## gnsea2024004

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---

### Introduction and overview
- Men and women each constitute roughly half of the population, but employment outcomes differ: the employment ratio (men-to-women employed) remained above 1.5 from 1991 to 2023, implying men are over 50 percent more likely to be employed than women over the 32-year period covered.
- The population ratio remains stable and close to one, indicating a near-equal number of men and women across the years.
- The persistent gap between population parity and employment inequality highlights enduring gender inequality in the global labor market and the need for policy intervention to increase women’s employment levels.
- The analysis covers 180 countries included in the ILO database that are part of the IMF membership.
- Data sources: IMF staff calculations using data from the IMF Gender Data Hub; original data from the International Labour Organization (ILO). Annex 2 describes the data used.

### Framework and analytical approach
- Decomposition framework components:
  - (1) gender differences in population distribution;
  - (2) gender differences in labor force participation (LFP) rates;
  - (3) gender differences in employment rates, conditional on belonging to the labor force.
- Measurement and math:
  - G^E_t = E_{m,t}/E_{f,t} = G^e_t · G^l_t · G^s_t.
  - Log decomposition: log(G^E_t) = log(G^e_t) + log(G^l_t) + log(G^s_t).
  - Rates of change: ∆log(G^E_t) = ∆log(G^e_t) + ∆log(G^l_t) + ∆log(G^s_t).
- Gaps measured as male-to-female ratios with logarithmic transformations to quantify level contributions and identify factors contributing most to the rate of change.
- Analysis covers different income groups and geographical areas; country groups use World Economic Outlook classifications as of April 2022.

### Principal drivers and global patterns (1991–2022)
- Global decomposition (ages 15+):
  - LFP rate component is the most significant contributor, explaining nearly the entire global employment gender gap.
  - Employment rate component is negligible compared with the LFP rate.
  - Population shares contribution is minimal and negative at the global level.
- Distributional snapshot (2022): about 94 percent of countries had positive employment gender gaps (men more likely to be employed than women).
- Table 2 country counts and frequencies in 2022 (exact values preserved):
  - World: Negative 11, Positive 168, Total 179; 6.1, 93.9, 100.0
  - Advanced economies: Negative 3, Positive 35, Total 38; 7.9, 92.1, 100.0
  - Emerging markets: Negative 4, Positive 79, Total 83; 4.8, 95.2, 100.0
  - Low-income countries: Negative 4, Positive 54, Total 58; 6.9, 93.1, 100.0
  - Africa: Negative 3, Positive 42, Total 45; 6.7, 93.3, 100.0
  - Asia and the Pacific: Negative 2, Positive 30, Total 32; 6.3, 93.8, 100.0
  - Europe: Negative 3, Positive 38, Total 41; 7.3, 92.7, 100.0
  - Middle East and Central Asia: Negative 2, Positive 28, Total 30; 6.7, 93.3, 100.0
  - Western Hemisphere: Negative 1, Positive 30, Total 31; 3.2, 96.8, 100.0
- Distribution of most and least important factors (2022) — percentages (Table 3):
  - Most Important (%): EMP Rate 0.6, LFP Rate 93.3, POP Share 6.1
  - Least Important (%): EMP Rate 68.7, LFP Rate 1.1, POP Share 30.2
  - By income level (Most Important %):
    - Advanced economies: EMP Rate 0, LFP Rate 97.4, POP Share 2.6
    - Emerging markets: EMP Rate 0, LFP Rate 94.0, POP Share 6.0
    - Low-income countries: EMP Rate 1.7, LFP Rate 89.7, POP Share 8.6
  - By income level (Least Important %):
    - Advanced economies: EMP Rate 89.5, LFP Rate 0, POP Share 10.5
    - Emerging markets: EMP Rate 62.7, LFP Rate 0, POP Share 37.3
    - Low-income countries: EMP Rate 63.8, LFP Rate 3.4, POP Share 32.8

