Paper 8 – Productivity And Technology In Sri Lankan Manufacturing Industries

Sunil Chandrasiri
9 This paper benefited from comments by participants at the SLEA Annual Sessions for the year 2004 on "Human Development in a Knowledge based Society." The author gratefully acknowledges valuable comments by A.D.V. de S. Indraratna and Harsha de Silva on earlier version of the paper. The statistical assistance of Sandeepa Hewagama is also gratefully acknowledged. Usual disclaimer applies.
Abstract

This study employs a simultaneous equation model to examine the relationship between productivity and technology in industry. The endogenous variables of the model include relative productivity, industry concentration and technology while relative capital intensity, relative median plant size, capital intensity in domestic industry, minimum efficient scale of production, skilled labour, managerial skills, growth, import intensity, export intensity, foreign direct investment and technological opportunity have been treated as instrumental variables. The term relative productivity is defined as the ratio of labour productivity in Sri Lankan manufacturing relative to its counterparts in the U.S. The data set covers 79 industry sub-sectors in Sri Lankan manufacturing at 4-digit level.

The results fail to recognize technology as a determinant of relative productivity in industry. Industry concentration however, was found to be an important determinant of productivity confirming the hypothesis that cost-minimizing efficiency dictates fewer producers. The positive and significant coefficient of scale economies variable in industry concentration equation lends further evidence in support of this conjecture. It is also interesting to note the significance of productivity as a determinant of technology intensity in industry. Our findings however, do not confirm Schumpeterian arguments relating to technology intensity in industry. In overall terms, the evidence supports the view that high concentration is associated with high productivity through cost minimization efforts. The negative and significant impact of profitability on technology development brings out yet another important issue in the context of Sri Lankan manufacturing. Of the two human capital variables employed in the analysis, the impact of managerial skills is positive as expected while skilled labour variable exerts a negative impact over relative productivity in Sri Lankan manufacturing. Foreign direct investment (FDI) was not found to be a significant determinant of productivity and research and development intensity in industry.

1. Introduction

Productivity is the prime determinant of a nation's standard of living. At national level, high productivity promotes growth and income while at firm level, it allows firms to be competitive both in domestic and foreign markets. The relatively low productivity levels in Sri Lanka especially, in manufacturing industries has long been a subject of interest among economists, policy makers and others interested in promoting competitive practices in industry. Existing evidence however, offer numerous explanations for the country's low productivity performance. On theoretical grounds, factors that might contribute to low productivity are relative market size, low investments in research and development (R&D) and human resources, inward oriented economic policies, poor management practices and lack of cooperation and commitment from employees.

The impact of technology on improved productivity has been extensively discussed in the literature. Both theoretical and empirical evidence at the firm and industry level reveals that the contribution of R&D to productivity is positive, significant and high. In the present context of highly globalized economic environment, companies recognize technology as an important strategy for competitive advantage both at domestic and foreign markets. In fact, competition by innovation is recognized as far more powerful than price competition. Some analysts note that the nature of competition itself is changing in response to new technologies (Best, 1990).

Technology has been recognized as one of the most important factors affecting productivity. Even a country blessed with natural and labour resources cannot be highly productive by international standards if it cannot use its resources efficiently in combination with appropriate technology. It entails the conscious building of 'technological capabilities', a mixture of information, skills, interactions and routines that firms need to handle the tacit elements of technology. As noted by Lall (2003), 'technological activity in developing countries consists less of 'innovation' than of engineering and technical work for learning, adaptation and improvement. Given its diffused nature, however, it is difficult to measure. What we can measure is formal R&D. This is still useful, since R&D becomes an important input into productivity and competitiveness especially in the context of developing countries'. A growing R&D base promotes better and faster technology diffusion within the economy and facilitates efficient use of local resources.

According to the estimates prepared by the National Science Foundation (NSF) the total R&D expenditure in the country was around Rs. 1810 million or 0.19 per cent of the GDP in 2000. This is far below the R&D activities performed by some of the neighboring countries in South East Asia. According to the Global Competitiveness Report (WEF, 2003), Sri Lanka's overall performance particularly with respect to technology related factors is very poor. In terms of technology index ranking, Sri Lanka is placed at 67th position out of a total of 80 countries.

The purpose of this paper is to examine inter-links between technology and productivity in Sri Lankan manufacturing using a cross-sectional research design. The term 'technology' is defined to include economic activities relating to invention, innovation and dissemination of information. The paper is organized into four main sections: a) theoretical considerations, b) data sources and methodology, c) empirical evidence and, d) conclusions.

Theoretical considerations

This study employs a simultaneous equation system to test the relationship between productivity and technology. It is based on structure-conduct-performance analysis of industrial organization theory. Following Schumpeterian arguments market structure and technology have been treated as endogenous variables. In addition, productivity has also been taken as an endogenous variable based on both theoretical and empirical evidence in industrial organization literature. The model, list of variables and theoretical explanations are given below.

• Productivity equation

Productivity is generally considered as the efficiency with which inputs are converted into useful output within the production process. Productivity measurement techniques can be classified into two broad categories: partial productivity measures and multi or total factor productivity (TFP) measures. The conventional approach to the measurement of productivity is based on labour productivity, which is simple and easy to compute, but does not explain the total effect of all factors of production. Productivity analysis at the firm or industry level can be carried out either relative to the best practice domestically or in an international context. 10 Work by Caves and Barton (1990) relating to U.S. manufacturing fall into this category. The analysis presented in this paper falls into the second category and it measures productivity in the domestic industry relative to its counterparts in the U.S. This is based on the conjecture that productivity in the U.S. manufacturing is highly correlated with the maximum level of productivity attainable, using the technological knowledge and management practices under most competitive conditions now available to the industrial countries. In developing the analytical model Cobb-Douglas type production function is used as the core of the model. In addition, several other variables have also been employed mainly to capture inter-industry variations in productive efficiency. Accordingly, the determinants of relative productivity for any industry are defined as follows:

Where

VPWi = Value added per employee in the domestic industry divided by value added per employee in the U.S. counterpart

EFFi = The ratio of two efficiency terms which accounts for all other influences on productivity other than quantities of capital and labour.

