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---

### I. Introduction — framing, definitions, and policy implications
- Gender gaps: observed differences between men and women or between boys and girls in relevant indicators.
- Gender inequality: the component of gender gaps driven by gender bias and unequal gender rights and opportunities; the remainder reflects preference/comparative advantage.
- Policy implication: policies should target reducing gender inequality (the bias-driven component), not necessarily closing every observed gender gap.
- Contextual facts and observations:
  - Gender gaps in nutritional intake reported in South Asia and sub-Sahara Africa.
  - Gender gaps in school enrollment narrowed rapidly for preprimary, primary and secondary education; tertiary education gaps remain and vary across countries.
  - Women more likely to report unmet healthcare needs (self-reported).
  - Access to formal financial services generally lower for women; saving and borrowing services more accessible to men.
  - Labor force participation gaps have narrowed but remain high with large cross-country variation; women overrepresented in low-status/low-pay sectors and under-represented in managerial and political leadership positions.
  - Women subject to more violence at home, commuting, and work; legal barriers remain pervasive—on average women have only three-quarters of the legal protections given to men during their working life.
  - Development and technology tend to narrow gender inequality but convergence is slow: around 82 percent of 40-year-old inventors are men; at current rate it will take another 118 years to reach gender parity.

### II. Definitions and drivers — decomposition and mechanisms
- Decomposition framework:
  - gender gaps = gender inequality (bias/unequal rights) + preference/comparative advantage between men and women.
- Illustrative examples:
  - In advanced economies female tertiary enrollment ~25 percent higher than male; likely reflects preferences/choices rather than inequality.
  - Registered nurses: males are a very small share in some countries, which may reflect social norms hindering male entry.
- Main drivers:
  - Comparative advantage: as countries develop women gain comparative advantage in mentally intensive tasks; reduces female-male wage gaps and raises female labor force participation.
  - Development, infrastructure, and technology: labor-saving home technologies and better infrastructure free women’s time and raise female labor force participation.
  - Gender bias and cultural/legal barriers: norms, statistical and taste-based discrimination, and legal restrictions (e.g., limits on owning assets, entering professions) significantly hamper female participation and entrepreneurship.
- Policy interactions:
  - Development, technology, and public policies affect both gender bias/social norms and comparative advantage.
  - Interventions that lower gender inequality in specific areas can in turn alter social norms.

### III. The evolving focus of gender inequality — education, labor market, financial access and legal barriers
- A. Education
  - Shift from access to quality and field of study.
  - Emerging and developing economies: preprimary, primary, secondary gaps narrowing; tertiary gaps persist.
  - Advanced economies: parity in access largely achieved; quality concerns remain (PISA: boys outperformed girls in mathematics by an average of eight points in 2015 and by 5 points in 2018).
  - Field of study global averages (2014–2016; percent female students in higher education):
    - Education: 71
    - Health and welfare: 68
    - Humanities and arts: 62
    - Social sciences, journalism and information: 61
    - Business, administration and law: 56
    - Natural science, mathematics and statistics: 55
    - Services: 49
    - Agriculture, forestry, fishery and veterinary: 46
    - Information and communication technologies: 28
    - Engineering, manufacturing and construction: 27
  - OECD new entrants into tertiary education (percent female):
    - Education: 78
    - Health and welfare: 75
    - Humanities and arts: 64
    - Social sciences, business and law: 57
    - Services: 49
    - Agriculture: 48
    - Sciences: 37
    - Engineering, manufacturing and construction: 24
  - STEM progress example (United States): in 1970 women received 9 percent of doctorates in science and engineering; by 2018 share was nearly 47 percent, with much driven by psychology and social sciences.

- B. Labor market
  - Labor force participation:
    - Gaps remain high but narrowed overall.
    - Advanced economies: smaller gaps and faster convergence.
    - Emerging economies: gaps widened over past two decades, driven largely by China and India declines in female labor force participation.
  - Sectoral distribution:
    - Advanced economies: female employment in services = 80 percent of employed women vs. 60 percent for men (OECD).
    - Emerging/developing economies: larger shares of women in agriculture; rapid movement into services.
  - Occupations in S&E and innovation:
    - By 2019 women made up 29 percent of S&E workers in the United States; concentration in non-S&E occupations is higher for female S&E-trained workers.
    - Women hold only 5.5 percent of commercialized patents; represent 10 percent of US patent inventors and 15 percent of inventors in life sciences.
    - Patent applications by women are more likely to be rejected, have fewer claims allowed, and receive fewer citations.
  - Leadership and management:
    - Proportion of seats held by women in national parliaments about doubled over two decades but remains low.
    - Little progress in senior and middle management over two decades.
    - Across EU27 only 25 percent of business owners with employees are women (2000–2010 marginal growth).
    - Gender quotas on corporate boards enacted in many countries; effectiveness debated.
  - Gender wage gap:
    - Declined in most countries over past two decades where data exist.
    - Average gap around 11 percent; varies substantially across countries.
    - Part explained by fewer hours, work interruptions, occupational concentration; a significant unexplained component suggests discrimination and noncognitive differences.
    - Example: in a large Chinese firm the gender wage gap small early in careers, becomes evident after marriage/children due to concentration in lower-level jobs.

