade has been found to be more strongly related to eighth grade and adult alcohol use among African American than Caucasian males (Horton 2007). In addition, African Americans are more than 20 times more likely than Caucasians to be incarcerated due to a drug-related offense and are more likely to make a drug-related emergency room visit, to die from an overdose, and to contract HIV due to intravenous drug use than Caucasians (Drucker 1999).

## Other Problem Indicators

### Suicide

Historically, African Americans have exhibited lower rates of suicidal behavior than Caucasians. However, increases in the rates of suicide among African Americans have meant that the gap in suicidal behavior between the two groups has closed in recent years. For example, currently the rates of suicide attempts are roughly equal between Caucasian and African American adolescents (Eaton et al. 2006). However, important differences emerge when African American boys are categorized according to ethnic group. Boys of Caribbean descent attempt suicide over their lifetime at a significantly higher rate, 7.5 %, than males of other African American ethnic groups (Joe et al. 2006).

### Special Education

The fact that African Americans are overrepresented in special education classes and among those classified as emotionally/behaviorally disturbed has been well documented. A recent report on the Implementation of the Individuals with Disabilities Act found that while there were no differences of rates of special education classification among toddlers and preschoolers, among individuals ages 6–21, there were substantial differences based on racial/ethnic background in special education placement. African Americans in this age range made up 20 % of the children receiving special education services, although they only make up roughly 12 % of the population. Furthermore, African Americans were classified as having an emotional disturbance at a higher rate than children of other ethnic backgrounds. Eleven percent of African American children were classified as being emotionally disturbed as compared to 8 % of Caucasian, 7.7 % of American Indian, 5 % of Asian, and 5 % of Hispanic youth with a disability (U.S. Department of Education 2005).

## Explaining the Mental Health Status of African American Males

It is important to underscore that over the range of psychological disorders, the prevalence of problems among African American males is quite similar to that of the rest of the population. Note that the prevalence for the most common emotional disorders is as low as it is for all other demographic groups. Buried in the mental health data are a few unanticipated findings. Males of color are more often diagnosed with schizophrenic-spectrum disorders, especially paranoid schizophrenia. However, as teens they have lower rates of abuse of alcohol and other substances, and they also have lower rates of suicide than White males, though the gap is closing. However, they do have higher rates of behavior problems. Accordingly, the most commonly expressed concerns about African American males include opposition, aggression, and challenging authority. How might these extraordinary differences in conduct problems be accounted for? This chapter attempts to account for the difference in behavior problems in terms of external factors such heightened stress and racial bias, internal factors such as biased social information processing, and interactional factors such as the differential effects of social moderators on male behavior.

Although African American males have prevalence rates of disorders that are similar to or higher than rates of psychological disorders observed in females and White male groups, a case can be made that they encounter higher levels of psychosocial stressors and challenges to maintaining mental health than other groups. The differential rates of mental health problems, particularly conduct disorder, among African American males may arise from social interactional processes such as prejudice and negative bias of adults as well as their own biases in social information processing. In addition, it appears that some protective factors which apparently promote resilience in girls and other ethnic groups do not have the same effect with African American males.

### Biased Responses to African American Males

African American males tend to be viewed in a more negative light by adults than other groups of boys. Evidence revealing bias in evaluations and responses to the behavior of African American males is compelling. African American males are viewed as more aggressive, more hostile and more oppositional than their peers. For example, Caucasian and Hispanic teachers have been found to rate African American boys higher on behavior problems than African American parents do (Zimmerman et al. 1995). Similarly, when focusing just on African American males, teachers have been found to give African American males higher scores for externalizing behaviors than African American boys give themselves or than their parents give them. At the same time, teachers' ratings do not differ in the same way from Caucasian caregivers' or Caucasian youth's self-rated externalizing behaviors (Youngstrom et al. 2000).

