Dynamic externalities and local growth A panel data analysis applied to Chinese provinces

Transcription

1 China Economic Review 13 (2002) Dynamic externalities and local growth A panel data analysis applied to Chinese provinces Cécile BATISSE* CERDI, University of Auvergne, 65, Bd Franc ois Mitterrand, Clermont-Ferrand 63000, France Abstract For 30 industry sectors, this paper provides empirical evidence on the relation between the local economic structure (local sectoral specialization, diversity, and competition) and the value-added growth of Chinese provinces. The econometric analysis shows first that while diversity and competition have a positive influence on local growth, speciliazation has a negative impact. Second, it appears that industry sectors located in coastal provinces and those located in interior provinces have been subject to different growth impulses. D 2002 Elsevier Science Inc. All rights reserved. JEL classification: L11; O11; O18; R3; R12 Keywords: Industrial structure; Growth; China; Panel data 1. Introduction Reforms in China have resulted in an unprecedented economic expansion since One of the key features of this evolution has been the growth in industrial production: between 1988 and 1994, industrial production grew at the average annual rate of 14%. Reform of China s internal market mechanisms was strongly reinforced by China s increasing presence in international markets, which facilitated the adoption of new * Tel.: ; fax: address: cecile.batisse@u-clermontl.fr (C. Batisse) X/02/$ see front matter D 2002 Elsevier Science Inc. All rights reserved. PII: S X(02)

2 232 C. Batisse / China Economic Review 13 (2002) technologies and increased foreign direct investment. However, China s transition to a market economy has created a growing inequality in economic performance between coastal and interior provinces. 1 Although several academic papers have studied the determinants of Chinese economic growth, 2 the issue of knowledge spillovers through local externalities (or agglomeration economies) has received little attention despite its important implication for productivity growth. China is a major source of empirical observations to test different hypotheses relative to economic geography, market forces, and growth. Recent empirical studies that have attempted to assess the importance of localization versus urbanization economies in explaining the location of activity and their relative impact in the process of local economic development include Glaeser, Kallal, Scheinkman, and Shleifer (1992) and Henderson, Kuncoro, and Turner (1995). They seek to answer the following key question: Do firms in a particular area learn more from firms in their own industry or from firms outside their own industry? In the first case, agglomeration externalities operate within an industry and arise primarily from local concentration of that industry. These scale externalities are called localization economies, or, in a dynamic context, Marshall Arrow Romer (MAR) externalities (in reference to contributions of Arrow, 1962; Marshall, 1920; Romer, 1990). 3 In contrast, if the variety of both local industrial environment and competition contributes to a faster adoption of knowledge, externalities are called externalities of urbanization, or, in a dynamic context, Jacobs (1969) externalities. Whether externalities are mainly of the localization or mainly of the urbanization type has broad implications for economic policy and industrial development. If they are localization or MAR externalities, firms are likely to cluster in a few cities or regions. However, if an industry is subject to urbanization or Jacobs externalities, it requires a diversified industrial environment to grow faster (Henderson, Lee, & Lee, 2001). Whereas Glaeser et al. (1992) find that diversity raises firm growth in their study of US cities between 1956 and 1987, Henderson et al. (1995) show that localization economies are important for urban growth between 1970 and The aim of this paper is to contribute to this debate by studying local economic development in China. We examine two sets of influences on industrial growth: local industry features and regional characteristics between 1988 and This article complements and deepens researches almost exclusively applied on developed countries. Do externalities for an industry rise from the localization of its own 1 The question of regional inequality in China has been extensively studied in recent years from both a microeconomic and a macroeconomic perspective (Démurger, 2000; Fleisher & Chen, 1997; Jian, Sachs, & Warner, 1996; Lyons, 1991; Raiser, 1998; Tsui, 1991; Wu, 1993, 1995, 1999, etc). Particularly, these studies on regional disparity in the postreform period highlight the growing gap in income levels, income growth rates, and productivity growth between the coastal and interior provinces. 2 To cite but a few, Naughton (1999), Woo (1998), and Wu (1995). Besides the English written literature, there is also an important Chinese written literature that is not cited here. 3 This terminology was adopted by Glaeser et al. (1992) and is used abundantly in the literature.

