CHAPTER VI ON PRIORITY SECTOR LENDING

Transcription

1 CHAPTER VI IMPACT OF PRIORITY SECTOR LENDING 6.1 PRINCIPAL FACTORS THAT HAVE DIRECT IMPACT ON PRIORITY SECTOR LENDING 6.2 ASSOCIATION BETWEEN THE PROFILE VARIABLES AND IMPACT OF PRIORITY SECTOR CREDIT 6.3 DETERMINANTS OF PRIORITY SECTOR LENDING AND IMPACT ON RURAL DEVELOPMENT 6.4 SUMMARY

2 222 CHAPTER VI IMPACT OF PRIORITY SECTOR LENDING The definition of priority sectors has evolved over a period of time and at present, priority sectors are broadly taken as those sectors of the economy which in the absence of inclusion in the priority sector categories would not get timely and adequate finance. Typically, these are small loans to small and marginal farmers for agriculture and allied activities, loans to Micro and Small Enterprises, loans for small housing projects, education loans and other small loans to people with low income levels. Presently, the target for aggregate advances to the priority sector is 40 per cent of the Adjusted Net Bank Credit (ANBC) or the credit equivalent of Off Balance sheet Exposure (OBE), whichever is higher for domestic banks. Foreign banks with 20 or more branches in the country are being brought on par with domestic banks for priority sector targets in a phased manner over a five year period starting from April 1, For foreign banks with less than 20 branches the overall target is fixed at 32 per cent. 1 The domestic banks, i.e, public and private sector, could not achieve the target of 40 percent for the year 2012 as can be evidenced from the table below. 1 Deepali Pant Joshi (2013). Indian Rural Banking Sector: Big Challenges and the Road Ahead, Reserve Bank of India.

3 223 Table 6.1 Priority Sector Lending by Commercial Banks in India (Public/Private/Foreign Banks) (Amount in ` billion) Sl. No. As on the Last Reporting Friday of March Public Sector Banks 10,215 (41.0) Priority Sector Lending by banks Private Sector Banks 2,491 (46.7) Foreign Banks 667 (39.7) ,299 (37.4) 2,864 (39.4) 805 (40.8) Source : RBI Annual Report 2012 Notes : Figures in parentheses are percentages to ANBC or credit equivalent of off balance sheet exposure (OBE), whichever is higher, in the respective groups As per 59 th NSS Survey, households with 2 hectare or less land accounted for 84 percent of all farmer households. The percentages of such small and marginal famers who have access to credit is only percent. A large section of farmers is still dependent on moneylenders for their financial needs. Table 6.2 Shares in Total debts of Cultivator Households (Figures in Percentage) Sl. No. Source of debt Year Institutional Non-Institutional Money Lenders Total Source : All India Debt and Investment Survey, NSSO, GoI, various rounds.

4 224 The revised priority sector guidelines lay emphasis on direct delivery of credit to the poor beneficiaries i.e. without the involvement of intermediaries, which will ensure better management of risks and also reduction in transaction, delivery and administrative costs for these loans, which being essentially small ticket, low value high volume loans, do generate profits translating to a stable low cost deposit stream for banks and to the fortune at the bottom of the pyramid. The cost of absence of bank lending to poor and vulnerable sections of the society is heavy, as the poor have no option but to borrow from the unorganized sector, the informal or alternative channels at usurious price. There is a need for reorienting the approach of banks to look at priority sector areas. The challenges in priority sector can be overcome only if banks consider priority sector lending as part of normal business operations of the banks and not as an obligation. Rural untapped market offers a big business opportunity to the banks. Banks need to innovate so that new products cater to the needs of farmers, weaker sections and other vulnerable sections of the society, develop new delivery channels and embrace technological developments which will reduce the delivery costs. Priority sector must be treated as a viable business proposition.

5 Principal Factors that have Direct Impact on Priority Sector Lending The impacts of priority sector credit were captured using a 23 item 5-point Likert Scale and the scale was tested for reliability Reliability of the Scale measuring impact: The impact of priority sector credit multiple items scale were analysed for internal consistency reliability through Cronbach s coefficient alpha which indicates the internal consistency of the scale. The alpha for the 23 item impact of priority sector credit scale was which indicate that the items form a scale that has reasonable internal consistency reliability. The corrected Item-Total correlation and the Alpha if item deleted were calculated to understand the correlation of each specific item with the sum/total of the other items in the scale.