These differences in teacher perceptions very likely contribute to their referring African American boys for disciplinary action at a higher rate than Caucasian students even for the same behavior. For example, in one study examining disciplinary reports at a middle school, African American males did not appear to engage in behavior that was qualitatively or quantitatively more disruptive than that of Caucasian males. Yet, African American males were referred more often for discipline. The most common infractions for which they were referred to the office with charges were heavily inferential and laced with subjective judgments such as "disrespect" or "excessive" noise (Skiba et al. 2002).

These distortions and overreactions to the behavior of African American males are not limited to school settings. In clinical settings, African Americans also tend to be overrepresented among children diagnosed with externalizing problems. Among adolescents hospitalized in a psychiatric facility, African Americans have been found to be more likely than Caucasians to be diagnosed with conduct disorder (Delbello et al. 2001). Furthermore, it appears that aspects of African American culture may put African American males at a greater risk for being perceived as needing special services. For example, one study found that teachers observing male adolescents with an African American cultural movement style, defined as a stylized, rhythmic way of walking, perceived those youth as being more aggressive, lower in achievement, and more likely to need special education services (Neal et al. 2003).

### Differential Experience of Trauma

Another reason for disproportionately high rates at which African American boys are diagnosed with conduct problems may be differential exposure to trauma. While, more work needs to be conducted in examining the levels of psychological difficulty experienced by African American boys, empirical work has demonstrated that they may experience stressful events often related to mental health problems at a higher frequency than other groups. For example, African American adolescents are more likely than adolescents of other ethnic backgrounds to experience the death of a family member and more likely than Caucasian adolescents to experience death of a friend (Rheingold et al. 2003). African American males report poorer physical health and more stressful life events than Caucasians (Brown et al. 1996). In addition, males and African Americans have been found to report higher exposure to environmental danger, such as witnessing or being victim to crime, than females and other ethnic groups. Higher exposure to environmental danger, in turn, has been found to be associated with lower attendance and tendency to avoid trouble (Bowen and Bowen 1999). Finally, among African American males, exposure to community violence has been found to be related to an elevated risk for PTSD symptoms and depression (Paxton et al. 2004). The long-term consequences are dire. The range of problems which boys of color disproportionately experience is broad and includes health, education, employment, and involvement in community life.

### Distinctive Moderating Factors

An additional source of difficulty for African American males are unspecified social processes, which apparently neutralize the effects of factors that are otherwise protective. Specifically, factors that appear to be protective for other children are not always protective for African American boys. For example, while maternal monitoring appears to reduce delinquency among African American female adolescents, it does not for males. Only the lack of association with deviant peers appears to be associated with less delinquency among African American male adolescents (Bowman et al. 2007). In addition, in one study, while academic self-efficacy was associated with girls' GPA, it had no association with boys' GPA, suggesting that for boys the belief that one can achieve academically is not sufficient on its own to lead them to be successful academically and achieve good grades (Saunders et al. 2004). Moreover, social support did not protect African American males from the psychological distress associated with community violence as it did for African American females (Paxton et al. 2004). In addition, the mental health of African American males appears to be disproportionately vulnerable to the adverse effects of family risk factors. For example, African American males growing up in a home without married parents have comparatively lower self-esteem than African American females (Mandara and Murray 2000).

### Social Information Processing of African American Males

African American males' differential exposure to traumatic events and biased ratings may in turn influence how they process information about social situations. The consequence may be that they are more guarded, suspicious, and less inclined to assume good or neutral intentions in others toward them. The additional trauma may make them feel more vulnerable and in turn lead them to take a defensive posture in dealing with others. The resulting biases in their social information processing may figure prominently in the high rates of conduct problems they exhibit. Many do not feel respected or affirmed at school. Though these feelings may have a basis in reality, they often represent an overreaction to perceived slights and fuel misinterpretations of others' intentions. Therefore, underlying the opposition and aggression are overreactions to perceived disrespect or anticipated threats. These may be cognitive distortions but that can powerfully move boys to anger and fighting. That is also why the respect of others is so important and figures so prominently in their struggles with themselves and others. Saving face is very important. Building a reputation as someone who will fight, who is not afraid, and who is cool is quite important and protects them from others. They talk back to teachers who discount and write them off. Their own sense of style and moral code dictates right and wrong. They follow a code which prizes respect almost above all else.

