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---

### Introduction — context and motivation
- Gender equality is foundational for a peaceful, prosperous, and sustainable world (United Nations, 2015).
- Women experience higher levels of poverty, unemployment, and other economic hardships (IMF, 2021).
- Women remain under-represented in the global financial system at all levels (Sahay and Cihak, 2018).
- Fintech defined as "newly developed digital technologies to support or enable financial services and processes" (Schüffel, 2016).
- Countries categorized into five groups (top 10%, 11–25%, 26–50%, 51–75%, 76–100%) according to the volume of fintech transactions in 2019.

### Potential channels through which fintech can affect gender inequality
- Increased access and usage of financial services:
  - Fintech can reach geographically marginalized communities via digital platforms.
  - World Bank Group’s Global Findex: more than one billion women still do not use or have access to the financial system; more than 70 percent of female-owned small and medium enterprises have inadequate or no access to financial services (World Bank, 2017; Demirguc-Kunt, 2018).
  - Fintech may provide greater convenience, privacy, and security for unbanked or underbanked women.
- Use of alternative data to assess creditworthiness:
  - Fintech leverages information generated on digital platforms to make lending decisions without relying solely on credit reports or scores, benefiting female applicants who lack credit reports or scores.
- Facilitating access to financing for female-headed households and businesses:
  - Estimated worldwide financing gap for formal, female-owned small businesses: $300 billion (IFC, 2022).
  - Fintech platforms with "big data, small credit" propositions can target SMEs, lower interest rates, and relax collateral requirements.

### Data and empirical approach
- Comprehensive fintech database covering 114 countries for 2011–20 combined with firm-level statistics including gender composition of owners, managers, and employees.
- Focused outcome: fintech’s impact on the gender employment gap (female employment).
- Global employment context: current global employment rate is less than 46% for women whereas 71% for men (International Labor Organization).
- Identification challenges: endogeneity from omitted variables and simultaneity.
- Strategies to address causality:
  - Include an array of controls and interacted fixed effects (country-industry and year).
  - Use lagged values of explanatory variables.
  - Interact fintech with firm characteristics (financial constraint, high-tech intensiveness, loan access) to reveal mechanisms and reduce reverse causality concerns.

### Baseline empirical findings (key statistics)
- A 1% increase in the scale of fintech usage is associated with:
  - a 1.4 percentage points increase in the number of female workers, and
  - a 0.4% increase in the ratio of female to total employees in the sample firms.
- Sample average percentage of female employees: 32%.
- Disaggregation:
  - Digital lending resembles debt financing; digital capital raising resembles equity financing.
  - Adoption of capital raising tools associated with a greater effect on the number of female workers because capital raising tools typically have no collateral requirements and do not increase financial distress when additional capital is needed.

### Fintech Variable — measurement and components
- Construct three main measures.
- Main measure (notation preserved): natural logarithm of the total volume of finance through digital platforms, denoted in U.S. dollars.
- Two category decompositions:
  - Lending: natural logarithm of the volume of lending instruments through digital platforms; comprises balance sheet lending, P2P/marketplace lending, debt-based lending, and invoice trading.
  - Capital Raising: natural logarithm of capital raising instruments through digital platforms; includes investment-based crowdfunding and non-investment-based crowdfunding (donation-based or reward-based).
- Data sources and verification:
  - Data collected from surveys and verified with publicly available information (platform websites, press releases, annual reports); web-scraping used to update data for prominent reward-based platforms.
  - Cambridge Alternative Finance Benchmark contains volume of finance through digital platforms from 191 jurisdictions spanning 2011–20.

### Female Employment Variable — measures and motivation
- Motivation: need for firm-level granularity beyond slow-moving country-level gender indices.
- Two measures from WBES:
  - natural logarithm of the number of female full-time employees (퐹퐹퐹퐹퐹퐹 푐푐푙푙 퐹퐹 퐸퐸퐹퐹푐푐𝑙𝑙 퐸퐸퐸퐸 퐹퐹퐹퐹푟푟),
  - ratio of female employees over total number of employees (퐹퐹퐹퐹퐹퐹푐푐푙𝑙 퐹퐹 푅푅푐푐퐹퐹퐹퐹퐸퐸).
- Distinction of firm types includes female-led firms (top manager female).

### Other firm-level variables and institutional indices
- Firm-level mediators:
  - Financial constraint dummy = 1 if access to finance is at least a minor obstacle; 0 otherwise.
  - Small business dummy = 1 if firm has fewer than 20 employees; 0 otherwise.
  - Loan access dummy = 1 if firm has no outstanding line of credit or loan; 0 otherwise.
  - Internet access dummy = 1 if firm has its own website; 0 otherwise.
- Institutional quality:
  - Worldwide Governance Indicators (WGI): government effectiveness, regulatory quality, rule of law (values and rankings; higher absolute value associated with worse institutional quality; ranking scale 1 to 100 where higher = higher institutional quality).
  - Women, Business and the Law index (0 to 100; higher = more progress toward gender equality in law).

