Dependent variable | Ordered probit model: hospital size |
|---|
(1) | (2) |
|---|
Higher education |
College diploma (dummy) | 0.513*** (0.017) | 0.559*** (0.022) |
Bachelor degree or above (dummy) | 1.759*** (0.016) | 1.717*** (0.027) |
Age | 0.018*** (0.001) | 0.019*** (0.001) |
Male | − 0.381*** (0.013) | − 0.406*** (0.022) |
Cut1 | Constant | 0.590*** (0.029) | − 24.252 (596.049) |
Cut2 | Constant | 1.860*** (0.030) | − 19.456 (596.048) |
Cut3 | Constant | 3.012*** (0.032) | − 5.556 (576.072) |
Fixed effects | | COUNTY |
Number of observations | 36 674 | 36 674 |
Log-likelihood function | − 4.03e+04 | − 1.25e+04 |
Chi squared | 14 936.433*** | 70 457.985*** |
Pseudo R-squared | 0.156 | 0.738 |
BIC | 80 659.477 | 26 115.339 |
- Source: 2009 Fujian province database is a cross-sectional database that collected basic characteristics of human resources for health in all of the health institutes
- *** denote statistical significance at the 1% levels. standard errors are reported in the parentheses
- cut1—this is the estimated cutpoint on the latent variable used to differentiate township-level hospital from county-level, city-level, and provincial-level hospitals when values of the predictor variables are evaluated at zero
- cut2—this is the estimated cutpoint on the latent variable used to differentiate township-level and county-level hospital from city-level and provincial-level hospitals when values of the predictor variables are evaluated at zero
- cut3—this is the estimated cutpoint on the latent variable used to differentiate township-level, county-level, and city-level hospitals from provincial level hospitals when values of the predictor variables are evaluated at zero
