Variable Measurements

For the bank-lending variable, we extract total gross loans from the BankScope database. A total gross loan of each bank is defined as total amount of loans to household

3 _{We cross-check the data within these three sources to ensure that the country mandatorily adopt IFRS and}

the effective date of the mandatory IFRS adoption is correct.

4 _{La Porta et al. (1998) provide data on the creditor rights index to measure the power of creditors in the vent}

of borrowers’ bankruptcy. Djankov et al. (2007) extend the dataset of LaPorta et al. (1998) on creditor rights index to include as many as 129 countries.

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and firms. The unit is expressed in term of million US dollars. In addition, we take a natural logarithm of total gross loans and take the difference between the natural logarithm of gross loans in the current period and the natural logarithm of gross loans in the previous period. Mathematically,

!"#$%_&,( = *+, "#$%_&,( − *+, "#$%_&,(./ (2-1)

Where *+, is a natural log function; "#$%&,( is total gross loans of bank ith at time t and

"#$%_&,(./ is total gross loans of bank ith _{at time t-1. We can interpret the changes in natural}

logarithms as percentage changes after multiplying by 100. We define the changes in the natural logarithms of gross loans as GLOAN.

2.3.1.1.2 Explanatory Variables

Credit Information Sharing Proxy

The key independent variable in our analysis the variable measuring the level of credit information sharing across countries. Generally, banks exchange information about their borrowers’ creditworthiness through information-sharing institutions. These information-sharing institutions exist as either privately held credit bureaus or publicly regulated credit registries. According to Djankov et al. (2007), a private credit bureau is a database maintained by a private commercial firm whereas a public credit registry is a database maintained by a public authority (e.g. central banks). Both information-sharing institutions consolidate information on the borrowers’ creditworthiness in the financial system and facilitate the exchange of credit information among banks and other financial institutions. However, the contents and scope of credit information available from credit information institutions may vary across countries. Some institutions may collect information on outstanding loans of large borrowers, while some others may provide extensive information consisting of demographic data, default records, late payment (delinquency), credit inquiries, ratings, payment of utility bills (Miller 2003; Djankov et al. 2007).

We thus use the depth of credit information sharing index (DEPTH) to capture the differences of information contents across countries. The index is taken from the World Bank’s Doing Business database. This index measures rules affecting the scope,

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accessibility, and quality of credit information available through either private credit bureau or public credit registry (Djankov et al. 2007; Houston et al. 2010). The depth of credit information sharing index ranges from zero to six with higher values indicating better scope, accessibility, and quality of credit information available from either private credit bureau or public credit registry. The value of zero indicates that there is no private credit bureau or public credit registry operating in a country. The value of one is then added to the index with each one of the following characteristics:

• Both positive information and negative information are distributed. Positive information is an information about loans outstanding and pattern of on-time repayments, whereas negative information is an information about late payments, number and amount of defaults, arrears or bankruptcies.

• Data on individuals (households) and firms are distributed.

• Data from retailers, trade creditors, and/or utility companies as well as financial institutions are distributed.

• More than 2 years of historical data are available. Registries that erase data on defaults as soon as they are repaid would receive a score of 0 for this indicator.

• Data are collected and distributed on loans with value below 1% of income per capita. A registry must have a minimum coverage of 1 percent of the adult population to score a 1 for this indicator.

• Laws give right to borrowers to inspect their own data. Information Environment Proxy

To measure the transparency of the information environment, we rely on two proxies which are the mandatory IFRS adoption and the Business Extent of Disclosure Index (BDI). Regarding mandatory IFRS adoption, we identify each country’s status of IFRS adoption and build a dummy variable whose value is equal to 1 for a country (and year) that mandatorily adopts IFRS and 0 otherwise. We name this dummy IFRS. For a country’s date

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of IFRS adoption, we refer to the effective date of IFRS implementation. We define countries with mandatory IFRS adoption to be more transparent than those without. IFRS dummy with a value of one is associated with more transparent information environment. The list of countries with mandatory IFRS adoption is in Appendix B.

Regarding BDI, the data is obtained from the World Bank’s Doing Business. This index measures the extent to which investors are protected through disclosure of ownership and financial information (World Bank’s Doing Business 2016). It ranges from 0 to 10 with a higher value indicating more disclosure of ownership and financial information to investors. Thus, a higher (lower) BDI indicates that the information environment is more (less) transparent. More detail of the components of BDI is in Appendix C.

Creditor Rights Index

The measure of creditor powers in the event of borrowers’ bankruptcy is an aggregate measure of creditor legal protection created based on the methodology proposed by LaPorta et al. (1998). The index is ranging from zero to four. The index consists of 4 components:

• Restrictions on reorganization: whether there are restrictions imposed, such as creditors’ consent or minimum dividend, when a debtor files for reorganization.

• No automatic stay: whether secured creditors can gain possession of assets after the petition for reorganization is approved, that is, whether there is no automatic stay or asset freeze imposed by the court on a creditor’s ability to seize collateral.

• Secured creditor paid first: whether secured creditors are ranked first in the distribution of proceeds of liquidating a bankrupt firm as opposed to other creditors such as government or workers.

• No management stay: whether the incumbent management does not stay in control of the firm during the reorganization, in other words, whether an administrator, not the management, is responsible for running the business during the reorganization.

