Table 1.
List of credit rating agencies.
This table presents the basic information about credit rating agencies in China’s local government bond market.
Table 2.
Local government bond issuance in China (2015–2021).
This table presents China’s local government bond issuance from 2015 to 2021. Data are obtained from the Wind Database.
Table 3.
This table reports the distribution of the sample bond observations by bond type, maturity, year, credit rating agency, and region. The percentage (%) is calculated as the number of bond issuances assigned to each breakdown category divided by the total number of 7941 observations.
Table 4.
This table presents definitions for the variables studied.
Table 5.
Summary statistics of variables.
This table reports the summary statistics of the variables included in the analysis for a sample of 31 local governments.
Table 6.
This table presents the correlation matrix of the variables.
Table 7.
Number of bonds rated by the credit rating agencies with high or low reputation in each region each year.
Table 8.
This table reports the estimates of the baseline regression Eq (1).
Fig 1.
Moderating effect of fiscal transparency.
Table 9.
Regression of risk premiums with Heckman correction.
This table reports the estimates of Heckman two-stage model to control for potential sample selection bias. The first stage reports the results of probit model, while the second stage shows the results of the baseline regression model.
Table 10.
Univariate tests of covariate balance for propensity-score matching.
This table reports descriptive statistics of the covariates for the sample of treatment bonds and the sample of control bonds. The results of the two-sample tests of mean and of the standardized bias for the covariates are provided for both unmatched and matched samples.
Table 11.
Difference-in-differences OLS regression results.
This table reports the regression results based on Eq (2).
Table 12.
Test of parallel trends assumption.
This table reports the results for the test of parallel trends assumption for difference-in-differences OLS regression.
Fig 2.
Predicted response versus true response of the trained optimizable tree model.
Fig 3.
Predicted response versus true response of the trained optimizable ensemble of tree model.
Table 13.
The validation of endogeneity based on the machine learning model.
This table reports the optimized hyperparameters of the two machine learning models (optimizable tree model and optimizable ensemble of trees model) and the statistics of counterfactual sets constructed by these two models. The last row reports the p-value of the t-test on the differences in risk premiums in the factual and counterfactual sets.
Table 14.
Regression results by excluding negative risk premium observations.
This table reports the estimates of the baseline regression Eq (1), excluding negative risk premium observations, with robust standard error.
Table 15.
Regression results based on the subperiod sample.
This table reports the estimates of the baseline regression Eq (1) from 2015 to 2019, with robust standard error.