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The COVID-19 pandemic, well-being, and transitions to post-secondary education
Malte Sandner1,
Alexander Patzina ORCID: orcid.org/0000-0001-5039-70161,2,
Silke Anger1,2,3,
Sarah Bernhard1 &
Hans Dietrich1
Review of Economics of the Household (2022)Cite this article
This study examines the immediate and intermediate effects of the COVID-19 pandemic on the well-being of two high school graduation cohorts (2020 and 2021) and how changes in well-being affect students' educational plans and outcomes. Our unique panel data on 3697 students from 214 schools in 8 German federal states contain prospective survey information on three dimensions of well-being: mental health problems, self-rated health, and life satisfaction. Data is collected several months before (fall 2019), shortly before and soon after (spring 2020) as well as several months after (fall/winter 2020/21) the beginning of the COVID-19 pandemic. Applying difference-in-differences designs, random effect growth curve models, and linear regression models, we find that school closures had a positive immediate effect on students' well-being. Over the course of the pandemic, however, well-being strongly declined, mainly among the 2021 graduation cohort. We show that a strong decline in mental health is associated with changes in educational and career plans and transition outcomes. As adverse life experiences in adolescence are likely to accumulate over the life course, this study is the first to exhibit potential long-lasting negative effects of the COVID-19 pandemic on education and careers of young individuals.
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The COVID-19 pandemic and the related policies to stop the spread of the coronavirus, particularly school closures, present a severe shock to mental and physical well-being for millions of young individuals worldwide. Distancing measures may affect the mental and physical health and life satisfaction of young individuals, as these measures massively change the schooling, learning processes and leisure activities of students, such as physical activity, time spent in front of screens, social contacts, substance use, and sleep time (Belot et al., 2021; Champeaux et al., 2020; Davis, 2021; Hisler & Twenge, 2021; Orgilés et al., 2020; Shanahan et al., 2020). Families with younger kids suffered from decreased well-being (Pisano et al., 2020; Stassart et al., 2021) but pandemic policies may especially impact the well-being of young individuals who are close to graduation because the measures not only affected schooling and leisure but also strongly reduced students' perceived career security and job and educational opportunities. As students' well-being presents a crucial resource in the process of educational decision-making and socioeconomic attainment (Haas, 2006), shocks to well-being may disrupt the transition from upper secondary to post-secondary education. Such transition disruptions at this stage may have negative consequences on future educational and labour market success, lifetime earnings and later life health (e.g., Leopold, 2018; Tamborini et al., 2015; Oreopoulos, 2007).
However, thus far, no empirical evidence exists on how school closures and the COVID-19 pandemic affect the well-being of students in their final high school years, and how effects on their mental and physical conditions relate to their educational and career plans and transition outcomes. We fill this research gap by using large-scale panel data on well-being, educational and career plans and transition outcomes of 3697 German high school students we sampled from the 2020 and 2021 graduation cohorts. These data have two key features. First, they entail three detailed indicators of well-being, i.e., mental health (10-item Hopkins Symptom Checklist; Derogatis et al., 1974), self-rated physical health (5-point scale; e.g., Mossey & Shapiro, 1982), and life satisfaction (11-point scale; e.g., Lucas, 2007). Second, these data contain both pre-pandemic information and information during the pandemic, as they stem from three survey waves in fall 2019, spring 2020, and fall/winter 2020/21.
Drawing on these data enables us to investigate (i) the immediate effects of nationwide school closures on students' well-being in spring 2020; (ii) the intermediate effects of the COVID-19 pandemic in general on students' well-being in fall/winter 2020/21; (iii) the heterogeneous effects of the COVID-19 pandemic on high school graduates who transition to post-secondary education and students still enrolled in high school; and (iv) the impact of decline in mental health on career and educational plans and educational decisions of graduates.
In the first step of our analysis, we separately investigate immediate and intermediate effects of the COVID-19 pandemic on well-being. This separation is important, particularly with respect to school policies, since students may perceive school closures as holidays or health protection in the short run (Helliwell & Wang, 2014), while in the long run, stressors due to adverse health, learning achievement, distancing measures or uncertainty about the future may prevail. To evaluate the immediate effects of nationwide school closures, the data allow the application of a difference-and-differences design exploiting the fact that some students within the second survey wave responded just before and other students shortly after the school closures. To elaborate on the intermediate effects of the pandemic (i.e., the developments prior to and during the crisis), we employ linear growth curve modelling.
In a second step, we investigate whether the COVID-19 pandemic has different effects on students who spend most of their two final high school years in times of the pandemic (2021 graduation cohort) and students who graduated from high school shortly after the outbreak of the pandemic (2020 graduation cohort). Differences in well-being between graduation cohorts might occur because students still enrolled in high school may face greater uncertainty about future decisions than graduates who already realized their transition to post-secondary education. However, school graduates face a completely unknown situation at their new educational institutions, since universities and vocational schools have similarly introduced distance learning (Crawford et al., 2020), which hardly enabled any interactions with new fellow students and apprentices. Additionally, the pandemic has reduced the available vocational training positions as alternative educational paths after high school as well as the number of student jobs, which may affect the financial situations of university students (Yükselen et al., 2020). Thus, it remains open whether the pandemic and related distancing measures affect students still at school or school graduates differently.
In a final analysis step, this study investigates to what extent a severe decrease in mental health leads to changes in educational and career plans and transition outcomes. Investigating such associations is important because earlier work showed that pre-transition health positively influences university enrolment decisions (Zheng, 2017). Furthermore, poor mental health increases the probability of educational dropout (Cornaglia, Crivellaro, & McNally, 2015). One potential mechanism explaining the importance of mental health for educational decision making might be that, for instance, depressive symptoms alter perceptions of the future (e.g., Leykin et al., 2011; Roepke & Seligman, 2016). Thus, students with decreasing mental health may lose confidence in their educational and career plans or opt for transitions they would not have made with a better mental health status. As other major societal crisis, e.g., the Great Depression, have cumulative negative effects for individuals over the life course (Hale, 2017), investigating changes in mental health and educational plans and outcomes may provide important insights on the potential long-term consequences of the COVID-19 pandemic.
In analysing the immediate and intermediate effects of the COVID-19 pandemic on students' well-being in two graduation cohorts and how changes in well-being relate to educational and career plans and transition outcomes, we extend the existing and rapidly emerging research on the effects of the COVID-19 pandemic on the well-being and mental health of young adults and teenagers (e.g., Elmer et al., 2020; Giuntella et al., 2021; Shanahan et al., 2020). Furthermore, we contribute to the literature on how lockdowns affect educational and career plans, which until now has concentrated on university students or employed individuals (Aucejo et al., 2020; Fiaschi & Tealdi, 2021). We combine these strands of literature and demonstrate that students who are close to the transition to post-secondary education are most vulnerable to shocks to their well-being due to the COVID-19 pandemic and that such shocks are related to educational and career plans and transition outcomes.
The participants of this study attended the highest track of secondary school in Germany, "Gymnasium", in the final 2 years. The educational system comprises three tracks of secondary school: the lower and intermediate tracks prepare students for vocational training, whereas the highest (academic) track results—after successful completion—in the high school diploma "Abitur", which qualifies students both to enrol at university or at a post-secondary vocational education or training. This academic track usually ends with final examinations at grade 12/13. These exams largely take place in March, and students receive their graduation diploma in the summer before they enrol at university or start vocational training in the fall.
After the outbreak of the COVID-19 pandemic at the beginning of 2020 and as one of the first nationwide pandemic prevention measures, all schools were closed after March 13th, 2020 in Germany. On April 23rd, 2020, the German federal states partly started reopening schools, albeit with very large regional and institutional variations: Since educational policy is the responsibility of the 16 federal states, there was no uniform school opening policy in Germany. Furthermore, local developments of the pandemic affected the closing of whole schools, class levels, or single classes. After the summer break, schools started on a regular basis and then went gradually back to limited schooling in November and December 2020, first by allowing only alternating groups of students from each classroom and then from January 2021 switching back to complete distance schooling.
The data used in this study were collected for the BerO study, which evaluates the effectiveness of intensive job counselling for students in the highest secondary school track. The baseline survey (wave 1) was conducted as a paper-and-pencil interview (PAPI) in 214 schools in 8 of 16 German federal states. Students in these schools could voluntary complete the questionnaire in school between September and November 2019 and were instructed by a professional data collection team.Footnote 1 In addition to these data, our analyses draw on data from two follow-ups, which took place outside the school context as a computer-assisted web or telephone interview (CAWI/CATI). Students were interviewed from February to June 2020 (wave 2) during the first wave of infection with some students answering before and others after school closures. Survey wave 3 took place from November to January 2021 during the second COVID-19 wave.Footnote 2 Fig. 1 gives an overview of the timeline of the data collection.
Timeline of data collection and COVID-19 infections in Germany
To answer the first and second research questions on the immediate and intermediate effects of the COVID-19 pandemic on the mental and physical well-being of young individuals, this study investigates changes over time in three outcomes: (i) mental health problems, (ii) life satisfaction, and (iii) self-rated health. The first three rows of Table 1 give an overview of the descriptive statistics of these outcome variables.