3 C. Batisse / China Economic Review 13 (2002) activity or from the diversity of the local economic activity? What are their influences on industrial growth in the Chinese provinces? Using panel data on the value added of 30 industrial sectors in 29 Chinese provinces, we seek to estimate the industrial growth differences in terms of MAR externalities and Jacobs externalities (measured by indexes of specialization, competition, and diversity), and to understand how initial industrial structure affects industrial growth. The remainder of this paper is organized as follows. Section 2 provides an overview of spatial dynamics of industrial sectors in Chinese provinces. Section 3 summarizes the different theories and previous studies on agglomeration externalities in a dynamic context. Section 4 presents our dataset and the econometric method. The results are given and interpreted in Section 5, while Section 6 concludes with some final observations and policy implications. 2. Spatial dynamics of industrial sectors in Chinese provinces There are important growth differences between coastal and interior provinces and across industrial sectors in China. These provincial and industrial disparities are caused not only by historical and geographical factors but also by political strategies that follow from the high involvement of the government in economic activities (Song, Chu, & Cao, 2000). Did the tendencies to agglomeration change through time? Did the sectors concentrate or decentralize? The answers to these questions will provide a better understanding of the evolution of provincial economic geography in China The evolution of Chinese regional policies of industrial localization The national government has played an important role in China s economy through its direct investments, regulation of resource allocation, fiscal transfers, and policies. Many authors have described the steps taken by China to reform its industrial policy (e.g., Jian, Sachs, & Warner, 1996; Laffont & Qian, 1999). There have been several distinct policy regimes since the beginning of the communist regime in Originally, Mao Zedong adopted a policy inspired both by the Soviet experience and by military security concerns. China followed the Soviet principle of central planning for resource allocation; priority was given to the development of heavy industries, and trade and financial linkages with the western economies were limited. Mao s strategy was to implement balanced regional development and to encourage relative regional autonomy. Therefore, regional economic self-sufficiency was incorporated into China s economic policy as a new principle of Maoism. A region should be self-sufficient not only in food production but also in industrial goods. The aim was primarily to achieve military security rather than to encourage economic growth. At the beginning of the 1960s, the growing military presence of the US in Vietnam and the worsening Sino Soviet relationship led Mao to reinforce regional economic self-sufficiency, seen as a key to China s protracted defense. Mao envisaged three lines of defense (coastal, western, and central), and ordered military industrial complexes constructed in western China, popularly called the Third

4 234 C. Batisse / China Economic Review 13 (2002) Front. This Third Front industrial policy involved heavy state investment in the interior and northwest provinces, with the bulk of it in Sichuan, Shaanxi, Hubei, Gansu, Henan, and Guizhou (Démurger, Sachs, Woo, & Bao, 2001). Thus, the core of Chinese industrialization was the development of heavy industry (Naughton, 1988). The authorities played a crucial role in the process of heavy industrialization by adopting numerous policy measures (low interest rates, low wages, and prices for intermediate goods, etc.). These policy measures had important consequences. For the period , heavy industry grew at an average rate that is 1.47 times higher than that of light industry (Lin, Cai, & Li, 1996). This system resulted in important inefficiencies, in terms of both resource allocation and productivity. As production, employment, investment, and product prices were planned by the central government, enterprises faced no competition, workers and managers lacked incentives, and industrial efficiency was low. Moreover, this policy resulted in a spatial dispersion of industry. In a sense, the economic geography was reshaped. Indeed, many industrial enclaves were established in the remote, interior regions, whereas coastal provinces with economic potential were left behind. With the implementation of reforms introducing market economy elements in the 1980s, more state capital was invested in the coastal provinces. The authorities indeed stipulated the importance of the priority development of these provinces, which were seen as growth centers that could diffuse positive externalities to the interior provinces. The fundamental element of the regional policy was the exploitation of comparative advantages. The task was to develop in coastal provinces consumer goods industries with high value added, to improve the technological content of traditional industries, and to transfer activities with high energy consumption levels to less industrialized provinces. The interior provinces were to produce energy, raw materials, and transformation industries, and to continue activities in the agricultural sector (Brun & Renard, 2001; Yang, 1997). The aim was more regional production complementarity and less self-sufficiency. This new policy was implemented through an open-door policy, which consisted of attracting foreign direct investment and promoting foreign trade in targeted zones where local governments had considerable latitude to provide investors with special privileges. 4 The establishment of regional preferential policies has led to the institution of 5 Special Economic Zones (SEZ), 5 14 Coastal Open Cities 6 entitled to establish their own Economic and Technological Development Zones (ETDZ), the establishment of Coastal Open Economic Zones and of an Open Coastal Belt. 7 The Deng Xiaoping southern tour in 1992 has been followed by further extension of these economic zones throughout China. The important role of this open-door policy in regional growth has been emphasized in several studies (e.g., Démurger, 2000; Mody & Wang, 1997). 4 See Démurger (2000) and Yang (1997). 5 These SEZ are Shenzhen, Zhuhai, and Shantou (located in Guangdong) established in 1979, Xiamen (located in Fujian) established in 1980, and Hainan island established in These costal open cities are Dalian (Liaoning), Qinhuangdao (Hebei), Tianjin, Yantai (Shandong), Qingdao (Shandong), Lianyungang (Jiangsu), Nantong (Jiangsu), Shanghai, Ningbo (Zhejiang), Wenzhou (Zhejiang), Fuzhou (Fujian), Guangzhou (Guangdong), Zhanjiang (Guangdong), and Beihai (Guangxi). 7 Further details on the timeline of these various China regional preferential policies can be found in Démurger et al. (2001).