6 226 Table 6.3 Cronbach s Alpha Item-Total Statistics Sl. No. Loan Impact Variables Cronbach's Alpha if Item Deleted 1. Made me more respectable in the society Improved the family economic status Improved the family educational status Marriage of children Reduced poverty Lead a comfortable life Given better health facilities to children Helped to save money Able to invest in the business Improved credit worthiness Relieved from the clutches of private moneylenders Expansion of activities on modern lines Repaid past debts with high interest rates Able to provide employment to few people Opportunity to be self employed Business had a higher turnover Opportunity to involve family members Gave an opportunity to understand modern technology Avoided buying on credit/ led to higher profit Purchased modern equipment Purchased land Modified or constructed house Purchased household articles 0.885

7 227 The uni-dimensionality of the scale was tested through Exploratory Factor Analysis (EFA) and the results are summed under table 6.4. Table 6.4 Total Variance of the Loan Impact of Priority Sector Lending Component Initial Eigen values Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative %

8 228 The above exploratory factor analysis resulted with the extraction of 7 component. The total variance explained table shows that the eigen value (6.574) for the first factor is quite a bit larger than the eigen value for the next factor (2.350). Also the first component accounts for 13 percent of the variance Impact of the Priority Sector Loan The objective of the priority sector loan is to ensure that the socially need are provided with directed credit and therefore lift them from the current social conditions. Therefore the impact of the priority sector loan was tested using 23 impact variables which complement the broader socioeconomic factors. The 23 impact variables were measured on a five point Likert scale with options from highly agree to highly disagree. The scores assigned on these scale were 5, 4, 3, 2 and 1 respectively. The research attempted to test the impact level of the priority sector loan availed by the beneficiaries using the one-sample t test which is used to compare one group to the hypothesized population mean. The mean scores for the different impact variables and the t statistics revealing the significance of the differences among the impact of loan of the beneficiaries according to the income group viz. low income and other income group. The results are tabulated in table 6.5.

9 229 Table 6.5 Impact of Loan on the Priority Sector Beneficiaries Sl. No Variables Low Income Income Group Other Income Made me more respectable in the society Improved the family economic status Improved the family educational status Marriage of children Reduced poverty Lead a comfortable life Given better health facilities to children Helped to save money Able to invest in the business Improved credit worthiness Relieved from the clutches of private money lenders Expansion of activities on modern lines Repaid past debts with high interest rates Able to provide employment to few people Opportunity to be self employed Business had a higher turnover Opportunity to involve family members Gave an opportunity to understand modern technology Avoided buying on credit/ led to higher profit Purchased modern equipment Purchased land Modified or constructed house Purchased household articles t Statistics * * * * * * * * * * * * * * Significant at 5 percent level.

10 230 Table 6.5 reveals the variables that have a direct impact on the priority sector lending. The significant difference among the beneficiaries of low income group and other income group were identified with the help of t test. It can be seen from the table that marriage of children, better health facilities for children, save money, improvement in credit worthiness, relieved from the clutches of private money lenders, expansion of activities on modern lines, high turnover in business, opportunity to involve family members, opportunity to understand modern technology, avoid buying on credit, purchase of modern equipment, purchase of land and modification of house are significant at 5 percent level Factor loading for the variables in impact factor The direct impact of the priority sector lending was narrated with the help of factor analysis. The scores on various variables were considered for testing. The analysis reveals four factors which influence impact of priority sector lending. The factors were improvement in social conditions, increase in savings and consumption, generate income and employment and asset creation. The resulted factors, the eigen values, percent of variation, the variables contributing to the factor and the reliability coefficient are presented in table 6.6.

11 231 Table 6.6 Important factors on impact of priority sector loan Factors (Eigen Values) Variables Factor Loading Reliability coefficient Percent of variation Improvement in social conditions (3.014) Increase in Savings and Investment (2.882) Generate Income and Employment (2.878) Asset Creation (1.198) Made me more respectable in the society Improved the family economic status Improved the family educational status Marriage of children Reduced poverty Lead a comfortable life Given better health facilities to children Helped to save money Able to invest in the business Improved credit worthiness Relieved from the clutches of private money lender Expansion of activities on modern lines Repaid past debts with high interest rates Able to provide employment to few people Opportunity to be self employed Business had a higher turnover Opportunity to involve family members Gave an opportunity to understand modern technology Avoided buying on credit/ led to higher profit Purchased modern equipment Purchased land Modified or constructed house Purchased household articles Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett's Test of Sphericity Approx. Chi-Square df 45 Sig

12 232 The narrated four factors explain the direct impact of priority sector lending to the extent of percent. The most important factor was improvement in social conditions which consist of seven variables with a reliability coefficient of 0.807, eigen value of and explains percent of variation. The second important factor was Increase in Savings and investment which consists of six variables with a reliability coefficient of 0.837, eigen value of and explains percent of variation. The third factor Generated income and employment which consists of six variables has a reliability coefficient of 0.815, eigen value of and explains percent of variation. The fourth factor Asset creation which consists of four variables has a reliability coefficient of 0.648, eigen value of and explains percent of variation. The KMO Measure of sampling adequacy was and the Bartlett s Test of Sphericity was significant. 6.2 Association Between the Profile Variables and Impact of Credit Since the profile of the priority sector beneficiaries has its own impact on the living conditions, the research attempted to analyse the profile of the priority sector beneficiaries and the impact of loan on the living conditions of those beneficiaries. The included profile variables were gender, age, marital status, education, occupation, family size, monthly income, monthly expenditure and monthly savings. The One Way

