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Credibility of Equal Access to Credit: Does Gender Matter?

This article examines the National Sample Survey Organisation unit record data pertaining to debt and investment (59th round) and highlights inequality in access to credit by certain segments of society. In particular, it shows that weaker sections such as female-headed households have a much lower access than their male counterpart even when they are involved in similar economic activities and consequently face significantly higher rates of interest.


Credibility of Equal Access to Credit: Does Gender Matter?

Meenakshi Rajeev, B P Vani, Manojit Bhattacharjee

Bhattacharjee and Rajeev 2010). The existing studies are usually based on the published data in the NSSO reports (Rao and Tripathi 2001).

A Brief Review of Literature

A number of studies examine the trends in

This article examines the National Sample Survey Organisation unit record data pertaining to debt and investment (59th round) and highlights inequality in access to credit by certain segments of society. In particular, it shows that weaker sections such as femaleheaded households have a much lower access than their male counterpart even when they are involved in similar economic activities and consequently face significantly higher rates of interest.

We acknowledge support of the Reserve Bank of India to Institute of Social and Economic Change which enabled us to carry out this work; however, the views expressed in this paper are strictly those of the authors and are not attributable to the RBI. We are grateful to R S Deshpande, G K Karanth and Vikas Kumar for valuable suggestions. We thank an anonymous referee for useful comments on an earlier draft of the paper. Usual disclaimer applies.

Meenakshi Rajeev ( and B P Vani ( are Faculty and Manojit Bhattacharjee is a doctoral scholar at the Institute for Social and Economic Change, Bangalore.

hile the importance of formal credit, especially for self-employed households has been wellrecognised, and consequently a financial inclusion drive is in progress in most states of India, a large percentage of the households continue to depend on informal sources of credit. Notwithstanding the introduction of no frill accounts and a mobile banking system (Dev 2006), the relationship of banks with small and marginal farmers or micro and small entrepreneurs has remained distant, raising doubts about the sustainability of the financial inclusion drive. In the literature on financial inclusion, socially backward classes are often highlighted but certain weaker sections do not attract as much discussion. Against this background, we explore the availability of credit to femaleheaded households and argue why they need special attention from the formal credit system. Even though the self-help group (SHG)-bank linkage programmes have established that women borrowers are comparatively less risky (Veershekharappa 2006 and Rajeev et al 2010), our analysis shows that they do not r eceive equal treatment when it comes to individual access to regular (non-scheme based) credit.

The present analysis utilises the unit level data for households from the All- India Debt and Investment Survey (AIDIS) and the Situation Assessment Survey (SAS) conducted as the part of the 59th round of National Sample Survey (NSS). The AIDIS provides information regarding household debt and investment for 1,43,285 rural and urban households engaged in a variety of occupations. Unlike AIDIS, the SAS confines itself to farmer households and the sample size for this data set is 51,770 households. While both these surveys provide rich micro-level information for large samples, very few studies carry out unit level analysis (Bhattacharjee and Vani 2009;

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formal sector lending for different economic activities or different sectors in the Indian economy using the bank level data from the Reserve Bank of India (Shetty 2005; Patnaik 2005; Chavan 2005; Basu 2006). Studies on the rural credit market observe that there was an increase in supply of credit to rural areas during the p eriod after the nationalisation of commercial banks (in 1969). However, after liberalisation (1991) there has been a d ecrease in rural banking net worth as reflected in indicators such as the number of rural branch offices of commercial banks (Rajeev and Vani 2011).

The problem of non-accessibility of formal sector credit to the poor and needy has been often highlighted in the literature. Even though the state made endeavours to address this problem by stipulating norms for compulsory lending to the agricultural sector, the formal lending agencies have not been successful in reaching out to the poor as seen from the NSSO (2005a, b) data (see also Siamwalla et al 1990; Bell 1990). The problem of access may be due to the lack of collateral, inability to comply with bureaucratic procedures, illiteracy, etc (see also Gupta and Choudhuri 1997; Lele 1981; Benjamin 1981). In the presence of these constraints, in spite of the considerable expansion of the formal credit network, informal lenders do a thriving business in the rural credit market in general, and in the agricultural credit market, in particular.