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A value of one is added to the index when a country’s laws and regulations provide each of these powers to secured lenders. A higher index indicates that secured lenders are better protected in case a borrower defaults.

2.3.1.1.3 Control Variables

We include a series of control variables to prevent the spurious relationship between credit information sharing and bank lending or avoid any relationship that could be driven by unobserved variables. To control for bank characteristics, we include bank’s size, profit margin, and efficiency. The size of each individual bank is empirically proxied by a natural logarithm of bank’s total assets (SIZE). We measure the profitability of each bank with a bank's net interest revenue as a share of its interest-bearing assets. This variable is a net interest margin (NIM). This variable measures the profitability of investing and lending activities. To control for the bank’s efficiency in operating on and off-balance sheet activities, we incorporate a ratio of total expenses to operating income (interest and non- interest income). This ratio is simply a cost-to-income ratio (EFFICIENCY). Beside bank’s size, profitability, and efficiency, we also include a ratio of total deposits to total assets (DEP) and a ratio of loan loss reserves to gross loans (LLR).

To control for country-specific macroeconomic performance, we include a growth rate of gross domestic product (GDPG) and an inflation rate (INF). All are collected from WDI. The growth rate of GDP is included to capture the development of the economy (Djankov et al. 2007). Inflation is proxied by a consumer price index (CPI) to control for the price movement and uncertainty in the credit market. Uncertainty in the credit market arises from the banks’ difficulty in assessing the quality of credit because profits in real term become harder to predict during periods of high inflation. We also control for the banking market structure by including the ratio of three largest bank’s assets in a country to the total assets in the banking system (CCT3). Lastly, we include a capital stringency index (CAPITAL_STR), measuring the extent of both initial and overall capital stringency in a country.

2.3.1.1.4 Summary Statistics

Table 2-1 summarizes all definitions and sources of variables as well as their symbols used in this chapter. Descriptive statistics for the main empirical results and robustness

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checks are displayed in Table 2-2. All variables are winsorized at the 1% and 99% levels. The sample consists of 16,009 banks in 113 countries over the period of 2005 to 2013. From the table, on average, the change of the natural logarithm of total gross loans (GLOAN) has a mean about 0.089 (or 8.9%). The depth of credit information sharing index (DEPTH) has mean (median) of 5.01 (5), indicating that most observations in the sample have a high scope, accessibility, and quality of credit information available through credit information sharing agencies. For other alternative measures of credit information sharing, the private credit bureau coverage (PRIV) is about 79%, while the public credit registries coverage (PUB) is about 9.8%. Regarding the proxies of information asymmetry, the table shows that the mean of IFRS adoption (IFRS) is 0.305, meaning that more than half of observations have no IFRS adoption, while the mean of the business extent of disclosure index (BDI) is 6.42. The mean of creditor rights index (CR) is 1.54, showing that on average the degree of creditor protection is not high around the globe.

The summary statistics of main control variables are also shown in the same table. According to the bank characteristics controls, the mean of the natural logarithm of bank’s assets (SIZE) is around 6; the mean of the net interest margin (NIM) is around 4%; the mean of the cost-to-income ratio (EFFICIENCY) is around 71.6%; the mean of the deposit to asset ratio (DEP) is around 78.3%; and the mean of loan loss reserves to gross loans ratio (LLR) is 2.5%. For the macroeconomic controls, on average, the growth rate of gross domestic product (GDP) is around 2%, while the inflation rate (INF) is approximately 3.1 percent. Regarding the banking market structure, the banking market concentration ratio (CCT3) is on average 42.8%. Lastly, on average, the degree of overall banking capital stringency (CAPITAL_STR) is around 6 to 7.

In addition to the variables used in the main regression, we also present the summary statistics of the variables used as additional controls in robustness tests. The dummy of deposit insurance (DEPOSIT_INS) has a mean of 0.961 and a median of 1, implying that most of the observations in the sample have a deposit insurance regime. The effect of deposit insurance regimes might be absent due to the cluster of values around 1. Another additional control variable is the political stability index (POLITIC). The mean of POLITIC is 0.397 on the scale of +/-2.5.

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Table 2-3 and Table 2-4 reports the correlation between variables. The table shows that DEPTH is positively correlated with GLOAN, indicating that there exists a positive relationship between credit information sharing and bank lending. Regarding alternative proxies of credit information sharing, PRIV is positively associated with GLOAN and highly correlated with DEPTH. This highly positive correlation between PRIV and DEPTH suggests that a country with the high depth of credit information tends to have high coverage of private credit bureaus or vice versa. However, each variable enters the regression individually, so the problem of multicollinearity should be less of a concern. Another proxy of credit information sharing, PUB, is, in contrast, negatively associated with GLOAN and negatively correlated with DEPTH. The two proxies of information asymmetry, IFRS and BDI, are positively correlated with GLOAN, suggesting that mandatory IFRS adoption and higher extent of disclosure index tend to promote lending and vice versa. The creditor rights index, CR, is also positively correlated with GLOAN. Notably, DEPTH is negatively correlated with each of proxy of information asymmetry (IFRS and BDI) and creditor rights index (CR), implying that their relationships are going in the opposite direction. However, their interaction effects on GLOAN will be examined further with multivariable regression analysis. The methodology will be explained in the next section.