2006 International Symposium on Contemporary Labor Economics (LABOR2006) December 16 - 18, 2006

1. 2006 International Symposium on Contemporary Labor Economics (LABOR2006) December 16 - 18, 2006

2. A Spatial Econometric Analysis of the Impact of Labor Migration on the Disparity of Regional Economic Development in Mainland China Zhengming Qian, Xiuhua Zhang, Yangping Yu and Pengfei Guo Xiamen University (361005) Xiamen, Fujian PRC

3. 1. Introduction 2. Analysis of Convergence of Regional Economic Growth of China 3. The Spatial Econometric Model 4. Data Set & Empirical Results 5. Conclusions & Suggestions

4. 1. Introduction Labor migration has significant influences on regional economic development. At present, the combination of economic growth and labor migration to study the impact of labor migration on regional economic growth becomes a hot spot. There exist two opposing viewpoints about the impact of labor migration on regional economic development in domestic academy. One view holds that labor migration presents positive impact on regional economic convergence, and it will narrows regional economic disparity, where labor migration could markedly reduce the disparity of resource among regions and effectively reduces regional economic disparity and realizes the‘conditional convergence’. The related studies include:

5. Duan Pingzhoug & Liu Chuangzhong(2005) point out that, floating population does obviously contribution to regional economic growth, and such contribution decreases gradually. Wang XiaoLu & Fan Guang(2004) think that labor migration among regions could narrow the disparity of labor’s return and per-GDP among regions; Yao Zhizhong & Zhou Shufang(2003) present that labor migration do contribute something to narrow regional economic disparity, but there exists great restriction on labor migration; Liu Qiang(2001) shows that great labor migration among regions is a important leading factor for the economic convergence of China’s regions;

6. The opposite viewpoint thinks that labor migration among provinces broaden regional economic disparity. The related studies include: Sun ZhiFeng(2006) points out that labor migration among provinces broadens regional economic disparity. He Qiang(2006) presents that rural labor migration boosts civilization, as well as broadens incoming disparity between city and country for the reason of human capital barrier.

7. We suggest that most of the above studies support the effect of labor migration through the index of net labor migration, but they haven’t separated the labor migration into labor inflow and labor outflow, and distinguished the East, the West and the Middle. And what’s more, most empirical studies are based on cross-sectional data and neglect the influence of time effect. So, this research constructs spatial econometric model based on the panel data of China’s regions to analyze how the direction and the power of inflow and outflow of labor influence the three region’s economic development.

8. 2．Analysis of Convergence of Regional Economic Growth of China In statistical analysis of economic growth convergence, researchers always like to use some coefficient or index such as Theil coefficient. Theil coefficient is a analytical method to measure regional spatial diversity, and it can be used to analyze the total change, inner-change and inter-change of regional disparity and the impact of the inner-change and inter-change of regional disparity on the total change of regional disparity. Therefore, we use the Theil coefficient to measure the economic disparity among the East, the West and the Middle of China.

9. The Theil coefficient can be defined as: （1） Where T denotes the Theil coefficient; yi denotes the share of the GDP of the ith region on total GDP; pi denotes the share of the population of the ith region on total population. The greater T value shows the greater disparity among regional economic development, while the smaller T value shows a smaller disparity among regional economic development.

10. Let province as a regional unit, and the total disparity of Theil coefficient can be defined as: （2） Where denotes the population of the jth province in the ith region, P denotes the total population. denotes the GDP of the jth province in the ith region, Y denotes the total GDP,

11. Based on the equation(2), define the disparity inner the ith region as: (3) Where denotes the GDP of the ith region, denotes the population of the ith region Based on the equation (3), the Theil coefficient of equation (2) can be separated into: （4） Where TWR denotes the inner-region disparity, TBR denotes the inter-region disparity.

