Repayment Performance of Self-Help Groups in Uttar Pradesh: An Empirical Investigation

Abstract

This paper aims to study the determinants of repayment performance of self-help groups in India’s Uttar Pradesh state, which has one of the highest numbers of defaulting self-help groups. The study is based on primary data collected in 2017 through a field survey covering 300 members across 100 self-help groups from the rural areas of Sultanpur and Faizabad districts. The survey reveals an overall repayment rate of about 55% with better pay-back performance seen among members of old self-help groups. The results, computed with the help of the Tobit model, show that factors such as group maturity (age of the group), ratio of family workers to household size and household income are negatively associated with the incidence of delinquency while peer group pressure and social ties associate positively with delays and overdues.

Keywords

Self-help groups microfinance group lending group maturity delinquency

Introduction

Over the past couple of decades a growing number of financial institutions have developed alternative lending mechanisms such as microfinance, challenging conventional wisdom that lending to poor households is doomed to fail (Al-Azzam et al., 2012). The primary objective of microfinance is to provide small amounts of credit to small farmers, business owners, entrepreneurs and other marginalized sections including women who do not have access to traditional financial resources for lack of collateral. Microfinance is considered an important tool for poor households to either start or expand their income-generating activities and pull themselves out of poverty, besides fulfilling the government vision of financial inclusion. Thus, government and non-government institutions cater to the needs of the hitherto under-served sections by providing loans, credit, insurance and access to savings accounts in addition to technical and skill development.

In India, the microfinance movement formally took shape in 1992 when the National Bank for Agriculture and Rural Development (NABARD) took up a pilot project called the Self-Help Group Bank Linkage programme (SHG-BLP) with the help of non-government organizations (NGOs) and linked self-help groups (SHGs) with public sector banks. SHGs are networks of poor people, belonging to the same community or village, which make regular savings and rotate them as loans among members. The groups follow the joint liability lending principle that all members are responsible for repaying the loan. It is mainly through group lending that loans are extended to such individuals as it is considered a key to loan repayment and risk minimization.

In the 28 years of its existence the SHG-BLP has grown into the largest microfinance initiative in the world with 10.2 million SHGs covering more than 124 million households. It boasts deposits of more than Rs. 260 billion, an annual off take of Rs. 770 billion and loans outstanding of Rs. 1000 billion (NABARD, 2020). But it is worrisome that although there has been a significant 16.58% increase in the number of SHGs that availed bank loans during 2019–20, their loan outstanding has also increased to a whopping Rs. 1080.75 billion in 2020. Table 5 shows that the average loan outstanding per SHG has increased by 50% in India during 2017–2020 as against an average loan disbursal of 21%. This is indicative of a low rise in credit penetration and low recoveries of bank loans to SHGs.

A study by the National Council of Applied Economic Research (NCAER, 2008) shows that only 69.2% of SHGs had an excellent record of loan recovery whereas the remaining had recoveries of less than 75%. Sahu (2013) also finds that the recovery rate of SHG loans is 59.4% and the share of non-performing assets (NPA) is 77% in Madhya Pradesh and Odisha. It has also been observed that a large number of SHGs are suffering from low internal (within group) repayment problems while financial institutions are merely concerned about their recovery from the SHG as a whole. Low or delayed repayment is a function of the inability of borrowers to repay the loan along with the non-professionalism and inability of microfinance promoters who evaluate a borrower’s credit-worthiness and recovery practices (Lascelles et al., 2014). It has also been observed that many SHGs do not maintain their books of accounts correctly. Anjaneyulu and Prakash (2009) point out that members of some SHGs were found to have distributed their savings but accounted for these distributions as loans given to members. These loans are neither repaid nor considered as delinquent loans. From an accounting perspective, there is no change in the SHGs’ corpus comprising members’ savings, but, in reality, it has decreased. Thus, it is very difficult to identify and measure overall delinquency in SHGs including internal repayment.

The performance of SHGs in Uttar Pradesh (UP) state is a classic example. The ratio of savings mobilized by SHGs to loan outstanding has significantly declined from Rs. 770 to Rs. 496 in UP during 2017–2020. One could say that the SHG members have been repaying loans on time (Rajasekhar, 2019), but the increasing share of NPAs in the state conveys a different story on repayment by SHGs. The NPA as ratio of loan outstanding is 43% in UP compared to 5% in India in 2020 (Table 5), which confirms poor recoveries of SHG loans in the state. Further, Gupta and Singh (2018) observe that the NPA level is high (more than 10%) in states where the poverty incidence is also high: for example, UP, Bihar, Jharkhand, Rajasthan and Arunachal Pradesh. There cannot be a simplistic view of repayment of SHGs in such a large and diverse country like India where SHGs have been nurtured differently. Thus, the present study tries to explore the factors contributing to poor repayment behaviour among SHG members in the country’s most populous state, UP.