Take the case of Jameel and Tyrone. Jameel was told that Tyrone was in the school cafeteria talking trash about him. Jameel confronted Tyrone. After trading words, Jameel pushed, Tyrone pushed back and in an instant they were in an all-out fight. They didn't seem to care that they were in the middle of a very crowded cafeteria. They fought on top of the table, rolled onto the floor, knocked over chairs, and spilled plates of food all over the floor. After being restrained by other students, both boys emerged from the scuffle with bruises. However, they were more concerned about loss of face than injuries to their bodies. For the two of them, maintaining reputation and winning respect were central motivators.

African American males may mask their anger, sadness, and fear with an air of "belle indifference" and develop a "devil may care" attitude while at the same time they remain prepared to fight in an instance, especially to defend their reputation. To that extent, the nonchalance is a pose, a posture, an affectation that conceals their fear and anxiety about dangerous situations and their sadness and hopelessness about their lives improving. Over time some may become indifferent to their own suffering or the suffering of others. Others become like tinderboxes that flair up with the slightest spark of provocation from others. Their aggression and opposition are concealing an emotional storm going on within. Accordingly, the outward observable manifestation of fighting, peer conflict, and opposition really represent a more complex set of issues that involve emotions such as anger, fear, sadness, and a sense of loss. These symptoms deserve special attention in the case of African American males because they may be at the root of the conduct problems and the aggression that by teacher reports are rampant among African American males. These emotions, we argue, are responses to actual or perceived denigration and negative feedback and must be addressed if we are to get a handle on the behavior that is both disruptive and self-destructive.

## Conclusion: Coping and Resilience

This discussion of differential stressors and challenges faced by African American males leads to very interesting questions about the existence of parallel or unique sources of resilience that help some overcome the strains and strengthen their capacity to cope with high levels of psychosocial stress, especially those often associated with economic disadvantage and discrimination. Some empirical work has begun to examine factors that may be protective for psychological well-being among African Americans males. There are several candidate protective factors, though no claim is made that they apply only to males. To start with, positive ethnic identity has been found to be protective for African American boys. For example, African American adolescents who feel positively about Black people have been shown to exhibit a host of positive outcomes including less frequent alcohol use (Caldwell et al. 2004) and greater psychological well-being (Sellers et al. 2006). In addition, African American youth who experience "cultural pride socialization" have been found to exhibit less lethargy and higher self-esteem whereas youth who experience "alertness to discrimination socialization" have been found to exhibit more helplessness (Davis and Stevenson 2006). Other personal qualities appear to be protective as well. For example, shyness in first-grade among African American males has been found to be associated with decreased risk of substance use in later life (Fothergill and Ensminger 2005). Finally, the involvement of a father or father-figure appears to be particularly helpful for African American males as compared to girls. African American adolescent boys who name their father as their role-model have been found to exhibit fewer behavior problems, better attitudes about school, and higher GPAs (Bryant and Zimmerman 2003). These protective factors provide a direction and are a source of hope for those who dedicate themselves to achieving better outcomes for African American males and promoting their mental health.

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Index

A

Achievement gap

closure

demographics

See(Demographics of America's children)

and discipline

educational attainment

GDP

postsecondary

Adoption

"AdoptUSKids" program

disproportionality issues, African American

families, children in care

AdoptUSKids program

AFDC.

