- Email: [email protected]
Is output growth of Korean manufacturing firms productivity-driven?
Journal of Asian Economics 14 (2003) 669–678
Short communication
Is output growth of Korean manufacturing firms productivity-driven? Renuka Mahadevana,1, Sangho Kimb,* a
School of Economics, The University of Queensland, Queensland 4072, Australia b Department of International Trade, Honam University, Kwangju, South Korea
Received 22 February 2003; received in revised form 27 May 2003; accepted 7 July 2003
Abstract This paper examines the sources of output growth and total factor productivity (TFP) growth of four selected South Korean manufacturing industries from 1980 to 1994. Unlike previous studies, this study is enriched by the use of firm level data within each industry and the application of the random coefficient frontier model. Empirical results show that output growth in the manufacturing industries is increasingly productivity-driven. But the varying sources of TFP growth (i.e. technical progress and gains in technical efficiency) within the industries present an urgent need to reexamine the effect of government policies and other factors to formulate specific policies for sustainable TFP growth. # 2003 Elsevier Inc. All rights reserved. Keywords: Total factor productivity; Technological progress; Technical efficiency
1. Introduction The industrial development of the largest of the East Asian miracle economies, South Korea, is generally considered to be one of the most successful cases of the postwar era before the 1997 financial crisis. From the devastation of the Korean war and from a small industrial base, the country succeeded in growing at 7% or more for all but a few years in the 1969–1996 period. Yet recent research indicates that much of Korea’s manufacturing output growth is input-driven. While many of these studies have applied the ‘Solow’ growth equation to account for economic growth and TFP growth (Dollar & Sokoloff, 1990; Moon, Jo, Whang, & Kim, * Corresponding author. E-mail addresses: [email protected] (R. Mahadevan), [email protected] (S. Kim). 1 Tel.: þ61-7-3365-6595; fax: þ61-7-3365-7299.
1049-0078/$ – see front matter # 2003 Elsevier Inc. All rights reserved. doi:10.1016/S1049-0078(03)00101-5
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R. Mahadevan, S. Kim / Journal of Asian Economics 14 (2003) 669–678
1991; Young, 1995; Pyo, Kong, Kwon, & Kim, 1992), other studies extended the ‘Solow’ growth accounting to include capital utilisation and economies of scale to analyse TFP growth for the Korean manufacturing sector (Kwon, 1986; Park & Kwon, 1995). However, this study on Korean manufacturing output and productivity growth is different in the following ways. First, manufacturing firm level data is used to obtain more accurate results. The panel data consists of a total of 135 firms in four manufacturing industries from 1980 to 1994. Second, with the exception of Kim and Han (2001) who used the stochastic frontier approach, all earlier studies on Korea used the growth accounting approach.2 The growth accounting method is flawed as it unrealistically assumes that all firms are technically efficient and hence TFP growth is synonymous with technical progress. Although Kim and Han’s (2001) stochastic frontier model overcomes this problem, it has two rigid assumptions in that, the production frontier shifts neutrally over time and technical efficiency is assumed (in an ad hoc way) to follow a truncated normal distribution with no theoretical reasoning. Here, the random coefficient frontier model is used as it relaxes both these assumptions. As the adopted model is estimated using the generalised least squares technique, it does not rely on any distributional assumptions about the error term and thus technical inefficiency is not specified to take on any rigidities. Also, here, the more realistic non-neutral shifting production frontier is estimated.3 Third, in addition to decomposing output growth into input growth and TFP growth, TFP growth is further decomposed into technical progress and gains from technical efficiency. In the productivity literature, technical progress is associated with shifts in the production frontier due to technological innovation while technical efficiency is related to movements towards the frontier which can be brought about by learning-by-doing, improved managerial practice, changes in the efficiency with which a known technology is applied, etc. As high rates of technological progress can coexist with deteriorating technical efficiency performance, specific policy actions are required to address the difference in the sources of variation in productivity growth. The plan of the paper is as follows: Section 2 sets out the theoretical framework underlying the random coefficient frontier model. While Section 3 explains data sources and the variables used, Section 4 presents the empirical results of the model and the decompositional analysis of output growth and TFP growth of the manufacturing firms. Section 5 concludes.
2. The random coefficient frontier model The concept underlying the stochastic frontier approach was initiated by Farrell (1957) and emphasises the idea of maximality. A production function methodology based 2 However there exist other studies such as Mahadevan (2000, 2001) that have used the stochastic frontier approach for Singapore and Malaysia, and Yang and Hu (2002) who did similar work for Taiwan. 3 Kalirajan and Shand (1994) argue that the marginal rate of technical substitution at any input combination should be allowed to change over time since with the same level of inputs, different levels of output are obtained by following different methods of applications. Hence the non-neutral shifting production frontier should be considered. See Kalirajan and Shand (1994) for technical details of the implementation in the model.
