Introduction

The objective of a policy of financial inclusion is to make financial services universally accessible and affordable. The current phase of India’s rural banking policy, which started in 2005, focuses on financial inclusion, defined by the Reserve Bank of India (RBI) as “the process of ensuring access to financial services, timely and adequate credit for vulnerable groups such as weaker sections and low-income groups at an affordable cost” (RBI 2008). The RBI later revised this definition to include “convenient access to a basket of basic formal financial products and services that should include savings, remittance, credit, government-supported insurance and pension products to small and marginal farmers and low-income households at a reasonable cost” (RBI 2015). This paper focuses on one such financial service, namely formal or regulated sources of credit, looking at the distribution of rural credit over the last decade (2014–24) in rural Uttar Pradesh. It uses secondary statistics as well as primary, village-level data to gauge the extent of financial inclusion. All figures in this paper are for rural areas only.

Although the policy of financial inclusion was introduced in 2005, 2014–24 is chosen as the timeframe for this paper because significant policy measures have been implemented since 2014. The biggest effort made in this direction by the Government of India, along with the Reserve Bank of India, is the Pradhan Mantri Jan-Dhan Yojana (PMJDY), which was initiated in 2014.

India’s financial inclusion journey began as early as 1955 with the nationalisation of the State Bank of India (earlier known as the Imperial Bank of India), followed by the nationalisation of life insurance companies in 1956, and then two rounds of bank nationalisation in 1969 and 1980 respectively. Rural banking policy in India can be categorised into three major phases (Chavan 2005). The first phase, from 1969 to 1991, focused on social and development banking, with the objective of increasing the supply of formal credit to rural areas at regulated interest rates. This began with the nationalisation of major commercial banks in 1969 and 1980, and was taken further through the implementation of priority sector lending – which mandated that a certain percentage of the loans be allocated to priority sectors, especially agriculture. Measures such as branch licensing policy, differential interest lending rates, lead bank scheme, and Integrated Rural Development Programme (IRDP) were introduced during this phase (ibid.).

The second phase, from 1991 to 2005, was marked by financial liberalisation and an emphasis on profitability and commercial operations of banks, as recommended by the Narasimham Committee. This phase saw the introduction of measures such as the relaxation of directed lending norms and removal of interest rate regulations on priority sector advances, among others. The branch licensing policy was modified in 1992, and new schemes such as micro-credit for self-help groups (SHG) and the Kisan Credit Card for farmers were launched (Chavan and Kamra 2022).

The third and current phase of India’s rural banking policy, of financial inclusion, started in 2005. The idea of inclusive finance is not new to the country. The focus of India’s banking policy in its very first phase of bank nationalisation was on being inclusive. This saw a reversal during the liberalisation phase, when the focus shifted to profitability, but it has since returned to inclusivity in the third phase. Since 2010, the RBI has implemented three rounds of financial inclusion plans involving three-yearly bank-level targets. Several measures have been undertaken as part of these plans. In 2011, the Swabhiman financial inclusion scheme was introduced, which was redesigned as the Pradhan Mantri Jan-Dhan Yojana (PMJDY) in 2014 (Chavan and Kamra 2022). “Basic Savings Bank Deposit Accounts” (BSBDAs) – introduced in 2005 by the RBI, earlier known as “no-frills accounts” and later called PMJDY accounts – were included as part of these plans.¹ The PMJDY aimed to provide affordable access to financial services such as opening bank accounts, and availing credit, insurance, and pensions, to all households in the country. Under this scheme, debit cards (RuPay), a small-sized credit facility, and a micro-insurance facility in the form of accidental and life insurance were introduced. The Kisan Credit Card (KCC) for farmers and the General Credit Card (GCC) for non-farm credit needs were issued. A new type of bank – small finance bank – was instituted in 2015, to cater to the rural population and low-income groups. This was in line with the push towards microfinance, which, although not included under the financial inclusion plans, aimed to provide funds through self-help groups (SHGs) to women from socio-economically weaker sections. This later became a core part of the National Strategy for Financial Inclusion (NSFI) 2019–24, initiated in 2019, with the objective of making financial services available, accessible, and affordable to all citizens in a safe and transparent manner. The formation of SHGs and joint liability groups (JLGs), whose members were primarily women, was encouraged through the provision of micro (small-sized) loans by banks to these groups (State Level Bankers’ Committee: North-Eastern States 2020). Additionally, the RBI introduced the Business Correspondent model to deepen rural banking access and to cater to individuals who resided in remote areas with no physical bank branches (Centre for Digital Financial Inclusion [CDFI] 2019).

