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Journal of the Asia Pacific Economy

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Did city cluster development help improve labor productivity in ?

Peixin Li, Chen Wang & Xueliang Zhang

To cite this article: Peixin Li, Chen Wang & Xueliang Zhang (2017) Did city cluster development help improve labor productivity in China?, Journal of the Asia Pacific Economy, 22:1, 122-135, DOI: 10.1080/13547860.2016.1261471 To link to this article: https://doi.org/10.1080/13547860.2016.1261471

Published online: 07 Dec 2016.

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Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=rjap20 JOURNAL OF THE ASIA PACIFIC ECONOMY, 2017 VOL. 22, NO. 1, 122–135 http://dx.doi.org/10.1080/13547860.2016.1261471

Did city cluster development help improve labor productivity in China?*

Peixin Li, Chen Wang and Xueliang Zhang School of Urban and Regional Science, University of Finance and Economics, Shanghai, China

ABSTRACT KEYWORDS In 2015, there were more than 20 city clusters in China, designated City cluster; labor by the central government. Based on panel data from 283 Chinese productivity; Yangtze River cities over the period of 2003–2013, this paper evaluates whether Delta; China cities within city clusters have higher productivity than those JEL CLASSIFICATION outside the clusters. The empirical results reveal that the city cluster O18; R11; R12; R58 strategy initiated in China’s 11th Five-Year Plan has not produced any significant impact on labor productivity, possibly due to market segmentation. However, an exception is the Yangtze River Delta Urban Economic Coordination Committee – the most developed city cluster in China, which has experienced growing productivity improvement.

1. Introduction There has been an increase of city clusters1 in the world (Gottmann 1957; Batten 1995;Hall and Pain 2006; Florida, Gulden, and Mellander 2008), including China. In the 1980s and 1990s, the Chinese government promoted development of small cities and towns while con- taining the growth of large cities. In the 2000s, large cities expanded rapidly, leading to vari- ous socioeconomic problems, such as congestion, environmental degradation and sky- rocketing housing prices. The strategy of city cluster development was proposed to ensure harmonious growth of all cities and towns. This strategy initially appeared in the 11th ‘Five-Year Plan’ of China and was highlighted in the 12th ‘Five-Year Plan’ (Fang 2015)and the National New-type Urbanization Plan (2014–2020) to build city clusters with efficient economic concentration, wide range of influence, optimal city systems and strong supple- mentary functions to support national economic growth, regional coordination and inter- national competition and cooperation.2 To date, more than 20 national city clusters have been formed (see Figure 1,forallincludedcitiesseeTable A1 in the Appendix). In 2013, the top 10 city clusters made up 14.7% of the nation’s land area, accounted for 46.9% of the total population, and produced 69.6% of the China’s gross domestic product (GDP).3 Theoretically, the concentration of economic activities results in agglomeration economies (Duranton and Puga 2004). However, the scope of agglomeration externalities

CONTACT Xueliang Zhang [email protected] *Earlier versions of this paper were presented at the Conference on New Urbanization and Real Estate Development during 14–15 November 2015 and The Spatial Economics Annual Meeting of China during 27–29 November 2015. © 2016 Informa UK Limited, trading as Taylor & Francis Group JOURNAL OF THE ASIA PACIFIC ECONOMY 123

Figure 1. Distribution of city clusters in China.

may not be confined to a single city but to the regional city cluster that consists of cities of different sizes (Burger and Meijers 2016). In addition, city cluster can also bring positive externalities which can be called ‘borrowed size’ or ‘urban network externalities’ (Capello 2000; Phelps, Fallon, and Williams 2001; Johansson and Quigley 2004; Meijers, Burger, and Hoogerbrugge 2016), because different cities’ proximity and integration not only help promote specialization, but also help reduce transportation costs and market segmenta- tion. Furthermore, a city cluster may stimulate economic and social interactions that improve the diffusion of technology and knowledge (Boix and Trullen 2007). Clearly, the externalities of city clusters depend on the degrees of spatial and functional integration of cities (Van Oort, Burger, and Raspe 2010; Meijers 2005). However, there is fierce interregional competition and market segmentation in China (Young 2000), which may restrict the city cluster externalities. Local governments would have an incentive to exercise protectionism, adversely affecting economic development (Wang et al. 2007). For example, some regions choose locally-made cars for taxi drivers to protect local automo- bile industry while some other regions list a choice set of cars with strict engine and emis- sion standard in favor of locally-made cars (Bai, Tao, and Tong 2008). Meanwhile, there exists multiple fees for foreign products and subsidies for local ones. Moreover, some regions enhance the same industry for the short-run development rather than cooperate with each other on developing industries according to their local comparative advantages (Lu, Chen, and Yan 2004). There exist a few studies on city clusters in China. While a positive correlation between growth and city clusters was found by Wu and Liu (2008), Yu and Wang (2011) and Wei, 124 P. LI ET AL.