R. Mahadevan, S. Kim / Journal of Asian Economics 14 (2003) 669–678
671
on Farrell’s method to measure firm performance is appealing as the frontier functions can indicate the maximum possible output from a combination of inputs and technology. The generalised version of the model can be written as: n X gijt ln Xijt ;
ln Yit ¼ g1it þ
(1)
j¼1
where i represents number of firms; j represents number of inputs used; t represents time period; Y is the output; X is the inputs used; g1it is the intercept term of the ith firm in the tth period; gijt is the slope concerning the method of application of the jth input used by the ith firm in the tth period. Since intercepts and slope coefficients can vary across firms and time, we can write: gijt ¼ gjt þ uijt
g1it ¼ g1t þ v1it ;
(2)
where gjt is the mean response coefficient of output with respect to the jth input in tth period; uijt and v1it are random disturbance terms; Eðgijt Þ ¼ gjt , Eðuijt Þ ¼ 0 and Varðuij Þ ¼ suit for j ¼ t and zero otherwise Combining Eqs. (1) and (2): ln Yit ¼ g1t þ
k n X X gjt ln Xijt þ uijt ln Xijt þ v1it : j¼1
(3)
j¼1
Following Aitken’s generalised least squares method suggested by Hildreth and Houck (1968) and the estimation procedure by Griffiths (1972), the firm-specific and inputspecific response coefficient estimates of the above model can be obtained. The highest magnitude of each response coefficient and intercept form the frontier coefficients of the potential production function. If g are the parameter estimates of the frontier production, then, gjt ¼ maxi fgijt g where i ¼ 1, 2, . . . n, j ¼ 1, 2, . . ., k and t ¼ 1, 2, . . ., T. The potential output of the firm can be realised when the ‘best practice’ techniques are used and this is given by ln Yit ¼ g1t þ
k X
gjt ln Xijt :
(4)
j¼1
The firm-specific technical efficiency is a measure of how well given inputs and technology are used and this is given by the ratio of the firm’s actual realised output to that of its potential output. Here, we consider the flexible translog production function to obtain more generalised estimates. In addition, a time trend is included to capture the effects of time-related variables on output. The model for each industry using firm level data (omitting time subscripts) is given by: lnðYÞ ¼ a0 þ a1 T þ b ln L þ a ln K þ d lnðKÞ lnðLÞþZ lnðKÞ lnðKÞþl lnðLÞ lnðLÞ; where Y is the real value added output; T is the time trend; L is the labor; K is the capital.
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R. Mahadevan, S. Kim / Journal of Asian Economics 14 (2003) 669–678
3. Data sources and construction of variables The four two-digit industries that are investigated according to the Korean Standard Industry Classification (KSIC) include light industries 31 (food, beverage and tobacco industry), and 32 (textile, wearing apparel and leather products industry), and heavy industries 35 (chemicals, petroleum and coal products industry) and 37 (fabricated metal products, machinery and equipment). Among these industries which explain about 80% of total Korean manufacturing output and about 70% of total exports of the fabricated metal products, machinery and equipment industry is the largest in terms of output and employment followed by the textile, wearing apparel and leather products industry. The sample firms in these industries cover manufacturing firms whose stock is listed on the Korean Stock Exchange. The firms are required to report their financial status in the Annual Report of Korean Companies by Korea Investors Service, from which the data for empirical investigation was compiled. The data from 1980 to 1994 consists of 30 firms for KSIC 31, 31 firms for KSIC 32, 41 firms for KSIC 35, and 33 firms for KSIC 37.The key variables used are value added output, capital and labour. The value added output of the firms was deflated by the wholesale price index of each industry with 1990 as the base year, obtained from the Monthly Bulletin by Bank of Korea. Labour was measured by the number of employed workers and capital stock was given by the amount of tangible fixed assets. As reported firms’ capital stock figures were already deflated but with varying base year prices, they were then made comparable with a common base year of 1990 using the gross domestic fixed capital formation deflator obtained from the National Accounts by the Bank of Korea.
4. Empirical results Table 1 shows the estimates of the production frontier for all four industries. The production frontier model for all four industries is valid given the statistical significance of the log likelihood ratio test of the one-sided error (u) obtained from the
Table 1 Estimates of the random coefficient frontier model Variable
Constant (a0) Time trend (a1) ln K (a) ln L (b) ln(K) ln(K) (Z) ln(L) ln(L) (l) ln(K) ln(L) (d)
Industry (KSIC) 31
32
35
38
2.08 (0.38) 0.09 (0.04) 0.381 (0.11) 0.492 (0.21) 0.016 (0.008) 0.051 (0.017) 0.056a (0.047)
4.36 (0.92) 1.04 (0.51) 0.402 (0.15) 0.516 (0.18) 0.052 (0.021) 0.043a (0.031) 0.028 (0.005)
1.77 (0.69) 0.88 (0.29) 0.591 (0.28) 0.371 (0.12) 0.030 (0.011) 0.029a (0.02) 0.061 (0.022)
1.52 (0.71) 0.74 (0.32) 0.518 (0.22) 0.425 (0.13) 0.021 (0.007) 0.018 (0.009) 0.034 (0.016)
Note: Figures in parenthesis are asymptotic standard errors. a Means that the coefficients are insignificant at the 5% level of significance.