This phenomenon has been observed not only in India but also in other developing countries including Pakistan (Aleem 1990), Thailand (Siamwalla et al 1990) and some countries in Africa (Udry 1990). A detailed account of the problem of accessibility of credit in agriculture and the nature of agricultural indebtedness can be found in the Report of the Expert Group on Agricultural Indebtedness (GoI 2007). Further, the Report of the Steering Committee on Empowerment of Women and the Development of Children for the Tenth

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Five-Year Plan (GoI 2001) notes that tradi

traditionally women members have been discriminated against in accessing credit and marketing facilities. An overwhelming majority of the female workforce in the country is employed in the agricultural sector. The report suggested that, concerted efforts should be made to ensure that the benefits of timely credit, training, extension and various programmes reach women in proportion to their numbers.

With regard to access to credit by women, the SHG-bank linkage programme of National Bank for Agriculture and R ural Development (NABARD) is worth mentioning since it has made credit available to poor women without the requirement of collateral (Rajeev et al 2010). Puhazhendi and Satyasai (2000) carried out an impact study of the SHG-bank linkage programme covering representative states at the all-India level. The study found poorer sections of society, especially women, benefiting from the programme. In another study on SHGs, Basu (2006) finds that a significant proportion (54% on an average) of the linkage groups are from poorer section but the figures vary across states.

Thus, in addition to the focus on inequality in access to credit by different groups in the society, the study of regional dispersions (often based on consolidated statistics published by the NSSO on access to credit) has also been of interest to scholars. For instance, to study the changes in indebtedness over time, Rao and Tripathi (2001) have used data published in the NSSO (AIDIS) reports of 1981 and 1991. Similarly, to study household indebtedness across various states, Narayanmoorthy and Kalamkar (2005) have used information provided by AIDIS reports of NSSO (59th round 2005a).

Table 1: Incidence of Indebtedness by Head of Household: All India – Rural

MPCE Classes (Rs) Male-headed (%) Female-headed (%) All (%)

0-254 23.90 16.63 23.14

254-338 26.13 15.36 25.04

338-425 27.13 18.97 26.30

425-510 28.51 18.83 27.48

510-600 28.65 15.03 27.11

600-700 30.38 19.45 29.20

700-950 30.19 19.71 28.80

950-1,500 27.72 15.32 26.28

1,500-3,000 33.88 13.14 29.53

Above 3,000 27.16 8.09 16.85

Total 27.59 17.44 26.51

MPCE = Monthly per capita consumption expenditure. Source: Authors’ analysis of NSSO 59th round data (Debt and Investment Survey).

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These studies have found that household indebtedness (both in incidence and size) has increased, over time (between 1981 and 1991), and it is more in agriculturally developed states as compared to other states. Interest rate and its determinants have been studied using econometric models by Bhattacharjee and Rajeev (2010) and Bhattacharjee and Vani (2009). A few other papers concentrate on the issue of indebtedness in the perspective of a particular region based on field surveys as well (Kumari 2005; Gothoskar 1988).

Access to Credit: Is There a Gender Bias?

The NSSO data provide information regarding households that have an outstanding loan on a pre-specified date (in this case as on 30 June 2002), based on which one can arrive at the percentage of households within a category of households (such as within an income category and so on) that have outstanding credit. This indicator, termed as the incidence of indebtedness (IOI), essentially represents the percentage of households having outstanding loans amongst the households of that category. A careful examination of the above data reveals that the IOI is higher for the higher income groups, and more economically advanced states have a higher level of IOI. Further, the scheduled tribe households in general have lower IOI than the general or Other Backward Classes (OBC) category households. Observing these characteristics, one is tempted to interpret the IOI more as a pointer of access to credit rather than an indicator of distress, though the latter possibility also cannot be ruled out, especially for the relatively poorer households.

