## Section 1: Introduction

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---

### Background and motivation
- Reliance on technology and adoption of digital financial services (DFSs), like using the mobile phone and internet to conduct financial transactions, progressed in the past decade and accelerated during the COVID-19 crisis.
- DFSs have been particularly helpful in advancing the goals of financial inclusion by bringing in individuals, households, and businesses into the system who were otherwise left out of the traditional financial sector.
- Gender gaps in financial inclusion persist: "Globally, 65 percent of women have an account, lagging that of men at 72 percent."
- Barriers hindering women from accessing financial institutions include distance to the nearest bank, insufficient documents for opening a bank account, and socio-economic and cultural factors.
- Financial technologies can help overcome some obstacles and financially empower women as ease of usage and accessibility increase.
- Financial inclusion indices (Sahay et al., 2020) suggest DFSs have helped narrow gender gaps in several countries, but disparities across regions and countries remain large.
- Greater digital financial inclusion is found to be positively associated with economic growth (Khera et al., 2021).

### Relevance of women leaders in finance and fintech
- Women hold less than 25 percent of board seats in banks and bank supervision agencies and account for about 5 percent of bank CEOs globally.
- Evidence suggests greater shares of women on bank boards and banking supervision boards are associated with greater bank stability.
- Given the increasing role of fintech firms in the finance industry, examining the role of women as leaders in fintech is important: female leaders could be pivotal in developing, marketing, and supplying financial products that better suit women’s needs.

### Contributions of the paper
- Uses a novel database on fintech firm-level leadership across 97 countries to comprehensively quantify gender gaps in leadership positions in the fintech industry.
- Examines the impact of having women leaders in the fintech industry on firm performance (revenue and funding).
- Explores drivers of gender gaps in digital financial inclusion across countries, focusing on digital financial services usage.

### Data overview (as summarized in Section 2)
- Combined Crunchbase data across 97 countries:
  - Descriptive information for over 12,000 fintech firms (size, location, year of establishment, revenue range, most recent round funding). The data includes firms established between 1690 and 2020.
  - Details on founders, executive board members and other employees for about 28,000 individuals in 9,922 firms established until 2020.
- Merging the two parts yields 5,256 firms with a total of 14,000 individuals in 83 countries.
- Snapshot nature of the data: provides current firms and latest firm performance indicators; historical trajectories of board diversity or firm performance are not observed.
- Firm characteristics: vast majority of fintech companies are less than 10 years old; roughly 75 percent of the firms are small (less than 50 employees); firms with more than 250 employees account for less than 8 percent.

### Key findings — three main results
1. Large gender gaps in fintech leadership:
   - Women represent less than 10 percent of leadership—both as founders and as members of executive boards of fintech firms.
   - The average share of firms with women founders has hovered around 10-15 percent over the last 20 years.
   - As of September 2020, the share of women executives in all fintech firms in the sample was around 7 percent.
   - Women’s representation on executive boards in fintech is lower than in technology firms (around 14.5 percent) and lower than in banks and banking supervision agencies (23 percent and 33 percent respectively).
   - Regional variation: Western Hemisphere, Asia and the Pacific, Africa, and Europe have around 11-14 percent of firms founded by women; the Middle East and Central Asia have the lowest fraction at 8 percent.
   - The share of women on executive boards is slightly higher in more recently founded firms (7 percent for firms cumulatively established until 2020 vs. less than 6 percent for firms established before 2000).
   - Share of women on executive boards tends to be higher at larger firms by revenue and by number of employees (example: firms with revenue less than $10 million have 9 percent women executives; firms with revenue between $10 million and $100 million have 14 percent).
2. Positive association between board gender diversity and firm performance:
   - A 10 percent higher share of women on executive boards is associated with roughly 13 percent higher revenue and funding earned by a firm.
   - In contrast, firms founded by women tend to make less revenue and receive less funding compared to firms founded by men.
   - Prior literature suggests potential mechanisms for weaker performance of women-founded firms: higher risk aversion of women in investment decisions and gender bias of investors (who are majority men).
3. Drivers of gender gaps in usage of digital financial services:
   - Gender gaps in DFS usage across countries are associated with gender gaps in financial literacy, digital literacy, and socio-cultural factors.
   - Using a composite measure of digital financial inclusion and a random effect panel data model for emerging and developing economies over two time periods, the paper finds:
     - Countries with a higher share of women graduating in STEM and with lower gender gaps in upper secondary education tend to have lower gender gaps in digital financial inclusion.

### Policy implications (summarized)
- Greater investment in the digital and financial literacy of women, including those left out of the education system, is important.
- Sustained efforts to increase the representation of women in STEM-related fields can help reduce gender gaps in digital financial inclusion.
- Policies that reduce barriers to supporting women entrepreneurs in the fintech industry or increase women’s representation on fintech boards could have economic benefits for society.