13 233 Analysis of Variance (ANOVA) between the profile variables and the impact of credit were attempted to understand the relationship. The null hypothesis that there is no significant difference among the groups of profile variable in relation to the impact of credit was tested and results tabulated in table 6.7. Table 6.7 Association between Profile Variables and Loan Impact F-Statistics Sl. No. Profile Variables Improvement in social conditions Increase in Savings and investment Generation of income and employment Asset Creation 1. Gender * Age * * Marital status * 7.318* Education 2.831* 4.430* 2.395* 3.745* 5. Occupation 2.731* 2.055* * 6. Family size 6.518* 4.460* 3.642* Monthly income 6.194* 8.425* * 8.208* 8. Monthly expenditure 8.336* 6.316* 8.686* 3.897* 9. Monthly savings 4.138* 3.293* 6.063* 6.052* * Significant at 5 percent level. From Table 6.7, it is concluded that certain profile variables are statistically significant at 5 percent level. Two levels of profile variable gender of priority sector beneficiaries have a statistically significant

14 234 difference on the improvement in social condition factor, F (1,504) = , p = Four levels of the profile variable age of priority sector beneficiaries have a statistically significant difference on two factors viz., Increase in savings and investment, F(3, 502) = 9.566, p = and Generation of income and employment, F (3, 502) = , p = Two levels of the profile variable marital status of priority sector beneficiaries have a statistically significant difference on the Generation of income and employment factor, F (1, 504) = 7.318, p = Eight levels of the profile variable education of priority sector beneficiaries have a statistically significant difference on all the four factors viz., Improvement in social conditions, F (6, 499) = 2.831, p = 0.010, Increase in savings and investment F (6, 499) = 4.430, p = 0.000, Generation of income and employment F (6, 499) = 2.395, p = and Asset creation F (6, 499) = 3.745, p = Eight levels of the profile variable occupation of priority sector beneficiaries have a statistically significant difference on three factors viz., Improvement in social conditions, F (7, 498) = 2.731, p = 0.009, Increase in savings and investment F (7, 498) = 2.055, p = 0.047, and Asset creation F (7, 498) = 5.500, p = Four levels of the profile variable Family size of priority sector beneficiaries have a statistically significant difference on three factors viz., Improvement in social conditions, F (3, 502) = 6.518, p = 0.000,

15 235 Increase in savings and investment F (3, 502) = 4.460, p = and Generation of income and employment F (3, 502) = 3.642, p = Five levels of the profile variable monthly income of priority sector beneficiaries have a statistically significant difference on all the four factors viz., Improvement in social conditions, F (4, 501) = 6.194, p = 0.000, Increase in savings and investment F (4, 501) = 8.425, p = 0.000, Generation of income and employment F (4, 501) = , p = and Asset creation F (4, 501) = 8.208, p = Five levels of the profile variable monthly expenditure of priority sector beneficiaries have a statistically significant difference on all the four factors viz., Improvement in social conditions, F (4, 501) = 8.336, p = 0.000, Increase in savings and investment F (4, 501) = 6.316, p = 0.000, Generation of income and employment F (4, 501) = 8.686, p = and Asset creation F (4, 501) = 3.897, p = Five levels of the profile variable monthly savings of priority sector beneficiaries have a statistically significant difference on all the four factors viz., Improvement in social conditions, F (4, 501) = 4.138, p = 0.003, Increase in savings and investment F (4, 501) = 3.293, p = 0.011, Generation of income and employment F (4, 501) = 6.063, p = and Asset creation F (4, 501) = 6.052, p =

16 Post hoc analysis of association between financial variables and loan impact The post hoc Tukey HSD test conducted for financial variable Monthly income indicate that there were significant mean differences on improvement in social conditions between lower income group and medium income group (p < 0.05, d = 2.00). The effect size is much larger than typical according to Cohen (1988) Likewise, the post hoc Games Howell Tests indicate that low monthly income group and high monthly income group differed significantly in the three factors viz., improvement of social conditions (p < 0.05, d= 0.52), generation of income and employment (p<0.05, d= 0.55), and asset creation (p < 0.05, d= 0.54) and the strength of this relationship is medium or typical according to Cohen (1988). The post hoc Tukey HSD test conducted for financial variable Monthly expenditure indicate that there were significant mean differences on improvement in social conditions (p < 0.05, d = 0.76)and generation of income and employment (p < 0.05, d = 0.65)between the low expenditure group and high expenditure group with the effect size of medium or typical according to Cohen (1988). Likewise, the post hoc Games Howell Tests indicate that low monthly expenditure group and high expenditure group differed significantly on increase in savings and investment (p < 0.05, d= 0.77)and asset creation (p < 0.05, d= 0.52) which according to Cohen (1988) is medium or typical effect.