- C. Financial access and legal barriers
  - Account ownership:
    - Advanced economies: largely closed the gender gap.
    - Emerging economies: gap narrowed from 23 percent in 2011 to 7 percent in 2021.
    - Low-income developing countries: gap remained around 27 percent.
  - Fintech:
    - Evidence limited on fintech closing gender gaps; Sahay and others (2020) find gender gaps lower on average in digital inclusion, but variations are large.
    - Chen and others (2021b): fintech usage — 29 percent of men vs. 21 percent of women.
  - Entrepreneurship financing:
    - Women less likely than men to report access to financing needed to start a business in all countries except Mexico and the United States; average gap of eight percentage points in OECD countries.
  - Legal barriers:
    - Women, Business and Law Index: advanced economies removed almost all legal barriers; significant gaps remain in emerging and developing economies.
    - Implementation challenges: legal reforms may not translate into practice due to lack of enforcement, education, cultural and economic constraints (examples: Ghana, Pakistan, Kenya/Rwanda/Uganda).

- D. Policy considerations (summary)
  - Gender inequality remains large across countries and dimensions; remaining gaps are often more subtle (field of study, sectoral distribution, entrepreneurship financing, innovation).
  - Closing more implicit and subtle gender inequality is challenging due to lower visibility and difficulty separating preference from bias.
  - Reducing gender inequality yields substantial social and economic benefits; policies should address both visible and subtle forms concurrently where appropriate.

### IV. Benefits of reducing gender inequality — distributional and macroeconomic effects
- Direct and household benefits:
  - Better career development, higher pay, less violence, more equal rights, improved human capital, entrepreneurship and finance access for women.
  - Maternal empowerment improves child welfare (example: suffrage reduced child mortality by 8–15 percent).
- Firm and team productivity:
  - Mixed-gender teams more productive and creative; mixed-gender patent teams commercialize patents more than single-gender teams.
  - Studies link gender diversity with increased sales revenue, more customers, and greater relative profits; evidence on corporate board quotas mixed.
- Macroeconomic benefits:
  - Higher female labor force participation can substantially boost economic growth and strengthen macro and financial stability.
  - Reallocation of talent: between 20 to 40 percent of growth in aggregate market output per person between 1960 and 2010 in the United States can be explained by improved allocation of talent.
  - In studies of PhDs, GDP per capita could be 0.6 to 4.4 percent higher if women and African Americans participated more fully in the innovation economy.
  - Reducing gender gaps lowers income inequality; financial inclusion benefits women disproportionately and thus can reduce overall income inequality.

### V. Policies and their designs matter — evidence on general versus targeted policies
- A. Role of policies
  - Government policies affecting gender gaps include: public investment in education/healthcare, childcare subsidies, paid parental leave, eliminating tax penalties for secondary earners, laws ensuring rights and opportunities.
  - Empirical illustration (fixed effects estimates, 1990–2019 sample) of Women, Business and the Law Index on five gender gaps:
    - Associations: gender laws/regulations linked to lower gender gaps in account ownership and proportion of seats held by women in national parliaments.
    - Non-significant estimates for labor force participation, female share of senior/middle management, and pay (Appendix Table 1).
    - Including a time trend reduces coefficient significance; excluding it yields larger, more significant estimates.
  - Interpretation: laws raise awareness and affect visible or low-barrier areas more rapidly; deeper structural change takes time.

