Fintech—new technology in finance that delivers financial services digitally—promises to promote financial inclusion and close the gender gap. Using a novel fintech data set, this study shows that fintech adoption significantly improves female employment and reduces gender inequality. The effect is more pronounced in firms without traditional financial access. Fintech not only increases the number and ratio of female employees in the workforce but also mitigates the financial constraints of female-headed firms. However, poor institutions and governance weaken such benefits.
Introduction
Gender equality lays the foundation for a peaceful, prosperous, and sustainable world (United Nations Development Programme 2020). Despite progress made over the past decades, as highlighted in Chapters 1 and 2, many challenges remain—women experience higher poverty levels, unemployment, and other economic hardships (IMF 2021). In the global financial system, women continue to be underrepresented at all levels, from depositors and borrowers to managers and regulators (Sahay and Cihak 2018).
Much hope exists that new technology in finance (fintech), which has spread quickly worldwide, will unlock vast potential for stimulating economic growth and increasing social welfare. Fintech could also help reduce gender inequality and promote female employment, particularly in the current context, where the COVID-19 pandemic has exacerbated the existing gender gap in employment (Figure 18.1). According to the World Bank, the current global employment rate is less than 46 percent for women but 71 percent for men. Boosting female employment could generate substantial growth benefits (IMF 2021; International Labour Organization 2022) and lay the foundation for other forms of gender equality, such as income and social status, while boosting macroeconomic outcomes.
Loko and Yang’s study (2022) was among the first to shed light on the link between fintech and gender inequality measured by female employment. Previous research had focused on specific case studies; however, Loko and Yang used a cross-country data set covering 114 economies and a range of gender inequality indicators at the micro level. They showed that women—traditionally marginalized by the formal financial system—can be included and experience improved welfare in the new fintech ecosystem.
Employment Rate by Gender
(Percent)
Sources: International Labour Organization; and author calculations.Note: Female (male) employment represents the labor force participation rate of female (male) population ages 15+ based on modeled International Labour Organization estimate.Using a novel fintech indicator and firm-level employment data, this study1 shows that fintech adoption significantly improves female employment and reduces gender inequality worldwide and in the sub-Saharan African region. Specifically, a 1 percent increase in the scale of fintech usage is associated with a 1.4 percent increase in the number of female workers and a 0.4 percent increase in the ratio of female to total employees in the sample firms. The economic significance is pronounced, given that female employees represent only 32 percent of employees in the sample. Fintech not only increases the number and ratio of female employees in the workforce but also mitigates the financial constraints of female-headed firms. The effects are more pronounced in firms without traditional financial access. However, the digital divide and poor institutions weaken such benefits.
Admittedly, a significant gender divide exists in accessing fintech services that can be ascribed largely to differences in attitudes toward privacy and technology (Chen and others 2021) and technological and institutional factors. This study identifies technological, legal, and regulatory barriers that have constrained fintech usage and proposes pathways to build a more gender-inclusive financial ecosystem.
Background
Using digital platforms, fintech could easily cross physical barriers and expand financial services to geographically marginalized communities. With these digital platforms making big data available, fintech firms can process borrower information more efficiently and overcome information asymmetry. Unlike their traditional counterparts with heavier compliance and capital requirements, fintech firms have lighter regulations, enabling them to operate nimbly in certain market segments, lend with less collateral, and better support the economy. When viewed through a gender lens, the benefits from fintech could therefore be significant.
First, fintech can leverage digital financial tools to increase access to and use of financial services, benefiting populations disproportionately excluded from the traditional financial system (Sahay and others 2020). According to the World Bank Group’s 2017 Global Findex report, more than 25 percent of women still do not use or have access to the financial system, and more than 70 percent of female-owned small and medium enterprises have inadequate or no access to financial services (World Bank 2017; Demirgüç-Kunt and others 2018). Developing fintech-enabled services will likely lead to greater convenience, privacy, and security for the traditionally unbanked or underbanked female population.
Second, fintech can better evaluate the creditworthiness of individuals whom the traditional financial system may have previously marginalized due to no or minimal credit history. Using alternative data—for example, information generated by and about consumers on digital platforms—fintech helps loan providers make lending decisions without relying on credit reports or scores. Many loan applicants, including female applicants with no credit reports or credit scores, would benefit from these innovative measures to assess credit risk and model creditworthiness.