Given the difference in the IOI across social communities, it is of importance to examine the access to credit not only by income class but also by different sections of society such as by the weaker sections designated by the female-headed households. When we examine the IoI pertaining to both formal and informal loans across male- and female-headed households, we observe that there is a significant difference between these two groups (see Table 1 for rural households). It is to be noted in this context that in the sample about 11% households are female-headed both in rural and urban regions, thus amounting to

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a sample of about 16,000 (female-headed) households. While for both groups IOI increases with the economic status of the household (except for the highest expenditure categories, which may be due to demand-side reasons) it is lower all through for female-headed households.

In order to test whether these differences are statistically significant or not, we conducted a test of the null hypothesis Ho: p1= p2, where, pi’s are the proportion of indebted male- and female-headed households, using a “Z-test”. The Z-statistic here turns out to be Z = {0.251 – 0.165}/ 0.00024 = 370.88. This being significant at 1% level, we reject the null hypothesis and conclude that there is a statistically significant difference in access to credit between the male- and female-headed households.

One may argue, however, that a loan taken by a female-headed household does not necessarily imply that credit is in the name of a woman only and that thereby indicates a gender differential in the credit market. Indeed, a man from a (femaleheaded) household can also take a loan. In order to examine this aspect, we have further analysed the characteristics of the f emale-headed households in the sample and observed that 70% of such households are headed by widows. Further analysing the profile of other members of these families it is found that 80% of the children in these families are non-adult. Thus, the majority of these households represent widow-headed households with small children.

We also look at inequality measured through Lorenz ratios for access to credit in case of male- and female-headed households (Figure 1, p 78). Interestingly, it is observed that inequality in access is more for female-headed households which i mplies that these households not only have lower access compared to their male counterparts, but poor females are much more discriminated against compared to their richer counterparts vis-à-vis the male-headed households. This observation calls for due attention of the policymakers.

Next, comparing rural and urban areas we observe that the IOI is much lower in urban areas and the difference between the maleand female-headed household is also lower. Thus, it appears that the access to credit is comparatively lower in urban areas (Table 2, p 78) for the expenditure categories.


Figure 1: Concentration Curves for Access to Credit (Amount of Loan Outstanding) across Male- and Female-Headed Households in Rural Areas

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0Male-headed HHs Female-headed HHs Cumulative Credit ProportionsLine of Equality
  • 0 0.2 0.4 0.6 0.8 1 Cumulative Population Proportions (arranged according to expenditure class).
  • Source: Authors’ computation using NSSO 59th round data.

    The IOI, no doubt, provides an indication of accessibility to credit. Some scholars, however, are critical of the concept as it in the nature of a dummy variable, and therefore, do not provide information about the

    Table 2: Incidence of Indebtedness by Head of Households: All India – Urban Area

    Urban (MPCE) (Rs) Male-headed (%) Female-headed (%) All (%)

    0-384 19.08 13.33 18.36

    384-511 18.53 15.88 18.22

    500-600 18.94 20.71 19.13

    600-700 18.16 18.73 18.22

    700-800 16.71 15.71 16.61

    800-950 18.35 14.26 17.95

    950-1,500 17.02 12.43 16.57

    1,500-2,500 20.93 6.33 19.32

    2,500-3,500 21.93 12.55 20.14

    Above 3,500 16.77 7.47 15.36

    Total 18.22 14.24 17.79

    Source: Authors’ analysis of NSSO 59th round data.

    Table 3: Average Amount of Loan Taken by Households (Rs)

    Regions Female-headed Male-headed Total Households Households

    Rural 17,006.3 20,869.9 20,600.8

    Urban 31,886.6 51,528.9 49,878.5

    Total 20,418.0 26,859.8 26,392.8

    Source: Authors analysis of NSSO data.

    quantum of the loan. In order to shed light on this aspect, we also look at the average amount of loan taken by the households. It can be clearly seen from Table 3 that in both rural and urban areas not only is accessibility lower for the f emale-headed households, the amount of loan received is also smaller.

