CHAPTER III FINANCIAL INCLUSION INITIATIVES OF COMMERCIAL BANKS

CHAPTER III FINANCIAL INCLUSION INITIATIVES OF COMMERCIAL BANKS "Efficient financial systems are vital for the prosperity of a community and a nation as whole. To ensure that poor people are included in the benefits of development, it is necessary that these vast numbers have consistent access to financial services, access that can translate into a key element of economic growth and poverty alleviation: options." - Jose Antonio Ocampo, Under Secretary General for Economic & Social Affairs. The Economic development of any Nation predominantly depends on the equitable regional growth of the country. India majorly being a rural economy, bulk of the population in the rural region is below the standard of living. The reason for below the standard of living is the very less circulation of money and employability in the rural areas leading to disparity of purchasing power than the urban centres. Henceforth the requirement of Commercial banks came in to existence as a threshold financial institution in rural India to bridge the gap between free flow of money and rural population. Commercial bank plays a significant role in the development of the nations and it constitutes the life blood of an advanced economic society. The growth of sound commercial banking will lead the nation at the top in the world; equivalent to the status of the developed economy. Commercial bank is a lever of the economy; it mobilizes the spreader saving of the society and redistributes them into more useful routes. It receives deposits from the public and it makes loans and advances out of the public deposits. In addition, they satisfy the financial needs of the various sectors such as agriculture, industry, trade, communication, whereby they play a significant role in the process of economic social needs. The functions performed by banks, since 78

recently, are becoming more people-oriented irrespective of rich and the poor. The service rendered by a modern commercial bank is immeasurable and it enables large payments to be made over the world through e-banking. The Government of India has taken a decision to include that population who are not availing the services in the fold of monetary products and services. Reserve Bank of India (RBI) initiated the commercial banks to serve that segment of the society in the rural India. There are certain initiatives taken by the commercial banks such as opening of No-frills accounts, Kisan Credit Card (KCC), General Credit Card (GCC), Over Draft (OD) facilities, appointment of Business Correspondents (BCs), Extension of Automated Teller Machine (ATM) centres etc., strengthening the lead bank scheme, opening the ultra small branches, automation of branches, speedy services are the further some. Today the banks are more willing to cover the financially excluded segment of the society by extending both their operations and acquiring the customers. In this respect, the banks have taken financial inclusion initiative on the basis of villages covered over the population of 2000, between the population 1600 and 2000, and the population below 1600 in the villages in India. The Commercial banks are generally classified in to Public sector banks, Private sector banks and Foreign banks. All the Public sector banks, Private sector banks and Foreign banks have the responsibility to spread their services in rural areas to strengthen the economy of the households thereby the Nation. Initiation has been taken by the banks for avoiding exclusion in a phase wise to cover the villages in India (excepting north eastern and major hill stations). In addition, Regional rural banks and Co-operative banks are also initiated by RBI through various commercial banks and NABARD respectively for attaining full financial inclusion in India. In this respect to understand the present position / effectiveness of Financial Inclusion 79

initiatives taken by the commercial banks in India and in particular Nagapattinam district, the quarterly data were collected from the RBI website since 2006 to 2012. The following statistical tools like t test, ANOVA, Discriminant Analysis, Regression and econometric tools like PANEL data regression are applied to see the fixed and random effects of the data to identify the influence of Financial Inclusion Initiatives. 3.1 PSBs AND OSCBs IN INDIA Table 3.1 One Way ANOVA between PSBs and OSCBs in India No. of offices No. of employees Business per employee Profit per employee Capital and Reserves & Surplus Deposits Investments Sum of Mean df Squares Square Between Groups 2.24E+10 3 7.47E+09 Within Groups 9.68E+08 28 34578306 Total 2.34E+10 31 Between Groups 3.83E+12 3 1.28E+12 Within Groups 3.5E+10 28 1.25E+09 Total 3.87E+12 31 Between Groups 14955.11 3 4985.03 Within Groups 23980.59 28 856.45 Total 38935.71 31 Between Groups 14.17 3 4.72 Within Groups 5.4 28 0.19 Total 19.57 31 Between Groups 4.15E+13 3 1.38E+13 Within Groups 2.34E+13 28 8.37E+11 Total 6.49E+13 31 Between Groups 6.45E+15 3 2.15E+15 Within Groups 2.65E+15 28 9.46E+13 Total 9.1E+15 31 Between Groups 7.73E+14 3 2.58E+14 Within Groups 2.5E+14 28 8.93E+12 Total 1.02E+15 31 F Sig. 216.12 0.00 1022.8 0.00 5.82 0.003 24.47 0.00 16.51 0.00 22.73 0.00 28.83 0.00 80

Sum of Mean df Squares Square Between Groups 3.51E+15 3 1.17E+15 Advances Within Groups 1.76E+15 28 6.28E+13 Total 5.26E+15 31 Between Groups 5.14E+13 3 1.71E+13 Interest income Within Groups 2.66E+13 28 9.51E+11 Total 7.8E+13 31 Between Groups 1.33E+12 3 4.43E+11 Other income Within Groups 4.01E+11 28 1.43E+10 Total 1.73E+12 31 Between Groups 2.19E+13 3 7.31E+12 Interest expended Within Groups 1.25E+13 28 4.46E+11 Total 3.44E+13 31 Between Groups 2.88E+12 3 9.6E+11 Operating expenses Within Groups 8.35E+11 28 2.98E+10 Total 3.72E+12 31 Between Groups 13.38 3 4.46 Cost of Funds Within Groups 14.4 28 0.51 (CoF) Total 27.79 31 Return on Between Groups 22.03 3 7.34 advances adjusted Within Groups 10.57 28 0.37 to CoF Total 32.61 31 Between Groups 266.56 3 88.85 Wages as % to total expenses Within Groups 238.21 28 8.5 Total 504.78 31 Between Groups 3.8 3 1.26 Return on Assets Within Groups 1.02 28 0.03 Total 4.83 31 Between Groups 28.89 3 9.63 CRAR Within Groups 22.33 16 1.39 Total 51.22 19 F Sig. 18.6 0.00 18 0.00 30.89 0.00 16.4 0.00 32.18 0.00 8.67 0.00 19.46 0.00 10.444 0.00 34.578 0.00 6.899 0.003 81