Li, and Tang (2013), Fang et al. (2013) concluded that the efficiency of city clusters was low. However, existing literatures only focus on cities within national city clusters without comparing cities’ productivity with and without national city clusters. This research ques- tion is of importance since it can provide evidence on the outcome of national city cluster policies. This study contributes to the discussions on the effectiveness of city cluster develop- ment in China by using data from 283 cities over the period, 2003–2013. Our results show that the development of city clusters has not led to general improvement in labor productivity but the cluster of Yangtze River Delta is found to be successful. The remainder of this paper is organized as follows. The data and modeling framework are described in Section 2. Section 3 presents empirical analyses. This is followed by a robustness check in Section 4. Finally, Section 5 concludes.

2. Modeling framework and data 2.1. Empirical framework

To apply the difference-in-difference (DID) method, cities are divided into the treatment or control groups. The former includes cities within the national city clusters (see Table A1 in the Appendix for clusters and cities) and the latter contains the remaining cities. By adding a dummy variable for the national city cluster to the model of Sveikaus- kas (1975), we have

D C C C C C e ; yit b0 b1National clusterit gXit ai mt it (1) where yit represents GDP per worker for city i in year t (in logarithm); National-clusterit is the dummy variable such that it takes the value of 1 if city i appeared in the national city- cluster plans in year t, and 0 otherwise. Because the plans were proposed in different years, the full number of cities in the treated group includes 18 cities in three city clusters in 2008; 25 cities in three city clusters in 2009; 33 cities in three city clusters in 2010 and 94 cities in 14 city clusters in 2011. Taking as an example, the national-cluster dummy variable is 0 for years 2003–2010 and 1 for 2011–2013 because Beijing was included in the Beijing–– city cluster in 2011.

Furthermore, Xit is a vector that contains the control variables; ai and mt are city and year fixed effects; the bs and g are unknown parameters to be estimated; and the eit represents white noise random errors. To explore whether the impacts of city clusters differ across cities of different sizes, we also add to the interactive variable between the national-cluster dummy and city size (the logarithm of population). Because the effect of cluster development may not be instant, it is appropriate to consider possible time-varying effects,

X3 D C C C C C e ; yit b0 b1ktreati dyeark gXit ai mt it (2) k D 1 JOURNAL OF THE ASIA PACIFIC ECONOMY 125

D where dyeark is the dummy variable with dyeark 1ifitisthekth year after the city was included in the national city cluster, and 0 otherwise. Because most cities in the treatment group joined city clusters in 2011, and our sample was for 2003–2013, fi three dummy variables are de ned, namely dyear1, dyear2 and dyear3.Forexample, D Beijing was included in the national cluster in 2011. Therefore, for Beijing, dyear1 D D D 1 for 2011, dyear1 0 for all other years and dyear2 1 for 2012, and dyear2 0 forallotheryears. Later, we also assess the effects of cluster – the most mature cluster in China where the treatment group contains counties that joined the cluster. Models (1) and (2) are used although FDI and traffic infrastructure could not be included because of data unavailability.

2.2. Data Our sample consists of 283 cities for the years 2003–2013 and the data were collected from the China Statistical Yearbook, the China City Statistical Yearbook and the China Statisti- cal Yearbook for the Regional Economy. As for the Yangtze River Delta, 131 counties in this region were examined for the period 1993–2012 by using data from the Shanghai Sta- tistical Yearbook, the Statistical Yearbook and the Statistical Yearbook. Regarding the productivity of the cities, the log form of GDP per worker is applied, with the prices appropriately deflated. Since the GDP deflation index at the city level is not available, the provincial level deflator is used, which is calculated based on the provin- cial GDP obtained from the China Statistical Yearbook. Our empirical analyses explicitly control a number of other determinants of productivity:

1) Capital stock per worker. According to economic growth theory, the most impor- tant factor for output per worker is the capital stock per worker. For the 283 cities in our study, we use 2003 as the base year and the city capital stock in the base year is calculated using the provincial capital-output ratio and city output, as considered by Zhang, Wu, and Zhang (2004); 2) Population density. An increase in population density can enhance the link between enterprises, decrease transaction costs, and thus improve productivity (Ciccone and Hall 1996). The logarithmic form of population per square kilometer is used to rep- resent the population density; 3) Human capital. Human capital is an important determinant for productivity according to new economic growth theory (Moretti 2004). Because no information is available regarding the average number of educational years of the population in cities and counties, the logarithmic form of the number of students attending senior school per ten thousand people is used as a proxy; 4) Government expenditure. The ratio of fiscal expenditure to GDP is accounted for because the government plays a vital role in economic development in China; 5) Foreign direct investment. In general, foreign investments can affect productivity through technology diffusion, demonstration and imitation effects, competition effects, industry-linkage effects and human resource mobility. The ratio of FDI to GDP is used in our analysis, discounted by the exchange rate; 126 P. LI ET AL.

12.0 11.8 11.6 11.4 11.2 11.0 10.8

Ln(GDP per worker) Ln(GDP per 10.6 10.4 10.2 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

cies not included in city clusters cies included in city clusters

Figure 2. Productivity trends for cities included and not included in national city clusters, 2003–2013.

6) Industry structure. Different industry sectors have different productivity and the industry structure may affect the productivity of the cities. Here, the percentage of secondary (manufacturing) industry is used in our empirical analyses; 7) Traffic infrastructure. Traffic infrastructure increases spatial accessibility and improves the efficiency of resource allocation. In this study, area of paved roads per capita is used to account for the effect of traffic infrastructure.

Summary statistics for variables are presented in Table A2 and Table A3 in the Appen- dix. Figure 2 compares cities’ productivity included and not included in national city clus- ters. It appears that, relative to the control group, the treatment group has higher labor productivity but shares similar trends of productivity increase over the period. This indi- cates that the treatment group does not change differently from the control group after being included in the city clusters and may not have significantly benefited from the national city cluster development strategy.

3. Empirical results The parameter estimates, presented in Table 1, show no significant positive effects of city clusters on the productivity of the cities involved. Model 1.1 only includes the national- cluster dummy, and the coefficient is negative and significant. In Model 1.2, we add the covariates. The coefficient of the national-cluster dummy is positive but not significant, suggesting that there is no evidence that the national clusters increased the productivity of the cities. Model 1.3 includes the interaction term of the national-cluster dummy and city size. The result shows that the city cluster has a positive but not significant effect. In Model 1.4, we present results corresponding to Equation (2). Again, none of the coeffi- cients are significant. It is worth noting, however, that the time-varying effects increased over time although they were not significant, suggesting that positive externalities of city clusters could take time to materialize. JOURNAL OF THE ASIA PACIFIC ECONOMY 127

Table 1. The effects of city clusters on the productivity of the cities. M1.1 M1.2 M1.3 M1.4 National-cluster dummy ¡0.045 0.005 0.002 (0.024) (0.013) (0.088) National-cluster dummyln(population) 0.001 (0.014) treati dyear1 0.0059 (0.0092) treati dyear2 0.0082 (0.0094) treati dyear3 0.015 (0.010) Ln(Capital per worker) 0.745 0.745 0.745 (0.022) (0.022) (0.021) Population density 0.101 0.101 0.103 (0.056) (0.056) (0.055) Human capital 0.042 0.043 0.043 (0.021) (0.021) (0.021) Government expenditure ¡1.02 ¡1.02 ¡1.02 (0.23) (0.23) (0.23) FDI ¡0.49 ¡0.49 ¡0.50 (0.22) (0.22) (0.22) Industry structure ¡0.00 ¡0.00 0.01 (0.15) (0.15) (0.15) Traffic infrastructure ¡0.0013 ¡0.0013 ¡0.0013 (0.0015) (0.0015) (0.0015) Constant 10.964 1.59 1.59 1.59 (0.016) (0.45) (0.45) (0.44) City fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes N 3113 3113 3113 3113 Adj-R2 0.614 0.895 0.895 0.895 F-value 85.2 276.9 262.3 252.1 Note: Cluster robust standard errors (SEs) are reported in parentheses to two significant digits and the coefficients are presented to the same number of digits behind the decimal points as the corresponding SEs. The asterisks , and indicate significance at the 10%, 5% and 1% levels, respectively.