### Descriptive statistics (Table 1 summary — exact reported values)
- Fintech: No. of Obs. 26447; Mean 15.238; Std. Dev. 2.927; Min 5.210; Max 21.780
- Lending: No. of Obs. 17021; Mean 16.365; Std. Dev. 2.192; Min 10.222; Max 21.766
- Capital Raising: No. of Obs. 17021; Mean 13.094; Std. Dev. 2.332; Min 5.210; Max 17.801
- Female Employees: No. of Obs. 26447; Mean 1.817; Std. Dev. 1.307; Min 0.000; Max 10.373
- Female Ratio: No. of Obs. 26447; Mean 0.324; Std. Dev. 0.275; Min 0.000; Max 1.000
- Female Led: No. of Obs. 26447; Mean 0.155; Std. Dev. 0.361; Min 0.000; Max 1.000
- GDP: No. of Obs. 26447; Mean 5.063; Std. Dev. 1.826; Min 0.291; Max 8.922
- GDP Growth: No. of Obs. 26447; Mean 0.117; Std. Dev. 0.081; Min -0.046; Max 0.782
- Openness: No. of Obs. 26447; Mean 0.769; Std. Dev. 0.409; Min 0.264; Max 3.801
- Sales: No. of Obs. 26447; Mean 16.551; Std. Dev. 3.030; Min 0.000; Max 32.053
- Age: No. of Obs. 26447; Mean 3.119; Std. Dev. 0.795; Min 0.180; Max 7.616
- Export Share: No. of Obs. 26447; Mean 0.117; Std. Dev. 0.273; Min 0.000; Max 1.000
- Foreign Ownership: No. of Obs. 26447; Mean 0.068; Std. Dev. 0.236; Min 0.090; Max 1.000
- Sector Specialization: No. of Obs. 26447; Mean 0.464; Std. Dev. 0.499; Min 0.000; Max 1.000
- Financial Constraint: No. of Obs. 26447; Mean 0.609; Std. Dev. 0.488; Min 0.000; Max 1.000
- Loan Access: No. of Obs. 26447; Mean 0.317; Std. Dev. 0.465; Min 0.000; Max 1.000
- Small Business: No. of Obs. 26447; Mean 0.314; Std. Dev. 0.474; Min 0.000; Max 1.000
- Internet Access: No. of Obs. 26447; Mean 0.556; Std. Dev. 0.497; Min 0.000; Max 1.000
- Government Effectiveness: No. of Obs. 26447; Mean -0.133; Std. Dev. 0.726; Min -1.680; Max 2.007
- Regulatory Quality: No. of Obs. 26447; Mean -0.106; Std. Dev. 0.734; Min -1.654; Max 1.906
- Rule of Law: No. of Obs. 26447; Mean -0.226; Std. Dev. 0.729; Min -1.656; Max 2.058
- Women Business Law: No. of Obs. 26447; Mean 73.025; Std. Dev. 17.443; Min 26.250; Max 100.000
- Additional descriptive statements reported verbatim:
  - The average number of female full-time employees is 20 people, and the average ratio of females over total employees is 32.4%.
  - A typical firm in the sample has a sales volume of 14.4 billion dollars, an operating experience of 17 years, 11.7% of its revenues from exports, and 6.8% of its shares held by foreign entities.
  - Around 46.4% of the sampled firms operate in the service sector.
  - After dropping observations with missing variables, average of 23.2 firms covered in a typical country in a typical year.

### Empirical strategy — baseline model and identification (notation preserved)
- Baseline specification:
  - 퐺퐺퐹퐹퐹퐹푙푙퐹퐹푟푟_{푖푖,푗푗,푡푡} = 훽_0 + 훽_1 퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹ℎ_{푖푖,푡푡−1} + 훽_2 푋_{푖푖,푡푡−1} + 훽_3 퐼_{푖푖,푗푗,푡𝑡−1} + 휂_{푖푖,𝑘𝑘} + 휇_{푡𝑡} + 휀_{푖푖,푗푗,푡𝑡}
- Alternative decomposition:
  - includes 퐿퐿퐹퐹퐹퐹푙푙퐹퐹퐹퐹푙푙_{푖,𝑡−1} and 퐶퐶푐푐푐푐퐹퐹퐹퐹𝑐𝑐𝑙𝑙 푅푅푐푐퐹퐹푟푟퐹퐹퐹퐹푙푙_{푖,𝑡−1}.
- Estimation details:
  - All explanatory variables lagged by one year.
  - Country-industry fixed effects 휂_{i,k} and year fixed effects 휇_t included.
  - Standard errors clustered at country and industry level.
- Identification via interaction model (notation preserved) to exploit heterogeneous firm-level exposure:
  - 퐺퐺퐹퐹퐹퐹푙푙퐹퐹푟푟_{푖,푗,푡} = 훼 + 훽 [ 퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹ℎ_{푖,푡−1} × 퐹퐹퐹퐹 푟푟퐹퐹_{푗} ] + γ 퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹ℎ_{푖,푡−1} + ρ 퐹퐹퐹퐹 푟푟퐹퐹_{푖} + δ 𝐼_{푖,푗,푡−1} + 휂_{푖,𝑘} + 휇_{𝑡} + 휀_{푖,푗,푡}
- Interpretation:
  - Interactions with firm-level financial constraint, loan access, internet access test mechanisms and aid identification. Positive significant 훽 indicates fintech disproportionately benefits specified firm types.

### Split-sample analysis
- Split by institutional quality: above-the-median WGI values → high governance group; others → low governance group.
- Additional splits: by income level (advanced vs emerging market economies) and by region to assess heterogeneities.

### Data sources (sample period 2011–20)
- Fintech adoption: Cambridge Alternative Finance Benchmark (2011–20).
- Female employment: World Bank Enterprise Survey (WBES) — establishment-level survey (excludes agriculture).
- Country-level controls: IMF World Economic Outlook (WEO).
- Institutional quality: Worldwide Governance Indicators (1996–2020) and Women, Business and the Law index.
- Sample period set to overlapping years of databases: 2011–20.