The above table 3.1 shows that there is a significant difference in No. of offices, No. of employees, Business per employee, Profit per employee, Capital and Reserves & Surplus, Deposits, Investments, Advances, Interest income, Other income, Interest expended, Operating expenses, Cost of Funds (CoF), Return on advances adjusted to CoF, Wages as % to total expenses, Return on Assets and CRAR by PSBs and OSCBs at 1% level of significance. It means the No. of offices, No. of employees, Business per employee, Profit per employee, Capital and Reserves & Surplus, Deposits, Investments, Advances, Interest income, Other income, Interest expended, Operating expenses, Cost of Funds (CoF), Return on advances adjusted to CoF, Wages as % to total expenses, Return on Assets and CRAR in PSBs and OSCBs were having significant differences at 1% level in India. 3.2. DISCRIMINANT ANALYSIS FOR POPULATION ON FINANCIAL INCLUSION Discriminant Analysis is used primarily to predict in Rural, Semi-urban, Urban, Metropolitan and No. of branches, Credit, Deposits. Tests of Equality of Group Means the results of univariate ANOVA s, carried out for each independent variable. An eigenvalue indicates the proportion of variance explained. A large eigenvalue is associated with a strong function. The canonical relation is a correlation between the discriminant scores and the levels of the dependent variable. A high correlation indicates a function that discriminates well. Wilks Lambda is the ratio of within groups sums of squares to the total sums of squares. A small lambda indicates that group means appear to differ. The associated significance value indicates whether the difference is significant. The Canonical Discriminant Function Coefficients indicate the unstandardized scores concerning the 82

independent variables. It is the list of coefficients of the unstandardized discriminant equation. Functions at Group Centroide indicates the average discriminant score for subjects in the two groups. Classification Results is a simple summary of number and percent of subjects classified correctly and incorrectly. Table 3.2.1 Group Statistics and Summary of Canonical Discriminant Functions - Eigenvalues for Population on Financial Inclusion Population Mean Std. Deviation Rural Semi-urban Urban Metropolitan Total No. of branches 909.3714 1067.45535 Credit 6322.4981 8297.87760 Deposits 10470.3241 12407.20642 No. of branches 544.9429 676.76059 Credit 7825.9575 11310.88555 Deposits 15057.4054 17738.80500 No. of branches 434.2526 494.52510 Credit 13433.6477 17889.92994 Deposits 22909.9157 24999.62436 No. of branches 389.7086 685.44603 Credit 54305.4456 156430.06070 Deposits 62332.9723 176325.65712 No. of branches 569.5689 786.86961 Credit 20471.8872 81425.72710 Deposits 27692.6544 91973.21943 Function Eigenvalue % of Variance Cumulative % Canonical Correlation 1.215 a 80.0 80.0.421 2.054 a 19.9 99.9.225 3.000 a.1 100.0.018 a. First 3 canonical discriminant functions were used in the analysis. Table 3.2.1 gives a canonical correlation of.421,.225 and.018 suggests the model explains 80.0%, 19.9% and 0.10% of the variation in the grouping variable. 83

Table 3.2.2 Wilks' Lambda for Population on Financial Inclusion Test of Function(s) Wilks' Lambda Chi-square df Sig. 1 through 3.781 864.813 9.000 2 through 3.949 183.391 4.000 3 1.000 1.146 1.284 This table 3.2.2 indicates the Wilks lambda significance of the discriminant function. A highly significant function (p <.000) and provides the proportion of total variability not explained, i.e. it is the converse of the squared canonical correlation. Table 3.2.3 Standardized Canonical Discriminant Function Coefficients for Population on Financial Inclusion Function 1 2 3 No. of branches 1.077.414.342 Credit.611 5.423-3.104 Deposits -1.587-5.079 3.648 The Table 3.2.3 interprets the discriminant coefficients (or weights). These three variables with large coefficients stand out as those that strongly predict allocation to the Rural, Semi-urban, Urban and Metropolitan. Table 3.2.4 Structure Matrix for Population on Financial Inclusion Function 1 2 3 Deposits -.428.485.763 * No. of branches.549.368.751 * Credit -.443.610.657 * Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. *. Largest absolute correlation between each variable and any discriminant function 84

Table 3.2.4 provides another way of indicating the relative importance of the predictors. The structure matrix table shows the corelations of each variable with each discriminate function. These Pearson coefficients are structure coefficients or discriminant loadings. Table 3.2.5 Canonical Discriminant Function Coefficients for Population on Financial Inclusion Function 1 2 3 No. of branches.001.001.000 Credit.000.000.000 Deposits.000.000.000 (Constant) -.476 -.147 -.578 Unstandardized coefficients The table 3.2.5 explains canonical discriminant function coefficient. These unstandardized coefficients are used to create the discriminant function (equation). It operates just like a regression equation. The discriminant function coefficients b or standardized form beta both indicate the partial contribution of each variable to the discriminate function controlling for all other variables in the equation. The difference in squared canonical correlation indicates the explanatory effect of the set of dummy variables. Table 3.2.6 Functions at Group Centroids for Population on Financial Inclusion Population Function 1 2 3 Rural.677.189.008 Semi-urban.091 -.166 -.028 Urban -.162 -.286.021 Metropolitan -.606.262 -.001 85