The paper is organized in ten sections. The following section provides a review of theoretical and empirical work on repayment in group lending as a whole and SHGs in particular. The next two sections dwell on the field study undertaken by the researchers including the sampling procedures and data collection. This is followed by a discussion of the econometric framework for analysis and the type of variables used in the estimation. A subsequent section describes how group maturity affects the delinquency rate followed by a description of the usage pattern of internal and bank loans by SHGs. Econometric results are presented in the penultimate section and the paper ends with a presentation of conclusions and policy suggestions.

Literature review

A large body of literature on group lending establishes that poor borrowers are excluded from the credit market due to credit rationing and collateral requirements. Stiglitz and Weiss (1981) observe that the collateral requirements to loosen credit rationing result in adverse selection and moral hazard problems. The limited liability of borrowers, for instance low income, also results in a lower rate of loan repayment. Armendáriz and Morduch (2005) note that this practice leads to an inefficient loan delivery system along with poor loan collection. Thus, group lending has the potential to reduce adverse selection and moral hazard problems through peer selection, peer monitoring and peer pressure.

Ghatak (1999) and Van Tassel (1999) show that in group lending only safe borrowers are selected in a group as everyone has information about each other, thus mitigating the adverse selection problem. Varian (1990) and Stiglitz (1990) have explained how borrowers’ mutual monitoring alleviates the moral hazard problems involved in lending to those with no collateral. Similarly, Banerjee et al. (1994) depict that the burden of moral hazard problem between borrowers and lenders falls on the monitoring members who are responsible for repaying the loan amount of defaulting members. Wenner (1995), in his study on repayment performance of 25 Costa Rican credit groups, shows that lending groups use private information to select their peers which increases the repayment performance of borrowing groups. In contrast, Wydick (1999), on evaluating the impact of peer monitoring, social ties and group pressure on repayment rates, group insurance and moral hazard in Guatemala, finds that neither social ties nor group pressure affects repayment rates, but peer monitoring plays a key role in increasing the repayment rate. However, Diagne et al. (2000) find that peer monitoring, peer pressure and joint liability have had little or negative impact on repayment performance in Malawi.

In addition, social ties and group homogeneity improve group dynamics. Sharma and Zeller (1997), based on the experience of 128 credit groups in Bangladesh, find that endogenously formed groups and a high degree of credit rationing improve repayment but social ties have a negative impact. Further, Zeller (1998), in his study to investigate the intra-group pooling of risky assets or projects on repayment rates by controlling community-level and programme design factors of 146 groups in Madagascar, finds that both social cohesion and risk diversification (up to a certain level) have a significant positive impact on repayment performance. But studies investigating the role of social ties in improving repayment rates may suffer from endogeneity problems.

Some other factors such as external borrowing options (informal credit) and group maturity also influence the repayment behaviour of borrower groups. Wydick (1999) and Paxton (1996) argue that availability of external credit options improves the repayment performance of a group. However, these external credit options are used to pay off loans received from the microfinance institutions pushing group members into a debt trap from informal sources. It has also been observed that group maturity plays a significant role in repayment performance. Godquin (2004), in his study of Bangladesh, finds that when a group becomes older in age its members are less likely to punish and monitor as they know each other better. However, Paxton (1996) shows that within groups, social links grow more with group maturity, therefore the ability of members to monitor each other increases leading to repayment. Besides, borrowers’ characteristics also matter in better repayment behaviour. Martin (1997) observes that education and land ownership (a proxy of borrowers’ wealth) has a positive impact on repayment performance but loan size does not have a significant impact on the repayment rate. He also notes that membership period is positively associated with default (the older a member’s association, the more likely the member is to default), and membership in multiple NGOs for access to loans has a negative impact on repayment performance. It is argued that family support improves repayment performance of women borrowers. Omorodion (2007), based on an ethnography study of microcredit borrowers in Nigeria, notes that husbands discourage women borrowers from travelling to the headquarters of the lending institutions, which affects repayments. He also observes that some men borrow money from their wives with the promise to pay when the lenders and government officials demanded full repayment, but do not keep their word, resulting in poor repayments.