---

### Regression analysis: firm performance and gender

### Regression setup and variables
- Dependent variable Yit: firm revenue or funding received for firm i founded in year t.
  - Revenue data expressed in eight categories; funding data is continuous.
  - Converted to categorical dependent variable: value 1 if revenue/funding is less than $10 million; value 2 if revenue/funding is between $10M-$100 million; value 3 if revenue/funding is more than $100M.
- WomanFounderit: 1 if firm i founded in year t was founded by a woman (solo or co-founder), 0 if founded by a man.
- FracWomenExecit: fraction of women on the executive board, takes values between 0 and 1.
- Sizeit: categorical firm size by employees: value 1 if less than 50; value 2 if between 50 and 250; value 3 if more than 250.
- Controls: Year FE and Country FE.
- Estimation: OLS regressions for revenue and funding; robustness check via ordered logit.
- Standard errors clustered at the country level.

### Key regression results — Revenue range (Table 1 highlights)
- WomanFounder coefficients across columns:
  - -0.025** (std. err. (0.010))
  - -0.016 (0.012)
  - -0.016 (0.014)
  - -0.067*** (0.015)
  - -0.049*** (0.018)
  - -0.043* (0.022)
- FracWomenExec coefficients when included:
  - 0.032 (0.023)
  - 0.029 (0.022)
  - 0.025 (0.019)
  - 0.102*** (0.035)
  - 0.080** (0.032)
  - 0.066* (0.034)
- Firm size coefficients (relative to firms with less than 50 employees):
  - 51-250: 0.257***; 0.223***; 0.233***; 0.277***; 0.234***; 0.242***; 0.254***; 0.222***; 0.232*** (standard errors shown in table)
  - 250+: 0.965***; 0.835***; 0.841***; 1.043***; 0.873***; 0.874***; 0.960***; 0.833***; 0.838*** (standard errors shown in table)
- Observations: 2393; 2377; 2377; 2726; 2699; 2699; 2393; 2377; 2377 (by column group)

### Key regression results — Funding range (Table 2 highlights)
- WomanFounder coefficients across columns:
  - -0.054* (0.030)
  - -0.052* (0.029)
  - -0.059* (0.031)
  - -0.139** (0.054)
  - -0.134** (0.055)
  - -0.142** (0.057)
- FracWomenExec coefficients when included:
  - 0.068** (0.032)
  - 0.064** (0.031)
  - 0.056* (0.033)
  - 0.222*** (0.074)
  - 0.214*** (0.078)
  - 0.218*** (0.077)
- Firm size coefficients (relative to firms with less than 50 employees):
  - 51-250: 0.676***; 0.644***; 0.653***; 0.673***; 0.644***; 0.653***; 0.670***; 0.641***; 0.648*** (standard errors shown in table)
  - 250+: 1.284***; 1.259***; 1.256***; 1.289***; 1.260***; 1.251***; 1.275***; 1.254***; 1.248*** (standard errors shown in table)
- Observations: 2281; 2279; 2279; 2468; 2466; 2466; 2281; 2279; 2279 (by column group)

### Robustness and economic significance
- Ordered logit robustness checks (Appendix B):
  - Controlling for country and year fixed effects and firm size, the odds of being a high revenue firm is 75 percent less if the firm is founded by a woman, while the diversity of the executive board does not matter.
  - The odds of receiving high funding are 77 percent less if the firm is founded by a woman.
  - A 1 percent increase in the fraction of women on executive boards is associated with the firm receiving higher funding by 1.3-2.7 percent.
- Limitations:
  - Possible survivorship bias could overestimate coefficients on WomanFounder and FracWomenExec.
  - Data limitations prevent identification of causal links; reverse causality (more profitable firms hiring more women) cannot be ruled out.
  - Multicollinearity tests (VIF and correlations) indicate multicollinearity is not a problem for these specifications.

### Interpretation and mechanisms
- Contrasting findings:
  - Firms founded by women are associated with lower revenue and lower funding (negative and often significant WomanFounder coefficients).
  - Firms with a higher fraction of women executives are associated with higher revenue and higher funding (positive and often significant FracWomenExec coefficients).
- Possible mechanisms for lower funding for women-founded firms:
  - Women invest less and appear more financially risk-averse (Charness and Gneezy, 2012).
  - Gender bias of investors and gender homophily affect startups led by women (Greenberg and Mollick, 2015; IFC, 2019; Ewens and Townsend, 2020).
  - Different questions asked by investors: male entrepreneurs receive promotion-focused questions; female entrepreneurs receive prevention-focused questions, affecting funding outcomes (Kanze et. al, 2018).
- Mechanisms for positive relation between gender diversity in executive ranks and firm performance:
  - Documented positive relationship between gender diversity and firm performance, particularly in sectors where women form a larger share of the labor force and where complementarities in skills are in demand (Christiansen et. al, 2016).
  - Complementarities in high tech and knowledge-intensive sectors could explain positive relationships between revenue/funding and the share of women executives in fintech firms.