Regarding the other covariates, the capital per worker has a positive effect on cities’ pro- ductivity and is highly significant. The population density and human capital also contribute positively to GDP per worker, suggesting that concentrated economic activities can improve cities’ economic productivity. However, these effects are quite small. With respect to the government expenditure and FDI, they have significantly negative effects on GDP per worker. The former finding implies that government may support some industries and firms that are not competitive. The effect of FDI may be due to the fact that, with the upgrading of industrial structure and export transformation, spillover effects of FDI may gradually weaken and may even inhibit the upgrading process, resulting in a loss of productivity. In Table 2, we present model estimates for the most developed city cluster in China – the Yangtze River Delta. It is interesting to see that the results differ. The Yangtze River Delta dummy is positive and significant in all regressions, suggesting that joining the Yangtze River Delta Urban Economic Coordination Committee improves the productivity of counties by approximately 6.1%. In Model 2.3, the Yangtze River Delta dummy variable is negative and significant whereas the interaction item with county size is positive and significant. Therefore, the total marginal effect of the Yangtze River delta dummy variable is ¡0.151C0.057 ln(pop- ulation). When the number of population is larger than 141.4 thousand, the effect of Yangtze River Delta Cluster becomes positive. In our samples, there are only three 128 P. LI ET AL.

Table 2. The effects of city clusters on the productivity of the cities and counties, the Yangtze River Delta. M2.1 M2.2 M2.3 M2.4 Yangtze River Delta dummy 0.055 0.061 ¡0.151 (0.024) (0.017) (0.065) Yangtze River Delta dummyln(population) 0.057 (0.017) treati dyear1-5 0.030 (0.013) treati dyear6-10 0.094 (0.025) treati dyear11-15 0.091 (0.022) Ln(capital per worker) 0.098 0.107 0.105 (0.023) (0.023) (0.023) Ln(population) ¡0.542 ¡0.561 ¡0.533 (0.050) (0.049) (0.049) Human capital 0.028 0.025 0.029 (0.019) (0.018) (0.018) Government expenditure ¡2.16 ¡2.17 ¡2.08 (0.42) (0.41) (0.42) Industry structure 0.84 0.89 0.84 (0.13) (0.13) (0.13) Cons 8.662 9.43 9.43 9.33 (0.015) (0.32) (0.31) (0.32) County fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes N 2620 2620 2620 2620 Adj-R2 0.940 0.971 0.971 0.971 F-value 584.9 958.0 919.5 1055.8 Note: Cluster robust standard errors (SEs) are reported in parentheses to two significant digits and the coefficients are presented to the same number of digits behind the decimal points as the corresponding SEs. The asterisks and indicate significance at the 5% and 1% levels, respectively.

counties of the treatment group whose population does not reach this threshold. In other words, almost all counties in the Yangtze River Delta enjoy the externalities of the city cluster. Moreover, the larger counties benefit more than the smaller ones. The estimates for Model 2.4 indicate that the productivity-enhancing effect of the Yangtze River Delta cluster becomes stronger year by year.

4. Robustness check Our models need to meet the exogeneity condition that the treatment group and control group have no systematic difference before the implementation of the cluster strategy. In other words, both groups should share a similar trend of economic development before the policy implementation. This can be verified by conducting the common trend test. Because the city cluster strategy was implemented from 2008 to 2011, we estimate four regressions with samples restricted to the years before 2008, 2009, 2010 and 2011, respec- tively, to test whether the corresponding treated group shows the same trend of productiv- ity as the control group. As shown in Table 3, all of the coefficients of the treatment group dummy variable are not significant with the first-order difference of labor productivity as the dependent variable, suggesting that the two groups share the common trend. We further examine the common trend using a counterfactual method (see Table 4). We artificially set the national-cluster dummy two and three years before the implantation JOURNAL OF THE ASIA PACIFIC ECONOMY 129

Table 3. Common trend test results. 4Ln (GDP per worker) Dependent variable M3.1 M3.2 M3.3 M3.4 Treated group 0.0016 0.0052 0.0047 0.0052 (0.0059) (0.0054) (0.0053) (0.0052) Control variable Yes Yes Yes Yes Location dummy Yes Yes Yes Yes Capital dummy Yes Yes Yes Yes Year dummy Yes Yes Yes Yes Sample Before 2008 Before 2009 Before 2010 Before 2011 N 1132 1325 1440 1449 Adj-R2 0.689 0.717 0.736 0.704 F-value 188.9 214.5 197.0 209.4 Note: Columns 1 through 4 show the test results corresponding to the samples from before 2008, 2009, 2010 and 2011, respectively.

