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The Political Economy of the BRICS Countries


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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.

      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.

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      Note: Others = Free/ Concessional; ***p < 0.01; **p < 0.05; *p < 0.10.

      Source: Computed based on AIDIS (2012).

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      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)

      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:

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      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); the vector X is a set of explanatory variables; µ are district fixed effects, and ε is the error term. We estimate the equation set separately for financial access and use and, likewise, separately for 2014 (short-run) and 2015 (long-run). This enables us to ascertain the short- and long-term impact of the JAM trinity. Each equation includes an appropriate set of control variables that enable us to clearly identify the equation, while allowing for possible interdependencies across equations.

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      Note: