Robustness checks using alternative samples and the PSM approach
| Dependent: IssueSpread | (1) | (2) | (3) |
|---|---|---|---|
| Post | 0.158** (2.553) | 0.086** (2.511) | 1.059*** (3.144) |
| Controls | Yes | Yes | Yes |
| Date | Yes | Yes | Yes |
| City | Yes | Yes | Yes |
| Industry | Yes | Yes | Yes |
| N | 3,215 | 8,603 | 1,743 |
| r2_a | 0.743 | 0.736 | 0.693 |
| Dependent: | (1) | (2) | (3) |
|---|---|---|---|
| 0.158** (2.553) | 0.086** (2.511) | 1.059*** (3.144) | |
| Yes | Yes | Yes | |
| Yes | Yes | Yes | |
| Yes | Yes | Yes | |
| Yes | Yes | Yes | |
| 3,215 | 8,603 | 1,743 | |
| r2_a | 0.743 | 0.736 | 0.693 |
Note(s): The table shows regressions of bond issuance spreads on the introduction of bankruptcy courts for alternative samples. The dependent variable is bond issuance spreads (IssueSpread). In column (1), bond issuances in Beijing, Shanghai, Chongqing and Tianjin are excluded. In column (2), bond issuances in provinces without bankruptcy courts are included. In column (3), the PSM approach is used to pair the treatment group with control group and the matched sample is employed. Control variables are the same as in Table 6. Variable definitions are given in Table 2. Robust t-statistics are reported in parentheses. *, ** and *** represent statistical significance at the 10, 5 and 1% level, respectively
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