Table 4. Common trend test results (continuous). Ln(GDP per worker) M4.1 M4.2 M4.3 M4.4 M4.5 National-cluster dummy 0.010 ¡0.004 (0.011) (0.016) L2. National-cluster dummy ¡0.007 (0.014) L3. National-cluster dummy ¡0.004 (0.014) Year04Treated group 0.006 (0.018) Year05Treated group ¡0.014 (0.019) Year06Treated group 0.003 (0.015) Year07Treated group ¡0.006 (0.013) Ln(Capital per worker) 0.744 0.744 0.745 0.745 0.806 (0.022) (0.022) (0.022) (0.022) (0.023) Population Density 0.101 0.101 0.102 0.101 0.078 (0.056) (0.056) (0.056) (0.056) (0.065) Human capital 0.043 0.042 0.042 0.043 0.000 (0.021) (0.021) (0.021) (0.021) (0.020) Government expenditure ¡1.04 ¡1.03 ¡1.03 ¡1.03 ¡1.13 (0.23) (0.23) (0.23) (0.23) (0.24) FDI ¡0.50 ¡0.50 ¡0.50 ¡0.49 ¡0.32 (0.22) (0.22) (0.22) (0.22) (0.26) Industry structure ¡0.01 ¡0.01 ¡0.00 ¡0.00 0.20 (0.15) (0.15) (0.15) (0.15) (0.22) Traffic infrastructure ¡0.0013 ¡0.0013 ¡0.0013 ¡0.0013 ¡0.0011 (0.0015) (0.0015) (0.0015) (0.0015) (0.0010) Constant 1.61 1.61 1.60 1.60 ¡0.31 (0.45) (0.45) (0.45) (0.45) (0.51) TrendTreated group No No No Yes No Year dummiesProvince dummies No No No No Yes City fixed effects Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes N 3113 3113 3113 3113 3113 Adj-R2 0.895 0.895 0.895 0.895 0.933 F-value 273.0 272.8 251.2 261.9 — Note: Cluster robust standard errors (SEs) are reported in parentheses to two significant digits and the coefficients are presented to the same number of digits behind the decimal points as the corresponding SEs. The asterisks , and indicate significance at the 10%, 5% and 1% levels, respectively. 130 P. LI ET AL. year equals to 1 in Model 4.1 and 4.2, respectively, in order to test whether the counterfac- tual city clusters before the implantation year has a significant effect or not. The results show that the coefficients of the counterfactual two- and three-year dummies (L2 and L3, respectively) are not significant, suggesting that both groups share a common trend. Moreover, Model 4.3 presents the results when accounting for the interactions between year dummy variables for 2004–2007 before the implementation of the cluster policies and the dummy variable for the treatment group, which is also to test whether samples satisfy a common trend. We find that all of those interaction terms are not significant, implying that our regression results are robust. Results under Model 4.4 show that, when the interaction between the dummy variable for the treatment group and the time trend are accounted for, the city-cluster dummy variable is still not significant. In Model 4.5, the interactions between province dummy variables and year dummy variables are added to account for other policies. Once again, the externality of city cluster is not significant. Robustness checks for models of the Yangtze River Delta are presented in Table 5. Based on the counterfactual method (see results under 5.1 and 5.2 in Table 5), the coeffi- cients of the counterfactual two-year and three-year Yangtze River Delta dummy variables are not significant, suggesting that both groups share a common trend and the Yangtze

