Abstract
Reduced inequality and gender equality are parts of the sustainable development goals (SDGs) towards global development, but the financial sector appears daunted in respect of financial inclusion for these noble goals. Concerns are more on gender inequality in the area of full utilisation of financial and human resources. Hence, this study investigated the impact of financial inclusion on gender inequality in sub-Saharan Africa. The study employed the generalised method of moments (GMM) estimation method on panel data on some countries in sub-Saharan Africa. The result of the study revealed that financial inclusion substantially reduced gender inequality. Financial inclusion access was found to drive down gender inequality more than usage. Female educational levels were found to have a substantial but negative impact on gender inequality. This study recommends that there is a need for an increase in commercial bank branches to increase accessibility to financial services. The government should increase its expenditure, and this should be channelled towards financial development and higher levels of education for females to improve financial literacy.
Introduction
With the growing rate of concern across the globe over the number of those that are financially excluded, data from many sub-Saharan African countries have revealed that huge amounts of funds are still not channelled into the formal financial system due to several factors. This is a threat to the sustainable development goals (SDGs) of reduced inequality and gender equality towards global development. The financial sector appears daunted in respect of financial inclusion for these noble goals. However, Researchers are more concerned on gender inequality in the area of full utilisation of financial and human resources. Hence, this study investigated the impact of financial inclusion on gender inequality in sub-Saharan Africa. Studies by Sharma and Kukreja (2013) and Adebayo et al. (2015) established that the Nigerian economy is largely cash-dominated, with a sizeable proportion of currency outside the banking system. Many sub-Saharan Africans, for several reasons, are unbanked and lack access to financial services. Given the broad spectrum of financial products in the areas of credit, savings, mobile banking, insurance, transfer payments and pensions, which the low-income earners (females) and vulnerable poor in rural areas are not able to afford, there is an absolute need to carry out an evaluation of financial inclusion and gender inequality in sub-Saharan African countries. In order to fully unveil the influence of financial inclusion on the selected variables, this study seek to provide answers to the following questions: (a) Can financial inclusion be utilised as a tool towards the reduction of gender inequality in sub-Saharan Africa? (b) What are the major channels of financial inclusion that can effectively reduce gender inequality in the region? (c) What is the nexus connecting financial inclusion and gender inequality? The objective of this study is to gauge the role of financial inclusion on gender inequality reduction in sub-Saharan Africa.
Data Sources and Methods
Data Sources
The data for the study were obtained from the World Bank (World Development Indicators), (2018b) and the Global Findex database (2018a) for 17 countries in sub-Saharan Africa: Ethiopia, Kenya, Rwanda, Tanzania, Uganda (East Africa), Angola, Botswana, Congo DR, Lesotho, Mozambique, Namibia, South Africa (Southern Africa), Cote d’Ivoire, Ghana, Liberia, Nigeria and Sierra Leone (West Africa). The selection of the countries was rooted on the availability of data, and at the same time, we ensured that the different parts of sub-Saharan Africa (Central, Eastern, Southern and Western) are covered in the study. The study covers the period 2011–2017 using three time series panels, given the limitation of data for financial inclusion analysis. The use of three round panel is for validation is more robust than a two-time series data (Wooldridge, 2002).
Methods
The comprehensive generalized method of moments (GMM) model for this study is specified below:
where
GII = Gender Inequality Index; CCB = commercial bank branches; VATM = volume of transactions on automated teller machines (ATMs); IB = internet banking (percentage that used the internet to pay bills or to buy something online in the past year [% age 15+]); FDC = female debit card ownership (% age 15+); FAH = female account holder (female who has an account [% age 15+]); FTE = female tertiary enrolment; INF = inflation; GXP = government expenditure; and RDGPpc = real gross domestic product per capita (measure of development).
The estimation of the above model was done by engaging the single-equation linear GMM. As compared to some other models (panel least square [PLS], maximum likelihood estimation [MLE]), the GMM has been more widely used (Omojolaibi, 2017) in the estimation of financial models, as it does not necessitate full knowledge of the distribution of the data. The GMM also has the ability to correct the problems of endogeneity, heteroscedasticity and cross-sectional dependency that are common in the panel data framework (Sarafidis, 2008; Sarafidis et al., 2008). The consistency of the GMM estimator, however, depends on the validity of the instruments, and this is usually addressed using the Sargen/Hansen test of over-identifying restrictions.
Descriptive Statistics
Gender inequality had a mean of 0.5, a standard deviation of 19.2 and a minimum value of 0.000 but a maximum of 0.679, which was experienced in Cote d’Ivoire in 2014. Commercial bank branches per 100,000 adults (CBB) as a measure of financial inclusion had a minimum of 0.000 and a maximum of 14.697, which was in Namibia in 2017. Examining the rate of female financial inclusion, female debit card ownership had the highest figure of 81, which was also in Namibia in 2017.
