NAL OF COMPARATIVE ECONOMICS 25, 220-236(1997) CLE NO. JE971462 The Coast-Noncoast Income Gap, Productivi and Regional Economic Policy in China Belton M. Fleisher The Ohio State University, Columbus, Ohio 43210 Jian Chen Kenyon College, Gambier. Ohio 43022 Received August 21. 1996: revised June 2. 1997 Fleisher, Belton M, and Chen, Jian-The Coast-Noncoast Income Gap, Productiv ity, and Regional Economic Policy in China We postulate that inferior factor productivity in Chinas noncoastal provinces is principal reason for their lower economic growth despite high investment rates relative to provincial GDP. We find that total factor productivity is roughly twice as high in the coastal provinces and estimate that investment in higher education and foreign direct investment helps explain the productivity gap. We speculate that despite its elatively modest estimated return, investment in infrastructure may be necessary to attract foreign direct investment and to retain university graduates in the interior Comp. Econom., October 1997, 25(2), pp. 220-236. The Ohio State University, Co- lumbus, Ohio, 43210; Kenyon College, Gambier, Ohio 43022. 0 1997 Academic Pres Journal of Economic Literature Classification Numbers: O15, 018, 047, 053 INTRODUCTION This paper is an attempt to understand the persistent and widening income gap between coastal and interior China and to suggest appropriate policies I This paper has benefited from the help of Dongwei Su and the comments of Mario Crucini, Pok-Sang Lam, Guang H, Wan, Shaowen Wu, Yong Yin, and two anonymous referees. We also thank Gary Jefferson and Barry Naughton, who offered extensive and valuable suggestions as scussants in the AEA session""Empirical Analysis of the Chinese Economy, New Orleans 1997, and participants in a seminar at the Center for Chinese Studies, University of Michigan, including Robert Dernberger, Junling Hu, David Li, Kenneth Lieberthal, and Albert Park. Xiaojun Wang provided excellent research assistance. Please send communications to B M. Fleisher, fleisher. l@osu. edu 0147-5967/97$2500 opyright e 1997 by Academic Press 220 All nights of reproduction in any form reserved
JOURNAL OF COMPARATIVE ECONOMICS 25, 220–236 (1997) ARTICLE NO. JE971462 The Coast–Noncoast Income Gap, Productivity, and Regional Economic Policy in China1 Belton M. Fleisher The Ohio State University, Columbus, Ohio 43210 and Jian Chen Kenyon College, Gambier, Ohio 43022 Received August 21, 1996; revised June 2, 1997 Fleisher, Belton M., and Chen, Jian—The Coast–Noncoast Income Gap, Productivity, and Regional Economic Policy in China We postulate that inferior factor productivity in China’s noncoastal provinces is a principal reason for their lower economic growth despite high investment rates relative to provincial GDP. We find that total factor productivity is roughly twice as high in the coastal provinces and estimate that investment in higher education and foreign direct investment helps explain the productivity gap. We speculate that despite its relatively modest estimated return, investment in infrastructure may be necessary to attract foreign direct investment and to retain university graduates in the interior. J. Comp. Econom., October 1997, 25(2), pp. 220–236. The Ohio State University, Columbus, Ohio, 43210; Kenyon College, Gambier, Ohio 43022. q 1997 Academic Press Journal of Economic Literature Classification Numbers: O15, O18, O47, O53. 1. INTRODUCTION This paper is an attempt to understand the persistent and widening income gap between coastal and interior China and to suggest appropriate policies 1 This paper has benefited from the help of Dongwei Su and the comments of Mario Crucini, Pok-Sang Lam, Guang H. Wan, Shaowen Wu, Yong Yin, and two anonymous referees. We also thank Gary Jefferson and Barry Naughton, who offered extensive and valuable suggestions as discussants in the AEA session ‘‘Empirical Analysis of the Chinese Economy,’’ New Orleans, 1997, and participants in a seminar at the Center for Chinese Studies, University of Michigan, including Robert Dernberger, Junling Hu, David Li, Kenneth Lieberthal, and Albert Park. Xiaojun Wang provided excellent research assistance. Please send communications to B. M. Fleisher, fleisher.1@osu.edu. 0147-5967/97 $25.00 Copyright q 1997 by Academic Press All rights of reproduction in any form reserved. 220 AID JCE 1462 / 6w10$$$121 09-30-97 14:16:24 cea