Table 5. Common trend test results, the Yangtze River Delta. M5.1 M5.2 M5.3 M5.4 M5.5 Yangtze River Delta dummy 0.064 0.057 (0.017) (0.017) L2. Yangtze River Delta dummy 0.025 (0.016) L3. Yangtze River Delta dummy ¡0.002 (0.016) Year94Treated group ¡0.049 (0.042) Year95Treated group ¡0.062 (0.041) Year96Treated group ¡0.031 (0.027) Ln(Capital per worker) 0.093 0.093 0.097 0.097 0.110 (0.023) (0.022) (0.023) (0.023) (0.027) Ln(population) ¡0.550 ¡0.554 ¡0.547 ¡0.543 ¡0.568 (0.050) (0.049) (0.050) (0.051) (0.052) Human capital 0.029 0.029 0.029 0.028 0.022 (0.020) (0.020) (0.020) (0.019) (0.018) Government expenditure ¡2.20 ¡2.21 ¡2.15 ¡2.18 ¡2.27 (0.42) (0.42) (0.40) (0.41) (0.48) Industry structure 0.81 0.81 0.81 0.84 0.78 (0.13) (0.13) (0.13) (0.13) (0.15) Cons 9.51 9.52 9.46 9.44 9.00 (0.32) (0.31) (0.33) (0.33) (0.51) TrendTreated group No No No Yes No Year dummiesProvince dummies No No No No Yes County fixed effects Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes N 2620 2620 2620 2620 2620 Adj-R2 0.970 0.970 0.970 0.971 0.974 F-value 1052.7 1103.2 965.9 953.3 – Note: Cluster robust standard errors (SEs) are reported in parentheses to two significant digits and the coefficients are presented to the same number of digits behind the decimal points as the corresponding SEs. The asterisk indi- cates significance at the 1% levels. JOURNAL OF THE ASIA PACIFIC ECONOMY 131

River Delta Urban Economic Coordination Committee contributed positively to the improvement in productivity. In addition, Model 5.3 presents the results when controlling for the interactions between the dummy variables for years (1994–1996) before the estab- lishment of the Committee and the dummy variable for the treatment group. We find that none of those interaction terms is significant. As shown in Model 5.4 and Model 5.5, we further examine the effect of the Yangtze River Delta dummy variable accounting for the interaction between the dummy variable for the treatment group and the variable for time trend and the interactions between year dummy variables and province dummy vari- ables, respectively. The regression results are not significantly different. Therefore, the results of the Yangtze River Delta case are also robust.

5. Conclusions This study investigates whether city clusters in China have contributed to productivity growth, by employing the popular DID impact evaluation methods to data from 283 Chi- nese cities over the period 2003–2012. Our results show that the development of city clus- ters has not had significant productivity-increasing effects so far. However, when we focus on the case of the Yangtze River Delta, which is the most mature cluster in China, the results confirm the benign effects on city productivity, with the effects increasing year by year. The different results of national city clusters and Yangtze River Delta could be explained by the different implementation periods, with the latter longer then the former. Regarding the national city clusters, it may be that it is still too early to expect its produc- tivity-enhancing effects. Some policies, for instance, in the area of cities’ cooperation, may only yield effects in the long run. Future evaluations should provide more insight in the externalities of national city clusters. Our policy implication is straight-forward: it is necessary to improve the effective coop- eration and integration of markets across cities, especially focus on the cities within national city clusters.

Notes

1. In the literature, the concepts of , mega-region, city network and urban agglomera- tion are used as synonyms for city cluster. 2. See http://www.gov.cn/gongbao/content/2014/content_2644805.htm. 3. The top 10 city clusters in 2013 included the Yangtze River Delta, the Beijing–Tianjin–Hebei, the , the -, the Peninsula, the Central Southern , the West Taiwan Straits, the -, the --, and the Central Plains.

Acknowledgments

We thank all of the participants of the above conferences, together with George Battese, Lixia Li, Yubo Liu, Meixia Meng, Cong Sun, Guanghua Wan and Daozhi Zeng, for their helpful comments and suggestions. 132 P. LI ET AL.

Disclosure statement No potential conflict of interest was reported by the authors.

Funding

The authors gratefully acknowledge financial support from China’s National Social Science Funds [grant number 14ZDB138]; the National Natural Science Funds [grant number 71473160]; the China Postdoctoral Science Foundation [grant number 2016M591645]; the Innovation Research Team from Shanghai University of Finance and Economics; the Excellent Ph.D. Dissertation Culti- vation Fund of Shanghai University of Finance and Economics.