### Findings by country group and region (levels and trends)
- World (1991–2022): Trend — Stable; Main Contributor — LFP rate; Least Contributor — EMP rate.
- Advanced economies: Trend — Decreasing employment gender gap; Main Contributor — LFP rate; Least Contributor — EMP rate. Population shares contribute negatively.
- Emerging markets: Trend — Increasing employment gender gap; Main Contributor — LFP rate; Least Contributor — EMP rate. Population shares contribute very small but consistently positive amounts.
- Low-income countries: Trend — Stable employment gender gap; Main Contributor — LFP rate; Least Contributor — EMP rate. Employment rate margin has grown in relevance over time; population shares contribute negatively.
- Geographic-area summary (Table 1):
  - Africa: U-shaped; Main Contributor — LFP rate; Least — EMP rate
  - Asia and the Pacific: Increasing; Main Contributor — LFP rate; Least — EMP rate
  - Europe: Decreasing; Main Contributor — LFP rate; Least — EMP rate
  - Middle East and Central Asia: Decreasing; Main Contributor — LFP rate; Least — POP share
  - Western Hemisphere: Decreasing; Main Contributor — LFP rate; Least — EMP rate

### COVID-19 "She-cession": decomposition of changes (2010–2022; focus 2019–22)
- Global pattern by subperiod:
  - 2019–20: employment gender gap widened significantly, primarily because of changes in gender gaps in the LFP rate; employment rate margin partially offset the widening.
  - 2020–21: large narrowing of the employment gender gap, driven mainly by improvements in LFP rates; employment rate margin slightly widened the gap.
  - 2021–22: employment gender gap increased again, driven by changes in all three margins, with LFP rate margin the most significant contributor.
- Table 4 summary of main contributors by period (selected exact entries):
  - World: 2019–20: +, LFP rate; 2020–21: −, LFP rate; 2021–22: +, LFP rate
  - Advanced economies: 2019–20: +, EMP rate; 2020–21: −, LFP rate; 2021–22: −, LFP rate
  - Emerging markets: 2019–20: +, LFP rate; 2020–21: −, LFP rate; 2021–22: +, LFP rate
  - Low-income countries: 2019–20: +, EMP rate; 2020–21: −, LFP rate; 2021–22: +, LFP rate
  - By geographical areas (selected):
    - Africa: 2019–20 +, EMP rate; 2020–21 +, LFP rate; 2021–22 +, LFP rate
    - Asia and the Pacific: 2019–20 +, LFP rate; 2020–21 −, LFP rate; 2021–22 +, EMP rate
    - Europe: 2019–20 −, EMP rate; 2020–21 −, LFP rate; 2021–22 −, LFP rate
    - Middle East and Central Asia: 2019–20 +, LFP rate; 2020–21 −, LFP rate; 2021–22 +, LFP rate
    - Western Hemisphere: 2019–20 +, LFP rate; 2020–21 −, LFP rate; 2021–22 −, LFP rate
- Group-specific notes:
  - Advanced economies: 2019–20 widening driven primarily by employment rate margin; 2020–21 and 2021–22 narrowing mostly driven by LFP improvements.
  - Emerging markets: 2019–20 widening driven mostly by LFP rate; 2020–21 narrowing driven by LFP recovery; 2021–22 increase driven by all three margins with LFP dominant.
  - Low-income countries: 2019–20 widening driven primarily by employment rate margin, followed by LFP and demographic margins; 2020–21 narrowing driven primarily by LFP recovery; 2021–22 increase driven mainly by LFP rate margin.

### Drivers limiting women’s labor force participation (supply and demand barriers)
- Supply-side constraints:
  - Gender differences in endowments (technical and socioemotional skills, assets, and networks).
  - Time limits due to household and care obligations.
  - Restricted mobility.
- Demand-side constraints:
  - Mismatch among skills, education, and job requirements.
  - Gender gaps and discrimination in recruiting and retention.
  - Insufficient benefits for childcare, maternity leave, reentry programs, and career progression.
  - Slow job creation and lack of business dynamism limiting new employment opportunities.
- Social, cultural, and policy factors:
  - Social and cultural norms, and restrictive policies and laws, often reinforce barriers and limit women’s ability to enter or reenter the labor market.