PROVINCIAL PRODUCTIVITY IN CHINA to help the lagging interior provinces catch up to their more prosperous counterparts Aware of the political danger and perhaps also sensitive to the inequity of favoring coastal development, the central government has taken steps to pro- mote the growth of enterprises in the interior, focusing particular attention on steps to encourage investment in rural enterprises(Yang and Wei, 1996) Evidently this strategy has yet to produce the desired results. We hypothesize that a use of the persistent and widening income gap between the coast and interior is lower factor productivity in the noncoastal provinces We report tests of hypotheses that total factor productivity(TFP)and TFP growth vary across provinces. We identify factors contributing to the produc tivity gap and derive implications for policies that may help the interior provinces approach parity with their coastal counterparts The rest of the paper proceeds as follows. In Section 2, we deal with methodological issues and outline the basic theoretical and econometric proce dure. Section 3 contains our econometric results. The last section summarizes and draws policy implications MODELING TFP AND TFP GROWTH 2.1. Methodological Issues The first methodological issue addressed is frontier- versus standard produc tion-function estimation Lau and Brada(1995) point out that an advantage of using the frontier approach is obtaining the relative contributions of techno- logical growth and improvements in technical efficiency to TFP growth, which is important in forecasting how long current growth trends will continue. We have chosen not to use a frontier estimation approach for two reasons. First, accuracy in allocating the" residual" of the production relationship between technical efficiency and technological progress depends critically on the accu- racy with which inputs have been measured Because we focus on all sectors of the Chinese economy at the provincial level we do not have access to accurate capital-stock data. 3 See, for example, Chen and Fleisher(1996)and Yang and Wei(1996). Chen and Fleisher contains references to earlier studies on the provincial distribution of income and production. In particular, rising per capita income in 10 coastal provinces, which we define to include Beijing because of its location and to exclude Guangxi and Hainan because of inadequate data, has outstripped growth in the interior, so that between 1978 and 1993 the coast/noncost ratio of mean GDP per capita grew from 2.53 to 2.82, or 11%. s As described below, we are able to estimate the desired production-function parameters without data on the capital stock because we estimate a growth model, which requires data investment. Discussion of difficulties in using capital stock data in China to estimate aggregate production functions can be found in Chen et al. (1988)and Chow(1984), especially pp. 202 205. We also attempt to correct for inclusion of nonproductive investment in the data described below
PROVINCIAL PRODUCTIVITY IN CHINA 221 to help the lagging interior provinces catch up to their more prosperous counterparts. Aware of the political danger and perhaps also sensitive to the inequity of favoring coastal development, the central government has taken steps to promote the growth of enterprises in the interior, focusing particular attention on steps to encourage investment in rural enterprises (Yang and Wei, 1996). Evidently this strategy has yet to produce the desired results.2 We hypothesize that a major cause of the persistent and widening income gap between the coast and interior is lower factor productivity in the noncoastal provinces. We report tests of hypotheses that total factor productivity (TFP) and TFP growth vary across provinces. We identify factors contributing to the productivity gap and derive implications for policies that may help the interior provinces approach parity with their coastal counterparts. The rest of the paper proceeds as follows. In Section 2, we deal with methodological issues and outline the basic theoretical and econometric procedure. Section 3 contains our econometric results. The last section summarizes and draws policy implications. 2. MODELING TFP AND TFP GROWTH 2.1. Methodological Issues The first methodological issue addressed is frontier- versus standard production-function estimation. Lau and Brada (1995) point out that an advantage of using the frontier approach is obtaining the relative contributions of technological growth and improvements in technical efficiency to TFP growth, which is important in forecasting how long current growth trends will continue. We have chosen not to use a frontier estimation approach for two reasons. First, accuracy in allocating the ‘‘residual’’ of the production relationship between technical efficiency and technological progress depends critically on the accuracy with which inputs have been measured. Because we focus on all sectors of the Chinese economy at the provincial level we do not have access to accurate capital-stock data.3 2 See, for example, Chen and Fleisher (1996) and Yang and Wei (1996). Chen and Fleisher contains references to earlier studies on the provincial distribution of income and production. In particular, rising per capita income in 10 coastal provinces, which we define to include Beijing because of its location and to exclude Guangxi and Hainan because of inadequate data, has outstripped growth in the interior, so that between 1978 and 1993 the coast/noncoast ratio of mean GDP per capita grew from 2.53 to 2.82, or 11%. 3 As described below, we are able to estimate the desired production-function parameters without data on the capital stock because we estimate a growth model, which requires data on investment. Discussion of difficulties in using capital stock data in China to estimate aggregate production functions can be found in Chen et al. (1988) and Chow (1984), especially pp. 202– 205. We also attempt to correct for inclusion of ‘‘nonproductive’’ investment in the data as described below. AID JCE 1462 / 6w10$$$121 09-30-97 14:16:24 cea