    Occupation-wise Analysis

    One may argue in this context that urban indebtedness or indebtedness of femaleheaded households may be lower due to lack of demand. As information on demand is not available, it is necessary to analyse the occupation-wise classifications to derive meaningful implications.

    One would expect that the households that are self-employed and with low incomes would need a regular flow of credit for their business activities, be it farm or non-farm. In this regard, our occupation-wise disaggregated analysis shows that in the rural areas 27% of the self-employed households engaged in non-farm activities are indebted. If we include the households engaged in agricultural activities, this percentage increases further. In the urban areas, on the other hand, only 17% of the selfemployed households are indebted. Expenditure category-wise about 18% of the urban self-employed households with MPCE below Rs 500 have access to credit. For rural self-employed (non-farm) households, the corresponding percentage increases to 26% (Rajeev and Vani 2011). In the urban areas, the female-headed households have much lower access to credit but the difference between male- and female-headed households is lower in urban areas.

    Thus, an occupation-wise disaggregated picture between male- and femaleheaded households (Table 4) reveals that in all categories there is a significant difference in access to credit between maleand female-headed households; the difference in access to credit amongst the agricultural households being most notable.

    Hence, when it comes to self-employed agricultural and non-agricultural households, who supposedly need credit on a regular basis, the female-headed households have much lesser access to credit.

    Sources of Credit

    Another important aspect in the context of credit is the source of credit. Informal

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    sources provide credit at unfavourable terms, that may in turn cause further distress to an already economically weaker household. While the analysis carried out so far is based on access to credit from both formal and informal sectors, we present a disaggregated picture concentrating on the formal sources in Table 5. It is observed that the female-headed households have a much lower access to formal sources of credit both in rural and urban areas and this is true for almost all income categories. This finding is of importance to policymakers, who are attempting to put in place a sustainable financial inclusion programme. Attempts need to be made to sensitise bank officials so that the weaker sections, especially in the lower income categories, receive better access to f ormal credit.

    In particular, for the lowest expenditure category in the rural areas (that is households below MPCE of Rs 254), one observes that of the total number of loans outstanding to male-headed households, about 40% of the loans are from formal lending agencies; but only 27% of borrowers from

    Table 4: IOI across Occupation for Male- and Female-Headed Households (%)

    Female- Male
    headed headed
    Self-employed in non-agriculture 19.08 28.02
    Agricultural labour 16.17 26.74
    Other labour 26.53 27.32
    Self-employed in agriculture 21.00 30.18
    Others 11.42 19.37
    Total 17.43 27.63
    Self-employed 16.27 18.01
    Regular wage or salary earning 17.71 19.81
    Casual labour 20.61 19.26
    Others 5.91 8.98
    Total 14.18 18.27

    Source: Author’s analysis of NSSO data.

    Table 5: Share of Formal Lending Agencies in Total Number of Loans Outstanding by Head of Household as on 30 June 2002: All India

    Rural (MPCE) Male- Female- Urban (MPCE) Male- Female
    headed headed headed headed
    (Rs) (%) (%) (Rs) (%) (%)
    0-254 39.5 27.2 0-384 21.6 20.3
    254-338 39.1 24.9 384-511 26.2 22.5
    338-425 37.9 23.4 500-600 29.0 32.6
    425-510 41.8 31.8 600-700 35.1 26.7
    510-600 43.9 35.5 700-800 42.5 32.1
    600-700 42.7 42.5 800-950 47.7 46.4
    700-950 54.2 47.2 950-1,500 60.7 62.9
    950-1,500 57.7 57.6 1,500-2,500 80.7 64.8
    1,500-3,000 72.4 45.1 2,500-3,500 90.1 85.9
    Above 3,000 89.3 0.0 Above 3,500 91.3 97.6
    Total 43.2 32.9 Total 46.1 38.6

    Source: Authors’ analysis of NSSO 59th round data.