Third, fintech can facilitate access to financing, especially for female-headed households and businesses. Worldwide, an estimated $300 billion financing gap exists for formal, female-owned small businesses (International Finance Corporation [IFC] 2017). Without such access, women face difficulties collecting and saving income, growing their own businesses (Sahay and others 2015), and pulling their families out of poverty. Many fintech-based platforms operating on “big data, small credit” propositions can contribute to women’s economic empowerment and entrepreneurship by targeting small and medium enterprises, lowering interest rates, and relaxing collateral requirements.
Most studies analyzing the nexus between fintech and inequality have focused on income inequality (Suri and Jack 2016; Asongu and Nwachukwu 2018; Demir and others 2020; Zhang and others 2020; Chinoda, Mashamba, and Vivian 2021). Several studies have pointed to either finance or technology as a positive force for improving female employment. Based on a sample of 48 African countries, Ngoa and Song (2021) concluded that information and communications technology (ICT) significantly stimulates female labor market participation, and financial development enhances the effect. Focusing also on Africa, Asongu and Odhiambo (2019) showed that promoting ICT beyond certain thresholds is necessary for ICT to mitigate inequality and increase female participation in the economy. Studies focused on Europe and Asia also found a positive impact of finance and technology on female employment (Nassani, Aldakhil, and Moinuddin 2019; Chen and others 2021). However, few, if any, studies have examined the intersection of female employment with fintech. This gap derives partly from the lack of data and partly because of the difficulty in establishing causality.
Identifying the causal effects of fintech development on female employment is challenging because of well-known endogeneity concerns, namely the potential correlation between explanatory variables and the error term that arises from omitted variable and simultaneity (Yang 2021). Building on the seminal work of Rajan and Zingales (1998), this study makes progress on establishing causality by including an array of controls and interacted fixed effects (country-industry and year) that allow accounting for a range of omitted variables.
As fintech expands access to financial services and credit, its adoption should disproportionately help firms with financial constraints, high-tech firms facing greater information asymmetry and thus higher borrowing costs, and firms without existing financial access. The study provides detailed evidence on economic mechanisms through which fintech development affects female employment by including several interaction terms between fintech and firm characteristics such as financial constraint, high-tech intensiveness, and loan access. In this way, the model specification captures the rich dynamics between fintech and firm variables, allowing for more reliable statistical inferences.
Link Between Gender Inequality and Fintech
Empirical Strategy
To estimate the relation between fintech and gender inequality, Loko and Yang (2022) constructed the following baseline model, as shown in equations (18.1) and (18.2):
where Genderi,j,t refers to the level of gender inequality of country i, firm j, in year t, measured by the number of female employees and the ratio of female employees over female and male employees in the sampling firms. Fintechi,t captures the fintech development of country i in year t, measured by the volume of alternative finance, which can be further classified into digital Lending and digital Capital Raising. Xi,t-1 is a vector of country-level controls, including the natural logarithm of per capita GDP, GDP growth rate, and trade openness. Ii,t-1 is a vector of firm-level controls, comprising firm size, firm age, export dependence, foreign ownership, and sector specialization. All explanatory variables are lagged by one year to mitigate endogeneity concerns.
Variable ηi,k accounts for country–industry fixed effect that absorbs variations in the financial environment between countries and industries, such as systematic differences in economic development, government policies, and industry-specific reforms. The μt denotes year fixed effect that picks up any variation in the outcome that happens over time and is not attributable to other explanatory variables. Standard errors are clustered at the country and industry levels to account for heteroskedasticity.
The coefficients of interest are β1, β΄1, and β΄2, which are associated with fintech variables. If they are positive and significant, it suggests that a higher level of fintech development is associated with higher female representation in the workforce, hence a lower degree of gender inequality. If they are negative and significant, a negative correlation between fintech development and gender equality can be inferred.
As discussed earlier, identifying the causal effects is a challenge because of the potential correlation between right-side variables and the error term arising most notably from omitted variables and reverse causality. On the one hand, omitted variables could bias the estimation that results from traditional cross-country regressions. Unobservable country or industry characteristics related to both fin-tech and female employment are left in the error term, making statistical inferences hard to draw. On the other hand, a higher female employment rate could increase the use of fintech.