---

### Gender gaps in usage of digital financial services (Section 3)

### Aggregate patterns and regional heterogeneity
- Digital financial inclusion indices (value range between 0 and 1, with 1 being the highest level of digital financial inclusion) constructed alongside traditional financial inclusion indices and male/female breakdowns; gender gaps measured as percentage difference of respective female to male index.
- Aggregate findings:
  - Gender gaps are lower on average in digital financial inclusion than in traditional financial inclusion (Figure 10).
  - Regional heterogeneity:
    - Africa and Middle East: gender gaps in digital financial inclusion were lower than in traditional inclusion.
    - Middle East: stark decline in the gender gap between 2014 and 2017.
    - Latin America: higher gender gap in digital financial inclusion but larger narrowing between 2014 and 2017.
    - Asia: gender gaps generally smaller than other regions, but the gap in digital inclusion was slightly higher than in traditional inclusion.
  - Between 2014 and 2017:
    - 31 of the 52 countries in the sample saw improvements in the gender gap in digital financial inclusion (24 of these also saw improvements in the traditional gap).
    - 21 countries saw a widening in the digital financial inclusion gender gap (about half of which also saw worsening in the traditional gap).

### Data and sample statistics (Table 3 highlights)
- Outcome variable: gender gap measure based on the digital financial inclusion index from Sahay et al. (2020).
- Sample sizes:
  - N = 26 countries in 2014 (countries with non-missing explanatory variables).
  - N = 36 countries in 2017.
- Key summary statistics:
  - Gender gap in Digital Financial Inclusion Index:
    - Mean: 0.028 (2014); 0.037 (2017)
    - Std. Dev.: 0.046 (2014); 0.032 (2017)
    - Min/Max: -0.130 / 0.119 (2014); -0.025 / 0.112 (2017)
    - Text summary: average gender gap is 2.8 percent in 2014 and 3.7 percent in 2017.
  - Gender gap in Upper Secondary education attainment:
    - Mean: 0.196 (2014); 0.220 (2017)
    - Std. Dev.: 0.279 (2014); 0.288 (2017)
    - Min/Max: -0.141 / 0.726 (2014); -0.220 / 0.726 (2017)
    - Text note: gender gap in upper secondary education increased from 19.6 percent to 22.0 percent between 2014 and 2017.
  - Female share of graduates in STEM (percent):
    - Mean: 15 (2014); 14.491 (2017)
    - Std. Dev.: 9.367 (2014); 8.818 (2017)
    - Min/Max: 4.488 / 47.336 (2014); 5.430 / 47.336 (2017)
  - Female/Male Labor Force Participation Ratio (percent):
    - Mean: 64.688 (2014); 68.382 (2017)
    - Std. Dev.: 20.599 (2014); 18.017 (2017)
    - Min/Max: 21.105 / 94.987 (2014); 21.105 / 93.992 (2017)
  - WBL Index (Women, Business and Law index; scale 1–100 where higher = greater gender equality):
    - Mean: 72.634 (2014); 72.325 (2017)
    - Std. Dev.: 13.066 (2014); 14.324 (2017)
    - Min/Max: 31.9 / 90.6 (2014); 31.9 / 95 (2017)

### Empirical approach
- Method: random effects panel regression model on cross-country data covering 36 EMDEs for two time periods (2014 and 2017).
- Dependent variable: Gender_Gap_it = (Male index − Female index) / Male index for digital financial inclusion.
- Main explanatory variables:
  - UpperSecondary_it: gender gap in upper secondary education attainment.
  - ShareinStem_it: female share of graduates from STEM (percent).
  - F/M LFP_it: ratio of female to male labor force participation rate (percent).
  - WBL_it: Women, Business and Law index (1–100).
  - RealGDPpc_it−1: lagged real per capita GDP.
- Controls: Year fixed effects and Country fixed effects included in preferred specifications.
- Estimation note: variables added recursively across specifications; heteroskedasticity-robust standard errors used.

### Main regression findings (Table 4 highlights)
- STEM graduates:
  - A higher share of women graduating in STEM is associated with a lower gender gap in digital financial inclusion.
  - Quantitative effect: a 1 percent increase in the share of women graduates in STEM is associated with a 0.2 percent decrease in the gender gap in digital financial inclusion.
  - Table 4 coefficient on Share(percent) in STEM: -0.002*** (and similar significant negative coefficients across specifications).
- Upper secondary education gap:
  - Countries with a larger gender gap in upper secondary education attainment are associated with larger gender gaps in digital financial inclusion.
  - Quantitative effect: a 1 percent increase in the gender gap in upper secondary education on average is associated with a 3 percent higher gender gap in digital financial inclusion.
  - Table 4 shows positive coefficients on Upper Secondary (e.g., 0.035**, 0.032**, 0.036** in preferred specifications with fixed effects).
- Labor force participation:
  - Some suggestive evidence that a lower gender gap in labor force participation (higher F/M LFP ratio) is associated with lower gender gaps in digital financial inclusion.
  - Effect weakens once year and regional fixed effects are included.
  - Table 4 reports small negative coefficients on F/M LFP (e.g., -0.001* in several columns).
- Socio-cultural and legal norms (WBL index):
  - Higher gender equality as measured by the WBL index is associated with lower gender gaps in digital financial inclusion.
  - Quantitative effect: a 10-point improvement in the WBL index is associated with a decrease in the gender gap in digital financial inclusion of 1 percent.
  - Table 4 reports negative and significant coefficients on WBL in the last specifications (e.g., -0.001*).
- Real GDP per capita:
  - Lagged real GDP per capita included to avoid endogeneity; coefficients reported as 0.000 (not a focal result).