### Annex III — baseline results and robustness (exact reported coefficients and findings)
- Sample after dropping missing values: 22,631 firms.
- Main finding (reported significance):
  - "A 1% increase in the volume of transactions through fintech platforms is associated with a 1.363 percentage points increase in the number of female full-time employees in our sample firms."
- Table 2 selected coefficients:
  - Fintech: 1.363*** (Model (1), Female Employees)
  - Fintech: 0.375*** (Model (2), Female Ratio)
  - Lending: -0.143* (Female Employees); -0.047*** (Female Ratio)
  - Capital Raising: 0.776*** (Female Employees); 0.152*** (Female Ratio)
  - GDP: 2.931** (Model (1) Female Employees); 0.642* (Model (2) Female Ratio); -1.344*** and -0.334*** in Models (3)/(4)
  - GDP Growth, Openness, Sales, Age, Export Share, Foreign Ownership: coefficients reported in Table 2 with significance levels as presented.
- Economic mechanisms (interaction evidence):
  - Fintech coefficients across selected models: 0.444*; 0.406*; 0.555**; 0.727** (standard errors reported in source).
  - Fintech*Financial Obstacle: 0.016*** (positive and significant, Model (1)).
  - Fintech*Female-Led: 0.014** (positive and significant).
  - Fintech*Small Business: 0.017*** (positive and significant).
  - Fintech*Service Sector: 0.044** (positive and significant).
  - Financial Obstacle: -0.272*** (negative).
  - Female-Led (main effect): -0.173* (negative).
  - Small Business (main effect): 0.175** (positive).
  - Service Sector (main effect): -0.424* (negative).
- Fintech interaction with firm characteristics (Table 5):
  - Fintech: 0.478** (Model (1)); 0.557* (Model (2))
  - Fintech*Loan Access: 0.011** (positive and significant)
  - Fintech*Internet Access: 0.016*** (positive and significant)
  - Loan Access (main effect): -0.502*** (negative)
  - Internet Access (main effect): -0.809*** (negative)
- Institutional splits — fintech coefficient by institutional quality and group (selected exact coefficients):
  - Government effectiveness:
    - High Government Effectiveness: Fintech 0.609*** (positive)
    - Low Government Effectiveness: Fintech -0.793*** (negative)
  - Regulatory quality:
    - High Regulatory Quality: Fintech 0.595*** (positive)
    - Low Regulatory Quality: Fintech -0.807*** (negative)
  - Rule of law:
    - Strong Rule of Law: Fintech 0.610*** (positive)
    - Weak Rule of Law: Fintech 0.019 (insignificant)
  - Women business law:
    - Stronger Law Protection: Fintech 1.441*** (positive)
    - Weaker Law Protection: Fintech 0.027 (insignificant)
  - By country income group:
    - Advanced Economies: Fintech 0.439*** (positive)
    - Emerging Markets: Fintech 0.149*** (positive)
    - Low-Income Countries: Fintech -3.929*** (negative)
  - By region (Female Ratio):
    - Sub-Saharan Africa: Fintech 0.142*** (positive)
    - Asia and Pacific: Fintech 0.041*** (positive)
    - Europe & Central Asia: Fintech 0.104*** (positive)
    - Middle East & North Africa: Fintech -0.032*** (negative)
    - Latin America & Caribbean: Fintech -0.012 (insignificant)
- Robustness checks:
  - Alternative fintech definition (ratio over GDP): Fintech (alternative) 0.754*** (Female Employees); 2.739*** (Female Ratio)
  - Including capital account openness and inflation: Fintech coefficients remain 1.357*** and 1.446*** in reported specifications.
  - Overall results robust to alternative fintech measurement and additional macro controls.

### Conclusion — key findings, mechanisms, and policy implications
- Key findings:
  - Fintech development leads to significant welfare improvement for women by increasing the number of female employees and the female-to-total employee ratio in the sample.
  - Positive impact observed in advanced economies and emerging markets; insignificant or negative in low-income countries.
  - Regional effects: positive in Sub-Saharan Africa, Asia and Pacific, and Europe; insignificant in Latin America and the Caribbean; negative in Middle East and North Africa.
- Mechanisms identified:
  - Fintech reduces firms’ financial constraints, enabling hires that support female workers (on-job training, maternity leave, flexible hours).
  - Effects concentrated among female-led firms, small firms, and service-sector firms.
  - Firms without loan access and with basic digital infrastructure (website) benefit more from fintech.
  - Institutional quality conditions the effect: weak institutions reduce fintech’s positive impact.
- Policy implications (reported recommendations):
  - Develop enabling infrastructure to expand access to fintech for women (including internet connectivity).
  - Improve institutional environment—government effectiveness, regulatory quality, rule of law, and women business law—to strengthen fintech’s positive impact on female employment.
  - Address technological, legal, and regulatory barriers that contribute to a significant gender divide in accessing fintech services.
  - Close the digital divide (e.g., OECD finding: worldwide 327 million fewer women than men have a smartphone and can access the mobile Internet (OECD, 2018)); inequality-reducing effects of fintech weaker in firms without internet access.
- Directions for future research (reported questions):
  - Does fintech help reduce firms’ earning inequality in addition to the gender employment gap?
  - What are distributional effects and welfare implications for female-led households and female entrepreneurs?
  - How will competition between banks and fintech lenders affect consumers and investors?
  - Do new forms of fintech financing demand new forms of regulation?

*Source: IMF WORKING PAPERS — Fintech and Gender Inequality (wpiea2022108-print-pdf).*

### Introduction............................................................................................................