### Policy implications and recommended focus areas
- Primary policy focus: increase women’s labor force participation (LFP) since LFP gaps are the dominant driver of employment gender gaps globally.
- Supply-side policy recommendations:
  - Improve access to skills, assets, and networks.
  - Reduce time burdens from unpaid care.
  - Enhance mobility.
- Demand-side policy recommendations:
  - Address hiring and retention discrimination.
  - Align education and training with job requirements.
  - Strengthen childcare, maternity leave, and reentry programs.
  - Support career progression.
- Job creation and private sector development:
  - Promote job creation and business dynamism to generate opportunities that incentivize female participation.
  - Policy tools could include tax incentives and subsidies to businesses that hire and retain female employees; promoting investment in female-led startups and small businesses; training and apprenticeship programs; enhancing access to finance for women entrepreneurs; attracting foreign investment; increasing investment efficiency.
- Labor market reforms and flexible work:
  - Continue reforms to make employment more flexible and inclusive.
  - Promote flexible employment arrangements (flexible schedules, work-from-home, part-time) and revise labor laws to protect rights while allowing adaptability.
- Digitalization and governance:
  - Accelerate digitalization; invest in digital infrastructure and digital literacy programs.
  - Ensure antidiscrimination laws are effectively enforced; reduce barriers to female entrepreneurship; strengthen institutions and anti-corruption measures.
- Evidence-based allocation of resources: prioritize interventions targeting components with the largest quantitative contributions to narrowing employment gender gaps, especially LFP-related interventions.
- Tailor policy responses to regional and income-group differences revealed by the decomposition (AEs, EMs, LICs).

### Data, diagnosis, and monitoring recommendations
- Leverage survey microdata with specific questions on reasons for nonparticipation to identify causes driving individuals, particularly women, out of the labor force.
- Where such data are lacking, integrate these questions into official surveys.
- Prioritize strategies that both reactively target and enhance female LFP and proactively prevent declines in participation.
- Short-term social protection and fiscal space:
  - Strengthen social protection measures and ensure sufficient fiscal space to provide targeted support for affected people in the short term to mitigate shocks and facilitate quicker recovery.

### Conclusion and methodological framing
- Gender gaps in LFP rates are a critical contributor to employment gender gaps globally, across income groups and regions.
- Addressing labor supply alone is insufficient; coordinated supply- and demand-side policies, social protection, labor market reforms, digitalization, governance, and private sector development are necessary.
- Data and coverage notes:
  - Analysis uses ILO Modelled Estimates for ages 15+ and covers the 180 ILO countries in IMF membership.
  - Employment data encompass both formal and informal employment and do not include disaggregation by hours worked, informality levels, or economic activity (sector).
- Framing and methodology (identity recap):
  - E_{g,t} = e_{g,t} · l_{g,t} · s_{g,t} · N_t, with log decomposition log(G^E_t) = log(G^e_t) + log(G^l_t) + log(G^s_t) and rates of change ∆log(G^E_t) = ∆log(G^e_t) + ∆log(G^l_t) + ∆log(G^s_t).

*Source: IMF Gender Note gnsea2024004 — Global Employment Gender Gaps (Diego B. P. Gomes and Dharana Rijal, December 2024). The analysis uses ILO Modelled Estimates; original ILO data accessed March 25, 2024.*

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

### Introduction

### Overview of global employment gender gaps
- Men and women each constitute roughly half of the population, but this parity is not reflected in employment outcomes.
- The employment ratio (men-to-women employed) has consistently remained above 1.5 from 1991 to 2023, implying that men are over 50 percent more likely to be employed than women over the 32-year period covered.
- The population ratio remains stable and close to one, indicating a near-equal number of men and women across the years.
- The persistent discrepancy between population parity and employment inequality underscores enduring gender inequality in the global labor market and the need for policy intervention to increase women’s employment levels.