First, as a widespread measure for mental health, in waves 2 and 3, this study employs data from a subscale of the well-established Hopkins Symptom Checklist (HSCL-58; Derogatis et al., 1974) to approximate individuals' risk for anxiety disorders and depression. The employed 10-item version (HSCL-10) has been shown to be a very good proxy for the longer HSCL-25 (Haavet et al., 2011; Schmalbach et al., 2021). This study uses a scale of four categories for each question ("No," "Yes, a little," "Yes, quite slightly," and "Yes, extremely," rated 1 to 4, respectively) and employs a binary measure, which indicates 1 if an individual's average score on the 10-item scale exceeds the widely used cut-off point of 1.85. We used this binary predictor because trials have indicated clinically relevant symptoms of anxiety and depression above this cut-off point (Strand et al., 2003).Footnote 3
Second, life satisfaction refers to "the degree to which an individual judges the overall quality of his/her own life as a whole favorably." (Veenhoven, 2012: 66). For waves 1, 2 and 3, we employ the established 11-point scale (e.g., Lucas, 2007) and rely on answers to the following question: "How satisfied are you currently with your life in general?" Respondents could answer on a scale ranging from 0 ("totally dissatisfied") to 10 ("totally satisfied"). Prior research on life satisfaction and mortality (e.g., Diener & Chan, 2011) indicates that such cognitive evaluations of individuals' lives predict mortality.
Third, for waves 1, 2 and 3, this study examines self-rated health. Empirically, we rely on the question "How would you describe your current state of health?" Respondents could answer on a five-point likert-type scale ranging from 1 ("poor") to 5 ("very well"). This question is a widely used item in many health studies in the social sciences and has been shown to be a strong predictor of mortality because it proxies general physical well-being (e.g., Mossey & Shapiro, 1982).
To elaborate on reasons for potential heterogeneity in well-being between graduation cohorts (i.e., between school students and school graduates), we additionally investigate whether students from the two cohorts perceive the COVID-19 situation differently. To this end, we explore whether differences in the current situation or whether worries about the future explain potential cohort variation. To approximate young individuals' current situation, we use questions addressing enjoyment with learning and the extent to which young individuals are burdened by distancing measures. We approximate worries about the future in using questions asking about students' worries about career plans (the descriptive statistics of these outcomes are shown in Table A1).
To answer the last research question, we identified severe drops in mental health between spring 2020 and fall/winter 2020/21 independent of the baseline value of mental health and the chosen cut-off point. A strong decrease is coded as 1 if values in the individuals' HSCL-10 scores changed by at least 0.4 scale points. This applies to a quarter of the sample.
To investigate the influence of these severe mental health drops on educational and career plans, we rely on five different measures. For graduation cohort 2020 analysed educational and career plans comprise probabilistic beliefs about finishing the current post-secondary education. For graduation cohort 2021 analysed plans comprise probabilistic beliefs about successfully finishing potential university education, the certainty about the future educational pathway, expected final grade point average (GPA), and probabilistic beliefs of studying a science, technology, engineering and mathematics (STEM) major. While the probability of studying STEM directly measures one important educational aspiration, probabilistic beliefs and GPA expectations address—according to rational action theory (e.g., Breen & Goldthorpe, 1997; Cameron & Heckman, 1998)—important determinants of educational decisions.
To investigate the influence of decreasing mental health on educational transitions, we rely on three different outcomes after graduation, which were measured in the 2020 graduation cohort. Analysed measures comprise satisfaction with the overall educational decision, satisfaction with the new learning institution, and satisfaction with the location of this institution. Analysing satisfaction measures appears important because research shows that satisfaction outcomes are associated with educational dropout (e.g., Sarra, Fontanella, & Di Zio, 2019).
Analytical strategy
Estimating the immediate effects of the first school closures
When investigating the effects of school closures, a pure outcome comparison between the students who answered before and after the school closures in March 2020 may be biased because the two groups of students may have different characteristics that could be related to our well-being outcomes under study. To solve this problem, we use the panel dimension of our data and apply a difference-in-differences estimation using data from waves 1 and 2 shown in Eq. 1:
$$Y_{it} = \beta _1SC_i + \beta _2W_{it} + \beta _3\left( {SC_iW_{it}} \right) + \varepsilon _{it}$$
where Yit is the outcome of interest of individual i at wave t (life satisfaction and self-rated health, which are both available for waves 1 and 2). SCi (School Closure) is a binary variable that takes a value of 1 for students who answered in March 2020 post school closure and 0 for students who answered in March 2020 pre school closure, and β1 captures the difference between those individuals. Wit (Wave) contains a wave dummy for Wave 2 interviews, where β2 captures the corresponding coefficient. εit is a standard error term. Finally, SCiWit is the interaction term of SCi and Wit that takes a value of 1 for students in wave 2 who answered the questionnaire post school closure. The coefficient β3 then measures the divergence in the outcome between those who answered post school closure, i.e., the treatment group, and those who answered pre school closure, i.e., the control group, which indicates the average treatment effect on the treated (ATT). This is the effect of the school closures.Footnote 4
Only for the Hopkins scale we do not have information at wave 1. Therefore, for this outcome, we compare students who answered before and after the school closures, including a rich set of individual characteristics, as controls (shown in Table 1). For the analysis of all three outcomes, we restrict the time window to individuals who responded in the second survey wave prior to school closures and during closures. In doing so, the difference-in-differences analysis excludes students who participate in the interviews after school reopenings. Before the closures, all students answered within a time window of two weeks. Therefore, we argue that it is rather unlikely that pandemic factors, such as the infection rate, explain differences in well-being before and after school closures.
Estimating the development of well-being during the COVID-19 pandemic
To investigate the development of the examined well-being outcomes during the COVID-19 pandemic, we use the following specifications of linear random effects growth curve modelsFootnote 5:
$$Y_{it} = \alpha + \beta _n\mathop {\sum}\limits_{n = 2}^3 {W_{nit}} + \lambda {^{\prime}}X_i + \mu {^{\prime}}X_{it} + \theta _i + \varepsilon _{it}$$
$$Y_{it} = \alpha + \beta _n\mathop {\sum }\limits_{n = 2}^3 W_{nit} + \gamma C_i + \delta _n\left( {C_i \times \mathop {\sum }\limits_{n = 2}^3 W_{nit}} \right) + \lambda {^{\prime}}X_i + \mu {^{\prime}}X_{it} + \theta _i + \varepsilon _{it}$$
In both equations, Yit represents either life satisfaction (0 to 10), self-rated health (1 to 5) or mental health problems, which are approximated by the risk for anxiety disorders and depression (0 vs. 1). θi represents a person-specific error term, which is modelled as a random variable. εit constitutes an idiosyncratic error term. Wnit indicates dummy variables for each survey wave. Xit indicates a vector with time-varying confounding variables, whereas Xi captures time-constant confounders. In Eq. 3, we introduce Ci indicating whether a respondent stems from the 2021 or 2020 graduation cohort. To allow for variation across graduation cohorts, we interact Ci with each wave dummy. While the multiplicative effect of γ captures heterogeneity between cohorts at wave 1, δn captures heterogeneity in well-being between cohorts over the course of the COVID-19 pandemic.
Finally, we apply two sets of ordinary least square regressions. In the first set, we specify a model to elaborate on differences between graduation cohorts 2020 and 2021 at wave 3 (fall/winter 2020/21). In the second set, we identify individuals with strong decreases in mental health between survey waves 2 and 3 to generate a binary variable (reference group: slight or no decrease in mental health) and regress educational and career plans and transition outcomes at wave 3 on this binary indicator.Footnote 6 In correlating these measures, we elaborate on the potential long-term impact of the COVID-19 pandemic. Although this procedure constitutes a correlative workaround due to potential reversed causality, we can rule out parts of endogenous selection bias by using our rich data. To this end, our model specifications condition on a vast set of individual characteristics as control variables, described in "Sample characteristics and control variables", and they also include the baseline level (i.e., survey responses given in fall 2019 that are independent of any COVID-19-related factors) of each dependent variable and the baseline level of mental health (measured at wave 2).
Sample characteristics and control variables
From the BerO baseline sample (N = 7192), we construct a balanced sample and restrict our analyses to students who participated in all three waves (fall 2019, spring 2020 and fall/winter 2020/21) with nonmissing information on our outcomes or metric control variables. If information is missing in a categorical control variable, we create a missing information category. As one main question of this study is as to what extent mental well-being declines correlate with perceived post-secondary transition outcomes, we further exclude individuals from the analytical sample that transitioned from high school to a "gap year" or standby state. In this year young individuals typically bridge the time between high school graduation and enrolling at university or starting vocational training. Since such a sample truncation has the potential to distort our main findings (e.g., Elwert and Winship, 2014), we re-run all analyses on a full sample. This robustness check reveals that our main results also hold if we include graduates in a "gap year" (Figures A2 to A4 in the Online Appendix). Overall, our balanced sample consists of 3697 students. 2451 students stem from graduation cohort 2021 and 1246 students from graduation cohort 2020.
The set of individual characteristics that we use as controls includes socio-demographics, i.e., cohort (graduation cohort 2021 dummy), gender (male dummy), migration background (1st/2nd generation migrant), parental education (at least one parent with university education), and educational achievement (GPA better than 2.5 on average on a scale from 1 – best grade to 6 – failed). For these characteristics, the strong gender imbalance is notable. This imbalance is partly because women are in general overrepresented in German high schools and because women tend to participate in questionnaires more often (e.g., Becker, 2021). Moreover, we use a rich set of preferences, i.e., risk aversion and myopia, and personality traits, for which we use constructs based on multiple items to measure self-efficacy, grit, and the Big Five personality dimensions openness, conscientiousness, extraversion, agreeableness, and neuroticism. We also include a dummy variable for interview mode to account for possible mode effects and, finally, rely on school fixed effects to take into account institutional and geographical variation in the data.
Table 1 presents the sample characteristics in the three waves, with the spring 2020 wave split into individuals who answered before and after the school closures. The first rows show the means of our overall well-being measures. They show strong variation among the waves and before and after the school closures. We will investigate these changes in detail in the next sections. The next rows depict the sociodemographic characteristics, educational achievement and educational choice, as well as preferences and personality traits of the sample. As we use a balanced sample, there are no differences in these characteristics between wave 1 (fall 2019) and wave 3 (fall/winter 2020/21). However, the figures reveal that the characteristics between those students who answered before and after the school closures differ; for example, more males and slightly worse performing students answered the questionnaire after the school closures compared to students who participated in the survey before the school closures. This finding supports our strategy to apply a difference-in-differences approach to rule out biases by this selection.
