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Structure, organizational behavior, and technical efficiency: The case of an Indian industry
Journal of Economic Behavior & Organization Vol. 34 (1998) 419±434
Structure, organizational behavior, and technical efficiency: The case of an Indian industry Murali Patibandla* Indian Institute of Management, Vastrapur, Ahmedabad 380015, India Received 4 December 1995; received in revised form 13 February 1997
Abstract The differences in certain aspects of organizational behavior across large and small firms could be explained as a response mechanism to structural conditions like capital market imperfections and high market transaction costs. This, in turn, could explain the relative production efficiency of large and small firms. This paper examines the issue on the basis of firm-level survey data for an Indian industry. # 1998 Elsevier Science B.V. JEL classi®cation: D21; L11; L22 Keywords: ef®ciency
Capital market imperfections; Structure; Competition; Organizational behavior; Technical
1. The background and issues In several Indian industries, a few large and a large number of small firms coexist and face quite divergent structural conditions in product and factor markets. This type of market structure is explained as a result of the Indian industrial policy regime and factor market duality (Desai, 1982, Gang, 1995). On the issue of firm size and production efficiency in Indian industry, the empirical study of Little et al. (1987) showed that there was no statistically significant relation between firm size and efficiency in three out of four industries they studied. These results imply that large and small firms realize similar levels of technical efficiency in production. This throws up important questions. If levels of efficiency in production are similar, how does one explain differences in firm size in * Corresponding author. 0167-2681/98/$19.00 # 1998 Elsevier Science B.V. All rights reserved PII S 0 1 6 7 - 2 6 8 1 ( 9 7 ) 0 0 0 8 3 - 8
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Indian industries? Why were large firms, which had better access to modern imported technologies and information than small firms, not more efficient in production? This paper attempts to answer these questions by adopting the argument that the structural imperfections function as a source of long market power to large firms and entry and mobility barriers to small firms. This facilitates large firms to remain large without having to be more efficient than small and medium firms. The divergent structural conditions faced by large and small firms determine the differences in organizational behavior in production which could be one of the dominant explanatory factors of the relative production efficiency of large and small firms. In Indian industries, large firms operate in the organized capital and labor markets and small firms operate mostly in the unorganized input markets. Consequently, large firms get access to capital at a lower price and pay higher wages to labor than small firms. The regulatory policies pursued in India combined with capital market imperfections and the presence of sub-optimal contractual arrangements cause high market transaction costs. High market transaction costs and capital market imperfections, as shown by the new institutional economics, are a source of long-run market power to large firms and entry and mobility barriers to small firms.1 Under these conditions, even if small- and mediumscale firms are more efficient in production, they cannot not grow into the strategic group of large firms.2 Organizational behavior, in this paper, refers to effort and involvement of managers, multi-product operations, internal coordination and flow of information, and choice of production techniques in terms of employment of permanent and temporary labor. Production efficiency is assumed to be synonymous with Farrell's (1957) technical efficiency (TE) which refers to output realized for a given level of inputs employed, given the most efficient technology frontier of an industry. In other words, technical efficiency is measured as the extent to which actual output falls short of potential output as determined by best practice performance. Under perfectly competitive product and factor markets and homogenous technology across firms in an industry, Technical efficiency differences across firms would be purely owing to organizational factors (X-inefficiency). If markets are imperfect, the observed differences in technical efficiency across firms could be owing to, not only organizational factors, but also the technology gap across firms, the product and factor prices (when output is taken in value terms), and economies of scale. This paper focuses on certain aspects of organizational behavior and their implications on the relative technical efficiency realized by large and small firms in Indian industry and examines this issue on the basis of survey data for the Indian engineering industry. To recapitulate, large firms, operating in the organized input markets, have greater access to modern (capital-intensive) imported technologies, skilled labor and better 1 Following Caves and Porter (1977), mobility barriers refer to small firms' ability to grow and move into the larger size group. In the context of Indian industry, this requires small firms to invest in advertisement and brandname creation and to deal with government bureaucracy. Secondly, mobility barriers to small firms can also arise from cross-product subsidization by large firms which is explained later. In other words, mobility barriers are a part of the competitive dimension for small firms as they have to compete with the brand names of large firms. 2 In other words, the ability to deal with the regulatory mechanism is a source of long-run market power but not necessarily of production efficiency.
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information. On the other hand, small and medium firms operate mostly in the unorganized input markets and operate with older and labor-intensive technologies (Little et al., 1987, Siddharthan, 1988, Patibandla, 1994). This technology gap across large and small firms facilitates large firms to determine the most efficient technology frontier and to be technically more efficient than small firms. But as this paper suggests, the more dominant factor in explaining the differences in the relative technical efficiency realized by large and small firms could be their organizational behavior as a response mechanism to structural conditions. It is generally argued that under India's import substitution policy regime, Xinefficiency in industrial performance was high owing to lack of competition (Goldar, 1986, Bruton, 1989, Majumdar, 1996). This might be more prominent in the case of large firms because, while large firms were able to derive long-run domestic market power, a large number of small firms sharing the residual demand faced highly competitive pressures and mobility barriers owing to capital market imperfections and high costs of market transactions.3 Under these structural conditions while large firms can derive excess profits irrespective of organizational slack, small firms that survive are those that operate with high levels of organizational efficiency which is discussed below. Taking maximum advantage of imported technologies depends to a large extent on the organizational efforts in adapting them to local conditions. Access to capital below its shadow price and lack of competitive conditions might not have forced large firms to undertake systematic organizational efforts in adapting imported technologies to local conditions most efficiently.4 Large-scale Indian firms had been observed to underutilize capital employed and were weak in labor management (Lall, 1987). A significant part of organizational X-inefficiency of large firms stemmed from poor coordination between different departments within a firm which got magnified in a non-competitive environment. As inferred from the qualitative information of the field study, on the production floor technicians or production managers could have been able to realize certain technical snags and possible solutions and improvements in the operation of machinery. But, generally, in large firms these findings had to be referred to R&D and other departments for their approval. If these departments were poorly coordinated, it would cause, not only substantial loss in information flow, but also dissipation of possible learning economies (internal to a firm) in production. The larger the number of hierarchies, the more imperfect was the two-way transfer of information between subordinates and superiors (Williamson, 1967, Leibenstein, 1987). In large Indian firms, the levels tended to be much higher in comparison with firms in developed countries. In a typical large Indian firm, hierarchy levels range between 15 and 20 categories. Within five or six broad categories, there are three or four sub-categories. 3 In pursuit of heavy industrialization, India's macro-economic policies were tilted towards capital-intensive large-scale firms. Bruch and Hiemenz (1983) have observed that in many newly industrializing countries large firms have concentrated in those areas where effective rates of protection are high whereas small firms have concentrated in those areas where the protection is very low. For example, in the case of India's automobile industry, large firms concentrate in the production of the final good which is subject to high rates of protection and small firms concentrate in the production of automobile components subject to very low rates of protection. In other words, small firms compete with imports also. 4 Ferrantino's (1992) empirical study of a few Indian industries shows that the purchasers of imported technology are less efficient in production than the non-purchasers.
