Chapter

Chapter 9B. Gender Gaps in Financial Inclusion and Income Inequality

Editor(s):
Kalpana Kochhar, Sonali Jain-Chandra, and Monique Newiak
Published Date:
February 2017
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Author(s)
Corinne Deléchat, Monique Newiak and Fan Yang 

One aspect of gender inequality that may increase income inequality is the gender gap in financial inclusion. An analysis of the relationship between the two concepts is particularly relevant for sub-Saharan Africa, where both gender and income inequality are significantly higher than in other regions and where access to formal financial services is low compared with other regions, particularly for women (Demirgüç-Kunt and others 2015; IMF 2015; IMF 2016).

Sub-Saharan Africa lags behind other developing regions in overall access to financial services—defined here as having an account at a formal financial institution—as well as in gender equality for financial inclusion, which also indicates the affordability of financial services (Figure 9.5). The region’s fragile states are exceptions only in that access levels are equally low for both genders. The gender gap is lower for informal activities, with more women than men in a savings club or saving with a person outside the family and with men and women equally likely to borrow from family and friends.

Figure 9.5.Indicators of Financial Inclusion in Sub-Saharan Africa, 2014

(Percent of male and female population, ages 15 and above)

Source: World Bank, Global Findex 2014.

Note: EMDE Asia = Asian emerging market and developing economies; HICs = high-income countries; LICs = low-income countries; MICs = middle-income countries; LAC = Latin America and the Caribbean; MENA = Middle East and north Africa; SSA = sub-Saharan Africa.

Narrower gender gaps in financial inclusion are associated with both higher economic development and lower income inequality (Figure 9.6):

  • More equal access for women and men to financial services is closely correlated with higher economic development, as measured by higher GDP per capita or lower poverty rates.

  • More equal opportunities for men and women are associated with a more equal income distribution (lower net Gini coefficient).

  • More equal labor force participation rates between men and women have been previously associated with higher growth (Cuberes and Teignier 2015) and a more equal income distribution (Gonzales and others 2015).

Figure 9.6.Gender Equality in Financial Inclusion and Macroeconomic Outcomes

Sources: Solt 2016; World Bank, Global Findex 2014; and World Bank, World Development Indicators.

Note: SSA = sub-Saharan Africa.

These stylized facts hold for a multidimensional measure of financial inclusion and for a large sample of 144 countries. The relationship between gender inequality in financial inclusion and income inequality is examined further here.

Linking Gender Gaps in Financial Inclusion to Income Inequality

More equality in financial inclusion between men and women is significantly associated with lower income inequality, even when accounting for other determinants of inequality (Table 9.3). Lower financial access among different groups of the population distorts the allocation of resources because it restricts investment in human and physical capital to the wealthier parts of the population (Galor and Zeira 1993; Honohan 2008). Using a broader index of formal financial inclusion in a cross section of countries,1 higher gender equality in financial inclusion is associated with lower income inequality. This effect comes on top of standard drivers of income inequality such as the structure of the economy, government expenditures, financial depth, and the level of economic development.

Table 9.3.Determinants of Income Inequality
Variables(1)(2)(3)(4)(5)
Financial inclusion gap–16.038***–15.874***–12.577**–12.628**–9.828**
(5.52)(5.56)(5.02)(4.96)(4.81)
GDP per capita–4.065***–5.043***–3.801***–6.923***–7.994***
(0.75)(1.18)(1.1)(2.22)(2.14)
Financial development4.44915.149***14.029***14.461***
(4.77)(4.96)(4.95)(4.7)
Financial development ×–13.615***–11.488***–7.248**
advance economies(3.24)(3.46)(3.62)
Agriculture share of GDP–0.359–0.529**
(0.22)(0.22)
Government consumption–0.524***
expenditure(0.19)
Constant89.459***96.596***80.421***113.097***129.694***
(7.58)(10.04)(9.75)(22.45)(22.12)
Observations7069696969
R-squared0.420.430.550.570.62
Source: IMF staff estimates.Standard errors in parentheses; *p < 0.1, **p < 0.05, ***p < 0.01.