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    the female-headed households have been able to access the formal sector.

    The source of credit naturally affects the terms of a loan, and hence the debt burden, especially through the rate of i nterest. Given the fact that the female-headed households have lower access to formal loans, it is not surprising that they pay on an average a higher interest rate. Since the formal rate of interest is always less than 15% we show in Table 6 the percentage of maleand female-headed households experiencing a rate of interest higher than 15%. Clearly the female-headed households facing a


    While the self-employed households need additional credit to sustain their incomegenerating activities, the above analysis reveals that the poorer sections among such households have less access to credit. More importantly, among the poor. The relatively weaker sections such as self-employed female-headed households (who are mainly widows with small children) have much less access to credit both from formal and informal sources. It is worthwhile to mention in this context that SHG-bank linkage programme has helped women access formal

    Table 6: Share of Loan Taken Below 15% Rate of Interest of Total Number of Loan Outstanding: All India

    Rural (MPCE) (Rs) Female-headed (%) Male-headed (%) All (%) Urban (MPCE) (Rs) Female-headed (%) Male-headed (%) All (%)

    0-254 18.6 15.4 15.7 0-384 36.5 28.8 29.5

    254-338 21.0 14.3 14.7 384-511 26.4 23.1 23.4

    338-425 16.6 14.7 14.8 500-600 15.8 25.2 24.1

    425-510 20.9 14.6 15.0 600-700 31.4 23.2 24.1

    510-600 13.6 12.3 12.3 700-800 14.9 22.0 21.4

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    GoI (2001): “Report of the Steering Committee on Empowerment of Women and the Development of Children for the Tenth Five-Year Plan (2002-07): Planning Commission”, Government of India, O ctober.

    – (2007): “Report of the Expert Group on Agricultural Indebtedness”, Ministry of Finance, Government of India.

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    600-700 14.8 11.6 11.8 800-950 23.8 24.2 24.1 Andhra Pradesh”, Indian Journal of Agricultural Economics, 60(3), pp 302-08.

    700-950 15.7 10.8 11.2 950-1500 33.1 20.9 21.7

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    950-1,500 15.6 9.9 10.2 1,500-2,500 19.8 15.0 15.2

    – ‘Household Indebtedness in India as on 1,500-3,000 41.5 14.7 16.4 2,500-3,500 26.5 6.8 9.0 30.06.2002’”, Ministry of Planning and Statistics

    Above 3,000 0.0 11.0 8.5 Above 3,500 0.0 10.7 10.1

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    Source: Authors’ analysis of NSSO 59th round data.

    higher rate of interest are larger in relative terms. It is worth -noting that at a higher rate of interest to say, 20% qualitatively the same result follows. While 49% of the female-headed households in rural areas pay a rate of interest higher that 20%, this percentage falls to 42% for rural male-headed households. Informal lenders not only charge a high interest rate, they also make sure that only the interest part gets repaid over time leaving the principal amount intact for a long period, consequently making the debt burden much higher than what is apparent from the rate of interest alone.

    As before, we once again test whether there exists a significant difference between the interest rate faced by the male- and the female-headed households. This is done using a t-test to test the null hypothesis H0: there is no significant difference in the rates of interest. We note that the average interest rate for the female-headed household is found to be 25% and the average interest rate for the male-headed household is 22%. The test statistic gives the value of t = 330.7, which being significant at the 1% level lead us to the conclusion that there exists a statistically significant difference between the interest rate faced by the male- and the female-headed households.

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    credit. However, in most cases, the credit accessed through SHGs by a member is limited in size and may not be adequate or frequent enough for carrying out regular farm activities. In addition, there is significant inequality among the female-headed households in access to credit and the banking sector needs to be sensitised about such trends. Thus, the challenge to the credit institutions is to remove such disparities and provide sustainable financial assistance to the weaker sections of the society who are badly in need of such support.


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