FACTORS AFFECTING BANK CREDIT IN INDIA

Chapter-6 FACTORS AFFECTING BANK CREDIT IN INDIA Banks deploy credit as per their credit or loan policy. Credit policy of a bank, basically, provides a direction to the use of funds, controls the size and composition of the loan portfolio and also influences credit decisions of a bank. A systematic credit policy helps a bank in attaining its goals and at the same time serving public at large. Banks require a clear-cut credit policy to conduct its lending business in an orderly and safe manner so that its loan portfolio remains balanced in terms of size, type, maturity, security thereby providing steady earnings. Banks also have a social obligation of meeting diverse credit needs of various sections of the community, but it cannot afford to lend funds universally and incur losses. So, banks have to frame an appropriate credit policy. The policy formulators in a bank must be cautious in framing its credit policy as lending activity of banks affect both the bank and public at large. There are several factors that influence banks credit policy thereby affecting credit deployment of funds by the banks. So, banks must consider such factors that are likely to influence the credit policies of a bank and its credit deployment. The present chapter is an attempt to study the factors affecting bank credit and also analyses selected factors in various bank groups in India affecting their credit deployment. 6.1 PRINCIPLES OF LENDING Major source of funds for lending comes through deposits, so banks must assure about the security of funds. Customers can anytime demand their deposits lying with the bank and banks are expected to honour their request. So, banks should follow certain principles of lending. These are safety, liquidity, security, diversification of risk, purpose, profitability and policy validation. 1. Safety: The safety of the fund is the most important principle of lending. Banks should take the calculated risk and ensure that the funds lent to the borrowers must be repaid by the borrowers in time. 219

2. Liquidity: Liquidity means the ability of the bank to convert the asset into cash. Banks are not allowed to lend the whole deposits of the customers lying with the banks. They have to keep their sizable proportion of deposits with the RBI in the form of CCR; and another portion should be invested in approved securities in the form of SLR, so that they can honour the demands made by the depositors. 3. Security: Here, security refers to collateral security. In case of higher risk, banks are required to take extra security in the form of any collateral security, viz. immovable property, shares, government approved securities, etc. Banks can sell the collateral security in case of default by the customer in the repayment of loan and can recover the lent amount. But this action will be taken as a last resort after taking all other steps. 4. Diversification of Risk: Banks should not lend the entire funds to particular individuals or for one type of industry. The banks should diversify the advances. The diversification means spreading of funds over a large number of borrowers over different maturity periods. It helps in minimizing the risk inherent in grants of loan. 5. Purpose of Loan: While granting the loans, banks should ensure about the purpose of loan. If the loan is given for productive purposes then it will be repaid by the borrowers from their earnings, but if the loan is granted for unproductive purposes or for some other speculative purposes then it may result into default because of inability of the borrower. So, banks must enquire about the purpose of loan. 6. Profitability: Banks have to make sufficient income to pay the interest on deposits as well as bear administrative expenses. The main income of the bank is the difference between the lending and the borrowing rate. But the banks should not make profits on the cost of safety and liquidity of the loans. 7. Policy Authentication: The lending should be refrained with: 220

(i) RBI s credit policy. (ii) Bank credit policy and various product specific parameters. (iii) Lending should not be opposed to national policy. (iv) Lending to national priorities sectors, viz. loans to priority sectors, export at concessional rate of interest. 6.2 FACTORS AFFECTING BANK CREDIT There are numerous factors that influence the credit policy of a bank. There are also certain factors that are considered in credit analysis of an applicant that affect credit deployment of a bank. The factors affecting bank credit have been categorized as general factors and credit analysis factors. A. General Factors These are the factors that influence and determine the credit policy of a bank. These include: 1. Capital Position: The capital position of a bank is an important factor influencing its credit policy. The capital of a bank serves as a cushion against losses which may arise in future. A bank with a strong capital base can afford to take more risk in lending than banks with a lower capital base. Banks with large capital structure can afford to follow a liberal lending policy and provide different types of loans which can be a difficult task for banks with weak capital position. Further, capital adequacy norms also determine the amount of risk assumed in lending operations. 2. Earnings Requirement: Earnings are essential for the successful operations of a bank. Banks, usually, consider earnings as an important factor in determining its credit policy. Banks with income as the primary objective in their lending policies, will follow an aggressive policy which may include providing large amount of term or consumer loans which are normally made at a higher rate of interest due to high risk involved in them. But fulfilling the objective of profitability banks should not take undue risks and 221