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One of the strategic responses of large firms to the structural conditions is vertical integration and multi-product operations within a broadly defined industry as it can facilitate imposition of mobility and entry barriers through cross-product subsidization (Patibandla, 1994).5 Multi-product operations increase organizational complexity and make organizations prone to high levels of X-inefficiency.6 Furthermore, another strategic response of large firms could be maintenance of excess capacity as an entry deterrence strategy (Leiberman, 1987). In such cases, the excess capacity of large firms results in lower technical efficiency in production. In contrast, small firms face two major interlinked structural constraints: limited access to capital and strong mobility barriers. These constraints get magnified in the Indian economy with high transaction costs. In the presence of capital market imperfections and sub-optimal contractual arrangements, small firms face higher transaction costs. For example, as derived from the field study, small firms had to wait for long periods for payment on domestic deliveries because of sub-optimal contractual arrangements and low bargaining power. This, in turn, raised the costs of working capital to small firms, especially since small firms got access to capital at higher rates than large firms. This is a significant factor because most small firms tended to have a high percentage of bought-in inputs than did large firms which were generally more vertically integrated (Sen, 1991 Patibandla, 1994, 1995).7 In this context, a successful small firm could be the one that adopted organizational behavior which minimized fixed and working capital requirements at all stages of operation.8 The above observation is illustrated with the help of the qualitative information collected from the field study of two typically successful small firms (located in Bangalore and Madras). Both firms were managed by technocrat±owner managers. In the case of the latter firm, three professional managers were employed. In both cases, organizational efforts were made towards minimizing costs in the installation of plant and machinery and in labor management through a high degree of involvement of the managers. This behavior is characterized as follows: 1. Small firms' relative advantage lies in specialization within a broadly defined industry (or a product). The first stage involves the choice of the product to be manufactured in relation to the intended scale of operation and available organizational and technological skills. 5 In those products where small firms are relatively more cost-efficient than large firms, large firms resort to cross-subsidization to block the growth of efficient small firms. The losses incurred in the subsidized products are recovered from excess profits made from those products in which competition is low. Another reason for multi-product operations by large firms is the realization of excess profits under import substitution cycles. 6 On the other hand, if there are economies of scope in multi-product operations, large multi-product firms should be in a position to derive higher efficiency in production than small firms. Apart from this, flexible manufacturing techniques are supposed to facilitate efficiency gains in multi-product operations. But flexible manufacturing technologies are uncommon in Indian industry. The other possible positive trade-offs of vertical integration and multi-product operations might be minimization of the costs of external trading (market transaction costs) and information collection (see Williamson, 1975). 7 Patibandla (1995) explains how several small firms in the Indian industry branched out into export markets in order to overcome the high costs of domestic market transactions. 8 Working capital constraint is generally observed to be one of the dominant reasons for the high incidence of sickness in the small-scale Indian industry (see Sen, 1991).
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2. The second stage is minimization of costs in buying and installing machinery. In the case of the first firm mentioned above, machinery cost was reduced substantially by buying secondhand machinery and renovating it with new components and parts secured from the domestic and overseas markets. 3. The third stage is minimization of variable costs along with maximum utilization of fixed capital with a high degree of involvement of owner±managers at every stage of operation. The working capital constraint was reduced by maintaining a consistent working relation with good quality raw material producers which helped in effective inventory management in relation to product demand conditions. The gains in raw material management could be pecuniary economies in bulk buying of materials which would be more significant to large firms and inventory management which is independent of firm size. As mentioned above, the pressure to do effective inventory management was more on small firms owing to working capital constraints. Secondly, management of labor is an important source of saving on working capital requirements. Since small firms operated mostly with unorganized labor, they had a relatively higher degree of flexibility in production organization. They could minimize the variable cost of labor by employing a few permanent skilled labor and temporary semi-skilled and unskilled labor. They could utilize permanent and temporary labor employed to the maximum extent. The labor intensity of small firms can be observed in terms of the intensity with which employed labor is utilized. Most small firms, as observed in the field study, tended to make laborers work long hours by paying low wage rates. Following from the above observation, the organization of labor markets could be one of the important factors in determining the relative technical efficiency of large and small firms. While capital market segmentation, as discussed earlier, works in favor of large firms, labor market imperfections might be a source of disadvantage to large firms in realizing optimum technical efficiency. The labor unions and labor laws in Indian industry restrict large firms from getting rid of excess labor and replacing inefficient labor with efficient labor. The degree of labor unionization in the sample was as follows. Small firms with sales turnover approximately below 20 million rupees tended to operate with unorganized labor. They could pursue a hire and fire policy towards labor. Medium-scale firms with sales turnover in the range of 25 to 200 million rupees employed semi-organized labor, especially if a firm was located in metropolitan cities. They had to provide certain minimum benefits to workers. However, most firms in this size group retained flexibility in replacing labor. The very large firms were fully subject to labor laws and unionization with little flexibility in labor replacement. The mobility of labor across firm size groups, caused by the labor market organization, would have implications on the relative technical efficiency realized by different size groups. Sen (1991) observes that there is generally considerable pressure on small firms to keep their wage bill down. As a result, there is a tendency for larger firms, which can financially afford to pay higher wages, to attract the best labor away from the financially weaker smaller firms. As mentioned earlier, in the case of very large firms, labor unions can effectively prevent the replacement of inefficient labor with more efficient new
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recruits. This, in turn, may result in a situation in which the larger of the small firms may be able to draw some of the best workers at the cost of smaller firms which have to do with relatively inexperienced labor. Thus, smaller firms may end up essentially as training grounds for medium-scale firms. 2. Empirical analysis The following empirical analysis estimates the association between firm-size and firmlevel relative technical efficiency indices and tests for the explanation of firm-level relative technical efficiency by certain organizational variables. If one accepts the assumption that large and small firms face different structural conditions in Indian industry, the relationship between firm size and efficiency sheds some light on this behavior. Given the data set, we could measure explicitly only two variables that capture some of the arguments of organizational aspects. These variables refer to the number of products produced by firms and the degree of vertical integration: the higher the values of these variables, the higher would be organizational complexity and consequent loss of efficiency.9 We take this exercise to provide certain empirical regularities and useful stylized facts.10 The main hypotheses are: (1) Under perfectly contestable markets, structure can be viewed as endogenous (Demsetz, 1973): larger firms are those which have higher production efficiency. On the other hand, under the presence of structural imperfections such as capital market imperfections and high market transaction costs as prevalent in the Indian economy, market structure in terms of firm size asymmetry can be seen as exogenous and stagnant. Differences in firm size have to be explained by structural imperfections rather than relative production efficiency of firms. The presence of long-run market power could facilitate large firms to remain large irrespective of their relative organizational inefficiency in production. If organizational inefficiency of large firms is more dominant than their relative advantage in access to superior technology and information, large firms would exhibit lower technical efficiency in production compared to small- and mediumscale firms. (2) Diversification into multi-products and higher levels of vertical integration by large firms are more towards exercising market power than realizing higher levels of production efficiency. This, in turn, causes higher organizational complexity and consequently lower levels of technical efficiency in production. 2.1. Data The firm-level data refer to the period 1983/84 and were drawn from the hand, small and cutting tools industry. Data were collected on the basis of an extensive field survey 9 The firm size variable also captures the owner±manager argument of the previous section as most small- and medium-scale firms are managed by owners while large firms are managed mostly by professional managers. 10 See Schmalensee (1989) for a detailed explanation of some of the problems involved in empirical studies with cross-sectional data in industrial economics.
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across the country. In the case of large public limited companies, the company balance sheets were also used. The sample consisted of 76 firms. Firm-size distribution of the sample, in terms of sales turnover, ranged from a maximum of 1592 million rupees to a minimum of 0.7 million rupees. One of the major problems of firm-level data, for any broadly defined (disaggregate level) industry, is the heterogeneity in products produced by firms within an industry. As mentioned earlier, in this industry large firms produce a wide range of hand and cutting tools and small firms concentrate on one or a few specialized product lines. Due to the heterogeneity in products, there is a certain degree of technological diversity across firms.11 Consequently, it causes a certain level of statistical noise in the estimation of firm-level technical efficiency indices. 2.2. Notations TE-1 TE-2 TE-3 S NP Y V K R L VI
firm-level relative technical efficiency, measured on the basis of a two-input production function (parametric approach) firm-level relative technical efficiency, measured on the basis of a three-input production function firm-level relative technical efficiency, measured on the basis of Data Envelopment Analysis (see Appendix A for a detailed discussion of the measurement issues of technical efficiency) firm-level sales turnover (normalized by the lowest value in the sample) as firmsize variable number of products produced by firms production in value terms value-added total capital employed in value terms total raw materials consumed in value terms number of permanent labor employed degree of vertical integration, (value-added/production).Its value ranges from 0 to 1. Firms that show values closer to 1 are highly vertically integrated in production.