Review of Literature

Using All-India Debt and Investment Survey (AIDIS) data (48th round, 1992, and 59th round, 2003), Chavan (2014) showed that commercial banks played an important role in providing formal credit to the Scheduled Castes. However, post liberalisation, between 1992 and 2012, the contribution of banks declined sharply and the gap was filled in by moneylenders. This led to a gradual exclusion of the Scheduled Castes in rural India from affordable formal sector credit. Using AIDIS 2013 data (70th round, 2012–13), Ghosh and Vinod (2017) concluded that female-headed households were 8 per cent less likely to access formal finance compared to male-headed households. Also, the size of loans taken by female-headed households was often smaller; they borrowed 20 per cent less in cash loans from formal sources than male-headed households (ibid.). Using the same AIDIS 2013 data, Karthick and Madheswaran (2018) showed that non-Scheduled Caste households had significantly better access to formal agricultural credit than Scheduled Caste households in rural India. The caste-based differences in farmers’ access to bank loans were reflected in lower approval rates for Scheduled Caste borrowers compared to others (ibid.). Using data from the AIDIS 2019 survey, Chavan and Kamra (2022) concluded that access to bank credit for asset-poor households in rural areas was low. However, they noted an overall increase in access compared to the levels recorded in the previous AIDIS round (70th, in 2013).

Kumar and Venkatachalam (2019) found evidence in the India Human Development Survey (IHDS) 2011–12, of both caste-based discrimination (prejudice against the oppressed castes) and statistical discrimination (perceived higher default risk), particularly in bank lending for production loans. Using the same IHDS data, Sangwan and Saha (2024) concluded that non-Scheduled Caste/Scheduled Tribe borrowers received significantly higher loan amounts on average than borrowers from marginalised castes. Govindapuram et al. (2023) used data from the 4th round of the National Family Health Survey (NFHS-4, 2015–16) to show that gender was an important factor in access to formal credit. Along with supply-side factors, individual and demographic characteristics played a significant role in determining women’s access to financial services.

Based on a survey of 780 rural households across 78 villages of Uttarakhand in 2019–20, Kandari et al. (2021) concluded that men had a greater likelihood of availing credit compared to women where financial literacy played a crucial role in credit uptake. Using primary data from field surveys in two villages of Maharashtra (2007) and two villages of Rajasthan (one in 2007 and the other in 2010), Swaminathan (2012) found that access to formal credit was extremely unequal across caste. Likewise, based on primary data from field surveys in two villages of Tamil Nadu (2019), Swaminathan and Dutta (2023) concluded that a high proportion of Scheduled Caste households were dependent on microfinance, while “Other Caste” households benefited from cheaper bank and cooperative loans. Using sample survey data collected from 15 representative districts in 2014–15 across regions in Uttar Pradesh, Tiwari et al. (2022) showed that caste-based disparities in Uttar Pradesh significantly affected financial inclusion, with the Scheduled Castes facing the most exclusion. Non-Scheduled Castes had better access to formal financial services, while Scheduled Castes and Muslims relied more on informal sources (friends, relatives, moneylenders). Loans for agriculture were common among the non-Scheduled Castes, whereas the marginalised castes borrowed for health, marriage, and emergencies (ibid.). In Tamil Nadu, it was found that access to credit was deeply intertwined with caste and social status (Guerin et al. 2023). Scheduled Caste women had stopped borrowing from landlords because it came with an additional sexual dependence, and had begun to rely on microfinance for their credit needs; non-Scheduled Caste women preferred borrowing from bank-linked organised SHGs (ibid.)