Notes on contributors

Peixin Li is an MD-PhD student at the School of Urban and Regional Science, Shanghai University of Finance and Economics, Shanghai, China. His research interests include regional economics and urban economics. His topic of recent publication is the economic mechanism of Chinese urban agglomeration. E-mail: [email protected]

Chen Wang is a lecturer at the School of Urban and Regional Science, Shanghai University of Finance and Economics, Shanghai, China. She has been affiliated as a research fellow at the Department of Economics of Leiden University since 2015. Her research interests include income inequality, poverty, consumption and economics of aging. Her topics of recent publications include trends and changes of income polarization in China, social investment and poverty reduction in European countries, the redistributive effect of tax-benefitsystemsinaffluent countries. E-mail: [email protected]

Xueliang Zhang is a professor at the School of Urban and Regional Science, Shanghai University of Finance and Economics, Shanghai, China. His research interests include city shrinking, transporta- tion development and economic growth. His topics of recent publications include transformation of regional economy and development of megalopolis economy in China, the spatial spillover effects of transport infrastructure in China, and regional economic convergence in the Yangtze River Delta. E-mail: [email protected]

References

Bai, C., Z. Tao, and Y. Tong. 2008. “Bureaucratic Integration and Regional Specialization in China.” China Economic Review 19 (2): 308–319. Batten, D.F. 1995. “Network Cities: Creative Urban Agglomerations for the 21st Century.” Urban Studies 32 (2): 313–327. Boix, R., and J. Trullen. 2007. “Knowledge, Networks of Cities and Growth in Regional Urban Systems.” Papers in Regional Science 86 (4): 551–574. Burger, M.J., and E.J. Meijers. 2016. “Agglomerations and the Rise of Urban Network Externalities.” Papers in Regional Science 95 (1): 5–15. Capello, R. 2000. “The City Network Paradigm: Measuring Urban Network Externalities.” Urban Studies 37 (11): 1925–1945. Ciccone, A., and R. Hall. 1996. “Productivity and the Density of Economic Activity.” American Eco- nomic Review 86 (1): 54–70. Duranton, G., and D. Puga. 2004. “Micro-Foundations of Urban Agglomeration Economies.” Handbook of Regional and Urban Economics 4: 2063–2117. Fang, C. 2015. “Important Progress and Future Direction of Studies on China’s Urban Agglomera- tions.” Journal of Geographical Sciences 25 (8): 1003–1024. JOURNAL OF THE ASIA PACIFIC ECONOMY 133

Fang, C., X. Guan, S. Lu, M. Zhou, and Y. Deng. 2013. “Input–Output Efficiency of Urban Agglom- erations in China: An Application of Data Envelopment Analysis (DEA).” Urban Studies 50 (13): 2766–2790. Florida, R., T. Gulden, and C. Mellander. 2008. “The Rise of the Mega-Region.” Cambridge Journal of Regions, Economy and Society 1 (3): 459–476. Gottmann, J. 1957. “Megalopolis or the Urbanization of the Northeastern Seaboard.” Economic Geography 33 (3): 189–220. Hall, P.G., and K. Pain. 2006. The Polycentric Metropolis: Learning from Mega-city Regions in Europe. London: Earthscan. Johansson, B., and J.M. Quigley. 2004. “Agglomeration and Networks in Spatial Economies.” Papers in Regional Science 83 (1): 165–176. Lu, M., Z. Chen, and J. Yan. 2004. “Increasing Return, Development Strategy and Regional Economic Segmentation.” [In Chinese] Economic Research Journal 1: 54–63. Meijers, E.J. 2005. “Polycentric Urban Regions and the Quest for Synergy: Is a Network of Cities More than the Sum of the Parts?” Urban studies 42 (4): 765–781. Meijers, E.J., M.J. Burger, and M.M. Hoogerbrugge. 2016. “Borrowing Size in Networks of Cities: City Size, Network Connectivity and Metropolitan Functions in Europe.” Papers in Regional Science 95 (1): 181–198. Moretti, E. 2004. “Human Capital Externalities in Cities.” Handbook of Regional and Urban Economics 4: 2243–2291. Phelps, N.A., R.J. Fallon, and C.L. Williams. 2001. “Small Firms, Borrowed Size and the UrbanRural Shift.” Regional Studies 35 (7): 613–624. Sveikauskas, L. 1975. “The Productivity of Cities.” The Quarterly Journal of Economics 89 (3): 393– 413. Van Oort, F., M. Burger, and O. Raspe. 2010. “On the Economic Foundation of the Urban Network Paradigm: Spatial Integration, Functional Integration and Economic Complementarities within the Dutch Randstad. Urban Studies 47 (4): 725–748. Wang, Y., Y. Zhang, Y. Zhang, Z. Chen, and M. Lu. 2007. “On China’s Development Model: The Costs and Benefits of China’s Decentralization Approach to Transition.” [In Chinese] Economic Research Journal 1: 4–16. Wei, S., T. Li, and D. Tang. 2013. “Dual-Agglomerated Externalities and Economic Performance of Urban Agglomerations in China.” [In Chinese.] Economic Management 9: 30–40. Wu, F., and Z. Liu. 2008. “Research on the Mechanism of How City Group Drive Economic Growth – Empirical Evidences from 16 Cities of Yangtze River Delta.” [In Chinese] Economic Research Journal 11: 126–136. Young, A. 2000. “The Razor’s Edge: Distortions and Incremental Reform in the People’s Republic Of China.” The Quarterly Journal of Economics 115 (4): 1091–1135. Yu, J., and C. Wang. 2011. “Analysis on Endogenous Mechanism of Metropolitan Area in Stimulat- ing the Regional Economic Growth: Based on the Metropolitan Area of Jing- Jin- Ji, Yangtze River Delta and Pearl River Delta.” [In Chinese] Economic Review 1: 69–78. Zhang, J., G. Wu, and J. Zhang. 2004. “The Estimation of China's Provincial Capital Stock: 1952– 2000.” [In Chinese.] Economic Research Journal 10: 35–44. 134 P. LI ET AL.

