clientele as commercial banks. While commercial banks might have the balance sheet strength to shield themselves from the vicissitudes of adverse economic outcomes, these smaller entities might find it difficult to successfully withstand serious market upheavals, jeopardizing financial stability.
Emerging Areas of Focus
In this section, we focus on several areas of financial inclusion that have assumed prominence in recent times. Salient among these include issues relating to gender, emerging role of technology, Pradhan Mantri Jan Dhan Yojana, Government-to-Person (G2P) payments, and new institutional initiatives. On a related note, we also touch upon some of the demand-side factors such as financial literacy and customer protection. We discuss each of these in turn.
Gender and Financial Inclusion
Gender disparity is commonplace in most economic and social spheres. Evidence suggests that on average, women earn 10–30% less than men for comparable work and have labor force participation rates of 55% as compared with 82% for men (World Bank, 2014a, 2014b). Not surprisingly, gender disparity is also quite pervasive with a view to access to finance. Globally, out of the 1.7 billion adults without a bank account, roughly 1 billion are women.
Evidence proffered by Demirguc-Kunt et al. (2015) from the FINDEX 2014 database suggests that 58% of women as compared with 65% of men worldwide have an account at a formal financial institution such as bank, credit union, cooperative, post office, or microfinance. Even the more recent FINDEX 2017 database documents a 7 percentage point gender gap (72% for men vs. 65% for women) across countries in 2017. These numbers mask the wide difference across countries. By way of example, the gender gap in account ownership is negligible in high-income OECD countries. In developing economies, however, 67% of men have an account, as compared with 59% of women.
Lower use of financial services by women can be explained by other gender-based differences. Men are likely to be in better-paying jobs, and hence more likely to require savings accounts. Female entrepreneurs may also choose to enter less capital-intensive industries which require less debt and therefore less finance. Legal and cultural norms also have a big impact on women obtaining equal (or any) access to financial services. For example, dissimilar gender treatment under law or customs may constrain women from entering into contracts under their own name, including the opening of a bank account (International Finance Corporation, 2011). Even after controlling for a host of individual characteristics including income, education, employment status, rural residency, and age, gender remains significantly related to access and usage of financial services (Demirguc-Kunt et al., 2013).
A number of studies show that some microfinance programs can have a positive impact on women’s empowerment (Mayoux, 2006). Recent randomized evaluations in South Africa (Karlan and Zinman, 2009) and India (Banerjee et al., 2015) for instance indicate that microfinance schemes can positively improve both women’s economic situation and indicators of broader empowerment. Studies in Peru (Trivelli, 2009) and the Philippines (Ashraf et al., 2009) demonstrate how savings schemes can improve women’s purchasing power, confidence, and decision-making power in the household, as well as reduce their vulnerability.
In India, evidence from the global Findex suggests that in 2017, 83% of men had an account at a financial institution as compared with 77% of women. The government has undertaken various steps to eliminate gender-based financial exclusion. As a step forward, loans to women (up to INR 50000) were placed under priority sector lending; also, to better cater to the needs of women, the Bhartiya Mahila Bank was established in November 2013. Notwithstanding these efforts, evidence from the All India Debt and Investment Survey (AIDIS) data published by the Government of India (2014c) suggests that women in urban and especially in rural areas are in a disadvantaged position with regard to the interest rates paid (Table 6).
Illustratively, the simple interest rate paid by female-headed households in rural areas is 25.3%. This is 3.5 percentage points higher as compared to males, and the difference is statistically significant. Although these differences are much lower in urban areas, in several instances, they are nonetheless quite compelling.
Table 6:Interest rate paid by male and female headed households.
Note: Others = Free/ Concessional; ***p < 0.01; **p < 0.05; *p < 0.10.
Source: Computed based on AIDIS (2012).
Table Access to and use of finance.
Note: Standard errors (clustered by state) within parentheses. ***p < 0.01; **p < 0.05; *p < 0.10.
Table 7 reports the multivariate regression results regarding access to and use of finance (Ghosh and Dharmarajan, 2017). With regard to access to formal finance, female-headed households (FHHs) are less likely to have access to formal finance as compared with men. Disaggregating by access to bank finance and informal finance shows that FHHs are more inclined to access the latter.
Looking at the use of finance, we find that even though female-headed households are less likely to borrow cash, in case they do so, their borrowings are 20% lower as compared to male-headed households.
To sum up, the evidence for India is in line with the international evidence which suggests that the gender gap in access to finance remains quite significant.
Pradhan Mantri Jan Dhan Yojana (PMJDY)
The approach of the State towards financial inclusion in recent times has been based on two major planks. First, setting up of new institutions and, second, provision of policy guidance for existing institutions to actively participate in the inclusion process. The launch of PMJDY was different in the sense that not only was the focus on access to institutional credit by unbanked households, but unlike prior attempts, was also combined with social security through insurance. More specifically, the idea was to ensure that every household in the country has access to a bank account. This account was bundled with an insurance cover, a debit card, and an insurance facility. Every household opening an account was also provided with personal accident insurance as well as life insurance of INR 30000, including an overdraft facility after a few months following a credit review.
In turn, these were bundled with biometric identification (Aadhaar) mobile phone number, thereby ensuring the operationalization of the JAM (Jan Dhan-Aadhaar-Mobile) trinity to provide subsidies to the poor in a targeted and less distortive manner (Government of India, 2016).1 As of May 2018, a total of 316 million beneficiaries have been opened under PMJDY; the total balance in these accounts stands at INR 812 billion. In 2014–2015, an amount of INR 440 billion was provided by expanding this trinity to 296 million beneficiaries (roughly a quarter of India’s population).
In order to ensure a broad perspective of the JAM trinity, we model PMJDY account, Aadhaar cards, and mobile telephony within a simultaneous equation setup (see, for example, Ghosh, 2017b). Accordingly, we estimate a three-equation model for household h in district d at time t:
where the left-hand side variables are all dummy coded equal to 1 if the respondent has/uses the account (e.g. PMJDY) or the facility (e.g. Aadhaar, mobile);