In India, the microfinance sector has seen mixed outcomes of repayment on group lending. States in the southern and eastern region have not only been leading in loan disbursements at 34% and 30%, respectively (Mathew, 2018), but are also leaders in loan repayments with NPAs from these regions at 4.46% (south) and 7.17% (east). In sharp contrast are the other regions wherein loan disbursals are low and NPAs also vary in the range of 13.43% (western region) to 24.70% (central region) – much above the national average of 6.12% (NABARD, 2018). Reddy and Reddy (2012) observe that departure of SHGs from formation norms, less focus on savings, poor maintenance of attendance registers and cash books, and pressure on banks to disburse subsidized loans are factors that restrict the natural process of SHGs’ growth. Further, excess control of group leaders on SHGs, who often take a larger share of loans, are among the major reasons for poor repayment rate. Deininger and Liu (2009) highlight that regular audits of financial books of SHGs are equally important for timely repayment. It is observed that high repayment frequency among SHG members increases the repayment rate of the group and reduces default risks. Field and Pande (2012) show that clients who follow the weekly or monthly repayment schedule manage to repay on time. This finding is consistent with Armendáriz and Morduch (2000).

In all, the existing studies on microfinance impact have considered a set of factors that affect repayment, namely, group size, group formation process, formation year of the group, criteria for group formation, caste structure and role of facilitator or NGO. Moreover, household characteristics and community-level factors also affect repayment rates. However, the degree of effectiveness of these factors varies across countries, regions and socio-economic classes. There are very few econometric studies on repayment performance of SHGs in India and none so far on rural Uttar Pradesh, which has the highest NPA percentage (43%) to loan outstanding in India (NABARD, 2020). Therefore, this paper, based on primary field study in the districts of a state with major defaulting issues, will help in understanding the impact of the various aforementioned factors on repayment among the selected SHGs nurtured through the Rajiv Gandhi Mahila Vikas Pariyojana, UP.

Rajiv Gandhi Mahila Vikas Pariyojana: A programme description

The Rajiv Gandhi Mahila Vikas Pariyojana (RGMVP) set up in 2002, is a flagship poverty alleviation and women’s empowerment programme of the Rajiv Gandhi Charitable Trust (RGCT) committed to building and strengthening community institutions for the poor. Since its inception, RGMVP has been involved in creating an innovative environment that can enable women’s empowerment. RGMVP has always been directed towards making all its initiatives self-sustainable and easily scalable rather than having a subsidy or charity-based approach. Thus, the Trust has partnered with commercial and rural banks to promote SHG–bank linkages in UP. With an outreach of 2 million households in 42 districts, it is the biggest programme for promotion of SHGs that NABARD supports in the state through the Small Industries Bank of India (SIDBI) and Access Assist, a microfinance institution. The programme covers about 67% of the state. About 66% of the very poor people are claimed to have benefitted through this programme. The scheme is based on a three-tier model: at the base is an SHG formed with 10 to 20 women in a village. The second tier comprises all the SHGs in the village which are federated into Village Organizations (VOs) representing 150–250 families. These VOs amalgamate into Block Organizations (BOs) representing 5000 to 7000 women (Vyas et al., 2016–17).

There are more than 70,000 SHG trainers, called samooh sakhis (group friends), and almost 6000 health and livelihood trainers providing knowledge about maternal care, child care and nutrition, and providing awareness and training on income-generating activities such as agriculture, organic composting, seeds, dairy and food security. RGMVP has also created a cadre of trained bank sakhis (friends) who liaise between SHGs and banks and engage in spreading financial literacy and creating awareness on bank procedures for effective credit utilization and management among SHG members. With its interventions, the RGCT continues to expand the programme in more districts, helping women fight poverty and various kinds of injustice. The SHG members conduct meetings on a monthly basis during which they collect the savings amount and discuss their individual problems, social and financial issues. The members use their collective wisdom and peer pressure to ensure the proper use of loans and timely repayment. However there is no peer group support in repayment of loans. There are no incentives to improve repayment, only a few SHGs (seven) were found to be using a progressive lending approach. It is also seen that the older groups actively take part in community development by addressing social issues and creating awareness pertaining to health, education and financial literacy. The broad functioning of SHGs is depicted in Figure 1.

Figure 1.

Functioning of self-help groups.

Although the intervention of RGMVP has provided opportunities to poor households to enhance their livelihood potential through SHGs, timely repayment of credit has been a big challenge for several groups. This study has therefore tried to examine the factors underlying poor repayment performance among the delinquent groups with a primary survey in the districts of Sultanpur and Faizabad.

