China's Rural Labor Migration and Its Economic Development. Xiaoguang Liu

of the main considerations: (1) It is an ideal research interval because it is generally believed that China’s reform and opening-up has entered a new stage, and the economic system of a comprehensive market has gradually been established after Deng Xiaoping’s southern talks in 1992.¹⁵ (2) Because of strict governmental control over labor mobility, the real climax of the transfer of labor did not begin until after Deng Xiaoping’s southern talks in 1992, though the transfer of agricultural labor in China started in the early stages of reform and opening-up. In fact, since 1992, the government’s attitude toward the transfer of labor has also changed from “allowed” to “encouraged”.¹⁶ Therefore, this section uses China’s provincial panel data regarding the period 1992–2010 for quantitative analysis to examine the driving factors of the transfer of agricultural labor.

      The explained variable is the transfer of agricultural labor, and the key explanatory variables include the urban–rural income gap, the growth rate of the GDP, the level of infrastructure, total factor productivity and agricultural labor productivity (measured by the ratio of the total agricultural machinery power to the population employed in agriculture). In addition, a series of important variables are also introduced, including the degree of openness (measured by the ratio of the foreign direct investment (FDI) and the total volume of exports and imports to the GDP), the proportion of the total output of state-owned enterprises (measured by the proportion of state-owned and state-owned holding units in the value of the total output of the industrial sector), the scale and efficiency of financial development (using the ratio of total loans to GDP as an indicator of the scale of financial development, and the ratio of total loans to total deposits as a proxy variable for financial efficiency¹⁷) and the level of public education expenditure (measured by the public education expenditure per capita). In addition to these influence variables, the impact of other potential factors is also further considered, including the urban unemployment rate (measured by the registered urban unemployment rate and the surveyed urban unemployment rate estimated using urban household survey data), return on capital (measured by the ratio of the total profits of industrial enterprises to the net value of fixed assets of industrial enterprises) and the level of inflation (measured by consumer price indicator (CPI)). Theoretically, all these factors may affect the transfer of labor, so a regression analysis should be carried out to investigate the impact, with the following details.

      (1) Key variables

      (i) The transfer of agricultural labor

      The number of rural employees and the number of employees in the rural primary industry in various provinces and regions from 1978 to 2008 can be found in the Compilation of Agricultural Statistics Data of 60 Years in New China (the Compilation). These two groups of data measure the distribution of employment of the rural registered labor force in agricultural and non-agricultural sectors. The Compilation “directly collects and calculates the employees who have lived outside the household for more than half a year, but whose income is linked to the family economy in the statistical caliber of rural population and rural employees used in relevant proportions”. Therefore, it is possible to accurately measure the number of members of the agricultural labor force who have transferred by subtracting the number of employees in the rural primary industry from the number of rural employees. In addition, the provincial and regional data with the same statistical calibers in the Compilation for 2009 and 2010 can be found in the China Statistical Yearbook, which facilitates the extension of the data regarding the transfer of labor to 2010.¹⁸ Based on this, the indicator of the transfer of agricultural labor is constructed to reflect that transfer.

      

      The amount of the transfer of agricultural labort = transfer of agricultural labort − transfer of agricultural labor_t−1, where, the transfer of agricultural labort = the number of rural employeest − the number of employees in the rural primary industry_t−1.

      Considering the complexity of the issue of the transfer of China’s labor force, it is necessary to carefully select and extract the information about the transfer of labor from multiple sets of data according to the needs of research. The data from the 2010 national census contain detailed information on population migration in the provinces and regions, but a certain discrepancy exists between the data on migration and the data regarding the transfer of employment, and the data are only cross-sectional data for 2010. In case of the individual effects of the provinces and regions beyond control, it is difficult to fully verify the exact relationship between infrastructure and the transfer of labor. The data from the second national agricultural census exclude the rural workers who have lived outside for half a year, significantly underestimating the number of rural employees and the transfer of labor.

      (ii) Urban–rural income gap

      The urban–rural income gap is one of the key explanatory variables in this section, as well as one of the main drivers of the transfer of agricultural labor according to the theory of developmental economics. In this chapter, the urban–rural income gap is measured by the difference between the per capita disposable income of urban residents and the per capita net income of rural residents where the per capita disposable income of urban residents and the per capita net income of rural residents are adjusted based on the CPI of various provinces and regions in 2000.

      (iii) Level of infrastructure

      First, the indicator of highway density is constructed using the ratio of the highway mileage of Chinese provinces and regions to the area of provinces and regions to be used as the main measurement indicator of the level of infrastructure. The data of highway mileage and land area in all provinces and regions were taken from the China Statistical Yearbook. The village roads were taken into account to calculate the highway mileage after 2006, causing an inconsistent caliber of statistics. Considering basically no impact on the transfer of the urban and rural labor force by village roads, the mileage of village roads is excluded from the highway mileage of the period 2006–2010 to calculate the highway density as the key measurement variable of the level of infrastructure. Specifically, in order to adjust the caliber, some data about village road mileage from 2006 to 2010 are first found from statistical yearbooks, traffic yearbooks and the official websites of the transportation departments of various provinces and regions and then excluded. The remaining data were estimated approximately by the average ratio of the village road data at the start and end of the period to the highway mileage data containing village roads. In addition, this chapter also examines the impact of the level of communications infrastructure on the transfer of agricultural labor and selects the ratio of the number of mobile phones and public telephones to the number of rural employees as the measurement indicator of the level of infrastructure.

      (iv) Total factor productivity

      Total factor productivity represents the difference in productivity that cannot be explained by the enterprise’s own factor inputs, such as the technical level, organizational efficiency and operating environment. In general, an increase in total factor productivity tends to raise the marginal productivity, thereby increasing the demand for labor and promoting the transfer of agricultural labor. Concerning the estimation of total factor productivity at the provincial level, a method of fixed effect for Solow residuals and a Generalized Method of Moment (GMM) estimation method of Arellano and Bover are provided in the literature.¹⁹ The method of fixed effect for Solow residuals may encounter two problems, namely, Simultaneity Bias, and Selectivity and Attrition Bias. The GMM method, especially the systematic GMM method, can solve the regression problem of macro variables to a large extent by introducing the level of the endogenous variable and the differential lag term as instrumental variables.²⁰ Therefore, the systematic method of GMM estimation is adopted to estimate the total factor productivity of provinces and regions by use of the industrial sector GDP, the net value of fixed assets of industrial enterprises above the designated size and the industrial enterprise labor panel data of 31 provinces, municipalities and autonomous regions in China from 1978 to 2010.

      (v) Agricultural labor productivity

      On