Methodology

According to our hypothesis H1, we expect that credit information sharing has a positive impact on bank lending. The regression equation is as follows:

"0%12%!_&,( = 3₄+ 3_/627_&,(./+ 3₈( ;_&,(./8 ₎

= 8>? + 3_@(A_&,(./@ ₎ B @>C + D₍+ E_& + F_&,( (2-2)

Where i, t and t-1 indicates the ith bank, year t and year t-1, respectively; "0%12%! is bank lending measured by the change in the natural logarithm of total gross loans (GLOAN); CIS is a credit information sharing variable proxied by the depth of credit information sharing index (DEPTH); X contains bank-specific variables, consisting of bank’s size (SIZE), net interest margin (NIM), a cost-to-income ratio (EFFICIENCY), a deposits to assets ratio (DEP), a loan-loss reserves to gross loans ratio (LLR); Y contains country-specific variables,

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consisting of GDP growth (GDPG), inflation (INF), banking concentration (CCT3) and capital stringency index (CAPITAL_STR); D₍ is the year fixed effects; E_& is the individual effects or the time-invariant component of the error term; and ε is an idiosyncratic error term or time-varying component of the error term. The coefficient 3_/ reflects the impact of credit information sharing on bank lending. Thus, according to the hypothesis H1, we expect the sign of 3_/ to be positive so that credit information sharing increases the volume of bank lending.

The year fixed effects. D(, are a set of time dummies included to control for economy-

wide events and technological innovation affecting all banks equally across countries, which vary over time. These year fixed effects capture, for example, the economy-wide institutional changes affecting the quality of rules and laws governing the country, fluctuation in the market interest rate shaping the supply and demand of credit, conditions in the public debt market, which also influence bank lending decision. The year fixed effects can also include exogenous macroeconomic shocks, such as the spread of the global financial crisis in 2008 and 2009. The individual (or bank) fixed effects, E&, captures the time-invariant

heterogeneity of banks. For instance, the bank-level heterogeneity is due to initial differences in the managerial practices, the age of establishment, etc. all of which could be potential confounding factors in estimating the effect of credit information sharing on bank lending.

According to the hypothesis H2, we expect the effect of credit information sharing on bank lending to be less pronounced in a more transparent information environment as proxied by IFRS adoption or BDI. To test this hypothesis, we augment Equation (2-2) with one of the two proxies of the information environment and their interactions with the credit information sharing measure. The new regression model thus expresses as follows:

"0%12%!_&,( = 3₄+ 3_/627_&,(./+ 3_?$7AG_&,(./+ 3_H$7AG_&,(./∗ 627_&,(./

+ 3₈(;_&,(./8 ₎ J 8>K + 3_@(A_&,(./@ ₎ // @>L + D₍+ E_& + F_&,( (2-3) Where i, t and t-1 indicates the ith bank, year t and year t-1, respectively; "0%12%! is bank lending measured by the change in the natural logarithm of total gross loans (GLOAN); CIS is a credit information sharing variable proxied by the depth of credit information sharing index (DEPTH); ASYM represents one of the two proxies of information environment,

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namely IFRS adoption (2MN7) and the business extent of disclosure index (O12); X contains bank-specific variables, consisting of bank’s size (SIZE), net interest margin (NIM), a cost- to-income ratio (EFFICIENCY), a deposits to assets ratio (DEP), a loan-loss reserves to gross loans ratio (LLR); Y contains country-specific variables, consisting of GDP growth (GDPG), inflation (INF), banking concentration (CCT3) and capital stringency index (CAPITAL_STR); D₍ is the year fixed effects; E_& is the individual effects or the time-invariant component of the error term; and ε is an idiosyncratic error term or time-varying component of the error term. The coefficient 3_H reflects the extent to which degree of information environment moderates the impact of credit information sharing on bank lending; thereby, according to the hypothesis H2, we expect the sign of 3_H to be negative such that the impact of credit information sharing on bank lending is less pronounced with a more transparent information environment.

According to the hypothesis H3, we expect that the impact of credit information sharing on bank lending is less pronounced under the environment with better creditor protection. To test for the hypothesis H3, we augment Equation (2-2) with an index measuring the level of creditor protection and its interaction with the credit information sharing measure. The new regression model is thus as follows:

"0%12%!_&,( = 3₄+ 3_/627_&,(./+ 3_?6N_&,(./+ 3_H6N_&,(./∗ 627_&,(./

+ 3₈(P;_&,(./8 ₎ J 8>K + 3_@(A_&,(./@ ₎ // @>L + D₍+ E_& + F_&,( (2-4) Where i, t and t-1 indicates the ith bank, year t and year t-1, respectively; "0%12%! is bank lending measured by the change in the natural logarithm of total gross loans (GLOAN); CIS is a credit information sharing variable proxied by the depth of credit information sharing index (DEPTH); CR is creditor rights index measuring the level of creditor protection through the legal system; X contains bank-specific variables, consisting of bank’s size (SIZE), net interest margin (NIM), a cost-to-income ratio (EFFICIENCY), a deposits to assets ratio (DEP), a loan-loss reserves to gross loans ratio (LLR); Y contains country-specific variables, consisting of GDP growth (GDPG), inflation (INF), banking concentration (CCT3) and capital stringency index (CAPITAL_STR); D( is the year fixed effects; E& is the

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error term or time-varying component of the error term. The coefficient 3_H reflects the extent to which degree of creditor rights affects the relationship between credit information sharing and bank lending; thereby, according to the hypothesis H3, we expect the sign of 3_H to be negative so that the impact of credit information sharing on bank lending is less pronounced with better creditor protection.

In the robustness test, we re-estimate Equation (2-2) to Equation (2-4) with a few modifications and augmentations. We employ alternative measures of credit information sharing in each by replacing DEPTH with private credit bureau coverages (PRIV) and public credit registries coverages (PUB). Also, we add more country-level control variables that could potentially affect the volume of bank lending, including a deposit insurance dummy (DEPOSIT_INS) and political stability (POLITIC). In addition, we provide an instrumental variable regression by employing a legal origin dummy (LEGALORIGIN), ethnic fractionalization (ETHNIC_FRAC) and latitude (LATITUDE) as instrumental variables for credit information sharing and bank lending.

2.4 Empirical Results and Robustness Tests