Sampling procedure and data description

A majority of SHGs under the RGMVP are concentrated in Rai Bareli, Sultanpur, Amethi and Faizabad districts. Therefore, to economize on resources and time for data collection the researchers purposively selected only two districts, namely, Faizabad and Sultanpur are in the eastern region of UP.¹ These two districts have high incidence of poverty: 54.62% of the population in Sultanpur and 48.22% of the population in Faizabad district are below the poverty line (Human Development Report, 2008). The extent of financial inclusion in the state is abysmal with almost one in five persons (19%) having no access to a bank (Bandyopadhyay, 2016). Ramakrishnan (2008) observes that in Bundelkhand and Eastern UP, which are among the least developed regions in the state, banks and cooperatives rarely lend money to small and marginal farmers. As a result, they are forced to borrow money from traditional moneylenders at exceptionally high rates of interest.² In such a scenario group lending can be considered a better source of credit to alleviate poverty. Thus, the selection of state and districts is ideal to understand the dynamics of microfinance and its impact on poor borrowers.

In the second stage, two developing blocks from each district – Harringtonganj and Milkipur from Faizabad; Dhanpatganj and Baldiram from Sultanpur – have been selected for data collection. Thereafter, the researchers randomly selected 300 SHG members (all women as SHGs are women only) – 150 from each district – to hold interviews with them over six months from December 2016 to May 2017. The selected SHG households were very poor (possessing less than 0.31 hectares of cultivable land) and engaged mainly in casual labour. Data obtained from the SHGs included a set of questions such as: initiation year of group, number of old and new group members, meetings schedule, reason for dropping out from the group, caste and class profile of the group, knowledge about sources of lending, loan size, frequency of loan taken by each member, number of delay days in repayment after the due date, actual loan outstanding of the SHG on the date of survey, reason for non-repayment of the old loan on time, utilization of loans for different purposes or activities, and so on.

Econometric analysis framework

Based on literature and the analysis of programme design, group dynamics and household characteristics, the researchers have estimated the determinants of delinquency among the selected SHGs. The dependent variable used in this study is the delinquency rate defined as a proportion of the total loan amount in arrears at the date when complete repayment was promised. The dependent variable is a continuous variable that is censored at a lower bound of 0 and an upper bound of 100. In this case, an ordinary least square model will lead to biased and inconsistent estimates. However, a Probit or multinomial model would sacrifice valuable information because it uses a dummy (Zeller, 1998). Therefore, researchers used the Tobit maximum likelihood technique to estimate the impact of explanatory variables on delinquency. The Tobit delinquency model may be performed by assuming that D_i* is the desired proportion of delinquency of each household when it exceeds 0, then

D_{i} = D_{i} * i f D_{i} * \geq 0; 0 o t h e r w i s e .

Further

E [D_{i} / X_{i}] = Φ (\frac{X_{i}^{I} β}{σ}) (X_{i}^{I} β + σ λ_{i})

where, $λ_{i} = \frac{(\frac{X_{i}^{I} β}{σ})}{Φ (\frac{X_{i}^{I} β}{σ})}$

and X_i is the vector of household, group, programme and community-level characteristics assigned to the corresponding household; Φ (.) is the standard normal distribution function; and ф (.) is the corresponding density.

Potential determinants of delinquency

The different categories of variables used in the econometric analysis are stated as follows.

Household-level variables

The researchers selected loan size borrowed from group (LOANSIZE), household size (HH SIZE), proportion of children to total household size (CHILDRENSIZE), ratio of workers to household size (WORKERRATIO), years of schooling of head of the household (HHEDU), ratio of adult literacy to household size (ADULTLIT), value of livestock (LIVESTOCK), household owned land in acres (HHLAND) and family income (HHINC) as household variables.

Control variables

The age of SHG is selected as a control variable. Ideally, as an SHG ages the default rate should reduce. The results of Paxton (1996) and Godquin (2004) show that the age of the group is predicted to have either a negative or positive impact on delinquency. This variable is divided into three categories: new SHGs (NEWSHG) which are three years old, middle-aged SHGs (MIDDLESHG) which have been in existence for three to five years and old SHGs (OLDSHG) which are more than five years old.

Selection variables

The selection process of group members always affects the performance of the group. The selection variable (SELECT) is measured by the sum (one for each affirmative answer) of the scores based on the following questions: whether the group reports ‘itself selected’, or the group explicitly reports that people have refused to join, whether members dropped out (except in the case of death or permanent migration) of the group and whether new members joined the group (Verhelle and Berlage, 2003). It has been observed that much of the screening of members occurs after a group has been formed (Wydick, 1999).

Peer-monitoring variables

Peer group monitoring (MONITORING) can mitigate the moral hazard problem, while group monitoring is itself a function of its cost (Al-Azzam et al., 2012). For peer monitoring, the variables included are group meetings every month (Stiglitz, 1990; Varian, 1990), group size (Diagne, 1998; Wenner, 1995) and the average distance between business activities of group members and their houses. It is observed that the greater the number of meetings, the better the repayment rates. Wydick (1999) has found that if the distances between the businesses or economic activities of group members are less than 1 km from their houses, the repayment rate is high. Finally, it is easy to monitor each member if it is a small group. The average distance is a dummy variable; however, group size and group meetings are quantitative variables. We assign ‘1’ to average distance if all group members are doing activities within 1-km distance of each other and ‘0’ otherwise.

