Independent variable

Two large-scale data collection projects provide data for the key variable PWB: the Microcredit Summit Campaign and the MIX Market. This study employs MIX data for

21 _{A test for potential endogeneity was conducted before estimating Equations (3.1) and (3.2). It is}

possible that providing funds to women could be easier in countries with greater gender equality. Further, GI and GNP per capita are potentially endogenous because improvements in GI may enhance economic development by, for instance, increasing the number of women in the labour market. This chapter performs Hausman–endogeneity tests and concludes that the variables can be treated as exogenous. Under the null hypothesis that PWB can be treated as exogenous, the chi-squared p-value from the Hausman test

two reasons. First, MIX reports data from more than 80 per cent of institutions worldwide compared with the Campaign (60 per cent). Second, the Campaign provides firm-level data that contain a considerable number of missing values and typographical errors, making it difficult to aggregate at the country level (Bauchet & Morduch, 2010). In contrast, MIX produces readily available country-level indicators. The key variable,

PWB, is logged for ease of interpretation.

Following other macroeconomic gender inequality models, this model controls for economic development measured by gross national income (GNI) per capita (logged), which is available from the World Bank’s World Development Indicators (Forsythe et al., 2000). Following Inglehart and Norris (2003) and Beer (2009), democracy is also included and is measured using an 11-point scale ranging from 0 to 10, where 0 represents no democracy and 10 represents full democracy. These data are obtained from the INSCR. Table 3.1 presents the summary statistics.

Table 3.1: Descriptive statistics

Obs. Mean Std Dev. Min. Max.

GII 267 0.47 0.12 0.14 0.72

GDI 297 0.64 0.16 0.28 0.87

Proportion of women borrowers 564 0.57 0.21 0.02 0.99

GNI per capita (log) 564 7.59 1.07 5.31 9.56

Democracy 564 5.89 3.17 0 10 EAP 564 0.09 0.28 0 1 ECA 564 0.20 0.40 0 1 LAC 564 0.29 0.45 0 1 MENA 564 0.06 0.23 0 1 SA 564 0.07 0.26 0 1 SSA 564 0.29 0.46 0 1

Notes: The GII is available from 2010 to 2014 and the GDI is available from 2003 to 2009.

3.5

Empirical results

Columns (1) and (4) of Table 3.2 present the results of Equation (3.1). Both specifications indicate that an increase in the proportion of women borrowers is associated with a decline in gender inequality. Column (1) shows that an increase in the proportion of women borrowers by 10 per cent is associated with an improvement in the GDI by 0.38 per cent, while column (4) indicates that a similar change in the proportion

of women borrowers is associated with a fall in the GII by 0.15 per cent, all else being equal.

Columns (2) and (5) present the results from Equation (3.2). The data suggest that the main results are driven by economies in the ECA and MENA regions. An increase in the proportion of women borrowers is associated with a decline in gender inequality in each region. A few reasons may explain why an increase in the proportion of women borrowers has a statistically significant effect in the ECA and MENA regions. First, because they are more conservative societies, an increase in the proportion of women borrowers in more gender-unequal societies can have a larger marginal effect on gender inequality than a similar increase in more gender-equal countries. Second, in these regions, microfinance is an emerging industry; therefore, its marginal effect on gender inequality is still positive. Third, MFIs in these regions may have some innovative ways of reducing gender inequality, such as a variety of training activities (Ngo & Wahhaj, 2012).

Table 3.2: FE estimation

Dependent variable GDI GII

(1) (2) (3) (4) (5) (6)

Log GNI per capita 0.0019 0.0028 0.0018 −0.079** −0.082** −0.078**

[0.17] [0.25] [0.17] [−2.20] [−2.24] [−2.11] Democracy 0.00068 0.00083 0.00061 −0.0061 −0.0071 −0.0063 [0.90] [1.31] [0.81] [−1.26] [−1.42] [−1.30] Log PWB 0.038*** 0.027* −0.015** −0.016* [3.43] [1.94] [−2.11] [−1.91] Log PWB*EAP −0.0022 0.011 [−0.066] [0.42] Log PWB*ECA 0.041** −0.037** [2.06] [−2.45] Log PWB*LAC −0.019 −0.0096 [−0.76] [−0.41] Log PWB*MENA 0.047** −0.077*** [2.66] [−3.51] Log PWB*SA −0.14*** 0.0085 [−6.06] [0.49] Log PWB*SSA 0.073** −0.014 [2.36] [−1.65] Log PWB*Muslim 0.026 0.0067 [1.18] [0.51]

Country and year FE? Yes Yes Yes Yes Yes Yes

Observations 297 297 297 267 267 267

R-squared 0.64 0.67 0.65 0.40 0.40 0.40

Notes: Numbers in square brackets are t-values. ***, ** and * represent significance at the 1%, 5% and 10% levels, respectively.