### Interpretation, caveats, and pandemic note
- Interpretation: gender differences in financial and digital literacy (proxied by upper secondary attainment gap and female share in STEM) are key drivers of gender gaps in digital financial inclusion.
- Heterogeneity: results vary across specifications and weaken for some variables when fixed effects are included.
- Data limitations and potential underestimation:
  - When gender breakdown is not available for certain indicators, the same country-level data is used for both male and female (primarily for access indicators), which may lead to underestimation of gender gaps.
- Pandemic caveat:
  - Results could be quantitatively altered by the COVID-19 crisis due to fast advances in usage of digital finance and increased reliance on digital tools for work and education; extending analysis to post-COVID data may change findings.

### Policy recommendations (targeted)
- Promote financial and digital literacy for women early:
  - Focus on improving women’s financial and digital literacy (e.g., increasing upper secondary attainment and STEM participation among women) to lower gender gaps in usage of DFS.
- Address socio-cultural and legal barriers:
  - Policies that equalize socio-cultural norms and legally back them up (improving the WBL index) can help narrow gender gaps in digital financial inclusion.
- Encourage female participation in STEM and labor markets:
  - Increase representation of women in STEM-related fields and advocate for policies that reduce gender gaps in employment to further gender equality in digital financial inclusion.
- Strengthen women’s economic independence:
  - Policies that increase female labor force participation and economic independence may reduce gender gaps in the usage of DFS.
- Support female leadership in fintech:
  - Address biases in funding and leadership to encourage more women as founders, executives, employees, and users of fintech services.
- Regulatory and government role:
  - Governments and regulators should ensure inclusion of women as users and leaders as adoption of DFS accelerates, including investing in digital and financial literacy.

---

### Appendix A — Firm characteristics (summary)
- Geographic distribution and sample composition:
  - 45 percent of firms are in the Western Hemisphere.
  - 34 percent of firms are in Europe.
  - 17 percent of firms are in Asia and Pacific.
  - Africa and Middle East and Central Asia represent less than 3 percent of the fintech firms in the database.
  - United States: over 3,000 firms.
  - United Kingdom: over 900 firms.
  - 88 percent of fintech companies in the database were founded after 2010.
- Industry classification (shares sum across multiple groups):
  - Software: 40 percent
  - Lending and Investment: 26 percent
  - Payments: 24.7 percent
  - Information Technology: 16.6 percent
  - Internet Services: 11 percent
  - Commerce and Shopping: 10 percent
  - Artificial Intelligence: 5.6 percent
  - Apps: 5.4 percent
- Firm size (employees) and revenues:
  - Roughly 75 percent of firms have less than 50 employees.
  - Firms with more than 250 employees account for less than 8 percent.
  - 84 percent of firms have revenues less than $10 million.
  - Around 3 percent of firms have revenue more than $100 million.
- Average firm age by revenue category:
  - Firms earning less than $10 million: 6.9 years on average.
  - Firms earning $10 million to $100 million: 11.5 years on average.
  - Firms earning more than $100 million: 30 years on average.
- Specific representation notes:
  - "13.5 percent for firms with 51-250 and more than 250 employees, respectively."
  - Of firms earning less than $10 million, 14 percent are founded by women; this falls to 7 percent for firms earning more than $100 million.

*Source: IMF Working Paper — Section 1: Introduction; Sections 3 and Appendix A (from wpiea2022140-print-pdf).*

### Section 1: Introduction.................................................................................................

### Section 1: Introduction

### Background and motivation
- Reliance on technology and adoption of digital financial services (DFSs), like using the mobile phone and internet to conduct financial transactions, has progressed in the past decade, and accelerated during the COVID-19 crisis.
- DFSs have been particularly helpful in advancing the goals of financial inclusion by bringing in individuals, households, and businesses into the system who were otherwise left out of the traditional financial sector.
- Gender gaps in financial inclusion persist: "Globally, 65 percent of women have an account, lagging that of men at 72 percent."
- Barriers hindering women from accessing financial institutions include distance to the nearest bank, insufficient documents for opening a bank account, and socio-economic and cultural factors.
- Financial technologies can help overcome some obstacles and financially empower women as ease of usage and accessibility increase.
- Financial inclusion indices (Sahay et al., 2020) suggest DFSs have helped narrow gender gaps in several countries, but disparities across regions and countries remain large.
- Greater digital financial inclusion is found to be positively associated with economic growth (Khera et al., 2021).