In their pioneering work, Rajan and Zingales (1998) proposed a fixed-effect identification strategy with interaction terms. They showed that better-developed financial markets lead to higher economic growth in industries heavily dependent on external finance. Loko and Yang (2022) built on this work and established the following model that extends their empirical framework to the fintech setting. By estimating various forms of the model, this study examines the effects of fintech on gender inequality, as shown in equation (18.3):
where Firmj is firm-level financial constraint, loan access, digital infrastructure, and other variables that capture economic mechanisms and help with identification. Note that only additional explanatory variables that vary both with country and firm need to be included. All explanatory variables are lagged by one year to mitigate simultaneity concerns.
One key virtue of the model is that it allows using interacted fixed effects (country–industry and year) to control for a range of omitted variables. Thus, the model treats country and firm characteristics in ways that previous cross-country empirical studies could not correct and will be less subject to criticism about the model’s specifications. When interpreting the results, the focus is on the signs and economic significance of β. A positive (negative) and significant coefficient indicates that fintech exerts a disproportionately positive (negative) effect on firms with financial constraints, high-tech intensiveness, loan access, and internet access. In addition, including various interaction terms clearly illustrates the specific mechanisms through which fintech affects female employment. These mechanisms are firmly grounded in economic theories, thus effectively addressing concerns of reverse causality.
Data
The data on fintech adoption derive from the Cambridge Alternative Finance Benchmark, which contains the volume of finance through digital platforms from the world’s 191 jurisdictions spanning 2011–20.2 The benchmark is based on an online survey hosted by the Cambridge Centre for Alternative Finance Judge Business School in partnership with the University of Agder (for the EU report), the University of Chicago Booth School of Business (for the Americas study), the University of Sydney Business School, the University of Tsinghua Graduate School at Shenzhen, Shanghai Jiaotong University Law School (for the Asia-Pacific regional study), and Nesta (for the UK report).
Data on female employment come from the World Bank Enterprise Survey (WBES). Since the 1990s, this renowned firm-level survey has covered a representative sample of firms in the world’s major economies. The WBES is a standard establishment-level survey representative of the nonagricultural, non-extractive private sector, covering registered establishments with five or more employees. The database covers various business environment topics, including access to finance, corruption, infrastructure, crime, competition, and performance measures. However, given the incidence of agriculture in female employment, the WBES database that only includes manufacturing and service industries has certain limitations.
In addition to the main variables of interest, the country-level control variables derive from the IMF’s World Economic Outlook (WEO) database. To estimate mediating effects, the study merges the existing data set with the Worldwide Governance Indicators proposed by Kaufmann and Kraay in 1999. The indicators report on six broad dimensions of governance—voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption—for more than 200 countries and territories over the period 1996–2020. The Women, Business and Law Index derives from the World Bank’s annual report under the same title that analyzes laws and regulations affecting women’s economic opportunity in 190 economies.
The sample period is 2011–20, the overlapping years of all previously referenced databases.
Results
Table 18.1 provides the baseline regression results. In column (1), equation (18.1) is estimated using the number of female employees as the dependent variable and the level of overall fintech finance as the independent variable. Dropping missing values leads to a sample of 22,631 firms. The fintech coefficient is positive and significant at the 1 percent level. The result appears consistent with the hypothesis that fintech development is associated with a significant increase in female employment. Specifically, a 1 percent increase in the volume of transactions through fintech platforms is associated with a 1.4 percent increase in the number of female full-time employees in sample firms.
A likely explanation is that with easier financial access enabled by fintech, firms have more financial resources to expand their businesses, make investments, and boost production. Since capital and labor are complements in the production process (Allen 1968), increased investments create more demand for laborers, including female laborers (Benmelech, Bergman, and Seru 2011).