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models. Most of the parameters are significant at the 5% level. However, the estimates do not directly indicate the production elasticities with respect to inputs. These elasticity estimates can be evaluated at their mean levels to show that capital contributes more to output than labour in the heavy industries than in the light industries. Using the estimates in Table 1 and the decompositional analysis set out in the Appendix A, the sources of output growth and TFP growth for firms in each industry were calculated and summarised in Table 2. For the decomposition for each industry, a weighted approach using the share of each firm’s value added to the total value added of the industry was adopted. Although all the manufacturing industries’ output growth was mainly input-driven, it can be seen that TFP growth increased over time for all industries except for the fabricated metal products, machinery and equipment industry. However, output growth for all four industries was increasingly productivity-driven. The contribution of TFP growth to output growth was also found to be higher for the export-oriented industries of the textile, wearing apparel and leather products industry as well as the fabricated metal products and machinery industry. These export industries which are exposed to international competition are well aware of the need to be cost competitive and are thus more conscious of productivity growth. With the light industries, the decline in input growth was negative in 1991–1994 for the textile, wearing apparel and leather products industry. This reflects protectionism in some advanced countries, the aggressive marketing of low-priced goods by the developing countries, the change in consumer tastes favouring foreign goods due to rising income as well as the world-wide sluggish textile market since the 1990s (The Korea Development Bank, 1994, p. 177). The main source of increasing TFP growth over time in the two light industries was found to be technological progress due to increasing use of advanced technology capital. The steep rise in high wages since 1988 has hurt these labour intensive industries (ibid.) which have now resorted to using more capital (with greater embodied technology) to substitute the expensive labour. However, the negative gains in technical efficiency are a major concern in these industries as this means that the firms are continuously moving away from the ‘best’ practice frontier. The lack of skilled labour coupled with rising wages has prevented firms from producing at full capacity due to poor applied knowledge of the workers. Also, the more skilled workers were attracted to the heavy industries which had been actively promoted by the government. With the chemicals, petroleum and coal products industry, input growth has been strong partly due to the tone set by the government’s drive to develop the heavy industries and chemical sector in the earlier period of 1973–1979. The liberalisation of facilities investment policies in January 1990 and the subsequent heavy investments in the following 2 years are other reasons for the continued high input use (The Korea Development Bank, 1994, p. 161). Nevertheless, TFP growth increased over time and this was brought about by gains from both technological progress and technical efficiency. Thus, firms were using more technologically advanced capital due to the massive new investment that embodied better technology. They also knew how to use it efficiently, leading to the steady movement of firms’ output toward its potential output. The fabricated metal products, machinery and equipment industry on the other hand was the only industry with falling TFP growth (although not negative) and this was caused by declining gains in both technological progress and technical efficiency, which were
674
1980–1984
1987–1990
Output growth Input growth TFP growth Technological progress Gains in technical efficiency
Food, beverage and tobacco 0.386 0.514 0.7 0.437 0.314 (81.3%) 0.077 (15%) 0.077 0.097 0.391 0.02
Output growth Input growth TFP growth Technological progress Gains in technical efficiency
Chemicals, petroleum and coal products 0.535 0.556 0.503 0.397 0.032 (6%) 0.159 (28.6%) 0.028 0.104 0.004 0.055
1991–1994
1980–1984
0.267 0.174 0.093 (35%) 0.099 0.006
Textile, wearing apparel and leather products 0.152 0.197 0.166 0.132 0.124 0.09 0.02 (13.2%) 0.073 (37%) 0.256 (154%) 0.06 0.212 0.272 0.04 0.139 0.016
0.699 0.439 0.26 (37.2%) 0.113 0.147
1987–1990
1991–1994
Fabricated metal products, machinery and equipment 1.22 0.603 0.36 0.83 0.392 0.216 0.39 (25%) 0.211 (35%) 0.144 (40%) 0.228 0.103 0.052 0.162 0.108 0.092
Note: Output growth ¼ ðY2 Y1 Þ/Y1. Figures in parenthesis show percentage contribution to output growth.
R. Mahadevan, S. Kim / Journal of Asian Economics 14 (2003) 669–678
Table 2 Decomposition of output growth and TFP growth for selected Korean industries
R. Mahadevan, S. Kim / Journal of Asian Economics 14 (2003) 669–678
675
nevertheless still positive. There was also a consistent decline in the industry’s input growth. Thus, unlike the chemicals, petroleum and coal products industry, this industry did not benefit as much from the government’s past promotion drive of the heavy industries. Neither did the consolidation of the automobile and electric equipment industry in Korean exports help. While the appreciation of the Korean Won under policy pressure from the U.S. eroded cost competitiveness during 1986–1989, the wage increases brought about by strong labour union pressure from 1990 to 1992 made it difficult for productivity growth to match rising costs of production in this industry. Thus, there was little incentive for firms to continuously keep abreast with the use of advanced technology (to obtain technological progress) and to provide training and cope with the lack of skilled workers (to enjoy gains from technical efficiency).