12. This research uses the GDP of total 30 provinces of China from 1996 to 2004 to estimate the Theil coefficient (see in Table 1). Table 1：Theil Coefficient（1996—2004） （Data Resource：《China Statistical Yea Book》(1996—2005), calculated）

13. As showed in Table 1, Firstly, in total, the Theil coefficients increased from the 0.1024 of 1996 to 0.1211 of 2004, which shows the process of disparity which widened among regions. Secondly, among the three regions, the Theil coefficients change just slightly: the East from 0.0461 to 0.0417, the West from 0.0213 to 0.0298, and the Middle from 0.0381 to 0.0396. All show a weak disparity within the three regions. Thirdly, the inner-region disparity TWR shows a weak decrease during this period (from 0.0394 to 0.039), while the inter-region disparity TBR shows a obvious increase (from 0.0629 to 0.0820). From Table 1 we can conclude that, in mainland China, the inner-region economic development presents gradual convergence, while the inter-region economic development presents gradual divergence.

14. Whether such a labor migration broadens or narrows regional economic disparity becomes an interesting issue. This research uses spatial econometric panel data model to analyze the impact of inflow and outflow of labor on regional economic disparity, and empirically discusses the quantitative characteristics of such impact.

15. 3. The spatial econometric panel data model 3.1 Spatial Correlation Index There are two statistical indices for testing the spatial correlation of regional economic growth in common use, one is Moran I which was first presented by Moran in 1950, the other is Greary c which was first presented by Geary in 1954. And the most used one is the Moran I, which is defined as: ， （5） Where ，Yi denotes the observed value of the ith region, n denotes the number of regions;

16. is the binary system neighbor spatial weight matrix, which is defined as: Based on the distribution of spatial data, the expectation and variance of Moran I can be calculated: We can use the following equation to test the spatial auto-correlation of the n regions: （8）

17. 3.2 Spatial Econometric Model The spatial econometric model includes spatial lag model and spatial error model. The spatial lag model includes explanatory variableand spatial lag term The spatial error model is the special case of regressive model which includes a auto-correlated error term. In its covariance matrix, the non-diagonal factors denote the structure of spatial correlation, which can be defined in different ways. The error variance-covariance matrix, which is a parameter vector.

18. 3.3 Panel Data Model Panel data is a data structure which mixes the two dimensions of time and space. The panel data model can be defined as: The most important step in panel data analyses is model selection. The estimation of the panel data equation depends on the assumption that is made of intercept and slope of the model. We first investigated whether the parameters of the dependent variable Yit were constant for all of the cross-sectional units and periods. The panel data model involves the following three situations, depending on both the intercept and the slope.

19. In Situation 1, both the intercept and the slope are the same (i = j, i = j); therefore, it is a pooled regression model. In Situation 2, the slope is the same but the intercept is different (i = j, i = j), and thus it is a variable intercept model. In Situation 3, both the intercept and the slope are different (i j, i j), and thus it a variable coefficient model. Based on the characteristics of sample data, the variable intercept modeland the variable coefficient model can be separated into fixed-effect model and random-effect model. The former model is suit for the case inferred from sample, while the latter model suits for the case inferred from the total. The analysis results indicate that the panel data fit nicely into Situation 2.

20. Panel data model can effectively solve the problems caused by small sample, and we could not use the traditional OLS method to estimate the panel data model because the panel data model doesn’t satisfy the classical assumption. So, in order to guarantee the validity of model estimation, this study uses GLS to estimate the model.

21. 4．The spatial econometric analysis of the impact of labormigration on regional economic development of China 4.1 Data Setand EmpiricalAnalysis Method We study the data of 30 provinces in mainland China from 2000 to 2004 (here we merge Chongqing into Sichun and include 30 provinces), then, We group these 30 provinces into three regions (the East, the Middle and the West ). The differences we intend to study are reflected mainly in the intercept, as previously indicated, and we adopt the model with fixed effects.

22. 4.2 Analysis of Spatial Auto-Correlation of China’s Economic Growth Firstly we test the dependence among provinces’ economic development by Moran I and Z value (See Table 2). Table 2：Moran I and Z value for the Per-GDP of Regional Economic Growth in China In Table 2, we can see that all Z value over the critical value (1.64) at 5% level, which means that there exists obvious positive spatial correlation among provinces’ economic growth of China, so they are spatially dependent. Therefore, it is necessary to integrate the spatial correlation factor into the model.