SeeAid to families with dependent children (AFDC)

African American disproportionality

Children's Defense Fund

"one-size-fits-all" approach

African Americans

childhood obesity

health status of

males

ABLE data

behavior and emotional difficulties

DSM

See(Diagnostic and Statistical Manual (DSM) disorders)

emotional distress

mental health

See(Mental health indicators)

prevalence rates, childhood disorders

special education

suicide

Age cohort analysis

Asian and White population

multiple-race combinations

White and Black population

Aging and health, Mexican Americans.

SeeMexican Americans

Aid to families with dependent children (AFDC)

Alternative questionnaire experiment (AQE)

Census Bureau

2020 census content testing

mailout/mailback questionnaires

quantitative analyses

race and Hispanic-origin reporting

telephone reinterview

Alternatives to suspension

disciplinary disparities

and expulsion

See(Expulsion)

school arrests and corporal punishment

American Community Survey

citizenship status, year of entry, and racial distribution

Hispanic population

race and ethnicity

White and Asian population

American poverty

child and health care

economic and political levels failingness

education/skills deficiencies

global competition

households

individualism and self-reliance

industrialized nations

inequality

See(Poverty inequality)

American poverty (cont.) injustice

public's attitudes

social security benefits

America's racial realism

Blacks report, Whites

causes, disparities

See(Causes of racial disparities)

slavery and discrimination

Armed Services Vocational Aptitude Battery (ASVAB)

Asset-building approaches

and CDC

See(Community Development Corporations (CDCs))

community/collective level

individual/household level

Attention, behavior, language and emotions (ABLE)

behavioral problems from early childhood

mental health problems

B

Behavioral risk factor surveillance system (BRFSS)

Biased responses, African American males

Black and White disparities

determinants

incidence and mortality

Black men

employment, education and incarceration

less-educated

policy implications

social and economic status

young

Black–White incarceration rate ratios

description

problems

socialization

Breakthrough series collaborative (BSC)

Breast cancer.

SeeHealth disparities

BRFSS.

SeeBehavioral risk factor surveillance system (BRFSS)

BSC.

SeeBreakthrough series collaborative (BSC)

C

Cancer.

SeeHealth disparities

Casey Family Programs

Causes of racial disparities

arrest and incarceration, Black males

criminal justice

economics

education

employment markets

family structure

health

housing market transaction

past discrimination and violence, Whites

residential segregation

Census

American Community Survey

Black/African American

citizenship status, entry and racial distribution, Hispanics

See(Hispanics racial distribution)

non-Hispanic population

numeric size

population, Hispanic/Latino origin

seeking group recognition

See(Seeking group recognition)

US Census Bureau

Center for Epidemiologic Studies Depression (CES-D) scale

The Center for Interdisciplinary Health Disparities Research (CIHDR) model

embodiment and accumulated disadvantage effects

health disparities, breast cancer

neighborhoods deterioration

social isolation

CES-D.

SeeCenter for Epidemiologic Studies Depression (CES-D) scale

Civil Rights Movement

Collective strategies

asset-building approaches

foundations

housing cooperatives

investment clubs

microenterprise cooperatives

shared risk pools

venture capital entities

worker-owned and farm cooperatives

College achievement

high school quality

See(High school quality)

racial gaps

College readiness

Communities Learning to Invest and Mobilize for Business (CLIMB) project

Community Development Corporations (CDCs)

Conduct disorders and delinquency

Coping

African American males

and resilience

Cox proportional hazard model

D

Demographics of America's children

educational researchers

federal poverty line

graduation and dropping out

opportunity gaps

retention, suspension and expulsion

test scores

Depression

Center for Epidemiologic Studies Depression (CES-D) scale

diabetes

disability and hazard ratio, death

functional disability

symptomatology and high rates

Depressive disorders

African American and Caucasian youth

presentations

Diagnostic and statistical manual (DSM) disorders

conduct disorders and delinquency

depressive disorders

description

mental illness

substance abuse

Dialogues.