### Relevance of women leaders in finance and fintech
- Women hold less than 25 percent of board seats in banks and bank supervision agencies and account for about 5 percent of bank CEOs globally.
- Evidence suggests greater shares of women on bank boards and banking supervision boards are associated with greater bank stability.
- Given the increasing role of fintech firms in the finance industry, examining the role of women as leaders in fintech is important: female leaders could be pivotal in developing, marketing, and supplying financial products that better suit women’s needs.

### Contributions of this paper
- Uses a novel database on fintech firm-level leadership across 97 countries to comprehensively quantify gender gaps in leadership positions in the fintech industry.
- Examines the impact of having women leaders in the fintech industry on firm performance (revenue and funding).
- Explores drivers of gender gaps in digital financial inclusion across countries, focusing on digital financial services usage.

### Data overview (as summarized in Section 2)
- Combined Crunchbase data across 97 countries:
  - Descriptive information for over 12,000 fintech firms (size, location, year of establishment, revenue range, most recent round funding). The data includes firms established between 1690 and 2020.
  - Details on founders, executive board members and other employees for about 28,000 individuals in 9,922 firms established until 2020.
- Merging the two parts yields 5,256 firms with a total of 14,000 individuals in 83 countries.
- Snapshot nature of the data: provides current firms and latest firm performance indicators; historical trajectories of board diversity or firm performance are not observed.
- Firm characteristics: vast majority of fintech companies are less than 10 years old; roughly 75 percent of the firms are small (less than 50 employees); firms with more than 250 employees account for less than 8 percent.

### Key findings (three main results)
1. Large gender gaps in fintech leadership:
   - Women represent less than 10 percent of leadership—both as founders and as members of executive boards of fintech firms.
   - The average share of firms with women founders has hovered around 10-15 percent over the last 20 years.
   - As of September 2020, the share of women executives in all fintech firms in the sample was around 7 percent.
   - Women’s representation on executive boards in fintech is lower than in technology firms (around 14.5 percent) and lower than in banks and banking supervision agencies (23 percent and 33 percent respectively).
   - Regional variation: countries in the Western Hemisphere, Asia and the Pacific, Africa, and Europe have around 11-14 percent of firms founded by women; the Middle East and Central Asia have the lowest fraction at 8 percent.
   - The share of women on executive boards is slightly higher in more recently founded firms (7 percent for firms cumulatively established until 2020 vs. less than 6 percent for firms established before 2000).
   - Share of women on executive boards tends to be higher at larger firms by revenue and by number of employees (example figures cited in the paper: firms with revenue less than $10 million have 9 percent women executives, firms with revenue between $10 million and $100 million have 14 percent).
2. Positive association between board gender diversity and firm performance:
   - A 10 percent higher share of women on executive boards is associated with roughly 13 percent higher revenue and funding earned by a firm.
   - In contrast, firms founded by women tend to make less revenue and receive less funding compared to firms founded by men.
   - Prior literature suggests potential mechanisms for weaker performance of women-founded firms: higher risk aversion of women in investment decisions and gender bias of investors (who are majority men).
3. Drivers of gender gaps in usage of digital financial services:
   - Gender gaps in DFS usage across countries are associated with gender gaps in financial literacy, digital literacy, and socio-cultural factors.
   - Using a composite measure of digital financial inclusion and a random effect panel data model for emerging and developing economies over two time periods, the paper finds:
     - Countries with a higher share of women graduating in STEM and with lower gender gaps in upper secondary education tend to have lower gender gaps in digital financial inclusion.

### Policy implications (summarized)
- Greater investment in the digital and financial literacy of women, including those left out of the education system, is important.
- Sustained efforts to increase the representation of women in STEM-related fields can help reduce gender gaps in digital financial inclusion.
- Policies that reduce barriers to supporting women entrepreneurs in the fintech industry or increase women’s representation on fintech boards could have economic benefits for society.

### Paper structure (as roadmap)
- Section 2: Stylized facts about women leaders in the fintech industry and the relationship between firm performance and gender of the founder and gender diversity on executive boards.
- Section 3: Gender gap in the usage of digital financial services and factors associated with digital financial inclusion.
- Section 4: Policy measures to tackle gender gaps in digital financial inclusion during and post COVID-19 and conclusion.

*Source: IMF Working Paper — Section 1: Introduction.*

### 13.5 percent for firms with 51-250 and more than 250

### 13.5 percent for firms with 51-250 and more than 250

### Representation of women and firm characteristics
- "13.5 percent for firms with 51-250 and more than 250 employees, respectively."
- Of firms earning less than $10 million, 14 percent are founded by women; this falls to 7 percent for firms earning more than $100 million.
- Positive correlation noted between the share of women on executive boards and funding received by firms.
- Firms founded by women tend to receive lower funding.