5 C. Batisse / China Economic Review 13 (2002) Fig. 1. Average evolution of concentration ( ) Concentration or dispersion of industrial sectors? We measure the degree of localization of Chinese industrial sectors in terms of value added. Denoting the value added of sector s (s=1,...,30) 8 in province p ( p=1,...,29) 9 as VA sp, the localization of sector s can be addressed by calculating an Isard index of localization: 10 L sp ¼ VA sp VA p VA sn VA n where VA sn is the value added of sector s in the whole nation; VA p is the total value added in the province p; VA n is the value added in the whole nation. Fig. 1 summarizes the average evolution of the index of localization for 30 industrial sectors. The aggregate index of localization shows a general increasing trend in concentration of industrial sectors. If we look at each industrial index (see Appendix C), we note that the strongest activity concentration is found for petroleum and natural gas-extracting sectors and for tobacco, sectors for which proximity of natural resources is essential. Energy production centers are primarily concentrated in the north and the northeast areas, in particular 8 See Appendix B. 9 Due to missing values, Tibet has been excluded from the statistical analysis. As Chongqing area was only given a municipality status in 1997, data before this year do not allow us to distinguish between Chongqing and Sichuan, which have been combined. 10 Several indicators can be used to measure the concentration or the dispersion of activities in an area. The main indicators generally give similar results (see Jayet, 1993 for an illustration).

6 236 C. Batisse / China Economic Review 13 (2002) Heilongjiang (for oil and natural gas extraction) and Shaanxi (coal) and in the western provinces such as Xinjiang (petroleum, natural gas). They are located far away from industrial consumption centers, which are situated inside industrial areas of the coastal provinces (indeed, among all coastal provinces, only three have significant energy production: Shandong, Liaoning, and Guangdong). In contrast, rather weak indexes of localization are found for market-oriented industries for which proximity of ultimate consumers dominates location decisions. They are characterized by strong value added activities. Example industries are the metal products, beverage, and chemical products sectors. They have developed within coastal provinces during the reform period. 3. The relationship between industrial structure and local growth 3.1. Specialization versus diversity/monopoly versus competition A debate has emerged in the recent literature on how underlying economic structure within a geographic unit may influence the diffusion of knowledge spillovers and promote industrial development and growth over time (Audretsch, 1998). This debate revolves around two key structural elements: the degree of specialization versus diversity, and the degree of local competition versus monopoly (market structure). The specialization thesis suggests that increased concentration of a particular sector within a specific geographic region facilitates knowledge diffusion among firms within the same sector, enhancing research, development, and innovative activities. When firms in the same sector locate near each other, they benefit from externalities that are external to the firm but internal to the sector. Benefits include the sharing of a specific labor market and of coded and implicit information as well as links within the sector. It asserts that concentration will also increase within-sector competition in the specific geographic unit of observation. Following Glaeser et al. (1992), we call this effect the MAR externality: accumulation of knowledge, development of an information network, and promotion of innovations among firms within the same sector are facilitated. These interactions affect local firms productivity and might favor growth. In contrast, Jacobs (1969) argues that the most important source of knowledge spillovers is external to the sector in which firms operate. She develops a theory according to which the diversity and variety of industries within a geographic region promote a greater return on the exchange of economic knowledge across firms. Indeed, a diversified industrial environment facilitates the transmission of technological or knowledge externalities and innovative activities, thereby promoting local economic growth. Insofar as some ideas diffuse and can be used by noncompeting enterprises, local industrial heterogeneity can promote a more rapid diffusion of ideas. Combes (2000b) points out, however, that the beneficial effects of industrial heterogeneity require that innovations of one sector can by used in another sector. In other words, a Jacobs effect is synonymous to urbanization economies. These two theories are not necessarily mutually exclusive, as analysed by Porter (1990), who suggests that local competition is more growth-conductive than is local monopoly, but who also agrees with MAR s assertion that intraindustry specialization is a source of growth.