### Framework and analytical approach
- Uses a structured accounting framework that decomposes employment gender gaps into three components:
  - (1) gender differences in population distribution;
  - (2) gender differences in labor force participation (LFP) rates;
  - (3) gender differences in employment rates, conditional on belonging to the labor force.
- Measures gaps as male-to-female ratios and applies logarithmic transformations to:
  - precisely quantify the factors responsible for the gap in levels, and
  - identify the factors contributing most to its rate of change.
- Annex 1 provides details of the framework; the analysis covers different income groups and geographical areas.

### Key findings (from 2022 data and broader trends)
- Most countries have higher employment for men than for women, driven mainly by LFP rate differences.
- Closing employment gender gaps is essential for economic stability and growth because:
  - equal workforce participation by women increases the labor supply;
  - it enriches the talent pool, fosters inclusive economic growth, and boosts productivity and innovation;
  - high-quality female employment raises household incomes and reduces inequalities, enhancing economic demand and stability.
- The analysis covers 180 countries included in the International Labour Organization (ILO) database that are part of the IMF membership.

### Role of the COVID-19 pandemic
- The COVID-19 pandemic worsened employment gender gaps, particularly through its impact on LFP rates.
- The study emphasizes the need for policies to boost female LFP by addressing both supply and demand issues in the labor market and supporting women’s entry and retention in the workforce.

### Data sources and scope
- Figure 1 and related analyses are based on IMF staff calculations using data from the IMF Gender Data Hub; the original source of the data is the International Labour Organization (ILO).
- The term “world” or “global” in this note refers to the 180 countries included in the ILO database that are part of the IMF membership.

*IMF | Gender Note NOTE/2024/004 — Global Employment Gender Gaps (Diego B. P. Gomes and Dharana Rijal, December 2024).*

### Annex 2 describes the data used.

### gnsea2024004 - Annex 2 describes the data used.

### Methodology and purpose
- The methodology decomposes employment gender gaps into three components: labor force participation (LFP) rate, employment rate, and population shares, measured in logarithmic units.
- Purpose:
  - Improves understanding of key factors contributing to employment gender gaps.
  - Offers a structured approach for evaluating progress and identifying enduring challenges.
  - Supports targeted interventions by quantifying each component’s contribution to the evolution of employment gender gaps.
  - Enables strategic allocation of resources to areas with the most potential to narrow gaps, supporting sustainable economic development and social equity.

### Principal drivers and global patterns (1991–2022)
- Global decomposition (ages 15+):
  - LFP rate component is the most significant contributor, explaining nearly the entire global employment gender gap.
  - Employment rate component is negligible compared with the LFP rate.
  - Population shares contribution is minimal and negative at the global level, consistent with near-equal men-to-women population ratios slightly less than one.
- Distributional snapshot (2022):
  - About 94 percent of countries had positive employment gender gaps in 2022, with men more likely to be employed than women.
  - Table 2 country counts and frequencies in 2022:
    - World: Negative 11, Positive 168, Total 179; 6.1, 93.9, 100.0
    - Advanced economies: Negative 3, Positive 35, Total 38; 7.9, 92.1, 100.0
    - Emerging markets: Negative 4, Positive 79, Total 83; 4.8, 95.2, 100.0
    - Low-income countries: Negative 4, Positive 54, Total 58; 6.9, 93.1, 100.0
    - Africa: Negative 3, Positive 42, Total 45; 6.7, 93.3, 100.0
    - Asia and the Pacific: Negative 2, Positive 30, Total 32; 6.3, 93.8, 100.0
    - Europe: Negative 3, Positive 38, Total 41; 7.3, 92.7, 100.0
    - Middle East and Central Asia: Negative 2, Positive 28, Total 30; 6.7, 93.3, 100.0
    - Western Hemisphere: Negative 1, Positive 30, Total 31; 3.2, 96.8, 100.0
- Distribution of most and least important factors (2022) — percentages (Table 3):
  - Most Important (%): EMP Rate 0.6, LFP Rate 93.3, POP Share 6.1
  - Least Important (%): EMP Rate 68.7, LFP Rate 1.1, POP Share 30.2
  - By income level (Most Important %: LFP dominates):
    - Advanced economies: Most Important — EMP Rate 0, LFP Rate 97.4, POP Share 2.6; Least Important — EMP Rate 89.5, LFP Rate 0, POP Share 10.5
    - Emerging markets: Most Important — EMP Rate 0, LFP Rate 94.0, POP Share 6.0; Least Important — EMP Rate 62.7, LFP Rate 0, POP Share 37.3
    - Low-income countries: Most Important — EMP Rate 1.7, LFP Rate 89.7, POP Share 8.6; Least Important — EMP Rate 63.8, LFP Rate 3.4, POP Share 32.8
  - By geographical areas (Most Important %: LFP dominates across regions; Least Important varies but EMP Rate often largest share).