2.3. Results The average values of technical efficiency for the sample are: 0.37 (with a standard deviation of 0.19) for TE-1, 0.50 (with a standard deviation of 0.13) for TE-2, and 0.59 (with a standard deviation of 0.17). The average value of technical efficiency estimated with a three-input function is significantly higher than the one estimated with a two-input
11 This issue is different from the argument of technological gap across firms in the production of a specific product.
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Fig. 1. TE-1 and firm size (in logs)
function. This indicates that there could be significant efficiency gains in raw material usage and management.12 Figs. 1±3 show the plots of TE-1, TE-2 and TE-3 against firm size. The association between technical efficiency and firm size appears to be non-linear. Technical efficiency is low for very large and small firms and high for medium-scale firms. In other words, if we ignore the few outlier observations in the middle, TE appears to increase with firm size until a critical level and thereafter to decline as firm size increases. This possible non-linear association between firm-level TE and firm size and a few variables that capture the organizational aspects is tested by a set of econometric equations. As the values of the dependent variable, TE, are bound to take values from 0 to 1, Ordinary Least Squares may provide biased estimates. To avoid this, the Tobit maximum likelihood method is adopted in estimating the regression coefficients (Maddala, 1983). The non-linear association between TE and firm size (S) is captured by introducing the size variable in quadratic terms. The number of products produced by firms (NP) and vertical integration (VI) capture certain organizational aspects of firms: the higher the degree of vertical integration and product diversification, the higher organizational complexity can be. This dimension operates through the number of labor employed. In other words, the larger the number of products produced and the higher the degree of 12 As discussed in Section 2, there are two types of efficiency gains in raw material management: pecuniary economies in bulk buying which could be significant for large firms and inventory management which applies for both large and small firms. Since raw material input enters the production function in value terms (price multiplied by quantity), pecuniary gains are captured by the input in the estimation.
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Fig. 2. TE-2 and firm size (in logs)
Fig. 3. TE-3 and firm size (in logs)
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Table 1 Econometric results Dependent variable
TE-1 TE-2 TE-3 TE-1 TE-2 TE-3 TE-1 TE-2 TE-3
Independent variables and the estimated coefficients 2
S
S
0.00116 (4.5)a 0.0013 (4.0)a 0.0015 (3.6)a 0.0005 (3.6)a 0.00018 (2.5)a 0.00008 (0.53) NP 0.0667 (8.0)a 0.0848 (9.1)a 0.105 (8.5)a
ÿ0.0000004 (3.35)a ÿ0.0000005 (2.9)a ÿ0.0000006 (2.6)a ÿ0.0000001 (1.63)b ÿ0.00000001 (0.6) 0.00000003 (0.51) NP*L ÿ0.0000045 (3.8)a ÿ0.000006 (3.8)a ÿ0.0000073 (3.7)a
VI
Log likelihood
VI*L ÿ30 ÿ50 ÿ70
0.70 (17)a 0.98 (49)a 1.27 (34)a
ÿ0.00012 (3.4)a ÿ0.000006 (3.3)a ÿ0.00008 (2.5)a
31 84 36 ÿ13 ÿ29 ÿ50
Note: figures in the parentheses are t values. a significant at 0.01 level. b significant at 0.05 level.
vertical integration for a given level of labor employed, the higher the organizational complexity and loss of control are likely to be. In order to capture this aspect, NP and VI as interactive terms with number of labor employed (L) are introduced into the estimations. The interactive terms capture the non-linearities in the explanation of TE by the organizational variables. The equations are estimated in different stages as the independent variables like firm size, number of products produced and the interactive variables are highly correlated.13 The results are presented in Table 1. The estimated coefficients associated with respect to variables S and S2 are statistically significant and have positive and negative signs respectively in all the three cases of technical efficiency variables. This confirms the non-linear association between technical efficiency and firm size. Technical efficiency increases till the firm size reaches about a 930 million rupees sales turnover and thereafter declines as firm size increases. The positive and negative signs of the statistically significant estimated coefficients associated with the number of products (NP) and the interactive variable (NP*L) imply that TE increases until about the level of 14,600 workers employed.14 Above this level, TE 13 Firm size (S) and the number of products produced (NP) are highly correlated with a correlation coefficient of 0.84. 14 This value is calculated by equating the first-order derivatives of the estimated equations with NP variable to zero.
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decreases as the number of products produced by firms increases. This result suggests that beyond the scale of 14,600 workers employed, diseconomies associated with loss of organizational control owing to labor performing diversified tasks in the production of larger number of products could turn out to be dominant.15 Similar implications apply to the results obtained with respect to vertical integration and its interactive variables. 3. Conclusion In the presence of capital market imperfections and high market transaction costs which are a source of entry and mobility barriers, the structure of an industry can be taken to be exogenously given. The structural imperfections in Indian industry can be traced to the factor market imperfections and the regulatory policy regime. The regulatory policy regime in India which causes over-bureaucratic apparatus, and the presence of suboptimal institutions (the contractual arrangements) are sources of high market transaction costs. The capital market imperfections combined with high market transaction costs function as a source of market power to large firms and entry and mobility barriers to small- and medium-scale firms. The implications of structural imperfections on the market structure can explain the differences in organizational behavior adopted by large and small firms which are one of the major determinants of their relative production efficiency. The empirical results of this study show that large firms in the Indian industry, despite their relative advantages in higher access to better technologies and information, are not the ones that can determine the most efficient technology frontier. The organizational inefficiency of large firms has been a more dominant factor than their relative advantages through the technology gap. Structural imperfections are the major explanatory factors for their low level of organizational efficiency irrespective of which they can remain large for sustained periods. Furthermore, the labor market segmentation in the Indian industry is also a major source of lower technical efficiency of large firms. Low TE levels in very small firms can be attributed to outdated technology and low labor skills. The explanation for the maximum TE realized by middle-size firms can be provided by the structural conditions as discussed in Section 2. Small firms that can adopt the right kind of organizational behavior in relation to the structural conditions they face are the ones that are in a position to realize higher levels of TE and grow into the middle scale. The movement of better trained labor into medium-size firms, caused by segmentation in the labor market, can also be one of the major explanatory factors for higher levels of TE in middle-size firms. But the ability of these firms to move into the large size group would be restricted by mobility barriers arising from capital market imperfections and high market transaction costs. On the question of generality of these conclusions, technical efficiency in production being the highest in medium-size firms might hold for many Indian industries in which small, medium, and large firms coexist and face the structural conditions as characterized 15 In other words, organizational diseconomies might offset the possible economies of scope in multi-product operations.
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in this study.16 One of the general explanations for lower efficiency of large firms is drawn from the argument of loss of organizational control because of the large scale of operation. In such a case, one way a firm can remain large is because of its relative advantage of either internally accumulated capital or external capital market imperfections. In the Indian context, the dominant factors are capital market imperfections and an institutional regime that cause high market transaction costs. The policy implications are in terms of the removal of these structural imperfections. Only then can one associate the larger size of firms to higher efficiency. Acknowledgements The author is grateful to Pranob Sen, Amal Sanyal, Pankaj Chandra, Richard H. Day, and two anonymous referees of this journal for several useful comments, and to the managers who provided information about their units during the field study. The author alone is responsible for any remaining errors. Appendix A Measurement of Technical Efficiency One way of measuring TE is through the parametric production function approach (Aigner and Chu, 1968, Richmond, 1974). The production relation can be expressed as: Y X : a u where Y is a vector of output observations, X is a matrix of input observations, a represents the parameters and u represents the one-sided error. The one-sided error forces Y f(X). In the general estimation of production functions, u is specified to be normally and identically distributed with zero mean and finite variance. Frontier estimations take u to have a negative expectation, indicating the presence of (technical) inefficiency in production. If we impose a Cobb±Douglas production function on the above form, it can be specified as Y f Xeÿu or log Y log f X ÿ u where u 0 and thus 0 eÿu 1 and where log f(X) is linear in the Cobb±Douglas case. It is assumed that X is exogenous and independent of u. As suggested by Richmond (1974),
16
The World Bank Country Study (1989) also points out this possibility in Indian industry in qualitative terms.