### Introduction

### Context and motivation
- Gender equality is foundational for a peaceful, prosperous, and sustainable world (United Nations, 2015).
- Despite progress, women experience higher levels of poverty, unemployment, and other economic hardships (IMF, 2021).
- Women remain under-represented in the global financial system at all levels (Sahay and Cihak, 2018).
- Fintech is defined as "newly developed digital technologies to support or enable financial services and processes" (Schüffel, 2016).
- Countries are categorized into five groups (top 10%, 11–25%, 26–50%, 51–75%, 76–100%) according to the volume of fintech transactions in 2019.

### Potential channels through which fintech can affect gender inequality
- Increased access and usage of financial services:
  - Fintech can reach geographically marginalized communities via digital platforms.
  - World Bank Group’s Global Findex: more than one billion women still do not use or have access to the financial system; more than 70 percent of female-owned small and medium enterprises have inadequate or no access to financial services (World Bank, 2017; Demirguc-Kunt, 2018).
  - Fintech may provide greater convenience, privacy, and security to the traditionally unbanked or underbanked female population.
- Use of alternative data to assess creditworthiness:
  - Fintech leverages information generated on digital platforms to make lending decisions without relying solely on credit reports or scores.
  - Many female loan applicants who lack credit reports or scores can benefit from these alternative assessment methods.
- Facilitating access to financing for female-headed households and businesses:
  - It is estimated that worldwide, a $300 billion gap in financing exists for formal, female-owned small businesses (IFC, 2022).
  - Fintech platforms operating on "big data, small credit" propositions can target SMEs, lower interest rates, and relax collateral requirements.

### Data and empirical approach
- Comprehensive fintech database covering 114 countries for the period 2011–20 combined with firm-level statistics that include gender composition of owners, managers, and employees.
- Focus: impact of fintech on gender employment gap (female employment).
- Global employment rates cited: current global employment rate is less than 46% for women whereas 71% for men (International Labor Organization).
- Identification challenges: endogeneity from omitted variables and simultaneity.
- Strategies to address causality:
  - Include an array of controls and interacted fixed effects (country-industry and year) to account for omitted variables.
  - Use lagged values of explanatory variables to mitigate simultaneity concerns.
  - Interact fintech with firm characteristics (financial constraint, high-tech intensiveness, loan access) to reveal mechanisms and reduce reverse causality concerns.

### Baseline empirical findings (key statistics)
- A 1% increase in the scale of fintech usage is associated with:
  - a 1.4 percentage points increase in the number of female workers, and
  - a 0.4% increase in the ratio of female to total employees in the sample firms.
- Sample average percentage of female employees is 32%.
- Disaggregation of fintech:
  - Digital lending resembles debt financing; digital capital raising resembles equity financing.
  - Adoption of capital raising tools is associated with a greater effect on the number of female workers.
  - Rationale: capital raising tools typically have no collateral requirements and do not increase financial distress when additional capital is needed.

### Heterogeneity and economic mechanisms
- Effects are substantially higher for firms that are:
  - Financially constrained.
  - Internet-connected.
  - Without outstanding loans or lines of credit.
- Interpretation: fintech reduces the cost of external financing, mitigates financial constraints, and increases inclusivity of credit—thus disproportionately benefiting firms in need of finance and those not already served by formal banking.

### Institutional and regional variation
- Institutional quality matters:
  - Positive correlation between fintech adoption and female employment in advanced economies and emerging markets.
  - Insignificant or negative effects in the low-income country group.
  - Splitting the sample by institutional measures (government effectiveness, regulatory quality, rule of law, women business law) shows fintech significantly increases female employment where governance, law, and regulatory quality are above median; benefits are weaker where institutional quality is below median.
- Regional results:
  - Positive effect in Sub-Saharan Africa, Asia and Pacific, and European countries.
  - Insignificant effect in Latin America and the Caribbean.
  - Negative effect in Middle East and North Africa.

### Contributions
- One of the first studies to examine the link between fintech and gender inequality as measured by female employment.
- Provides cross-country evidence covering 114 economies worldwide and uses micro, firm-level gender indicators.
- Identifies technological, legal, and regulatory barriers constraining equitable access and usage of fintech.

### Policy implications and suggested pathways
- Develop enabling infrastructure to expand access to fintech for women (including internet connectivity).
- Improve institutional environment—government effectiveness, regulatory quality, rule of law, and women business law—to strengthen the positive impact of fintech on female employment.
- Address technological, legal, and regulatory barriers that contribute to a significant gender divide in accessing fintech services.

*Source: IMF WORKING PAPERS — Fintech and Gender Inequality, Introduction.*

### 1.    Fintech Variable

### 1. Fintech Variable

### Fintech measurement
- Construct three main measures.
- First measure, 퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹ℎ, is the natural logarithm of the total volume of finance through digital platforms, denoted in U.S. dollars.
- 퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹ℎ can be divided into two categories:
  - digital 푙푙퐹퐹퐹퐹푙푙퐹퐹퐹퐹 푙푙 (lending)
  - digital 퐶퐶푐푐푐푐퐹퐹퐹퐹푐푐푙푙 푅푅푐푐퐹퐹푟푟퐹퐹퐹퐹푙푙 (capital raising)

### Components
- 퐿퐿퐹퐹퐹퐹푙푙퐹퐹퐹퐹푙푙: natural logarithm of the volume of lending instruments through digital platforms; comprises balance sheet lending, P2P/marketplace lending, debt-based lending, and invoice trading.
- 퐶퐶푐푐푐푐퐹퐹퐹퐹푐푐푙푙 푅푅푐푐퐹퐹푟푟퐹퐹퐹퐹푙푙: natural logarithm of capital raising instruments through digital platforms; includes investment-based crowdfunding and non-investment-based crowdfunding (donation-based or reward-based).
- Level 3 categorization is provided in Annex II; platforms can multi-select business models and provide detailed breakdowns.