7 The magnitude of these externalities and their impact on economic activity are not directly observable and are thus very difficult to assess empirically. However, in the last few years, there has been a burst of academic research in this area. A brief and incomplete summary of recent developments, illustrating methodological issues and focusing on influential papers, follows Empirical research C. Batisse / China Economic Review 13 (2002) Some empirical research works on agglomeration economies have attempted to assess the effects of different economic structures in explaining the location of activity and the process of local economic development. These studies are based on the idea that geographical proximity facilitates and intensifies transmission of information. They try to assess the specialization and growth patterns of the studied area and focus on employment or production growth between two periods. Glaeser et al. (1992) measure economic performance in terms of employment growth. Their data set is composed of the six largest sectors in 170 US cities between 1956 and They assess the nature of local externalities through a specialization index (MAR externality), an industrial diversity index (Jacobs externality), and a local competition index (market structure). They also include a convergence effect (catch-up) with the level of sectoral employment. All explanatory variables are measured at the initial date. Their results suggest that local competition and urban diversity are more important than specialization in generating employment growth, which is consistent with Jacobs theory. These results suggest that important knowledge spillovers may occur between industries rather than within. Henderson et al. (1995) question the magnitude and the nature of these externalities. They study the period and use data on eight specific manufacturing industries in 224 US metropolitan areas. They run their regressions for each sector separately and find evidence of MAR externalities for traditional industries and of both Jacobs and MAR externalities for new high-technology industries. The papers by Glaeser et al. (1992) and Henderson et al. (1995) gave rise to several criticisms and extensions. They also encouraged the application of the two methodologies to other countries. 11 While studies that investigate the relationship between industrial structure and sectoral growth in developing countries are relatively scarce, we might mention papers by Henderson et al. (2001) on Korean industry and Mody and Wang (1997) on coastal Chinese provinces. Exploiting a sample of 23 industrial sectors in seven coastal provinces over the period, Mody and Wang find a negative impact of specialization and a positive effect of competition on local growth, as well as the existence of a quadratic relationship between the explained variable and each of these two explanatory variables. In this paper, we 11 For example, Cainelli and Leoncini (1999) follow the model developed by Glaeser et al. (1992) to test the impact of dynamic externalities on industrial employment growth rates in Italy during the period The sample is composed of 16 industrial sectors and 92 provinces. They estimate the relation for each of the four Italian geographical areas in order to estimate how various local structures may explain long-term employment growth in these four zones.

8 238 C. Batisse / China Economic Review 13 (2002) investigate whether their results about industrial structure can be replicated for the period Methodology and model specification 4.1. A model with agglomeration externalities Our aim is to determine how the growth of a particular sector in a given province is affected by local specialization, competition, and industrial diversity. Theories of MAR, Porter, and Jacobs can be summarized in a simple growth model specification: lng s;p ¼ blnðk s;p ÞþclnðS s;p ÞþdlnðT s;p Þþjlnðdiv s;p ÞþgðX p Þþa s þ u s;p where G represents the ratio of the industrial value added of sector s (s=1,...,30) in province p ( p=1,...,29) 12 between 1988 and 1994; K is the growth of capital per worker; S, T, and div are the explanatory variables corresponding, respectively, to specialization, local competition, and local industrial diversity; X is provincial characteristic; and u s,p is the error term. The industry-level data set used for estimation comes from the China Industrial Economic Statistical Yearbook (State Statistical Bureau, ). It contains data on 30 manufacturing industries across 29 provinces over 7 years (see Appendices A and B). Our study is based on couples province sector, by analogy, to couples city industry of Glaeser et al. (1992). Due to missing data for some sector province couples (either because they are unavailable or because they are not disclosed), the number of observations is reduced by 5%, to 833 from 870. Regressions are estimated on pooled data for all manufacturing sectors, as in Glaeser et al. The data set being a panel of sectors over provinces, the error term can be expressed as follows: u s,p =m s +e s,p, where m is a sector-specific parameter. The Breusch Pagan test (Lagrange multiplier test) confirms the presence of specific sector-level effects (value of for a c 2 with 1 df). Moreover, the Hausman test indicates that the fixed-effect specification is preferred to a random-effect specification. We thus opt for a panel specification with fixed effects by sector. The White estimator of variance is used to correct for potential heteroskedasticity (White, 1980). The dependent variable is the logarithm of the value added growth of sector s in province p between 1988 and The choice of the period is consistent with historical 12 The choice of provinces as unit of analysis is tied to the fact that the provincial level of investigation is the only one for which it is possible to find necessary data to estimate the model. The adoption of other units of analysis, such as metropolitan areas (as in Glaeser et al., 1992), would be more appropriate, but as Cainelli and Leoncini (1999) note, it would have required stronger hypotheses during the construction of the data set. However, insofar as the Chinese provinces represent the administrative units of the country and have a share of autonomy in the control of their economic policy, we can regard them as relatively independent entities. 13 Note that, besides our focus on growth, we tried to conduct the analysis in terms of productivity, but the results were not significant.