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the above specification can be estimated by ordinary least squares (OLS) by simple modification. If we let w be the mean of u, we can write: log Y ao ÿ w
n X
log X ÿ u ÿ w
i1
where the new error term has a zero mean. The error term satisfies all the usual properties except normality. The above specification can be estimated by OLS to obtain the best linear unbiased estimates of (ao ÿw) and ai. The estimated residuals can be used to correct the OLS constant. The constant term can be corrected by shifting it up till no residual is positive and one is zero. The extent of the deviations of the rest of the observations can be used to estimate firm-level relative production efficiency indices. The relative efficiency indices take values from 0 to 1; firms which take the value of 1 are the ones that determine the efficiency frontier. The correction procedure depends on the assumption made about the distribution of u. Following Richmond (1974) it has been assumed that u has a gamma distribution. As argued in Section 2, there could be substantial efficiency gains in the management of procurement and utilization of raw materials to firms. In such a case, a three-input production function in which production volume is a function of labor, capital and raw material is a more appropriate choice than a two-input function with value-added as output measure. However, the results are presented for both cases in order to get a broad picture of the significance of raw material management. As economies of scale are insignificant in this industry, the Cobb±Douglas functional form is an appropriate choice. In measuring technical efficiency, the frontier is taken to be deterministic. Stochastic models in which the error term is decomposed into the effect of efficiency and a symmetric component that captures measurement error and other statistical noise is more justifiable empirically than deterministic models. Secondly, one reason for allowing for noise is model misspecification, which is especially important when a parametric model is used. But the frontier being stochastic it is not possible to obtain estimates of efficiency for each observation with cross-section data. One can argue that the frontier function for Indian industries is stochastic because of supply side, power and transport bottlenecks. But over the years Indian industrial units might have learned to predict these bottlenecks, and consequently the uncertainty element in these bottlenecks gets reduced. Secondly, as our objective is to observe the association between firm-size and firm-level TE indices rather than the extent of TE at the industry level, we can harmlessly assume the stochastic elements to be similar to all firms in the sample. The alternative methodology of measuring TE that overcomes some of the shortcomings of the parametric approach is the Data Envelopment Analysis (DEA) which uses fractional linear programming techniques. One of the advantages of DEA is that it is relatively insensitive to model specification which avoids noise because of model misspecification in the estimation of TE. As developed by Charnes et al. (1978), DEA is a technique for comparative efficiency measurement. Each observation is evaluated against itself and all other observations. The DEA programming method constructs the production frontier by floating a piece-wise linear surface to rest on top of the
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observations. The degree of inefficiency is quantified and partitioned by a series of metrics that measure various distances from the frontier (for a detailed discussion of this methodology see Seiford and Thrall, 1990,Leibenstein and Maital, 1992; see Majumdar, 1996, for the application of DEA to Indian industry in a time series framework). In order to increase the reliability of the results of this study, relative technical efficiency indices for each observation have been estimated by using the DEA approach also. By taking the three-input case, technical efficiency indices are measured by adopting the inputminimizing DEA. DEA constructs a piece-wise linear approximation to the true frontier by minimizing the quantities of the m inputs required to meet stated level of the output. The model is as follows; ! m t X X ÿ Sk Si c hp ÿ i1
k1
subject to Xkp :hp ÿ S k Yip Sÿ i
z X
Xkc c ; k 1; . . . ; m
c1
z X
Yic c ; i 1; . . . ; t
c1
and 0; c 1; . . . ; p; . . . ; Z weights on branches Sk 0; k 1; . . . m input slacks Siÿ 0; i 1; . . . t
output slacks
The parameters of the model are; Xkp amount of input k used by DMUp (Data Measurement Unit) amount of output i produced by DMUp Yip e an infinitisimal constant Z 76, m 3, t 1 The inputs are labor, capital, and raw materials consumed. The pth branch is relatively efficient if and only if the efficiency ratio, hp equals unity and the slack variables are all zero. As in the case of production functions, the values of hp for each observation range from 0 to 1 and provide an index of relative efficiency (or inefficiency) for each unit. Accurate measurement of relevant variables in production function frameworks is always subject to problems due to incompleteness of available data. In our model, capital in historical costs and number of permanent labor employed were used for capital (K) and labor (L) inputs. The rental value of capital is a better measure of capital. But it requires information regarding the vintage and the expected life span of machinery employed by firms which is not available. An alternative measure is the one that reduces the capital stock by depreciation and interest rate. But the problem in this case is that depreciation figures reported by firms are influenced by tax considerations and are not reliable.
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Because of these problems, capital in historical costs was used. In the case of labor input, heterogeneity in the employees in terms of skills and experience, extent of effort, differences in the extent of use of temporary labor, etc, across firms can result in measurement errors in the case of permanent labor employed as a labor input. An alternative measure is salaries and wages paid. But to recapitulate, large and small firms pay different wage rates to labor owing to labor market segmentation which could bias the results when salaries and wages paid is used as a labor input. Because of this reason, permanent labor employed was used as labor input. However, the production functions, the results for which are presented below, were also estimated with salaries and wages paid as labor input. The results are more or less similar. The results for the three-input and two-input Cobb±Douglas production functions are as follows. lnV 3:2 0:68lnL 0:26lnK 7:3a
8:1a
3:1a
Adjusted R20.94 F624 N76 lnY 1:75 0:22lnL 0:07lnK 0:70lnR 7a
4:5a
1:77b
14a
Adjusted R20.98 F2158 N76 Figures in brackets are t values. a significant 0.01 level; b significant at 0.05 level. The sum of the output elasticities estimated in each case of the functions indicates that increasing returns to scale are insignificant. A comparison of the results of the functions indicates that the contribution of raw materials to the explanation of production is very significant; to the extent it is dominating the share of capital (K). In other words, raw material management might be an important source of relative efficiency gains. References Aigner, D.J., and Chu, S.F., 1968. On estimating the industry production function, American Economic Review 9758 (September), 826±839. Bruch, M., Hiemenz, Urick, 1983. Small and medium scale manufacturing establishments in Asian countries: Perspectives and policy Issues, Report No. 9. Economics Office Report Series, Asian Development Bank. Bruton, H., 1989. Import Substitution, Vol. 2, Handbook of Development Economics. North Holland, Amsterdam. Caves, R., and Porter, M., 1977. From entry barriers to mobility barriers: Conjectural decisions and contrive deterrence to new competition, Quarterly Journal of Economics 97, 247±261. Charnes, A., Cooper, W.W., and Rhodes, E., 1978. Measuring the efficiency of decision making units, European Journal of Operational Research 2 (November), 429±444. Demsetz, H., 1973. Industry structure, market rivalry and public policy, Journal of Law and Economics 16, 1±10. Desai, A.V., 1982. Market structure and technology: Their interdependence in Indian industry, National Council for Applied Economic Research, New Delhi, mimeo. Farrell, M.J., 1957. The measurement of production efficiency, Journal of Royal Statistical Society, Series A 120, 252±267. Ferrantino, M.J., 1992. Technology expenditures, factor intensity, and efficiency in Indian manufacturing, The Review of Economics and Statistics 74, 689±700.
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Gang, N. Ira, 1995. Small firms in India: A discussion of issues. In: Dilip Mookherjee (Eds.), Indian Industry: Policies and Performance, Oxford University Press, New Delhi. Goldar, B.N., 1986. Productivity and Growth in Indian Industry, Allied Publishers, New Delhi. Lall. S., 1987. Learning to Industrialize, Macmillan Press, London. Leibenstein, H., 1987. Inside the Firm: The Inefficiencies of Hierarchy, Harvard University Press, Cambridge, MA. Leibenstein, H., and Maital, S., 1992. Empirical estimation and partitioning of X-inefficiency: A dataenvelopment approach, American Economic Review 82, 428±433. Leiberman, M.B., 1987. Excess capacity as a barrier to entry: An empirical appraisal, Journal of Industrial Economics 35, 213±228. Little, M.D., Majumdar, D, Page, J.M., 1987. Small Manufacturing Enterprises: A Comparative Study of India and Other Economies, Oxford University Press, Oxford. Maddala, G.S., 1983. Limited Dependent and Qualitative Variables in Econometrics, Cambridge University Press, Cambridge. Majumdar, S.K., 1996. Government policies and industrial performance: An institutional analysis of the Indian experience, Journal of Institutional and Theoretical Economics 152(2), 380±411. Patibandla, M., 1995. Firm size and export behaviour: An Indian case study, The Journal of Development Studies 31, 868±882. Patibandla, M., 1994. Firm size and export behaviour, Ph.D thesis, Jawaharlal Nehru University, New Delhi. Richmond, J., 1974. Estimating the efficiency of production, International Economic Review 15, 515±521. Schmalensee, R., 1989. Inter-industry Studies of Structure and Performance, Vol. 2, Handbook of Industrial Organization, North Holland, Amsterdam. Seiford, L.M., and Thrall, R.M., 1990. Recent developments in DEA, Journal of Econometrics 46, 7±38. Sen, P., 1991. Small scale enterprises and economic development: Some conceptual issues, paper presented at the International Workshop on Promotion of Small and Medium Scale Enterprises, The Institut Superieur de Gestion et de Plantification, Algiers.. Siddharthan, N.S., 1988. In house R and D, imported technology and firm size: Lessons from Indian experience, Developing Economies 26, 212±221. Williamson, O.E., 1967. Hierarchical control and optimum firm size, Journal of Political Economy 75, 123±138. Williamson, O.E., 1975. Markets and Hierarchies: Analysis and Antitrust Implications, Free Press, New York. World Bank Country Study, 1989. India: An Industrializing Economy in Transition, World Bank, Washington DC.