Peer pressure variable

It is observed that peer group pressure (PEERPRESURE) and social coercion are more effective instruments to increase repayment of old loans and reduce moral hazard (Besley and Coate, 1995) among the less developed and poor societies in rural areas. In this study, researchers have assigned ‘1’ to peer pressure if group members apply social pressure on the defaulter after the due date of repayment and ‘0’ otherwise.

Social capital variable

In a diverse Indian society it is common that members in groups with a homogeneous background have better information about each other. Thus, in the social capital (SOCIALCAPITAL) variable, researchers have used a measure similar to Al-Azzam et al. (2012) and considered the following variables: same-sex of the group members, same caste, same religion, same age category, same occupation and same district.

Group maturity and repayment performance

The researchers were able to construct the credit history of SHG borrowers. For this, they collected information on loan cycles, loan amounts, loans outstanding, the share of loan amount to be paid, reasons for non-repayment of old loans in time, number of delayed days in repayment after due date, among others. Information on the aforementioned parameters was taken only for the last three years preceding the date of the survey. It has been observed that SHG members initially borrow a small loan, which increases as the group ages. From Table 1 it is clear that there is a variation in the size of loans to different borrowers across the age of SHGs. The average loan size borrowed by members of old SHGs is almost double the average loan size borrowed by members of younger and newer SHGs. However, the variance of the allocated loan size increases in relatively older groups, which reflects that older groups tend to become more homogeneous as a result of active participation of a few members who fall in line with the dominant members or group leader. The other reason for high variance of the allocated loan for older group is the use of loans for life cycle events such as construction of a house or marriage. The average minimum and maximum loan size also increases as the groups grow in age even as the number of borrowers decreases (Godquin, 2004). Besides, the researchers have also found that older group members have taken loans more frequently than members of new groups indicating that older SHGs are higher risk seekers so they avail themselves of loans more frequently.

Table 1.

Variation in loan size with the age of self-help groups (in Rs.).

Age of SHGs	Loan cycle	Mean	Std. dev.	Min	Max
New SHGs	1.30	9584.746	9508.686	500	50,000
Middle SHGs	1.66	15,963.3	13,373.5	500	62,000
Old SHGs	1.60	18,297.3	13,334.61	1500	54,000
All groups	1.55	14,548.78	12,748.13	500	62,000

Source: Computed from field survey data.

From Table 2 it is clear that the average size of loan outstanding is higher among old SHGs than the new ones. However, the share of the loan outstanding to total loan decreases as a group ages. The results show that in new SHGs the share of the loan outstanding to the total loan is 53.96%. However, it is only 13.67% in old SHGs. The survey has also obtained information about the percentage of group members who had fully repaid before the due date. On an average, about 55% of group members have fully repaid their old loans before the due date; the share was highest among old SHGs at 60%. The study has also examined the percentage of group members who have repaid at least 50% of their old loan before the due date. Does it vary as the groups grow older? In old SHGs, 31.58% of members have repaid at least 50% of their old loan before the due date of repayment. However, in new SHGs only 13.33% of members have repaid 50% of the old loan amount. The average interest rate varies across SHGs; it is lower in old SHGs and higher in the new ones.

Table 2.

Loan outstanding and repayment details by group age.

	New SHGs	Middle SHGs	Old SHGs	All groups
Average interest rate (% per annum)	18.59	14.86	14.92	15.93
Average loan outstanding (in Rs.)	1823.94	843.57	1937.64	1294.86
Loan outstanding as share of total loan amount (%)	53.96	32.37	13.67	47.93
Percentage of group members fully repaid	38.02	68.57	60.87	55.37
Percentage of group members partially repaid (50%)	13.33	22.22	31.58	18.71

SHGs: self-help groups.

Source: Computed from field survey data.

Usage pattern of loans

It is observed that the repayment performance depends on the usage of the loans taken by members of SHGs. As the groups become older and savings increase with time, SHG members use a larger proportion of their bank loan for income generating purposes which helps in repayment. Gadenne and Vasudevan (2007) argue that with time members become more confident about their repaying capacity, encouraging them to be more willing to take production-oriented loans which involve greater risk. From Table 3 it is clear that almost 50% of bank loans are used for income-generating activities in contrast to 40% of internal loans³ being used for income-generating activities. A significant proportion of bank loans are used for buying livestock (21.9%), starting new businesses (19.04%), or marriage of son or daughter (19.04%). The use of internal loans for these activities is low compared to bank loans. It thus seems that bank loans are used more for establishment of businesses and income-generating activities while internal loans are used as working capital for these businesses; 4.91% of internal loans are spent on purchasing stock for existing businesses in our survey. Moreover, a substantial amount of internal loan has been spent on repayment of debts other than from SHGs, on children’s education and on consumption purposes.