23. 4.3 Model Setting, Estimation and Analysis First, we found that there existed obvious spatial dependence among China’s economy from 4.2. We adopt the spatial lag model as well as the spatial lag factor to construct the panel data model. To separate the respective impact of labor inflow and labor outflow on regional economic development, we set the spatial panel data model as follows: （16） Where the per-GDP is the dependent variable, the independent variables includes labor inflow (noted as MIGIN) which measures the population inflow from the other provinces, labor outflow (noted as MIGOUT) which measures the population outflow from the province to other provinces, and the spatial lag factor (noted as WLn (GDP)) which is the product of regional spatial weight matrix by regional per-GDP.

24. We use the GLS to estimate the spatial panel data model and the result showed in Table 3. Table 3：Estimating Result of the Spatial Panel Data

25. From Table 3, we can see that the R values, F value and t value all pass the tests. The DW value shows unobvious serial correlation, which means a good fit of the model. Thus we can conclude that the model with fixed effects performs well. The labor migration significantly influences regional economic development. And the t value of spatial lag factor passes tests at 1% level, which confirms the spatial dependence among regional economic development. Then we analyze the impacts of labor inflow and labor outflow on regional economic development for the East, the West and the Middle. First - the impact on the East. The coefficient of labor inflow is positive, which means the labor inflow boosts the economic development of the East. The 1% increase of labor inflow can generate a 0.1143% growth of GDP for the East. Whereas, the coefficient of labor outflow is negative, which means the labor outflow counteracts the economic development of the East. The 1% increase of labor outflow would make a 0.1016% decrease of GDP for the East.

26. Second - the impact on the Middle. The coefficient of labor inflow is negative, which means the labor inflow counteracts the economic development of the Middle. The 1% increase of labor inflow can generate a 0.1047% decrease of GDP for the Middle. Whereas, the coefficient of labor outflow is positive, which means the labor outflow boosts the economic development of the Middle. The 1% increase of labor outflow would make a 0.7065% growth of GDP for the Middle. Third - the impact on the West. The coefficient of labor inflow is positive, which means the labor inflow boosts the economic development of the West. The 1% increase of labor inflow can generate a 0.0867% growth of GDP for the West. The coefficient of labor outflow is also positive and is much greater, which means the labor outflow can greatly boosts the economic development of the Middle. The 1% increase of labor outflow would make a 0.3820% growth of GDP for the West.

27. 4.4 Factor Analysis • We can see from the findings of the empirical study that both labor inflow and outflow have different effects on different regions. Even within the same region, such effects are also different for labor inflow and outflow. • We find that the two main reasons which cause such differences: (1) the issue of human capital associated with labor flows; and (2) the distribution of flow associated with labor migration. • So, Next step, we analyze the reason for such different impact of labor migration on regional economic development. There are two different factors: (1)Human Capital Barrier to the Labor Migration . (2) Unbalanced Distribution of Labor Migration (please see my paper).

28. 5. Conclusions and Suggestion Since the opening and reform policy implemented, China’s regional economic development has presented an obvious ‘club convergence’, and the disparity among the three regions becomes wider. The labor migration did impact the regional economic disparity. In particular, the inflow and outflow of labor have different impacts on regional economic growth and their combined effect results in the disparity of the regional economic development. On the whole, the labor inflow leads to a difference of labor in both quantity and quality, which in turn broadens the disparity of regional economic development. The labor outflow transfers the income from the East to the West and the Middle, which in turn narrows the disparity of regional economic development.

29. Because of the opposite impact of laborinflow and outflow on regional economic development, the negative impact on regional economic convergence of unreasonable labor inflow finally counteracts the positive impact on regional economic convergence of labor outflow. Thus the total impact of labor migration on regional economic convergence is negative, which means the labor migration totally widens the disparity of regional economic development. In order to ensure the healthy impact of labor migration on regional economic development, the government needs to balance the direction, quantity and quality of labor migration.

30. The government should implement some feasible policies to direct labor migration. Firstly, It has to put more attention to the ‘Big’ West Development, encourage and allure much more high qualified labor to the West, which can produce the great effect of human capital. Secondly, it has to put the same attention to the Middle by addressing the low quality and quantity of labor migration. It needs to encourage and allure more high qualified labor to the Middle, and to reverse the impact of the direction of labor inflow on regional economy.

31. Thank you for participation