Structure, organizational behavior, and technical efficiency: The case of an Indian industry Murali Patibandla* Indian Institute of Management, Vastrapur, Ahmedabad 380015, India Received 4 December 1995; received in revised form 13 February 1997
Abstract The differences in certain aspects of organizational behavior across large and small firms could be explained as a response mechanism to structural conditions like capital market imperfections and high market transaction costs. This, in turn, could explain the relative production efficiency of large and small firms. This paper examines the issue on the basis of firm-level survey data for an Indian industry. # 1998 Elsevier Science B.V. JEL classi®cation: D21; L11; L22 Keywords: ef®ciency
Capital market imperfections; Structure; Competition; Organizational behavior; Technical
1. The background and issues In several Indian industries, a few large and a large number of small firms coexist and face quite divergent structural conditions in product and factor markets. This type of market structure is explained as a result of the Indian industrial policy regime and factor market duality (Desai, 1982, Gang, 1995). On the issue of firm size and production efficiency in Indian industry, the empirical study of Little et al. (1987) showed that there was no statistically significant relation between firm size and efficiency in three out of four industries they studied. These results imply that large and small firms realize similar levels of technical efficiency in production. This throws up important questions. If levels of efficiency in production are similar, how does one explain differences in firm size in * Corresponding author. 0167-2681/98/$19.00 # 1998 Elsevier Science B.V. All rights reserved PII S 0 1 6 7 - 2 6 8 1 ( 9 7 ) 0 0 0 8 3 - 8
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Indian industries? Why were large firms, which had better access to modern imported technologies and information than small firms, not more efficient in production? This paper attempts to answer these questions by adopting the argument that the structural imperfections function as a source of long market power to large firms and entry and mobility barriers to small firms. This facilitates large firms to remain large without having to be more efficient than small and medium firms. The divergent structural conditions faced by large and small firms determine the differences in organizational behavior in production which could be one of the dominant explanatory factors of the relative production efficiency of large and small firms. In Indian industries, large firms operate in the organized capital and labor markets and small firms operate mostly in the unorganized input markets. Consequently, large firms get access to capital at a lower price and pay higher wages to labor than small firms. The regulatory policies pursued in India combined with capital market imperfections and the presence of sub-optimal contractual arrangements cause high market transaction costs. High market transaction costs and capital market imperfections, as shown by the new institutional economics, are a source of long-run market power to large firms and entry and mobility barriers to small firms.1 Under these conditions, even if small- and mediumscale firms are more efficient in production, they cannot not grow into the strategic group of large firms.2 Organizational behavior, in this paper, refers to effort and involvement of managers, multi-product operations, internal coordination and flow of information, and choice of production techniques in terms of employment of permanent and temporary labor. Production efficiency is assumed to be synonymous with Farrell's (1957) technical efficiency (TE) which refers to output realized for a given level of inputs employed, given the most efficient technology frontier of an industry. In other words, technical efficiency is measured as the extent to which actual output falls short of potential output as determined by best practice performance. Under perfectly competitive product and factor markets and homogenous technology across firms in an industry, Technical efficiency differences across firms would be purely owing to organizational factors (X-inefficiency). If markets are imperfect, the observed differences in technical efficiency across firms could be owing to, not only organizational factors, but also the technology gap across firms, the product and factor prices (when output is taken in value terms), and economies of scale. This paper focuses on certain aspects of organizational behavior and their implications on the relative technical efficiency realized by large and small firms in Indian industry and examines this issue on the basis of survey data for the Indian engineering industry. To recapitulate, large firms, operating in the organized input markets, have greater access to modern (capital-intensive) imported technologies, skilled labor and better 1 Following Caves and Porter (1977), mobility barriers refer to small firms' ability to grow and move into the larger size group. In the context of Indian industry, this requires small firms to invest in advertisement and brandname creation and to deal with government bureaucracy. Secondly, mobility barriers to small firms can also arise from cross-product subsidization by large firms which is explained later. In other words, mobility barriers are a part of the competitive dimension for small firms as they have to compete with the brand names of large firms. 2 In other words, the ability to deal with the regulatory mechanism is a source of long-run market power but not necessarily of production efficiency.
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information. On the other hand, small and medium firms operate mostly in the unorganized input markets and operate with older and labor-intensive technologies (Little et al., 1987, Siddharthan, 1988, Patibandla, 1994). This technology gap across large and small firms facilitates large firms to determine the most efficient technology frontier and to be technically more efficient than small firms. But as this paper suggests, the more dominant factor in explaining the differences in the relative technical efficiency realized by large and small firms could be their organizational behavior as a response mechanism to structural conditions. It is generally argued that under India's import substitution policy regime, Xinefficiency in industrial performance was high owing to lack of competition (Goldar, 1986, Bruton, 1989, Majumdar, 1996). This might be more prominent in the case of large firms because, while large firms were able to derive long-run domestic market power, a large number of small firms sharing the residual demand faced highly competitive pressures and mobility barriers owing to capital market imperfections and high costs of market transactions.3 Under these structural conditions while large firms can derive excess profits irrespective of organizational slack, small firms that survive are those that operate with high levels of organizational efficiency which is discussed below. Taking maximum advantage of imported technologies depends to a large extent on the organizational efforts in adapting them to local conditions. Access to capital below its shadow price and lack of competitive conditions might not have forced large firms to undertake systematic organizational efforts in adapting imported technologies to local conditions most efficiently.4 Large-scale Indian firms had been observed to underutilize capital employed and were weak in labor management (Lall, 1987). A significant part of organizational X-inefficiency of large firms stemmed from poor coordination between different departments within a firm which got magnified in a non-competitive environment. As inferred from the qualitative information of the field study, on the production floor technicians or production managers could have been able to realize certain technical snags and possible solutions and improvements in the operation of machinery. But, generally, in large firms these findings had to be referred to R&D and other departments for their approval. If these departments were poorly coordinated, it would cause, not only substantial loss in information flow, but also dissipation of possible learning economies (internal to a firm) in production. The larger the number of hierarchies, the more imperfect was the two-way transfer of information between subordinates and superiors (Williamson, 1967, Leibenstein, 1987). In large Indian firms, the levels tended to be much higher in comparison with firms in developed countries. In a typical large Indian firm, hierarchy levels range between 15 and 20 categories. Within five or six broad categories, there are three or four sub-categories. 3 In pursuit of heavy industrialization, India's macro-economic policies were tilted towards capital-intensive large-scale firms. Bruch and Hiemenz (1983) have observed that in many newly industrializing countries large firms have concentrated in those areas where effective rates of protection are high whereas small firms have concentrated in those areas where the protection is very low. For example, in the case of India's automobile industry, large firms concentrate in the production of the final good which is subject to high rates of protection and small firms concentrate in the production of automobile components subject to very low rates of protection. In other words, small firms compete with imports also. 4 Ferrantino's (1992) empirical study of a few Indian industries shows that the purchasers of imported technology are less efficient in production than the non-purchasers.
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One of the strategic responses of large firms to the structural conditions is vertical integration and multi-product operations within a broadly defined industry as it can facilitate imposition of mobility and entry barriers through cross-product subsidization (Patibandla, 1994).5 Multi-product operations increase organizational complexity and make organizations prone to high levels of X-inefficiency.6 Furthermore, another strategic response of large firms could be maintenance of excess capacity as an entry deterrence strategy (Leiberman, 1987). In such cases, the excess capacity of large firms results in lower technical efficiency in production. In contrast, small firms face two major interlinked structural constraints: limited access to capital and strong mobility barriers. These constraints get magnified in the Indian economy with high transaction costs. In the presence of capital market imperfections and sub-optimal contractual arrangements, small firms face higher transaction costs. For example, as derived from the field study, small firms had to wait for long periods for payment on domestic deliveries because of sub-optimal contractual arrangements and low bargaining power. This, in turn, raised the costs of working capital to small firms, especially since small firms got access to capital at higher rates than large firms. This is a significant factor because most small firms tended to have a high percentage of bought-in inputs than did large firms which were generally more vertically integrated (Sen, 1991 Patibandla, 1994, 1995).7 In this context, a successful small firm could be the one that adopted organizational behavior which minimized fixed and working capital requirements at all stages of operation.8 The above observation is illustrated with the help of the qualitative information collected from the field study of two typically successful small firms (located in Bangalore and Madras). Both firms were managed by technocrat±owner managers. In the case of the latter firm, three professional managers were employed. In both cases, organizational efforts were made towards minimizing costs in the installation of plant and machinery and in labor management through a high degree of involvement of the managers. This behavior is characterized as follows: 1. Small firms' relative advantage lies in specialization within a broadly defined industry (or a product). The first stage involves the choice of the product to be manufactured in relation to the intended scale of operation and available organizational and technological skills. 5 In those products where small firms are relatively more cost-efficient than large firms, large firms resort to cross-subsidization to block the growth of efficient small firms. The losses incurred in the subsidized products are recovered from excess profits made from those products in which competition is low. Another reason for multi-product operations by large firms is the realization of excess profits under import substitution cycles. 6 On the other hand, if there are economies of scope in multi-product operations, large multi-product firms should be in a position to derive higher efficiency in production than small firms. Apart from this, flexible manufacturing techniques are supposed to facilitate efficiency gains in multi-product operations. But flexible manufacturing technologies are uncommon in Indian industry. The other possible positive trade-offs of vertical integration and multi-product operations might be minimization of the costs of external trading (market transaction costs) and information collection (see Williamson, 1975). 7 Patibandla (1995) explains how several small firms in the Indian industry branched out into export markets in order to overcome the high costs of domestic market transactions. 8 Working capital constraint is generally observed to be one of the dominant reasons for the high incidence of sickness in the small-scale Indian industry (see Sen, 1991).
