Cell Phone Use and Women’s Financial Inclusion: A Statistical Analysis

Author: Dillan Jacobson, 2019.

“This study examines the relationship between women’s use of cellphones and the level of financial inclusion of women in a country; specifically, whether the level of financial inclusion of women in a country is affected by women’s use of cellphones.”

Image Source: Pixabay. 

In 2017, women accounted for 56 percent of the 1.7 billion people worldwide without a bank account or a mobile money provider (Demirgüç-Kunt et.al 36). According to the World Bank’s Global Findex Data Base, “Owning an account is an important first step toward financial inclusion” (55). Digital technologies, particularly mobile money services and payment cards, are used increasingly as tools for accessing financial services, impacting the overall socio-economic development of a country (59).

This study examines the relationship between women’s use of cellphones and the level of financial inclusion of women in a country; specifically, whether the level of financial inclusion of women in a country is affected by women’s use of cellphones. The central hypothesis is that higher levels of women’s cell phone use relate to an increase in the level of financial inclusion of women in a country. To test this hypothesis, the study utilizes OLS regression, controlling for the effect of three other variables – GDP per capita, political and civil rights, and the average number of years for which women are educated in a country. The regression output demonstrates a statistically significant positive relationship between women’s use of cell phones and the level of financial inclusion of women in a country. Therefore, the significance of the results indicates that the null hypothesis – that women’s use of cellphones is unrelated to the level of financial inclusion of women–  can be rejected. The following section discusses some of the literature that informed the study’s main hypothesis and the selection of relevant control variables.

Theory and Hypothesis

The study’s main hypothesis – that higher levels of women’s use of cellphones relate to an increase in the level of financial inclusion of women in a country– is informed by extensive literature on the importance of Information Communication Technologies (ICT), particularly mobile phone technologies, on economic development (Aker and Mbiti 208).  Access to mobile money services­– instead of, or along with obtaining a bank account– is a central indication of the level of financial inclusion in a country (Lal and Sachdev 3). The positive effects of mobile services are evident in poverty reduction efforts, as well as in healthcare and business development (Demirgüç-Kunt et.al 1). In Sub-Saharan Africa, mobile telephony enhances the connection between individuals, markets, and trade services, transforming the socio-economic landscape in a region with limited access to electricity and landline services (Aker and Mbiti 207). Multiple studies demonstrate substantial economic benefits that result from lower communication costs, such as improvements in agricultural and market efficiency, and consumer welfare (Jensen). The mobile service sector has also created new employment opportunities, some of which include phone operators, device manufacturers, tech-startup innovators, and mobile entrepreneurs (“The Mobile Economy Sub-Saharan Africa” 19). Groupe Spéciale Mobile Association’s (GSMA) 2018 report indicates that “The mobile ecosystem makes a significant contribution to the economy in Sub-Saharan Africa, with an economic value added of $110 billion, equivalent to 7.1% of the regions GDP in 2017” (17).

While the prevalence of ICTs evidently relates to the level of socio-economic development and financial inclusion in a country, the fact that women represent approximately half of the global population means that greater use of these technologies should have significant effects on a country’s development.

The Dimensions of Gender and Economic Development

In 2017, 65 percent of women worldwide owned a bank account, compared to 72 percent of men (Demirgüç-Kunt et.al 4). Multiple factors contribute to this gender gap, including the presence of discriminatory laws that prevent women’s control over financial assets and property, as well as gender-preferences regarding the provision of identification materials–  such as birth certificates and national identity cards– that are essential for verification at a financial institution (“Women’s Financial Inclusion and the Law” 1).

The introduction of ICTs as a mechanism for accessing financial services is a significant opportunity to lower this gender gap and increase the level of financial inclusion of women in a country. In 2015, women were 14 percent less likely than men to own a cellphone– a central medium for accessing financial services digitally (“Bridging the Gender Gap” 6).  As discussed by scholar Tonia Warnecke, “at the macro-level, gender gaps in technology lead to underutilization of human capital, with impacts on economic growth, as women are not able to contribute as much as they could to the economy” (309). Intel estimates that increasing internet access services – often supported by mobile platforms– to 150 million women in developing countries could increase GDP by over $13 billion U.S. dollars annually (“Women and the Web” 10).

The literature on the importance of using digital technologies to increase the level of economic development and financial inclusion in a country informs the study’s main hypothesis that higher levels of women’s use of cell phones relate to an increase in the level of financial inclusion of women in a country. An alternative hypothesis is that there is a negative relationship between the two variables; higher levels of women’s cell phone use relate to a decreasein the level of financial inclusion of women in a country.