Table 3.

Utilization of internal and bank loans (in %).

	Internal loan	Bank loan
Starting new business	10.43	19.04
Buying agriculture inputs	10.82	8.43
Buying stock for existing business	4.91	0.00
Buying livestock	14.34	21.90
Total income-generating activities	40.50	49.38
Health	10.67	6.41
Marriage	14.36	19.04
Home repairing	12.31	19.23
Repayment of old debt	7.37	0.00
Education	7.67	3.51
Social ceremonies and other consumption expenditure	7.12	2.42
Total	100.00	100.00

Source: Computed from field survey data.

Determinants of repayment: Econometric results

The results of the Tobit estimations of the repayment behaviour measured as the delinquency rate are presented in Table 4. The researchers first define regressors, present hypotheses and interpret results. Each hypothesis is marked (+) or (−) according to whether the regressor is expected to have a positive or negative relationship with the delinquency rate. Several scholars have argued that an increase in average loan size will increase the leverage of gains from default. Therefore, the average loan size is hypothesized to be positively associated with an increase in the delinquency rate. The possible reason, as Sharma and Zeller (1997) in their study of Bangladesh and Feroze et al. (2011) in India find, is that in case of a project failure the borrowers will find it more challenging to meet the repayment schedule. As a result, the default ratio will increase. Also, it has been observed that multiple borrowing from other formal and informal sources by households also leads to default. Our results show that loan size (LOANSIZE) is a positive sign but statistically insignificant. This result is in line with the finding of Al-Azzam et al. (2012). It has been observed in the survey that delay in loan repayment is not necessarily due to negative intentions but because of beneficiaries’ selection of long-maturity projects for investment. The return on the project coming late could have a bearing on the repayment schedule. Godquin (2004) argues that large loans do not meet the borrowers’ needs and are not suited to the local economy.

Table 4.

Determinants of delinquency in self-help groups.

	Coef.	Std. err.	P > t	dy/dx
LOANSIZE	0.003	0.001	0.437	0.002
HHSIZE	−0.053	0.047	0.265	−0.002
WORKERRATIO	−1.617	0.660	0.015	−0.051
HHEDU	−0.028	0.020	0.158	−0.001
ADULTLIT	0.567	0.412	0.170	0.018
HHLAND	0.330	0.171	0.055	0.010
HHINCOME	−0.003	0.001	0.006	−0.001
MIDDLESHG	−0.348	0.176	0.049	−0.011
OLDSHG	−0.498	0.246	0.044	−0.016
SELECT	0.106	0.107	0.323	0.003
MONITORING	−0.416	0.070	0.000	−0.013
PEERPRESSURE	0.460	0.225	0.042	0.014
SOCIALCAPITAL	0.027	0.084	0.743	0.001
Constant	−0.205	0.669	0.760
Log likelihood	−59.3021
Observation	300
Prob > χ²	0
Pseudo R²	0.5367

Source: Computed from field survey data.

HHSIZE shows the household size. It is hypothesized that there is a negative relationship between household size and default rate which could be valid only if dependency ratio within the household is low and more household members are employed. This measures as WORKERRATIO (ratio of employed members to household size within each household). The results reveal that HHSIZE is negatively related to delinquency but insignificant. However WORKERRATIO has a significant impact on the default rate. The negative sign of HHSIZE and WORKERRATIO establishes the hypothesis that more employment and job opportunities decrease the probability of default in group lending. Besides this, it is argued that female borrowers in group lending programmes in India play only an intermediary role of channelling loans from SHGs to households. The funds are actually used by other household members (Srinivasan, 2010) leading to increased defaults in repayment.

The results indicate that the higher the level of education of the head of the household (HHEDU), the lower the delinquency rate; however the level of adult education (ADULTLIT) within the household is positively related to the default rate, but both coefficients are insignificant. HHLAND is the mean size of land owned by the household. Scholars have postulated two hypotheses: First, greater land size would enhance the capacity of borrowers to repay loans on time. The second hypothesis positively associates the size of landholding with default rate. It is valid for rural areas because many of the borrower farmers are unable to repay old loans as they have invested a substantial amount of the loans in agriculture and could not meet the pay-back target due to failure of crops and consequent shortfall in returns from cultivation (Sarangi, 2007). The current findings support the second hypothesis as the size of household land is positively associated with the delinquency rate, the coefficient being significant at the 10% level. In the survey some respondents reported that they had invested a large share of their bank loan and SHG credit in agriculture but returns had been meagre during the last three years. Thus, they could not repay their old loans before the due date.