There are studies which show that microfinance has enlarged women’s access to formal credit, particularly in regions with limited banking infrastructure (Khandker 2005; Pitt and Khandker 1998). It has also led to reduced dependence on pawnbrokers and landlords (Guérin et al. 2013), and helped the shift away from informal lenders who charge exploitative rates of interest (60–120 per cent) to microfinance institutions (MFIs) (charging 20–30 per cent interest) (Rutherford 2009).

While access to credit by caste or by gender has been independently studied, there is little literature on the intersection of gender and caste in financial inclusion in India, particularly in the context of Uttar Pradesh. This paper aims to examine and understand the distribution of formal credit in rural Uttar Pradesh across gender and caste. Using secondary data as well as primary data from the Project on Agrarian Relations in India (PARI) of the Foundation for Agrarian Studies (FAS), it explores borrowing patterns, loan size, sources of credit, and the socio-economic dynamics affecting access to credit for women and persons belonging to the Scheduled Castes.

Data and Methodology

The main official source of data on indebtedness in India is the All-India Debt and Investment Survey (AIDIS).² In this paper, I have used and analysed unit-level microdata of the latest round of the AIDIS, conducted in 2018–19. While details of the caste to which household members belong have been collected and are available in existing secondary data sources, the interlinkage of caste and gender in access to credit is not captured by these sources. This gave rise to a need to look at primary data on both caste and gender. I used the data for the two villages initially surveyed in 2006 by the Foundation for Agrarian Studies (FAS) as part of its Project on Agrarian Relations in India (PARI),³ and resurveyed in 2023.⁴ I was part of the team that resurveyed both villages in 2023. The primary research was conducted in 2023 through a census survey of 130 households in Harevli village, Bijnor district in western Uttar Pradesh, and a sample survey of 107 households in Mahatwar village, Ballia district in eastern Uttar Pradesh. The sample constituted 50 per cent of the village population, and the households were selected using stratified random sampling. The research instrument was a questionnaire comprising questions on loans,⁵ and on the demographic and economic features of the households. The primary respondent was the decision-making head of the household, but information was collected on all members of the household.

The variables on which information regarding credit was collected were:

1. Principal: loan amount or principal borrowed from the lending agency
2. Rate of interest: annual rate levied on the loan amount
3. Collateral: provided to the lender as a guarantee of repayment
4. Source of borrowing: the name of the lending agency
5. Purpose of borrowing: reason for loan.

While the AIDIS provides information on debt outstanding, measured by the sum of the loan amount (principal) and the interest accrued on the loan, the data in this paper use “principal borrowed.” Principal borrowed is used here for two reasons: first, because an individual’s recollection of the loan amount is usually more accurate than the rate of interest;⁶ secondly, because there is not much difference between principal borrowed and debt outstanding, if the overall proportions of total amount borrowed in the village data are considered. For consistency, the term “debt outstanding” is used henceforth for both variables.

While the magnitude of financial inclusion can be understood at the national level by gender and at the State level by caste, no secondary data source provided information on credit by gender and by caste, for rural India and the States. The primary data filled this gap. Scheduled Tribe households have been excluded as they constituted only 1 per cent of the population of Uttar Pradesh, and castes other than Scheduled Castes have been categorised together as non-Scheduled Castes.

Brief overviews of the two villages are as follows.

Mahatwar village is situated in the eastern part of the State, close to the Bihar border. Sixty-five per cent of households in the village were Scheduled Caste (as opposed to 60 per cent in 2006).⁷ There was only one Scheduled Tribe household, which has not been included in the analysis for simplification. The primary source of income for this six-member household was a government job, along with a pension. They owned 0.6 acre of land and did not engage in agriculture.