Appendix Table A1 shows the descriptives of the measures, which are only available for wave 3 (fall/winter 2020/21). The variables contain information on attitudes and worries, i.e., how students deal with the COVID-19 pandemic and distancing measures, as well as on educational and career plans and further sociodemographic characteristics. Appendix Table A2 shows the aggregated values over the three waves for the variables shown in Table 1.
Table 1 Sample characteristics by wave
Immediate effects of first school closures on well-being
Table 2 presents the immediate effects of school closures on the three well-being outcomes. Using the difference-in-differences approach explained in Eq. 1 demonstrates that while school closures did not affect life satisfaction, self-rated health weakly increased after school closures by 0.21 standard deviations (see online Appendix Figure A1 for a graphical illustration of the effects). Investigating the immediate effect of school closure on mental health based on a within-wave 2-comparison reveals that the risk of mental health problems—while controlling for the variables introduced in "Sample characteristics and control variables"—is 5 percentage points lower for the students who participated in the survey after the closures. Overall, these results indicate that school closures had a positive effect on overall health in the first weeks after their implementation, as indicated by improvements in two of the three measures.
Table 2 Immediate effects of school closures on well-being: Results from difference-in-differences and OLS regressions
To test whether our treatment indicator actually picks up the effect of school closures, we ran a placebo test. In doing so, we assigned all individuals who answered in survey wave 2 in calendar week eleven (one week before the school closures) and onwards to the post school closure group. As a consequence the post school closure comprises a large amount of respondents (around 1700) who were not affected by school closures. The results from this robustness check suggest that the increase in self-rated health and the decrease in mental health problems are substantially smaller and statistically non-significant. Thus, our main specification appears to pick up the immediate effects of nationwide school closures.
Development of well-being before and during the first and second waves of the COVID-19 pandemic
In this section, we investigate the effects of school closures and distancing regulations eight months after the pandemic started. Figure 2 shows the development of the three outcomes at wave 1, at wave 2 before and after the school closures and at wave 3 calculated by applying Eq. 2 (see Appendix Table A3 for point estimates and significance levels). In line with the previous section, we see an immediate increase in self-rated health and a decrease in mental health problems in wave 2 after the school closures. However, from spring to fall, we observe a strong decrease in life satisfaction and self-rated health and a particularly strong increase in mental health problems. Overall, the data suggest that after students' overall health improved in the short term, it strongly declined in the longer term.
Development of well-being since fall 2019. Results from random effect growth curve models. Notes: Outcomes: Life satisfaction (0 to 10); self-rated health (1 to 5); Dummy for being above the clinical threshold for a high anxiety and depression risk (HSCL-10). N Life Satisfaction = 11,091; N SRH = 11,091; N HSCL-10 = 7394. Controls: school fixed effects, gender, migration status, parental education, school performance at wave 1, self-efficacy, Grit, Big Five personality traits, graduation cohort, risk aversion, time preferences, and interview mode. Data: BerO study wave 1 to 3
COVID-19 effects on mental and physical well-being by graduation cohorts 2020 and 2021
We now investigate how the effects on the three well-being measures differ over time between the 2020 and 2021 graduation cohorts. Using Eq. 3, Fig. 3 shows that none of the three measures differed significantly at wave 1 or 2 (before and after the school closures) between the two cohorts (see Appendix Table A4 for the point estimates and significance levels). However, at wave 3 (fall/winter 2020/21), the graduation cohort 2021 (graduating in spring 2021) showed significantly worse outcomes for all three well-being measures. The difference was most pronounced for mental health problems, for which the increase for graduation cohort 2021 from wave 2 to wave 3 amounts to almost 20 pp, while the increase was 5 pp for the 2020 graduation cohort.
Development of mental and physical well-being by graduation cohort. Results from random effect growth curve models. Notes: Outcomes: Life satisfaction (0 to 10); self-rated health (1 to 5); Dummy for being above the clinical threshold for a high anxiety and depression risk (HSCL-10). N Life Satisfaction = 11,091; N SRH = 11,091; N HSCL = 7394. Controls: school fixed effects, gender, migration status, parental education, school performance at wave 1, self-efficacy, Grit, Big Five personality traits, risk aversion, time preferences, and interview mode. Data: BerO study waves 1 to 3
After showing substantial variation between graduation cohorts in fall/winter 2020/21, we now investigate two potential mechanisms that might explain cohort differences. In particular, we test whether differences in the current enjoyment of education and burdens induced by distancing measures or whether worries about the future can explain cohort variation. We assume that the mechanisms under study are important because young individuals spend a great amount of their daily time with being at school and with school work (Anger et al., 2020). Moreover, research indicates that perceived career insecurity is detrimental to well-being (e.g., Kopasker, Montagna, & Bender, 2019).
For this purpose, Columns 1 and 2 of Table 3 present the results of two sets of questions that the students in both cohorts answered at wave 3, and Column 3 shows the differences between the two graduation cohorts. Students who had just started their final school year (graduation cohort 2021) reported higher levels of burden due to distancing measures and less enjoyment of learning in fall 2020 than those who already had left school (graduation cohort 2020) and had already attended university study (84%) or vocational training (16%). Analysing respondents' worries about their future reveals that students from graduation cohort 2021 were more worried than those in graduation cohort 2020 about their occupational futures and expected negative effects of distancing policies on their future careers. They also claim deficits with respect to receiving relevant career information.
Table 3 Attitudes of the 2020 and 2021 graduation cohorts regarding distancing measures and worries (Fall 2020)
Next, we investigate to what extent these higher concerns of the graduation cohort 2021 can explain the observed differences in well-being between the two cohorts. Table 4 shows the results of the estimations in which we regress the three well-being measures on the control variables (column 1) and on the two sets of questions that may explain the difference between the cohorts in well-being (Columns 2 and 3). In line with the results in Fig. 3, Column 1 demonstrates that graduation cohort 2021 reported significantly worse outcomes in the health measures than graduation cohort 2020. However, the results in column 2 show that including the present attitudes completely absorbs the difference between the cohorts for all outcomes and explains the largest share of the difference in life satisfaction, self-rated health, and mental health between the cohorts. The present attitudes explain even more of the gap than future worries, which also reduces the effect of the graduation cohort but does not fully absorb it (column 3).
Table 4 Mechanisms explaining differences in dimensions of well-being between graduation cohorts 2020 and 2021
Associations between mental health decreases and educational and career plans and transition outcomes
In this section, we analyse whether and to what extent the decrease in mental health that we observe from spring 2020 to fall/winter 2020/21 is related to students' educational and career plans and transition outcomes. We focus on mental health, for which we find the strongest decrease between spring 2020 and fall/winter 2020/21. As described in "Analytical Strategy", we regress educational and career plans and transition outcomes, measured in fall/winter 2020/21, on a dummy that takes a value of 1 if a student showed a strong decline in mental health (i.e., an increase on the HSCL-10 scale above a value of 0.4, which represents the upper quartile) from spring to fall/winter. The first model controls for student characteristics, while the second model additionally uses the panel dimension of our data and includes mental health values at wave 2 (i.e., the first measure of mental health available) and the dependent variable of the model, i.e., the educational and career plans at wave 1 (i.e., the first measure that is independent of the COVID-19 situation) as control variables. Models investigating transition outcomes include life satisfaction at wave 1 as a control. These two additional controls exclude the possibility that our estimates merely capture the effect of a student's generally low mental health and that those students with a decrease in mental health would have already stated less ambitious educational and career plans and dissatisfaction before the decrease.
Table 5 shows the results of the estimations for the 2020 graduation cohort (Columns 1 and 2) and for the 2021 graduation cohort (Columns 3 and 4). In both cohorts, a strong decline in mental health was related to a lower success probability of the current educational path or potential future university education. The next rows present the results for educational and career plans among graduation cohort 2021 and transition outcomes among graduation cohort 2020. For the 2020 graduation cohort, the results reveal that students with a strong decline in mental health were less satisfied with their overall current educational decision, location, and institution than students with a lower or no decline. In line with these results, students with a strong decline in mental health in the 2021 graduation cohort stated that they felt less secure about their future career paths, they expected a worse GPA, and they had a lower probability of wanting to study STEM subjects. Columns 2 and 4 demonstrate that these results hold in the very tight specification, which includes lagged mental health and lagged educational and career plans and satisfaction. The sizes of the effects (between 10 and 15% of a standard deviation) are higher for the 2020 graduation cohort than for the 2021 cohort. However, for the 2021 cohort, the effects were in a relevant range, with approximately five percent of a standard deviation.
Table 5 Associations between strong decreases in mental health and educational and career plans and transition outcomes in fall/winter 2020/21
We start the discussion of the results with the question of why school closures create positive effects on well-being in the short run. Our first explanation for the positive short-run effects refers to the idea that students perceived school closures as a relief, hence resembling additional holidays. This explanation is based on the finding that individuals show higher well-being on weekends and during holidays (e.g., Ryan et al., 2010), which in the case of students may be caused by the fact that studying provides less well-being than other leisure activities (Helliwell & Wang, 2014). Additional studies have shown that if high school students are not at school, they feel less stressed because of reduced pressure and bullying, which in extreme cases even leads to less suicide during the holiday months (Hansen & Lang, 2011, Kim & Leventhal, 2008). The finding that students spend much fewer hours studying during school closures than in normal times supports the holiday explanation (Anger et al., 2020 for the present sample; Grewenig et al., 2020, Grätz & Lipps, 2021).