An inspection of the data showed that gender inequality is also relatively low in Namibia as compared to other countries. The country had maintained a GII of less than 0.5 over the period of the study. Hence, one may draw the conclusion that the stage of financial development and financial inclusion contributes to the lower rate of gender inequality. However, the final conclusion was drawn from the regression analysis. The diagnostics statistics also indicated that all the variables were normally distributed, given their Jarque–Bera values.
Correlation Result
The extent of multicollinearity was examined by the correlation matrix. There is an absence of perfect multicollinearity among the variables. The result showed that all financial inclusion variables came out to be negatively correlated to gender inequality. The result of the negative correlation showed that financial inclusion improvement in these countries will reduce the rate of gender inequality, thereby enhancing the development of these countries.
Panel Data Estimation, Generalized Method of Moments
GMM Estimate of Gender Inequality and Financial Inclusion
GMM Estimate of Gender Inequality and Financial Inclusion
GMM Estimation Result
The model was found to pass the Hansen test of valid instrument as shown in Table 1. The null hypothesis was accepted that all instruments are valid, given Hansen/J statistics of 4.738541 and a probability of 0.053123. The fattiness of the model was examined, and the model was found to be well fitted, given an R2 of 0.663488, indicating that 69 per cent of the variation in the dependent variable is accounted for by the explanatory variables. The Durbin–Watson (DW) statistics was 1.298964. This, however, does not jeopardise the model, given that the use of the GMM method of estimation can also correct the problem of heteroscedasticity and serial correlation that may be present in the model.
Examining the relationship and impact of the independent variables, the result showed that CCB, IB, FAH, RGDPpc and GXP came with a negative significant impact on GII, in line with theoretical and our expectations, while VATM, FDC and FTE showed a positive significant impact on GII. Although the positive relationships were against our expectation, they were also found significant, indicating that policy measures towards reducing gender inequality through financial inclusion should not totally neglect these variables. The substantial impact of FTE points to the need for enhancing the tertiary education of females, because that will give them more opportunity to be technologically developed, as well giving them the opportunity of securing better-paid jobs at a higher level of income, thereby reducing gender inequality. Of all the financial inclusion variables, only FDC was found not to have a major impact on GII. The result also showed that inflation came with a negative, noteworthy impact on GII, which was contrary to our expectation.
Implications of the Findings and Policy Inferences
The outcome of the study exhibited a negative and substantial impact of general access to financial inclusion on gender inequality. This also turned out to have the highest magnitude in the reduction of gender inequality. While internet banking as an index of usage of financial inclusion was also negatively significant, VATM was shown not to reduce inequality, though being significant. Financial access tends to reduce gender inequality more than usage, as recommended by Clamara et al. (2014) and supported by the findings of Mutsonziwa (2016). The implication of this is that females tend to prefer going to the bank rather than using the recent technological advancements in financial development. Hence, this study recommends an upsurge in commercial bank branches, which will increase and encourage female access to financial services. This will also help to build their financial capacity, thereby reducing the gender gap.
The result of the impact of VATM on gender inequality was strengthened by the outcome of female debit card use, which had no significant impact on the reduction of gender inequality. This can be attributed to the low level of education, particularly tertiary level of education. Hence, the result showed a positive impact of education on gender inequality, which was contrary to expectations. This is attributed to the low amount of female tertiary education in most of these countries. Clamara et al. (2014) have shown that degree of education results in higher intensity of financial inclusion which also make them also plan their expenditure and build their financial capacity. The study therefore recommends the encouragement of higher degrees of education among females to reduce the educational gap, advancing financial literacy through financial education. This strategy will improve their level of income through higher qualified jobs, and increasing their usage of financial services by better knowledge of the gains in financial services thereby reducing the gender inequality. High level of educational gap will increase inequality (gender) with increase financial inclusion.
The results also showed that growth in government expenditure significantly reduces gender inequality. Hence, a continued upsurge in government expenditure is highly recommended, especially towards the development of human capital (education).
The level of development in a country was also established to increase financial inclusion, thereby reducing gender inequality. Financial inclusion has also been found to increase the level of economic development of a country.
Conclusion
The upshot of this study is that financial inclusion substantially reduces gender inequality. However, access to financial services tends to have a greater effect on lowering gender inequality than the usage gauge of financial inclusion. Higher levels of education among females is strongly recommended to improve financial literacy, thereby enhancing gender usage and shrinking the gender inequality gap. This is of paramount importance, since the educational gap has been found to increase inequality in the face of financial exclusion. The above findings are absolutely important for policymakers and the governments of sub-Saharan African countries in paving the way forward to reduce gender inequality and accelerate financial inclusion.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Funding
The authors received no financial support for the research, authorship and/or publication of this article.