### Data collection and verification
- Underlying data collected from surveys and verified with publicly available information (platform website, press releases, annual reports).
- Prominent reward-based platforms sometimes unable to participate in survey; data for these platforms collected and automatically updated by web-scraping techniques.

### Key dataset coverage (mentioned later in Data Sources)
- Cambridge Alternative Finance Benchmark contains volume of finance through digital platforms from the world’s 191 jurisdictions spanning 2011–20.

---

### Female Employment Variable

- Motivation: existing country-level gender indices (UNDP GDI, GEM; WHO GII; GEI; GGGI; Social Institutions and Gender Index) are aggregated and slow-moving; need firm-level granularity.
- Focus: female workers in firms and how firms make employment decisions under influence of fintech.
- Two measures constructed from WBES:
  - 퐹퐹퐹퐹퐹퐹 푐푐푙푙 퐹퐹 퐸퐸퐹퐹푐푐푙푙 퐸퐸퐸퐸 퐹퐹퐹퐹푟푟: natural logarithm of the number of female full-time employees.
  - 퐹퐹퐹퐹퐹퐹푐푐푙푙 퐹퐹 푅푅푐푐퐹퐹퐹퐹퐸퐸: ratio of female employees over total number of employees.
- Distinguished firm types:
  - 퐹퐹퐹퐹퐹퐹 푐푐푙푙 퐹퐹−퐿퐿퐹퐹푙푙 퐹퐹퐹퐹 푟푟퐹퐹푟푟: firms with female as top manager (female-led firms).
- Literature: female labor force participation linked to productivity and development; estimated losses from economic disempowerment of women range from 10 percent of GDP in advanced economies to more than 30 percent in developing countries (Kochhar et al., 2017). More recent research suggests larger economic benefits (Sahay and Cihak, 2018).

---

### Other Firm-Level Variables

- Financial constraint dummy:
  - 퐹퐹퐹퐹퐹퐹 푐푐퐹퐹퐹퐹퐹퐹 푐푐𝑙𝑙 퐶퐶퐸퐸퐹퐹푟푟퐹퐹푟푟푐푐퐹퐹퐹퐹퐹퐹 = 1 if access to finance is at least a minor obstacle; 0 otherwise.
- Small business dummy:
  - 푆푆퐹퐹푐푐𝑙𝑙𝑙𝑙 퐵퐵퐵퐵 푟푟퐹퐹퐹퐹퐹퐹푟푟푟푟 = 1 if firm has fewer than 20 employees; 0 otherwise.
- Loan access dummy:
  - 퐿퐿퐸퐸𝑐𝑐퐹퐹 퐴퐴퐹퐹퐹퐹퐹퐹푟푟푟푟 = 1 if the firm has no outstanding line of credit or loan from a financial institution; 0 otherwise.
- Internet access dummy:
  - 퐼퐼퐹퐹퐹퐹퐹퐹푟푟퐹퐹퐹퐹퐹퐹 퐴퐴퐹퐹퐹퐹퐹퐹푟푟푟푟 = 1 if the firm has its own website; 0 otherwise.
- These variables capture mediating factors in how fintech affects female employment.

---

### Institutional Quality Indices

- Use Worldwide Governance Indicators (WGI) to measure institutional quality; focus on three dimensions:
  - government effectiveness
  - regulatory quality
  - rule of law
- Both values and ranking available for each dimension.
  - Higher absolute value is associated with worse institutional quality.
  - On a scale from 1 to 100, higher ranking = higher institutional quality.
- Use Women, Business and the Law index (World Bank) on a scale from 0 to 100; higher value = more progress toward gender equality in law.

---

### Control Variables

- Country-level controls:
  - 퐺퐺퐺퐺퐺퐺 푙푙퐹퐹푙푙 퐹퐹푙푙: natural logarithm of a country’s GDP, denoted in billion U.S. dollars.
  - 퐺퐺퐺퐺퐺퐺 푙푙푟푟퐸퐸푔푔  퐹퐹ℎ: percentage change of a country’s GDP.
  - 푂푂푐𝑐퐹퐹퐹퐹퐹퐹퐹퐹𝑟𝑟𝑟𝑟: sum of export and import volumes over total GDP (trade openness).
- Firm-level controls:
  - 푆푆푐𝑐𝑙𝑙퐹퐹푟푟: natural logarithm of total annual sales.
  - 퐴퐴푙𝑙퐹퐹: natural logarithm of firm operating years (current year minus start year).
  - 퐸퐸퐸퐸 푐𝑐퐸퐸푟푟퐹퐹 푆푆ℎ푐𝑐푟푟퐹퐹: share of sales that are direct or indirect exports.
  - 퐹퐹퐸퐸푟푟퐹퐹퐹퐹 푙푙퐹퐹 푂푂푔푔퐹퐹퐹퐹푟푟푟푟 ℎ퐹퐹푐푐: share owned by private foreign entities.
  - 푆푆퐹퐹퐹퐹퐹퐹퐸퐸푟푟 푆푆𝑐𝑐퐹퐹𝑅𝑅 differentiates manufacturing vs service sectors.