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2. The second stage is minimization of costs in buying and installing machinery. In the case of the first firm mentioned above, machinery cost was reduced substantially by buying secondhand machinery and renovating it with new components and parts secured from the domestic and overseas markets. 3. The third stage is minimization of variable costs along with maximum utilization of fixed capital with a high degree of involvement of owner±managers at every stage of operation. The working capital constraint was reduced by maintaining a consistent working relation with good quality raw material producers which helped in effective inventory management in relation to product demand conditions. The gains in raw material management could be pecuniary economies in bulk buying of materials which would be more significant to large firms and inventory management which is independent of firm size. As mentioned above, the pressure to do effective inventory management was more on small firms owing to working capital constraints. Secondly, management of labor is an important source of saving on working capital requirements. Since small firms operated mostly with unorganized labor, they had a relatively higher degree of flexibility in production organization. They could minimize the variable cost of labor by employing a few permanent skilled labor and temporary semi-skilled and unskilled labor. They could utilize permanent and temporary labor employed to the maximum extent. The labor intensity of small firms can be observed in terms of the intensity with which employed labor is utilized. Most small firms, as observed in the field study, tended to make laborers work long hours by paying low wage rates. Following from the above observation, the organization of labor markets could be one of the important factors in determining the relative technical efficiency of large and small firms. While capital market segmentation, as discussed earlier, works in favor of large firms, labor market imperfections might be a source of disadvantage to large firms in realizing optimum technical efficiency. The labor unions and labor laws in Indian industry restrict large firms from getting rid of excess labor and replacing inefficient labor with efficient labor. The degree of labor unionization in the sample was as follows. Small firms with sales turnover approximately below 20 million rupees tended to operate with unorganized labor. They could pursue a hire and fire policy towards labor. Medium-scale firms with sales turnover in the range of 25 to 200 million rupees employed semi-organized labor, especially if a firm was located in metropolitan cities. They had to provide certain minimum benefits to workers. However, most firms in this size group retained flexibility in replacing labor. The very large firms were fully subject to labor laws and unionization with little flexibility in labor replacement. The mobility of labor across firm size groups, caused by the labor market organization, would have implications on the relative technical efficiency realized by different size groups. Sen (1991) observes that there is generally considerable pressure on small firms to keep their wage bill down. As a result, there is a tendency for larger firms, which can financially afford to pay higher wages, to attract the best labor away from the financially weaker smaller firms. As mentioned earlier, in the case of very large firms, labor unions can effectively prevent the replacement of inefficient labor with more efficient new
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recruits. This, in turn, may result in a situation in which the larger of the small firms may be able to draw some of the best workers at the cost of smaller firms which have to do with relatively inexperienced labor. Thus, smaller firms may end up essentially as training grounds for medium-scale firms. 2. Empirical analysis The following empirical analysis estimates the association between firm-size and firmlevel relative technical efficiency indices and tests for the explanation of firm-level relative technical efficiency by certain organizational variables. If one accepts the assumption that large and small firms face different structural conditions in Indian industry, the relationship between firm size and efficiency sheds some light on this behavior. Given the data set, we could measure explicitly only two variables that capture some of the arguments of organizational aspects. These variables refer to the number of products produced by firms and the degree of vertical integration: the higher the values of these variables, the higher would be organizational complexity and consequent loss of efficiency.9 We take this exercise to provide certain empirical regularities and useful stylized facts.10 The main hypotheses are: (1) Under perfectly contestable markets, structure can be viewed as endogenous (Demsetz, 1973): larger firms are those which have higher production efficiency. On the other hand, under the presence of structural imperfections such as capital market imperfections and high market transaction costs as prevalent in the Indian economy, market structure in terms of firm size asymmetry can be seen as exogenous and stagnant. Differences in firm size have to be explained by structural imperfections rather than relative production efficiency of firms. The presence of long-run market power could facilitate large firms to remain large irrespective of their relative organizational inefficiency in production. If organizational inefficiency of large firms is more dominant than their relative advantage in access to superior technology and information, large firms would exhibit lower technical efficiency in production compared to small- and mediumscale firms. (2) Diversification into multi-products and higher levels of vertical integration by large firms are more towards exercising market power than realizing higher levels of production efficiency. This, in turn, causes higher organizational complexity and consequently lower levels of technical efficiency in production. 2.1. Data The firm-level data refer to the period 1983/84 and were drawn from the hand, small and cutting tools industry. Data were collected on the basis of an extensive field survey 9 The firm size variable also captures the owner±manager argument of the previous section as most small- and medium-scale firms are managed by owners while large firms are managed mostly by professional managers. 10 See Schmalensee (1989) for a detailed explanation of some of the problems involved in empirical studies with cross-sectional data in industrial economics.
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across the country. In the case of large public limited companies, the company balance sheets were also used. The sample consisted of 76 firms. Firm-size distribution of the sample, in terms of sales turnover, ranged from a maximum of 1592 million rupees to a minimum of 0.7 million rupees. One of the major problems of firm-level data, for any broadly defined (disaggregate level) industry, is the heterogeneity in products produced by firms within an industry. As mentioned earlier, in this industry large firms produce a wide range of hand and cutting tools and small firms concentrate on one or a few specialized product lines. Due to the heterogeneity in products, there is a certain degree of technological diversity across firms.11 Consequently, it causes a certain level of statistical noise in the estimation of firm-level technical efficiency indices. 2.2. Notations TE-1 TE-2 TE-3 S NP Y V K R L VI
firm-level relative technical efficiency, measured on the basis of a two-input production function (parametric approach) firm-level relative technical efficiency, measured on the basis of a three-input production function firm-level relative technical efficiency, measured on the basis of Data Envelopment Analysis (see Appendix A for a detailed discussion of the measurement issues of technical efficiency) firm-level sales turnover (normalized by the lowest value in the sample) as firmsize variable number of products produced by firms production in value terms value-added total capital employed in value terms total raw materials consumed in value terms number of permanent labor employed degree of vertical integration, (value-added/production).Its value ranges from 0 to 1. Firms that show values closer to 1 are highly vertically integrated in production.
2.3. Results The average values of technical efficiency for the sample are: 0.37 (with a standard deviation of 0.19) for TE-1, 0.50 (with a standard deviation of 0.13) for TE-2, and 0.59 (with a standard deviation of 0.17). The average value of technical efficiency estimated with a three-input function is significantly higher than the one estimated with a two-input
11 This issue is different from the argument of technological gap across firms in the production of a specific product.
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Fig. 1. TE-1 and firm size (in logs)
function. This indicates that there could be significant efficiency gains in raw material usage and management.12 Figs. 1±3 show the plots of TE-1, TE-2 and TE-3 against firm size. The association between technical efficiency and firm size appears to be non-linear. Technical efficiency is low for very large and small firms and high for medium-scale firms. In other words, if we ignore the few outlier observations in the middle, TE appears to increase with firm size until a critical level and thereafter to decline as firm size increases. This possible non-linear association between firm-level TE and firm size and a few variables that capture the organizational aspects is tested by a set of econometric equations. As the values of the dependent variable, TE, are bound to take values from 0 to 1, Ordinary Least Squares may provide biased estimates. To avoid this, the Tobit maximum likelihood method is adopted in estimating the regression coefficients (Maddala, 1983). The non-linear association between TE and firm size (S) is captured by introducing the size variable in quadratic terms. The number of products produced by firms (NP) and vertical integration (VI) capture certain organizational aspects of firms: the higher the degree of vertical integration and product diversification, the higher organizational complexity can be. This dimension operates through the number of labor employed. In other words, the larger the number of products produced and the higher the degree of 12 As discussed in Section 2, there are two types of efficiency gains in raw material management: pecuniary economies in bulk buying which could be significant for large firms and inventory management which applies for both large and small firms. Since raw material input enters the production function in value terms (price multiplied by quantity), pecuniary gains are captured by the input in the estimation.