Appendix

Table A1. Cities included in the China’s city-cluster plans and their enrolled years. Year City cluster Cities included in the city cluster 2008 Wuhan, , , , ,, , , Qianjiang Changsha-Zhuzhou-Xiangtan Changsha, Zhuzhou, Xiangtan, , ,, , Beibu Gulf , , , 2009 Pearl River Delta , , , , , , , , West Taiwan Straits , , , , , , , , - Xian、、、、Weinan、、Tianshui 2010 Yangtze River Delta Shanghai, , , , , , , , Taizhou, , , , , , , Taizhou Jiang-Huai , , , , , Maanshan, , Circle Poyang Lake , , , , , Fuzhou, Yichun, , Jian 2011 Beijing-Tianjin-Hebei Beijing, Tianjin, , , , , , , , , , , Central Southern Liaoning , , , , , , , , , Shandong Peninsula , , , , , , , Central , , , , , Luliang Huhhot--Erdos-Yulin Huhhot, Baotou, Erdos, Yulin Harbin-Changchun Harbin, , , , Changchun, , Yanbian, Eastern Longhai , , Rizhao Central Plains , , , , , , , , Chengdu-Chongqing Chongqing, Chengdu, , , , , Suining, , Yaan, , , , , , , Guangan Central , , , , Qiandongnan, Qiannan Central , , , Chuxiong - Lanzhou, Xining, , , Linxia - , Wuzhong, , Northern Tianshan Mountains Urumqi, , , , , Bortala, Ili, ,

Table A2. Summary statistics for variables in the empirical models (283 cities, 2003–2013). Variables N Mean SD min max Log of GDP per worker 3113 11.39 0.49 9.741 13.335 City cluster dummy variable (UA) 3113 0.21 0.41 0.000 1.000 Log of capital stock per worker 3113 12.26 0.59 10.378 14.443 Log of population density 3113 5.72 0.91 1.547 7.887 Human capital 3113 6.38 0.25 4.143 8.262 Government expenditure 3113 0.142 0.077 0.031 0.858 Foreign direct investment (FDI) 3113 0.022 0.025 0.000 0.376 Industry structure 3113 0.49 0.11 0.090 0.910 Traffic infrastructure 3113 9.6 6.8 0.310 108.37 Log of population 3113 5.85 0.69 2.795 8.119 JOURNAL OF THE ASIA PACIFIC ECONOMY 135

Table A3. Summary statistics for variables in the Yangtze River Delta models (131 counties, 1993–2012). Variables N Mean SD min max Log of GDP per worker 2620 9.80 0.86 7.614 12.072 Yangtze River Delta dummy variable 2620 0.45 0.50 0.000 1.000 Log of capital stock per worker 2620 9.4 1.3 5.275 13.471 Log of population 2620 3.73 0.75 1.445 7.017 Human capital 2620 7.8 1.0 4.664 22.388 Government expenditure 2620 0.083 0.052 0.014 0.522 Industry structure 2620 0.49 0.11 0.136 0.770