FLEISHER AND CHEN Our second reason is, in a sense, philosophical and rests on the belief that there is an inherent arbitrariness in distinguishing between the levels of technology and technical efficiency. One source of this arbitrariness is the need to specify the mathematical form of the time paths of technical progress and technical efficiency. The allocation of TFP change between technical progress and changes in technical efficiency depends on the time paths as- umed. Arbitrariness also arises in attempting to allocate the causes of failure to adopt""best'available technology, which may arise from: (i) failure to invest in physical capital in which the technology is embodied; (ii) lack of human capital, or knowledge of the best available technology; and (iii)adverse incentives due to market institutions. government controls. etc. Economic reforms since 1979 are designed to take care of item(iii) and are evidently reflected in the increased efficiency identified by Lau and Brada in the early years of the reform era. If TFP is below its maximum due to(i) or(ii),is this necessarily"inefficient?" The answer depends in part on one's view of capital markets, available resources, and capital constraints. This study fo- cuses on(i)and (ii)as possible explanations of provincial differences in TFP The second methodological issue is specification of the form of the produc tion function In our empirical work, we assume a Cobb-Douglas production function with Hicks-neutral technology. G. S. Maddala(1979) points out that within the class of functions... Cobb-Douglas, generalized Leontief, homogeneous translog, and homogeneous quadratic, differences in the func tional form produce negligible differences in measures of multi-factor produc- tivity. ' Imposing the Cobb-Douglas specification in the context of the Solow growth model is analogous to the standard growth-accounting technique of using hypothetical factor elasticities to compute TFP or TFP change as a esidual. We, however, estimate our(constant)factor elasticities simultane- ously with our estimates of TFP and TFP growth 2.2. An Empirical Model The Cobb-Douglas production function with Hicks-neutral technology is Y,,=A,KhLiI'e'u where i and t index the provinces and time, respectively. We specify Ais A, oe,+2. as the systematic component of TFP at time t, which includes all factors contributing to output other than labor L and physical capital K at Another potentially serious problem, however, is pointed out by Guang H. Wan(1995), who argues that alternative specifications, e.g, Hicks-neutral, Harrod-neutral, can influence estimates of the degree of technical change
222 FLEISHER AND CHEN Our second reason is, in a sense, philosophical and rests on the belief that there is an inherent arbitrariness in distinguishing between the levels of technology and technical efficiency. One source of this arbitrariness is the need to specify the mathematical form of the time paths of technical progress and technical efficiency. The allocation of TFP change between technical progress and changes in technical efficiency depends on the time paths assumed. Arbitrariness also arises in attempting to allocate the causes of failure to adopt ‘‘best’’ available technology, which may arise from: (i) failure to invest in physical capital in which the technology is embodied; (ii) lack of human capital, or knowledge of the best available technology; and (iii) adverse incentives due to market institutions, government controls, etc. Economic reforms since 1979 are designed to take care of item (iii) and are evidently reflected in the increased efficiency identified by Lau and Brada in the early years of the reform era. If TFP is below its maximum due to (i) or (ii), is this necessarily ‘‘inefficient?’’ The answer depends in part on one’s view of capital markets, available resources, and capital constraints. This study focuses on (i) and (ii) as possible explanations of provincial differences in TFP. The second methodological issue is specification of the form of the production function. In our empirical work, we assume a Cobb–Douglas production function with Hicks-neutral technology. G. S. Maddala (1979) points out that ‘‘within the class of functions . . . Cobb–Douglas, generalized Leontief, homogeneous translog, and homogeneous quadratic, differences in the functional form produce negligible differences in measures of multi-factor productivity.’’ Imposing the Cobb–Douglas specification in the context of the Solow growth model is analogous to the standard growth-accounting technique of using hypothetical factor elasticities to compute TFP or TFP change as a residual. We, however, estimate our (constant) factor elasticities simultaneously with our estimates of TFP and TFP growth.4 2.2. An Empirical Model The Cobb–Douglas production function with Hicks-neutral technology is given by Yi,t Å Ai,tKb i,tL10b i,t eei,t , (1) where i and t index the provinces and time, respectively. We specify Ai,t Å Ai,0eg1i t/g2i t 2 as the systematic component of TFP at time t, which includes all factors contributing to output other than labor L and physical capital K at 4 Another potentially serious problem, however, is pointed out by Guang H. Wan (1995), who argues that alternative specifications, e.g., Hicks-neutral, Harrod-neutral, can influence estimates of the degree of technical change. AID JCE 1462 / 6w10$$$122 09-30-97 14:16:24 cea