---

### Descriptive Statistics (Table 1 summary)
- Variable observations and summary statistics exactly as reported:
  - Fintech: No. of Obs. 26447; Mean 15.238; Std. Dev. 2.927; Min 5.210; Max 21.780
  - Lending: No. of Obs. 17021; Mean 16.365; Std. Dev. 2.192; Min 10.222; Max 21.766
  - Capital Raising: No. of Obs. 17021; Mean 13.094; Std. Dev. 2.332; Min 5.210; Max 17.801
  - Female Employees: No. of Obs. 26447; Mean 1.817; Std. Dev. 1.307; Min 0.000; Max 10.373
  - Female Ratio: No. of Obs. 26447; Mean 0.324; Std. Dev. 0.275; Min 0.000; Max 1.000
  - Female Led: No. of Obs. 26447; Mean 0.155; Std. Dev. 0.361; Min 0.000; Max 1.000
  - GDP: No. of Obs. 26447; Mean 5.063; Std. Dev. 1.826; Min 0.291; Max 8.922
  - GDP Growth: No. of Obs. 26447; Mean 0.117; Std. Dev. 0.081; Min -0.046; Max 0.782
  - Openness: No. of Obs. 26447; Mean 0.769; Std. Dev. 0.409; Min 0.264; Max 3.801
  - Sales: No. of Obs. 26447; Mean 16.551; Std. Dev. 3.030; Min 0.000; Max 32.053
  - Age: No. of Obs. 26447; Mean 3.119; Std. Dev. 0.795; Min 0.180; Max 7.616
  - Export Share: No. of Obs. 26447; Mean 0.117; Std. Dev. 0.273; Min 0.000; Max 1.000
  - Foreign Ownership: No. of Obs. 26447; Mean 0.068; Std. Dev. 0.236; Min 0.090; Max 1.000
  - Sector Specialization: No. of Obs. 26447; Mean 0.464; Std. Dev. 0.499; Min 0.000; Max 1.000
  - Financial Constraint: No. of Obs. 26447; Mean 0.609; Std. Dev. 0.488; Min 0.000; Max 1.000
  - Loan Access: No. of Obs. 26447; Mean 0.317; Std. Dev. 0.465; Min 0.000; Max 1.000
  - Small Business: No. of Obs. 26447; Mean 0.314; Std. Dev. 0.474; Min 0.000; Max 1.000
  - Internet Access: No. of Obs. 26447; Mean 0.556; Std. Dev. 0.497; Min 0.000; Max 1.000
  - Government Effectiveness: No. of Obs. 26447; Mean -0.133; Std. Dev. 0.726; Min -1.680; Max 2.007
  - Regulatory Quality: No. of Obs. 26447; Mean -0.106; Std. Dev. 0.734; Min -1.654; Max 1.906
  - Rule of Law: No. of Obs. 26447; Mean -0.226; Std. Dev. 0.729; Min -1.656; Max 2.058
  - Women Business Law: No. of Obs. 26447; Mean 73.025; Std. Dev. 17.443; Min 26.250; Max 100.000

- Additional descriptive interpretations reported verbatim from the source:
  - The average number of female full-time employees is 20 people, and the average ratio of females over total employees is 32.4%.
  - A typical firm in the sample has a sales volume of 14.4 billion dollars, an operating experience of 17 years, 11.7% of its revenues from exports, and 6.8% of its shares held by foreign entities.
  - Around 46.4% of the sampled firms operate in the service sector.
  - After dropping observations with missing variables, average of 23.2 firms covered in a typical country in a typical year.

---

### B. Empirical Strategy

### 1. Baseline Model
- Baseline econometric specification (notation preserved):
  - 퐺퐺퐹퐹퐹퐹푙푙퐹퐹푟푟_{푖푖,푗푗,푡푡} = 훽_0 + 훽_1 퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹ℎ_{푖푖,푡푡−1} + 훽_2 푋_{푖푖,푡푡−1} + 훽_3 퐼_{푖푖,푗푗,푡푡−1} + 휂_{푖푖,𝑘𝑘} + 휇_{푡𝑡} + 휀_{푖푖,푗푗,푡푡}
  - Alternative specification:
    - 퐺퐺퐹퐹퐹퐹푙푙퐹퐹푟푟_{푖푖,푗푗,푡푡} = 훽_0' + 훽_1' 퐿퐿퐹퐹퐹퐹푙푙퐹퐹퐹퐹푙푙_{푖푖,푡푡−1} + 훽_2' 퐶퐶푐푐푐푐퐹퐹퐹퐹푐푐𝑙𝑙 푅푅푐푐퐹퐹푟푟퐹퐹퐹퐹푙푙_{푖푖,푡푡−1} + 훽_3' 𝑋_{푖푖,푡푡−1} + 훽_4' 𝐼_{푖푖,푗푗,푡푡−1} + 휂_{푖푖,𝑘𝑘} + 휇_{푡𝑡} + 휀_{푖푖,푗푗,푡푡}
- Definitions:
  - 퐺퐺퐹퐹퐹퐹푙푙퐹퐹푟푟_{푖,푗,푡}: level of gender inequality for country i, firm j, year t (number of female employees and female ratio).
  - 퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹ℎ_{i,t}: fintech development measured by volume of alternative finance.
  - X_{i,t−1}: country-level controls (log per capita GDP, GDP growth rate, trade openness).
  - I_{i,j,t−1}: firm-level controls (firm size, age, export dependence, foreign ownership, sector specialization).
- Estimation details:
  - All explanatory variables lagged by one year.
  - Include country-industry fixed effects 휂_{i,k} and year fixed effects 휇_t.
  - Standard errors clustered at country and industry level.
- Interpretation:
  - Coefficients of interest: 훽_1, 훽_1', 훽_2'. Positive and significant → higher fintech associated with higher female representation (lower gender inequality). Negative and significant → opposite.