Fintech Development and Female Employment
Fintech Development and Female Employment
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Variables | Female Employees | Female Ratio | Female Employees | Female Ratio |
| Fintech | 1.363*** (0.456) |
0.375*** (0.112) |
||
| Lending | -0.143* (0.083) |
-0.047*** (0.011) |
||
| Capital Raising | 0.776*** (0.112) |
0.152*** (0.019) |
||
| GDP | 2.931** (1.318) |
0.642* (0.330) |
-1.344*** (0.114) |
-0.334*** (0.020) |
| GDP Growth | -0.627* (0.329) |
-0.094 (0.084) |
0.098** (0.046) |
0.060*** (0.006) |
| Openness | 6.764** (2.774) |
1.543** (0.702) |
-0.023 (0.826) |
0.595*** (0.110) |
| Sales | 0.354*** (0.005) |
-0.003*** (0.001) |
0.366*** (0.006) |
-0.006*** (0.001) |
| Age | 0.081*** (0.010) |
-0.003 (0.002) |
0.086*** (0.012) |
-0.002 (0.003) |
| Export Share | 0.109*** (0.041) |
-0.014* (0.007) |
0.027 (0.050) |
-0.021** (0.009) |
| Foreign Ownership | 0.306*** (0.033) |
0.025*** (0.006) |
0.311*** (0.043) |
0.027*** (0.008) |
| Sector Specialization | 0.068 (0.448) |
-0.056 (0.076) |
0.020 (0.434) |
-0.056 (0.077) |
| Constant | -29.434*** (8.811) |
-6.675*** (2.161) |
-3.488*** (0.945) |
0.228 (0.155) |
| Country–industry Fixed Effects | Yes | Yes | Yes | Yes |
| Year Fixed Effects | Yes | Yes | Yes | Yes |
| Observations | 22631 | 26447 | 14631 | 17021 |
| R2 | 0.390 | 0.263 | 0.393 | 0.270 |
Fintech Development and Female Employment
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Variables | Female Employees | Female Ratio | Female Employees | Female Ratio |
| Fintech | 1.363*** (0.456) |
0.375*** (0.112) |
||
| Lending | -0.143* (0.083) |
-0.047*** (0.011) |
||
| Capital Raising | 0.776*** (0.112) |
0.152*** (0.019) |
||
| GDP | 2.931** (1.318) |
0.642* (0.330) |
-1.344*** (0.114) |
-0.334*** (0.020) |
| GDP Growth | -0.627* (0.329) |
-0.094 (0.084) |
0.098** (0.046) |
0.060*** (0.006) |
| Openness | 6.764** (2.774) |
1.543** (0.702) |
-0.023 (0.826) |
0.595*** (0.110) |
| Sales | 0.354*** (0.005) |
-0.003*** (0.001) |
0.366*** (0.006) |
-0.006*** (0.001) |
| Age | 0.081*** (0.010) |
-0.003 (0.002) |
0.086*** (0.012) |
-0.002 (0.003) |
| Export Share | 0.109*** (0.041) |
-0.014* (0.007) |
0.027 (0.050) |
-0.021** (0.009) |
| Foreign Ownership | 0.306*** (0.033) |
0.025*** (0.006) |
0.311*** (0.043) |
0.027*** (0.008) |
| Sector Specialization | 0.068 (0.448) |
-0.056 (0.076) |
0.020 (0.434) |
-0.056 (0.077) |
| Constant | -29.434*** (8.811) |
-6.675*** (2.161) |
-3.488*** (0.945) |
0.228 (0.155) |
| Country–industry Fixed Effects | Yes | Yes | Yes | Yes |
| Year Fixed Effects | Yes | Yes | Yes | Yes |
| Observations | 22631 | 26447 | 14631 | 17021 |
| R2 | 0.390 | 0.263 | 0.393 | 0.270 |
In column (2), the dependent variable Female Employees was replaced with the ratio of female employees over total employees and we found a significantly positive correlation between fintech development and female ratio. The results indicate that fintech adoption not only leads to more jobs for women but also raises the ratio of female to male workers.
Next, the fintech indicator was disaggregated into fintech lending and fintech capital raising instruments. Interestingly, opposite signs were observed on the estimated coefficients. Despite the overall positive influence of fintech on female employment, the correlation between fintech lending and female employment is negative and significant. In contrast, the coefficient on capital raising is positive and significant at the 1 percent level, consistent with the hypothesis that equitylike instruments are more effective tools to mitigate financial distress than debtlike instruments. The results highlight the importance of distinguishing between different fintech tools in estimating the economic impact.