5. Conclusion This study shows that output growth in all the four manufacturing industries was increasingly productivity-driven (based on contribution to output growth) since the mid1980s. Also, the export-oriented industries experienced a higher contribution of TFP growth to output growth. The use of the random coefficient frontier model further showed that the sources of TFP growth varied in the selected industries. While in both the light industries’ productivity growth was caused by technological progress only, that of the chemicals, petroleum and coal products industry was driven by both technological progress and gains in technical efficiency. The fabricated metals products, machinery and equipment industry on the other hand, experienced declining technological progress and technical efficiency. However, unlike the light industries, both the heavy industries did enjoy positive technical efficiency gains. The variation in the source and trend of TFP growth in the heavy industries question the after-effects of the past government policy on promoting these industries, that is, what caused these industries to respond differently to the same government policy? More importantly, the import liberalisation policy (with the loosening of controls on import licenses, quotas and tariffs) and the structural adjustment policy (the reduction in government intervention to encourage competition rather than protection and to promote equal sectoral development) of the mid-1980s also seemed to have different effects on the heavy industries as well as the light industries. Thus, in light of the results obtained, it is important to reassess the effects of government policies on the two sources of productivity growth in these industries. Previous studies did not decompose TFP growth and any analysis to weed out factors affecting TFP growth would have masked the possibly different effects of these factors on technological progress and technical efficiency. This would only lead to broad policy measures which may not have the desired effect if not directed at the right source of poor TFP growth. Hence, it is a useful exercise to devise specific policies aimed at improving both technological progress and technical efficiency for sustainable TFP growth as the balance between the components of TFP growth would determine the trend and magnitude of long run economic growth. In particular, for the two light industries where sluggish technical efficiency gains are a major concern, a policy to enhance the efficient use of existing technology is recommended
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to catch up to frontier technology. This entails improvements in learning-by-doing processes and in managerial practices through on the job training, promotion of market competition through opening up of the domestic market, and reducing government intervention. Grooming firms to become larger or via mergers is also known to increase technical efficiency. For the fabrication industry where falling TFP growth was caused by declining gains in both technical efficiency and technical progress, a policy that promotes technological progress should be pursued along with a technical efficiency-promoting policy. Promotion of technological progress is possible through a policy that induces technological innovation through increased R&D spending by providing government subsidies. With firm level data available, industry-specific policies can be drawn using a regression analysis and this is an avenue for future research as the identified factors such as firm size, industrial concentration, education level of workers, research and development, and advertising expenditure may affect technological progress and technical efficiency differently.
Appendix A. Decomposition of output growth and TFP growth
Assume that the industry faces production frontiers F1 and F2 in period 1 and period 2, respectively. If the industry is technically efficient, output would be on the frontier, that is, industry would be able to produce output y1 ; in period 1, using x1 input level and output y 2 in period 2, using x2 input level. However, in periods 1 and 2, industry may be producing
R. Mahadevan, S. Kim / Journal of Asian Economics 14 (2003) 669–678
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output y1 and y2, respectively, due to technical inefficiency in production. Technical inefficiency in terms of output forgone is represented by the distance between the frontier output and actual output of a given industry in the figure. The industry in period 1 is said to experience TE1 in period 1 if it is able to increase production from y1 to y1 and TE2 in period 2 if it is able to increase production from y2 to y 2 . Thus, change in technical efficiency over time is the difference between TE1 and TE2 and technological progress is measured by the distance between frontier 2 and frontier 1 given by, y 1 y1 evaluated at x1 input level. The input growth between the two periods denoted by Dyx causes output growth of y 2 y2 . This output growth can be decomposed into three components, i.e. input growth, technological progress and improvements in technical efficiency, the sum of the latter two constitutes total factor productivity (TFP) growth. The decomposition can be mathematically expressed as follows: D ¼ y2 y1 ¼ A þ B þ C ¼ ½y1 y1 þ ½y 1 y1 þ ½y2 y1
¼ ½y1 y1 þ ½y 1 y1 þ ½y2 y1 þ ½y2 y2
¼ ½y1 y1 þ ½y 1 y1 ½y2 y2 þ ½y2 y1
¼ fðy1 y1 Þ ðy 2 y2 Þg þ ðy1 y1 Þ þ ðy2 y1 Þ
¼ TE þ TP þ yx ¼ TFP þ yx where y2y1 is the output growth between two periods; TE is the change in technical efficiency; TP is the technological progress; y x is the change in output due to input growth; TFP is the total factor productivity growth. Source: Mahadevan and Kalirajan (1999).
References Bank of Korea. (2003a). Monthly Bulletin. Seoul. Bank of Korea. (2003b). National Accounts. Seoul. Dollar, D., & Sokoloff, K. (1990). Patterns of productivity growth in south Korean manufacturing industries, 1963–1979. Journal of Development Economics, 33, 309–327. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society A, General 120(3), 253–281. Griffiths, W. E. (1972). Estimation of actual response coefficients in the Hildredth-Houck random coefficient model. Journal of The American Statistical Association, 67, 633–635. Hildredth, C., & Houck, J. K. (1968). Some estimators for linear model with random coefficients. Journal of The American Statistical Association, 63, 764–768. Moon, H. -w., Jo, B. -t., Whang, I. -h., & Kim, K. -b. (1992). Total factor productivity in Korea: An analysis of 27 manufacturing industries. Seoul: Korea Productivity Center. Kalirajan, K. P., & Shand, R. (1994). Economics in disequilibrium: An approach from the frontier. India: Macmillan Limited. Kim, S., & Han, G. (2001). A decomposition of total factor productivity growth in Korean manufacturing industries: A stochastic frontier approach. Journal of Productivity Analysis, 16(3), 269–281. Korea Investor Services. (2003). Annual Report of Korean Companies. Seoul. Kwon, Jene K. (1986). Capital utilization, economies of scale and technical change in the growth of total factor productivity: An explanation of South Korea manufacturing growth. Journal of Development Economics, 24(1), 75–89.