Gender equality in financial inclusion may be influencing income inequality through its effect on female labor force participation. Theoretically, financial inclusion can empower women economically and therefore contribute to higher female labor force participation. An account at a financial institution provides women with a place outside the home to store money safely (CGAP 2015), and access to borrowing can allow women to start a business, thus contributing to increases in entrepreneurship and self-employment. These channels are particularly important in sub-Saharan Africa where women are overrepresented in the informal sector, with a large part of the overall population in nonwage employment.

Indeed, the results from an empirical cross-country analysis suggest that greater gender equality in financial inclusion is significantly and positively associated with equality in labor force participation rates (Table 9.4):

  • Narrowing the gender gap in financial inclusion by 10 percentage points is associated with a decrease in gender gaps in labor force participation by 2 to 3 percentage points globally. This finding holds when controlling for previously identified determinants of female labor force participation, such as the level of development (Duflo 2012; Tsani and others 2012), the gender gap in education (Eckstein and Lifshitz 2011; Steinberg and Nakane 2012), fertility rate (Bloom and others 2009; Mishra and Smyth 2010), the male-female age differential at the time of the first marriage (a proxy for a society’s attitude toward women), and an index of women’s rights (Gonzales and others 2015; IMF 2015).

  • Likely driven by the region’s labor market structure, the relationship between financial inclusion and labor force participation is stronger in sub-Saharan Africa than for the global sample, with a 10 percentage point reduction in female labor force participation being associated with a more than 4 percentage point decrease in labor force participation gaps.

Table 9.4.Determinants of Female Labor Force Participation
Variables(1)(2)(3)(4)(5)(6)(7)(8)
Financial inclusion gap0.491***0.399***0.319***0.220***0.235***0.241***0.260***0.211***
(0.068)(0.075)(0.078)(0.077)(0.078)(0.078)(0.070)(0.075)
GDP per capita–0.483***–0.67***–0.489***–0.708***–0.736***–0.678***–0.468–0.529***
(0.15)(0.159)(0.157)(0.166)(0.171)(0.191)(0.177)(0.188)
GDP per capita squared0.025***0.034***0.023***0.036***0.037***0.034***0.023**0.026***
(0.008)(0.009)(0.009)(0.009)(0.009)(0.01)(0.01)(0.01)
Education gap0.324***0.1680.219**0.202*0.206**0.0130.087
(0.1)(0.104)(0.101)(0.102)(0.103)(0.101)(0.105)
Marriage age differential–0.044***–0.022–0.031**–0.033**–0.042***–0.04***
(0.013)(0.014)(0.014)(0.015)(0.013)(0.014)
Equal rights to get a job (dummy)0.177***
(0.047)
Female legal rights index0.045***0.049***0.033**0.039**
(0.013)(0.014)(0.013)(0.014)
Fertility rate0.014–0.05**–0.029**
(0.02)(0.022)(0.023)
SSA (dummy)0.266***
(0.055)
SSA × Financial inclusion gap0.223***
(0.071)
Constant2.564***3.339***3.003***3.719***3.663***3.292***2.765***2.936***
(0.662)(0.7)(0.692)(0.713)(0.72)(0.902)(0.816)(0.868)
Observations1371221079999999999
R-squared0.340.400.400.440.430.430.540.49
Source: IMF staff estimates.Note: SSA = sub-Saharan Africa. Standard errors in parentheses; *p <0.1, **p <0.05, ***p <0.01.

Conclusions

This analysis suggests that policies targeted at improving women’s financial inclusion would help enhance both gender equality in labor force participation and income inequality. In turn, more equal labor force participation rates would unlock growth benefits and contribute to reducing income inequality. However, the results should be interpreted with caution. The associations among different gender gaps are complex, and further work and more data on financial inclusion across countries and over time are needed to make more definitive statements about the direction of causality at the macroeconomic level.