Women comprised 47 per cent of Mahatwar’s population. About 20 per cent of the households in the village did not own agricultural land, and 71 per cent owned less than 1 acre. There was no bank in Mahatwar; the closest bank branch – Baroda Uttar Pradesh Gramin Bank, a regional rural bank under the sponsorship of Bank of Baroda – was located at a distance of 6 kms. There was a single Business Correspondent (BC), with the license in the wife’s name and the husband operating the shop situated in the Harijan basti (Scheduled Caste settlement).^8,9 Banks and MFIs were the prevailing formal sources of lending, while pawn shops and friends/relatives were the common informal sources.¹⁰ One notable feature was the absence of professional moneylenders.

Harevli is situated in the north-western part of Uttar Pradesh, close to the Uttarakhand border. At the time of the survey, 32 per cent of households in the village were Scheduled Caste (as opposed to 38 per cent in 2006).¹¹ Women comprised 46 per cent of the village population. Land distribution was unequal with one-third of households owning no agricultural land. There was neither a physical bank nor a BC in Harevli. The nearest bank branches were at a distance of 7.5 km: Bank of Baroda; a scheduled commercial public sector bank; and Prathama U. P. Gramin Bank, a regional rural bank sponsored by Punjab National Bank. The Sugarcane Development Cooperative Society, a primary agricultural credit society (PACS), was situated at a distance of 11 km from the village. Formal sources of lending were further classified into formal public sources (State Bank of India, Punjab National Bank, Bank of Baroda, Prathama U. P. Gramin Bank, HDFC Bank, Kotak Mahindra Bank, Nainital Bank, and the Sugarcane Development Cooperative Society) and formal private sources (Asirvad Micro Finance Limited, Tata Capital, Muthoot Finance, TVS Credit, Bharat Financial Inclusion Limited, Chaitanya India Fin Credit Private Limited, Samasta Microfinance, and IIFL Samasta Finance Limited). The informal sources were landlord/employer, and friends and relatives.¹² No professional moneylenders were active in this village either.

In this paper, sources of borrowing have been classified into formal sources (regulated by the Reserve Bank of India – such as scheduled commercial public and private banks, credit cooperatives, regional rural banks, small finance banks, non-banking financial companies or NBFCs, and microfinance institutions or MFIs) and informal sources (unregulated, such as friends and relatives, pawn shops, moneylenders, and so on). In my analysis, using the argument provided by Swaminathan and Dutta (2023), I have further categorised the formal sector into formal public sources (scheduled commercial public and private banks, credit cooperatives, and regional rural banks) and formal private sources (small finance banks, NBFCs, and MFIs). This classification was made based on the type of loan, cost of borrowing, and collateral required. Loans from SHGs were classified based on their source of funds – for example, whether they were bank-related or MFI-related.

Insights from Secondary Data: Status of Financial Inclusion

Banking services are defined by two variables: access to a deposit account in a financial institution, and access to credit facilities. In India, these data are collected as part of the AIDIS. The findings from microdata of AIDIS 2018–19 are discussed below.

1. Uttar Pradesh fared better (69 per cent) than the national average (64 per cent) in 1991, in the share of formal debt out of total debt (Table 1). Post-liberalisation, nationally, there was a decline in the share of formal debt in total debt. This decline was steeper for Uttar Pradesh as compared to the national average. Although the State’s share has increased since the financial inclusion policy was introduced (63 per cent in 2019), it has not been able to surpass pre-liberalisation levels, while the national figure (66 per cent in 2019) has barely surpassed the 1991 figure.
2. AIDIS 2019 shows that in Uttar Pradesh, the share of scheduled commercial banks in formal sector credit was almost 60 per cent as against around 50 per cent nationally. The dominance of commercial banks in formal sector debt continued despite the emergence of newer formal financial institutions.
3. Out of every 100 women/men in Uttar Pradesh, 80 per cent women and 86 per cent men had deposit accounts in financial institutions. The gender gap in deposit account ownership in Uttar Pradesh was 6 percentage points, while it was 7 percentage points in India as a whole.¹³ There has been progress in providing ownership of accounts across gender, but no official data are available on access to credit services by gender.
4. The Scheduled Castes accounted for a disproportionately low share of credit disbursed. The average loan size, measured in terms of the sum total of all loans divided by the number of loans, of non-Scheduled Castes in Uttar Pradesh was thrice that of Scheduled Castes (Table 3). The corresponding ratio for rural India was 2.5. If the share in formal debt outstanding by share in population was considered, in Uttar Pradesh, the ratio for Scheduled Castes was 0.5 as compared to 1.1 for non-Scheduled Castes (Table 2); at the national level, the corresponding ratios were 0.6 and 1.2, respectively.