### Findings by country group (levels and trends)
- World (1991–2022): Trend — Stable; Main Contributor — LFP rate; Least Contributor — EMP rate.
- Advanced economies (AEs): Trend — Decreasing employment gender gap; Main Contributor — LFP rate; Least Contributor — EMP rate. Population shares contribute negatively (higher proportion of women but lower female employment relative to men).
- Emerging markets (EMs): Trend — Increasing employment gender gap; Main Contributor — LFP rate; Least Contributor — EMP rate. Population shares contribute very small but consistently positive amounts.
- Low-income countries (LICs): Trend — Stable employment gender gap; Main Contributor — LFP rate; Least Contributor — EMP rate. Employment rate margin has grown in relevance over time; population shares contribute negatively.

- Summary of geographical-area trends (Table 1):
  - Africa: U-shaped; Main Contributor — LFP rate; Least — EMP rate
  - Asia and the Pacific: Increasing; Main Contributor — LFP rate; Least — EMP rate
  - Europe: Decreasing; Main Contributor — LFP rate; Least — EMP rate
  - Middle East and Central Asia: Decreasing; Main Contributor — LFP rate; Least — POP share
  - Western Hemisphere: Decreasing; Main Contributor — LFP rate; Least — EMP rate

### COVID-19 She-cession: decomposition of changes (2010–2022; focus 2019–22)
- Global pattern around the pandemic:
  - 2019–20: The employment gender gap widened significantly, primarily because of changes in gender gaps in the LFP rate. Employment rate margin partially offset the widening by decreasing gender gaps in employment rates.
  - 2020–21: Large narrowing of the employment gender gap, driven mainly by improvements in LFP rates; employment rate margin slightly widened the gap (i.e., offsetting the improvement).
  - 2021–22: Employment gender gap increased again, driven by changes in all three margins, with LFP rate margin the most significant contributor.
- Table 4 summary of main contributors by period:
  - World:
    - 2019–20: +, LFP rate
    - 2020–21: −, LFP rate
    - 2021–22: +, LFP rate
  - Advanced economies:
    - 2019–20: +, EMP rate
    - 2020–21: −, LFP rate
    - 2021–22: −, LFP rate
  - Emerging markets:
    - 2019–20: +, LFP rate
    - 2020–21: −, LFP rate
    - 2021–22: +, LFP rate
  - Low-income countries:
    - 2019–20: +, EMP rate
    - 2020–21: −, LFP rate
    - 2021–22: +, LFP rate
  - By geographical areas (selected entries from Table 4):
    - Africa: 2019–20 +, EMP rate; 2020–21 +, LFP rate; 2021–22 +, LFP rate
    - Asia and the Pacific: 2019–20 +, LFP rate; 2020–21 −, LFP rate; 2021–22 +, EMP rate
    - Europe: 2019–20 −, EMP rate; 2020–21 −, LFP rate; 2021–22 −, LFP rate
    - Middle East and Central Asia: 2019–20 +, LFP rate; 2020–21 −, LFP rate; 2021–22 +, LFP rate
    - Western Hemisphere: 2019–20 +, LFP rate; 2020–21 −, LFP rate; 2021–22 −, LFP rate

