CIRCUITO HIDRÁULICO

This section describes the sources of data used in our study. To get a homogenous sample we focus on only the commercial banks in India for the period 2004 to 2011.15 The main source of data on the bank’s balance sheets, income statements and annual reports is from the Reserve Bank of India. Our dataset is considered to be superior to the BankScope

14 _{The vector of dummies is not included.}

15 _{For India, financial year starts from 1 April and ends on 31 March of every year. Therefore, 2004 denotes}

the 2003-2004 financial year and so on.

database; however, for the sake of accuracy, we have cross-checked them with the data obtained from BankScope.16 _{Regarding foreign commercial banks’ data we had to depend} exclusively on the income statements and balance sheets collected from RBI since BankScope does not provide any data regarding foreign banks operating in India. Since we have eliminated observations with missing data for the variables, we have therefore an unbalanced panel dataset. It should be noted that we have applied rules to eliminate outliers based on the 1st _{and 99}th _{percentiles of the distributions of the dependent variable in the} profit equation. The sample includes 73 commercial banks operating in the Indian banking industry. This accounts for the Indian banks holding more than 95% of total assets. We augment our RBI dataset with the country level macroeconomic indicators retrieved from the World Bank database.17 Descriptive statistics and the correlation coefficients of independent variables are now discussed.

Table 2.2 shows the descriptive statistics of basic variables used in the estimation of profit equations. The descriptive statistics provide very interesting insights. The return on equity (ROE) varies between -46.9% and 38.3% with an average of 13.5%, while the return on assets (ROA) varies between -2.1% and 6.71% with an average of 1.3%. In both profitability measures the minimum values are negative. The probable explanation is that during the 2004-05 fiscal years the profitability of many banks had shrunk drastically due to the rising interest rate environment in India. The average of CR5 (40.71%) and HHI (582) indicate that the Indian banking industry is moderately concentrated18. The maximum value (18.6%) of market share (MS) indicates weak evidence of market competition in India. The logarithm of total assets varies between 5.71 and 16.32 reflecting the massive heterogeneity of bank sizes in the industry. The credit risk (NPL) varies

16 _{BankScope is maintained by Fitch/IBCA/Bureau Van Dijk.}

17_{http://data.worldbank.org/indicator} 18 _{HHI is multiplied by 10,000.}

between 0% and 76%, with mean of 2% and standard deviation of 6.07%. The average NPL ratio over the years shows a minimum of 0.78% in 2008 and maximum of 5.35% in 2004. It shows that some banks suffer from the huge burden of bad assets. The income diversification (DIV) varies between -12.66% and 83.22%, with mean of 19.13% and standard deviation of 14.25%. The mean value suggests that one-fifth of the total income was generated from non-interest income sources. The capitalisation (EqA) varies between 1.0% and 98.1% with a mean of 13.5% indicating the healthy status of the Indian banking industry. However, there are some banks (e.g., SCBs) still under-capitalised compared with the international competitive norm. The operational inefficiency varies between 0.32% and 10.6%. The ratio of loans to total assets varies between 0% and 75.8%, while growth rate of total assets varies between 76.9% and 917.5%. Finally, for the macroeconomic indicators, the inflation (INF) rate varies between 3.8% and 12%, the real interest rate (INT) varies between -0.5% and 6.87%. The mean value GDP growth rate is 8%, however it reached almost 11% in 2006-07 followed by 4% in 2007-08 because of the global financial crisis.

Panels A and B of Table 2.3 show the comparative study on mean values of the dependent and some selected explanatory variables in terms of bank-size groups and ownership types, respectively.19 We find wider variations comparing the statistics across bank-size groups. Panel A shows that large banks (17.1%) have almost three times higher ROE than small banks (6.5%). Regarding credit risk, small banks have the highest average credit risk (3.7%), followed by medium-sized banks (1.6%) and large banks (1.1%). On the other hand, small banks (25.5%) have the highest non-interest income, followed by medium-sized banks (18%) and large banks (15%). The mean comparison tests show that

19 _{Based on total assets, three size classes have been considered. These are: small banks: assets up to Rs. 35}

billion, medium-sized banks: assets between Rs. 35 billion to Rs. 685 billion, large banks: assets above Rs. 685 billion.

the credit risk is significantly different between small and medium banks and between small and large banks. Overall, the results suggest that bank size heterogeneity does matter in the management of credit risk and in the pursuit of non-interest income.

We also find wider variations comparing statistics across ownership types. Panel B shows that private foreign banks have the highest average credit risk (2.8%), followed by public banks (1.7%) and private domestic banks (1.6%). Regarding non-interest income, the differences between public and private foreign banks are enormous. Private foreign banks (29%) earn almost twice as much as public (13.5%) and private domestic banks (15.1%) from non-interest sources of income. Private foreign banks in India face enormous restrictions on licensing/ branching and acquisition activities by RBI. Since foreign banks have prior experience, and better financial networks, they reasonably emphasise non- interest sources of income to render advisory services to the ever growing corporate sector (Pennathur, Subrahmanyam and Vishwasrao, 2012). The comparison tests of the means of all three ownership types are significantly different. Therefore, the overall results suggest that ownership does matter for maintaining credit risk and in the pursuit of non-interest income.

Figure 2.3 shows the Kernel density plots (based on Gaussian kernel) of credit risk and income diversification for all three banking size groups and ownership types. The upper panel shows an asymmetric distribution for all size-groups and ownership types, indicating small banks and the private foreign banks have the highest credit risk. Similarly, the lower panel of Figure 2.3 shows the kernel density plots of income diversification. It reveals an asymmetric distribution for all size-groups and ownership types, indicating small banks and private foreign banks have the most diversified portfolios.

Figure 2.3

Kernel density estimates (KDE) for Credit Risk and Income Diversification by bank size groups and ownership types.

Note: Based on total assets, three size classes have been considered. These are: small banks: assets up to Rs. 35 billion, medium-sized banks: assets between Rs. 35 billion to Rs. 685 billion, large banks: assets above Rs. 685 billion. Since credit risk ratio is limited by zero, we applied a logistical transformation to create kernel density plot. Kernel density plots on the upper panel show the credit risk, while plots on the lower panel show the income diversification for all size groups and ownership types.

The correlation matrix between independent variables is presented in table 2.4. It suggests that the variables used in our study do not possess a serious multicollinearity problem. Gujarati (2003) explains that a serious multicollinearity problem will arise if the pair-wise correlation coefficient between two regresses exceeds 0.8.20