### Regression specification and variables
- Dependent variable Yit: firm revenue or funding received for firm i founded in year t.
  - Revenue data expressed in eight categories; funding data is continuous.
  - Converted to categorical dependent variable: value 1 if revenue/funding is less than $10 million; value 2 if revenue/funding is between $10M-$100 million; value 3 if revenue/funding is more than $100M.
- WomanFounderit: 1 if firm i founded in year t was founded by a woman (solo or co-founder), 0 if founded by a man.
- FracWomenExecit: fraction of women on the executive board, takes values between 0 and 1.
- Sizeit: categorical firm size by employees: value 1 if less than 50; value 2 if between 50 and 250; value 3 if more than 250.
- Controls: Year FE and Country FE.
- Estimation: OLS regressions for revenue and funding; robustness check via ordered logit.

### Key regression results (Table 1: Outcome Variable - Revenue Range)
- WomanFounder coefficients across columns (1)–(9):
  - -0.025** (std. err. (0.010))
  - -0.016 (0.012)
  - -0.016 (0.014)
  - -0.067*** (0.015)
  - -0.049*** (0.018)
  - -0.043* (0.022)
- FracWomenExec coefficients when included:
  - 0.032 (0.023)
  - 0.029 (0.022)
  - 0.025 (0.019)
  - 0.102*** (0.035)
  - 0.080** (0.032)
  - 0.066* (0.034)
- Firm size coefficients (relative to firms with less than 50 employees):
  - 51-250: 0.257***; 0.223***; 0.233***; 0.277***; 0.234***; 0.242***; 0.254***; 0.222***; 0.232*** (standard errors shown in table)
  - 250+: 0.965***; 0.835***; 0.841***; 1.043***; 0.873***; 0.874***; 0.960***; 0.833***; 0.838*** (standard errors shown in table)
- Observations: 2393; 2377; 2377; 2726; 2699; 2699; 2393; 2377; 2377 (by column group)
- Country FE and Year FE included selectively as shown in table.
- Standard errors are clustered at the country level.

### Key regression results (Table 2: Outcome Variable - Funding Range)
- WomanFounder coefficients across columns (1)–(9):
  - -0.054* (0.030)
  - -0.052* (0.029)
  - -0.059* (0.031)
  - -0.139** (0.054)
  - -0.134** (0.055)
  - -0.142** (0.057)
- FracWomenExec coefficients when included:
  - 0.068** (0.032)
  - 0.064** (0.031)
  - 0.056* (0.033)
  - 0.222*** (0.074)
  - 0.214*** (0.078)
  - 0.218*** (0.077)
- Firm size coefficients (relative to firms with less than 50 employees):
  - 51-250: 0.676***; 0.644***; 0.653***; 0.673***; 0.644***; 0.653***; 0.670***; 0.641***; 0.648*** (standard errors shown in table)
  - 250+: 1.284***; 1.259***; 1.256***; 1.289***; 1.260***; 1.251***; 1.275***; 1.254***; 1.248*** (standard errors shown in table)
- Observations: 2281; 2279; 2279; 2468; 2466; 2466; 2281; 2279; 2279 (by column group)
- Country FE and Year FE included selectively as shown in table.
- Standard errors are clustered at the country level.

### Robustness and economic significance
- Ordered logit regression robustness check (Tables B1 and B2 in Appendix B) yields:
  - Controlling for country and year fixed effects and firm size, the odds of being a high revenue firm is 75 percent less if the firm is founded by a woman, while the diversity of the executive board does not matter.
  - The odds of receiving high funding are 77 percent less if the firm is founded by a woman.
  - A 1 percent increase in the fraction of women on executive boards is associated with the firm receiving higher funding by 1.3-2.7 percent.
- Limitations:
  - Possible survivorship bias could overestimate coefficients on WomanFounder and FracWomenExec.
  - Data limitations prevent identification of causal links; reverse causality (more profitable firms hiring more women) cannot be ruled out.
  - Multicollinearity concerns between WomanFounder and FracWomenExec were tested; variance inflation factor and correlation tests indicate multicollinearity is not a problem for these specifications.

### Interpretation and mechanisms
- Contrasting findings:
  - Firms founded by women are associated with lower revenue and lower funding (negative and often significant coefficients for WomanFounder).
  - Firms with a higher fraction of women executives are associated with higher revenue and higher funding (positive and often significant coefficients for FracWomenExec).
- Possible mechanisms for lower funding for women-founded firms:
  - Women invest less and appear more financially risk-averse (Charness and Gneezy, 2012).
  - Gender bias of investors and gender homophily affect startups led by women (Greenberg and Mollick, 2015; IFC, 2019; Ewens and Townsend, 2020).
  - Different questions asked by investors: male entrepreneurs receive promotion-focused questions; female entrepreneurs receive prevention-focused questions, affecting funding outcomes (Kanze et. al, 2018).
- Mechanisms for positive relation between gender diversity in executive ranks and firm performance:
  - Documented positive relationship between gender diversity and firm performance, particularly in sectors where women form a larger share of the labor force and where complementarities in skills are in demand (Christiansen et. al, 2016).
  - Complementarities in high tech and knowledge-intensive sectors could explain positive relationships between revenue/funding and the share of women executives in fintech firms.