Gender Inequality, Fintech, and Firm Characteristics
How does fintech disproportionately increase the number of female employees? First, fintech reduces firms’ financial constraints. With more financial resources made available through fintech, employers might be able to hire female workers who require on-job trainings, maternity leave, flexible hours, and other benefits (Liu and others 2017; Lim and Zabek 2021; Kalev and Dobbin 2022). If this hypothesis is true, a more pronounced effect should occur in firms with financial constraints. This study tests this hypothesis by interacting the fintech indicator with a firm’s financial constraint; Table 18.2 presents the results. Consistent with the hypothesis, the coefficient on the interaction term is positive and significant (column 1), suggesting a stronger effect for financially constrained firms. Thus, fintech promotes female employment by providing firms with more financial resources to hire more employees, especially female employees.
Fintech adoption could disproportionately benefit female-led firms, which are more likely to hire female workers (West and Sundaramurthy 2019). Numerous studies have documented a gender divide in financial access (Organisation for Economic Co-operation and Development [OECD] 2016). Women are less likely than men to obtain the financing needed to start a business because of a lack of collateral guarantees, a lack of credit history, or the bank’s practice of gender discrimination. With a lower collateral requirement and alternative ways to establish credit worthiness, fintech is expected to provide more convenient access to finance for female borrowers. The results, reported in Model 2 of Table 18.2, confirmed the conjecture. The effect of fintech is positive and significant in female-led firms, suggesting that fintech can contribute to a more equal distribution of financial resources between genders.
Gender Inequality, Fintech, and Firm Characteristics
Gender Inequality, Fintech, and Firm Characteristics
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Variables | Female | Employees | ||
| Fintech | 0.444* (0.238) |
0.406* (0.236) |
0.555** (0.282) |
0.727** (0.291) |
| Fintech × Financial Obstacle | 0.016*** (0.006) |
|||
| Fintech × Female-Led | 0.014** (0.006) |
|||
| Fintech × Small Business | 0.017*** (0.005) |
|||
| Fintech × Service Sector | 0.044** (0.018) |
|||
| Financial Obstacle | -0.272*** (0.088) |
|||
| Female-Led | -0.173* (0.093) |
|||
| Small Business | 0.175** (0.075) |
|||
| Service Sector | -0.424* (0.255) |
|||
| Age | 0.186*** (0.011) |
0.186*** (0.011) |
0.174*** (0.011) |
0.189*** (0.011) |
| Export Share | 0.004*** (0.000) |
0.004*** (0.000) |
0.003*** (0.000) |
0.003*** (0.000) |
| Foreign Ownership | 0.008*** (0.000) |
0.008*** (0.000) |
0.008*** (0.000) |
0.008*** (0.000) |
| Constant | -5.524 (3.601) |
–4.981 (3.580) |
21.222* (12.399) |
16.140 (12.873) |
| Country–industry Fixed Effects | Yes | Yes | Yes | Yes |
| Year Fixed Effects | Yes | Yes | Yes | Yes |
| Observations | 25,120 | 25,120 | 25,120 | 25,142 |
| R2 | 0.150 | 0.150 | 0.173 | 0.096 |
Gender Inequality, Fintech, and Firm Characteristics
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Variables | Female | Employees | ||
| Fintech | 0.444* (0.238) |
0.406* (0.236) |
0.555** (0.282) |
0.727** (0.291) |
| Fintech × Financial Obstacle | 0.016*** (0.006) |
|||
| Fintech × Female-Led | 0.014** (0.006) |
|||
| Fintech × Small Business | 0.017*** (0.005) |
|||
| Fintech × Service Sector | 0.044** (0.018) |
|||
| Financial Obstacle | -0.272*** (0.088) |
|||
| Female-Led | -0.173* (0.093) |
|||
| Small Business | 0.175** (0.075) |
|||
| Service Sector | -0.424* (0.255) |
|||
| Age | 0.186*** (0.011) |
0.186*** (0.011) |
0.174*** (0.011) |
0.189*** (0.011) |
| Export Share | 0.004*** (0.000) |
0.004*** (0.000) |
0.003*** (0.000) |
0.003*** (0.000) |
| Foreign Ownership | 0.008*** (0.000) |
0.008*** (0.000) |
0.008*** (0.000) |
0.008*** (0.000) |
| Constant | -5.524 (3.601) |
–4.981 (3.580) |
21.222* (12.399) |
16.140 (12.873) |
| Country–industry Fixed Effects | Yes | Yes | Yes | Yes |
| Year Fixed Effects | Yes | Yes | Yes | Yes |
| Observations | 25,120 | 25,120 | 25,120 | 25,142 |
| R2 | 0.150 | 0.150 | 0.173 | 0.096 |
A stronger effect of fintech on small businesses should also be expected. Small firms are often unable to pledge collateral due to the lack of collateral assets (Nguyen and Qian 2012). In the meantime, small firms tend to hire more women than larger firms do, as the latter prefer more educated workers (Paik 2008). This study tests this hypothesis by interacting the fintech indicator with a dummy that indicates small business. The positive and significant sign on the interaction term in Model (3) of Table 18.2 confirms the stronger impact on small businesses.
