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

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СКАЧАТЬ 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 the one hand, an increase in the productivity of agricultural labor has raised the output of the unit agricultural labor and the income of rural residents and weakened the driving forces of the transfer of agricultural labor. On the other hand, an increase in the output of agricultural products has enabled the agricultural labor to be released from industrial sectors and prompted its transfer. The total mechanical power of agricultural labor (the ratio of the total agricultural machinery power to the population employed in agriculture) is selected to measure the productivity of agricultural labor, as analyzed in the subsequent sections.

      (2) Important variables

      Other important variables selected in this section include the degree of openness, the proportion of state-owned enterprises, the level of public education expenditure and the scale and efficiency of financial development.

      (i) Degree of openness

      With reference to the practices in the previous literature, the degree of openness is measured by the ratio of the FDI and the total volume of exports–imports to GDP. The degree of openness may affect the transfer of agricultural labor through multiple channels. On the one hand, the higher the degree of openness of a region is, the more conducive to the introduction of advanced technology, the absorption of advanced management experience and the improvement of total factor productivity, thus further affecting the transfer of agricultural labor; on the other hand, China is now still at the peak of the transfer of labor, and the higher degree of openness leads to more active economic activities and a greater deepening of capital, thus promoting the transfer of labor from the agricultural sector to the non-agricultural sector. In addition, the degree of openness may also affect the transfer of agricultural labor from the perspective of factor allocation. Bentolila and Saint-Paul point out that any factor affecting the degree of imperfect market competition may affect factor allocation.²¹ As for the specific situation of the Chinese market, the FDI and the total volume of imports–exports can be used to measure the degree of competition in the product market. The strengthening of market competition will reduce the cost of the transfer of agricultural labor.

      (ii) Proportion of state-owned enterprises

      The restriction of the system of household registration makes it difficult for the agricultural labor to become the staff of state-owned enterprises. A large proportion of state-owned enterprises indicates high monopoly power of state-owned enterprises, which tends to reduce the transfer of agricultural labor. In this chapter, the ratio of the total output of stateowned and state-owned holding industrial enterprises above the designated scale to the total output of all industrial enterprises above the scale has been adopted as a measurement of the proportion of state-owned enterprises.

      (iii) The level of public education expenditure

      The per capita public education expenditure is used to measure the level of public education expenditure. In fact, public education expenditure is a resource allocation. Generally speaking, the urban residents have a higher level of education than rural residents, thus leading to a higher marginal output of investment in rural residents by public education expenditure. In case of СКАЧАТЬ