- B. General policies versus targeted gender policies — tradeoffs and design considerations
  - Definitions:
    - General policies: apply to all genders (e.g., macro policies: fiscal, monetary, exchange rate, macro-financial, macro-structural).
    - Targeted policies: specifically directed at a gender (micro or programmatic interventions).
  - Observations:
    - Macro and fiscal policies are not gender-neutral (example: family-based income taxation raises marginal tax rates for secondary earners—often women).
    - General micro policies can benefit girls more where baseline access is lower.
    - Targeted policies may be less efficient by excluding potentially better-suited males; general policies can be inefficient if gender bias leads to benefits accruing disproportionately to men.
  - Empirical evidence on effectiveness:
    - Evans and Yuan (2022): general educational interventions deliver average gains for girls comparable to girl-targeted interventions across 267 interventions in 54 low- and middle-income countries.
    - Some targeted policies yield unintended consequences (example: Mexico City women-only subway cars reduced sexual harassment but increased aggression incidents among men; corporate board quotas sometimes associated with negative returns or limited broader impacts).
  - Design recommendations:
    - For areas with limited role for comparative advantage (e.g., basic education, healthcare), fully closing gaps is unlikely to distort efficiency.
    - Where competition for fixed positions exists (e.g., leadership seats), targeted policies (quotas) may be necessary but should be conservative, phased, and carefully evaluated.
    - General policies tend to introduce fewer gender-specific distortions and may be suitable when it is difficult to separate bias-driven gaps from preference-driven gaps (example: childcare subsidies vs. wage subsidies for women).

### VI. Policy actions do not have to start with those targeted at the root causes
- Root causes: gender bias and social norms that restrict rights and opportunities.
- Rationale for multiple entry points:
  - Changing social norms is slow; interventions (education, role models) can accelerate change and produce persistent behavior shifts (example: classroom gender-equality discussions in India produced persistent effects).
  - Policies addressing specific areas (education, labor market, finance) can yield immediate benefits and can indirectly shift norms (examples: gender quotas in political leadership boosted adolescent girls’ aspirations; financial inclusion programs in rural India incentivized female work and liberalized norms).
  - Policy sequencing: while direct targeting of root causes is preferred for full elimination, practical constraints justify using a mix of general and targeted policies to achieve near-term gains and gradual norm change.
- Conclusion: policies that reduce gender inequality in specific areas can be effective and help pave the way toward addressing root causes; ultimately, eliminating gender inequality requires tackling social norms and biases.

### VII. Discussions — research gaps, data, and implementation challenges
- Five key lessons:
  - Distinguish gender inequality from gender gaps for effective policy design.
  - The focus of gender inequality evolves; as visible gaps close, more subtle gaps emerge.
  - Reducing gender inequality benefits everyone — women, children, firms, and the macroeconomy.
  - Policy design matters; both general and targeted policies have roles and tradeoffs.
  - Long-run elimination of gender inequality requires addressing root causes though interim policies are useful.
- Research and data needs:
  - Empirical separation of gender inequality from preference-driven gaps remains challenging; more data collection and methodological work required.
  - Lack of gender-disaggregated data persists, especially in low-income countries, despite improvements (IMF Financial Access Survey; World Bank WDI).
- Implementation challenges:
  - Laws and regulations (e.g., Women, Business and the Law) do not automatically translate into improved outcomes; enforcement and administrative capacity matter.
  - Examples of differing effectiveness across contexts: conditional cash transfers highly effective in some countries but not in others; underscores need for context-sensitive design and implementation.

*IMF Working Paper chapter: "1. Gender inequality, gender gaps and their causes" — IMF Working Papers Tackling Gender Inequality: Definitions, Trends, and Policy Designs*

### References .............................................................................................................

### References ........................................................................................................................................................................ 31

### FIGURES
- FIGURES

*https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022232-print-pdf.pdf*

### 1. Gender inequality, gender gaps and their causes......................................................................

### 1. Gender inequality, gender gaps and their causes

### I. Introduction — framing, definitions, and policy implications
- Gender gaps: observed differences between men and women or between boys and girls in relevant indicators.
- Gender inequality: the component of gender gaps driven by gender bias and unequal gender rights and opportunities; the remainder reflects preference/comparative advantage.
- Policy implication: policies should target reducing gender inequality (the bias-driven component), not necessarily closing every observed gender gap.
- Key contextual facts and observations from literature:
  - Gender gaps in nutritional intake reported in South Asia and sub-Sahara Africa.
  - Gender gaps in school enrollment narrowed rapidly for preprimary, primary and secondary education; tertiary education gaps remain and vary across countries.
  - Women more likely to report unmet healthcare needs (self-reported).
  - Access to formal financial services generally lower for women; saving and borrowing services more accessible to men.
  - Labor force participation gaps have narrowed but remain high with large cross-country variation; women overrepresented in low-status/low-pay sectors and under-represented in managerial and political leadership positions.
  - Women subject to more violence at home, commuting, and work; legal barriers remain pervasive—on average women have only three-quarters of the legal protections given to men during their working life.
- Development and technology tend to narrow gender inequality but the convergence is slow (example: around 82 percent of 40-year-old inventors are men; at current rate it will take another 118 years to reach gender parity).