In addition, more women cluster in the service industry because of their comparative advantages, for example in interactive tasks (Georgieva and others 2020). In contrast to manufacturing sector firms, service sector firms have more intangible assets such as goodwill and trademarks. The limited collateral value of intangible assets restricts the use of traditional financial instruments such as bank loans (Hsu, Tian, and Xu 2014). The traditional financial system often discriminates and marginalizes service sector firms, which are thus more likely to face financial constraints. Consistent with this hypothesis, the coefficient on the interaction term in Model (4) of Table 18.2 is positive and significant, suggesting that fintech adoption brings additional benefits to firms in service sectors.
In summary, fintech promotes female employment mainly through a favorable allocation of financial resources to firms that are more female-labor intensive, and more likely to have financial constraints, such as female-led and small firms and firms in service sectors.
Gender Inequality, Fintech, and Country Characteristics
The impact of fintech development on female employment could vary with the heterogeneity in countries’ institutional quality. To test this hypothesis, sample firms were divided based on the institutional quality of the country that hosts their headquarters. The study examines four governance dimensions—female business law, government effectiveness, regulatory quality, and rule of law. Table 18.3 displays the results and shows that women living in countries with better legal protection are likely to benefit more from fintech.
Gender Inequality, Fintech, and Country Characteristics
Gender Inequality, Fintech, and Country Characteristics
| Stronger Law Protection | Weaker Law Protection | |
|---|---|---|
| (1) | (2) | |
| Variables | Female Employees | |
| Fintech | 1.441*** (0.452) |
0.027 (0.025) |
| GDP | 3.238** (1.312) |
0.391*** (0.091) |
| GDP Growth | -0.714** (0.327) |
0.064*** (0.005) |
| Openness | 7.533*** (2.766) |
1.873** (0.753) |
| Sales | 0.393*** (0.006) |
0.269*** (0.008) |
| Age | 0.083*** (0.011) |
0.069*** (0.018) |
| Export Share | 0.095** (0.047) |
0.090 (0.077) |
| Foreign Ownership | 0.285*** (0.037) |
0.291*** (0.074) |
| Sector Specialization | 0.001 (0.458) |
0.607** (0.251) |
| Constant | -31.661*** (8.754) |
-7.905*** (1.017) |
| Country–industry Fixed Effects | Yes | Yes |
| Year Fixed Effects | Yes | Yes |
| Observations | 15,849 | 6,782 |
| R2 | 0.406 | 0.370 |
Gender Inequality, Fintech, and Country Characteristics
| Stronger Law Protection | Weaker Law Protection | |
|---|---|---|
| (1) | (2) | |
| Variables | Female Employees | |
| Fintech | 1.441*** (0.452) |
0.027 (0.025) |
| GDP | 3.238** (1.312) |
0.391*** (0.091) |
| GDP Growth | -0.714** (0.327) |
0.064*** (0.005) |
| Openness | 7.533*** (2.766) |
1.873** (0.753) |
| Sales | 0.393*** (0.006) |
0.269*** (0.008) |
| Age | 0.083*** (0.011) |
0.069*** (0.018) |
| Export Share | 0.095** (0.047) |
0.090 (0.077) |
| Foreign Ownership | 0.285*** (0.037) |
0.291*** (0.074) |
| Sector Specialization | 0.001 (0.458) |
0.607** (0.251) |
| Constant | -31.661*** (8.754) |
-7.905*** (1.017) |
| Country–industry Fixed Effects | Yes | Yes |
| Year Fixed Effects | Yes | Yes |
| Observations | 15,849 | 6,782 |
| R2 | 0.406 | 0.370 |
In untabulated results, the details of which can be found in the original paper, countries with greater government effectiveness, regulatory quality, and the rule of law experience greater welfare improvement via fintech. To some extent, institutional quality could increase the risk aversion of the country’s investors, discouraging the development of fintech innovations and thus limiting its impact on gender inequality.