An alternative explanation for the increase in well-being might be that students perceive school closures as a measure to protect their health and relieve their fear of becoming infected with COVID-19. The finding that a positive effect exists for self-rated health and mental health problems while the effect is absent for life satisfaction may support this explanation. In addition, the decline in self-rated health and life satisfaction from Wave 1 to Wave 2 for the students who answered the questionnaire before the school closures supports the health protection explanation, as the decline may have resulted from COVID-19 fear. Finally, self-rated health for students who answered the questionnaire after the school closures remained at the same level as in wave 1 and did not increase. In the case of a holiday effect, we may have expected an increase in this outcome. However, interview mode or honesty-in-reporting effects may also explain the decline in life satisfaction and self-rated health from wave 1 to wave 2, which supports the holiday explanation (Chadi, 2013; Warren & Halpern-Manners, 2012). Irrespective of the final explanation for the increase in well-being shortly after school closures, the findings are policy relevant, as they demonstrate that short-term school closures are not harmful to students' well-being.
Next, we discuss why well-being declines in the long run and why this decline is stronger for students who are still enrolled at high schools. In the longer run, the burdens of school closures and other distancing measures may accumulate over time because students suffer more from social distancing and home schooling and may be afraid about a loss of human capital. Additionally, students increasingly perceive the pandemic not only as a short-term event but also as a long-term condition. This is an important finding, because our results implicate that physical and mental well-being developed very dynamically during the pandemic and measuring well-being at one point in time during a crisis may be misleading. Svaleryd & Vlachos, (2022) describe positive and negative effects on mental health and well-being in their literature review. For example, Sachser et al., (2021) also found positive immediate effects of lockdown measures on mental health in a representative sample of the German population without assessing longer-term outcomes. In contrast, there are studies which also found short-term decreases in well-being, particularly for mothers and working parents in general (e.g., Cheng et al., 2021; Huebener et al., 2021). Taken together, the results suggest the importance of differentiating by mechanisms, groups, and time points when researching mental health and well-being.
As an explanation for the observed long-run decline in well-being of the 2021 graduation cohort, we find that students who were still in school were more worried about their future careers and were more burdened by the current COVID measures, in particular school closures, social distancing measures during phases of reopened schools and perceived future career uncertainty. These differences in perceptions explain the difference in decline in overall well-being almost completely.
The existence and explanation of the difference between the cohorts are surprising, as the high school graduates from the 2020 cohort were also strongly affected by distancing measures, such as having online lectures or prohibitions of freshman events, and uncertainties about the future. However, in the first post-high school year, individuals from graduation cohort 2020 appeared to cope better with the situation. One explanation for the finding may be that freshmen were not familiar with universities, vocational schools or training firms without distancing measures and that therefore, they did not miss anything, for example, on-site lectures. Additionally, after having made a successful transition, worries about the future may have decreased. In contrast, high school students still have to make the transition out of school and are therefore exposed to greater uncertainty. Overall, the results indicate that students who were locked down while still in school are most vulnerable to an overall health decline, which should be considered in prevention or support services.
Finally, it is important that the decline in well-being is related to educational and career plans and satisfaction with the chosen educational path. As students from the 2020 graduation cohort with a decline in mental health reported less satisfaction with their choices, they were more likely to drop out, causing high costs for the individual and society. The same is true for the 2021 graduation cohort: our results, i.e., that these students feel less secure about the future, suggest that they will make decisions that they would have not made without the pandemic, which may also lead to high individual and societal costs.
Although our study has many strengths, it also shows limitations. First, we cannot state whether the COVID-19 distancing measures causally generated the decline in physical and mental well-being, as we do not have a control group who was not affected by the pandemic policies. However, it is very unlikely that only time, seasonal effects or any other event caused the reduction, as the decline was too strong for these explanations. Similar declines only occur in regions where a war started or in individuals who have experienced a stroke of fate, such as becoming widowed or disabled (Coupe & Obrizan, 2016; Infurna et al., 2017; Oswald & Powdthavee, 2008). Furthermore, other studies showed that well-being remained stable during the final years of high school education for cohorts who graduated before the pandemic (Herke et al., 2019). A second limitation might be that the size of the relationship between the decline in mental health and the transition outcomes in our estimates is not very large. Nevertheless, the relationships are meaningful because a rich body of research based on the notion of cumulative (dis)advantage shows that even small changes in this critical stage of the life course can have long-lasting accumulating effects over the life course (e.g., DiPrete & Eirich, 2006).
This paper analyses how the COVID-19 pandemic and the related measures to reduce the spread of the coronavirus have affected the well-being, educational and career plans and transition outcomes of students from the 2020 and 2021 high school graduation cohorts. The results show that after an immediate increase in physical and mental health around the time of the first school closures, well-being strongly declined in the longer run, particularly for students in the 2021 graduation cohort, who were still in school at the time of the survey in fall/winter 2020/21. Additionally, our results demonstrate that this decline in well-being was related to several educational and career plans and transition outcomes. The results clearly indicate that the COVID-19 pandemic, including school closures and distancing measures, has had negative effects on current graduation cohorts, which may cause life-long harm. Next, disentangling the effects of different pandemic policies, e.g., school closures or shutdown of leisure activities, would be crucial to evaluate the true costs and benefits of such policies. This is particularly important because the effectiveness of school closures as one of the main policies to prevent COVID-19 infections is disputed (Courtemanche et al., 2021; Isphording et al., 2021; van Bismarck-Osten et al., 2021). Finally, we address the question of intergenerational equality, as our findings demonstrate that the younger generation is likely to bear the long-term costs of pandemic policies, while the benefits of distancing measures in terms of lower infections are likely to be higher for older individuals.
The voluntary participation in the first wave within schools and in the follow-up waves outside the school context means that the survey data is not necessarily representative for the whole population of German high school students in their final years. However, we compare some of the outcome measures (self-rated health, life satisfaction) of our sample with data from the representative SOEP Youth Survey and find similar pre-pandemic values. Furthermore, to account for different response rates and any other characteristics which may differ between schools, we include school fixed effects.
To investigate whether attrition between the panel waves may be selective, we analyzed the determinants of sample inclusion in the balanced panel. In particular, individuals from the younger graduation cohort (cohort 2021) were more likely to stay in the sample, which can be explained by the higher mobility of the older graduation cohort (cohort 2020) after their high school degree. While there seems a slightly positive selection of individuals in the balanced panel (e.g., high-performing students), there is no indication that our outcome variables determine the participation in all three waves.
As a robustness check, we also conducted the analysis using the original variable for mental health to allow for more variation in the outcome variable, which confirmed the results of our main analysis.
As those individuals who were exposed to the school closures also have experienced a significantly longer exposure to the pandemic in general, our school closure effects may also capture effects of the pandemic in general. To address this threat, in Table A6 in the Appendix we show that intra-individual differences in interview timing between wave 2 and 3 after the outbreak of the COVID-19 pandemic (see Figure A5 for an overview of differences in response timing), which we use as a proxy for pandemic exposure, has no substantial effect on well-being outcomes under study. In respect to our Diff. in Diff. approach, the results presented in Table A6 suggest that the positive short-term school closure effects on wellbeing outcomes are a lower bound as the time effect points in another direction and leads to a decline in well-being (although mainly not statistically significant and very small in size). As the employed fixed-effects approach uses a subsample of respondents who answered in survey wave 2 after school closures and in survey wave 3 (N=1,405) and relies on strong exogeneity assumptions, we want to stress that these results constitute rather stylized findings on pandemic exposure on our well-being outcomes. Nevertheless, they strengthen our results retrieved from Eq. 1.
We use linear random effects growth curve models mainly because these models allow for individual-specific changes in the outcomes under study over time (i.e., they incorporate an individual- and time-specific random effect). To this end, the random effect model uses individual-specific and between-person variation thereby allowing to investigate how group differences emerge over time, a special feature of the model we heavily rely on in our study. The use of within-person variation constitutes a major advantage compared to pooled OLS. The advantage over FE models constitutes the fact that RE growth curve models allow investigating group differences (of course at the cost of potential bias due to individual-specific time constant unobserved heterogeneity). In Table A7 in the appendix we show, however, that results from RE, OLS and FE models do hardly differ.
We show results of linear probability models, as these are more intuitive and easy to interpret. However, to take into account that using a non-linear binary choice estimator may be more accurate for binary outcome variables, we also conduct analyses using an ordered logit model for life satisfaction and self-rated health, as well as a logit model for mental health. The coefficients of these non-linear models are very similar to those revealed by our main analyses.
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Open Access funding enabled and organized by Projekt DEAL.
Institute for Employment Research (IAB), Nuremberg, Germany
Malte Sandner, Alexander Patzina, Silke Anger, Sarah Bernhard & Hans Dietrich
University of Bamberg, Bamberg, Germany
Alexander Patzina & Silke Anger
Institute of Labor Economics (IZA), Bonn, Germany
Silke Anger
Malte Sandner
Alexander Patzina
Sarah Bernhard
Hans Dietrich
Correspondence to Alexander Patzina.
Sandner_etal_COVID_Wellbeing_HighSchoolStudents_Appendix_
Sandner, M., Patzina, A., Anger, S. et al. The COVID-19 pandemic, well-being, and transitions to post-secondary education. Rev Econ Household (2022). https://doi.org/10.1007/s11150-022-09623-9
High school graduates
Mental and physical well-being
School-to-work transition