maintain more liquid secondary reserve or include securities with shorter maturity periods and less credit risk in investment account. 3. Variability of Deposits: Fluctuations in deposits influence the loan policy of a bank. Banks experiencing wide fluctuations in deposits or declining deposits will follow a conservative lending policy and cannot take risk of making term loans. Whereas banks with stable and growing deposits can afford to be more liberal in their lending policy and take more chances with loans. 4. State of Economy: The economic conditions of an area being served also have a significant bearing on banks credit policy. A bank operating in an area experiencing seasonal and cyclical fluctuations cannot afford to have a liberal lending policy, whereas stable economy is conducive to a liberal lending policy as possibility of fluctuations in the level of deposits and loan demands is limited. Banks should also consider national economy as the factors affecting nation as a whole may eventually affect local conditions. 5. Monetary Policy: The lending policy of a bank has relationship with the monetary policies framed by the central bank. The monetary policy determines the lending capacity of banks by bringing variations in cash reserve ratio (CRR) and statutory liquidity ratio (SLR) requirements. The availability of deployable funds with the banks depends on the monetary policy. If the CRR and SLR requirements are increased, lending capacity of banks is restricted. 6. Ability and Experience of Loan Officers: Loan officers of a bank play a pivotal role in implementation of loan policies. A Bank should consider skill and competence of its loan officers while framing its loan policy. When a bank has knowledgeable and experience staff it can operate in diverse forms of loan. Banks 222

should also take steps to train and educate staff in each and every field of lending to cater varied needs of public. 7. Competitive Position: The competitive position of a bank may also influence its loan policy. Banks refrain from entering the loan fields where strong competing institutions exist. Some competing banks are such experts in certain fields of lending that their presence may affect the credit policy of other banks. 8. Credit Needs of the Area Served: The lending policy of a bank shall take note of the area being served by it. A bank is supposed to meet the loan demands of all the local borrowers, and if it fail to do so then there will be little justification for its existence in that area. Mostly, the credit needs in an area arise from the dominant economic activity of the area. If a bank is located in an area where economy is predominantly dependent on agriculture, bank must tailor its credit policy to meet loan demands of the farmers. But banks must meet the loan requests that are logically and economically sound. Thus, there are various factors which affect credit policy of a bank and also many aspects of banks credit policy are determined from the guidelines of Reserve Bank of India (RBI). 6.3 FACTORS AFFECTING BANK CREDIT BY VARIOUS BANK GROUPS IN INDIA There are numerous factors that affect allocation of credit by various bank groups in India. The variables selected under the study as factors affecting deployment of bank credit by various bank groups in India are capital, deposits, borrowings, non-performing assets (NPAs), profits, number of employees and number of offices. Capital of a bank means financial resources available for use; deposits are the money placed into a banking institution for safekeeping; borrowing represents amount received in exchange for an obligation to pay back usually at a greater value at a particular time in the future; NPAs refer to the loans that are in jeopardy of 223

default; profits are the money a bank makes after accounting for all the expenses; number of employees represent human assets of a bank; and number of offices indicate branches of a bank in various areas. The analysis of factors affecting bank credit has been done on the basis of various statistical techniques like descriptive statistics, viz. minimum, maximum, range, average, standard deviation, coefficient of variation, and exponential growth rate. Kurtosis, skewness and one sample Kolmogorov-Smirnov test have been applied for checking the normality of the data. Correlation has been applied to study the association between different factors which affect the credit deployment pattern; and step-wise multiple regression analysis has been used to look for different combinations of variables that explain variation in advances of the bank groups in India. The regression is also helpful in eliminating some of independent variables which are not required for the purpose, as some of them being correlated with other variables don t add any value to the regression model. However, the following variables have been examined for the purpose of this study: Dependent Variable: Y = advances (Rs. in crore) Independent Variables: X1 = Capital (Rs. in crore) X2 = Deposits (Rs. in crore) X3 = Borrowings (Rs. in crore) X4 = Investments (Rs. in crore) X5 = NPAs (Rs. in crore) X6 = Profits (Rs. in crore) X7 = Number of employees X8 = Number of offices. 224