PROVINCIAL PRODUCTIVITY IN CHINA time t, g, as the rate of technological change, and Eu as an error term with the usual properties, which may also be viewed as random productivity shocks. The labor force evolves as Lio e"! where n, is the rate of labor force growth. Output per worker, a close correlate of income per capita, is y,=A.Ai- -b where y= Y/L and k= K/L From Eq (1), we specify a Solow growth equation B B which, on the basis of the assumption that convergence to the steady state occurs at the rate x(0<x< 1), leads to In In (In A1, 0+g1,+ g2/) (1-e-)ny/-1+l(3) Our TFP estimates are based on Eq. (3), which allows us to obtain all produc- tion-function parameters directly and simultaneously and does not require data on the capital stock 2. 3. Explaining Technological Change Making the standard growth accounting assumption that the error term in e above equations, uil, represents provincial productivity shocks, we define TFP in year t as TH Ai0 +814+ g2F + ui, and specify the following regression equation to explain provincial TFP differentials In amAmis-1+ asC+ ant agf+aoCI aioN Ti-I +U,(4) where the right-hand variables and hypothesized qualitative relationship with TFP are The quadratic trend term was suggested by an anonymous referee to capture a possible slowdown in TFP growth that may have occurred around 1985. This may be inferred by comparing the empirical results of Lau and Brada(1990)with those of wu(1995)
PROVINCIAL PRODUCTIVITY IN CHINA 223 time t, gi as the rate of technological change, and ei,t as an error term with the usual properties, which may also be viewed as random productivity shocks.5 The labor force evolves as Li,0 Å eni t , where ni is the rate of laborforce growth. Output per worker, a close correlate of income per capita, is yi,t Å Ai,tk10b i,t where y å Y/L and k å K/L. From Eq. (1), we specify a Solow growth equation ln yi,t Å 1 1 0 b (ln Ai,0 / g1i / g2i t 2 ) / b 1 0 b ln si,t01 0 b 1 0 b ln ni,t / wi,t , (2) which, on the basis of the assumption that convergence to the steady state occurs at the rate l(0 õ l õ 1), leads to ln yi,t 0 ln yi,t01 Å (1 0 e0lt )F 1 1 0 b (ln Ai,0 / g1i / g2i t 2 ) / b 1 0 b ln si,t01 0 b 1 0 b ln ni,tG 0 (1 0 e0lt )ln yi,t01 / ui,t . (3) Our TFP estimates are based on Eq. (3), which allows us to obtain all production-function parameters directly and simultaneously and does not require data on the capital stock. 2.3. Explaining Technological Change Making the standard growth accounting assumption that the error term in the above equations, ui,t , represents provincial productivity shocks, we define TFP in year t as ti,t Å Ai,0 / g1i t / g2i t 2 / ui,t and specify the following regression equation to explain provincial TFP differentials. ln ti,t Å a0 / ∑ 5 mÅ1 amxm,i,t01 / a6C / a7t / a8t 2 / a9Ct / a10ln ti,t01 / £i,t , (4) where the right-hand variables and hypothesized qualitative relationship with TFP are 5 The quadratic trend term was suggested by an anonymous referee to capture a possible slowdown in TFP growth that may have occurred around 1985. This may be inferred by comparing the empirical results of Lau and Brada (1990) with those of Wu (1995). AID JCE 1462 / 6w10$$$122 09-30-97 14:16:24 cea
24 FLEISHER AND CHEN x, is a measure of investment in housing(to correct for the inclusion of expenditure on new housing in total investment ), and is negative, x2 is a measure of the vintage of the physical capital stock, and egative, xs is a measure of investment in human capital, and is positive xa is a measure of infrastructure(highways, railways, and waterways) and is positive, xs is foreign direct investment(FDI) as a share of total investment, an Is positive, C is a dummy variable I for coastal provinces and Beijing t is the year of observation(1979=1..1993= 15), and U is an i.i.d. error term. We follow Wolff (1991)in including lagged TFP, Ti-l, in Eq. (4). Full definitions and sources of the variables are included in the Appendix 7 The rationale for including vintage as a contributing factor to TFP and TFP growth is neatly summarized by Wolff (1991). Although Wolff uses the rate of change of the capital stock as a proxy for vintage, we have chosen to define variable x2 as a weighted average of the age of existing capital, specifi I(y2i-I Iuxt-j+ 1)l, where I is real accumulation of fixed assets 8 The contribution of human capital to production is by now part of received knowledge. It would be appropriate to include investment in human capital in conjunction with physical-capital investment in Eq (3) We do not do this because data on the actual magnitude of human-capital (1988) stock data have been purged of housing and other nonproductive capital. Jefferson et al.