### II. Definitions and drivers — decomposition and mechanisms
- Decomposition: gender gaps = gender inequality (bias/unequal rights) + preference/comparative advantage between men and women.
- Examples illustrating decomposition:
  - In advanced economies female tertiary enrollment ~25 percent higher than male (Figure 1); this likely reflects preferences/choices rather than inequality.
  - Registered nurses: males are a very small share in some countries, which may reflect social norms hindering male entry.
- Main drivers of gender gaps and gender inequality:
  - Comparative advantage: as countries develop women gain comparative advantage in mentally intensive tasks; this reduces female-male wage gaps and raises female labor force participation.
  - Development, infrastructure, and technology: labor-saving home technologies and better infrastructure free women’s time and raise female labor force participation.
  - Gender bias and cultural/legal barriers: norms, statistical and taste-based discrimination, and legal restrictions (e.g., limits on owning assets, entering professions) significantly hamper female participation and entrepreneurship.
- Policy interactions:
  - Development, technology, and public policies affect both gender bias/social norms and comparative advantage.
  - Interventions that lower gender inequality in specific areas can in turn alter social norms.

### III. The evolving focus of gender inequality — education, labor market, financial access and legal barriers
A. Education
- Shift in focus: from access to quality and field of study.
- Emerging and developing economies: gaps in preprimary, primary, secondary narrowing; gaps persist for tertiary education.
- Advanced economies: largely achieved parity in access; remaining concerns are quality (e.g., PISA: boys outperformed girls in mathematics by an average of eight points in 2015 and by 5 points in 2018) and field of study distribution.
- Field of study: girls underrepresented in science and engineering and overrepresented in social sciences; global distributions similar across developed and developing economies.
- STEM example (United States): in 1970 women received 9 percent of doctorates in science and engineering; by 2018 share was nearly 47 percent, with much driven by psychology and social sciences.

B. Labor market
- Labor force participation:
  - Gaps remain high but have narrowed overall (Appendix Figure 3a).
  - Advanced economies: smaller gaps and faster convergence.
  - Emerging economies: gaps have widened over past two decades, driven largely by China and India declines in female labor force participation.
- Sectoral distribution:
  - Advanced economies: women less likely in agriculture and industry, more in services (OECD: female employment in services = 80 percent of employed women vs. 60 percent for men).
  - Emerging/developing economies: larger shares of women in agriculture; rapid movement into services.
- Occupations in S&E and innovation:
  - By 2019 women made up 29 percent of S&E workers in the United States; concentration in non-S&E occupations is higher for female S&E-trained workers.
  - Women hold only 5.5 percent of commercialized patents; represent 10 percent of US patent inventors and 15 percent of inventors in life sciences.
  - Patent applications by women are more likely to be rejected, have fewer claims allowed, and receive fewer citations.
- Leadership and management:
  - Proportion of seats held by women in national parliaments about doubled over two decades but remains low.
  - Little progress in senior and middle management over two decades (Appendix Figure 4b).
  - Women with business ownership: across EU27 only 25 percent of business owners with employees are women (2000–2010 marginal growth).
  - Gender quotas on corporate boards enacted in many countries; effectiveness debated.
- Gender wage gap:
  - Declined in most countries over past two decades where data exist.
  - Average gap around 11 percent; varies substantially across countries (Appendix Figure 5).
  - Part explained by fewer hours, work interruptions, occupational concentration; a significant unexplained component suggests discrimination and noncognitive differences.
  - Example: in a large Chinese firm the gender wage gap small early in careers, becomes evident after marriage/children due to concentration in lower-level jobs.

C. Financial access and legal barriers
- Account ownership:
  - Advanced economies: largely closed the gender gap.
  - Emerging economies: gap narrowed from 23 percent in 2011 to 7 percent in 2021.
  - Low-income developing countries: gap remained around 27 percent.
- Fintech:
  - Evidence limited on fintech closing gender gaps; Sahay and others (2020) find gender gaps lower on average in digital inclusion, but there are significant variations.
  - Chen and others (2021b): fintech usage — 29 percent of men vs. 21 percent of women.
- Entrepreneurship financing:
  - Women less likely than men to report access to financing needed to start a business in all countries except Mexico and the United States; average gap of eight percentage points in OECD countries (Appendix Figure 6c).
- Legal barriers:
  - Women, Business and Law Index: advanced economies removed almost all legal barriers; significant gaps remain in emerging and developing economies (Appendix Figure 6b).
  - Implementation challenges: legal reforms may not translate into practice due to lack of enforcement, education, cultural and economic constraints (examples: Ghana, Pakistan, Kenya/Rwanda/Uganda).