The sample countries are also examined in regional groups, based on the geographical classification in WBES. The results, summarized in Table 18.4, point to a greater effect of fintech on female employee ratio in sub-Saharan Africa than in other regions, as suggested by the larger magnitude of the coefficient in the sub-Saharan Africa subsample. Fintech positively affects female employment in Asia and the Pacific, Europe, and Central Asia whereas it negatively affects female employment in Middle Eastern and northern African countries.
Interestingly, a positive association exists between fintech and female employment in sub-Saharan Africa. With less developed financial markets, sub-Saharan African countries are home to fewer entrenched players than advanced economies. As a result, they may offer more opportunities for innovation, as disrupting the equilibrium faces less resistance.
Gender Inequality and Fintech in Different Regions
Gender Inequality and Fintech in Different Regions
| Sub-Saharan Africa | Asia and Pacific | Europe and Central Asia | Middle East and North Africa | Latin America and Caribbean | |
|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | |
| Variables | Female Ratio | ||||
| Fintech | 0.142*** (0.041) |
0.041*** (0.006) |
0.104*** (0.016) |
-0.032*** (0.004) |
-0.012 (0.012) |
| GDP | -9.370*** (2.664) |
-0.068*** (0.015) |
0.043 (0.096) |
0.032 (0.023) |
0.006 (0.026) |
| GDP Growth | 0.966*** (0.276) |
0.004 (0.017) |
-0.037 (0.063) |
0.000 (0.002) |
0.008 (0.011) |
| Openness | -0.607*** (0.171) |
0.066* (0.036) |
0.661* (0.373) |
0.063* (0.033) |
-0.091 (0.197) |
| Sales | -0.001 (0.001) |
0.001 (0.002) |
-0.007*** (0.001) |
-0.003 (0.002) |
-0.005** (0.002) |
| Age | -0.008** (0.004) |
-0.012** (0.006) |
0.000 (0.003) |
0.010** (0.005) |
0.002 (0.007) |
| Export Share | -0.026* (0.014) |
0.006 (0.020) |
-0.034*** (0.011) |
0.061** (0.024) |
0.030 (0.028) |
| Foreign Ownership | -0.014 (0.010) |
0.055* (0.033) |
0.049*** (0.009) |
0.058* (0.031) |
0.028* (0.016) |
| Sector | -0.059 (0.068) |
0.135** (0.064) |
-0.293*** (0.024) |
-0.114 (0.101) |
0.261*** (0.062) |
| Constant | -1.451*** (0.416) |
-0.170 (0.365) |
-0.379 (0.327) |
0.547*** (0.143) |
0.328 (0.312) |
| Country–industry Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Year Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Observations | 6,614 | 3,765 | 10,766 | 2,629 | 2,673 |
| R2 | 0.136 | 0.283 | 0.296 | 0.084 | 0.087 |
Gender Inequality and Fintech in Different Regions
| Sub-Saharan Africa | Asia and Pacific | Europe and Central Asia | Middle East and North Africa | Latin America and Caribbean | |
|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | |
| Variables | Female Ratio | ||||
| Fintech | 0.142*** (0.041) |
0.041*** (0.006) |
0.104*** (0.016) |
-0.032*** (0.004) |
-0.012 (0.012) |
| GDP | -9.370*** (2.664) |
-0.068*** (0.015) |
0.043 (0.096) |
0.032 (0.023) |
0.006 (0.026) |
| GDP Growth | 0.966*** (0.276) |
0.004 (0.017) |
-0.037 (0.063) |
0.000 (0.002) |
0.008 (0.011) |
| Openness | -0.607*** (0.171) |
0.066* (0.036) |
0.661* (0.373) |
0.063* (0.033) |
-0.091 (0.197) |
| Sales | -0.001 (0.001) |
0.001 (0.002) |
-0.007*** (0.001) |
-0.003 (0.002) |
-0.005** (0.002) |
| Age | -0.008** (0.004) |
-0.012** (0.006) |
0.000 (0.003) |
0.010** (0.005) |
0.002 (0.007) |
| Export Share | -0.026* (0.014) |
0.006 (0.020) |
-0.034*** (0.011) |
0.061** (0.024) |
0.030 (0.028) |
| Foreign Ownership | -0.014 (0.010) |
0.055* (0.033) |
0.049*** (0.009) |
0.058* (0.031) |
0.028* (0.016) |
| Sector | -0.059 (0.068) |
0.135** (0.064) |
-0.293*** (0.024) |
-0.114 (0.101) |
0.261*** (0.062) |
| Constant | -1.451*** (0.416) |
-0.170 (0.365) |
-0.379 (0.327) |
0.547*** (0.143) |
0.328 (0.312) |
| Country–industry Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Year Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Observations | 6,614 | 3,765 | 10,766 | 2,629 | 2,673 |