Group centroids table 3.2.6 shows the further way of interpreting discriminant analysis results is to describe each group in terms of its profile, using the group means of the predictor variables. These group means are called centroids. Table 3.2.7 Classification Statistics - Prior Probabilities for Groups for Population on Financial Inclusion Population Prior Cases Used in Analysis Unweighted Weighted Rural.250 875 875 Semi-urban.250 875 875 Urban.250 875 875 Metropolitan.250 875 875 Total 1.000 3500 3500 Table 3.2.7 provides Prior Probabilities for Groups. The table shows the unweighted weighted cases used in the analysis. Table 3.2.8 Classification Results for Population on Financial Inclusion Population Predicted Group Membership Rural Semi-urban Urban Metropolitan Rural 378 122 368 7 (43.2) (13.9) (42.1) (.8) Semi-urban 205 110 560 0 (23.4) (12.6) (64.0) (.0) Urban 94 59 698 24 (10.7) (6.7) (79.8) (2.7) Metropolitan 28 16 671 160 (3.2) (1.8) (76.7) (18.3) Total 875 (100) 875 (100) 875 (100) 875 (100) The classification results (Table 3.2.8) reveal that 43.2%, 64%, 79.8%, and 76.7% of respondents were classified correctly into Rural, Semi-urban, Urban and Metropolitan. This overall predictive accuracy of the discriminant function is called the hit ratio. 86

3.3 DISCRIMINANT ANALYSIS FOR REGION ON FINANCIAL INCLUSION Discriminant Analysis is used to predict membership in Central Region, Eastern Region, North Eastern, Northern, Southern, Western and No. of branches, Credit, Deposits. Table 3.3.1 Group Statistics and Summary of Canonical Discriminant Functions - Eigenvalues for Region on Financial Inclusion Region Mean Std. Deviation No. of branches 796.1540 1109.71670 Central Region Eastern Region North Eastern Northern Southern Western Total Credit 10310.0677 12802.63735 Deposits 22485.7630 28663.55652 No. of branches 670.4860 718.91422 Credit 11034.3894 25173.13651 Deposits 22157.2451 30746.64007 No. of branches 75.8529 156.83534 Credit 823.5542 1723.64095 Deposits 2248.8401 4685.92816 No. of branches 489.8329 538.69650 Credit 22826.3161 71254.74725 Deposits 31114.5204 91560.55245 No. of branches 927.9183 862.34149 Credit 32076.8120 45270.69482 Deposits 35170.7721 45621.78884 No. of branches 614.8800 831.07938 Credit 50356.7606 185250.58049 Deposits 60291.9412 202882.06536 No. of branches 569.5689 786.86961 Credit 20471.8872 81425.72710 Deposits 27692.6544 91973.21943 87

Function Eigenvalue % of Variance Cumulative % Canonical Correlation 1.160 a 58.6 58.6.371 2.086 a 31.4 90.0.281 3.027 a 10.0 100.0.163 a. First 3 canonical discriminant functions were used in the analysis. Table 3.3.2 gives a canonical correlation of.371,.281 and.163 suggests the model explains 58.6%, 31.4% and 10.0% of the variation in the grouping variable. Table 3.3.2 Wilks' Lambda for Region on Financial Inclusion Test of Function(s) Wilks' Lambda Chi-square df Sig. 1 through 3.773 899.911 15.000 2 through 3.897 381.576 8.000 3.973 94.251 3.000 This table 3.3.2 indicates the Wilks lambda significance of the discriminant function. A highly significant function (p <.000) and provides the proportion of total variability not explained, i.e. it is the converse of the squared canonical correlation. Table 3.3.3 Standardized Canonical Discriminant Function Coefficients for Region on Financial Inclusion Function 1 2 3 No. of branches 1.112 -.013 -.295 Credit 1.361 6.224-1.189 Deposits -1.583-6.013 2.263 The Table 3.2.3 interprets the discriminant coefficients (or weights). These three variables with large coefficients stand out as those that strongly predict allocation to the Central Region, Eastern Region, North Eastern, Northern, Southern and Western. 88

Table 3.3.4 Structure Matrix for Region on Financial Inclusion Function 1 2 3 No. of branches.959 * -.166.230 Deposits.256.127.958 * Credit.249.283.926 * Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. *. Largest absolute correlation between each variable and any discriminant function Table 3.3.4 provides another way of indicating the relative importance of the predictors. The structure matrix table shows the corelations of each variable with each discriminate function. These Pearson coefficients are structure coefficients or discriminant loadings. Table 3.3.5 Canonical Discriminant Function Coefficients for Region on Financial Inclusion Function 1 2 3 No. of branches.002.000.000 Credit.000.000.000 Deposits.000.000.000 (Constant) -.726.257 -.160 Unstandardized coefficients The table 3.3.5 explains canonical discriminant function coefficient. These unstandardized coefficients are used to create the discriminant function (equation). It operates just like a regression equation. The discriminant function coefficients b or standardized form beta both indicate the partial contribution of each variable to the discriminate function controlling for all other variables in the equation. The difference 89