Tables 6.1 to 6.12 show descriptive statistics, correlation and stepwise multiple regression of various selected variables which affect bank credit. 6.4.1 STATE BANK OF INDIA (SBI) & ITS ASSOCIATES The descriptive statistics of various selected variables as factors affecting bank credit in the case of SBI & its Associates during the period 1997-98 to 2011-12 have been presented in Tables 6.1 to 6.3. Table 6.1 DESCRIPTIVE STATISTICS OF SBI & ITS ASSOCIATES Factors Indicators Advances (Y) Capital (X1) Deposits ( X2) Borrowings Investments (X3) (X4) NPAs (X5) Profits (X6) Number of Employees (X7) Number of Offices (X8) Mean 435722.60 1090.67 623847.20 51883.20 240799.33 20219.93 6739.67 280725 15246 Std. Deviation 351220.64 136.29 395095.84 53249.15 108075.84 8916.71 4363.12 17623.48 2280.59 C.V. (%) 80.61 12.50 63.33 102.63 44.88 44.10 64.74 6.28 14.96 EGR (%) 20.69 1.01 15.62 26.68 11.54 3.52 16.84-1.00 2.87 Minimum 97567.00 1036.00 173603.00 8851.00 72703.00 12541.00 1466.00 249008.00 13334.00 Maximum 1151991.00 1566.00 1405024.00 158782.00 417322.00 48215.00 15334.00 308817.00 20260.00 Range 1054424.00 530.00 1231421.00 149931.00 344619.00 35674.00 13868.00 59809.00 6926.00 Skewness 0.903 3.451 0.817 1.053 0.194 2.522 0.661 0.089 1.239 Kurtosis -0.497 12.524-0.591-0.397-0.891 7.246-0.771-0.278 0.208 One sample Kolmogorov- Smirnov Sig. 0.588 0.057 0.705 0.380 0.893 0.180 0.703 0.860 0.244 Table 6.1 reveals that in the case of SBI & its Associates, on an average, Y = Rs. 435722.60 crore, X1 = Rs.1090.67 crore, X2 = Rs. 623847.20 crore, X3 = Rs. 51883.20 crore, X4 = Rs. 240799.33 crore, X5 = Rs. 20219.93 crore and X6 = 6739.67 crore during the period of study. The number of employees (X7) and offices (X8) are 280725 and 15246 respectively. A huge variation exists in X3, Y, X6 and X2 exhibiting the percentages of 102.63, 80.61, 64.74, and 63.33 respectively. In SBI & its Associates, variables such as borrowings (26.68%), advances (20.69%), profits (16.84%), deposits (15.62%), and investments (11.54%) grew significantly, whereas number of employees recorded a negative growth of - 225

1.00 per cent during the period of study. NPAs, number of offices and capital recorded a nominal growth of 3.52 per cent, 2.87 per cent and 1.01 per cent respectively during the study period. All the variables are found to be normally distributed except X1 and X5 which have kurtosis greater than 3 indicating more than a normal distribution and having high probability for extreme values. Table 6.2 CORRELATION COEFFICIENT MATRIX OF SBI & ITS ASSOCIATES Y X1 X2 X3 X4 X5 X6 X7 X8 Y X 1 0.364 (0.183) X 2 0.995** X 3 0.993** X 4 0.918**) X 5 0.695** (0.004) X 6 0.977** X 7-0.506 (0.054) X 8 0.986** 0.343 (0.211) 0.312 (0.257) 0.275 (0.320) 0.082 (0.770) 0.376 (0.167) -0.543* (0.037) 0.326 (0.235) 0.987** 0.951** 0.686** (0.005) 0.985** -0.526* (0.044) 0.981** 0.900** 0.722** (0.002) 0.963** -0.418 (0.121) 0.994** 0.585* (0.022) 0.957** -0.591* (0.020) 0.898** 0.632* (0.011) 0.099 (0.725) 0.780** (0.001) ** Correlation is significant at 0.01 level (2-tailed). * Correlation is significant at 0.05 level (2-tailed). -0.570* (0.027) 0.955** -0.381 (0.161) Table 6.2 shows the correlation among all the selected variables of SBI & its Associates. The standardized values shown in the correlation table fall in the range from 0-1. The table depicts a highly positive and statistically significant correlation between Y (advances) and X2 (deposits), X3 (borrowings), X4 (investments), X6 (profits), and X7 (number of bank offices) at 1 per cent level of significance, whereas Y (advances) has been found negatively but moderately, and statistically significantly correlated with X6 (number of employees) at 5 per cent level of significance. Y has been found positively and moderately correlated with X1 (capital) and X4 (NPAs) with the values of 0.357 and 0.501 respectively. Thus, in order to augment advances in SBI & its Associates and their deposits (X2), borrowings(x3), and number 226

