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Financial Access and Entrepreneurship by Gender: Evidence from Rural India

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Does improved access to financial sources increase entrepreneurship across gender? We explore this question in the Indian context, by constructing a novel measure of financial access defined as the distance of each unbanked village to the nearest banked centre. Using economic census data at the village level, we find that the proximity of an unbanked village to a banked centre within 5 km increases entrepreneurship in the non-agricultural sector. While exploring the mechanisms, we find that the impact on women is driven by the uptake of institutional credit. The prevailing norms around gender influence the gains from bank proximity as the impact on women enterprises occurs mainly in villages which have liberal social norms. Results hold when we use the number of branches within 5 km as an alternate measure of financial access. Results are robust to several additional tests. Our results show that the lack of nearby banking facilities represents a key constraint for women, and hence, widespread banking outreach can boost female entrepreneurship in rural areas.

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Close proximity to banks increases entrepreneurship among women in the non-agricultural sector by strengthening their formal credit uptake. Women are under-represented in entrepreneurial activities in several countries. A prominent reason for this could be the difficulty they face in accessing formal finance. Lack of credit often restricts women from entering more productive sectors of the economy, particularly in traditional societies like India, where women are restricted from venturing too far from their residences. In such cases, a proximate bank branch within a female entrepreneur’s neighbourhood should be beneficial. We show that the women-owned non-agricultural enterprises increase in a village when a new bank branch opens within 5 km of the village. The proximity to a branch improves accessibility, which further leads to credit uptake by women entrepreneurs. Thus, credit plays an important role for women entrepreneurship. Men-owned enterprises, on the other hand, move out of agriculture and enter the non-agricultural sector. These results show the structural transformational potential of the financial development in rural India. Our findings indicate that it is advisable for policymakers to expand financial access, particularly for women, to ensure women’s economic emancipation alongside the structural transformation of the rural economy of India.

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  1. See, Pradhan Mantri Gram Sadak Yojana (PMGSY) at Pradhan Mantri Gram Sadak Yojana (

  2. See, Rajiv Gandhi Grameen Vidyutikaran Yojana (RGGVY) at Committee Reports (

  3. IFC reported a large unmet financing gap in the Micro, Small and Medium Enterprise (MSME) sector across the world, particularly in the developing world. Nearly 21 percent of micro enterprises world-wide are fully financially constrained, and 19 percent are partially constrained. For SMEs, these figures stand at 30 and 14 percent, respectively. This problem was observed to be highest for the South Asia region, where 56 percent of micro enterprises (highest) and 50 percent of SMEs (second highest) were either fully or partially financially constrained.

  4. The sectors not covered in EC are the following: in the case of agricultural activity, establishments classified under 011 and 012 of Section A of NIC 2008; in the case of non-agricultural activity, establishments engaged in Section O of NIC 2008 (public administration, defence, compulsory social security), Section T of NIC 2008 (territorial organization and bodies) and Section R of NIC 2008 (illegal gambling and betting activities).

  5. Several measures have been used in the literature as a proxy of access to banks/finance. The first of these is the geographic and demographic penetration of bank branches, where the total number of bank branches is divided by either total area or total population (Alessandrini et al., 2010; Beck et al., 2007a, b, 2008; Zhao and Jones-Evans, 2017). Recent studies have used different measures such as straight-line distance and travel distance to the nearest bank branch or distance that users are willing and able to travel for the service (Koomson et al., 2020; Langford et al., 2021; Camacho et al., 2021).

  6. The spatial data we use are the GIS shape files, which provide us with the location of each village—specifically, the latitude and longitude of the boundary of each village. These data are obtained from the research team at the World Bank. These GIS shape files are compatible with Population Census 2011.

  7. The spatial data for Arunachal Pradesh were not available, so that state is not included in the analysis.

  8. A total of 274,009 unbanked villages had a bank branch within 5 km prior to the treatment period (2005). As these villages were already proximate, their characteristics are not comparable to the distant villages. Additionally, we do not observe a sizeable change in distance for these villages. Therefore, we exclude them from our analysis.

  9. The literature shows that socio-economic factors—such as size and density of population, level of education, the share of urban population, size of the profitable market, growth rate, unemployment rate, and level of economic activity—are significant drivers of bank branch availability.