Findings

Mahatwar

In Mahatwar, 51 per cent of households had active loans. Among them, 79 per cent had ongoing loans from the formal sector. On average, each household had 1.5 loans.

In 2006, 49 per cent of debt in the village was from the formal sector, dominated by banks and cooperatives. In 2023, formal sources accounted for 80 per cent of all loans and 83 per cent of debt (see Appendix Table 1). Within the formal sector, 48 per cent of debt was from public sources, with Baroda Uttar Pradesh Gramin Bank (RRB) alone accounting for 36 per cent of loans and 24 per cent of debt.¹⁴ While the share of debt from formal public sources (banks and cooperatives) did not change much, the increase in share of formal credit was due to the emergence of private sources. Formal private sources accounted for 52 per cent of the formal sector debt. Cashpor Micro Credit (NBFC–MFI) alone accounted for 44 per cent of all formal private loans (and 71 per cent of all MFI loans). The microfinance institutions operating in Mahatwar were Cashpor Micro Credit, Bandhan Bank, Satin Creditcare Network Limited, and Satya MicroCapital Ltd.

The cost of borrowing or the annual rate of interest levied is an important component of credit. While scheduled commercial rural banks lend at relatively low cost against physical collateral, formal private sources charge higher rates of interest (Table 5). In Mahatwar village, there are NBFCs and MFIs who lend at rates in the range of 12 to 31 per cent per annum. These are loans collected by MFI agents on a weekly or fortnightly basis, depending on the type of repayment.

The cost of borrowing varies by gender (see Appendix Tables 3 and 4). The median rate at which women in Mahatwar borrowed (14 per cent) was higher than that for men (11 per cent). The cost of borrowing varied between Scheduled Caste and non-Scheduled Caste women, with Scheduled Caste women paying 4 per cent more for a loan. Women as a whole took 71 per cent of their loans from the formal private sector, and Scheduled Caste women took 65 per cent of loans from the formal private sector (MFIs).

In 2023, the share of Scheduled Castes in total credit was almost half their share in the population (Table 4); their share in formal debt was even lower. The 2006 PARI survey found that the share of Scheduled Castes in total village debt was 44 per cent, which was lower than their share in the population (60 per cent); their share in formal debt was 49 per cent. Despite all the policy measures undertaken over the years, the share of Scheduled Castes declined in 2023, both in terms of share in total debt and formal debt, while their share in the population remained unchanged.

House repairs and house construction were major reasons for borrowing for all households: 49 per cent for Scheduled Castes and 25 per cent for non-Scheduled Castes. Since these are non-income-generating activities, they increased the burden of repayment on the households. The Scheduled Castes borrowed largely for non-income-generating purposes (91 per cent).

Women’s share in formal credit was exactly half their share in the population. Further, while the average size of loan taken by women was Rs 55,000, it was Rs 195,000 for men.¹⁵ The average size of loan taken by women did not vary much across caste groups: Rs 54,000 for Scheduled Castes and Rs 64,000 for non-Scheduled Castes, while among men there was a big gap (Table 7).

Women borrowed mainly through group-based microfinance: 75 per cent of their debt was in the form of group loans from MFIs and other financial institutions. In Mahatwar, a group of women came together and became affiliated with an NBFC–MFI. Multiple all-women SHGs linked to NBFC–MFIs were active in the village. But there was no mechanism of savings in these groups. Loans were sanctioned in the name of individual women by the financial institutions, and missed repayments were not allowed.