of employees (X7) need to be increased; and the level of NPAs (X5) is required to be curtailed considerably. Table 6.3 MULTIPLE REGRESSION ANALYSIS OF SBI & ITS ASSOCIATES Steps Intercept X2 X3 X7 X5 R 2 Adjusted R 2 I II III IV V -116277.895 (-6.708) -32626.116 (-1.135) 446326.352 (2.245) 729745.80 (6.299) 907299.059 (8.391) 0.885** (37.330) 0.527** (4.753) 0.250 (1.696) F-ratio _ 0.991 0.990 1393.527** 2.691** (3.271) 4.520** (4.414) 6.245** (47.027) 5.761** (29.873) 0.995 0.994 1222.020** -1.429* (-2.427) -2.201** (-5.487) -2.970** (-7.327) The figures in parentheses represent the t-values. ** Refers to 1 per cent significance level * Refers to 5 per cent significance level _ 0.997 0.996 1148.785** _ 0.996 0.995 1488.777** 3.124* 0.998 0.997 1642.975** (2.971) Table 6.3 highlights the results of step-wise multiple regression analysis for the study period. It can be seen from the table that variable X2 (deposits) enters in the regression model at the first step, singularly explaining 99.00 per cent variation in Y (advances) with regression coefficient 0.885. At the second step, variable X3 (borrowings) enters the analysis and together with X2 explains 99.40 per cent of variation in the advances. One unit of increase in X3 leads to 2.691 units increase in Y. At the third step, variable X7 (number of employees) enters with regression coefficient -1.429 along with the variables X3 and X2. But X2 becomes less significant. This is due to the principle of multi-collinearity which means that there is some dependency between independent variables. In the fourth step, X2 is removed, borrowings with regression coefficient of 6.245 and number of employees with regression coefficient -2.201 collectively explain 99.50 per cent variation in advances. In the last step, X5 enters into the analysis and finally borrowings with regression coefficient of 5.761, number of employees with regression coefficient -2.970 and NPAs with the regression coefficient 3.124 collectively explain 99.70 per cent variation in advances. F- test for the model is found to be highly significant at 1 per cent level of significance. 227

The multivariate analysis for the period concludes: Y = 907299.059+ 5.761 X3 2.970 X7 +3.124 X5 + e. Where, e is the error term. After the fifth step, no other variable was found to significantly affect advances of the bank. The regression coefficients of three variables X3, X7 and X5 explain 99.70 per cent variation in Y. So, only these three variables were found to significantly affect the advances of SBI & its associates during the period 1997-98 to 2011-12. 6.4.2 NATIONALISED BANKS The descriptive statistics of various variables as factors affecting bank credit in Nationalised Banks during 1997-98 to 2011-12 have been presented in Tables 6.4 to 6.6. Table 6.4 DESCRIPTIVE STATISTICS OF NATIONALISED BANKS Factors Indicators Advances (Y) Capital (X1) Deposits ( X2) Borrowings Investments (X3) (X4) NPAs (X5) Profits (X6) Number of Employees (X7) Number of Offices (X8) Mean 919392.73 13643.47 1357960.33 85845.13 473377.13 35185.47 13365.93 494434 37777 SD 828218.69 1928.73 1041748.88 98082.48 288126.11 10679.29 11114.79 37171.88 5392.92 C.V. (%) 90.08 14.14 76.71 114.26 60.87 30.35 83.16 7.52 14.28 EGR (%) 23.40 0.34 18.15 37.10 14.26 1.92 24.80-1.17 2.74 Minimum 162308.00 11294.00 358126.00 5069.00 154399.00 24786.00 1792.00 466063.00 33263.00 Maximum 2725316.00 17958.00 3596989.00 306151.00 1089948.00 69048.00 34180.00 570595.00 50729.00 Range 256308.00 6664.00 3238863.00 301082.00 935549.00 44262.00 32388.00 104532.00 17466.00 Skewness 1.101 0.997 1.096 1.215 1.007 2.479 0.809 1.436 1.367 Kurtosis 0.105 0.796 0.052 0.458 0.073 7.619-0.559 0.507 One sample Kolmogorov- Smirnov Sig. 0.605 0.795 0.553 0.379 0.619 0.127 0.672 0.121 0.361 Table 6.4 reveals that in the case of nationalised banks, the mean value of Y = Rs. 919392.73 crore, X1 = Rs.13643.47 crore, X2 = Rs. 1357960.33 crore, X3 = Rs. 85845.13 crore, X4 = Rs. 473377.13 crore, X5 = Rs. 35185.47 crore, and X6 = Rs. 13365.93 crore during the period of study. The number of employees (X7) and offices (X8) are 494437 and 37777 228

