Instrumental Variable Approach

We perform an instrumental variable approach to avoid any potential endogeneity problem that could exist due to the reverse causality between credit information sharing and bank lending7. We select the instrumental variables based on the existing literature on law and finance (Easterly & Levine 1997; LaPorta et al. 1998; La Porta et al. 1999; Beck et al. 2003; Acemoglu & Johnson 2005). Specifically, we employ legal origins, ethnic fractionalization, and latitude as instrumental variables for DEPTH8. They are previously used in Barth et al. (2009), Houston et al. (2010), Büyükkarabacak and Valev (2012) and Fu et al. (2014) as instruments. Because our instruments consist of time-invariant variables, we use a two-stage least square (2SLS) with pooled OLS estimations rather than fixed effects estimations.

To test for the endogeneity of DEPTH, we perform the Durbin-Wu-Hausman test of endogeneity. The Durbin-Wu-Hausman tests for endogeneity in a regression estimated with IV approach, the null hypothesis for which states that an OLS estimator of the same equation would yield consistent estimates: that is, any endogeneity among the regressors would not

7 _{The reverse causality between credit information sharing and bank lending is less problematic in our study}

because we investigate the effect of credit information sharing agencies on the volume of bank lending of individual bank firms.

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have harmful effects on OLS estimates (Durbin 1954; Wu 1974; Hausman 1978; Baum et al. 2007). A rejection of the null indicates that the effects of endogenous regressors on the estimates are meaningful, and instrumental variables approaches are necessary. This rejection means that DEPTH can be treated as exogenous under the null hypothesis. After we perform the Durbin-Wu-Hausman tests for endogeneity, the estimation shows that the p- value is 0.7816 so the null hypothesis cannot be rejected. Thus, we can treat depth of credit information sharing as exogenous. Nonetheless, we perform robustness tests for Equation (2-2) to (2-4) by employing an instrumental variable approach. The results are presented in Table 2-15, Table 2-16 and Table 2-17.

To test for the relevance and validity of the instruments of the credit information sharing, we perform the First Stage F-test and the Hansen’s J test. Specifically, with regards to the relevance of these instruments, we conduct an F-test of the excluded instruments in the corresponding first-stage regression. The null hypothesis of the test is that the instruments do not explain cross-sectional differences in the credit information measure. On all tables, we reject the null hypothesis at the 1% level in all regressions. In addition, the Hansen J-test of over-identifying restrictions cannot be rejected suggesting that the instruments are valid instruments, uncorrelated with the error term and correctly excluded from the estimated equation9.

As we have confirmed the relevance and validity of our instruments, we continue to analyze the IV regression results of each table. First, we analyze the IV regression results for Equation (2-2). On Table 2-15, the first column reports the second stage regression, while the second column reports the first stage regression. The main result is still robust and consistent with our first hypothesis H1. The coefficient of DEPTH remains positive and significant. The result with IV approach confirms our main finding that bank lending increases with credit information sharing. Moreover, the IV coefficient is much larger than the coefficient of the fixed effect regression, indicating the presence of potential measurement error, which inflates the IV coefficient. Nonetheless, our conclusion does not

9 _{By confirming the relevance and validity of our instruments, we are not claiming that these variables are the}

best instrumental variables, but we hold that these instruments are reasonably exogenous and have adequate explanatory power for the credit information sharing measure

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depend on the instrumentation approach because DEPTH is not endogenous and poses no concern of endogeneity.

For Equation (2-3) and (2-4), we split the sample into two subsamples based on each of information environment proxies and the creditor rights index. The regression results of Equation (2-3) are reported in Table 2-16. The first four columns of Table 2-16 present the IV regressions of two subsamples that are split based on IFRS as a proxy of information environment transparency. The results are robust and consistent with our second hypothesis H2. The coefficient of DEPTH is only significant in the subsample without the mandatory IFRS adoption suggesting that the impact of credit information sharing on bank lending is more pronounced when the information environment is less transparent. Similar results are applied to the subsample based on BDI. In column 5 to column 8 of Table 2-16, the coefficient of DEPTH is only significant in the subsample with LOW BDI suggesting that the impact of credit information sharing on bank lending is more pronounced when the information environment is less transparent.

Regarding Equation (2-4), Table 2-17 presents the IV regressions of two subsamples which are split based on the value of creditor rights index. The value of creditor rights index above the median value of the sample is corresponded to the high level of creditors’ protection (HIGH CR), while the value of creditor rights index below the median value of the sample corresponds to the low level of creditors’ protection (LOW CR). The coefficient of DEPTH is positive and significant only in the subsample with LOWCR. This result does not pose a serious problem to our main results because our IV approach is based on pooled OLS estimations rather than fixed effects estimations. With pooled OLS estimations, the estimates may be biased and inefficient.