  10. Other proxies include withdrawing women from school at menarche (Field and Ambrus, 2008; Khanna, 2021), low desired levels of education for women (Maertens, 2013), historical transition of societies from hunter-gatherer to agricultural (Hansen et al., 2015), living in joint families (Dhanaraj & Mahambare, 2018) and other restrictions on women’s movement away from the house (Dean & Jayachandran, 2019) and women’s use of mobile phones (Scott et al., 2021).

  11. We conduct another test wherein we choose the control group using PSM. Results are presented in Table 14 (Appendix). Our results remain robust.


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The authors would like to thank the editor of this journal and two anonymous reviewers for their very constructive comments and suggestions. We also thank World Bank for providing us with Indian village-level shape files. We also appreciate the feedback from Sanjukta Das (NCAER), Sam Asher (John Hopkins University), Paul Novosad (Dartmouth College). We also thank seminar participants and discussants at the NCAER, the Institute of Economic Growth, the Delhi Winter School 2020; the STEG Annual Conference 2021; the South Asian Economic Development Conference 2021; 3rd Annual Conference on ‘Women in the Economy’, IWWAGE, ISI, New Delhi; the 3rd Annual Indian Public Policy Network Annual Conference, Indian School of Business, March 2021; the Annual conference of Development Studies Association (DSA) 2021; the Xavier School of Economics, Bhubaneshwar; ISB-NBER conference 2022; Annual Conference on Growth and Development at ISI Delhi, 2022; University of Manchester 2023; and the STEG/PSDRN workshop London 2023 for their feedback. An earlier version of this paper was circulated under the title “Impact of Financial Access on Gender Gap in Entrepreneurship and Financial Inclusion: Evidence from India”.

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Correspondence to Sandhya Garg.

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In this appendix, we provide additional robustness tests.

1.1 Assessment using propensity scores for matching treatment and control group

We have shown robustness of results using a matched control group, where the matching was performed using Coarsened Exact Matching process (Iacus et al., 2012). To demonstrated robustness of that result, we use propensity score matching method as an alternative matching process. Under this method, we estimate the propensity of treatment for each village. The matched control group is then obtained by comparing the propensities of control group villages against treatment group villages.

Table 14 shows the results for our main specification but with the control group limited to the villages matched with the Treatment villages. Here as well, our main results remain robust. Female non-agricultural entrepreneurship increases while male-owned enterprises increase in non-agricultural sector and decreasing in the agricultural sector.

Table 14 Model with matched control group using propensity scores

In Table 15, we provide the covariate balance t-test from the propensity score matching. The two groups are balanced across most covariates. We find a higher proportion of the matched control group with road. However, that only shows the direction of bias is against our main effects.

Table 15 Covariate balancing test

Figures 4, 5 and 6 plot the distribution of the continuous covariates—distance to town, population and literacy rate, respectively—for the treated and matched control groups.

Fig. 4
figure 4

Distribution of distance to town

Fig. 5
figure 5

Distribution of population

Fig. 6
figure 6

Distribution of literacy rates

1.2 Assessment using entropy balancing for matching treatment and control group

Further, we conduct another robustness check on our baseline results. We use entropy balancing techniques to match the treatment group with the control group as proposed by Hainmueller (2012). Table 16 shows the results. Here as well, our main results remain robust. Both female and male owned enterprises increase in the non-agricultural sector.

Table 16 Model with matched control group using entropy balancing

1.3 Impact on male and female employment

Our results suggest that the improved proximity to financial services increases entrepreneurship through flow of credit. We further study the impact on gender-wise employment. The EC data provides size of employment of each enterprise. We use that information to measure the impact on gender-wise employment in enterprises. Employment indicators should also be influenced by improved entrepreneurship since the Indian rural economy is labour intensive.

Tables 17 shows the effects on labour markets by sector and gender of workers. Total number of female workers increases by a magnitude of 0.908 (column 1). While it declines in agricultural enterprises by 0.417 (column 2), a more than compensating increase of 1.258 occurs in the non-agricultural enterprises (column 3).

Similarly, we observe that, male workforce declines by -0.698 in the agricultural sector (column 5) and increases by 1.647 in the non-agricultural enterprises (column 6). Thus, labour market results reaffirm the evidence on structural transformation (Table 17).

Table 17 Impact on employment

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Garg, S., Gupta, S. & Mallick, S. Financial Access and Entrepreneurship by Gender: Evidence from Rural India. Small Bus Econ (2024).

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