In Table 7, we examine whether the earnings gap between rural migrant and urban workers varies based on the ownership type of firms, educational attainment level of workers, and the region where workers are employed. We find that the hourly earnings gap between rural migrant and urban workers is much larger among workers employed in SOEs than in non-SOEs. In column 1, when our SOE dummy is added, the coefficient on the rural migrant dummy variable is still negative and significant (compared with the result in column 4 of Table 6), but the magnitude becomes slightly smaller (−0.073 versus −0.084). In column 2, we add a variable interacting the SOE dummy variable and the rural migrant dummy variable. As shown, the coefficients on the rural migrant dummy variable and the interaction term are both negative and significant. These findings suggest that an earnings gap exists in both SOEs and non-SOEs, and that the two gaps are significantly different. Specifically, the hourly earnings gap is 6.4% in non-SOEs, which is 14 percentage points smaller than the 20.4% gap observed for SOEs (significant at the 1% level), suggesting that urban workers in SOEs are more protected by the government for certain political reasons (Lin, Cai, and Li 1998; Dong and Putterman 2003; Bai, Lu, and Tao 2006).
. | Hourly Earnings in Logarithm . | |||||
---|---|---|---|---|---|---|
. | (1) . | (2) . | (3) . | (4) . | (5) . | (6) . |
Rural migrant | −0.073*** | −0.064*** | −0.157*** | −0.124*** | −0.084*** | −0.109*** |
(0.014) | (0.015) | (0.014) | (0.016) | (0.014) | (0.020) | |
SOE | 0.144*** | 0.183*** | ||||
(0.023) | (0.029) | |||||
Rural migrant × SOE | −0.140*** | |||||
(0.045) | ||||||
College | 0.454*** | 0.503*** | ||||
(0.016) | (0.022) | |||||
Rural migrant × college | −0.109*** | |||||
(0.030) | ||||||
Hubei | −0.239*** | −0.238*** | −0.211*** | −0.210*** | −0.229*** | −0.258*** |
(0.013) | (0.013) | (0.013) | (0.013) | (0.013) | (0.022) | |
Rural migrant × Hubei | 0.050* | |||||
(0.026) | ||||||
Male | 0.214*** | 0.215*** | 0.255*** | 0.255*** | 0.218*** | 0.219*** |
(0.012) | (0.012) | (0.012) | (0.012) | (0.012) | (0.012) | |
Age | 0.066*** | 0.066*** | 0.066*** | 0.065*** | 0.067*** | 0.067*** |
(0.004) | (0.004) | (0.005) | (0.005) | (0.004) | (0.004) | |
Age squared | −0.001*** | −0.001*** | −0.001*** | −0.001*** | −0.001*** | −0.001*** |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
Years of schooling | 0.085*** | 0.084*** | 0.086*** | 0.086*** | ||
(0.002) | (0.002) | (0.002) | (0.002) | |||
R-squared | 0.276 | 0.277 | 0.237 | 0.238 | 0.272 | 0.273 |
Number of observations | 7,799 | 7,799 | 7,799 | 7,799 | 7,799 | 7,799 |
. | Hourly Earnings in Logarithm . | |||||
---|---|---|---|---|---|---|
. | (1) . | (2) . | (3) . | (4) . | (5) . | (6) . |
Rural migrant | −0.073*** | −0.064*** | −0.157*** | −0.124*** | −0.084*** | −0.109*** |
(0.014) | (0.015) | (0.014) | (0.016) | (0.014) | (0.020) | |
SOE | 0.144*** | 0.183*** | ||||
(0.023) | (0.029) | |||||
Rural migrant × SOE | −0.140*** | |||||
(0.045) | ||||||
College | 0.454*** | 0.503*** | ||||
(0.016) | (0.022) | |||||
Rural migrant × college | −0.109*** | |||||
(0.030) | ||||||
Hubei | −0.239*** | −0.238*** | −0.211*** | −0.210*** | −0.229*** | −0.258*** |
(0.013) | (0.013) | (0.013) | (0.013) | (0.013) | (0.022) | |
Rural migrant × Hubei | 0.050* | |||||
(0.026) | ||||||
Male | 0.214*** | 0.215*** | 0.255*** | 0.255*** | 0.218*** | 0.219*** |
(0.012) | (0.012) | (0.012) | (0.012) | (0.012) | (0.012) | |
Age | 0.066*** | 0.066*** | 0.066*** | 0.065*** | 0.067*** | 0.067*** |
(0.004) | (0.004) | (0.005) | (0.005) | (0.004) | (0.004) | |
Age squared | −0.001*** | −0.001*** | −0.001*** | −0.001*** | −0.001*** | −0.001*** |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
Years of schooling | 0.085*** | 0.084*** | 0.086*** | 0.086*** | ||
(0.002) | (0.002) | (0.002) | (0.002) | |||
R-squared | 0.276 | 0.277 | 0.237 | 0.238 | 0.272 | 0.273 |
Number of observations | 7,799 | 7,799 | 7,799 | 7,799 | 7,799 | 7,799 |
Notes: Robust standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. Rural migrant workers are those who work in manufacturing (nonagriculture sector) and have a rural hukou. Hourly earnings are measured by dividing the monthly earnings measure by average hours worked per month. College is a dummy variable where 1 denotes vocational college and above and 0 denotes high school and vocational high school and below. SOE is a dummy variable where 1 denotes firms with state ownership and 0 denotes firms with nonstate ownership. Hubei is a dummy variable where 1 denotes Hubei province and 0 denotes Guangdong.
Source: Authors’ calculations.