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Fig. 2. TE-2 and firm size (in logs)
Fig. 3. TE-3 and firm size (in logs)
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Table 1 Econometric results Dependent variable
TE-1 TE-2 TE-3 TE-1 TE-2 TE-3 TE-1 TE-2 TE-3
Independent variables and the estimated coefficients 2
S
S
0.00116 (4.5)a 0.0013 (4.0)a 0.0015 (3.6)a 0.0005 (3.6)a 0.00018 (2.5)a 0.00008 (0.53) NP 0.0667 (8.0)a 0.0848 (9.1)a 0.105 (8.5)a
ÿ0.0000004 (3.35)a ÿ0.0000005 (2.9)a ÿ0.0000006 (2.6)a ÿ0.0000001 (1.63)b ÿ0.00000001 (0.6) 0.00000003 (0.51) NP*L ÿ0.0000045 (3.8)a ÿ0.000006 (3.8)a ÿ0.0000073 (3.7)a
VI
Log likelihood
VI*L ÿ30 ÿ50 ÿ70
0.70 (17)a 0.98 (49)a 1.27 (34)a
ÿ0.00012 (3.4)a ÿ0.000006 (3.3)a ÿ0.00008 (2.5)a
31 84 36 ÿ13 ÿ29 ÿ50
Note: figures in the parentheses are t values. a significant at 0.01 level. b significant at 0.05 level.
vertical integration for a given level of labor employed, the higher the organizational complexity and loss of control are likely to be. In order to capture this aspect, NP and VI as interactive terms with number of labor employed (L) are introduced into the estimations. The interactive terms capture the non-linearities in the explanation of TE by the organizational variables. The equations are estimated in different stages as the independent variables like firm size, number of products produced and the interactive variables are highly correlated.13 The results are presented in Table 1. The estimated coefficients associated with respect to variables S and S2 are statistically significant and have positive and negative signs respectively in all the three cases of technical efficiency variables. This confirms the non-linear association between technical efficiency and firm size. Technical efficiency increases till the firm size reaches about a 930 million rupees sales turnover and thereafter declines as firm size increases. The positive and negative signs of the statistically significant estimated coefficients associated with the number of products (NP) and the interactive variable (NP*L) imply that TE increases until about the level of 14,600 workers employed.14 Above this level, TE 13 Firm size (S) and the number of products produced (NP) are highly correlated with a correlation coefficient of 0.84. 14 This value is calculated by equating the first-order derivatives of the estimated equations with NP variable to zero.
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decreases as the number of products produced by firms increases. This result suggests that beyond the scale of 14,600 workers employed, diseconomies associated with loss of organizational control owing to labor performing diversified tasks in the production of larger number of products could turn out to be dominant.15 Similar implications apply to the results obtained with respect to vertical integration and its interactive variables. 3. Conclusion In the presence of capital market imperfections and high market transaction costs which are a source of entry and mobility barriers, the structure of an industry can be taken to be exogenously given. The structural imperfections in Indian industry can be traced to the factor market imperfections and the regulatory policy regime. The regulatory policy regime in India which causes over-bureaucratic apparatus, and the presence of suboptimal institutions (the contractual arrangements) are sources of high market transaction costs. The capital market imperfections combined with high market transaction costs function as a source of market power to large firms and entry and mobility barriers to small- and medium-scale firms. The implications of structural imperfections on the market structure can explain the differences in organizational behavior adopted by large and small firms which are one of the major determinants of their relative production efficiency. The empirical results of this study show that large firms in the Indian industry, despite their relative advantages in higher access to better technologies and information, are not the ones that can determine the most efficient technology frontier. The organizational inefficiency of large firms has been a more dominant factor than their relative advantages through the technology gap. Structural imperfections are the major explanatory factors for their low level of organizational efficiency irrespective of which they can remain large for sustained periods. Furthermore, the labor market segmentation in the Indian industry is also a major source of lower technical efficiency of large firms. Low TE levels in very small firms can be attributed to outdated technology and low labor skills. The explanation for the maximum TE realized by middle-size firms can be provided by the structural conditions as discussed in Section 2. Small firms that can adopt the right kind of organizational behavior in relation to the structural conditions they face are the ones that are in a position to realize higher levels of TE and grow into the middle scale. The movement of better trained labor into medium-size firms, caused by segmentation in the labor market, can also be one of the major explanatory factors for higher levels of TE in middle-size firms. But the ability of these firms to move into the large size group would be restricted by mobility barriers arising from capital market imperfections and high market transaction costs. On the question of generality of these conclusions, technical efficiency in production being the highest in medium-size firms might hold for many Indian industries in which small, medium, and large firms coexist and face the structural conditions as characterized 15 In other words, organizational diseconomies might offset the possible economies of scope in multi-product operations.
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in this study.16 One of the general explanations for lower efficiency of large firms is drawn from the argument of loss of organizational control because of the large scale of operation. In such a case, one way a firm can remain large is because of its relative advantage of either internally accumulated capital or external capital market imperfections. In the Indian context, the dominant factors are capital market imperfections and an institutional regime that cause high market transaction costs. The policy implications are in terms of the removal of these structural imperfections. Only then can one associate the larger size of firms to higher efficiency. Acknowledgements The author is grateful to Pranob Sen, Amal Sanyal, Pankaj Chandra, Richard H. Day, and two anonymous referees of this journal for several useful comments, and to the managers who provided information about their units during the field study. The author alone is responsible for any remaining errors. Appendix A Measurement of Technical Efficiency One way of measuring TE is through the parametric production function approach (Aigner and Chu, 1968, Richmond, 1974). The production relation can be expressed as: Y X : a u where Y is a vector of output observations, X is a matrix of input observations, a represents the parameters and u represents the one-sided error. The one-sided error forces Y f(X). In the general estimation of production functions, u is specified to be normally and identically distributed with zero mean and finite variance. Frontier estimations take u to have a negative expectation, indicating the presence of (technical) inefficiency in production. If we impose a Cobb±Douglas production function on the above form, it can be specified as Y f Xeÿu or log Y log f X ÿ u where u 0 and thus 0 eÿu 1 and where log f(X) is linear in the Cobb±Douglas case. It is assumed that X is exogenous and independent of u. As suggested by Richmond (1974),
16
The World Bank Country Study (1989) also points out this possibility in Indian industry in qualitative terms.