text:
Pseudopanurgus fuliginosus is een vliesvleugelig insect uit de familie Andrenidae. De wetenschappelijke naam van de soort is voor het eerst geldig gepubliceerd in 1973 door Timberlake.

Andrenidae

redpajama_set_name: RedPajamaWikipedia


I woke to fresh snowfall outside my window, but it wasn't the glittering field of white that caught my eye, it was the speckles of red in the white. I woke up and pulled on my robe and slippers and blundered into the brilliant glare. There in the snow, not thirty feet from my house, I found the red in the snow. It was clearly blood, and a lot of it. I felt a cold that had nothing to do with the snow. I kicked at the snow. Perhaps, somewhere, there was a clue to what had happened in the field, and yet I couldn't bring myself to touch the sullied snow.
My dog, Clover, ran out from the house, through the door I'd left standing wide open. He bounded over, initially happy to see me, but after a moment concerned himself with the patch of snow as well. He didn't have my compunctions about the blemished snow, and instead buried his face into it, seeking the heart of the problem.
He brought his face up, smeared with red and frost. And in his mouth was a pendant, with the sign of a saint I didn't know.
"Good job, boy!" I said, and Clover dropped the chain in my hand, and proceeded to kiss me with his scarlet smeared mouth. I screamed and ran back into the house, someone or something's sticky blood all over my hands. Clover cocked his head to the side and followed behind me. I washed my hands and then I went to the computer to look up this saint.
This entry was posted in Methods, writing and tagged blood, dog, fun, inspiration, methods, motivation, mystery, prompts, red, science fiction, scifi, snow, timed writing, writer's block, Writing, writing prompts on January 30, 2014 by Vironevaeh.


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redpajama_set_name: RedPajamaGithub


Slots can be grouped into a few categories based on their overall structure, in the broadest sense. Classic slots that have three reels are the most simple form of slots with relation to design, rules and wagering options, that's exactly why they are recommended to beginners.
Video slots having a five-reel layout boast of an immense variety of themes and bonus features. Some slots have a modest betting range with the low minimum bet and are thus appealing and affordable to players on a budget. Other slots have a high maximum stake that may reach hundreds of dollars per spin which makes them a desirable slot choice for high-rollers. In video slots, graphics are richer, features are more numerous and diverse, and payouts are usually bigger, as compared with classic slot machines.
Progressive slots may feature either five or three reels, but one major distinction that sets them apart is a progressive jackpot which sometimes grows into millions of dollars. Hitting a progressive is a challenge yet a single spin of the reels can turn a lucky one into a millionaire.


Together with our partners, AIR has conducted the two largest studies of the implementation of the No Child Left Behind Act to date: The National Longitudinal Study of the No Child Left Behind Act (NLS-NCLB) and the Study of State Implementation of NCLB (SSI-NCLB). While the first study collected two waves of survey data from a nationally-representative sample of districts officials, principals, teachers, and paraprofessionals, the SSI-NCLB gathered interview and extant data from state directors of assessment and accountability, teacher quality, Title III, and supplemental educational services in all 50 states, the District of Columbia, and Puerto Rico. Together, these studies constitute the major data sources for the Congressionally-mandated National Assessment of Title I, the purpose of which is to inform Congress prior to the next reauthorization of ESEA. To date, AIR and our partners have produced eight reports on the implementation of key provisions of NCLB, including accountability, teacher quality, parental choice, and resource allocation.


text:
Category: 'university'
University place demand to grow by 300,000 by 2030
Category: university
Hundreds of thousands more university places will be needed by 2030 in order to keep up with demand for degrees, according to a study.
University of Bath vice-chancellor quits in pay row
The UK's highest paid vice-chancellor is retiring from the University of Bath amid a row over claims of excessive pay among university leaders.
Oxford and Cambridge named best two universities in the world
The top two universities in the world are to be found in the UK, according to a new league table. Oxford held on to first place in the latest annual Times Higher Education World University Rankings, with Cambridge rising from fourth place to take second.
George Osborne takes sixth job as university professor
George Osborne, the editor of the Evening Standard and former chancellor of the exchequer, has added a sixth job to his portfolio – that of honorary professor of economics at the University of Manchester.
Earlier this year the government proposed new plans to increase the price of tuition fees for students attending university to £9,250 per year of study. This is a £250 rise from the current £9,000 fee and will take effect from 2017.

redpajama_set_name: RedPajamaCommonCrawl


text:
(64975) Gianrix est un astéroïde de la ceinture principale.

Description
(64975) Gianrix est un astéroïde de la ceinture principale. Il fut découvert le  à Campo Imperatore par le projet CINEOS. Il présente une orbite caractérisée par un demi-grand axe de 2,95 UA, une excentricité de 0,26 et une inclinaison de 1,6° par rapport à l'écliptique.