- Group-specific pandemic dynamics:
  - Advanced economies:
    - 2019–20: Widening driven primarily by employment rate margin (men more likely to retain/find jobs); LFP margin contributed negatively (slight decrease in participation gap).
    - 2020–21 and 2021–22: Narrowing mostly driven by LFP rate improvements; employment rate margin mixed effects.
  - Emerging markets:
    - 2019–20: Widening driven mostly by LFP rate (larger decline in women’s participation); employment rate margin often negative (offsetting).
    - 2020–21: Narrowing driven by LFP recovery; employment rate margin shifted to widen gap.
    - 2021–22: Increase driven by all three margins with LFP dominant.
  - Low-income countries:
    - 2019–20: Widening driven primarily by employment rate margin, followed by LFP and demographic margins.
    - 2020–21: Narrowing driven primarily by LFP recovery; employment rate margin continued to increase the gap.
    - 2021–22: Increase driven mainly by LFP rate margin with small contributions from other margins.

### Drivers limiting women’s labor force participation (supply and demand barriers)
- Supply-side constraints:
  - Gender differences in endowments (technical and socioemotional skills, assets, and networks).
  - Time limits due to household and care obligations.
  - Restricted mobility.
- Demand-side constraints:
  - Mismatch among skills, education, and job requirements.
  - Gender gaps and discrimination in recruiting and retention.
  - Insufficient benefits for childcare, maternity leave, reentry programs, and career progression.
  - Slow job creation and lack of business dynamism limiting new employment opportunities.
- Social, cultural, and policy factors:
  - Social and cultural norms, and restrictive policies and laws, often reinforce barriers and limit women’s ability to enter or reenter the labor market.

### Policy implications and recommended focus areas
- Primary policy focus: increase women’s labor force participation (LFP) since LFP gaps are the dominant driver of employment gender gaps globally.
- Policy measures should address both supply and demand constraints:
  - Supply-side: improve access to skills, assets, networks; reduce time burdens from unpaid care; enhance mobility.
  - Demand-side: address hiring and retention discrimination; align education and training with job requirements; strengthen childcare, maternity leave, and reentry programs; support career progression.
  - Promote job creation and business dynamism to generate opportunities that incentivize female participation.
  - Reform restrictive laws and policies and target social-norm interventions to enable labor market entry and reentry for women.
- Evidence-based allocation of resources: prioritize components with the largest quantitative contributions to narrowing employment gender gaps, especially LFP-related interventions.
- Tailor policy responses to regional and income-group differences revealed by the decomposition (AEs, EMs, LICs), given divergent dynamics and pandemic impacts.

*Source: IMF staff calculations based on data from the IMF Gender Data Hub. The original source of the data is obtained from the International Labour Organization (ILO).*

### Conclusion

### gnsea2024004 - Conclusion

### Key findings
- Gender gaps in LFP rates are a critical contributor to employment gender gaps globally, across different income groups, and geographical areas.
- Gender gaps in labor supply are the most prominent factor explaining gender employment gaps, but addressing labor supply alone is not sufficient.
- The COVID-19 pandemic required temporary shutdowns in certain sectors for public health reasons, leading to unavoidable declines in LFP, particularly for women.
- The analysis uses ILO Modelled Estimates for individuals aged 15 and older and covers the 180 countries included in the ILO database that are part of the IMF membership. Country groups are based on the World Economic Outlook classifications as of April 2022.
- The employment data used in the analysis encompass both formal and informal employment and do not include data disaggregated by hours worked (part-time vs full-time), levels of informality (formal vs informal), or economic activity (sector).