See alsoIntergroup dialogue

color and White students

credit-bearing course

inequality and poverty

multi-university research evaluation

national studies

post-racial youth generation

See(Youth views)

racial and gender inequality

racial/ethnic inequalities

See(Inequality)

University of Michigan students

Disability and diabetes

epidemiologic transition

Hispanic EPESE

medical conditions

Discrimination.

See alsoCauses of racial disparities

Black home-buyers

in criminal justice policies

employment

housing practices

labor market

and racial disparities, eradicated

and violence, Whites

Disparities.

SeeHealth disparities

Disproportionality

African American

disparities

See(Racial and ethnic disparities)

FGDM

Juvenile Justice Contact

negative relationship, school climate

racial disproportionality, prison

See(Racial disproportionality, prison)

school dropout

school engagement/achievement

Drug markets

African Americans

dealers and predecessors

drug market intervention (DMI)

incarceration

DSM.

SeeDiagnostic and Statistical Manual (DSM) disorders

E

Earned Income Tax Credit (EITC)

childless adults

federal

labor market entry

men and women

noncustodial fathers

work supports

Earnings

child support

GED/high school diploma

Ecological model, health behavior

Economic development

inextricable connection

in Native American communities

Economic risk

Education

dropping out of school

HCV

on-time graduation

opportunity

racial/ethnic disparities

White and children of color

Emotional distress

Employment

educational and behavioral outcomes

Hispanic men

young men and women

Ethnicity.

See alsoRace

census 2010

See(Census)

classification

data collection

dialogues

See(Dialogues)

Hispanics

multiple-race reporting

See(Age cohort analysis)

OMB

See(Office of Management and Budget (OMB), US Standards)

US federal government

Expulsion

juvenile justice system

minor infractions

and suspension

See(Suspension)

F

Family group decision making (FGDM)

Family structures

"AdoptUSKids" program

AFDC

African American adoptions

challenges

consistency and guidance, children development

description

disruptions and placement

educational and employment opportunities

parental incarceration rates

prohibition of marriage, issues

promising practices

See(Promising practices)

society, racial inequities

Federal housing administration (FHA)

FGDM.

SeeFamily group decision making (FGDM)

FHA.

SeeFederal housing administration (FHA)

Foster care

African American families

federal initiatives

resources

Frailty, older Mexican Americans

characteristics

healthy and pathologic aging

Hispanic EPESE

mortality profiles

pre-frail and frail individuals vs. non-frail individuals

risk factors

G

Gap closers

Grade point average (GPA)

H

Harlem Children's Zone (HCZ)

Health disparities

in black and white women

See(Black and White disparities)

case study

CIHDR

See(The Center for Interdisciplinary Health Disparities Research (CIHDR) model)

community partnerships

early detection

environmental factors

faith-based organizations

health equity

hospitalizations

institutional-healthcare system factors

interconnectedness factors, population health

interpersonal behaviors

media coverage

patient behaviors

patient-centered medical home, care

Patient Protection and Affordable Care Act (ACA)

Patient's life history and clinical problems

physicians

prenatal care and labor challenges

provider behaviors

proximal and distal determinants

social determinants

See(Social determinants)

social indicators with clinical information

social isolation

sources

Health equity

High school quality

academic achievement

ethno-racial gaps

high-income

K-12 achievement

longitudinal and retrospective survey

meta-analysis

performance gaps

postsecondary attendance

school quality

SES and academic achievement

Texas college admissions and school quality debate

See(Texas higher education)

violence and college orientation

Hispanic Established Population for the Epidemiological Study of the Elderly (Hispanic EPESE)

baseline and Waves 2-6

medical conditions and disabilities

Southwestern states

Hispanic paradox

"epidemiologic paradox", Southwestern Hispanics

life expectancy, age and gender

mortality/health advantage, Mexican-origin population

"salmon bias"

Hispanics/Latinos populations

Hispanics racial distribution

Caribbean origin

census

See(Census)

Central American origin

and Cuban origin

foreign-born

Guatemalan, Salvadoran and Honduran

Hispanic/Latino population

and Latino origin

OMB race categories

origin and race question

Panamanian population

percentage, Latino population

race question

reproduction, questions

South American origin

Homewood Children's Village (HCV)

academic achievement

collaborative approach

evidence-based strategy

phases and goals

YMCA and YWCA

HOPE program

I

Incarceration.

