of public education expenditure, the increase in the level of per capita education expenditure is conducive to promoting the transfer of agricultural labor. However, the unevenly distributed public education expenditure between urban and rural areas may hinder the transfer of agricultural labor.
(iv) Scale and efficiency of financial development
This chapter introduces the ratio of the total amount of loans to the GDP as an indicator of the scale of financial development and the ratio of the total amount of loans to total deposits as a proxy variable for financial efficiency. The two indicators may affect the urban–rural income gap and the transfer of agricultural labor to varying degrees in the specific environment of China’s economic development. In terms of the distribution of financial resources, China’s financial system shows a clear tendency toward urbanization, which is inclined to the state sector in credit allocation. Such an unbalanced development may hinder the transfer of agricultural labor.22 According to the analysis of Zhang Qi et al. and Ye Zhiqiang et al., financial development has significantly expanded the urban–rural income gap.23 They also note that the improvement in financial efficiency with the development of financial scale may alleviate the tendency of urbanization and state-owned enterprises, and financial development may bring about the narrowing of the urban–rural income gap. Yao Yaojun’s analysis shows that the efficiency of financial development is negatively correlated with the income gap between the urban and rural areas, despite the positive correlation between the scale of financial development and the urban–rural income gap.24
(3) Other factors
In addition to the foregoing variables, the variable of the rate of urban unemployment and the variable of return on capital are also taken into account because they may affect the rate of the transfer of labor by affecting the demand for labor in the urban sector. In the regression analysis, the former is measured by the changes in the rate of urban unemployment and the latter is measured by the ratio of the total profit of industrial enterprises to the net value of fixed assets of industrial enterprises. In the benchmark regression, the rate of urban unemployment is measured by the rate of registered urban unemployment. Because of statistical problems, the indicator of the rate of registered urban unemployment cannot reflect the unemployment rate of China’s urban sectors well. The more ideal measurement indicator is the rate of surveyed urban unemployment. However, the National Bureau of Statistics does not fully disclose the data regarding the rate of surveyed urban unemployment, and only microdata from the urban household surveys in some provinces are made available. Through the calculation of the microdata from the survey on urban households, it is possible to obtain the estimated data regarding the surveyed urban unemployment rate in the nine provinces from 1992 to 2009. Therefore, the surveyed urban unemployment rate is used to facilitate the regression analysis (the results show no significant difference, as reported in Exhibit B of Appendix B).
To eliminate the possible impacts caused by price changes, the data for the nominal variable have been adjusted based on the CPI of various provinces and regions in 2000, such as per capita public education expenditure. In addition, the impact of inflation is controlled. The statistics of the above variables are reported in Table 2.1.
Table 2.1. Statistics of Regression Variables of the Driving Factors of the Transfer of Agricultural Labor.
Source: China Statistical Yearbook, statistical yearbooks of various provinces and regions, traffic yearbooks of various provinces and regions, official website of the provincial department of transportation, Compilation of Statistical Data of 60 Years in New China, Compilation of Agricultural Statistics Data of 60 Years in New China and CEIC Database; the rate of surveyed urban unemployment has been estimated using the microdata from urban household surveys. The sample interval of each variable is from 1992 to 2010, and the sample interval of the communications infrastructure is from 1998 to 2010. Due to the missing data of some observations in individual provinces, municipalities and autonomous regions, the number of observations of each variable was not completely equal.
2. The results of the empirical analysis
With the use of the panel data of China’s provinces and regions, the transfer of agricultural labor has been used as an explained variable to analyze the determinants of the transfer of agricultural labor. By reference to the practices in the previous literature, both SAR and SEM models are used for analysis in this section to overcome the influence of a potential spatial correlation and to carry out a maximum likelihood estimation. The same is true also for the setting of the spatial weight matrix. The weight coefficient of adjacent provinces is set as 1, and the weight coefficient of non-adjacent provinces is set as 0.25 The weight matrix is standardized in the estimation of the specific measurement. For comparison purposes, the regression results under the two models are reported symmetrically in the regression table.
Table 2.2 shows the results of the baseline regression, indicating that the variable of the urban–rural income gap is significantly positive. This means that a larger urban–rural income gap leads to a stronger motivation for the transfer of agricultural labor and the greater transfer volume of agricultural labor, which is in line with the expectations. The coefficient of the growth rate of the GDP is significantly positive, indicating that economic growth increases the demand for non-agricultural labor, which is conducive to promoting the transfer of agricultural labor. The coefficient of the level of the scale of infrastructure is significantly positive, indicating that the improvement of the level of infrastructures is conducive to reducing labor transfer costs and promoting the transfer of agricultural labor, which is consistent with the expectations.
The estimated results of other influencing variables are also roughly in line with the expectations. The coefficient of the proportion of state-owned enterprises is significantly negative, indicating that the larger the proportion of state-owned enterprises is the smaller the volume of labor transfer will be. The possible explanation is that the restriction of the household registration system may make it difficult for the agricultural labor to become the staff of state-owned enterprises, and thus, the agricultural labor mainly flows to the non-state-owned enterprises, leading to an increase in the proportion of state-owned enterprises. This indicates that the increased power of monopoly of state-owned enterprises may weaken the transfer of labor. The coefficient of the level of public education expenditure is significantly negative, indicating that the increase in the level of public education expenditure is not conducive to the rapid transfer and employment of agricultural labor. As a result, the biased expenditure of public education further reduces the employment opportunities for agricultural labor to cities in reality. Similarly, the current biased financial development is also not favorable for promoting the transfer of agricultural labor, and the improvement of financial efficiency plays a positive role to some extent. The improvement of agricultural labor productivity represented by the level of agricultural mechanization has a significant negative impact on the transfer of agricultural labor. This is possibly because of the fact that the opportunity costs of the transfer of agricultural labor increase with the increase in the output of agricultural products and agricultural labor income. In addition, the coefficient of capital return is significantly positive, indicating that the higher rate of capital return leads to greater investment incentives and a higher demand for non-agricultural labor, which is more favorable for promoting the transfer of agricultural labor.
Table 2.2. Baseline Regression Results of the Driving Factors of the Transfer of Agricultural Labor.