respectively. There is a the huge variation in X3, Y, X6, X2 and X4 of 114.26 per cent, 90.08 per cent, 83.16 per cent, 76.71 per cent and 60.87 per cent respectively. In Nationalised banks, variables like borrowings (37.10%), profits (24.80%), advances (23.40%), deposits (18.15%), and investments (14.26%) grew significantly, whereas number of employees recorded a negative growth of -1.17 per cent during the period under study. Number of offices, NPAs and Capital recorded a nominal growth of 2.74 per cent, 1.92 per cent and 0.34 per cent respectively during the study period. All the variables were found to be normally distributed except X5 which has kurtosis greater than 3, indicating sharper distribution than a normal one and having high probability for extreme values. Table 6.5 CORRELATION COEFFICIENT MATRIX OF NATIONALISED BANKS Y X1 X2 X3 X4 X5 X6 X7 X8 Y X 1 0.419 (0.120) X 2 0.999** X 3 0.991** X 4 0.990** X 5 0.604* (0.017) X 6 0.985** X 7-0.451 (0.091) X 8 0.995** 0.424 (0.115) 0.472 (0.075) 0.427 (0.112) 747** (0.001) 0.389 (0.152) 0.166 (0.554) 0.464 (0.081) 0.990** 0.993** 0.609* (0.016) 0.988** -0.463 (0.082) 0.994** 0.984** 0.632* (0.011) 0.969** -0.405 (0.134) 0.988** 0.628* (0.012) 0.991** -0.521* (0.046) 0.982** 0.550* (0.034) -0.037 (0.896) 0.665** (0.007) ** Correlation is significant at 0.01 level (2-tailed). * Correlation is significant at 0.05 level (2-tailed). -0.540* (0.038) 0.969** -0.381 (0.161) Table 6.5 depicts the correlation among all the selected variables of nationalised banks. The study exhibits a highly positive and statistically significant relation between Y (advances) and X2 (deposits), X3 (borrowings), X4 (investments), X6 (profits), and X8 (number of bank offices) ranging from 0.985 to 0.999, whereas Y has been found to be having a negative but 229

statistically significant correlation with X7 (number of employees). The analysis exhibits a moderately statistically significant association between Y (advances) and X5 (NPAs) with the regression coefficient of 0.604. In order to increase the deployment of credit by nationalised banks, their deposits (X2), borrowings (X3), investments (X4), NPAs (X5), Profits (X6), and number of branches (X8) needs to be increased. Further, in certain cases, independent variables are highly correlated with each other which indicates that only one or two of them can be used to predict the dependent variable (advances). Table 6.6 MULTIPLE REGRESSION ANALYSIS OF NATIONALISED BANKS Step Intercept X2 R 2 Adjusted R 2 F-ratio I -159475.283 0.794** 0.999 0.999 9379.724** (-11.502) (96.804) The figures in parentheses represent the t-values. **Refers to 1 per cent significance level The results of step-wise multiple regression analysis for the study period are exhibited in Table 6.6. The table reveals that X2 (deposits) enters in the regression model at the first step. It singularly explains 99.90 per cent variation in Y (advances) with regression coefficient of 0.794. Thus, it means that one unit of increase in X2 leads to 0.794 units increase in Y. After the first step, no other variable was found to significantly affect Y in the case of nationalised banks. Thus, the regression coefficient of X2 explains 99.90 per cent variation in advances during 1997-98 to 2011-12. F-Test for the model is also highly significant at 1 per cent level of significance. Based on the model the equation can be written as: Y= -159475.283 + 0.794 X2 + e. Where, e is the error term. 6.4.3 PRIVATE SECTOR BANKS The descriptive statistics of various variables as factors affecting bank credit in private sector banks during the period 1997-98 to 2011-12 have been presented in Tables 6.7 to 6.9. 230

