## _sdn1520

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---

### Executive summary — links between gender inequality and income inequality: key findings
- Gender wage gaps and gaps in labor force participation directly contribute to income inequality by generating inequality of earnings between sexes and increasing household income dispersion.
- Several dimensions of gender inequality are strongly associated with income inequality across time and across almost 140 countries covered in the study.
- An increase in the multi-dimensional Gender Inequality Index from 0 (perfect gender equality) to 1 (perfect gender inequality) is associated with an increase in net inequality (measured by the Gini coefficient) by almost 10 points.
- Relevant dimensions vary by country income level:
  - Advanced countries: income inequality arises mainly through gender gaps in economic participation.
  - Emerging markets and low-income countries: inequality of opportunity—particularly gender gaps in education and health, and political empowerment—appear to pose the main obstacle to a more equal income distribution.
- Gender inequality is negatively associated with GDP per capita and GDP per capita growth:
  - R² = 0.5876 for the relationship between GII and log of GDP per capita.
  - R² = 0.616 for the relationship between GII and GDP per capita growth.
- Country- and region-level summaries:
  - World average score on the GII is 0.451.
  - Regional averages range from 0.13 percent among European Union countries to nearly 0.58 percent in Sub-Saharan Africa.

### Empirical approach and data notes
- Extended the United Nations’ Gender Inequality Index (GII) to construct a long time series covering two decades for almost 140 countries (constructed GII: 1990–2010; original UNDP GII available for 2008, and from 2011 to 2013; underlying data available from 1990 onward).
- GII combines dimensions of outcome and opportunity: labor market (gap between male and female labor force participation rates), education (difference between secondary and higher education rates for men and women), empowerment (female shares in parliament), and health (maternal mortality ratio and adolescent fertility).
- Primary income inequality data source: Standardized World Income Inequality Database (SWIID). Robustness check: Luxembourg Income Study (LIS) for a subset.
- Data caveat: SWIID includes missing observations generated via model-based multiple imputation estimates.
- Interpolation: data available only every five years were linearly interpolated for 1990–2010; constructed GII correlation with actual GII = 0.97.

### Quantitative results and illustrative statistics
- Growth:
  - A 0.1 reduction in the GII is associated with almost 1 percentage point higher economic growth.
  - Table 1 growth regressions report coefficients for Log (Initial income per capita): –0.1068***, –0.0975***, –0.0539***, –0.0202*** (standard errors reported in the source).
  - UNDP Gender Inequality Index (GII) coefficients: –0.1120**, –0.1131**, –0.3818***, –0.0885* (standard errors reported in the source).
- Inequality correlations (visual R² reported):
  - Income inequality (Net Gini) and re-estimated GII: R² (HIC) = 0.4711; R² (LIC) = 0.0863; R² (MIC) = 0.0757; overall R² = 0.3323.
  - Income share of poorest 10 percent and GII: R² (HIC) = 0.3679; R² (LIC) = 0.3074; R² (MIC) = 0.2196; overall R² = 0.1337.
  - Poverty headcount ratio at US$2 a day (PPP) and GII: R² (HIC) = 0.104; R² (LIC) = 0.3057; R² (MIC) = 0.1871.
  - Poverty headcount ratio at US$1.25 a day (PPP) and GII: R² = 0.104.

### Key empirical findings on distribution (Table 2 highlights)
- GII (United Nations Gender Inequality Index) coefficients and significance:
  - Net GINI: 9.761*.
  - Top 10: 16.81*.
  - Top 60: 10.09**.
  - Bottom 40: –9.367**.
  - Bottom 20: –5.934**.
- Magnitudes across distribution:
  - If the GII increases from the median to the highest levels, the income share of the top 10 percent increases by 5.8 percentage points (difference between Norway and Greece).
  - If the GII increases from the median to the highest levels, the income share of the bottom 20 percent declines by 2 percentage points (similar to the difference between Estonia and Uganda).
- Other covariates (selected coefficients):
  - Financial Openness: 0.0422*** (Net GINI); 0.0310*** (Top 10); 0.0347*** (Top 60); –0.0291*** (Bottom 40); –0.0141** (Bottom 20).
  - Financial Deepening: 0.0233** (Net GINI); 0.0230*** (Top 10); 0.0208** (Top 60); –0.0200** (Bottom 40); –0.00876** (Bottom 20).
  - Educational Attainment: –0.793** (Net GINI); –0.504 (Top 10); –0.481** (Top 60); 0.546*** (Bottom 40); 0.292*** (Bottom 20).
  - Government Spending: –0.320*** (Net GINI); –0.356*** (Top 10); –0.112** (Top 60); 0.132** (Bottom 40); 0.0660** (Bottom 20).
- Sample sizes and fit:
  - Observations (five-year averages): 338 (Net GINI), 208 (Top 10), 244 (Top 60), 244 (Bottom 40), 244 (Bottom 20).
  - Countries: 97 (Net GINI), 66 (Top 10), 89 (Top 60), 89 (Bottom 40), 89 (Bottom 20).
  - Adjusted R-squared: 0.236 (Net GINI), 0.421 (Top 10), 0.359 (Top 60), 0.345 (Bottom 40), 0.305 (Bottom 20).