### Recommendations: data and diagnosis
- Policymakers should leverage survey microdata that contain specific questions regarding the reasons for nonparticipation in the labor force to obtain a clean identification of the potential causes driving individuals, particularly women, out of the labor force.
- Where such data are lacking, efforts must be made to integrate these questions into official surveys to uncover and address barriers to female LFP.
- Policymakers should prioritize strategies that both reactively target and enhance female LFP and proactively prevent declines in participation.

### Recommendations: short-term social protection and fiscal space
- Strengthen social protection measures and ensure a sufficient fiscal space to provide targeted support for affected people in the short term to mitigate negative effects from shocks (for example, pandemic-related shutdowns) and facilitate quicker recovery.

### Recommendations: demand-side policies and private sector development
- Target the demand side of the labor market alongside supply-side measures; job creation is a two-sided matching process and increasing female LFP without corresponding demand from firms may not lead to sustainable improvements in women’s employment.
- Integrate policies that foster job creation, business dynamism, and private sector development into strategies aimed at enhancing women’s employment and reducing employment gender gaps.
- Policy tools could include:
  - Tax incentives and subsidies to businesses that hire and retain female employees, especially in sectors where women are underrepresented (Rubolino 2022).
  - Promoting investment in female-led startups and small businesses to spur innovation and create job opportunities aligned with women’s skills and needs (Caliendo and Künn 2015).
  - Implementing training and apprenticeship programs in collaboration with industries to match women’s skills with market demands.
  - Enhancing access to finance for women entrepreneurs and supporting business incubators and accelerators focusing on female entrepreneurship.
  - Attracting foreign investment by creating a stable and predictable business environment, reducing bureaucratic hurdles, and offering incentives for foreign companies to establish operations.
  - Increasing investment efficiency by streamlining regulatory processes, improving infrastructure, and directing investments toward high-effect projects that generate significant employment opportunities for women.

### Recommendations: labor market reforms and flexible work
- Continue labor market reforms to make employment more flexible and inclusive.
- Promote flexible employment arrangements (flexible work schedules, work-from-home opportunities, part-time work) to accommodate diverse needs of women, particularly those with caregiving responsibilities.
- Revise labor laws to protect workers’ rights while allowing for more adaptable work arrangements that can accommodate the needs of both employers and employees.
- Evidence cited: Bloom, Han, and Liang 2022, 2024; Tito, 2024.

### Recommendations: digitalization and governance
- Accelerate digitalization and invest in digital infrastructure and digital literacy programs to create new job opportunities, particularly in technology-driven sectors and remote work, which can be more accessible to women balancing work and family responsibilities (Loko and Yang 2022; Yin, Zhang, and Choi 2023; Yang and others 2024).
- Enhance governance and create a transparent, inclusive business environment by:
  - Ensuring antidiscrimination laws are effectively enforced.
  - Reducing barriers to female entrepreneurship.
  - Fostering an inclusive corporate culture.
  - Strengthening institutions, improving transparency, and ensuring anti-corruption measures are effectively implemented.

### Framing and methodology (brief)
- The employment level identity: E_{g,t} = e_{g,t} · l_{g,t} · s_{g,t} · N_t, where e is the employment rate, l is the LFP rate, s is the population share, and N is total population size.
- Employment gender gap measured as ratios: G^E_t = E_{m,t}/E_{f,t} = G^e_t · G^l_t · G^s_t, with log decomposition giving log(G^E_t) = log(G^e_t) + log(G^l_t) + log(G^s_t).
- Rates of change use log differences: ∆log(G^E_t) = ∆log(G^e_t) + ∆log(G^l_t) + ∆log(G^s_t), allowing attribution of changes in the employment gender gap to employment rates, LFP rates, and population shares.

*Source: IMF Gender Note gnsea2024004 — Conclusion (uses ILO Modelled Estimates; original ILO data accessed March 25, 2024).*

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_Source: https://www.imf.org/-/media/files/publications/gns/2024/english/gnsea2024004.pdf_