VIRUS DE LA HEPATITIS C

Before proceeding to the regression results, we conduct model selection and diagnostic tests, which are shown on Table 3-7. All tests are applied to Equation (3-9) without interaction terms and the chosen estimation technique are then applied to Equation (3-10) and (3-11)17. As shown on Table 3-7, the estimators from the pool OLS regression are biased and inconsistent. Also, the fixed effect regression is preferable to the random effect regression. Furthermore, to account for the problems of heteroscedasticity and serial correlation, we adjust standard errors that are robust to heteroscedasticity and cluster standard errors at bank-level to account for within-cluster correlation of the error term18

The regression results of Equation (3-9) are displayed in Table 3-8 regarding the impact of credit information sharing on bank risk. T-statistics are reported in parentheses.

Bank risk is measured by Z_SCORE (Once again, a higher Z_SCORE implies lower risk)

and the level of credit information sharing is measured by DEPTH. The coefficient of DEPTH (or @_A in Equation (3-9)) is positive and significant (at 5% level), showing that

17 _{Adding interaction terms would not significantly change the overall results of the tests much.} 18 _{More detail of model selection tests and diagnostic tests can be found in the Appendix F.}

121

Z_SCORE increases with higher DEPTH. This result is consistent with our hypothesis H1, suggesting that bank risk is lower with higher level of credit information sharing.

By assessing the marginal effect of credit information sharing on bank risk holding all other variables at their sample mean, we find that a one-unit increase of DEPTH is associated with a change in Z_SCORE of 0.117. Hence, switching from a regime without credit information sharing to a regime with fully-fledged credit information sharing (DEPTH=6) can increase bank Z-score by up to 0.702. This finding lends support to the argument that credit information sharing can decrease bank risk because information sharing among banks helps alleviate adverse selection problems in lending and the post-lending moral hazard problems (Pagano & Jappelli 1993; Padilla & Pagano 1997), increase the incentives for debt repayment (Klein 1992; Vercammen 1995; Padilla & Pagano 2000) and reduce over-borrowing (Bennardo et al. 2009, 2014).

Our finding is in line with previous studies that attempt to examine the effect of credit information sharing on bank risk. Regarding to the early empirical analysis of the effects of information sharing on credit markets, Jappelli and Pagano (2002) support that credit risk is lower in countries where lenders share information about their borrowers. However, there is a shortcoming of the results of Jappelli and Pagano (2002) because of the weak quality of their proxy of a default rate (or credit risk), which is based on the International Country Risk Guide (ICRG) survey of leading international bankers. The ICRG indicator is imperfectly correlated with the likelihood of default on bank loans and it may also reflect other financial risks. Recently, with smaller sample and different sample period, Houston et al. (2010) utilize bank-level data in 69 countries and measure bank risk with a better indicator, Z-Score, which is similar to the one we use in this analysis. They also find that bank risk is positively related to credit information sharing. Our study complements Houston et al. (2010) by using recent data and more banks and countries in the sample. Similarly, based on cross-country empirical investigation, Büyükkarabacak and Valev (2012) also support the contributed effect of credit information sharing to the likelihood of banking crises by showing that credit information sharing reduces the likelihood of banking crises.

Investigating the coefficients of various control variables, we find a few interesting results. The significantly positive coefficient for bank size (SIZE) suggests that larger banks face less risk. Laeven and Levine (2009), Houston et al. (2010) and Fu et al. (2014) also find

122

the same result. In addition, by including a square of bank size (SIZE_SQR), we find an inverse U-shape relationship between bank size and bank risk. Banks that are classified as too-big-too-fail (TBTF) engage in more risk-taking. Less efficient banks with high cost-to- income ratio (EFFICIENCY) tends to be relatively riskier. Regarding to macroeconomic variables, we find that higher inflation rate (INF) is associated with higher risk.

By considering banking regulatory environment, the results reveal several interesting results. First of all, the evidence does not show that an existence of deposit insurance regime (DEPOSIT_INS) is significantly associated with bank risk, contrasting the moral hazard argument stating that bank act imprudently when a financial safety net is available. As point out by Barth et al. (2006), a deposit insurance regime intensifies the moral hazard problem in banking because depositors no longer face the risk of losing their savings, which diminishes their incentives and efforts to monitor bank activities (Houston et al. 2010). Our finding about the relationship between deposit insurance and bank risk is inconsistent with previous studies (e.g. Demirgüç-Kunt and Detragiache (2002)). Secondly, we find that the overall capital stringency (CAPITAL_STR) is significantly and positively related to lower bank risk. This suggests that stringent capital requirement promote more bank stability. Lastly, bank risk is lower with higher asset diversification (ASSET_DIV) indicating that banks are less risky when the regulation on asset diversification allows banks to diversify asset across sectors and aboard.

3.4.1.2 The Impact of Information Asymmetry on the Relationship between Credit

Information Sharing and Bank Risk

Information asymmetry can be problematic for banks since the adverse selection and moral hazard problems in lending are exacerbated. Nonetheless, asymmetric information can be less of a problem when the information environment is more transparent. When the information environment is more transparent, the benefit of credit information sharing can potentially decrease. In the previous section, we show that credit information sharing reduces bank risk because the sharing scheme helps to overcome the information problem and bridge the information gaps between banks and borrowers, such that banks can lend safely and borrowers behave well.

123

In this section, we present the regression results of Equation (3-10) testing whether the relationship between credit information sharing and bank risk varies with different degree of information environment. The results are shown Table 3-9. T-statistics are reported in parentheses. Bank risk is measured by Z_SCORE (Once again, a higher Z_SCORE implies lower risk) and the level of credit information sharing is measured by DEPTH. IFRS and BDI are used as proxies for information environment transparency.

The results with IFRS as a proxy of information environment are presented in the column 2 and column 3 of Table 3-9. The coefficient of the interaction term between DEPTH and IFRS (or @_F in Equation (3-10)) is negatively significant (at 1% level). Since the value one of the IFRS dummy proxies for more transparent information environment, the negative coefficient of the interaction indicates that IFRS attenuates the impact of credit information sharing on bank risk. This result supports our hypothesis H2 that the impact of credit information sharing on bank risk is less pronounced in a country with more transparent information environment as proxied by mandatory IFRS adoption. We also evaluate the moderating effect of IFRS on the relationship between credit information sharing and bank risk. When the country does not adopt IFRS, a one-unit increase of DEPTH will increase Z_SCORE by 0.495. However, when the country adopts IFRS, a one-unit increase of DEPTH will increase Z_SCORE by 0.087. That is 0.408 or approximately 82.4% less pronounced with IFRS adoption.

The results with BDI as a proxy of information environment are presented in the column 4 and column 5 of Table 3-9. The coefficient of the interaction term between DEPTH and BDI (or @_F in Equation (3-10)) is negative and significant (at 5% level). As higher BDI indicates more transparent information environment, the negatively significance of the interaction term suggests that BDI mitigate the impact of credit information sharing on bank risk. To measure the moderating effect of BDI on the relationship between credit information sharing and bank risk, the interaction term is evaluated at the 25th and 75th percentiles of BDI. DEPTH can increase bank Z_SCORE by between 0.035 and 0.104, depending on the degree of BDI. Specifically, a unit-increase in DEPTH is associated with a 0.104 increase in Z_SCORE when BDI is at the 25th percentile. The impact reduces to 0.035 when BDI is at the 75th percentile. We can securely conclude that the benefit of credit information sharing decreases with the business extent of disclosure index. In other words, the impact of credit

124

information sharing on bank risk is less pronounced when the information environment is more transparent. This evidence strengthens our hypothesis H2.