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Mahadevan, R. (2000). Sources of output growth in Singapore’s services sector. Empirical Economics, 25, 495–506. Mahadevan, R. (2001). Assessing the output and productivity growth of Malaysia’s manufacturing sector. Journal of Asian Economics, 12(4), 587–597. Mahadevan, R., & Kalirajan, K. P. (1999). On measuring total factor productivity growth in Singapore’s manufacturing industries. Applied Economics Letters, 6, 295–298. Park, S.-R., & Kwon, J. K. (1995). Rapid economic growth with increasing returns to scale and little or no productivity growth. Review of Economics and Statistics, XVII(2), 332–351. Pyo, H. -k., Kong, B. -h., Kwon, H. -y., & Kim, E. -j. (1992). An analysis of the causes of growth and productivity by industry for the republic of Korea, 1970–1990. Seoul: Korean Economics Research Institute. The Korea Development Bank. (1994). Korean Industry in The World 1994. Yang, L., & Hu, J. L. (2002). Technical efficiency and location choice of small and medium-sized enterprises. Small Business Economics, 19(1), 1–12. Young, A. (1995). The tyranny of numbers: Confronting the statistical realities of the East Asian growth experience. Quarterly Journal of Economics, 110(3), 643–680.
Short communication
Is output growth of Korean manufacturing firms productivity-driven? Renuka Mahadevana,1, Sangho Kimb,* a
School of Economics, The University of Queensland, Queensland 4072, Australia b Department of International Trade, Honam University, Kwangju, South Korea
Received 22 February 2003; received in revised form 27 May 2003; accepted 7 July 2003
Abstract This paper examines the sources of output growth and total factor productivity (TFP) growth of four selected South Korean manufacturing industries from 1980 to 1994. Unlike previous studies, this study is enriched by the use of firm level data within each industry and the application of the random coefficient frontier model. Empirical results show that output growth in the manufacturing industries is increasingly productivity-driven. But the varying sources of TFP growth (i.e. technical progress and gains in technical efficiency) within the industries present an urgent need to reexamine the effect of government policies and other factors to formulate specific policies for sustainable TFP growth. # 2003 Elsevier Inc. All rights reserved. Keywords: Total factor productivity; Technological progress; Technical efficiency
1. Introduction The industrial development of the largest of the East Asian miracle economies, South Korea, is generally considered to be one of the most successful cases of the postwar era before the 1997 financial crisis. From the devastation of the Korean war and from a small industrial base, the country succeeded in growing at 7% or more for all but a few years in the 1969–1996 period. Yet recent research indicates that much of Korea’s manufacturing output growth is input-driven. While many of these studies have applied the ‘Solow’ growth equation to account for economic growth and TFP growth (Dollar & Sokoloff, 1990; Moon, Jo, Whang, & Kim, * Corresponding author. E-mail addresses: [email protected] (R. Mahadevan), [email protected] (S. Kim). 1 Tel.: þ61-7-3365-6595; fax: þ61-7-3365-7299.
1049-0078/$ – see front matter # 2003 Elsevier Inc. All rights reserved. doi:10.1016/S1049-0078(03)00101-5
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R. Mahadevan, S. Kim / Journal of Asian Economics 14 (2003) 669–678
1991; Young, 1995; Pyo, Kong, Kwon, & Kim, 1992), other studies extended the ‘Solow’ growth accounting to include capital utilisation and economies of scale to analyse TFP growth for the Korean manufacturing sector (Kwon, 1986; Park & Kwon, 1995). However, this study on Korean manufacturing output and productivity growth is different in the following ways. First, manufacturing firm level data is used to obtain more accurate results. The panel data consists of a total of 135 firms in four manufacturing industries from 1980 to 1994. Second, with the exception of Kim and Han (2001) who used the stochastic frontier approach, all earlier studies on Korea used the growth accounting approach.2 The growth accounting method is flawed as it unrealistically assumes that all firms are technically efficient and hence TFP growth is synonymous with technical progress. Although Kim and Han’s (2001) stochastic frontier model overcomes this problem, it has two rigid assumptions in that, the production frontier shifts neutrally over time and technical efficiency is assumed (in an ad hoc way) to follow a truncated normal distribution with no theoretical reasoning. Here, the random coefficient frontier model is used as it relaxes both these assumptions. As the adopted model is estimated using the generalised least squares technique, it does not rely on any distributional assumptions about the error term and thus technical inefficiency is not specified to take on any rigidities. Also, here, the more realistic non-neutral shifting production frontier is estimated.3 Third, in addition to decomposing output growth into input growth and TFP growth, TFP growth is further decomposed into technical progress and gains from technical efficiency. In the productivity literature, technical progress is associated with shifts in the production frontier due to technological innovation while technical efficiency is related to movements towards the frontier which can be brought about by learning-by-doing, improved managerial practice, changes in the efficiency with which a known technology is applied, etc. As high rates of technological progress can coexist with deteriorating technical efficiency performance, specific policy actions are required to address the difference in the sources of variation in productivity growth. The plan of the paper is as follows: Section 2 sets out the theoretical framework underlying the random coefficient frontier model. While Section 3 explains data sources and the variables used, Section 4 presents the empirical results of the model and the decompositional analysis of output growth and TFP growth of the manufacturing firms. Section 5 concludes.