CAPi = Assets per employee in the domestic industry divided by assets per employee in the U.S. counterpart

TPi = Median plant size in the domestic industry

When we take logarithms of the VPWi regression and add a disturbance term, we have a linear stochastic relationship between comparative productivity and comparative efficiency, comparative capital intensity and comparative plant size. This can be used as the basis for the analysis of productivity in industry. The variable CAP and TP are observable, and the regression coefficients will provide estimated values for α+β-1. In other words, these two variables explain the difference between points D and F in figure 1 which represents the actual factor combinations in Sri Lanka and the U.S. respectively. The variable EFF captures all influences on productivity other than quantities of capital and labour. Using Figure 1, this can be explained as the ratio of OF/OE (the residual measure of efficiency), which captures the qualitative differences of inputs and all other factors that bear on pure differences in efficiency.

However, neoclassical core element of the equation assumes that the output elasticities of capital and labour are the same for all sampled industries in both countries. If this assumption is violated, the pure cross-section research design breaks down. Davies and Caves (1987) suggest the following as a strategy to overcome this problem:

a) include the values of CAP and TP relating to the country under study as two more terms in the regression model. The coefficients of these two variables are expected to capture conditions, which are specific to the country under study.

b) Include dummy slope variables for CAP and TP for those industries thought to differ from the factor intensity and returns to scale.

Since the production function is defined in relative form, the efficiency term makes it possible to capture systematic differences in the residual efficiency of Sri Lankan manufacturing relative to its counterparts in the U.S. The selection of efficiency variables is guided by both theoretical and empirical evidence on industry structure, conduct and performance. The U.S. has been selected as the normalizing country due to its pro-market policy orientation and relatively high efficiency standards.
11 One might also consider Japan, Germany or some of the East Asian countries for purposes of computing relative productivity.
Theoretical considerations discussed above indicate the relevance of capital intensity as one of the important variables to be included in a model relating to relative productivity. In the present study, relative capital intensity (KLD) is measured by dividing the capital-labour ratio of Sri Lankan industry by the capital labour ratio of the U.S. counterpart industry. Standard production theory assumes a positive relation between KLD and VPW. This study also employs capital intensity in Sri Lankan industry (KLDL) as an explanatory variable in order to allow for systematic differences of production functions between the two countries. Its effect on relative productivity can be either positive or negative.

Relative plant size is the other variable that needs to be employed in our productivity equation. We propose employing MP, median plant size in Sri Lankan industry divide by median plant size in the U.S. counterpart, and expect it to have a positive effect on VPW in a typical manufacturing industry. Once again, we need to allow for the differences in scale economies between Sri Lanka and the U.S. Thus, we propose to include the median plant size in Sri Lankan industry, MEP, and expect it to be positively related to VPW, based on the argument that Sri Lankan plant sizes are small enough to yield substantial economies in industry. A negative coefficient for MEP should indicate that greater returns to scale are obtained in the U.S.

Having covered the neo-classical core of the model relating to relative productivity, we are now in a position to develop hypotheses relating to efficiency differences (EFFi) across industries in Sri Lankan manufacturing. In the present study, we propose to employ six different variables with a view to examine efficiency differences on inter-industry basis: industry concentration, research and development activities, quality of human resources and exposure to foreign competition.

In the literature, one can find arguments that concentration may affect productivity, both positively and negatively. The positive relation between concentration and productivity arises from the hypothesis that it may lessen the prevalence of sub-optimal capacity and thereby increase the productivity of industry. The negative effect of concentration on relative productivity is based on the argument that the umbrella effect of market power may encourage survival of inefficiently small firms. That is, incompletely exploited market power enables small, inefficient firms to earn normal profits by charging high prices. Thus, the relation between relative productivity and concentration in industry is ambiguous.

R&D intensity is another potential explanation of relative productivity in industry. Firms, which undertake high R&D work, are more likely to be innovative and effective in their efforts to reduce costs and make quality improvements. This implies a positive relation between R&D intensity and productivity in industry. As argued in the literature, Sri Lanka lags behind other countries in the diffusion of new ideas, and commercially oriented R&D activities.12 For details see Chandrasiri (2003) Accordingly, we hypothesize a positive relation between R&D intensity (RD) and relative productivity in industry. Exposure to foreign competition may also affect productivity in industry. It is common to expect an industry's efficiency and productivity to increase with both the extent of import competition that it faces and the degree to which it sells on export markets. In the present study, we assess the effect of import penetration (IMI) and expect a positive relation between productivity and import intensity in industry. The argument is that foreign competition through imports will induce high efficiency among producers in the domestic market. Moreover, IMI should yield a positive influence on technical efficiency, as should export intensity (EXI) if indeed it exerts a pro-competitive force. However, previous research on exporting activities indicate that exporting activity is spread quite unevenly among the typical exporting industry's production units, apparently due to fixed distribution or related costs that are large relative to those entailed for domestic sales. As a consequence, large production units are more likely to export than small ones, and small units that export are likely to export large proportions of their outputs. Such a pattern will expand the variance of margins and could well raise the revenue production frontier and increase apparent technical inefficiency.