Table 6.7 DESCRIPTIVE STATISTICS OF PRIVATE SECTOR BANKS Factors Indicators Advances (Y) Capital (X1) Deposits ( X2) Borrowings Investments (X3) (X4) NPAs (X5) Profits (X6) Number of Employees (X7) Number of Offices (X8) Mean 337706.40 3350.73 450526.40 76332.73 196000.13 10597.33 6729.87 116394.80 7542.53 SD 298850.48 1169.09 357601.85 75131.82 151647.94 5270.20 6749.48 61163.49 2752.74 C.V. (%) 88.49 34.89 79.37 98.43 77.37 49.73 100.29 52.55 36.50 EGR (%) 27.80 8.73 22.88 34.90 22.36 11.82 28.60 11.52 7.19 Minimum 35420.00 1689.00 69516.00 2085.00 26590.00 3186.00 709.00 59374.00 4941.00 Maximum 966403.00 4783.00 1174587.00 258420.00 525982.00 18768.00 22718.00 248284.00 13976.00 Range 930983.00 3094.00 1105071.00 256335.00 499392.00 15582.00 22009.00 188910.00 9035.00 Skewness 0.836-0.212 0.752 1.247 0.873 0.301 1.272 0.802 1.270 Kurtosis -0.390-1.573-0.619 1.028-0.091-1.209 0.857-0.482 0.733 One sample Kolmogorov- Smirnov Sig. 0.684 0.845 0.707 0.571 0.743 0.886 0.489 0.535 0.576 Table 6.7 reveals that in private sector banks the mean value of Y = Rs. 337706.40 crore, X1 = Rs.3350.73 crore, X2 = Rs. 450526.40 crore, X3 = Rs. 76332.73 crore, X4 = Rs. 196000.13 crore, X5 = Rs. 10597.33, and X6 = Rs. 6729.87 crore during the period of study. The number of employees (X7) and offices (X8) are 116394.80 and 7542.53 respectively. Value of one sample Kolmogorov-Smirnov in all the variables is found to be greater than 0.05; and all the variables have less than 3 value of kurtosis which indicates that these were normally distributed. There is a huge variation in X6, Y, X3, X2 and X5 of 100.29 per cent, 98.43 per cent, 88.49 per cent, 79.37 per cent and 77.37 per cent respectively. In private sector banks, exponential growth rate has revealed a growth of 34.90 per cent in borrowings, 28.60 per cent in profits, 27.80 per cent in advances, 22.88 per cent in deposits, 22.36 per cent in investments, 11.82 per cent in NPAs, 11.52 per cent in number of employees, 7.19 per cent in number of offices and 8.73 per cent in capital. 231