| R2 | 0.136 | 0.283 | 0.296 | 0.084 | 0.087 |
Taken together, the findings not only confirm the previous estimates about the association between fintech and female employment but also show that prevailing governance ineffectiveness, poor regulatory quality, and weak rule of law associated with less developed countries constitute major obstacles to fintech adoption in these economies.
Conclusion
Fintech development leads to significant welfare improvements for women. It not only increases the number of female employees in the workforce but also raises the ratio of female relative to male employees. This study also sheds light on the economic mechanisms—fintech provides easier financial access to firms with financial constraints, especially female-led firms, small firms, and firms in service sectors that traditionally hire more female workers.
Weak institutions reduce the positive effect of fintech. Fintech can significantly increase female employment in countries with good governance, law, and regulations, while it has weaker benefits in countries whose institutional quality is below the median. At the regional level, the effect of fintech is positive in sub-Saharan African, Asian and Pacific, and European countries.
These results provide important policy implications. First, closing gender gaps in digitalization is critical to fully reap fintech benefits on gender equality. In most countries, unequal access to mobile phones and other electronic devices creates financial inclusion gaps. For example, according to the OECD (2018), worldwide, 327 million fewer women than men have a smartphone and can access the mobile internet. The findings indicate that the inequality-reducing effects of fintech are significantly weaker in firms without access to the internet compared to firms with such access. Thus, the digital divide must be addressed, for example, by investing in technological innovation and increasing the supply of digital infrastructure, to fully leverage fintech benefits. Second, policymakers need to promote good governance, laws, and regulations to ensure that fintech effectively reduces gender inequality.
Valuable avenues of research exist that are worth exploring: Does fintech help reduce firms’ earning inequality in addition to the gender employment gap? What are the fintech-related distributional effects and welfare implications on female-led households and female entrepreneurs who start businesses? If banks and fin-tech lenders compete on credit provision, how will that affect consumers and investors? Do the new forms of financing introduced by fintech demand new forms of regulation? Answering these questions allows a comprehensive approach to building more gender-inclusive economies.
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This chapter draws upon Loko and Yang 2022.
Loko and Yang (2022) chose the current fintech indicator over other fintech-related indexes, including Global FINDEX compiled by WB and FAS compiled by IMF for the following reasons: (1) They are published in waves and the only available years are 2011, 2014, and 2017, making it difficult to perform reliable panel-based analysis; (2) the fintech landscape has been changing rapidly since 2017, making it preferable to use the latest data available to reflect the most recent developments; (3) Global FINDEX and FAS do not make the distinction between lending and equity financing, which is economically important given the vastly different natures of, and incentives offered by, debt and equity financing. The final index on alternative finance includes financial channels and instruments that emerge outside the traditional financial system. Mobile money and internet banking that are often operated by traditional banks are thus not included.