See alsoBlack–White incarceration rate ratios

Black–White differences

crack-powder-cocaine disparity

mandatory-minimum sentencing laws

"victim discounting"

Individual development accounts (IDAs)

Individual retirement accounts (IRAs)

Inequality

Black–White disparities

Civil Rights Act

discrimination

economic inequality, US

Latinos and Asian Americans

measures of attributions

on-line survey

poverty rate

racial resentment

stereotype pattern

zero-sum game

Institutional-healthcare system factors

African and Hispanic Americans

health insurance

infant mortality

institutional factors

interconnectedness factors, population health

team-based setting

Intergroup dialogue

color students vs. White students

communication processes

critical-dialogic model

curriculum model

educational approach

identity involvement and motivation

multi-item measures

Multi-University Intergroup Dialogue Project

pre and post data

structural and institutional influences

J

Job market

K

Korean Churches for Community Development (KCCD)

L

Legitimacy

description

high-crime neighborhoods, Chicago

informal social control

Life course

American children

and economic instability

nonWhites

Limited liability companies (LLCs)

M

Males.

SeeAfrican Americans

Mental health indicators.

See alsoAfrican American

behavior problems

biased responses

differential experience, trauma

distinctive moderating factors

paranoid schizophrenia

prejudice and negative bias, adults

social information processing

Mexican Americans

depressive symptoms, diabetes and health outcomes

diabetes and disability

frailty, older

See(Frailty, older Mexican Americans)

Hispanic EPESE

See(Hispanic Established Population for the Epidemiological Study of the Elderly (Hispanic EPESE))

Hispanic/Latinos population

Hispanic paradox

See(Hispanic Paradox)

obesity, disability and mortality

Minorities.

SeeHealth disparities

Mortality, breast cancer.

SeeHealth disparities

Multi-University Intergroup Dialogue Project

N

National Assessment of Educational Progress (NAEP)

The National Health and Nutrition Examination Survey (NHANES)

National Longitudinal Survey of Youth (NLSY97)

National Network for Safe Communities (NNSC)

National origin

and ethnic checkboxes

French-speaking Haitian

race and color

Neighborhood poverty

New Jim Crow

NHANES.

SeeThe National Health and Nutrition Examination Survey (NHANES)

NNSC.

SeeNational Network for Safe Communities (NNSC)

O

Obesity

awareness and resources

cultural factors

description

disability and mortality

faith-based organizations

family factors

geographic location

health behaviors and consequences

individual influences

insurance companies, preventive-health services

medical costs

national school nutrition guidelines

neighborhood influences

program contents

public policy

race/ethnicity

See(Race)

weight status

Office of Management and Budget (OMB), US Standards

categories

census 2010

See(Census)

data collection

government-wide race and ethnicity

principles

revision

Organization for Economic Cooperation and Development (OECD)

P

Patient behaviors

Patient Protection and Affordable Care Act (ACA)

Policing

description

intrusive and abusing

racism

Poverty inequality.