*Source: Authors’ calculations and analyses based on Crunchbase data, as presented in the IMF Working Paper excerpt.*

### Section 3: Gender Gaps in Usage of Digital

### Section 3: Gender Gaps in Usage of Digital Financial Services

### Overview of digital vs traditional gender gaps
- Digital financial inclusion indices (value range between 0 and 1, with 1 being the highest level of digital financial inclusion) were constructed alongside traditional financial inclusion indices and male/female breakdowns; gender gaps measured as percentage difference of respective female to male index.
- Aggregate findings:
  - Gender gaps are lower on average in digital financial inclusion than in traditional financial inclusion (Figure 10).
  - Regional heterogeneity:
    - Africa and Middle East: gender gaps in digital financial inclusion were lower than in traditional inclusion.
    - Middle East: stark decline in the gender gap between 2014 and 2017.
    - Latin America: higher gender gap in digital financial inclusion but larger narrowing between 2014 and 2017.
    - Asia: gender gaps generally smaller than other regions, but the gap in digital inclusion was slightly higher than in traditional inclusion.
  - Between 2014 and 2017:
    - 31 of the 52 countries in the sample saw improvements in the gender gap in digital financial inclusion (24 of these also saw improvements in the traditional gap).
    - 21 countries saw a widening in the digital financial inclusion gender gap (about half of which also saw worsening in the traditional gap).

### Data source and key summary statistics (sample coverage)
- Outcome variable: gender gap measure based on the digital financial inclusion index from Sahay et al. (2020).
- Sample sizes:
  - N = 26 countries in 2014 (countries with non-missing explanatory variables).
  - N = 36 countries in 2017.
- Key summary statistics (2014 and 2017 as reported in Table 3):
  - Gender gap in Digital Financial Inclusion Index:
    - Mean: 0.028 (2014); 0.037 (2017)
    - Std. Dev.: 0.046 (2014); 0.032 (2017)
    - Min/Max: -0.130 / 0.119 (2014); -0.025 / 0.112 (2017)
    - Text summary: average gender gap is 2.8 percent in 2014 and 3.7 percent in 2017.
  - Gender gap in Upper Secondary education attainment (gap between percentage of men and women 25+ with upper secondary attainment):
    - Mean: 0.196 (2014); 0.220 (2017)
    - Std. Dev.: 0.279 (2014); 0.288 (2017)
    - Min/Max: -0.141 / 0.726 (2014); -0.220 / 0.726 (2017)
    - Text note: gender gap in upper secondary education increased from 19.6 percent to 22.0 percent between 2014 and 2017.
  - Female share of graduates in STEM (percent):
    - Mean: 15 (2014); 14.491 (2017)
    - Std. Dev.: 9.367 (2014); 8.818 (2017)
    - Min/Max: 4.488 / 47.336 (2014); 5.430 / 47.336 (2017)
    - Text note: mean share of women in STEM fields has not changed much between 2014 and 2017.
  - Female/Male Labor Force Participation Ratio (percent):
    - Mean: 64.688 (2014); 68.382 (2017)
    - Std. Dev.: 20.599 (2014); 18.017 (2017)
    - Min/Max: 21.105 / 94.987 (2014); 21.105 / 93.992 (2017)
  - WBL Index (Women, Business and Law index; scale 1–100 where higher = greater gender equality):
    - Mean: 72.634 (2014); 72.325 (2017)
    - Std. Dev.: 13.066 (2014); 14.324 (2017)
    - Min/Max: 31.9 / 90.6 (2014); 31.9 / 95 (2017)

### Empirical approach (model and controls)
- Method: random effects panel regression model on cross-country data covering 36 EMDEs for two time periods (2014 and 2017).
- Dependent variable: Gender_Gap_it = (Male index − Female index) / Male index for digital financial inclusion.
- Main explanatory variables:
  - UpperSecondary_it: gender gap in upper secondary education attainment.
  - ShareinStem_it: female share of graduates from STEM (percent).
  - F/M LFP_it: ratio of female to male labor force participation rate (percent).
  - WBL_it: Women, Business and Law index (1–100).
  - RealGDPpc_it−1: lagged real per capita GDP.
- Controls: Year fixed effects and Country fixed effects included in preferred specifications to account for time and country specific level effects.
- Estimation note: variables added recursively across specifications; heteroskedasticity-robust standard errors used.