### 2. Identification Strategy
- Challenges: omitted variables and reverse causality.
- Adopt fixed-effect identification with interaction terms inspired by Rajaa and Zingales (1998).
- Interaction model (preserved notation):
  - 퐺퐺퐹퐹퐹퐹푙푙퐹퐹푟푟_{푖,푗,푡} = 훼 + 훽 [ 퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹ℎ_{푖,푡−1} × 퐹퐹퐹퐹 푟푟퐹퐹_{푗} ] + γ 퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹퐹ℎ_{푖,푡−1} + ρ 퐹퐹퐹퐹 푟푟퐹퐹_{푖} + δ 𝐼_{푖,푗,푡−1} + 휂_{푖,𝑘} + 휇_{𝑡} + 휀_{푖,𝑗,𝑡}
- 퐹퐹퐹퐹 푟푟퐹퐹_{푗}: firm-level financial constraint, loan access, digital infrastructure, etc., used to capture mechanisms and aid identification.
- Model strengths:
  - Interacted fixed effects control for wide range of omitted variables.
  - Interaction terms test heterogeneous effects (e.g., fintech effects on firms with financial constraints, high-tech intensiveness, loan access, internet access).
  - Positive (negative) significant 훽 indicates fintech exerts disproportionately positive (negative) effect on specified firm types.

### 3. Split Sample Analysis
- Partition sample by institutional quality:
  - Countries with above-the-median WGI values → high governance group; others → low governance group.
  - Test whether governance (government effectiveness, regulatory quality, rule of law) modulates fintech’s effect on female employment.
- Additional splits:
  - By income level (advanced vs emerging market economies) to test capacity to invest/adopt fintech.
  - By geographical region to assess regional heterogeneities.

---

### C. Data Sources

- Fintech adoption:
  - Cambridge Alternative Finance Benchmark: volume of finance through digital platforms for 191 jurisdictions spanning 2011–20.
  - Benchmark based on online survey (Cambridge Centre for Alternative Finance, with partner institutions listed in source).
- Female employment:
  - World Bank Enterprise Survey (WBES): establishment-level survey representative of non-agricultural, non-extractive private sector (registered establishments with 5 or more employees); covers access to finance, corruption, infrastructure, crime, competition, performance measures.
  - Note: WBES excludes agriculture; limitation given agriculture’s incidence in female employment.
- Country-level controls:
  - IMF’s World Economic Outlook (WEO) database.
- Institutional quality:
  - Worldwide Governance Indicators (Kaufmann and Kraay, 1999): six dimensions for over 200 countries, period 1996–2020.
  - Women, Business, and Law index (World Bank annual report): laws and regulations affecting women’s economic opportunity in 190 economies.
- Sample period set to overlapping years of databases: 2011–20.
- The complete list of countries and variable definitions summarized in the Annex (per source).

*IMF Working Papers — Fintech and Gender Inequality (content unit: "wpiea2022108-print-pdf - 1.    Fintech Variable")*

### Annex III.

### Annex III.

### A. Baseline Results
- Sample: after dropping missing values, sample of 22,631 firms.
- Main finding:
  - Fintech coefficient positive and significant at the 1% level.
  - "A 1% increase in the volume of transactions through fintech platforms is associated with a 1.363 percentage points increase in the number of female full-time employees in our sample firms."
- Table 2 selected coefficients (Model (1) and (2)):
  - Fintech: 1.363*** (Model (1), Female Employees)
  - Fintech: 0.375*** (Model (2), Female Ratio)
  - Lending: -0.143* (Female Employees); -0.047*** (Female Ratio)
  - Capital Raising: 0.776*** (Female Employees); 0.152*** (Female Ratio)
  - GDP: 2.931** (Model (1) Female Employees); 0.642* (Model (2) Female Ratio); -1.344*** and -0.334*** in Models (3)/(4)
  - GDP Growth, Openness, Sales, Age, Export Share, Foreign Ownership: coefficients reported in Table 2 with significance levels as presented.
- Observed contrast:
  - Fintech lending correlates negatively with female employment.
  - Fintech capital raising (equity-like instruments) correlates positively and significantly at the 1% level.

### B. Economic Mechanisms
- Hypothesized channel: fintech reduces firms’ financial constraints, enabling hires that support female workers (on-job training, maternity leave, flexible hours).
- Evidence from interaction with financial constraint (Table 3):
  - Fintech: coefficients across models range (examples) 0.444*; 0.406*; 0.555**; 0.727** (standard errors reported)
  - Fintech*Financial Obstacle: 0.016*** (positive and significant, Model (1))
- Heterogeneous effects (Table 3 / Table 4 continuation):
  - Fintech*Female-Led: 0.014** (positive and significant)
  - Fintech*Small Business: 0.017*** (positive and significant)
  - Fintech*Service Sector: 0.044** (positive and significant)
  - Financial Obstacle: -0.272*** (negative)
  - Female-Led (main effect): -0.173* (negative)
  - Small Business (main effect): 0.175** (positive)
  - Service Sector (main effect): -0.424* (negative)
- Summary of mechanism:
  - Fintech promotes female employment mainly by reallocating financial resources toward firms that are more female-labor-intensive and more likely to be financially constrained: female-led firms, small firms, and firms in service sectors.

