Additional Tests and Robustness Checks 74

In 2005, there was an important reform in the Bank which lead to a revolutionary change in its ICR system. Following I am going to test the impact of this reform on the Bank’s ICR system.

3.4.1.

Rollover Test

In the following test, I look at both Beijing and Guangdong branches and test whether 2005 reform leads to any changes in the rating outcomes. In the dataset, there is a special loan indicator for rollover loans. As shown in the descriptive statistics of these two samples in Table 3.10, the percentage of rollover loans that defaulted or firms that become financially distressed are much lower for post-2005 period compared with the pre-2005 period.

Following the probit regression model in (7), I test whether the Bank changes its criteria in granting rollover loans after the reform.

′ (7)

The result, as presented in Table 3.11, show that in general, it is less likely to get rollover loans after 2005. The characteristics of firms that received “rollover” loans also changed over time. Firms with lower profit are more likely to receive rollover loans in the post-2005 period. Firms that have past financial distress, lower leverage, lower capital turnover (for Beijing sample) or lower productivity (for Guangdong sample) are more likely to get rollover loans and these characteristics do not significantly change before and after the 2005 reform.

While the other factors are intuitive, why would firms that have experienced past financial distress and lower profit have a higher probability of rolling over their loans? I suspect the reason could be that the ratings after the reform were artificially inflated. The Bank may have intentionally used rollover loans to reduce the loan default rates.

3.4.2.

Revised Test 1

Similar to Section 3.3.2 Test 1, following model (8), I run probit regression to test the likelihood getting rating 2 vs. 1 on firms’ hard information in two time periods - before 2005 and after 2005 in order to test whether there is any change in the prediction power of hard information on ICRs before and after the reform. However, this time, I use the propensity matching sample for the test. Other than the original PS matching sample without replacement, I also create a PS matching sample with replacement. The hard information includes size, FD, and five breakdown components of Z-score1.

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2 ′ (8)

The results are presented in Table 3.12. For the Beijing sample of post-2005 period, consistent with my earlier result, when firm has small size, has past occurrence of financial default, has a low productivity and high leverage, there is a higher probability of it getting a loan rating of 2 verses 1 and the results are statistically significant. However, the pre-2005 period sample shows no statistical significance on past occurrence of financial default. It appears that 2005 reform has improved the prediction power and accuracy of hard information for the Beijing branch.

The Guandong branch has similar result for the post-2005 sample as Beijing. Except for Guangdong, higher capital turnover and higher productivity factors can also increase the probability of getting rating 2 vs. 1. This could be because these two factors can be associated with a smaller firm and smaller firms have a higher probability of getting rating 2 vs. 1. Guangdong sample does have a consistent prediction power from past financial distress for both the pre-2005 and post-2005 sample. In my future research, I will conduct test to find out whether this difference is due to the different political environment in Guangdong and Beijing. In another word, I suspect that Guandong provides an environment with less political influence on both the Bank and firms and thus Guangdong branch’s ICRs have less noise than Beijing’s which were contaminated because of the political environment until after the 2005 reform.

As a robustness test, I run the same test only on firms in the biggest industry in my sample – manufacturing industry. Results are similar and are presented in Table 3.13.

3.4.3.

Revised Test 3

Now that we know the rating system appears to have improved after 2005 reform, I will continue the tests on the prediction of soft information on future loan default and finance distress by following model (9) and (10). I conduct this test on Beijing sample with different specifications of the model and present the results in Table 3.14.

2 2 ∗ (9)

2 2 ∗ (10)

Here post takes the value of 1 if time is after 2005 and 0 if it is before 2005. The coefficient I am most interested in is 2 ∗ . The results indicate that, in general, the ICRs’ prediction power for future loan default increased significantly after 2005 but the impact on financial distress is unclear.

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In summary, it seems that 2005 reform has improved the Bank’s ICR system. However, when combining the rollover results and the fact that only the prediction power of loan default has improved and not financial distress, which is a cleaner and less manipulative measure of a firm’s health, this seemingly improvement could be man-made. More tests need to be done in order to find out the true effect of the reform.