D. Policy considerations (summary)
- Gender inequality remains large across countries and dimensions; remaining gaps are often more subtle (field of study, sectoral distribution, entrepreneurship financing, innovation).
- Closing more implicit and subtle gender inequality is challenging due to lower visibility and difficulty separating preference from bias.
- Reducing gender inequality yields substantial social and economic benefits; policies should address both visible and subtle forms concurrently where appropriate.

### IV. Benefits of reducing gender inequality — distributional and macroeconomic effects
- Direct benefits to women: better career development, higher pay, less violence, more equal rights, improved human capital, entrepreneurship and finance access.
- Benefits to children: maternal empowerment improves child welfare (examples: suffrage reduced child mortality by 8–15 percent; maternal education linked to compensatory investments in child outcomes).
- Firm and team productivity:
  - Mixed-gender teams more productive and creative; mixed-gender patent teams commercialize patents more than single-gender teams.
  - Studies link gender diversity with increased sales revenue, more customers, and greater relative profits; evidence on corporate board quotas mixed.
- Macroeconomic benefits:
  - Higher female labor force participation can substantially boost economic growth and strengthen macro and financial stability.
  - Reallocation of talent: between 20 to 40 percent of growth in aggregate market output per person between 1960 and 2010 in the United States can be explained by improved allocation of talent (Hsieh and others, 2019).
  - In studies of PhDs, GDP per capita could be 0.6 to 4.4 percent higher if women and African Americans participated more fully in the innovation economy.
  - Reducing gender gaps lowers income inequality; financial inclusion benefits women disproportionately and thus can reduce overall income inequality.

### V. Policies and their designs matter — evidence on general versus targeted policies
A. Role of policies
- Government policies affecting gender gaps include: public investment in education/healthcare, childcare subsidies, paid parental leave, eliminating tax penalties for secondary earners, laws ensuring rights and opportunities.
- Empirical illustration: fixed effects estimates (1990–2019 sample) of Women, Business and the Law Index on five gender gaps show:
  - Associations: gender laws/regulations linked to lower gender gaps in account ownership and proportion of seats held by women in national parliaments.
  - Non-significant estimates for labor force participation, female share of senior/middle management, and pay (Appendix Table 1).
  - Including a time trend reduces coefficient significance; excluding it yields larger, more significant estimates.
- Interpretation: laws raise awareness and affect visible or low-barrier areas more rapidly; deeper structural change takes time.

B. General policies versus targeted gender policies — tradeoffs and design considerations
- Definitions:
  - General policies: apply to all genders (e.g., macro policies: fiscal, monetary, exchange rate, macro-financial, macro-structural).
  - Targeted policies: specifically directed at a gender (micro or programmatic interventions).
- Observations and considerations:
  - Macro and fiscal policies are not gender-neutral in distributional effects (e.g., family-based income taxation raises marginal tax rates for secondary earners—often women).
  - General micro policies (e.g., conditional cash transfers, public education) can benefit girls more where baseline access is lower.
  - Targeted policies may be less efficient by excluding potentially better-suited males, but general policies can be inefficient if gender bias leads to benefits accruing disproportionately to men.
  - Empirical evidence on effectiveness:
    - Evans and Yuan (2022): general educational interventions deliver average gains for girls comparable to girl-targeted interventions across 267 interventions in 54 low- and middle-income countries.
    - Some targeted policies yield unintended consequences (examples: Mexico City women-only subway cars reduced sexual harassment but increased aggression incidents among men; corporate board quotas sometimes associated with negative returns or limited broader impacts).
  - Recommendations on design:
    - For areas with limited role for comparative advantage (e.g., basic education, healthcare), fully closing gaps is unlikely to distort efficiency.
    - Where competition for fixed positions exists (e.g., leadership seats), targeted policies (quotas) may be necessary but should be conservative, phased, and carefully evaluated.
    - General policies tend to introduce fewer gender-specific distortions and may be suitable when it is difficult to separate bias-driven gaps from preference-driven gaps (example: childcare subsidies vs. wage subsidies for women).