### C. Fintech Interaction with Firm Characteristics
- Variables examined: loan access and internet access (firm-level).
- Table 5 selected coefficients:
  - Fintech: 0.478** (Model (1)); 0.557* (Model (2))
  - Fintech*Loan Access: 0.011** (positive and significant)
  - Fintech*Internet Access: 0.016*** (positive and significant)
  - Loan Access (main effect): -0.502*** (negative)
  - Internet Access (main effect): -0.809*** (negative)
- Interpretation:
  - Fintech tends to complement gaps in traditional finance: firms without existing loan access benefit more from fintech adoption.
  - Firms with basic digital infrastructure (proxied by existence of own website) are more able to take advantage of fintech innovation, especially equity financing via fintech platforms.
- Summary:
  - Positive effect of fintech on female employment is more pronounced in firms with basic digital infrastructure and without access to traditional finance.

### D. Weak Institution Reduces Benefits of Fintech
- Split-sample by governance dimensions: government effectiveness, regulatory quality, rule of law, women business law.
- Government effectiveness (Table 5):
  - High Government Effectiveness: Fintech 0.609*** (positive)
  - Low Government Effectiveness: Fintech -0.793*** (negative)
- Regulatory quality (Table 6):
  - High Regulatory Quality: Fintech 0.595*** (positive)
  - Low Regulatory Quality: Fintech -0.807*** (negative)
- Rule of law (Table 7):
  - Strong Rule of Law: Fintech 0.610*** (positive)
  - Weak Rule of Law: Fintech 0.019 (insignificant)
- Women business law (Table 8):
  - Stronger Law Protection: Fintech 1.441*** (positive)
  - Weaker Law Protection: Fintech 0.027 (insignificant)
- By country income group (Table 9):
  - Advanced Economies: Fintech 0.439*** (positive)
  - Emerging Markets: Fintech 0.149*** (positive)
  - Low-Income Countries: Fintech -3.929*** (negative)
- By region (Table 6 regional results for Female Ratio):
  - Sub-Saharan Africa: Fintech 0.142*** (positive)
  - Asia and Pacific: Fintech 0.041*** (positive)
  - Europe & Central Asia: Fintech 0.104*** (positive)
  - Middle East & North Africa: Fintech -0.032*** (negative)
  - Latin America & Caribbean: Fintech -0.012 (insignificant)
- Interpretation:
  - Fintech’s positive impact on female employment is conditional on institutional quality, regulatory environment, legal protections for women, and country development stage.
  - In weak institutional settings and low-income countries, fintech can be associated with negative or insignificant effects on female employment.

### Robustness Checks
- Alternative fintech definition: ratio of fintech finance over total GDP (Table 7 / Alternative Fintech Definitions):
  - Fintech (alternative): 0.754*** (Female Employees); 2.739*** (Female Ratio)
- Additional control variables (Table 8 / Additional Control Variables):
  - Including capital account openness (KA Openness) and inflation does not change main results.
  - Fintech coefficients remain: 1.357*** and 1.446*** in reported specifications.
- Overall robustness:
  - Results are robust to alternative fintech measurement and inclusion of additional macro controls.

*IMF Working Paper — Annex III of wpiea2022108-print-pdf*

### Conclusion

### Conclusion

### Key findings
- Study objective: evaluate if fintech has an equally positive effect on gender inequality, measured by female employment, using a cross-country fintech database that covers 114 countries and a fixed-effects identification strategy.
- Fintech development leads to significant welfare improvement for women:
  - Increases the number of female employees in the workforce.
  - Raises the ratio of female relative to male employees.
- Fintech has a positive impact on the number of female employees in advanced economies and emerging markets, but the effect appears insignificant or even negative in low-income countries.
- Regional effects:
  - Positive in Sub-Saharan African, Asian and Pacific, and European countries.
  - Insignificant in the Latin American and Caribbean sample.
  - Negative in countries in Middle East and North Africa.

### Mechanisms identified
- Fintech provides easier financial access to firms with financial constraints.
- The easing of financial constraints is especially pronounced for:
  - Female-led firms.
  - Small firms.
  - Firms in service sectors that traditionally hire more female workers.
- Institutional quality conditions the effect:
  - Weak institutions reduce the positive effect of fintech.
  - Fintech can significantly increase female employment in countries with good governance, law and regulations, while benefits are weaker in countries whose institutional quality is below median.

### Policy implications
- Closing fintech gender gaps is critical to fully reap fintech’s benefits on gender equality.
  - Unequal access to mobile phones and other electronic devices opens up financial inclusion gaps.
  - Example: according to OECD, worldwide 327 million fewer women than men have a smartphone and can access the mobile Internet (OECD, 2018).
  - Inequality-reducing effects of fintech are significantly weaker in firms without access to internet as compared to firms with such access.
  - Recommendation: address the digital divide by investing in technological innovation and increasing the supply of digital infrastructure.
- Policymakers need to promote good governance, law and regulations to ensure fintech effectively reduces gender inequality.

### Directions for future research
- Does fintech help reduce firms’ earning inequality in addition to the gender employment gap?
- What are the distributional effects and welfare implications of fintech on female-led households and female entrepreneurs who start their own businesses?
- If banks and fintech lenders are competing on credit provision, how will consumers and investors be affected?
- Do the new forms of financing introduced by fintech demand new forms of regulation?
- The authors contend that answering these questions will allow a comprehensive evaluation of the effects of fintech on the economy and provide important policy advice.

*IMF WORKING PAPERS Fintech and Gender Inequality — INTERNATIONAL MONETARY FUND*

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_Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022108-print-pdf.pdf_