### Mechanisms linking gender inequality to macro outcomes
- Inequality of economic outcomes:
  - Gender wage gaps and participation gaps raise earnings dispersion.
  - Higher female incidence in informal sector (with lower earnings) widens gender earnings gap and overall income inequality.
- Inequality of opportunities:
  - Gender gaps in education reduce equality of opportunity, lowering expected future incomes and increasing income inequality.
  - Lower financial inclusion among women constrains entrepreneurship and investment in human capital.
- Macroeconomic channels:
  - Gender gaps in economic participation can reduce total factor productivity and GDP growth.
  - Raising female economic participation can mitigate labor force shrinkage effects and support pension system stability.

### Heterogeneity by country group (Table 3 and Figure 10 highlights)
- Across all countries:
  - Labor Force Participation Gender Gap: 0.150* (Net GINI).
  - Education Attainment Gender Gap: 2.520*** (Net GINI).
- Advanced markets (AM):
  - Labor Force Participation Gender Gap: 0.239* (Net GINI).
  - Education Attainment Gender Gap: 0.549 (Net GINI).
  - Interpretation: gender gap in labor force participation is the key aspect affecting income inequality in advanced countries.
- Emerging markets and developing countries (EMDC / EM):
  - Education Attainment Gender Gap: 4.423*** (All countries EMDC/EM subset), 3.997** (EM).
  - Maternal mortality and adolescent fertility show varied significance across groups.
  - Interpretation: in EMDC/EM, gender gaps in opportunities (education and health) are important drivers of income inequality; in low-income countries women’s health is an important driver.
- Table 3 sample sizes and fit:
  - Observations (five-year averages): 338 (All), 139 (AM), 199 (EMDC), 162 (EM).
  - Number of Countries: 97 (All), 32 (AM), 65 (EMDC), 48 (EM).
  - Adjusted R-squared: 0.279 (All), 0.336 (AM), 0.367 (EMDC), 0.357 (EM).

### Causality, instruments, robustness (Table 4 and IV evidence)
- Concern: potential reverse causality between income inequality and gender inequality.
- Instrumental strategy:
  - Instruments for labor force participation gender gap: legal restrictions on women’s economic participation (Guaranteed Equality under the law; Daughter Inheritance rights) and lag of share of female tertiary teachers.
  - Rationale: legal rights affect income inequality only indirectly through labor force participation gap.
- Table 4 highlights:
  - First-stage: Guaranteed Equality = –6.219**; Daughter Inheritance = –2.203** (both strong instruments).
  - Second-stage: Labor Force Participation Gender Gap coefficient = 0.257* (First Stage reported), and education attainment gender gap in second stage = 2.646***.
  - Observations (five-year averages): 241 (both stages).
  - Number of Countries: 64.
  - Angrist-Pischke F-test (P-value): [0.0008].
  - Hansen J-test (P-value): [0.877].
- Conclusion from IV results: widening of the gender gap in labor force participation leads to greater income inequality; results robust to alternative instruments.