### VI. Policy actions do not have to start with those targeted at the root causes
- Root causes: gender bias and social norms that restrict rights and opportunities.
- Rationale for multiple entry points:
  - Changing social norms is slow; interventions (education, role models) can accelerate change and produce persistent behavior shifts (example: classroom gender-equality discussions in India produced persistent effects).
  - Policies addressing specific areas (education, labor market, finance) can yield immediate benefits and can indirectly shift norms (examples: gender quotas in political leadership boosted adolescent girls’ aspirations; financial inclusion programs in rural India incentivized female work and liberalized norms).
  - Policy sequencing: while direct targeting of root causes is preferred for full elimination, practical constraints justify using a mix of general and targeted policies to achieve near-term gains and gradual norm change.
- Conclusion: policies that reduce gender inequality in specific areas can be effective and help pave the way toward addressing root causes; ultimately, eliminating gender inequality requires tackling social norms and biases.

### VII. Discussions — research gaps, data, and implementation challenges
- Five key lessons summarized:
  - Distinguish gender inequality from gender gaps for effective policy design.
  - The focus of gender inequality evolves; as visible gaps close, more subtle gaps emerge.
  - Reducing gender inequality benefits everyone — women, children, firms, and the macroeconomy.
  - Policy design matters; both general and targeted policies have roles and tradeoffs.
  - Long-run elimination of gender inequality requires addressing root causes though interim policies are useful.
- Research and data needs:
  - Empirical separation of gender inequality from preference-driven gaps remains challenging; more data collection and methodological work required.
  - Lack of gender-disaggregated data persists, especially in low-income countries, despite improvements (IMF Financial Access Survey; World Bank WDI).
- Implementation challenges:
  - Laws and regulations (e.g., Women, Business and the Law) do not automatically translate into improved outcomes; enforcement and administrative capacity matter.
  - Examples of differing effectiveness across contexts: conditional cash transfers highly effective in some countries but not in others; underscores need for context-sensitive design and implementation.

*IMF Working Paper chapter: "1. Gender inequality, gender gaps and their causes" — IMF Working Papers Tackling Gender Inequality: Definitions, Trends, and Policy Designs*

### Appendix Figure 1. Gender Gaps in Education

### wpiea2022232-print-pdf - Appendix Figures and Table: Gender Gaps

### Education: enrollment female-to-male ratios (Appendix Figure 1)
- Measures: female-to-male gross enrollment ratios with dashed line at ratio of one.
- Levels shown (axis tick marks preserved): 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4.
- Panels:
  - a. Preprimary education enrollment rate: female-to-male ratio — country groups: Advanced, Emerging, Developing.
  - b. Primary education enrollment rate: female-to-male ratio.
  - c. Secondary education enrollment rate: female-to-male ratio.
  - d. Tertiary education enrollment rate: female-to-male ratio.

### Field of study gender composition (Appendix Figure 2)
- Sources: UNESCO Institute for Statistics; OECD (2017); author’s calculation. Global data for 2014-2016; OECD data for 2014.
- a. Percent of female students enrolled in higher education, global average (field-level values shown on chart axis up to 80):
  - Education: 71
  - Health and welfare: 68
  - Humanities and arts: 62
  - Social sciences, journalism and information: 61
  - Business, administration and law: 56
  - Natural science, mathematics and statistics: 55
  - Services: 49
  - Agriculture, forestry, fishery and veterinary: 46
  - Information and communication technologies: 28
  - Engineering, manufacturing and construction: 27
- b. Percent of new entrants into tertiary education in each field that are female, OECD (axis up to 90):
  - Education: 78
  - Health and welfare: 75
  - Humanities and arts: 64
  - Social sciences, business and law: 57
  - Services: 49
  - Agriculture: 48
  - Sciences: 37
  - Engineering, manufacturing and construction: 24

### Labor force participation and employment by sector (Appendix Figure 3)
- a. Labor force participation rate for age 14-64: female-to-male ratio — axis tick marks: 0.5, 0.6, 0.7, 0.8, 0.9, 1.0. Country groups: Advanced, Emerging, Developing, Emerging excluding China, India and MENAP.
- Sectoral employment gender differences (female share minus male share):
  - b. Agriculture: axis from -2 to 6 (ticks: -2, -1, 0, 1, 2, 3, 4, 5, 6) — reported as "female (% of female employment) minus male (% of male employment)".
  - c. Industry: axis from -25 to 0 (ticks: -25, -20, -15, -10, -5, 0).
  - d. Services: axis from -5 to 25 (ticks: -5, 0, 5, 10, 15, 20, 25).