Table 6.8 CORRELATION COEFFICIENT MATRIX OF PRIVATE SECTOR BANKS Y X 1 0.912** (.000) X 2 0.999** X 3 0.972** X 4 0.997** X 5 0.885** X 6 0.988** X 7 0.989** X 8 Y X1 X2 X3 X4 X5 X6 X7 X8 0.921** 0.837** 0.910** 0.840** 0.849** 0.894** 0.966** 0.996** 0.886** 0.984* 0.988** 0.981** 0.923** 0.985** 0.952** 0.904** 0.990** 0.981** 0.880** 0.845** 0.970** 0.985**.839**.981** 0.000.984**.986**.874**.997** ** Correlation is significant at the 0.01 level (2-tailed)..970** Table 6.8 reveals the correlation among all the selected variables of private sector banks. The analysis reveals that Y (advances) has a highly significant correlation with all the selected variables, viz. X1 (capital), X2 (deposits), X3(borrowings), X4 (investments), X5 (NPAs,) X6 (profits), X7 (number of employees), and X8 (number of offices) with the values ranging from 0.885 to 0.999. Thus, all the selected variables have been found to be affecting Y which indicates a fairly good set of independent variables to correlate with advances. It is further revealed that the independent variables are highly correlated with each other which indicates that they are not independent of each other. Table 6.9 MULTIPLE REGRESSION ANALYSIS OF PRIVATE SECTOR BANKS Steps Intercept X2 X6 R 2 Adjusted R 2 I II -38528.494 (-7.685) 0.835** (94.575) F-ratio _ 0.999 0.998 8944.465** -27884.971 0.714** 6.507** 0.999 0.999 7923.886** (-5.636) (19.303) (3.319) The figures in parentheses represent the t-values. ** Refers to 1 per cent significance level 232

Table 6.9 reveals the results of step-wise multiple regression analysis for the study period. It can be seen from the table that X2 (deposits) enters in the regression model at the first step, singularly explaining 99.80 per cent variation in Y (advances) with regression coefficient 0.835. In the second step, X6 (profits) enters the analysis and together with X2 explains 99.90 per cent of variation in the advances. One unit of increase in X6 leads to 6.507 units increase in Y. F-test for the model is found to be highly significant at 1 per cent level of significance. The multivariate analysis for the period concludes: Y = -27884.971 + 0.714 X2 + 6.507 X6 + e Where, e is the error term. 6.4.4 FOREIGN BANKS The descriptive statistics of various variables as factors affecting bank credit in Foreign Banks during the period 1997-98 to 2011-12 have been presented in Tables 6.10 to 6.12. Table 6.10 DESCRIPTIVE STATISTICS OF FOREIGN BANKS Factors Indicators Advances (Y) Capital (X1) Deposits ( X2) Borrowings Investments (X3) (X4) NPAs (X5) Profits (X6) Number of Employees (X7) Number of Offices (X8) Mean 100871.87 13646.20 127912.40 45806.73 76602.60 3487.00 3621.47 20754.00 259.80 SD 67158.94 13482.27 81937.67 31863.69 59332.55 1789.01 2942.81 7584.72 39.10 C.V. (%) 66.58 98.80 64.06 69.56 77.46 51.31 81.26 36.55 15.05 EGR (%) 17.27 27.89 15.71 16.46 17.77 7.48 22.11 6.97 3.05 Minimum 29290.00 1780.00 42873.00 9855.00 18382.00 1928.00 630.00 11703.00 196.00 Maximum 229849.00 40631.00 276948.00 120422.00 200651.00 7134.00 9426.00 33969.00 323.00 Range 200559.00 38851.00 234075.00 110567.00 182269.00 5206.00 8796.00 22266.00 127.00 Skewness 0.598 0.936 0.628 1.115 1.039 1.224 0.778 0.375 0.241 Kurtosis -1.064-0.627-1.246 0.586-0.330-0.108-0.809-1.543-0.903 One sample Kolmogorov- Smirnov Sig 0.633 0.371 0.421 0.498 0.269 0.075 0.368 0.449 0.991 233

Table 6.10 reveals that in the case of foreign banks, on an average, Y = Rs. 100871.87 crore, X1 = Rs. 13646.20 crore, X2 = Rs. 127912.40 crore, X3 = Rs. 45806.73 crore, X4 = Rs. 76602.60 crore, X5 = Rs. 3487.00 crore, and X6 = 3621.47 crore during the period of study. The number of employees (X7) and branches (X8) are 20754 and 259.80 respectively. X1 recorded the highest variation of 98.80 per cent followed by profits (81.26) per cent, investments (77.46 per cent), borrowing (69.56 per cent), advances (66.58 per cent), deposits (64.06 per cent), and NPAs (51.31 per cent), whereas the lowest variation has been found with regard to number of offices, i.e., 15.05 per cent followed by number of employees, i.e., 36.55 per cent. All the selected variables are found to be normally distributed. The study has reflected a significant growth in variables like capital (27.89%), profits (22.11%), investments (17.77%), advances (17.27%), borrowings (16.46%) and deposits (15.71%), whereas exponential growth rate has shown a small growth of 6.97 per cent and 3.05 per cent in the number of employees and branches respectively during the study period. Table 6.11 CORRELATION COEFFICIENT MATRIX OF FOREIGN BANKS Y X 1 0.978** X 2 0.993** X 3 0.973** X 4 0.963** X 5 0.761** (0.001) X 6 0.981** X 7 0.868** X 8 Y X1 X2 X3 X4 X5 X6 X7 X8 0.928** 0.988** 0.974** 0.994** 0.845** 0.951** 0.803** 0.929** 0.967** 0.980** 0.823** 0.965** 0.862** 0.937** 0.973** 0.804** 0.966** 0.755** (0.001) 0.935** 0.884* 0.931* 0.757** (0.001) 0.930**.731** (0.002).555* (0.032).796** 0.853** 0.887** ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). 0.798** 234