Control Variables

To capture the relationship between women’s use of cell phones and the level of financial inclusion of women in a country, the study controls for three other variables that also could affect women’s financial inclusion. The first control variable is the effect of women’s educational attainment– particularly, the average years of school completed by women– on the level of women’s financial inclusion in a country. According to a 2018 report on the cost of not educating girls:

“Women with primary education (partial or completed) earn only 14 to 19 percent more than those with no education at all. By contrast, women with secondary education may expect to make almost twice as much, and women with tertiary education almost three times as much as those with no education. Secondary and tertiary education are also associated with higher labor force participation, and especially full-time work.” (Wodon et al. 4)

The 2018 Global Education Monitoring Report indicates that “Only 66% of countries have achieved gender parity in primary education, 45% in lower secondary and 25% in upper secondary” (“Global Education Monitoring Report Gender Review”11). The World Bank further estimates that more women completing higher education would have significant effects on the annual GDP growth rate in a country (Chaaban and Cunningham).

In relation to the economic impact of the global educational disparity between men and women, the study also controls for the effect of a country’s GDP per capita on the level of financial inclusion of women in a country. Analyses conducted in the 2015 Global Gender Gap Report indicate strong correlations between levels of gender equality– reflected in the Global Gender Gap Index score– and GDP per capita, levels of human development, and competitiveness in a country (“The Global Gender Gap Index” 36). Eastin and Prakash’s study on the relationship between economic development and gender equality reveals an “S-shaped” curve; the effects of development at first increase gender equality, then decrease, and once again increase after a certain phase of development (158). In keeping with these findings, as well as other evidence for a curvilinear relationship between environmental indicators and economic growth (Grossman and Krueger), the current study specifies the GDP per-capita variable as a quadratic term, which indicates a non-linear relationship.

Finally, the study controls for the presence of civil and political liberties, measured by Freedom House’s total aggregate score of a country’s political and civil rights, as a reflection of the overall status of democracy in a country. The correlation between the democratic strength of a country and indicators of gender equality, particularly women’s representation in parliament, has been well documented (Ingelhart et al. 2). Further, well-established democracies– that is, countries with long-term democratization processes– demonstrate higher scores on multiple indicators of gender equality (Beer). As succinctly captured by scholars Ingelhart, Norris, and Welzel, “the link between women’s representation and democracy should be self-evident, since women account for over half the population of most societies: if the majority doesn’t have full political rights, the society is not democratic” (2).

The following section describes the study’s dependent and main independent variable, by reporting key descriptive statistics: the mean, median, standard deviation and range of the two variables.

Data & Methods

The study’s main independent variable is the level of women’s cell phone use in a country. The data was obtained from the 2017 Women’s Peace and Security (WPS) Index, which defined the variable as the “Percentage of women ages 15 years and older who report having a mobile phone that they use to make and receive personal calls.” Data for the dependent variable– the level of financial inclusion of women in a country– was also obtained from the WPS Index, and defined as the “Percentage of women ages 15 and older who report having an individual or joint account at a bank or other financial institution or who report using a mobile money service in the past year”. Both variables are coded on a scale that ranges from 0 to 100 percent.

Descriptive statistics

The mean percentage of women’s cellphone use among the 164 countries in the sample is 77.92 percent, while the median is slightly higher at 83.73 percent. The standard deviation in the sample is approximately 17.50, with a range of 92.37 between the lowest and highest percentages of women’s cell phone use in a country.

For the dependent variable, the results indicate that the mean percentage of women who own a bank account or use mobile money services in a country is 48.14 percent, with a slightly lower median at 41.27 percent. The standard deviation is 31.54 percent, with a range of 98.43. The following tables present the descriptive statistics for both the dependent and main independent variable.

Results

The study utilizes OLS regression, controlling for the effects of women’s level of education – specifically, the mean number of years that women attend school– the level of civil and political liberties, and the GDP per capita in a country. The regression output demonstrates a statistically significant relationship between each of the four independent variables and the level of financial inclusion of women in a country– the percentage of women with an individual or joint account at a bank or other financial institution or using a mobile money service (See Table 2). The Adjusted R-squared score is relatively high at 0.77, which indicates that the variables in the regression model account for 77 percent of the variation in the level of financial inclusion of women in a country.