### Leadership positions (Appendix Figure 4)
- a. Proportion of seats held by women in national parliaments (percent) — axis 0 to 40 (ticks: 0, 10, 20, 30, 40). Country groups: Advanced, Emerging, Developing.
- b. Female share of employment in senior and middle management (percent) — axis 0 to 40 (ticks: 0, 5, 10, 15, 20, 25, 30, 35, 40).
- Note: downward trend for emerging economies between 2003 and 2010 is noted as not appearing to be driven by the unbalanced panel.

### Gender wage gap in selected economies (Appendix Figure 5)
- Source: OECD gender wage gap indicator (accessed on 02 March 2022); author’s calculation.
- Definition: difference between median earnings of men and women relative to median earnings of men, for full-time employees; missing-year substitution rules noted.
- Countries and OECD/Average axis 0 to 45 (ticks: 0, 5, 10, 15, 20, 25, 30, 35, 40, 45) include (listed on chart):
  - BGR, LUX, BEL, ROU, NZL, CRI, NOR, DNK, ITA, COL, SWE, SVN, IRL, CHL, ESP, POL, GRC, MEX, HUN, TUR, EU27, SVK, MLT, LTU, PRT, FRA, AUS, GBR, CZE, HRV, NLD, ISL, AUT, CHE, DEU, CYP, CAN, EST, USA, FIN, LVA, JPN, ISR, KOR, OECD Average; series shown for 2000 and 2020.

### Financial access and legal barriers (Appendix Figure 6)
- Sources: World Development Indicators, World Bank; Entrepreneurship at a Glance 2016, OECD; author’s calculation.
- a. Account ownership at a financial institution or with a mobile-money-service provider: female-to-male ratio — time points 2011, 2014, 2017, 2021; axis tick marks: 0.2, 0.4, 0.6, 0.8, 1.0, 1.2. Country groups: Advanced, Emerging, Developing. Country averages based on unbalanced samples and weighted by population size.
- b. Women, Business and the Law Index (highest possible score 100) — axis tick marks: 40, 50, 60, 70, 80, 90, 100. Country groups: Advanced, Emerging, Developing.
- c. Perception on having access to entrepreneurship financing, 2013 (percent scale 0 to 70; ticks: 0, 10, 20, 30, 40, 50, 60, 70): comparison Women vs Men. Note: data for New Zealand refer to 2014 and data for China, India and Indonesia refer to 2015. Data for panel c based on question: “Do you have access to money you would need if you wanted to start or grow a business?”

### Cross-country variation (Appendix Figure 7)
- Sources: World Development Indicators; OECD gender wage gap indicator (accessed on 02 March 2022); author’s calculation. Based on latest year of available data between 2015 and 2019.
- Country group labels: Advanced; CEE/CIS = Emerging Europe and Commonwealth of Independent States; EDA = Emerging and Developing Asia; LAC = Latin America and the Caribbean; MENAP = Middle East, North Africa, Afghanistan, and Pakistan.
- Panels plot indicators vs Log(per capita GDP in PPP):
  - a. Account ownership at a financial institution or with a mobile-money-service provider: female-to-male ratio (axis 0.0 to 1.4).
  - b. Labor force participation rate: female-to-male ratio (axis 0.0 to 1.4).
  - c. Female share of employment in senior and middle management (percent) (axis 0 to 90).
  - d. Proportion of seats held by women in national parliaments (percent) (axis 0 to 60).
  - e. Gender wage gap (percent) (axis 0 to 35).
  - f. Women business and the law index (axis 0 to 120).