Compléments

Articles connexes
 Liste des planètes mineures (64001-65000)
 Ceinture d'astéroïdes

Références

Planète mineure découverte en 2002
Astéroïde de la ceinture principale
Objet céleste découvert par le programme CINEOS
Objet céleste découvert sur le site Campo Imperatore de l'observatoire de Rome


text:
Communicating with Donors: Interview with Fundraiser Harvey McKinnon
by Harvey McKinnon, on 2/21/13 8:00 AM
Harvey McKinnon, author of The 11 Questions Every Donor Asks and the Answers All Donors Crave, recently spoke with his publisher about donor relations. GuideStar has published an excerpt from the book, and we're pleased to share Mr. McKinnon's additional thoughts with you.
One of the questions you cite in your book is "How will I be treated?" In your experience, are donors treated well today?
I'll let you judge for yourself. In a recent "mystery shopper test" we sent gifts to 28 hospitals on the same day. One organization sent back a warm thank you immediately. But the average response for the others was four weeks. And one hospital didn't respond with a thank you and receipt for 90-something days.
Ouch! Ninety days?
A long time, for sure. A client of ours has a great policy. As soon as they receive a gift of a thousand or more, they immediately call the donor. The person making the call has the highest-ranking title in the office that day. A few months ago, they received $10,000 in the mail. But there were no senior staff around—none. So the gift processor picked up the phone herself. She thanked the donor and learned that he'd sent $10,000 to five organizations, and her group was the first to thank him. To recognize their efficiency and how they treated him, he gave an additional stock gift of $122,000. I'm pretty sure the other four organizations don't know what they lost.
You talk a lot about storytelling in your book. You're convinced it's critical in fundraising, aren't you?
Yes. And for the simple reason that stories make needs come alive. You might call them the connective tissue. Right this minute in Niger there are a thousand starving children, you tell me. But that's a number, an abstraction to me. Tell me the story of Samira—so hungry she digs through the garbage for a fruit pit with pulp on it—and I begin to understand the desperation. I get emotionally connected. I start to visualize the suffering child and want to help.
Generally speaking, what about the solicitor matters most to the would-be donor?
Trust is absolutely essential. Without it, a gift is unlikely. Trust in and of itself may not be enough, but it's the critical component.
And what's not so important?
How smart you are. I know some brilliant people who aren't good person-to-person fundraisers. They don't connect well with people. They're not great at reading social cues and can't engage in the kind of conversation that motivates donors to part with their money. And then there are other fundraisers of average intelligence, who because of their charm, passion, and integrity raise a fortune.
Urgency is a word you hear a lot in fundraising, that donors need to feel their gift is needed NOW. That's easy to communicate when there's a disaster or when, for example, you're a health agency fighting a deadly disease. But that doesn't describe the mission of many organizations. What are they to do?
The first thing I'd say is tell a compelling story. If you speak of a child in need, a river that'll only be saved if action is taken now, the donor is more likely to understand the urgency. Another approach is to use deadlines or milestones. For instance, you can send a child such as Claire to a summer camp if you respond by May 31. Helpful, too, are specific dollar targets. If you can say that a $5,000 scholarship will help a student enroll in college and achieve their dreams, that adds urgency.
By economy of scale, a national organization, whether it's engaged in health or social welfare or youth development, is able to spend less per dollar on fundraising than a grassroots group or a start-up agency with perfectly noble goals. Is it really fair, then, for a potential donor to put a lot of emphasis on overhead?
I can understand why rating agencies, and even some donors, want a simple tool to evaluate organizations. It's partly because they have so many choices. But I'm convinced that looking only at overhead and ratios is a horrible metric on which to base your giving. What you want to look for is impact. Keep in mind that it's all too easy for organizations to allocate funds in a slightly dishonest way. Meaning money that should really be allocated to fundraising or overhead is conveniently counted in the "education category." This means that nonprofits that are more transparent and honest will be penalized. That's wrong. I give to a number of organizations that spend practically all their money on overhead because I know they're going to grow, and I like what they're doing.
Fundraising through social media—right now is it real or much ado about not much?
For emergency campaigns it can be lucrative. Social media can also work reasonably well with peer-to-peer fundraising. The problem here, though, is that people aren't giving to the cause, which they frequently don't care about, they're giving to their friend. And they're difficult to convert to long-term donors. If you think the attrition rate for direct mail is bad, take a close look at donors who come in through social media. It's appalling.
Anything we can do about it?
Yes. Don't rely solely on the Internet to renew online donors. From our experience, and it's extensive, you have to try to convert these individuals using a combination of channels, including the mail and telephone.
Pick the most important of the 11 questions in your book and tell me why.
First a caveat. The most important question varies from donor to donor. There's virtually nothing that applies to 100 percent of any population. But if you forced me to choose one question that's most important to the majority of donors it would be: Will my gift make a difference? All else equal, if you can answer that question honestly, and compellingly, your chances of getting a gift soar.
And I assume since you discussed it last, the question "How will you measure results?" isn't as pressing for donors?
It's extremely important for a certain percentage of people—usually high-dollar donors. But many others simply trust that a well-known organization will use their money responsibly. But make no mistake about it. Organizations that fail to report back on their successes—essentially how a donor's gift was used—ultimately raise less money and lose more donors.
© 2013, Emerson & Church, Publishers
Harvey McKinnon is one of North America's leading fundraising experts and president of the Vancouver/Toronto-based fundraising consultancy Harvey McKinnon Associates. In addition to The 11 Questions Every Donor Asks and the Answers All Donors Crave, his works include Hidden Gold (Taylor); the audio CD How Today's Rich Give (Jossey-Bass); Tiny Essentials of Monthly Committed Giving (White Lion Press); and (as co-author) the international bestseller The Power of Giving (Tarcher/Penguin), which was selected as an Amazon Best Book for 2005.


text:
Alain Baudry, né le  et décédé le  à Paris, est un éditeur universitaire français, Officier des Palmes Académiques.

Biographie

Après des études de droit et de lettres, il est appelé, en 1964, à collaborer à la première grande librairie universitaire sur le campus de Montréal, où il fonde la collection littéraire « Musique en Nouvelle France ».

De retour en France en 1971, il reprend ultérieurement la « Librairie Marceau », spécialisée dans les livres anciens, avenue Marceau, dans le  de Paris.

En 1984, il reprend la société « Aux Amateurs de Livres International »,  de l'exportation d'ouvrages français, vers les bibliothèques universitaires des États-Unis, du Canada et du Japon, y adjoignant une activité d'agence d'abonnements. Il y ajoute par la suite, une activité éditoriale de thèses littéraires d'auteurs français des  et , et crée des revues spécialisées dans cette période.

En 1990, il décide de reprendre les Éditions Klincksieck, fondées en 1842 rue de Lille, à Paris. Il déménage le siège des éditions en rachetant la « Boutique des cahiers » de Charles Péguy, rue de la Sorbonne. Le fond des éditions (1,3 million de volumes) est déplacé dans l'ancienne abbaye cistercienne du , de Droiteval, dans les Vosges.

De 1990 à 2000, il édite de très nombreux ouvrages et thèses universitaires de renom, dans des domaines aussi différents que les langues orientales et anciennes, l'archéologie, la musicologie, l'histoire des littératures et des idées, l'étymologie, l'esthétique, l'histoire de l'art et l'iconographie, la philologie, l'histoire du livre et des bibliothèques, l'histoire des religions. Il est également le diffuseur des revues de nombreuses sociétés savantes.

En 1994, son groupe intègre les éditions Cicero (théâtre classique) et la maison Didier érudition (collections d'études anglaises, germaniques et littératures comparées).

Après la vente du groupe Klincksieck, en 2000, aux éditions Les Belles Lettres, il reste dans l'édition en fondant en 2006, la société Alain Baudry et Cie qui publie notamment la collection « République des Lettres, République des Arts », sous l'autorité de Marc Fumaroli de l'Académie française.

En près de 50 ans, Alain Baudry aura, sous différentes enseignes, édité près de 2200 titres, créé 97 collections et diffusé 67 revues littéraires.