To illustrate the process, I describe the lending structure of Cashpor Micro Credit, an NBFC–MFI that was popular in Mahatwar. Cashpor lent for a duration of one to two years, with a weekly payment structure. There were no processing charges for the sanction of a loan. All loans were issued in the women’s names. A life insurance component was deducted from the principal amount. The minimum loan sanctioned in Mahatwar was Rs 5,000, and the highest was Rs 150,000. There was a 100 per cent repayment rate, and the onus was on the group leader to ensure that all payments were made without fail. Additionally, the Credit Information Bureau (India) Limited (CIBIL)¹⁶ score of the applicant was verified. These loans were granted to individual women who belonged to the same SHG. Similar to Swaminathan’s (2025) finding from her village studies in Tamil Nadu, I found that MFIs used existing SHGs in the village as lending structures, which in turn served as guarantors. Anyone who wished to take a fresh loan (someone who was not a member of the group yet) and resided nearby could approach the collection agent, and arrangements were made for inclusion in the group after due scrutiny. The applicant’s household income was checked through discussion with other group members before a loan was sanctioned.

During the survey, we found that multiple financial scams associated with chit funds and microfinance had taken place in the village in the last five years, which directly affected the women as they were the ones who were scammed of their money.

Harevli

In Harevli village, 74 per cent of households had an active loan, while 81 per cent had borrowed from the formal sector. On average, each household had two ongoing loans.

In 2006, the formal sector (which then comprised only banks and cooperatives) accounted for 80 per cent of debt, while in 2023, formal sources accounted for 82 per cent of all loans and 87 per cent of debt in the village (Appendix Table 2). Of the total formal debt, 78 per cent was from formal public sources, mainly public banks, including regional rural banks (RRBs). Between 2006 and 2023, the share of debt of the traditional banking sector remained unchanged. The rest of the formal sector debt was lent using the group National Rural Livelihood Mission (NRLM) linkage model. Loans granted under the NRLM programme have been treated as formal public sector loans based on loan size, type of collateral, lending mechanism, and rates of interest.

A large portion (82 per cent) of the formal public debt in Harevli came from crop loans (KCC), loans taken against land, for agriculture and allied activities, from RRBs and cooperatives. The KCC loans were taken by land-owning households, and all were non-Scheduled Caste households. Land owners were eligible for KCC loans. A significant lender of KCC loans was the Prathama U. P. Gramin Bank, an RRB, lending at a subsidised rate of 3 per cent per annum; it accounted for 35 per cent of all formal public sector debt (and 28 per cent of total village debt). Another 10 per cent was given by the Sugarcane Development Cooperative Society, a primary agricultural credit society (PACS), for seeds and other inputs, also at a subsidised rate of 3 per cent per annum. Members of the Society were eligible for loans, and almost all these loans were taken by men as the land records were largely in their names. The average loan size, however, was very different across sources: Rs 265,000 from the Gramin Bank and Rs 59,000 from the Sugarcane Development Cooperative Society. Loans from the Gramin Bank ranged from Rs 50,000 to Rs 900,000, and were mainly given to landed Tyagi farming households.

There was a noticeable improvement in access to credit by Scheduled Caste households in the village. The share of Scheduled Castes in total debt doubled from 7 per cent in 2006 to 14 per cent in 2023, while their share in the village population declined from 38 per cent to 30 per cent during the same period. More than 60 per cent of the credit of the non-Scheduled Castes was for productive reasons, such as purchase of land, crop cultivation, business, and dairy activities (Table 10). It was different for the Scheduled Castes – 50 per cent of their debt was to meet the expenses of daily requirements, education, for medical purposes, and marriages.