### Appendix Table 1: Alternative specifications on effects of laws and regulations on selected gender gaps
- Source: World Development Indicators; OECD gender wage gap indicator (accessed on 02 March 2022); author’s calculation.
- Notes: Robust standard errors reported; * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.
- Dependent variables (columns grouped in threes: FE, FE, RE specifications): 
  - Log of Account ownership at a financial institution or with a mobile-money-service provider: female-to-male ratio (Number of observations: 527, 527, 527; Adjusted R2: 0.090, 0.085, 0.083).
  - Labor force participation rate: female-to-male ratio (Number of observations: 4,826, 4,826, 4,826; Adjusted R2: 0.337, 0.186, 0.337).
  - Female share of senior and middle management (percent) (Number of observations: 1,312, 1,312, 1,312; Adjusted R2: 0.117, 0.084, 0.113).
  - Proportion of seats held by women in national parliaments (percent) (Number of observations: 4,038, 4,038, 4,038; Adjusted R2: 0.423, 0.338, 0.423).
  - Gender wage gap (percent) (Number of observations: 632, 632, 632; Adjusted R2: 0.195, 0.132, 0.195).
- Key reported coefficients (by column ordering across the 15 specifications):
  - Log women business and the law index (one lag):
    - 0.482***, 0.543***, 0.415***, -0.018, 0.202***, -0.003, 0.129, 0.476***, 0.282*, 0.506**, 1.398***, 0.610***, -0.080, -0.832, -0.131
    - Corresponding reported standard errors for first three: (0.105), (0.103), (0.075) [subsequent standard errors are shown in table but not repeated here].
  - Log GDP per capita in PPP:
    - -0.065, -0.003, 0.065***, -0.051**, 0.034*, -0.049**, -0.058, 0.135, 0.045, 0.119, 0.603***, 0.096**, 0.190, -0.367, 0.265
    - Example standard errors for first three: (0.090), (0.075), (0.011).
  - Time trend coefficients (selected across specifications):
    - 0.003, 0.001, 0.007***, 0.007***, 0.011***, 0.008***, 0.032***, 0.031***, -0.023***, -0.024*** (standard errors shown as (0.002), (0.002), (0.001), (0.001), (0.003), (0.003), (0.003), (0.003), (0.004), (0.003)).
  - Constants (selected values across columns):
    - -1.710*, -2.472***, -2.595***, 0.046, -1.528***, -0.032, 3.113**, -0.082, 1.511**, -1.177, -8.769***, -1.416*, 1.411, 10.168***, 0.764 (standard errors reported in table).
- Fixed/Random effects indicators per column: FE, FE, RE, FE, FE, RE, FE, FE, RE, FE, FE, RE, FE, FE, RE.

*Source: IMF Working Paper (Appendix Figures and Table as provided in the supplied PDF content).*

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### Education, human capital, and long-term outcomes
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### Finance, fintech, and inclusion
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### Laws, social norms, and institutions
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### Innovation, patents, corporate boards, and firm outcomes
- Bell, Alex, Raj Chetty, Xavier Jaravel, Neviana Petkova, and John Van Reenen, 2019, “Who Becomes an Inventor in America? The Importance of Exposure to Innovation,” The Quarterly Journal of Economics, 134(2): 647-713.  
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### Social norms, gender roles, and cultural determinants
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- Jayachandran, Seema, 2021, “Social Norms as a Barrier to Women’s Employment in Developing Countries,” IMF Economic Review, 69(3): 576-595.  
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### Methodology, measurement, and databases
- Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, 2015, “The Global Findex Database 2014: Measuring Financial Inclusion around the World,” Policy Research Working Paper No. WPS 7255, World Bank, Washington DC.  
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- World Economic Forum (WEF), 2021, “Global Gender Gap Report 2021,” World Economic Forum, Geneva.

### Selected field experiments and program evaluations
- Beaman, Lori, Esther Duflo, Rohini Pande, and Petia Topalova, 2012, “Female Leadership Raises Aspirations and Educational Attainment for Girls: A Policy Experiment in India,” Science, 335 (6068): 582-586.  
- Bernhardt, Arielle, Erica Field, Rohini Pande, Natalia Rigol, Simone Schaner, and Charity Troyer-Moore, 2018, “Male Social Status and Women’s Work,” AEA Papers and Proceedings, 108: 363-67.  
- Field, Erica, Seema Jayachandran, Rohini Pande, 2010, “Do Traditional Institutions Constrain Female Entrepreneurship? A Field Experiment on Business Training in India,” American Economic Review 100 (2): 125-29.  
- Dinkelman, Taryn, 2011, “The Effects of Rural Electrification on Employment: New Evidence from South Africa,” American Economic Review, 101 (7): 3078-3108.  
- Pitt, Mark, Shahidur Khandker, Omar Haider Chowdhury, and Daniel Millimet, 2003, “Credit Programs for the Poor and the Health Status of Children in Rural Bangladesh," International Economic Review, Volume44, Issue1: 87-118.  
- Buehren, Niklas, Markus Goldstein, Kenneth Leonard, Joao Montalvao, and Kathryn Vasilaky, 2022, “Spillover Effects of Girls’ Empowerment on Brothers’ Competitiveness: Evidence from a Lab-in-the-Field Experiment in Uganda,” Economic Development and Cultural Change, Volume 70, Number 2: 653–670.

*Tackling Gender Inequality: Definitions, Trends, and Policy Designs — Working Paper No. WP/2022/232*

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_Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022232-print-pdf.pdf_