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the above specification can be estimated by ordinary least squares (OLS) by simple modification. If we let w be the mean of u, we can write: log Y ao ÿ w
n X
log X ÿ u ÿ w
i1
where the new error term has a zero mean. The error term satisfies all the usual properties except normality. The above specification can be estimated by OLS to obtain the best linear unbiased estimates of (ao ÿw) and ai. The estimated residuals can be used to correct the OLS constant. The constant term can be corrected by shifting it up till no residual is positive and one is zero. The extent of the deviations of the rest of the observations can be used to estimate firm-level relative production efficiency indices. The relative efficiency indices take values from 0 to 1; firms which take the value of 1 are the ones that determine the efficiency frontier. The correction procedure depends on the assumption made about the distribution of u. Following Richmond (1974) it has been assumed that u has a gamma distribution. As argued in Section 2, there could be substantial efficiency gains in the management of procurement and utilization of raw materials to firms. In such a case, a three-input production function in which production volume is a function of labor, capital and raw material is a more appropriate choice than a two-input function with value-added as output measure. However, the results are presented for both cases in order to get a broad picture of the significance of raw material management. As economies of scale are insignificant in this industry, the Cobb±Douglas functional form is an appropriate choice. In measuring technical efficiency, the frontier is taken to be deterministic. Stochastic models in which the error term is decomposed into the effect of efficiency and a symmetric component that captures measurement error and other statistical noise is more justifiable empirically than deterministic models. Secondly, one reason for allowing for noise is model misspecification, which is especially important when a parametric model is used. But the frontier being stochastic it is not possible to obtain estimates of efficiency for each observation with cross-section data. One can argue that the frontier function for Indian industries is stochastic because of supply side, power and transport bottlenecks. But over the years Indian industrial units might have learned to predict these bottlenecks, and consequently the uncertainty element in these bottlenecks gets reduced. Secondly, as our objective is to observe the association between firm-size and firm-level TE indices rather than the extent of TE at the industry level, we can harmlessly assume the stochastic elements to be similar to all firms in the sample. The alternative methodology of measuring TE that overcomes some of the shortcomings of the parametric approach is the Data Envelopment Analysis (DEA) which uses fractional linear programming techniques. One of the advantages of DEA is that it is relatively insensitive to model specification which avoids noise because of model misspecification in the estimation of TE. As developed by Charnes et al. (1978), DEA is a technique for comparative efficiency measurement. Each observation is evaluated against itself and all other observations. The DEA programming method constructs the production frontier by floating a piece-wise linear surface to rest on top of the
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observations. The degree of inefficiency is quantified and partitioned by a series of metrics that measure various distances from the frontier (for a detailed discussion of this methodology see Seiford and Thrall, 1990,Leibenstein and Maital, 1992; see Majumdar, 1996, for the application of DEA to Indian industry in a time series framework). In order to increase the reliability of the results of this study, relative technical efficiency indices for each observation have been estimated by using the DEA approach also. By taking the three-input case, technical efficiency indices are measured by adopting the inputminimizing DEA. DEA constructs a piece-wise linear approximation to the true frontier by minimizing the quantities of the m inputs required to meet stated level of the output. The model is as follows; ! m t X X ÿ Sk Si c hp ÿ i1
k1
subject to Xkp :hp ÿ S k Yip Sÿ i
z X
Xkc c ; k 1; . . . ; m
c1
z X
Yic c ; i 1; . . . ; t
c1
and 0; c 1; . . . ; p; . . . ; Z weights on branches Sk 0; k 1; . . . m input slacks Siÿ 0; i 1; . . . t
output slacks
The parameters of the model are; Xkp amount of input k used by DMUp (Data Measurement Unit) amount of output i produced by DMUp Yip e an infinitisimal constant Z 76, m 3, t 1 The inputs are labor, capital, and raw materials consumed. The pth branch is relatively efficient if and only if the efficiency ratio, hp equals unity and the slack variables are all zero. As in the case of production functions, the values of hp for each observation range from 0 to 1 and provide an index of relative efficiency (or inefficiency) for each unit. Accurate measurement of relevant variables in production function frameworks is always subject to problems due to incompleteness of available data. In our model, capital in historical costs and number of permanent labor employed were used for capital (K) and labor (L) inputs. The rental value of capital is a better measure of capital. But it requires information regarding the vintage and the expected life span of machinery employed by firms which is not available. An alternative measure is the one that reduces the capital stock by depreciation and interest rate. But the problem in this case is that depreciation figures reported by firms are influenced by tax considerations and are not reliable.
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Because of these problems, capital in historical costs was used. In the case of labor input, heterogeneity in the employees in terms of skills and experience, extent of effort, differences in the extent of use of temporary labor, etc, across firms can result in measurement errors in the case of permanent labor employed as a labor input. An alternative measure is salaries and wages paid. But to recapitulate, large and small firms pay different wage rates to labor owing to labor market segmentation which could bias the results when salaries and wages paid is used as a labor input. Because of this reason, permanent labor employed was used as labor input. However, the production functions, the results for which are presented below, were also estimated with salaries and wages paid as labor input. The results are more or less similar. The results for the three-input and two-input Cobb±Douglas production functions are as follows. lnV 3:2 0:68lnL 0:26lnK 7:3a
8:1a
3:1a
Adjusted R20.94 F624 N76 lnY 1:75 0:22lnL 0:07lnK 0:70lnR 7a
4:5a
1:77b
14a
Adjusted R20.98 F2158 N76 Figures in brackets are t values. a significant 0.01 level; b significant at 0.05 level. The sum of the output elasticities estimated in each case of the functions indicates that increasing returns to scale are insignificant. A comparison of the results of the functions indicates that the contribution of raw materials to the explanation of production is very significant; to the extent it is dominating the share of capital (K). In other words, raw material management might be an important source of relative efficiency gains. References Aigner, D.J., and Chu, S.F., 1968. On estimating the industry production function, American Economic Review 9758 (September), 826±839. Bruch, M., Hiemenz, Urick, 1983. Small and medium scale manufacturing establishments in Asian countries: Perspectives and policy Issues, Report No. 9. Economics Office Report Series, Asian Development Bank. Bruton, H., 1989. Import Substitution, Vol. 2, Handbook of Development Economics. North Holland, Amsterdam. Caves, R., and Porter, M., 1977. From entry barriers to mobility barriers: Conjectural decisions and contrive deterrence to new competition, Quarterly Journal of Economics 97, 247±261. Charnes, A., Cooper, W.W., and Rhodes, E., 1978. Measuring the efficiency of decision making units, European Journal of Operational Research 2 (November), 429±444. Demsetz, H., 1973. Industry structure, market rivalry and public policy, Journal of Law and Economics 16, 1±10. Desai, A.V., 1982. Market structure and technology: Their interdependence in Indian industry, National Council for Applied Economic Research, New Delhi, mimeo. Farrell, M.J., 1957. The measurement of production efficiency, Journal of Royal Statistical Society, Series A 120, 252±267. Ferrantino, M.J., 1992. Technology expenditures, factor intensity, and efficiency in Indian manufacturing, The Review of Economics and Statistics 74, 689±700.
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M. Patibandla / J. of Economic Behavior & Org. 34 (1998) 419±434
Gang, N. Ira, 1995. Small firms in India: A discussion of issues. In: Dilip Mookherjee (Eds.), Indian Industry: Policies and Performance, Oxford University Press, New Delhi. Goldar, B.N., 1986. Productivity and Growth in Indian Industry, Allied Publishers, New Delhi. Lall. S., 1987. Learning to Industrialize, Macmillan Press, London. Leibenstein, H., 1987. Inside the Firm: The Inefficiencies of Hierarchy, Harvard University Press, Cambridge, MA. Leibenstein, H., and Maital, S., 1992. Empirical estimation and partitioning of X-inefficiency: A dataenvelopment approach, American Economic Review 82, 428±433. Leiberman, M.B., 1987. Excess capacity as a barrier to entry: An empirical appraisal, Journal of Industrial Economics 35, 213±228. Little, M.D., Majumdar, D, Page, J.M., 1987. Small Manufacturing Enterprises: A Comparative Study of India and Other Economies, Oxford University Press, Oxford. Maddala, G.S., 1983. Limited Dependent and Qualitative Variables in Econometrics, Cambridge University Press, Cambridge. Majumdar, S.K., 1996. Government policies and industrial performance: An institutional analysis of the Indian experience, Journal of Institutional and Theoretical Economics 152(2), 380±411. Patibandla, M., 1995. Firm size and export behaviour: An Indian case study, The Journal of Development Studies 31, 868±882. Patibandla, M., 1994. Firm size and export behaviour, Ph.D thesis, Jawaharlal Nehru University, New Delhi. Richmond, J., 1974. Estimating the efficiency of production, International Economic Review 15, 515±521. Schmalensee, R., 1989. Inter-industry Studies of Structure and Performance, Vol. 2, Handbook of Industrial Organization, North Holland, Amsterdam. Seiford, L.M., and Thrall, R.M., 1990. Recent developments in DEA, Journal of Econometrics 46, 7±38. Sen, P., 1991. Small scale enterprises and economic development: Some conceptual issues, paper presented at the International Workshop on Promotion of Small and Medium Scale Enterprises, The Institut Superieur de Gestion et de Plantification, Algiers.. Siddharthan, N.S., 1988. In house R and D, imported technology and firm size: Lessons from Indian experience, Developing Economies 26, 212±221. Williamson, O.E., 1967. Hierarchical control and optimum firm size, Journal of Political Economy 75, 123±138. Williamson, O.E., 1975. Markets and Hierarchies: Analysis and Antitrust Implications, Free Press, New York. World Bank Country Study, 1989. India: An Industrializing Economy in Transition, World Bank, Washington DC.