Regarding the level of women’s cell phone use, the results indicate that a one percent increase in the percent of women in a country using a cellphone predicts a 0.44 percent increase in the percent of women with a bank account or who use mobile money services. The positive direction of the relationship– that an increase in the main independent variable relates to an increase in the dependent variable– is consistent with the study’s main hypothesis. The results are statistically significant at the 0.001 level, which indicates that the null hypothesis can be rejected.

The results for the education variable demonstrate that a one-year increase in the average number of years of a woman’s education predicts a 1.61 percent increase in the percent of women with a bank account or who use mobile money services. Regarding the level of civil and political liberties in a country, a one-point increase in the total aggregate score of a country’s political and civil rights[1] – with 0 as the lowest score and 100 as the highest – predicts a 0.22 percent increase in the percent of women with a bank account or who use mobile money services. The quadratic (non-linear) GDP per capita variable is also statistically significant, demonstrating that at a certain GDP per capita level – approximately $60, 320 USD – the level of women’s financial inclusion starts to decrease (See Figure 2).

Additional Analyses

The study utilized additional statistical analyses to test the robustness of the results. Since the main independent variable and the dependent variable were both obtained from the Women, Peace, and Security Index, the following analyses examined whether the results were affected by multicollinearity– high correlations between the independent variables included in the regression model. Specifically, the test observed whether the main independent variable of interest – the percent of women using a cell phone in a country– is predicted by one or more of the other independent variables in the model, such as the level of women’s education, democratic strength, and GDP in a country. While high multicollinearity between variables does not introduce bias into the regression estimates, it becomes more difficult to differentiate the effect of the main independent variable on the dependent variable of interest. The following tests will help verify the statistical significance obtained from the regression results.

Tests for Multicollinearity

The first test for multicollinearity was a correlation analysis between each of the independent variables in the model, which examined the degree to which the variables were interrelated (See Table 3). High pairwise correlations were anticipated, particularly since three out of the five variables in the model were obtained from the same index, which fundamentally relies on a certain degree of correlation.

The results demonstrate that the percent of women’s cell phones use is significantly related to the other independent variables, especially the average number of years that women are educated. The correlation analysis was accompanied by an investigation of the Variance Inflation Factors (VIFs); the higher the VIF value, the more severe the effect of multicollinearity. This additional test did not demonstrate high VIF values,[2] indicating that the robustness of the regression results was not challenged by high multicollinearity.

While not an issue in this study, one of the remedies for high multicollinearity is to create an index that includes the highly-correlated variables.  Most of the variables in this study are already present in the Women, Peace, and Security Index; however, the other statistically significant variables in this study – total aggregate score for civil and political liberties and GDP per capita – could be considered for inclusion in a future version of the index, or inform the creation of a new index that focuses on indicators of gender equality and economic development.

Discussion & Conclusion

This study examined the relationship between women’s use of cellphones and the level of financial inclusion of women in a country. The central hypothesis was that higher levels of women’s cell phone use relate to an increase in the level of financial inclusion of women in a country. To test this hypothesis, the study utilized OLS regression, controlling for the effect of three other variables – GDP per capita, political and civil rights, and the average number of years for which women are educated in a country.

Each of the independent variables included in the model demonstrated a statistically significant relationship with the dependent variable, in which three were significant at the 0.001 level. Most important for this study, the results demonstrated a statistically significant positive relationship between the percent of women using cell phones and the level of financial inclusion of women in a country. This was consistent with the study’s main hypothesis; the statistical significance (p<0.001) further indicated that the null hypothesis could be rejected.

Implications of the Study

These findings could inform future programs and policies that focus on advancing gender equality and/or regional socio-economic development.  Given the literature concerning the negative impacts of excluding women from the formal economy, investing in the provision of more cell phones to women globally, particularly in regions with minimal access to electricity or formal banking institutions, could positively affect levels of economic development.

While the statistical findings could have significant implications for future program and policy operations, these findings could further inform the creation of a new index, or an expansion of the Women, Peace, and Security Index to include other statistically significant variables that were found in this study – the level of civil and political liberties in a country and GDP per capita. This study ultimately provides a solid foundation for program and policy development, as well as new avenues for research on gender equality and socio-economic development.

[1]The study utilized Freedom House’s total aggregate score based on 25 different indicators of political and civil liberties in a country.

[2]In general, a VIF score of 5 or above indicates severe multicollinearity. The VIF value obtained in this case was 5.62; however, further examination revealed that the GDP per capita variables (the quadratic and non-quadratic term) were the only independent variables in the model that demonstrated VIF values higher than 5, and thus cumulatively raised the average VIF score. Therefore, multicollinearity did not seem to be a problem in this model.

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