Références

Liens externes
 http://le-monde-de-l-edition.tout-le-net-en-1-site.com/editeur-Alain_Baudry.html
 http://alexandrie.ira-lyon.gouv.fr/Record.htm?Record=19143521280919617030&idlist=1

Éditeur français
Naissance dans le 12e arrondissement de Paris
Naissance en septembre 1944
Décès en juillet 2018
Décès dans le 5e arrondissement de Paris
Décès à 73 ans


#6687A5 Hex Color, Includes conversions, schemes, html color codes and much more.
This page lists many detailed information about the hex color #6687A5. Its Hexadecimal code is 6687A5. In a RGB color space, which is consist of 40% red, 52.94% green and 64.71% blue, and that corresponding RGB values are 102, 135, 165. In a CMYK color space, it is consist of 38.2% cyan, 18.2% magenta, 0% yellow and 35.3% black, whereas that corresponding CMYK values are 0.38, 0.18, 0, 0.35. Other more information such as degree, lightness, saturation, please browses the following content.


text:
package com.example.android.bitmapfun.util;

import android.content.Context;
import android.graphics.Bitmap;
import android.net.ConnectivityManager;
import android.net.NetworkInfo;
import android.os.Build;
import android.text.TextUtils;
import android.util.Log;
import android.widget.Toast;

import com.example.android.bitmapfun.util.ImageCache.ImageCacheParams;
import com.example.android.bitampfun.BuildConfig;
import com.example.android.bitampfun.R;

import java.io.BufferedInputStream;
import java.io.BufferedOutputStream;
import java.io.File;
import java.io.FileDescriptor;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.OutputStream;
import java.net.HttpURLConnection;
import java.net.URL;

/**
 * A simple subclass of {@link ImageResizer} that fetches and resizes images fetched from a URL.
 */
public class ImageFetcher extends ImageWorker {
    private static final String TAG = "ImageFetcher";
    private static final int HTTP_CACHE_SIZE = 24 * 1024 * 1024; // 10MB
    private static final String HTTP_CACHE_DIR = "http";
    private static final int IO_BUFFER_SIZE = 8 * 1024;

    private DiskLruCache mHttpDiskCache;
    private File mHttpCacheDir;
    private boolean mHttpDiskCacheStarting = true;
    private final Object mHttpDiskCacheLock = new Object();
    private static final int DISK_CACHE_INDEX = 0;

    private static ImageFetcher sNormalImageFecther;
    private static ImageFetcher sBitImageFetcher;

    public static void create(Context context){
    	ImageFetcher fetcher = new ImageFetcher(context);

    	ImageCacheParams cacheParams = new ImageCacheParams(context, "imagecaches");
    	cacheParams.diskCacheDir=null;//user http cache dir, so disable default cache dir

        cacheParams.setMemCacheSizePercent(0.15f); // Set memory cache to 25% of app memory

        // The ImageFetcher takes care of loading images into our ImageView children asynchronously
        fetcher = new ImageFetcher(context);
        fetcher.addImageCache("vdisk_image_caches", cacheParams);
        sNormalImageFecther = fetcher;

    	cacheParams = new ImageCacheParams(context, "bigimagecaches");
    	cacheParams.diskCacheDir=null;//user http cache dir, so disable default cache dir

        cacheParams.setMemCacheSizePercent(0.20f); // Set memory cache to 25% of app memory

        // The ImageFetcher takes care of loading images into our ImageView children asynchronously
        fetcher = new ImageFetcher(context);
        fetcher.addImageCache("vdisk_big_image_caches", cacheParams);
        sBitImageFetcher = fetcher;
    }
    public static ImageFetcher normalFetcher(){
    	return sNormalImageFecther;
    }

    public static ImageFetcher bigFetcher(){
    	return sBitImageFetcher;
    }

    /**
     * Initialize providing a target image width and height for the processing images.
     *
     * @param context
     * @param imageWidth
     * @param imageHeight
     */
    public ImageFetcher(Context context) {
    	super(context);
        init(context);
    }

    private void init(Context context) {
        checkConnection(context);
        mHttpCacheDir = ImageCache.getDiskCacheDir(context, HTTP_CACHE_DIR);
    }

    @Override
    protected void initDiskCacheInternal() {
        super.initDiskCacheInternal();
        initHttpDiskCache();
    }

    private void initHttpDiskCache() {
        if (!mHttpCacheDir.exists()) {
            mHttpCacheDir.mkdirs();
        }
        synchronized (mHttpDiskCacheLock) {
            if (ImageCache.getUsableSpace(mHttpCacheDir) > HTTP_CACHE_SIZE) {
                try {
                    mHttpDiskCache = DiskLruCache.open(mHttpCacheDir, 1, 1, HTTP_CACHE_SIZE);
                    if (BuildConfig.DEBUG) {
                        Log.d(TAG, "HTTP cache initialized");
                    }
                } catch (IOException e) {
                    mHttpDiskCache = null;
                }
            }
            mHttpDiskCacheStarting = false;
            mHttpDiskCacheLock.notifyAll();
        }
    }

    @Override
    protected void clearCacheInternal() {
        super.clearCacheInternal();
        synchronized (mHttpDiskCacheLock) {
            if (mHttpDiskCache != null && !mHttpDiskCache.isClosed()) {
                try {
                    mHttpDiskCache.delete();
                    if (BuildConfig.DEBUG) {
                        Log.d(TAG, "HTTP cache cleared");
                    }
                } catch (IOException e) {
                    Log.e(TAG, "clearCacheInternal - " + e);
                }
                mHttpDiskCache = null;
                mHttpDiskCacheStarting = true;
                initHttpDiskCache();
            }
        }
    }

    @Override
    protected void flushCacheInternal() {
        super.flushCacheInternal();
        synchronized (mHttpDiskCacheLock) {
            if (mHttpDiskCache != null) {
                try {
                    mHttpDiskCache.flush();
                    if (BuildConfig.DEBUG) {
                        Log.d(TAG, "HTTP cache flushed");
                    }
                } catch (IOException e) {
                    Log.e(TAG, "flush - " + e);
                }
            }
        }
    }

    @Override
    protected void closeCacheInternal() {
        super.closeCacheInternal();
        synchronized (mHttpDiskCacheLock) {
            if (mHttpDiskCache != null) {
                try {
                    if (!mHttpDiskCache.isClosed()) {
                        mHttpDiskCache.close();
                        mHttpDiskCache = null;
                        if (BuildConfig.DEBUG) {
                            Log.d(TAG, "HTTP cache closed");
                        }
                    }
                } catch (IOException e) {
                    Log.e(TAG, "closeCacheInternal - " + e);
                }
            }
        }
    }

    /**
    * Simple network connection check.
    *
    * @param context
    */
    private void checkConnection(Context context) {
        final ConnectivityManager cm =
                (ConnectivityManager) context.getSystemService(Context.CONNECTIVITY_SERVICE);
        final NetworkInfo networkInfo = cm.getActiveNetworkInfo();
        if (networkInfo == null || !networkInfo.isConnectedOrConnecting()) {
            Toast.makeText(context, R.string.no_network_connection_toast, Toast.LENGTH_LONG).show();
            Log.e(TAG, "checkConnection - no connection found");
        }
    }

    @Override
    protected Bitmap processBitmap(BitmapData data) {
        if (BuildConfig.DEBUG) {
            Log.d(TAG, "processBitmap - " + data);
        }

        final String key = ImageCache.hashKeyForDisk(data.getDiskKey());
        FileDescriptor fileDescriptor = null;
        FileInputStream fileInputStream = null;
        DiskLruCache.Snapshot snapshot;
        synchronized (mHttpDiskCacheLock) {
            // Wait for disk cache to initialize
            while (mHttpDiskCacheStarting) {
                try {
                    mHttpDiskCacheLock.wait();
                } catch (InterruptedException e) {}
            }

            if (mHttpDiskCache != null) {
                try {
                    snapshot = mHttpDiskCache.get(key);
                    if (snapshot == null) {
                        if (BuildConfig.DEBUG) {
                            Log.d(TAG, "processBitmap, not found in http cache, downloading...");
                        }

                        final String url = data.getData().toString();
                        if(TextUtils.isEmpty(url))
                        	return null;
                        DiskLruCache.Editor editor = mHttpDiskCache.edit(key);
                        if (editor != null) {
                            if (downloadUrlToStream(url,
                                    editor.newOutputStream(DISK_CACHE_INDEX))) {
                                editor.commit();
                            } else {
                                editor.abort();
                            }
                        }
                        snapshot = mHttpDiskCache.get(key);
                    }
                    if (snapshot != null) {
                        fileInputStream =
                                (FileInputStream) snapshot.getInputStream(DISK_CACHE_INDEX);
                        fileDescriptor = fileInputStream.getFD();
                    }
                } catch (IOException e) {
                    Log.e(TAG, "processBitmap - " + e);
                } catch (IllegalStateException e) {
                    Log.e(TAG, "processBitmap - " + e);
                } finally {
                    if (fileDescriptor == null && fileInputStream != null) {
                        try {
                            fileInputStream.close();
                        } catch (IOException e) {}
                    }
                }
            }
        }

        Bitmap bitmap = null;
        if (fileDescriptor != null) {
            bitmap = BitmapUtils.decodeSampledBitmapFromDescriptor(fileDescriptor,
            		data.getDesiredWidth(),
                    data.getDesiredHeight(),
                    getImageCache());
        }
        if (fileInputStream != null) {
            try {
                fileInputStream.close();
            } catch (IOException e) {}
        }
        return bitmap;
    }