2. The random coefficient frontier model The concept underlying the stochastic frontier approach was initiated by Farrell (1957) and emphasises the idea of maximality. A production function methodology based 2 However there exist other studies such as Mahadevan (2000, 2001) that have used the stochastic frontier approach for Singapore and Malaysia, and Yang and Hu (2002) who did similar work for Taiwan. 3 Kalirajan and Shand (1994) argue that the marginal rate of technical substitution at any input combination should be allowed to change over time since with the same level of inputs, different levels of output are obtained by following different methods of applications. Hence the non-neutral shifting production frontier should be considered. See Kalirajan and Shand (1994) for technical details of the implementation in the model.
R. Mahadevan, S. Kim / Journal of Asian Economics 14 (2003) 669–678
671
on Farrell’s method to measure firm performance is appealing as the frontier functions can indicate the maximum possible output from a combination of inputs and technology. The generalised version of the model can be written as: n X gijt ln Xijt ;
ln Yit ¼ g1it þ
(1)
j¼1
where i represents number of firms; j represents number of inputs used; t represents time period; Y is the output; X is the inputs used; g1it is the intercept term of the ith firm in the tth period; gijt is the slope concerning the method of application of the jth input used by the ith firm in the tth period. Since intercepts and slope coefficients can vary across firms and time, we can write: gijt ¼ gjt þ uijt
g1it ¼ g1t þ v1it ;
(2)
where gjt is the mean response coefficient of output with respect to the jth input in tth period; uijt and v1it are random disturbance terms; Eðgijt Þ ¼ gjt , Eðuijt Þ ¼ 0 and Varðuij Þ ¼ suit for j ¼ t and zero otherwise Combining Eqs. (1) and (2): ln Yit ¼ g1t þ
k n X X gjt ln Xijt þ uijt ln Xijt þ v1it : j¼1
(3)
j¼1
Following Aitken’s generalised least squares method suggested by Hildreth and Houck (1968) and the estimation procedure by Griffiths (1972), the firm-specific and inputspecific response coefficient estimates of the above model can be obtained. The highest magnitude of each response coefficient and intercept form the frontier coefficients of the potential production function. If g are the parameter estimates of the frontier production, then, gjt ¼ maxi fgijt g where i ¼ 1, 2, . . . n, j ¼ 1, 2, . . ., k and t ¼ 1, 2, . . ., T. The potential output of the firm can be realised when the ‘best practice’ techniques are used and this is given by ln Yit ¼ g1t þ
k X
gjt ln Xijt :
(4)
j¼1
The firm-specific technical efficiency is a measure of how well given inputs and technology are used and this is given by the ratio of the firm’s actual realised output to that of its potential output. Here, we consider the flexible translog production function to obtain more generalised estimates. In addition, a time trend is included to capture the effects of time-related variables on output. The model for each industry using firm level data (omitting time subscripts) is given by: lnðYÞ ¼ a0 þ a1 T þ b ln L þ a ln K þ d lnðKÞ lnðLÞþZ lnðKÞ lnðKÞþl lnðLÞ lnðLÞ; where Y is the real value added output; T is the time trend; L is the labor; K is the capital.
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3. Data sources and construction of variables The four two-digit industries that are investigated according to the Korean Standard Industry Classification (KSIC) include light industries 31 (food, beverage and tobacco industry), and 32 (textile, wearing apparel and leather products industry), and heavy industries 35 (chemicals, petroleum and coal products industry) and 37 (fabricated metal products, machinery and equipment). Among these industries which explain about 80% of total Korean manufacturing output and about 70% of total exports of the fabricated metal products, machinery and equipment industry is the largest in terms of output and employment followed by the textile, wearing apparel and leather products industry. The sample firms in these industries cover manufacturing firms whose stock is listed on the Korean Stock Exchange. The firms are required to report their financial status in the Annual Report of Korean Companies by Korea Investors Service, from which the data for empirical investigation was compiled. The data from 1980 to 1994 consists of 30 firms for KSIC 31, 31 firms for KSIC 32, 41 firms for KSIC 35, and 33 firms for KSIC 37.The key variables used are value added output, capital and labour. The value added output of the firms was deflated by the wholesale price index of each industry with 1990 as the base year, obtained from the Monthly Bulletin by Bank of Korea. Labour was measured by the number of employed workers and capital stock was given by the amount of tangible fixed assets. As reported firms’ capital stock figures were already deflated but with varying base year prices, they were then made comparable with a common base year of 1990 using the gross domestic fixed capital formation deflator obtained from the National Accounts by the Bank of Korea.