in squared canonical correlation indicates the explanatory effect of the set of dummy variables. Table 3.3.6 Functions at Group Centroids for Region on Financial Inclusion Region Function 1 2 3 Central Region.261 -.449 -.070 Eastern Region.089 -.369 -.039 North Eastern -.637.171 -.146 Northern -.141 -.043.083 Southern.609.400 -.130 Western.007.159.353 Unstandardized canonical discriminant functions evaluated at group means Group centroids table 3.3.6 shows the further way of interpreting discriminant analysis results is to describe each group in terms of its profile, using the group means of the predictor variables. These group means are called centroids. Table 3.3.7 Classification Statistics - Prior Probabilities for Groups for Region on Financial Inclusion Region Prior Cases Used in Analysis Unweighted Weighted Central Region.167 500 500 Eastern Region.167 500 500 North Eastern.167 700 700 Northern.167 700 700 Southern.167 600 600 Western.167 500 500 Total 1.000 3500 3500 Table 3.2.7 provides Prior Probabilities for Groups. The table shows the unweighted weighted cases used in the analysis. 90

Table 3.3.8 Classification Results for Region on Financial Inclusion Predicted Group Membership Region Central Region Eastern Region North Eastern Northern Southern Western Total Central Region 115 59 188 80 (23.0) (11.8) (37.6) (16.0) Eastern Region 103 105 154 56 (20.6) (21.0) (30.8) (11.2) North Eastern 7 14 637 24 (1.0) (2.0) (91.0) (3.4) Northern 135 67 340 60 (19.3) (9.6) (48.6) (8.6) Southern 64 0 234 16 (10.7) (.0) (39.0) (2.7) Western 88 26 275 14 (17.6) (5.2) (55.0) (2.8) 55 (11.0) 66 (13.2) 18 (2.6) 87 (12.4) 260 (43.3) 82 (16.4) 3 (.6) 16 (3.2) 0 (.0) 11 (1.6) 26 (4.3) 15 (3.0) 500 (100) 500 (100) 700 (100) 700 (100) 600 (100) 500 (100) The classification results (Table 3.3.8) reveal that 37.6%, 30.8%, 91%, 48.6%, 39% and 55% of respondents were classified correctly into Central Region, Eastern Region, North Eastern, Northern, Southern and Western. This overall predictive accuracy of the discriminant function is called the hit ratio. 91

3.4 BANKING PROGRESS AND BANKING PRODUCTS ON FINANCIAL INCLUSION IN INDIA Table 3.4.1 Banking Progress and Financial Inclusion in India Sl.No Particulars Mar 10 Mar 11 Mar 12 June 12 1 2 3 4 5 6 7 Total No. of Branches 85457 91145 99242 99771 Percentage Increase - 6.66 16.13 4.18 No. of Rural Branches 33433 34811 37471 37635 Percentage Increase - 4.12 12.08 3.14 Banking outlets in Villages with population >2000 37791 66447 112130 113173 Percentage Increase - 75.82 96.71 49.86 Banking outlets in Villages with population <2000 29903 49761 69623 74855 Percentage Increase - 66.40 32.82 37.58 Banking Outlets through Brick & Mortar Branches 33378 34811 37471 37635 Percentage Increase - 4.29 12.26 3.19 Banking Outlets through BCs 34174 80802 141136 147167 Percentage Increase - 34.44 12.99 82.66 Urban Locations covered through BCs 447 3771 5891 6968 Percentage Increase - 43.62 1217.89 364.70 8 Banking Outlets through Other Modes 142 595 3146 3226 Percentage Increase - 319.01 2115.49 542.95 Source: www.rbi.org.in The table 3.4.1 interprets that that the number of branches, number of rural branches, villages covered on the basis of population size above and below 2000, brick and mortar branches, BCs, ULBCs and other modes have been raised since 2010. The maximum number BCs and other modes rose to 1217.89% and 2115.49% under Financial Inclusion Initiatives year ended March 2012 in India. 92

Table 3.4.2 Progress of Banking Products and Financial Inclusion in India Sl. No 1 2 3 4 5 6 7 8 9 10 Particulars Mar 10 Mar 11 Mar 12 June 12 No Frill A/Cs (No. In millions) 73.45 104.76 138.50 147.94 Percentage Increase - 42.62 88.56 25.35 Amount in No Frill A/Cs (Amt In billions) 55.02 76.12 120.41 119.35 Percentage Increase - 38.34 118.84 29.23 No Frill A/Cs with OD (No. in millions) 0.18 0.61 2.71 2.97 Percentage Increase - 238.89 1405.56 387.5 No Frill A/Cs with OD (Amt In billions) 0.10 0.26 1.08 1.21 Percentage Increase - 160 980 277.5 KCCs-Total-No. In million 24.31 27.11 30.23 30.76 Percentage Increase - 11.51 24.35 6.64 KCCs-Total-Amt In billion 1240.07 1600.05 2068.39 2094.00 Percentage Increase - 29.02 66.79 17.21 GCC-Total-No. in million 1.39 1.70 2.11 2.29 Percentage Increase - 22.30 51.79 16.18 GCC-Total-Amt In billion 35.11 35.07 41.84 43.21 Percentage Increase - (-0.11) 19.16 5.76 ICT A/Cs-through BCs (No. in millions) 13.26 31.65 57.08 62.77 Percentage Increase - 138.68 330.46 93.35 ICT A/Cs-Transactions (No. In millions) 26.52 84.16 141.09 45.96 Percentage Increase - 217.34 432.01 18.32 Source: www.rbi.org.in The table 3.4.2 explains that the number / amounts of no-frills account and nofrills account with OD, number / amounts of KCC and GCC and ICT based accounts through BCs / transactions have been raised since 2010. The maximum number / amounts of no-frills account with OD were 1405.56% and 980% in the year ended March 2012. It is clear that the GOI having more initiative on no-frills account to attain full financial inclusion in India. 93