17 237 The post hoc Games-Howell test conducted for financial variable Monthly savings indicate that there were significant mean differences on all the impact factor between the high savings group and low savings group on improvement in social conditions (p < 0.05, d = 0.47), increase in savings and investment (p < 0.05, d = 0.35), generation of income and employment (p < 0.05, d = 0.43), and asset creation (p < 0.05, d = 0.45), the effect size of which according to Cohen (1988) is small or smaller than typical. Hence it can be concluded that the credit to the beneficiaries have an improved impact on the socio-economic conditions. The higher income group have high effect size. 6.3 Determinants of Priority Sector Lending and Impact on Rural Development The analysis of the impact of priority sector lending reveals that various factors determine the overall priority sector outcome in Kanyakumari district. The demographic variables, income patterns, expenditure and saving patterns and business dimensions play a vital role. The research attempted to understand the level of influence, the independent factors have on the determination of priority sector impact, hence multiple regression analysis is attempted. Though the factors determining the impact of priority sector lending differs with various

18 238 independent variables, the overall trend is understood by the application of the regression model. The multiple regressions conducted to determine the best linear combination of gender, marital status, size of family, monthly income, monthly savings, increase in savings and investment, generate income and employment and asset creation for predicting the improvement in social condition of the beneficiaries of priority sector credit. The means standard deviations, and inter-correlations have been tabulated in table 6.8. Table 6.8 Means and Standard Deviations for Improvement in Social Conditions and Predictor Variables (N=506) Sl. No. Variables Mean Standard Deviation Improvement in social condition Predictor variables 1. Gender Marital Status Size of Family Monthly Income Monthly Savings Increase in Savings and Investment Generate Income and Employment Asset Creation

19 239 When the combinations of variables in table 6.8 are used the ANOVA value is F (8,497) = , p = which indicates that the combination of predictors significantly combine together to predict the improvement in social conditions. Table 6.9 Simultaneous Multiple Regression Analysis Summary Sl. No. Variable Regression Coefficient Standard Error β 1. Gender * 2. Marital Status * 3. Size of Family * 4. Monthly Income Monthly Savings * 6. Increase in Savings and Investment 7. Generate Income and Employment * * 8. Asset Creation * Constant Note: R 2 = 0.25, F(8,497) = , p, * Significant at 5 percent level. The beta coefficients in table 6.9 suggest that except monthly income all the other predictor variables significantly predict improvement in social conditions when all the eight variables are included. The adjusted

20 240 R 2 value indicates that at 25 per cent of the variance in social condition is explained by this model. According to Cohen (1988), this is larger than typical effect. equation as, The results of the multiple regression co-efficient is presented in the Y = b b b b b b b b 8 Where, Y= Improvement in social conditions b 1 Gender, b 2 Marital Status, b 3 size of family, b 4 Monthly income, b 5 monthly savings, b 6 increase in savings and investment, b 7 generation of income and employment, b 8 asset creation. While analysing the multiple regression equation with standardised coefficient, it is clear that the variables that are impacted by priority sector lending had the higher level of influence in the determination of improvement in social condition. It indicate that one unit of change in the social condition change make a change of units change on the dependent variable ie, asset creation, income and employment generation and savings and investment increase. The other variables that have a significant impact on the dependent variable are gender, marital status, size of family and monthly income.

21 Summary The impact of priority sector lending by commercial banks on rural development has been determined by several independent variables. There have been 23 variables identified that have a vast influence on the priority sector beneficiaries. A part of the variables have positive influence and certain variable have negative influence on priority sector lending. Out of the said variables, a certain group of variables had a significant influence on the priority sector lending. The factor analysis (principal axis method) helped in reducing the variables in to broader factors, so that the understanding of the impact of the lending was visible and the latent variables provided more insight in the determination of the benefits. The determinants were further understood through multiple regression which provided the model that connects the entire scheme of things. Under the model, the impact factors played the vital role and thus it can be concluded that the priority sector lending benefited the beneficiaries in a major way and the broader areas of improvement were in social conditions, savings and investment, employment generation and asset creation. Thus it can be established that the priority sector credit has a direct bearing on the livelihood of the beneficiaries and more targeted credit will uplift the bottom of the pyramid to a higher income strata.