4. Empirical results Table 1 shows the estimates of the production frontier for all four industries. The production frontier model for all four industries is valid given the statistical significance of the log likelihood ratio test of the one-sided error (u) obtained from the
Table 1 Estimates of the random coefficient frontier model Variable
Constant (a0) Time trend (a1) ln K (a) ln L (b) ln(K) ln(K) (Z) ln(L) ln(L) (l) ln(K) ln(L) (d)
Industry (KSIC) 31
32
35
38
2.08 (0.38) 0.09 (0.04) 0.381 (0.11) 0.492 (0.21) 0.016 (0.008) 0.051 (0.017) 0.056a (0.047)
4.36 (0.92) 1.04 (0.51) 0.402 (0.15) 0.516 (0.18) 0.052 (0.021) 0.043a (0.031) 0.028 (0.005)
1.77 (0.69) 0.88 (0.29) 0.591 (0.28) 0.371 (0.12) 0.030 (0.011) 0.029a (0.02) 0.061 (0.022)
1.52 (0.71) 0.74 (0.32) 0.518 (0.22) 0.425 (0.13) 0.021 (0.007) 0.018 (0.009) 0.034 (0.016)
Note: Figures in parenthesis are asymptotic standard errors. a Means that the coefficients are insignificant at the 5% level of significance.
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models. Most of the parameters are significant at the 5% level. However, the estimates do not directly indicate the production elasticities with respect to inputs. These elasticity estimates can be evaluated at their mean levels to show that capital contributes more to output than labour in the heavy industries than in the light industries. Using the estimates in Table 1 and the decompositional analysis set out in the Appendix A, the sources of output growth and TFP growth for firms in each industry were calculated and summarised in Table 2. For the decomposition for each industry, a weighted approach using the share of each firm’s value added to the total value added of the industry was adopted. Although all the manufacturing industries’ output growth was mainly input-driven, it can be seen that TFP growth increased over time for all industries except for the fabricated metal products, machinery and equipment industry. However, output growth for all four industries was increasingly productivity-driven. The contribution of TFP growth to output growth was also found to be higher for the export-oriented industries of the textile, wearing apparel and leather products industry as well as the fabricated metal products and machinery industry. These export industries which are exposed to international competition are well aware of the need to be cost competitive and are thus more conscious of productivity growth. With the light industries, the decline in input growth was negative in 1991–1994 for the textile, wearing apparel and leather products industry. This reflects protectionism in some advanced countries, the aggressive marketing of low-priced goods by the developing countries, the change in consumer tastes favouring foreign goods due to rising income as well as the world-wide sluggish textile market since the 1990s (The Korea Development Bank, 1994, p. 177). The main source of increasing TFP growth over time in the two light industries was found to be technological progress due to increasing use of advanced technology capital. The steep rise in high wages since 1988 has hurt these labour intensive industries (ibid.) which have now resorted to using more capital (with greater embodied technology) to substitute the expensive labour. However, the negative gains in technical efficiency are a major concern in these industries as this means that the firms are continuously moving away from the ‘best’ practice frontier. The lack of skilled labour coupled with rising wages has prevented firms from producing at full capacity due to poor applied knowledge of the workers. Also, the more skilled workers were attracted to the heavy industries which had been actively promoted by the government. With the chemicals, petroleum and coal products industry, input growth has been strong partly due to the tone set by the government’s drive to develop the heavy industries and chemical sector in the earlier period of 1973–1979. The liberalisation of facilities investment policies in January 1990 and the subsequent heavy investments in the following 2 years are other reasons for the continued high input use (The Korea Development Bank, 1994, p. 161). Nevertheless, TFP growth increased over time and this was brought about by gains from both technological progress and technical efficiency. Thus, firms were using more technologically advanced capital due to the massive new investment that embodied better technology. They also knew how to use it efficiently, leading to the steady movement of firms’ output toward its potential output. The fabricated metal products, machinery and equipment industry on the other hand was the only industry with falling TFP growth (although not negative) and this was caused by declining gains in both technological progress and technical efficiency, which were
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1980–1984
1987–1990
Output growth Input growth TFP growth Technological progress Gains in technical efficiency
Food, beverage and tobacco 0.386 0.514 0.7 0.437 0.314 (81.3%) 0.077 (15%) 0.077 0.097 0.391 0.02
Output growth Input growth TFP growth Technological progress Gains in technical efficiency
Chemicals, petroleum and coal products 0.535 0.556 0.503 0.397 0.032 (6%) 0.159 (28.6%) 0.028 0.104 0.004 0.055
1991–1994
1980–1984
0.267 0.174 0.093 (35%) 0.099 0.006
Textile, wearing apparel and leather products 0.152 0.197 0.166 0.132 0.124 0.09 0.02 (13.2%) 0.073 (37%) 0.256 (154%) 0.06 0.212 0.272 0.04 0.139 0.016
0.699 0.439 0.26 (37.2%) 0.113 0.147
1987–1990
1991–1994
Fabricated metal products, machinery and equipment 1.22 0.603 0.36 0.83 0.392 0.216 0.39 (25%) 0.211 (35%) 0.144 (40%) 0.228 0.103 0.052 0.162 0.108 0.092
Note: Output growth ¼ ðY2 Y1 Þ/Y1. Figures in parenthesis show percentage contribution to output growth.