Table 6.11 explains the correlation among all the selected variables of foreign banks. It is evident that Y (advances) has a significant correlation with all the variables, i.e., X1 (capital), X2 (deposits), X3 (borrowings), X4 (investments) X5 (NPAs,) X6 (profits), X7 (number of employees) and X8 (number of offices) with regression coefficient of 0.978, 0.993, 0.973, 0.963, 0.761, 0.981, 0.868 and 0.928 respectively. It implies that all the selected variables have a positive and statistically highly significant association between the dependent and independent variables. This indicates toward a choice of good set of independent variables to correlate with advances. Thus, all the selected variables are found to be affecting the advances. The advances can be increased, if all the variables show an improvement in their performance. The correlation matrix further reveals that the independent variables are found to be highly correlated with each other. Table 6.12 MULTIPLE REGRESSION ANALYSIS OF FOREIGN BANKS Steps Intercept X2 X6 R 2 Adjusted R 2 I II -3211.686 (-0.783) 0.814** (29.824) F-ratio _ 0.986 0.984 889.458** 2954.235 0.551** 7.590** 0.993 0.992 873.062** (0.871) (7.373) (3.650) The figures in parentheses represent the t-values. ** Refers to 1 per cent significance level. The results of step-wise multiple regression analysis for the study period have been presented in Table 6.12 which indicate that at the first step X2 (deposits) enters in the regression model and singularly explains 98.40 per cent variation in advances with regression coefficient of 0.814. At the second step, X6 (profits) enters into the analysis and together with X2 explains 99.20 per cent of variation in Y (advances). One unit of increase in X6 leads to 7.590 units increase in Y. The equation can be written as: Y = 2954.234 + 0.551 X2 +7.590 X6 + e Where, e is the error term. 235

After the second step, no other variable has been found to be significantly affecting advances of the bank. The regression coefficients of two variables, i.e., X 2 and X 6 explain 99.20 per cent variation in Y. Only these two variables are found to be significantly affecting the advances of foreign banks during the period 1997-98 to 2011-12. F-test for the model is also highly significant at 1 per cent level of significance. 6.5 CONCLUSION One of the main functions of a bank is to meet the credit needs of different sectors of the economy by using the funds of its depositors. But banks cannot lend the funds indiscriminately. Banks should follow general principles of lending, i.e., safety, liquidity, risk and profitability. Descriptive analysis has revealed a significant growth in the distribution of bank credit by all bank groups, i.e., SBI & its Associates (20.69%), nationalised banks (23.42%), private sector banks (27.80%) and foreign banks (17.27%). Correlation matrix depicts a significant association of all the independent variables with advances in the case of private sector banks and foreign banks, whereas in public sector banks (SBI & its Associates and nationalised banks) advances have a significant association with deposits, borrowings, investment, NPAs, profits and number of offices. The analysis further reveals that in the case of SBI & its Associates, out of selected factors, advances are found to be significantly associated and affected with the borrowings, number of employees and NPAs. However, in the case of nationalised banks, credit deployment has been found to be significantly associated and affected by deposits, whereas in private sector banks and foreign banks, advances are found to be significantly associated and affected by deposits and profits. Thus, analysis reveals that credit deployment of banks is significantly affected by certain factors, and banks need to consider such factors while deploying bank credit. 236