References

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    Consultative Group to Assist the Poor (CGAP). 2015. “Women and Financial Inclusion.http://www.cgap.org/topics/women-and-financial-inclusion.

    Cuberes, David, and MarcTeignier. 2015. “Aggregate Costs of Gender Gaps in the Labor Market: A Quantitative Estimate.Journal of Human Capital, forthcoming.

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Annex 9.2. Data Sources and Description
VariableDescriptionSource
Income inequalityThe traditional Gini measure of inequality. In this paper, we use “net” Gini but find similar results with “market” Gini. This is the dependent variable in the analysis on the gender determinants of income inequality. A value of 0 represents perfect equality.Solt 2016: Standardized World Income Inequality Database
Labor force participationThe ratio of labor force participation rate of females to males. A value of 1 represents perfect equality.World Bank, World Development Indicators (WDI) database
Financial inclusion gapThe result of a principal components analysis (PCA) estimate on five FINDEX time series variables: (1) has an account at a financial institution, (2) has a credit card, (3) has a debit card, (4) saved at a financial institution, and (5) borrowed from a financial institution. Each variable is disaggregated by gender. We first calculated ratios of female to male inclusion for each component before performing PCA on these ratios; the final variable is the fitted value using the principal component. A value of 1 represents perfect equality.World Bank, Global FINDEX 2014
GDP per capitaThe logged GDP per capita (in constant 2011 international dollars).World Bank, WDI database
Financial developmentThis is an index published in an IMF Staff Discussion Note (SDN) that aims to measures financial development. The index takes on values in the continuum between 0 and 1, where 1 represents maximum development. Please refer to the SDN for details on the methodology.Sahay and others (2015)
Financial development × advanced economiesAn interaction term of financial development with a dummy variable that takes on value 1 for advanced economies, as defined by the IMF.Sahay and others (2015)
Agriculture share of GDPThe value-added share of agriculture as a percentage of GDP.World Bank, WDI database
Government consumption expenditureGovernment consumption expenditure as a percentage of GDP.World Bank, WDI database
GDP per capita squaredThe squared value of the GDP per capita variable.World Bank, WDI database
Education gapThe difference between the mean years of schooling for females and males across all educational attainment levels. A positive value represents more female schooling. The source data are from the 2013 United Nations’ Human Development Report (UN 2013).Barro and Lee 2013; UNESCO Institute for Statistics 2013; and United Nations 2013
Marriage age differentialFrom the UN World Marriage database, we extract the singulate mean age at marriage (SMAM), which is the average length of single life expressed in years among those who marry before age 50. We take the difference of this between male and female SMAM, such that a positive value is how much (on average) older the male spouse is compared with the female.United Nations, Department of Economic and Social Affairs, Population Division, World Marriage Data 2012
Equal rights to get a job (dummy)This is a dummy variable that takes on value 1 when the answer to the World Bank, Women, Business and the Law (WBL) database question “Can a married woman get a job or pursue a trade or profession in the same way as a married man?” is “yes.”World Bank, WBL database
Female legal rights indexThe sum of 10 binary indicators representing existence of selected (unmarried and married) women’s legal rights. Takes on value of 0 (no rights) to 10 (all selected rights). Rights include obtaining identification, signing contracts, inheritance, ownership of property, and favorability of the default marital regime.World Bank, WBL database
Fertility rateThe number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.World Bank, WDI database
Sub-Saharan Africa (SSA) (dummy)A dummy variable with value 1 for SSA countries, as defined by the IMF.IMF staff estimates
SSA × financial inclusion gapAn interaction term of the financial inclusion gap with the SSA dummy variable.IMF staff estimates

We construct an index of formal financial inclusion using data from the World Bank, Global FINDEX database, using a principal components approach. The index covers the following dimensions, defined as ratio of female to male, as a share of the total population ages 15 and older: having a bank account at a formal financial institution, having a debit card, having a credit card, saving in a formal financial institution, and borrowing from a formal financial institution.

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