9 C. Batisse / China Economic Review 13 (2002) events because this period marks the turning point in industrial reforms with the coastal development strategy and the speeding up of industrial reforms announced in However, the length of the sample period is constrained by the fact that comprehensive industrial data are available only from 1988 to Data of the 1995 industrial census are not directly comparable with the data published in the Industrial Statistical Yearbook. In 1997, the State Statistical Bureau published industry-level data, but 1997 figures are not quite consistent with the period data because of modifications and revisions to the industrial enterprise categorization. Since then, to our knowledge, no industrial data have been published. All explanatory variables are measured at the initial date, This is consistent with the idea that there is a lag between the emergence of agglomeration economies and their impact on firms location and provincial growth; the stock of local specific knowledge accumulates over time. The addition of 1994 variables to the specification (as in Henderson et al., 1995) makes it more difficult to interpret the econometric results. All variables are measured in logarithms, so the estimated parameters can be interpreted in terms of elasticities. The explanatory variable, K is the growth of capital per worker, which is generally a significant source of observed differences in industrial growth operating through increased worker productivity and through technological progress. Other variables whose influence on industrial performances and efficiency have been analyzed in the theoretical and empirical literature and which must be included in our econometric work are included in the set of explanatory variables, X, which contains provincial and other control variables (GDP per capita and geographical dummies). Studies of convergence (e.g., Barro & Sala-I-Martin, 1998) introduce the initial sectoral employment level in order to account for catching-up effects and convergence (a negative sign is expected because of decreasing returns). Glaeser et al. (1992) and Henderson et al. (1995) introduce specialization (from which they expect a positive sign due to local increasing returns). The lack of theoretical grounds in the debate led Glaeser et al. and Henderson et al. to introduce simultaneously these two variables. Combes (2000a) shows that inclusion of this variable strongly changes the interpretation of the specialization variable and leads to an overestimation of localization economies. 14 Consequently, we include per capita GNP (GNPp) 15 rather than the initial local sectoral value added. This variable controls for the initial development of the province. It can be seen as the effect of initial conditions on later growth of local sectors. A final set of explanatory variables is included to capture differences in growth performance related to geographical location. We include a dummy variable (SEZ) equal to 1 for the provinces of Guangdong, Hainan, and Fujian, and 0 for other provinces. Both the 14 When the signs obtained for the effect of convergence and of specialization are opposite (e.g., negative/ positive), the local industrial structure, which is more favorable to industrial growth, might also correspond to a low level of sectoral employment and to a strong share of the sector in regional employment. In other words, having a low level of employment in the province is the single solution compatible with these results. Interpretations in terms of MAR externalities then disappear completely. For example, a positive effect of the specialization on local growth can simply reflect the negative effect of the regional employment. 15 Data used come from the 1996 edition of China Regional Economy: A Profile of 17 Years of Reform and Opening Up, compared with information from the Comprehensive Statistical Data and Materials on 50 Years of New China published by the State Statistical Bureau (1999) (see Appendix A).

10 240 C. Batisse / China Economic Review 13 (2002) favorable policy treatment of these provinces and their geographical position near Hong Kong and Taiwan favor faster growth. Favorable policies have given local governments in these provinces greater autonomy to invest in new business and infrastructure. They have provided strong incentives to exporting firms and foreign investors, including tax exemptions or reductions, low land use fees, and more. SEZ provinces are characterized by recently emerging industrial structure composed of small nonstate enterprises and using modern production techniques. They have greater flexibility to respond to external stimuli and market trends (Mody & Wang, 1997). Another dummy variable (cot) takes the value of 1 when a province other than an SEZ province has an opening to the sea and value of 0 if it does not. 16 We hypothesize that coastal provinces have natural growth advantages. Moreover, they have also benefited from the opendoor policy. We observe that these provinces have experienced greater profits and more rapid growth of nonstate enterprises (collective, private, and individual). Another factor influencing the economic success of coastal areas has been their better infrastructure quality, including human capital, transportation, and telecommunication (Song et al., 2000). Lucas (1988) and Romer (1990) emphasize the importance of these factors in providing a better environment for economic growth. In particular, physical infrastructures such as transports and telecommunications can improve the productivity of production inputs, and thus increase long-term growth by facilitating exchange and emergence of externalities among firms. As infrastructure develops, it becomes easier for entrepreneurs to adopt new technologies, leading to technical progress and economic growth (Shleifer, 1990). This phenomenon is all the more important in China since distances are significant and since technical progress is most often imported rather than created locally by research and development activities (Démurger, 2001). Finally, due to their geographic isolation, interior provinces have limited access to outside markets, both national and international. They have few hometown connections with overseas Chinese, who are responsible for a very large share of direct foreign investment We tried several functional forms, including quadratic specifications in our regressions, choosing the form that performs best in terms of fit. 17 The substantial features of our econometric results are robust when estimated by alternative, not affected by choice of functional forms Measuring externalities We postulate that the growth rate in an industrial sector in a given province can be influenced by the degree of specialization (MAR externality), the degree of industrial diversity (Jacobs externality), and the market structure measured at the beginning of the period. In the absence of perfect information about these externalities, we measure them with proxies similar to those used in the empirical literature on agglomeration economies and 16 The other coastal provinces are Beijing, Tianjin, Hebei, Liaoning, Shanghaï, Jiangsu, Zhejiang, and Shandong. 17 Specification link tests have been conducted (Pregibon, 1979).

11 industrial concentration. Each variable is divided by the value it takes at the national level for the considered sector to allow comparisons between sectors. The specialization index we consider is the ratio of the share of sector s in province p to its average share across China: S s;p;t ¼ VA sp=va p VA sn =VA n where VA sn is the value added in sector s at the national level; VA p is the total value added for province p; and VA n is the value added at the national level. If S is greater than 1, then province p has a relatively high concentration of value added generated in sector s. Knowledge spillovers in the sector are assumed to be greater when S is higher. The index of diversity is the inverse of a normalized Herfindhal index of sectoral concentration: 18, P 1 S VA s 0 2 p VA p VA sp s div s;p;t ¼ 0 6¼s, 1 P S s 0 6¼s 2 VA s 0 n VA n VA sn where VA s 0p is the value added in industrial sectors other that the one that is studied. A positive relationship between industrial diversity and the sector s growth in a given province is evidence supporting Jacobs theory. Note that this indicator represents the sectoral diversity faced by sector s in province p; a high value of this index does not necessarily imply that sector s is not concentrated in province p. Another important industry characteristic is the degree of market competition within a sector. As in other recent empirical studies, 19 we calculate a competition index as the ratio of the number of firms to total value added in sector s in province p divided by the national average value of this ratio: T s;p;t ¼ NBE sp=va sp NBE sn =VA sn C. Batisse / China Economic Review 13 (2002) where NBE sp is the number of firms in sector s in province p and NBE sn is the number of firms in sector s at the national level. Glaeser et al. consider this variable to be a good index of product market competition. However, this variable corresponds more to the average plant size of sector s located in province p. In terms of effects on industrial growth, this variable should allow us to determine whether competition (positive relationship between T and G according to Jacobs and Porter) or monopoly (negative relationship between T and G according to MAR) is more effective in enhancing growth and innovation. The use of an indicator of competition based on data at the establishment level would be desirable, but we are constrained by available data. 18 Ellison and Glaeser (1997) also use Herfindhal indexes to study geographic concentration in US industries. 19 We follow Glaeser et al. (1992); Henderson et al. (1995), Maurel (1996), and Mody and Wang (1997).

12 242 C. Batisse / China Economic Review 13 (2002) Empirical results 5.1. Regressions on all sectors We estimate our model using the fixed-effects method for unbalanced panels of sectors across provinces. With an unbalanced panel, we have to deal with some heteroskedastic disturbances, which may lead to bias in the within estimator. To reduce this source of bias, we have transformed our model in order to achieve homoskedastic disturbances, following the procedure proposed by Sevestre and Matyas (1992) (see Appendix D). Estimation results reported in Table 1 provide evidence of the role played by industrial structure and region-specific variables in the economic performance of industrial sectors in Chinese provinces. The variable K performs as expected: more capital per worker increases labor productivity growth and thus value added growth. The initial level of sectoral specialization has a large and negative influence on local sectoral growth (Column 1). This means that sectors that are more heavily concentrated in a given province than they are in China as a whole experience slower growth. This result contradicts implications of MAR model. The negative specialization effect can be interpreted, as in Combes (2000b) and Glaeser et al. (1992), in terms of a product s life cycle: products first develop in a few places and then diffuse across space. We replicate the results found by Mody and Wang (1997) on the period. These results also replicate for Chinese industrial sectors as a whole the absence of intrasectoral local externalities reported by Glaeser et al. for US industry, by Combes for French industry and by Cainelli and Leoncini (1999) for Italian industry. The initial level of the competition index has a positive estimated impact on sector-based growth (Column 2). The positive sign implies that, in concordance with the implications of Jacobs and Porter, the presence of many small firms is a trigger of dynamic spillovers, and hence of growth. Mody and Wang (1997) also find a positive relation between competition and industrial growth for the period from 1985 to It is generally recognized that young establishments have greater flexibility and capacity to adapt to new conditions. This property is reinforced in China insofar as new enterprises mainly belong to the nonstate sector (collective, private, and individual). This nonstate sector generally exhibits greater flexibility and productivity than do Chinese government enterprises that have notable readjustment problems today (Jian et al., 1996). Cainelli and Leoncini (1999) and Glaeser et al. (1992) find similar results in their studies of developed economies. Sectoral diversity positively affects growth (Column 3). The positive influence of this variable supports hypotheses that stress the importance of interindustry externalities. Firms benefit from operating in an environment with a great variety of industries. However, as we are working at a relatively high level of aggregation, a positive effect of the diversity might reflect the existence of commercial relationships between sectors, rather than the sharing and exploitation of technological complementarities between sectors. Column 4 reports the estimation results for a regression equation in which all three variables that may represent various externalities are included. The results generally confirm those observed in the first three columns, where the three variables are included one at a time.

13 Table 1 Determinants of sector province value added growth between 1988 and 1994 a (1) Total sample (2) Total sample (3) Total sample (4) Total sample (5) Coastal provinces sample b (6) Landlocked provinces sample Production factor Capital per worker, K (5.670) (5.080) (5.040) (5.404) (3.144) (4.926) Industrial structure Specialization index, S ( 3.363) ( 3.110) ( 2.382) ( 2.090) Competition index, T (3.024) (1.183) (2.059) ( 0.399) Diversity index, div (2.098) (2.975) (5.166) (2.626) Region-specific GDP per capita ( 2.652) ( 0.523) ( 2.675) ( 1.509) (1.206) ( 0.015) SEZ dummy (4.519) (3.967) (3.400) (3.684) variable Coastal dummy (3.832) (3.807) (3.377) (3.681) without SEZ Number of observations Estimation method Fixed effects with unbalanced panel Fixed effects with unbalanced panel Fixed effects with unbalanced panel Fixed effects with unbalanced panel Fixed effects with unbalanced panel Fixed effects with unbalanced panel Adjusted R a Regressions 1 6 include sectoral dummy variables. The estimated standard deviations have been corrected using a White matrix. The values shown between brackets are the Student s t values. b Coastal provinces are Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Shandong, Guangdong, Fujian, and Hainan. C. Batisse / China Economic Review 13 (2002)

14 244 C. Batisse / China Economic Review 13 (2002) The estimated coefficient of initial GDP per capita is negative, supporting the hypothesis of conditional convergence among Chinese provinces. This result suggests that relatively backward provinces tend to grow faster than the more advanced provinces, other things equal. The significant and positive estimated coefficient of dummy variables cot and SEZ reflects faster growth for sectors located in coastal provinces, other things equal. These results seem to confirm that the implementation of reforms openness to foreign investments, human and infrastructure endowments, development of the nonstate sector, etc. has especially played an important role in the industrial growth process of coastal provinces. 20 Briefly, our econometric results lend support to Jacobs hypothesis that there is a positive impact of local industrial diversity and of level of local competition on sectoral growth. They contradict the hypotheses associated with Marshall, Arrow, and Romer that there is a positive impact of initial concentration of a sector on local growth. They are mixed with respect to hypotheses generated by Porter s work. Although our results confirm the existence of agglomeration externalities over the period , the impact is quantitatively small Influence of the geographic localization Given the strong significance of the coastal dummy variable, we also estimated the same regressions on two subgroups of provinces. The results are reported in Columns 5 and 6 of Table We are asking if the relation between industrial growth and industrial structure is a general characteristic of the Chinese manufacturing industry, or if only some sectors are sensitive to these market mechanisms according to their geographical localisation. Our estimations confirm the overall results shown in Column 4. The signs of explanatory variables remain the same, but their significance changes because the number of observations is lower in this case than in the estimations at aggregate level. These estimations show that the industrial structure effects are more important because elasticities are systematically higher. Our findings show that the growth of sectors located in coastal provinces is positively affected by interindustry externalities, and the existence of a strong initial concentration of the sector in a given province has a significant and negative impact on the sector s growth. Including multiplicative variables to capture possible interaction between SEZ and industrial structure revealed no sector effects specific to the SEZ. While our estimation results imply that sectors located in the coastal provinces are sensitive to the diffusion of dynamic externalities, they do not support the importance of such effects in interior provinces. 20 When we included provincial variables representing human capital and physical infrastructures, specifically secondary school enrolment rates and the density of road, and rails and waterway network, multicollineary precluded obtaining statistically significant estimates of several regressors. 21 It is possible that differences exist between industrial sectors located in coastal provinces, which experienced a faster growth, and the same industrial sectors located in interior provinces, which were mainly developed during the first two phases of industrial development and experienced less growth today. The Chow test of coefficients nonstability justifies this procedure of differentiation in two subsamples.

15 C. Batisse / China Economic Review 13 (2002) Conclusion This paper investigates whether dynamic externalities associated with specialization, industrial diversity, and competition have influenced industrial growth in Chinese provinces between 1988 and Using panel data for 30 industrial sectors in 29 Chinese provinces, the estimation of a growth equation shed light on several issues. We find evidence of dynamic externalities influence on local growth. Our results are consistent with hypotheses generated by Jacobs (1969) and tend to contradict those asserted in the work of Arrow (1962), Marshall (1920), and Romer (1990). They are mixed with regard to hypotheses suggested in research reported by Porter s (1990) theory. We find that sectors located in coastal provinces and sectors located in interior provinces have been subject to different growth impulses. Namely, it appears that interior provinces have not yet benefited from economic reform to the extent enjoyed in coastal regions, at least in the time period encompassed by our research. It is not an easy task to deduce policy implications of these results. Nevertheless, the local industrial structure seems to exert a real influence on the growth of value added. A better understanding of these phenomena is required for the formulation of policies to create incentives for local specialization and competition that may result in rather long-term effects. Policy making will also be complicated by the need for the Chinese central government to face the challenge of complying with to WTO rules. Acknowledgments This paper has benefited from the comments and suggestions of S. Démurger, M.-F. Renard, and one anonymous referee. I also thank all the participants in Third International Conference on the Chinese Economy Has China become a market economy?, held at CERDI, Clermont-Ferrand, France on May, 17 18, 2001, for useful comments and suggestions. I remain, nevertheless, responsible for errors and insufficiencies. Appendix A. Statistical appendix: sources A.1. Source The database has been constructed from the Chinese editions of China Industrial Economic Statistical Yearbook (State Statistical Bureau, ), of China Regional Economy. A Profile of 17 Years of Reform and Opening Up (State Statistical Bureau, 1996), and of Comprehensive Statistical Data and Materials on 50 Years of New China (State Statistical Bureau, 1999). All current values are expressed in local currency (RMB Yuan). We have a database covering the whole Chinese territory except Tibet. The available data are composed of province sectors couples, by analogy with the city industry couples of Glaeser et al. (1992).