It is also hypothesized that higher household income lowers the delinquency rate. The coefficient of household income (HHINCOME) is negative and highly significant at the 1% level, which confirms that household income is a critical determinant. Most female borrowers admit that one of the primary sources of repayment of old loans is regular curtailment from daily consumption expenditure. This small saving could be increased if the household monthly income is high.

An essential objective of this paper is to understand the repayment behaviour of borrowers and how group maturity (age of the group) affects delinquency. Whether the default rate is lower in old groups than new groups or not, all groups behave similarly. It is hypothesized that there is a negative relationship between old groups and delinquency rate as borrowers of old groups use a substantial amount of their loans on income-generating activities compared to new-group borrowers who use the additional money to supplement household income. Thus, the former manage to quickly repay their loans. The results show that both coefficients of group maturity (MIDDLESHG and OLDSHG) are negative and significant at the 5% level. This negative relationship between group age and delinquency rate is similar to results of Noglo and Androuais (2015), but not to the observations of Martin (1997). Reddy and Reddy (2012) show that the repayment rate from SHGs to banks varies from 0 to 100% with an average of 72%. The average repayment rate is high in young SHGs (three years old) or the oldest SHGs (older than 12 years).

The selection variable (SELECT) is measured by the sum of the positive response about self-selection, the probability of refusing to join the group, dropping/opting out of the group and new members joining the group. It is a broad measure to include the ex-ante and ex-post self-selection as much of the screening of members occurs after a group is formed. A careful selection of group mitigates the adverse selection problem of moneylenders. The results indicate that selection does not have a significant impact on the delinquency rate. Verhelle and Berlage (2003) and Feroze et al. (2011) in their study in India find the same relationship between selection variable and delinquency rate. The possible reason, as Verhelle and Berlage (2003) highlight, is the indirect influence of NGO personnel in the selection process of group members. Preferences always favour the wealthier people in order to reduce risks involved in loan distribution. Contrary to this finding, Wenner (1995), Zeller (1998) and Ghatak (1999) report that the selection criterion of groups always lowers the probability of default.

The peer group monitoring (MONITORING) variable is negatively associated with delinquency and is highly significant at the 1% level, which confirms that the small size of groups, frequency of group meetings and small distance between business activities and group members’ houses reduce the probability of default. Stiglitz (1990) and Varian (1990), however, observe that if the frequency of monthly group meetings is seven or more, that kind of regular monitoring has a positive effect on repayment of old loans because it mitigates the probability of ex-ante moral hazard. Wenner (1995) and Onyeagocha et al. (2012) believe that large group size is not a source of efficient monitoring as it can lead to strategic delinquency. Deininger and Liu (2009) find similar results on examining data of 3350 expired group loans from 300 Indian villages. According to them, regular monitoring and audits, high repayment frequency and consumption smoothing support through rice credits, significantly increase the repayment rate, but the magnitude of the effect varies across the socio-economic profile of borrowers. In contrast, Feroze et al. (2011), based on 60 dairy SHGs and 60 non-SHG members from Haryana, report that proper monitoring leads to low delinquency rates.

It is observed that peer group pressure reduces the delay days in late repayments and the possibility of ex-post moral hazard (Ahlin and Townsend, 2007; Karlan, 2007; Wydick, 1999). Contrary to their findings, here the estimated result of peer pressure (PEER PRESSURE) is positively associated with delinquency, which is significant at the 5% level. The impact of peer pressure works if there is less cooperation among group members. Cooperation among group members may dilute the willingness to create pressure on delinquent members which encourages late repayment. In the survey some borrowers admitted that they sometimes found it difficult to apply pressure on delinquent members due to strong social ties related to same caste, religion, occupation, age group and sex which prevail over money matters. This is confirmed by our results which show a positive relationship between social capital (SOCIALCAPITAL) and delinquency. This result is in line with the finding of Sharma and Zeller (1997). But Sahu (2013), in his study of Madhya Pradesh and Odisha, has concluded that group homogeneity and economic background do not affect the repayment performance of SHGs. The repayment rates of SHGs having members from same community are not significantly different from groups having members from different communities.

However, Al-Azzam et al. (2012) and Tesfaye et al. (2014) find that social sanction reduces opportunistic tendencies (ex-post moral hazard). In the same vein, Coleman (1988) and Fukuyama (1995) observe that social capital creates trust which is a strong prevention mechanism against asymmetric information; thus helping in improving the repayment rate under group lending. Karlan (2007), in his study of Peruvian microfinance organization FINCA, also finds that groups with stronger social ties are more likely to repay the loan amount.