The share of women in formal credit was disproportionate to their share in the population (Table 11); it was one-third their share in the population. The average size of loans taken by women was Rs 22,300, whereas it was Rs 149,000 for men.¹⁷ There was variation based on caste as well. The average loan taken by Scheduled Caste women was half of that taken by non-Scheduled Caste women (Table 11).¹⁸

The cost of borrowing varied between public and private sources (Table 9). While the formal public sector had a median interest of 7 per cent per annum, the formal private sector had a median rate that was four times higher, at 27 per cent. KCC loans taken from banks and cooperatives were charged an interest rate of 3 per cent per annum, while NRLM loans were charged 24 per cent. MFIs too charged high rates of interest (27 per cent); there was not much difference between the interest rates on NRLM and MFI loans.

A cost differential was visible across gender and caste (Appendix Table 3). The median rate at which women borrowed (24 per cent) was substantially higher than that at which men borrowed (4 per cent). The median rate of interest charged to Scheduled Caste men was higher (7 per cent) than that charged to non-Scheduled Caste men (4 per cent). There was no such difference across caste for women, with the median rate at 24 per cent for both Scheduled Caste women and non-Scheduled Caste women.

Group lending through both public sector banks and private agencies existed in Harevli. In addition to the MFI group lending model, there were SHGs associated with public sector banks under the NRLM programme.¹⁹ While NRLM lent at an annual rate of 23 per cent, MFIs lent at 30 per cent (see Appendix Table 5).

The National Rural Livelihoods Mission (NRLM), also known as Aajeevika, is a flagship programme of the Ministry of Rural Development, Government of India. Its primary aim is to reduce poverty by providing sustainable livelihood opportunities to the rural poor. In addition to facilitating access to affordable financial services, NRLM focuses on skill enhancement, enabling women to gain access to employment opportunities. The objective of the NRLM–SHG linkage is to provide women the scope for engaging in income-generating activities so that they can become financially independent and to safeguard them against exploitation. The women of the village engaged in small-scale economic activities, as they could avail the initial capital to set up businesses through loans. There were instances of women who borrowed through NRLM using the loan to open a small business, purchase cattle, or repair their house. Other activities observed among the women included setting up a grocery (kirana) store within their homestead, opening a clothing store, and buying a sewing machine for stitching garments.

I interviewed the SHG representative (known as Samuha Sakhi) of this village, and learned that the first SHG in Harevli was formed in July 2019. The SHG representative, belonging to the Dhimar sub-caste, was selected, trained, and appointed as an Internal Community Resource Person (ICRP) of the government; was paid a monthly salary; and was entrusted with responsibilities such as conducting weekly meetings, collecting weekly payment instalments, and other administrative tasks.

There were 48 active SHGs in Harevli that were part of a cluster-level federation (CLF). The RRB gave loans at 6 per cent to the federation while the federation lent at 12 per cent to the SHG, and the group members in turn borrowed at 24 per cent. The surplus amount, arising out of the difference in interest rates, went into the corpus of the SHG and was used for the benefit of the group. Individuals who could not pay instalments in full could choose to pay only the interest component, which was not uncommon. However, the duration of the loan could not exceed two years, within which they were required to fully repay the loan with interest, and a new loan was not issued till the existing loan was entirely repaid.

All recognised SHGs were linked to a bank – in this case, the Prathama U. P. Gramin Bank. There were nine active SHGs in the village (a total of 12 SHGs were listed on the NRLM website, but two were dormant and one had been inactive for a while). The distribution of social groups across households was as follows: four belonging to Other Backward Classes, three of Scheduled Castes, and one each of Muslims and Other Caste Hindus. The cap on the number of members of an SHG was 15, but there was a preference for a 10-member group as that allowed for a reduced division of allotted funds, thereby increasing the size of individual loans. Most of the groups repaid their debts within the stipulated time and regular repayments were rewarded with a doubling of the loan amount by the bank. This acted as an incentive for the women to repay on time without incurring any additional cost, and eventually acted as a boost for the women, the group, and the bank.