3.5 MULTIDIMENSIONAL SCALING - PROXSCAL Table 3.5.1 Goodness of Fit Stress and Fit Measures Normalized Raw Stress.01299 Stress-I Stress-II S-Stress.11396 a.27335 a.03151 b Dispersion Accounted For (D.A.F.).98701 Tucker's Coefficient of Congruence.99349 PROXSCAL minimizes Normalized Raw Stress. a. Optimal scaling factor = 1.013. b. Optimal scaling factor = 1.048. Table 3.5.2 Common Space Final Coordinates Dimension 1 2 No-frill a/c numbers.950 -.007 KCC numbers -.269 -.258 No-frill a/c with OD numbers -.630.085 GCC numbers -.052.179 The below Figure 3.1, show the common space plot of the four selected products on financial inclusion. The Table 3.5.1 PROXSCAL multi-dimensional scaling technique goodness of fit test shows the Stress I and Stress-II values to be 0.11396 and 0.27335. The configurations derived from dimension 1 and 2 for the selected financial inclusion products using multidimensional scaling technique (Table 94

3.5.2), distinguishes the positive and negative dimensions, thus identifying the positive dimensions leading to financial inclusion products success. 3.1 OBJECT POINTS COMMON SPACE 3.6 REGRESSION FOR VILLAGES COVERED AND OFFICES, BCs AND OTHER MODES Table 3.6.1 Regression for Villages Covered and Offices Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1.986 a.973.959 26978.80 a. Predictors: (Constant), vc total branches 95

ANOVA a Model Sum of Squares df Mean Square F Sig. Regression 52231076463.79 1 52231076463.79 71.760.014 b 1 Residual 1455711826.95 2 727855913.47 Total 53686788290.75 3 a. Dependent Variable: villages covered gt b. Predictors: (Constant), vc total branches Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -1225038.14 166630.82-7.35.018 Vc total branches 58.884 6.951.986 8.47.014 a. Dependent Variable: villages covered gt The table 3.6.1 shows that there is a significant influence of total number of branches on the number of villages covered at 5% level of significance. The beta coefficient of 0.986 shows that every one unit increase in the number of branches there will be almost one unit increase in the number of villages covered. The R 2 Value of 0.973 depicts that the explained variance is higher where the explanatory variable (No. of. Branches) explains the dependent variable (No. of. Villages covered) in a better manner. Table 3.6.2 Regression for Villages Covered and BCs Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 1.000 a 1.000 1.000 1240.561 a. Predictors: (Constant), VCbc 96

1 ANOVA a Model Sum of Squares df Mean Square F Sig. Regression 53683710308.762 1 53683710308.762 34882.407.000 b Residual 3077981.988 2 1538990.994 Total 53686788290.750 3 a. Dependent Variable: villages covered gt b. Predictors: (Constant), VC bc Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 21158.618 1060.797 19.946.003 VC bc 1.027.006 1.000 186.768.000 a. Dependent Variable: villages covered gt The table 3.6.2 gives that there is a significant influence of total number of BCs on the number of villages covered at 5% level of significance. The beta coefficient of 1.000 shows that every one unit increase in the number of bbcs there will be almost one unit increase in the number of villages covered. The R 2 Value of 1.000 depicts that the explained variance is higher where the explanatory variable (No. of. BCs) explains the dependent variable (No. of. Villages covered) in a better manner. Table 3.6.3 Regression for Villages Covered and Other Modes Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1.998 a.996.995 9829.737 a. Predictors: (Constant), VCothers 97

ANOVA a Model Sum of Squares df Mean Square F Sig. Regression 53493540831.128 1 53493540831.128 553.627.002 b 1 Residual 193247459.622 2 96623729.811 Total 53686788290.750 3 a. Dependent Variable: villagescoveredgt b. Predictors: (Constant), VCothers Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 39906.625 7782.338 5.128.036 VCothers 144.064 6.123.998 23.529.002 a. Dependent Variable: villagescoveredgt The table 3.6.3 interprets that there is a significant influence of total number of other modes on the number of villages covered at 5% level of significance. The beta coefficient of 0.998 shows that every one unit increase in the number of other modes there will be almost one unit increase in the number of villages covered. The R 2 Value of 0.996 depicts that the explained variance is higher where the explanatory variable (Other modes) explains the dependent variable (No. of. Villages covered) in a better manner. 3.7 PANEL DATA REGRESSION MODEL FOR PENETRATION OF OFFICES AND EMPLOYEES, BUSINESS PER EMPLOYEE, INTEREST INCOME AND OTHER INCOME Table 3.7.1 Random-Effects for Number of Offices & Employees Mean dependent var 12660860 S.D. dependent var 13030964 Coefficient Std. Error t-ratio p-value const 987077 1.3347e+06 0.7395 0.46552 No. of offices 929.275 173.563 5.3541 <0.00001 No of employee -39.4415 13.4954-2.9226 0.00666 Hausman test - Asymptotic test statistic: Chi-square (2) = 2029.69 with p-value = 0 98