    /**
     * Download a bitmap from a URL and write the content to an output stream.
     *
     * @param urlString The URL to fetch
     * @return true if successful, false otherwise
     */
    public boolean downloadUrlToStream(String urlString, OutputStream outputStream) {
        disableConnectionReuseIfNecessary();
        HttpURLConnection urlConnection = null;
        BufferedOutputStream out = null;
        BufferedInputStream in = null;

        try {
        	Log.d(TAG, "downloadUrlToStream: "+urlString);
            final URL url = new URL(urlString);
            urlConnection = (HttpURLConnection) url.openConnection();
            urlConnection.setConnectTimeout(10*1000);
            urlConnection.setReadTimeout(10*1000);
            in = new BufferedInputStream(urlConnection.getInputStream(), IO_BUFFER_SIZE);
            out = new BufferedOutputStream(outputStream, IO_BUFFER_SIZE);

            int b;
            while ((b = in.read()) != -1) {
                out.write(b);
            }
            return true;
        } catch (final IOException e) {
            Log.e(TAG, "Error in downloadBitmap - " + e);
            e.printStackTrace();
        } finally {
            if (urlConnection != null) {
                urlConnection.disconnect();
            }
            try {
                if (out != null) {
                    out.close();
                }
                if (in != null) {
                    in.close();
                }
            } catch (final IOException e) {}
        }
        return false;
    }

    /**
     * Workaround for bug pre-Froyo, see here for more info:
     * http://android-developers.blogspot.com/2011/09/androids-http-clients.html
     */
    public static void disableConnectionReuseIfNecessary() {
        // HTTP connection reuse which was buggy pre-froyo
        if (Build.VERSION.SDK_INT < Build.VERSION_CODES.FROYO) {
            System.setProperty("http.keepAlive", "false");
        }
    }
}


text:
/*
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.jdbi.v3.core;

import static org.assertj.core.api.Assertions.assertThat;

import org.jdbi.v3.core.rule.H2DatabaseRule;
import org.junit.Rule;
import org.junit.Test;

public class TestHandle
{
    @Rule
    public H2DatabaseRule dbRule = new H2DatabaseRule();

    @Test
    public void testInTransaction() throws Exception
    {
        Handle h = dbRule.openHandle();

        String value = h.inTransaction(handle -> {
            handle.insert("insert into something (id, name) values (1, 'Brian')");
            return handle.createQuery("select name from something where id = 1").mapToBean(Something.class).findOnly().getName();
        });
        assertThat(value).isEqualTo("Brian");
    }

    @Test
    public void testSillyNumberOfCallbacks() throws Exception
    {
        try (Handle h = dbRule.openHandle()) {
            h.insert("insert into something (id, name) values (1, 'Keith')");
        }

        String value = dbRule.getJdbi().withHandle(handle ->
                handle.inTransaction(handle1 ->
                        handle1.createQuery("select name from something where id = 1").mapTo(String.class).findOnly()));

        assertThat(value).isEqualTo("Keith");
    }

    @SuppressWarnings("resource")
    @Test
    public void testIsClosed() throws Exception
    {
        Handle h = dbRule.openHandle();
        assertThat(h.isClosed()).isFalse();
        h.close();
        assertThat(h.isClosed()).isTrue();
    }
}


NL18-1787628 HALFSISTER FIRST LADY is from the father of FIRST LADY paired to CORRIE from the lines of three super pigeons, FAVORIET, COR and the PERPIGNAN DOFFER.
HALFSISTER FIRST LADY is from the father of FIRST LADY paired to CORRIE from the lines of three super pigeons, FAVORIET, COR and the PERPIGNAN DOFFER.
CORRIE is a superbreedinghen and from inbreed to the FAVORIET x grdaughter of COR x PERPIGNAN DOFFER.
COR is the 1st International Ace Extreme long distance 2008.


text:
Gorges-du-Tarn-Causses – gmina we Francji, w regionie Oksytania, w departamencie Lozère. W 2013 roku populacja ludności wynosiła 975 mieszkańców. Przez gminę przepływa rzeka Tarn.

Gmina została utworzona 1 stycznia 2017 roku z połączenia trzech ówczesnych gmin: Montbrun, Quézac oraz Sainte-Enimie. Siedzibą gminy została miejscowość Sainte-Enimie.

Uwagi

Przypisy

Miejscowości w departamencie Lozère


text:
Attorney for Georgia lawmaker calls charges 'overreach'
Jeff Amy
Atlanta – An attorney for a Georgia lawmaker who was charged with two felonies after knocking on the door to the governor's private office said authorities overreached in the case, which unfolded while the governor spoke on live television about a sweeping overhaul of state elections.
State police arrested state Rep. Park Cannon, an Atlanta Democrat, on Thursday after she said she wanted to see Republican Gov. Brian Kemp sign the law that places new restrictions on voting by mail and gives lawmakers more power to oversee elections.
Cannon was charged with obstruction of law enforcement and disruption of the General Assembly. She was released from jail late Thursday.
A state police spokesman said Cannon knocked on the door to the public lobby of the governor's office, and then shifted to knocking on a door to a private area.
"She was advised that she was disturbing what was going on inside and if she did not stop, she would be placed under arrest," Lt. W. Mark Riley wrote in a statement.
Subscribers: Arrest of Georgia lawmaker sharpens debate on voting bills
Kemp signed the bill before he began speaking. He interrupted his televised remarks while Cannon was knocking and later resumed the speech.
"This was a law enforcement overreach on all the charges, and my hope is that after examining the file, the district attorney will dismiss the charges," Cannon's lawyer, Gerald Griggs, said Friday. Griggs said he had already spoken to Fulton County District Attorney Fani Willis.
The new law requires a photo identification to vote absentee by mail. It also shortens how long voters have to request an absentee ballot and limits where ballot drop boxes can be placed and when they can be used. Republicans said Georgia needed to restore the confidence of voters who believed President Donald Trump's baseless claims of widespread fraud. They also noted that the law increases the number of in-person early voting days on weekends.
"We're expanding the right to vote in Georgia," Kemp said Thursday.
Democrats say the law is a power grab by Republicans who are threatened by Joe Biden's presidential victory in Georgia in November and the twin Democratic victories of U.S. Sens. Raphael Warnock and John Ossoff in January.
President Joe Biden criticized the law again Friday, saying its Republican sponsors were trying "to deny people the right to vote" and that the measure was "a blatant attack on the Constitution and good conscience."
Biden renewed his call for Congress to pass nationwide voting standards that would include automatic voter registration nationwide, allow former felons to vote and limit the ways states can remove registered voters from the rolls. Such a bill passed the Democratic-controlled House earlier this month but faces opposition from Republicans who call it a federal voting takeover. It would expand voting by mail, promote early voting and give states money to track absentee ballots.
"This is Jim Crow in the 21st century," Biden said in a statement. "It must end. We have a moral and constitutional obligation to act."
Three voter-mobilization groups sued late Thursday to block the law, alleging that it violates the First and 14th Amendments of the U.S. Constitution and illegally hurts Black voters under the federal Voting Rights Act.
Georgia's constitution says lawmakers "shall be free from arrest" during meetings of the General Assembly "except for treason, felony, or breach of the peace."
Griggs questioned whether felony charges, with mandatory prison terms, were merited. A warrant claims Cannon resisted arrest by stomping on an officer's feet, but Griggs said he's seen no video supporting that accusation.
Griggs questioned whether the felony charge for disruption of the General Assembly could be applied because first and second offenses are misdemeanors, while only third and later offenses are felonies. Cannon has never been charged or convicted under that law before, Griggs said.
The law against disrupting a legislative meeting was challenged as unconstitutional in a lawsuit filed last year after people including Nikema Williams, then a state senator and now a U.S. representative, were arrested at the Capitol while protesting election results in 2018. All charges were dismissed in 2019.
Cannon had another run-in with officers in February. She and others held a two-hour sit-in on a Capitol stairway after Cannon said it was improper that a police officer led her away while Democrats were protesting voting bills. Cannon put her ear up to the bullhorn of an officer ordering protesters to disperse and then demanded an apology from another officer who moved her away.
A crowd of Cannon supporters including Warnock gathered Thursday outside the Fulton County jail awaiting Cannon's release.
"We will not live in fear, and we will not be controlled," Cannon wrote on social media after being released. "We have a right to our future and a right to our freedom."
Warnock said Cannon is a member of Ebenezer Baptist Church, where he is the pastor, and he visited her inside the jail before her release.
"We are praying for her, we are praying for her family, but also for the soul of our democracy," Warnock told reporters outside the jail. "We are witnessing right now, a kind of wrestling in the soul of Georgia. Will we go forward or will we go backwards? We will not allow a few politicians, in their craven lust for power, to take us back."