### Main regression findings (drivers of gender gap in digital financial inclusion)
- STEM graduates:
  - A higher share of women graduating in STEM is associated with a lower gender gap in digital financial inclusion.
  - Quantitative effect reported: a 1 percent increase in the share of women graduates in STEM is associated with a 0.2 percent decrease in the gender gap in digital financial inclusion.
  - Table 4 reports the coefficient on Share(percent) in STEM as -0.002*** (and similar significant negative coefficients across specifications).
- Upper secondary education gap:
  - Countries with a larger gender gap in upper secondary education attainment are associated with larger gender gaps in digital financial inclusion.
  - Quantitative effect reported: a 1 percent increase in the gender gap in upper secondary education on average is associated with a 3 percent higher gender gap in digital financial inclusion.
  - Table 4 shows positive coefficients on Upper Secondary in several specifications (e.g., 0.035**, 0.032**, 0.036** in preferred specifications with fixed effects).
- Labor force participation:
  - Some suggestive evidence that a lower gender gap in labor force participation (higher F/M LFP ratio) is associated with lower gender gaps in digital financial inclusion.
  - Effect weakens once year and regional fixed effects are included.
  - Table 4 reports small negative coefficients on F/M LFP (e.g., -0.001* in several columns).
- Socio-cultural and legal norms (WBL index):
  - Higher gender equality as measured by the WBL index is associated with lower gender gaps in digital financial inclusion.
  - Quantitative effect reported: a 10-point improvement in the WBL index is associated with a decrease in the gender gap in digital financial inclusion of 1 percent.
  - Table 4 reports negative and significant coefficients on WBL in the last specifications (e.g., -0.001*).
- Real GDP per capita:
  - Lagged real GDP per capita included to avoid endogeneity; coefficients reported as 0.000 (not a focal result in text).

### Interpretation and caveats
- Key interpretation: gender differences in financial and digital literacy (proxied by upper secondary attainment gap and female share in STEM) are key drivers of gender gaps in digital financial inclusion.
- Heterogeneity: results vary across specifications and weaken for some variables when fixed effects are included.
- Data limitations and potential underestimation:
  - When gender breakdown is not available for certain indicators, the same country-level data is used for both male and female (primarily for access indicators), which may lead to underestimation of gender gaps.
- Pandemic caveat:
  - Results could be quantitatively altered by the COVID-19 crisis due to fast advances in usage of digital finance and increased reliance on digital tools for work and education; extending analysis to post-COVID data may change findings.

### Policy implications and recommendations
- Promote financial and digital literacy for women early:
  - Focus on improving women’s financial and digital literacy (e.g., increasing upper secondary attainment and STEM participation among women) to lower gender gaps in usage of DFS.
- Address socio-cultural and legal barriers:
  - Policies that equalize socio-cultural norms and legally back them up (improving the WBL index) can help narrow gender gaps in digital financial inclusion.
- Encourage female participation in STEM and labor markets:
  - Increase representation of women in STEM-related fields and advocate for policies that reduce gender gaps in employment to further gender equality in digital financial inclusion.
- Strengthen women’s economic independence:
  - Policies that increase female labor force participation and economic independence may reduce gender gaps in the usage of DFS.
- Support female leadership in fintech:
  - While Section 3 focuses on usage, the broader paper finds low shares of women founders and executive board representation in fintech; addressing biases in funding and leadership may encourage more women as employees and users.
- Regulatory and government role:
  - Governments and regulators should ensure inclusion of women as users and leaders as adoption of DFS accelerates, including investing in digital and financial literacy.

*Italic Source: IMF Working Paper — Section 3: Gender Gaps in Usage of Digital Financial Services (from wpiea2022140-print-pdf)*

### APPENDIX A – FIRM CHARACTERISTICS

### APPENDIX A – FIRM CHARACTERISTICS

### Geographic distribution and sample composition
- Table A1 reports the number of firms in each country in the database (country-level counts provided in the source table).
- Regional shares:
  - 45 percent of firms are in the Western Hemisphere.
  - 34 percent of firms are in Europe.
  - 17 percent of firms are in Asia and Pacific.
  - Africa and Middle East and Central Asia represent less than 3 percent of the fintech firms in the database.
- Country-level highlights:
  - United States: over 3,000 firms.
  - United Kingdom: over 900 firms.
- Founding year:
  - 88 percent of fintech companies in the database were founded after 2010.

### Industry classification
- All firms classified under “Financial Services” but span multiple industry groups with the following shares:
  - Software: 40 percent
  - Lending and Investment: 26 percent
  - Payments: 24.7 percent
  - Information Technology: 16.6 percent
  - Internet Services: 11 percent
  - Commerce and Shopping: 10 percent
  - Artificial Intelligence: 5.6 percent
  - Apps: 5.4 percent

### Firm size (employees) and financial scale (revenues)
- Firm size by employees:
  - Roughly 75 percent of firms have less than 50 employees.
  - Firms with more than 250 employees account for less than 8 percent.
- Firm revenues:
  - 84 percent of firms have revenues less than $10 million.
  - Around 3 percent of firms have revenue more than $100 million.
- Average firm age by revenue category:
  - Firms earning less than $10 million are on an average 6.9 years old.
  - Firms earning $10 million to $100 million are on an average 11.5 years old.
  - Firms earning more than $100 million are on an average 30 years old.

*Source: APPENDIX A – FIRM CHARACTERISTICS, Women in Fintech: As Leaders and Users (Working Paper No. WP/2022/140).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022140-print-pdf.pdf_