Table 3.
Robustness checks on the impact of the NRCMS on health care utilisation and medical expenditure
| Formal care | Preventive care | Folk doctor use | Inpatient care | Village clinics | Township health centres | County hospitals | City hospitals | Pr(OOP > 0) | Log of OOP if positive | |
|---|---|---|---|---|---|---|---|---|---|---|
| (1) IV of county NRCMS status and number of years of the NRCMS coverage | ||||||||||
| NRCMS treatment effect | − 0.000 (0.022) | 0.005 (0.014) | − 0.037* (0.022) | 0.024 (0.034) | 0.116* (0.068) | − 0.029 (0.085) | 0.016 (0.054) | − 0.077* (0.046) | 0.023 (0.082) | − 0.298 (0.279) |
| F statistics for weak identification test | 242.467 | 248.267 | 262.863 | 166.822 | 190.580 | 190.580 | 190.580 | 190.580 | 251.227 | 183.438 |
| Sargan’s over-identification test (prob) | 0.753 | 0.127 | 0.718 | 0.208 | 0.303 | 0.113 | 0.288 | 0.594 | 0.796 | 0.597 |
| N | 17,397 | 17,467 | 14,361 | 1754 | 1744 | 1744 | 1744 | 1744 | 2688 | 2021 |
| (2) Non-linear IV models | ||||||||||
| NRCMS treatment effect | − 0.024 (0.118) | 0.188 (0.243) | − 0.443** (0.219) | 0.218 (0.321) | 0.418** (0.206) | − 0.185 (0.301) | 0.019 (0.233) | − 0.603 (0.372) | 0.073 (0.249) | − 0.320 (0.277) |
| N | 20,324 | 20,431 | 17,322 | 2048 | 2042 | 2042 | 2012 | 1932 | 3136 | 2389 |
Notes: The first panel shows results from IV estimations in linear probability models (LPM) using 2 IVs: the introduction of the NRCMS at county level and the number of years covered by the insurance, with F statistics for weak identification test and probability of Sargan’s over-identification test. Sargan’s over-identification test is calculated as N*R-squared from a regression of the IV residuals on both instruments. The joint null hypothesis is that the instruments are valid instruments; a rejection casts doubt on the validity of the instruments. The second panel shows the IV probit estimation results for health care utilisation. Two different models are applied to the two parts of the OOP payments: an IV probit model for the probability that an individual made any OOP payment and OLS, applied only to sub-sample with non-zero OOP payments, for the log of OOP payments. Robust standard errors clustered at county level in brackets.
Other independent variables include age, gender, household size, marital status, ethnicity, eastern region, central region, household income, asset index, education level, occupation, number of major diseases, severity of illness in the last month, health risk variables and urbanicity index at community level.
*Indicates statistical significant at the 10% level; **indicates statistical significant at the 5% level.