Conclusion and policy suggestions

The group lending model is now an essential element of poverty alleviation in India but its success has always been questioned due to poor repayment performance. This paper has used primary data covering 300 SHG members across 100 SHGs in UP where the repayment is significantly poor to find out the reasons for this. The credit history of SHG members highlights several issues. The average loan amount increases as the groups become older. The findings also show that members of older SHGs tend to avail themselves of loans more frequently than members of new ones, which indicates that the former are greater risk seekers. During focus group discussions with respondents of the field survey, it has been observed that the share of each SHG member in total loan borrowed from a bank is minimal. As a result, a borrower cannot start any income-generating activity from the loan amount which is more often than not spent on complementing consumption needs, especially by members of new SHGs. In some cases, it has also been found that the group leader or dominant women members in the group use a large share of the group corpus even though each member makes an equal contribution towards group savings. Another essential point that has been observed from the credit history of SHG members is that more than 60% of old groups have completed their full repayment before the due date compared to 38% among new SHGs. The regression results also support this fact, confirming that group maturity (measured in terms of age of SHG) improves repayment. Thus, Godquin (2004) suggests that there is a need to develop new incentives for experienced borrowers to avoid decreasing repayment performance and adverse domino effects.

The results of the Tobit regression also bring out the fact that the number of family members employed and household income have a negative relation with delinquency, confirming that income generation is essential for poor borrowers. If they can find a way to generate income, it will help in repayment of the old loan. In this case, starting a new business could be a better option for income generation. The central government has made provisions in the Union budget 2019–20 to develop entrepreneurial capacity of women. It has proposed to grant every woman who is a verified SHG member and has a Jan Dhan bank account, an overdraft of Rs. 5000. One woman from each SHG would also be eligible for Rs. 100,000 under the MUDRA (Micro Units Development and Refinance Agency) scheme. As of August 2020, the share of women borrowers including schedule caste, schedule tribe, other backward caste and other minorities, under different schemes of MUDRA loan, stands at 63% by number of accounts and 43% by sanction of loan amount (MUDRA, 2020). However, given the low skills of poor SHG members, the success of the scheme is doubtful.

Thus, with adequate credit support a holistic development model needs to be developed so as to improve the skills set of SHG members. It may include training for starting new businesses, or strengthening an existing business, business management, financial literacy and even awareness about government programmes and schemes. It has been observed that banks sanction loans to SHGs based on previous loan repayment records and group savings, and do not take into account their financial management and internal repayment performance. Besides, there is poor monitoring of loans by banks and no dedicated staff for SHG lending due to high work pressure. It has also been noticed that banks do not follow the basic appraisal system of SHG lending (CRFIM and BIRD, 2015). Grading of SHGs is either not done or, if done, it is not done properly. Thus, proper monitoring and grading of SHGs and, accordingly, loan allocation may improve the repayment performance of SHGs.

Footnotes

Annexure

Table 5.

Loan carrying capacity of self-help groups (SHGs) and non-performing assets (NPAs) against loan outstanding.

Years		Savings per SHG (Rs.)	Loan disbursed per SHG (Rs.)	Loan outstanding per SHG (Rs.)	Ratio of savings to outstanding loan amount per Rs. 100 of savings	NPA as % loan outstanding
2019–20	UP	11,550.70	95,604.05	57,292.18	496.01	42.97
2019–20	India	25,530.83	246,850.92	190,371.18	745.65	4.92
2018–19	UP	10,217.18	70,511.40	61,379.29	600.75	44.45
2018–19	India	23,291.31	216,119.29	171,543.15	736.51	5.19
2017–18	UP	8539.40	71,434.59	61,082.68	715.30	30.9
2017–18	India	22,405.23	208,682.54	150,583.79	672.09	6.12
2016–17	UP	8380.50	80,883.44	64,569.77	770.48	27.79
2016–17	India	18,787.99	204,313.51	127,016.62	676.05	6.5

Source: NABARD (2016–17, 2017–18, 2018–19 and 2019–20).

Values reported in the table include all three type of banks – commercial bank, regional rural bank and cooperative bank.

Acknowledgements

We are grateful to Dr Amish Dugar, Dr Arun Keshav and Professor Nripendra Kishore Mishra for their helpful suggestions and encouragement. We are thankful to RGMVP officials who supported us in the fieldwork. We also thank the three anonymous referees for their incisive comments.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Udai Bhan Singh

Notes

Author biographies

Udai Bhan Singh is an Economics faculty at Amity Business School, Amity University Rajasthan, India.

Tanushree Gupta is a Finance faculty at Amity Business School, Amity University Chhattisgarh, India.

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