(1992) use corrected data for state and collective industry. It would be ideal for such net capital stock data for each province, but constructing such data is a task that is far beyond our urrent resources. In order to solve the problem that annual data for this variable are not available 1978 to 1993, we use an instrument for housing in estimating Eq.(4). The instrument is obtained by regressing a measure of housing area(square meters) per capita on per capita real income. The predicted level of per capita housing is then used as the measure of variable x Unfortunately, variables x(infrastructure)and xs(FDI) are not available annually from 1978 to 1993. Therefore in our empirical work we have treated them nvironmental variables Details are contained in the notes to table 2 Data for accumulation of fixed assets is available after 1952 for all provinces in our sample. We deflate using a price index obtained from series on construction both in nominal prices and in]fixed Chen et al. (June, 1988)assert that the data on construction in fixed prices are unreliable. However, our alternative is to use the provincial national income deflator that can be obtained by comparing national income and national income at fixed prices. We chose to use the construction deflator on the assumption that it provides an index closer to the correct one for accumulation than any alternative. We also used the same data to construct a variable(AK/ K)=(n2-o Iy), which is conceptually similar to the variable used by Wolff. The empirical results are not very sensitive to which of these variables is used to estimate Eq (4)
224 FLEISHER AND CHEN x1 is a measure of investment in housing (to correct for the inclusion of expenditure on new housing in total investment), and is negative,6 x2 is a measure of the vintage of the physical capital stock, and is negative, x3 is a measure of investment in human capital, and is positive, x4 is a measure of infrastructure (highways, railways, and waterways), and is positive, x5 is foreign direct investment (FDI) as a share of total investment, and is positive, C is a dummy variable Å 1 for coastal provinces and Beijing, t is the year of observation (1979 Å 1 rrr 1993 Å 15), and £i,t is an i.i.d. error term. We follow Wolff (1991) in including lagged TFP, tt01 , in Eq. (4). Full definitions and sources of the variables are included in the Appendix.7 The rationale for including vintage as a contributing factor to TFP and TFP growth is neatly summarized by Wolff (1991). Although Wolff uses the rate of change of the capital stock as a proxy for vintage, we have chosen to define variable x2 as a weighted average of the age of existing capital, specifi- cally, Vi,t Å (t jÅ1 [(Ii,j/(t jÅ1 Ii,j)(t 0 j / 1)], where Ij is real accumulation of fixed assets.8 The contribution of human capital to production is by now part of received knowledge. It would be appropriate to include investment in human capital in conjunction with physical-capital investment in Eq. (3). We do not do this because data on the actual magnitude of human-capital 6 Chen et al. (1988) report estimates of production functions for state industry in which capitalstock data have been purged of housing and other nonproductive capital. Jefferson et al. (1992) use corrected data for state and collective industry. It would be ideal for us to use such net capital stock data for each province, but constructing such data is a task that is far beyond our current resources. In order to solve the problem that annual data for this variable are not available for 1978 to 1993, we use an instrument for housing in estimating Eq. (4). The instrument is obtained by regressing a measure of housing area (square meters) per capita on per capita real income. The predicted level of per capita housing is then used as the measure of variable x1 . 7 Unfortunately, variables x4 (infrastructure) and x5 (FDI) are not available annually from 1978 to 1993. Therefore in our empirical work we have treated them as ‘‘environmental’’ variables. Details are contained in the notes to Table 2. 8 Data for accumulation of fixed assets is available after 1952 for all provinces in our sample. We deflate using a price index obtained from series on construction both in nominal prices and in fixed prices. Chen et al. (June, 1988) assert that the data on construction in fixed prices are unreliable. However, our alternative is to use the provincial national income deflator that can be obtained by comparing national income and national income at fixed prices. We chose to use the construction deflator on the assumption that it provides an index closer to the correct one for accumulation than any alternative. We also used the same data to construct a variable (DK/ K)i,t Å (Ii,t/(t jÅ0 Ii,j), which is conceptually similar to the variable used by Wolff. The empirical results are not very sensitive to which of these variables is used to estimate Eq. (4). AID JCE 1462 / 6w10$$$122 09-30-97 14:16:24 cea