Column (2) shows that the proportion of women borrowers is associated with worsening gender inequality in the SA region. This is consistent with findings from some regional studies. Leach and Sitaram (2002) found that the rising proportion of women borrowers has often marginalised men, who have responded by sabotaging projects, appropriating funds and sometimes being violent. Agier and Szafarz (2013) found a glass ceiling effect that hurts female entrepreneurs undertaking large projects. In this sense, microfinance worsens gender equality. Apart from these explanations, another potential reason may lie in the context of SA, as previously suggested by Kabeer (2001, 2005) and Mahmood (2011). Microfinance began in SA and later gained popularity in other regions (Armendáriz & Morduch, 2010). Driven by the expectation of improving gender inequality, microfinance has been so overemphasised in SA that its marginal effect has turned negative. The results are inconclusive for SSA. Column (2) suggests that the proportion of women borrowers is associated with improving gender inequality, but column (5) does not confirm this relationship. One possible explanation may be the difference between the GII and the GDI. The GDI focuses more on income, while the GII focuses on health, education, political representation and labour market participation (United Nations Development Programme, 2010).

Given that ECA and MENA are predominantly Muslim, columns (3) and (6) estimate models using Muslim-nation dummies interacting with the proportion of women borrowers.22 The results from both columns suggest that while cultural factors are likely to play a role in determining how microfinance interacts with gender inequality, Islam cannot explain this role.

Finally, when using the GII as the dependent variable, the results show that an increase in GNI per capita is associated with a decline in gender inequality. However, this is not the case for GDI, perhaps because it is measured using income differentials between men and women.

22 _{Muslim-country dummies are obtained from Grim and Karim (2011), who defined a nation as Muslim}

3.6

Conclusion

As microfinance has increased in popularity around the world, a better understanding of the effect of microfinance on gender equality from a macroeconomic perspective is essential. This chapter tests for macroeconomic evidence of a relationship between women’s participation in microfinance and gender inequality using panel data for 64 developing and emerging countries over the period 2003–2014. Gender inequality is measured using two main indices from the UN—namely, the GDI and the GII. The key variable of significance in the analysis is a gendered indicator of microfinance usage, which is defined as the proportion of women borrowers. This measure is constructed using microfinance data from the MIX Market, which is a microfinance auditing firm. The findings do not support the hypothesis. Rather, this chapter found that by providing women with access to credit, MFIs can potentially reduce gender inequality by strengthening women’s decision-making power within the household and society. This chapter also considers that microfinance does not automatically empower women because country-level and cultural characteristics can influence the gender inequality– microfinance nexus. In this case, the relationship is driven by economies in the MENA and ECA regions. It is found that Islam—the prominent religion in these regions—is not a significant determining factor. Therefore, other unobserved country-specific or cultural characteristics are likely to play a role in determining how microfinance interacts with gender inequality. These factors include the degree to which a society is conservative and gender-equal, the status quo of the microfinance industry and the way in which microfinance innovates to reduce gender inequality. For instance, many firms acknowledge the difficulties associated with women working outside the home in certain communities, so they help women to establish small businesses at home, sometimes pulling resources together across households. Future studies should explore this area.

Given that gender inequality is measured as composite indices of health, education and income indicators, it is natural to conclude that greater access to credit in women’s hands will mean greater access to education and health, as well as income-generating opportunities. Thus, more microcredit in developing nations is good news for women.

Given these positive outcomes, governments and international organisations in developing countries should continue to promote microcredit institutions to indirectly empower women. However, they must take into account that microfinance does not automatically empower women. Country-specific and cultural factors play a key role in determining how microfinance interacts with gender inequality, and these factors should be considered when assessing the effect of microcredit in the developing world.