Fixed-Effects Mean dependent var 12660860 S.D. dependent var 13030964 R-squared 0.997905 P-value(F) 6.04e-34 Coefficient Std. Error t-ratio p-value const -2.05824e+07 1.64871e+06-12.4839 <0.00001 No. of offices 1460.97 36.5392 39.9836 <0.00001 No of employees -24.3676 6.07895-4.0085 0.00046 Table 3.7.1 gives the results of Random effect from which it can be seen Number of offices and Number of employees has an effect on the advances with the t stat significant at less than 1%. Hausman test results show that there is no fixed effect in the data as the chi square value is insignificance even at 10 %. Therefore the right model for the data is Random effect model which is given in table. From the Random effect model it can be seen that the Regression equation is significant at <1 % rejecting the null hypothesis that the beta is equal to zero. The explained variance of 99 % showing the amount of variation the independent variable is able to explain on the dependent variable, even though it is multiple effects. Table 3.7.2 Random-Effects for Number of Offices & Business Per Employee Mean dependent var 17055878 S.D. dependent var 17133637 Coefficient Std. Error t-ratio p-value const -2.41563e+07 4.53104e+06-5.3313 0.00001 No of offices 1137.27 92.852 12.2482 <0.00001 Business per employee 89929.5 21329 4.2163 0.00022 Hausman test - Asymptotic test statistic: Chi-square (2) = 179.764 with p-value = 9.22162e-040 99

Fixed-Effects Mean dependent var 17055878 S.D. dependent var 17133637 R-squared 0.996779 P-value(F) 1.62e-31 Coefficient Std. Error t-ratio p-value const -3.06593e+07 1.04038e+06-29.4693 <0.00001 No of offices 1552.77 50.5175 30.7373 <0.00001 Business per employee 24551 10150.6 2.4187 0.02288 Table 3.7.2 shows the results of Random effect from which it can be seen Number of offices and Business per employees has an effect on the deposits with the t stat significant at less than 1%. Hausman test results show that there is no fixed effect in the data as the chi square value is insignificance even at 10 %. Therefore the right model for the data is Random effect model which is given in table. From the Random effect model it can be seen that the Regression equation is significant at <1 % rejecting the null hypothesis that the beta is equal to zero. The explained variance of 99 % showing the amount of variation the independent variable is able to explain on the dependent variable, even though it is multiple effects. TABLE 3.7.3 Random-Effects for Interest Income & Other Income Mean dependent var 1576717 S.D. dependent var 1446755 Coefficient Std. Error t-ratio p-value const 40600 62604.6 0.6485 0.52176 Interest income 0.516644 0.0811416 6.3672 <0.00001 Other income 2.663 0.544795 4.8881 0.00003 Hausman test - Asymptotic test statistic: Chi-square (2) = 32.6431 with p-value = 8.15898e-008 Fixed-Effects Mean dependent var 1576717 S.D. dependent var 1446755 R-squared 0.988991 P-value(F) 1.39e-24 Coefficient Std. Error t-ratio p-value const -83131 85812.1-0.9688 0.34159 Interest income 0.657327 0.0791393 8.3060 <0.00001 Other income 2.31066 0.644589 3.5847 0.00137 100

Table 3.7.3 explains the results of Random effect from which it can be seen Interest income and other income has an effect on the Capital and Reserve & Surplus with the t stat significant at less than 1%. Hausman test results show that there is no fixed effect in the data as the chi square value is insignificance even at 10 %. Therefore the right model for the data is Random effect model which is given in table. From the Random effect model it can be seen that the Regression equation is significant at <1 % rejecting the null hypothesis that the beta is equal to zero. The explained variance of 99 % showing the amount of variation the independent variable is able to explain on the dependent variable, even though it is multiple effects. 3.8 PUBLIC SECTOR BANKS (PSBs) AND OTHER SCHEDULED COMMERCIAL BANKS (OSCBs) IN RURAL AND SEMI-URBAN AREAS IN NAGAPATTINAM DISTRICT H 0,1 : There is no significant difference between number of PSBs branches in Rural and Semi- Urban regions. H 0,2 : There is no significant difference between amount of credit sanctioned by PSBs in Rural and Semi-Urban regions. H 0,3 : There is no significant difference in the amount of Deposits made in PSBs in Rural and Semi-Urban regions. H 0,4 : There is no significant difference between number of OSCBs branches in Rural and Semi-Urban regions. H 0,5 : There is no significant difference between amount of credit sanctioned by OSCBs in Rural and Semi-Urban regions. H 0,6 : There is no significant difference between amount of Deposits made in OSCBs in Rural and Semi-Urban regions. 101

PSB Offices PSB Credit PSB Deposit OSCB Offices OSCB Credit OSCB Deposit Table 3.8 Independent Samples Test for PSB & OSCB Branches, Credit, Deposit and Rural & Semi urban areas in Nagapattinam District Area of Operation N Mean Std. Deviation Std. Error Mean Rural 26 44.4231 11.99724 2.35285 Semi- Urban 26 34.0000 3.82623.75038 Rural 26 690.6154 293.52684 57.56535 Semi- Urban 26 589.7308 184.67670 36.21808 Rural 26 588.8846 223.61204 43.85393 Semi- Urban 26 1280.1923 278.84218 54.68545 Rural 26 6.8846.32581.06390 Semi- Urban 26 16.6923 3.10830.60959 Rural 26 30.6923 12.10543 2.37407 Semi- Urban 26 180.0385 88.59141 17.37421 Rural 26 65.6154 17.88424 3.50739 Semi- Urban 26 414.2308 128.92643 25.28455 t Sig. (2- tailed) 4.221.000 1.483.144-9.862.000-16.001.000-8.517.000-13.657.000 From the above table 3.8, shows that the mean value of PSBs offices, credit (`. in Crore), deposits (`. in Crore) in rural and semi-urban areas were 44.4231, `.690.6154, `.588.8846 and 34.0000, `.589.7308, `.1280.1923 respectively. The mean value of OSCBs offices, credit and deposits in rural and semi-urban areas were 6.8846, `.30.6923, `.65.6154 and 16.6923, `.180.0385, `.414.2308 respectively. The t-test results shows that there is a significant difference in PSBs offices, deposits in rural and semi-urban areas and OSCBs offices, credit and deposits in rural and semiurban areas at 1 % level. It also shows that there is no significant difference in PSBs credits rural and Semi-urban areas in the Nagapattinam district, Tamil Nadu. Hence H 0,1, H 0,3, H 0,4, H 0,5, H 0,6, were rejected and H 0,2 is not rejected. 102

3.9 PUBLIC SECTOR BANKS AND OTHER SCHEDULED COMMERCIAL BANKS BRANCHES, CREDIT AND DEPOSIT IN NAGAPATTINAM DISTRICT H 0,7 : There is no significant difference between number of branches in rural and semi urban area of PSBs and OSCBs. H 0,8 : There is no significant difference between Credit sanctioned in rural and semi urban area by PSBs and OSCBs. H 0,9 : There is no significant difference between Deposits made in rural and semi urban area by PSBs and OSCBs. TABLE 3.9 One Way ANOVA for Branches, Credit and Deposit in Nagapattinam District between PSB and OSCB Sum of Squares df Mean Square Between Groups 22215.46 3 7405.15 Offices Within Groups 4208.53 100 42.08 Total 26424.00 103 Between Groups 7858766.69 3 2619588.89 Credit Within Groups 3206461.76 100 32064.61 Total 11065228.46 103 Between Groups 20337461.00 3 6779153.66 Deposit Within Groups 3617429.46 100 36174.29 Total 23954890.46 103 Source: www.rbi.org.in Computed by Researcher F Sig. 175.955.000 81.697.000 187.403.000 The above table 3.9 interprets that there is a significant difference in branches, credit sanctioned and the deposits made for both the rural and the semi-urban areas by the PSBs and OSCBs at 1% level of significance. It means the PSBs and OSCBs locating their branches, accepting deposits and credit sanctioned in rural and semiurban branches, were having significant differences in the Nagapattinam district, Tamil Nadu. Hence H 0,7, H 0,8 and H 0,9 were rejected at 1% level. 103

3.10 RURAL & SEMI-URBAN AREAS AND PUBLIC SECTOR BANKS & OTHER SCHEDULED CBs IN NAGAPATTINAM DISTRICT H 0,10 : There is no significant difference between Rural and Semi - Urban areas and number of PSB branches. H 0,11 : There is no significant difference between Rural and Semi - Urban areas and amount of credit sanctioned by PSBs. H 0,12 : There is no significant difference between Rural and Semi - Urban areas and the amount of Deposits made in PSBs. H 0,13 : There is no significant difference between Rural and Semi - Urban areas and number of OSCB branches. H 0,14 : There is no significant difference between Rural and Semi - Urban areas and amount of credit sanctioned by OSCBs. H 0,15 : There is no significant difference between Rural and Semi - Urban areas and the amount of Deposits made in OSCBs. Table 3.10 Independent Samples Test for Rural & Semi urban Branches, Credit, Deposit and PSB & OSCB in Nagapattinam District (`. in Crore) Bank Type N Mean Std. Deviation Std. Error Mean t Sig. (2- tailed) Rural PSB 26 44.4231 11.99724 2.35285 Offices OSCB 26 6.8846.32581.06390 15.949.000 Rural PSB 26 690.6154 293.52684 57.56535 Credit OSCB 26 30.6923 12.10543 2.37407 11.454.000 Rural PSB 26 588.8846 223.61204 43.85393 Deposit OSCB 26 65.6154 17.88424 3.50739 11.894.000 Semi- PSB 26 34.0000 3.82623.75038 Urban 17.902.000 OSCB 26 16.6923 3.10830.60959 Offices Semi- PSB 26 589.7308 184.67670 36.21808 Urban 10.199.000 OSCB 26 180.0385 88.59141 17.37421 Credit Semi- PSB 26 1280.1923 278.84218 54.68545 Urban 14.373.000 OSCB 26 414.2308 128.92643 25.28455 Deposit 104

The above table 3.10, shows that the mean value of PSBs rural and semiurban offices, credit (`. in Crore) and deposits (`. in Crore) were 44.4231, `.690.6154, `.588.8846 and `.589.7308, `.1280.1923 respectively. The mean value of OSCBs rural and semi-urban offices, credit and deposits were 34.0000, `.30.6923, `.65.6154 and 16.6923, `.180.0385, `.414.2308 respectively. The t-test results show that there is a significant difference in rural and semi-urban offices, credit, deposits and PSBs & OSCBs at 1 % level in the Nagapattinam district, Tamil Nadu. Hence H 0,1, H 0,11, H 0,12 H 0,13, H 0,14, H 0,15 were rejected. 3.11 PUBLIC SECTOR BANKS AND OTHER SCBs BRANCHES, CREDIT AND DEPOSIT IN RURAL & SEMI-URBAN AREAS IN NAGAPATTINAM DISTRICT Table 3.13 Chi-Square Test Pearson chi-square Value df 105 Asymp. Sig. (2-sided) PSB Offices and Rural Offices 496.487 180.000 PSB Offices and Rural Credit 897.000 828.048 PSB Offices and Rural Deposit 936.000 828.005 PSB Offices and Semi Urban Offices 512.783 288.000 PSB Deposit and Rural Deposit 2392.000 2254.021 OSCB Offices and Rural Offices 116.158 80.005 OSCB Offices and Rural Deposit 416.000 368.043 OSCB Offices and Semi-urban Offices 392.487 128.000 OSCB Credit and Semi-urban Offices 832.000 752.022 OSCB Deposit and Semi-urban Offices 832.000 768.054 From the above table 3.11 it is clear that the total number of PSBs offices and the PSBs offices in rural and semi-urban, deposits in rural areas were significantly associated at 1% level, and PSBs offices and credits, PSBs deposits and deposits in rural areas were significantly associated at 5% level. And the total number of OSCBs