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Table 2 Decomposition of output growth and TFP growth for selected Korean industries
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nevertheless still positive. There was also a consistent decline in the industry’s input growth. Thus, unlike the chemicals, petroleum and coal products industry, this industry did not benefit as much from the government’s past promotion drive of the heavy industries. Neither did the consolidation of the automobile and electric equipment industry in Korean exports help. While the appreciation of the Korean Won under policy pressure from the U.S. eroded cost competitiveness during 1986–1989, the wage increases brought about by strong labour union pressure from 1990 to 1992 made it difficult for productivity growth to match rising costs of production in this industry. Thus, there was little incentive for firms to continuously keep abreast with the use of advanced technology (to obtain technological progress) and to provide training and cope with the lack of skilled workers (to enjoy gains from technical efficiency).
5. Conclusion This study shows that output growth in all the four manufacturing industries was increasingly productivity-driven (based on contribution to output growth) since the mid1980s. Also, the export-oriented industries experienced a higher contribution of TFP growth to output growth. The use of the random coefficient frontier model further showed that the sources of TFP growth varied in the selected industries. While in both the light industries’ productivity growth was caused by technological progress only, that of the chemicals, petroleum and coal products industry was driven by both technological progress and gains in technical efficiency. The fabricated metals products, machinery and equipment industry on the other hand, experienced declining technological progress and technical efficiency. However, unlike the light industries, both the heavy industries did enjoy positive technical efficiency gains. The variation in the source and trend of TFP growth in the heavy industries question the after-effects of the past government policy on promoting these industries, that is, what caused these industries to respond differently to the same government policy? More importantly, the import liberalisation policy (with the loosening of controls on import licenses, quotas and tariffs) and the structural adjustment policy (the reduction in government intervention to encourage competition rather than protection and to promote equal sectoral development) of the mid-1980s also seemed to have different effects on the heavy industries as well as the light industries. Thus, in light of the results obtained, it is important to reassess the effects of government policies on the two sources of productivity growth in these industries. Previous studies did not decompose TFP growth and any analysis to weed out factors affecting TFP growth would have masked the possibly different effects of these factors on technological progress and technical efficiency. This would only lead to broad policy measures which may not have the desired effect if not directed at the right source of poor TFP growth. Hence, it is a useful exercise to devise specific policies aimed at improving both technological progress and technical efficiency for sustainable TFP growth as the balance between the components of TFP growth would determine the trend and magnitude of long run economic growth. In particular, for the two light industries where sluggish technical efficiency gains are a major concern, a policy to enhance the efficient use of existing technology is recommended
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to catch up to frontier technology. This entails improvements in learning-by-doing processes and in managerial practices through on the job training, promotion of market competition through opening up of the domestic market, and reducing government intervention. Grooming firms to become larger or via mergers is also known to increase technical efficiency. For the fabrication industry where falling TFP growth was caused by declining gains in both technical efficiency and technical progress, a policy that promotes technological progress should be pursued along with a technical efficiency-promoting policy. Promotion of technological progress is possible through a policy that induces technological innovation through increased R&D spending by providing government subsidies. With firm level data available, industry-specific policies can be drawn using a regression analysis and this is an avenue for future research as the identified factors such as firm size, industrial concentration, education level of workers, research and development, and advertising expenditure may affect technological progress and technical efficiency differently.
Appendix A. Decomposition of output growth and TFP growth
Assume that the industry faces production frontiers F1 and F2 in period 1 and period 2, respectively. If the industry is technically efficient, output would be on the frontier, that is, industry would be able to produce output y1 ; in period 1, using x1 input level and output y 2 in period 2, using x2 input level. However, in periods 1 and 2, industry may be producing
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output y1 and y2, respectively, due to technical inefficiency in production. Technical inefficiency in terms of output forgone is represented by the distance between the frontier output and actual output of a given industry in the figure. The industry in period 1 is said to experience TE1 in period 1 if it is able to increase production from y1 to y1 and TE2 in period 2 if it is able to increase production from y2 to y 2 . Thus, change in technical efficiency over time is the difference between TE1 and TE2 and technological progress is measured by the distance between frontier 2 and frontier 1 given by, y 1 y1 evaluated at x1 input level. The input growth between the two periods denoted by Dyx causes output growth of y 2 y2 . This output growth can be decomposed into three components, i.e. input growth, technological progress and improvements in technical efficiency, the sum of the latter two constitutes total factor productivity (TFP) growth. The decomposition can be mathematically expressed as follows: D ¼ y2 y1 ¼ A þ B þ C ¼ ½y1 y1 þ ½y 1 y1 þ ½y2 y1
¼ ½y1 y1 þ ½y 1 y1 þ ½y2 y1 þ ½y2 y2
¼ ½y1 y1 þ ½y 1 y1 ½y2 y2 þ ½y2 y1
¼ fðy1 y1 Þ ðy 2 y2 Þg þ ðy1 y1 Þ þ ðy2 y1 Þ
¼ TE þ TP þ yx ¼ TFP þ yx where y2y1 is the output growth between two periods; TE is the change in technical efficiency; TP is the technological progress; y x is the change in output due to input growth; TFP is the total factor productivity growth. Source: Mahadevan and Kalirajan (1999).
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