Concluding Observations

From 2005, the Government of India has implemented programmes to enhance financial inclusion and increase access to financial services. A visible area of improvement has been an increase in the ownership of deposit accounts, with around 90 per cent of the Indian population owning a deposit account in 2019 (National Statistical Office 2019). This paper focuses on access to formal credit, drawing on official data from the All-India Debt and Investment Survey (AIDIS) 2019. It is important to note that borrowing patterns have changed since then, due to the COVID-19 pandemic. In recent years, there has been a rapid spread of microfinance institutions (MFIs) in rural areas, and Uttar Pradesh features among the top three States of India in terms of MFI borrowers and amounts borrowed in 2024 (Sa-Dhan 2024). This increase in microfinance loans has not been captured by the AIDIS. Also, while AIDIS 2019 has information on credit availed by caste groups, there is no gender disaggregation of credit.

I used primary data from two villages of Uttar Pradesh surveyed in 2023 under the Project on Agrarian Relations (PARI) of the Foundation for Agrarian Studies (FAS), to study access to credit by gender and caste. A summary of my main findings are as below.

1. Contrary to secondary data (AIDIS 2019), which showed the share of the formal sector in Uttar Pradesh to be 63 per cent, and earlier village studies (cf. Swaminathan 2012), which concluded that the informal sector thrived in all villages and access to the formal sector was unequal across households in Uttar Pradesh, in my study, I found that access to formal credit was relatively high in the two Uttar Pradesh study villages. In both villages, the share of the formal sector in total debt was more than 80 per cent.
2. The distribution of debt within formal sources varied across the two villages. In Mahatwar, public and private formal sources had an equal share. Banks gave loans for agriculture and allied activities to land-owning households, while MFIs gave group loans to women. In Harevli, formal public sources dominated. Banks and cooperatives gave crop loans for cultivation expenses, including for seeds and inputs. Bank-linked NRLM loans were given to women for income-generating activities.
   1. a. The distribution of formal sector debt was unequal across caste and gender.
   2. b. The share of Scheduled Caste households in formal credit, and in total credit, was lower than their share of the population in Uttar Pradesh and in the two villages, as observed from both secondary and primary data sources. The average size of loan of Scheduled Castes was lower than that of non-Scheduled Castes.
3. Women lagged behind men in access to credit. The share of women in total debt, formal debt, and average size of loan was disproportionately low as compared to men. Women also paid a higher cost for loans as compared to men. Scheduled Caste women in both the study villages were worse off than non-Scheduled Caste women, highlighting the fact that gender, along with caste, acted as a major constraint.
4. In Mahatwar, an important source of funds were the newer financial institutions, namely loans advanced by non-banking financial companies (NBFCs) and MFIs. While this lending model provided women with easy access to loans that came with simpler allocation methods and no requirement of physical collateral, they posed difficulties in the form of high interest rates and strict repayment schedules. These loans were taken primarily for house repair and consumption expenses, and were not linked to economic activities.
5. In Harevli, microfinance was mainly routed through the National Rural Livelihood Mission (NRLM) programme, which not only provided credit to women but also assisted them with skills training. These loans were sanctioned to individual women by the group leader (Samuha Sakhi). All NRLM groups in Harevli were linked to a single regional rural bank.

While microfinance lending to women was observed in both villages, there were differences between NRLM and MFI credit in terms of group formation and loan allocation procedures. The NRLM had an appointed group head who was responsible for all the groups in the village, while the MFI groups had individual leaders. The NRLM groups had a corpus that could be used for various group activities, while the MFI groups did not have any such funds. NRLM loans were usually associated with economic activities, unlike MFI loans. The rate of interest for NRLM loans was lower than that of MFIs. The average size of loans given by MFIs was higher than NRLM loans in both villages.

In summary, evidence from the PARI village survey suggests that although overall access to formal credit has improved, variations exist in the structure of formal credit. New private financial institutions (NBFCs and MFIs) are functioning actively alongside traditional institutions like banks and cooperatives. The new financial institutions provide easier access to credit, but at a higher cost. Access to formal credit is differentiated by caste and gender, which together act as a limitation on affordable finance.