16 246 C. Batisse / China Economic Review 13 (2002) Modifications in the nomenclature of two sectors appeared since 1993: the food manufacture sector (Sector 10) and the machine building sector (Sector 15). Each of these two sectors has been decomposed in two sectors. Using data of the previous years and taking account of the Chinese industrial history, it appears that summations of these two groups of sectors correspond to the sectors that existed until A.2. Choice of industrial value added data We preferred data on industrial value added rather than data on employment as opposed to what most studies on the industrial structure do. This choice is motivated by the existence of the household registration system in China since 1958 (the Hukou system), which stipulates the urban or rural membership and forbids change of residence without preliminary authorization. However, in the 1980s, after the success of the rural reforms, the agricultural production released high surpluses of labor, which intensified the informal migrations. The Chinese authorities then adopted a policy intending to limit migrations. For the Labor Ministry, the goal of this policy is to limit the movements of interregional workers at the current level. The rural urban migration increased, but the interregional migration was relatively limited. Zhang and Chi (1996), in a study on six interior provinces, found that more than 96% of the rural migration towards the cities was intraprovincial. Thus, although there is a greater mobility for skilled workers (Zhang & Chi, 1996); labor markets remain fragmented along provincial lines. Besides prohibition to migrate, Chinese labor market does not appear to be competitive. It is characterized in particular by employment quotas fixed by the Labor Ministry and the social role played by the state enterprises in terms of social protection, health, education, retirement, etc. (Kanbur & Zhang, 1999). Lastly, we can assume that in a transitional economy like China, some massive dismissals can occur without meaning deceleration of local growth or a fall in productivity. Appendix B. List of sectors Beverage manufacturing Chemical fibers Chemical material and products manufacturing Clothing and other chemical fibers products Coal mining and preparation Cultural, educational, and sports articles manufacturing Electric equipment and machinery manufacturing Electronic and telecommunications equipment manufacturing Ferrous metals mining and preparation Food manufacture Furniture manufacturing Instruments, meters, and other measuring equipment manufacturing Leather, furs, and manufactured goods

17 C. Batisse / China Economic Review 13 (2002) Logging and transport of timber and bamboo Machine building Medical and pharmaceutical products Metal products Nonmetal mineral products Paper making and manufactured goods Petroleum and natural gas extraction Plastics manufactured goods Power generation, steam, and hot water production and supply Printing and record medium manufacturing Rubber-manufactured goods Running water production and supply Smelting and pressing of ferrous metals Textile manufacturing Timber processing, bamboo, cane, palm bribe, and straw products Tobacco manufacturing Transportation equipment manufacturing Appendix C. Isard s coefficients of localization: industrial sectors, The table presents Isard s coefficients of localization for 30 Chinese industrial sectors. Sectors Beverage manufacturing Chemical fibers Chemical material and products manufacturing Clothing and other chemical fibers products Coal mining and preparation Cultural, educational, and sports articles manufacturing Electric equipment and machinery manufacturing Electronic and telecommunications equipment manufacturing Ferrous metals mining and preparation Food manufacture Furniture manufacturing

18 248 C. Batisse / China Economic Review 13 (2002) Instruments, meters, and other measuring equipment manufacturing Leather, furs, and manufactured goods Logging and transport of timber and bamboo Machine building Medical and pharmaceutical products Metal products Nonmetal mineral products Paper making and manufactured goods Petroleum and natural gas extraction Plastics manufactured goods Power generation, steam, and hot water production and supply Printing and record medium manufacturing Rubber manufactured goods Running water production and supply Smelting and pressing of ferrous metals Textile manufacturing Timber processing, bamboo, cane, palm fibre, and straw products Tobacco manufacturing Transportation equipment manufacturing Mean Source: Author s calculation based on Industrial Statistical Yearbook (various years), State Statistical Bureau.