See alsoAmerican poverty

empirical analysis

food stamps

intergenerational mobility

labor market and lack of benefits

social problems

social welfare program

Program for International Student Assessment (PISA)

Promising practices

Casey Family Programs

community and cultural resources

family group decision making (FGDM)

formal and informal support

Provider behaviors

Q

Questionnaire design

AQE

See(Alternative questionnaire experiment (AQE))

census

See(Census)

mailout/mailback

reproduction, Hispanic origin

streamlined question

US decennial census

R

Race

in America

See(America's racial realism)

educational attainment

and ethnicity concepts

See(Ethnicity)

ethnicity, obesity

non-Hispanic Black men

prevalence among boys and girls

prevalence among men and women

intergroup dialogue

See(Intergroup dialogue)

post-racial generation

socioeconomic opportunity gaps

structural attributions

test scores

Racial and ethnic disparities

college achievement

See(College achievement)

disciplinary disproportionality counters expectations

disruptive behavior

poverty

prediction

school discipline

See(School discipline)

Racial disproportionality, prison

African Americans

Black–White incarceration rate ratios

See(Black–White incarceration rate ratios)

crimes

drug markets

See(Drug markets)

HOPE program

incarceration

See(Incarceration)

reduction factors, crime

state criminal-record repositories

US incarceration rate

Racial/ethnic status

Black–White breast cancer mortality disparities

Federal Housing Administration (FHA)

residential segregation

Racial inequities

Racial problems

See alsoRace

Racial profiling

Black citizens

Body Count

"Ceasefire" approach

criminal justice system

DMI

institutional and social racism

legitimacy

National Network for Safe Communities (NNSC)

"New Jim Crow" argument

police chiefs, prosecutors

social science evaluations

"super-predators"

Racial wealth gap

community resources

poverty

private equity

racial economic inequality

Racism

alcohol and cigarettes abuse

drug use

and gender differences

and heightened stress

S

School discipline

culturally responsive intervention

data disaggregation

data interpretation

disproportionality

out-of-school suspension and expulsion

racial and ethnic disparities

School-wide positive behavior supports (SWPBS)

Seeking group recognition

Afro-Caribbean community

Hispanic origin

Iranian/Arab nationalities

OMB race categories

uncodable responses

Write-Ins classification

Severe mental illness

Social class

achievement gaps

race/ethnicity

Social determinants

Black–White breast cancer mortality disparities

gender

public health researchers

racial/ethnic status

social class/socioeconomic position

Social information processing

cognitive distortions

symptoms

traumatic events and biased ratings

Social justice

The State of Black America

Substance abuse

Caucasian males

drug use by race

racial differences

Survey.

See alsoAmerican Community Survey

anti-Black than anti-White discrimination

Blacks report

General Social Survey

Income and Program Participation

Michigan's Institute for Social Research

Texas public school graduates

young people

Suspension

Black and White

and expulsion

Latino students

T

Texas A&M University (TAMU)

Asian and Hispanic freshmen

Hispanic-White achievement gap

scholarship program

socioeconomic composition

4-year graduation rates

Texas higher education

algebra completion

Black students

characteristics, first time freshmen

economic heterogeneity

economic strata

first semester GPA determinants

first time freshmen distribution

four-year graduation disparities

Hispanic and Black UT freshmen

institutional selectivity and demographic group

modeling strategy

race-neutral admission

sixth semester GPA

socioeconomic status

TAMU

TEA measure

test score

UTSA

weak academic preparation

White high school classmates

4-year graduation

Trauma

differential experience

social information processing

U

Underserved

University of Texas at Austin (UT)

achievement disparities

open admissions

saturation

and TAMU

See(Texas A&M University (TAMU))

White counterparts

University of Texas–San Antonio (UTSA)

administrative records

attrition rates

stark comparison

Urban Innovation

W

Wealth

asset-building strategies

See(Asset-building approaches)

blueprint

CLIMB

economic security

funds dedication

governmental policies

Hispanics

practical gap-closing case studies

quality education and homeownership

racial disparities

Servicemen's Readjustment Act

unemployment

Urban Institute study

White households

The Weathering hypothesis

Y

Young Black men

activity and institutions

crime rates

labor demand

labor market opportunities

"labor supply"

less-educated

market opportunities

previous incarceration

Youth views

anti-Black and anti-White discrimination

Black and Latino

level of convergence

perspective differences, college students

post-racialism

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