offices and the OSCBs offices in rural and semi-urban areas were significantly associated at 1% level. The total number of OSCBs offices and the deposits and credits to/from OSCBs in rural were significantly associated at 5% level. The total amount of OSCBs deposit and the OSCBs offices in semi-urban were significantly associated at 10% level. These reveals that there is a significant proportion of PSBs offices with rural offices, credit, deposits and semi-urban offices and PSBs deposits with rural deposits, OSCBs offices with rural offices, deposits, semi-urban offices, OSCBs credit with semi-urban offices, OSCBs deposits with semi-urban offices. 3.12 PSBs OFFICES, DEPOSIT & CREDIT IN RURAL AND URBAN CENTRES IN NAGAPATTINAM DISTRICT The below charts 3.1, shows that the number of branches, total amount of credits and deposits in rural and semi-urban areas of public sector banks since 2005 2006 Q3 in Nagapattinam district. The numbers of PSBs branches are increasing trend in semi-urban areas than the rural areas. The amount of Deposits attracted and credit sanctioned are more in the semi-urban branches. Recently all PSBs are taking more consideration on extending number of branches, offering credits and attracting deposits in the semi-urban areas giving a tough competition to the OSCBs. Even though there is a steady increase in the rural branches of PSBs in rendering services to the semi-urban at a steady rate. In other words, it has become very much necessary to concentrate equally in the rural and semi-urban regions to avoid exclusion of banking services. 106

CHART 3.1 PSBs Offices, Deposit & Credit in Rural Areas CHART 3.1 PSBs Offices, Deposit & Credit in Urban Areas Source: www.rbi.org.in Compiled by Researcher 107

3.13 OSCBs OFFICES, DEPOSIT & CREDIT IN RURAL AND URBAN CENTRES IN NAGAPATTINAM DISTRICT The below charts 3.2, shows that the number of branches, total amount of credits and deposits in rural and semi-urban areas of other scheduled commercial banks since 2005 2006 Q3 in Nagapattinam district. The numbers of OSCBs branches are increasing trend in semi-urban areas than the rural areas. The amount of Deposits attracted and credit sanctioned are more in the semi-urban branches. Recently all OSCBs are taking more consideration on extending number of branches, offering credits and attracting deposits in the semi-urban areas giving a tough competition to the PSBs. CHART 3.2 OSCBs Offices, Deposit & Credit in Rural Areas 108

CHART 3.2 OSCBs Offices, Deposit & Credit in Urban Areas Source: www.rbi.org.in Compiled by Researcher 3.14 PSBs AND OSCBs OFFICES, DEPOSIT & CREDIT IN RURAL AND URBAN CENTRES IN NAGAPATTINAM DISTRICT In the below charts 3.3, shows that the number of branches, total amount of credits and deposits in rural and semi-urban areas of public sector banks and other scheduled commercial banks since 2005 2006 Q3 in Nagapattinam district. The numbers of PSBs branches are higher in rural areas lesser in the semi-urban areas compare to OSCBs. The amount of deposits attracted and credit sanctioned by the OSCBs are higher than the PSBs in rural and semi-urban areas. 109

CHART 3.3 PSBs and OSCBs Offices, Deposit & Credit in Rural Areas Source: www.rbi.org.in Compiled by Researcher 110

CHART 3.3 PSBs and OSCBs Offices, Deposit & Credit in Urban Areas Source: www.rbi.org.in Compiled by Researcher 111

3.15 CONCLUSION The financial inclusion initiatives taken by the commercial banks in India over a period of time shows a significant improvement. The overall analysis made the credit penetration, deposit penetration and branch penetration by the PSBs and OSCBs in rural and semi-urban areas in Nagapattinam district and India. Since 2005 2006 Q3 the number of branches, total amount of credits and deposits in rural and semi-urban areas of PSBs and OSCBs in Nagapattinam district. The numbers of PSBs branches are higher in rural areas and lesser in the semi-urban areas compare to OSCBs. The amount of deposits attracted and credit sanctioned by the OSCBs are higher than the PSBs in rural and semi-urban areas. Thus commercial banks played a major role in the improvement of financial status of economy through financial inclusion covering Central Region, Eastern Region, North Eastern, and Northern, Southern and Western including rural, semi-urban, urban and metropolitan population in our country. Small loans can transform lives, especially the lives of women and children. The poor can become empowered instead of disenfranchised. Homes can be built, jobs can be created, businesses can be launched, and individuals can feel a sense of worth again. - Natalie Portman, Actress Spokesperson for the International Year of Microcredit 2005 FINCA International Ambassador of Hope. 112