### Synthetic Control Method application: Chile (Box 5)
- Method: Synthetic Control Method (SCM) constructs counterfactual using weighted combination of donor countries matching pre-intervention trajectory.
- Chile case:
  - Intervention: 1999 constitutional amendments (articles 1 and 19) legally guaranteeing equality between men and women.
  - Prior evidence: such a change associated with a decrease of 1.3 percent in the labor force participation (LFP) gender gap in emerging markets.
  - SCM results:
    - Chile’s LFP gap decreased a further almost three percent after five years relative to synthetic Chile.
    - The constitutional change induced a reduction in the LFP gap and, using the relationship between LFP gaps and income inequality, had an effect in lowering income inequality relative to synthetic Chile.
  - Caveat: interpret with caution due to potential unobserved confounders (e.g., concurrent redistributive policy changes).

### Policy implications and recommendations
- Overarching message: leveling the economic playing field between men and women reduces overall income inequality and supports growth.
- Prioritize reforms according to country context:
  - Advanced countries: focus on reducing gender gaps in economic participation (labor market policies, barriers to full employment, childcare support, retirement and pension design where relevant).
  - Emerging markets and low-income countries: prioritize equalizing opportunities in education, health, political empowerment, and financial inclusion.
- Remove legal and other obstacles preventing women from reaching full economic potential and exercising equal choice to participate in the economy.
- Redistribution complements but is not a substitute for gender-specific policies; targeted gender-specific interventions are required alongside redistribution to address deeper inequality of opportunities.
- Examples of policy measures discussed:
  - Remove gender-based legal restrictions (constitutional or statutory guarantees of equality; equal inheritance rights).
  - Anti-discrimination laws.
  - Create fiscal space for priority expenditures: foster education (cash transfers conditional on daughters’ school attendance), develop infrastructure (roads, electricity, water), improve access to health services.
  - Revise tax policies: replace family taxation with individual taxation or reduce marginal taxes on second earners; consider tax credits for low-wage earners.
  - Implement well-designed family benefits: parental leave, affordable child care, parity between paternity and maternity leave, flexible work arrangements.
  - Budget gender-responsively (gender-responsive budgeting examples: Bangladesh, Morocco).
  - Make finance accessible to women (microfinance successes in LIDCs).
- Implementation note: significant decreases in gender gaps require integrated policies across many dimensions, including anti-discrimination laws and revision of tax policies; some policies require collaboration with other organizations.

*Source: _sdn1520 (IMF).*

### EXECUTIVE SUMMARY ___________________________________________________________________________ 4

### EXECUTIVE SUMMARY

### Links between gender inequality and income inequality: key findings
- Gender wage gaps and gaps in labor force participation directly contribute to income inequality by generating inequality of earnings between sexes and increasing household income dispersion.
- Several dimensions of gender inequality are strongly associated with income inequality across time and across almost 140 countries covered in the study.
- An increase in the multi-dimensional Gender Inequality Index from 0 (perfect gender equality) to 1 (perfect gender inequality) is associated with an increase in net inequality (measured by the Gini coefficient) by almost 10 points.
- The relevant dimensions of gender inequality vary by country income level:
  - Advanced countries: income inequality arises mainly through gender gaps in economic participation.
  - Emerging markets and low-income countries: inequality of opportunity—particularly gender gaps in education and health, and political empowerment—appear to pose the main obstacle to a more equal income distribution.
- Gender inequality is negatively associated with GDP per capita and GDP per capita growth:
  - Figures report R² = 0.5876 for the relationship between GII and log of GDP per capita.
  - Figures report R² = 0.616 for the relationship between GII and GDP per capita growth.
- Country- and region-level summaries:
  - The world average score on the GII is 0.451.
  - Regional averages range from 0.13 percent among European Union countries to nearly 0.58 percent in Sub-Saharan Africa.

### Empirical approach and data notes
- The study extends the United Nations’ Gender Inequality Index (GII) to construct a long time series covering two decades for almost 140 countries.
- The GII combines dimensions of outcome and opportunity: the labor market (gap between male and female labor force participation rates), education (difference between secondary and higher education rates for men and women), empowerment (female shares in parliament), and health (maternal mortality ratio and adolescent fertility).
- Primary income inequality data source: Standardized World Income Inequality Database (SWIID). Robustness check: Luxembourg Income Study (LIS) data used for a subset of countries.
- Data caveat: SWIID includes missing observations generated via model-based multiple imputation estimates.

### Quantitative results and illustrative statistics
- A 0.1 reduction in the GII is associated with almost 1 percentage point higher economic growth (Box 1).
- Table 1 (growth regressions) reports estimated coefficients including:
  - Log (Initial income per capita): –0.1068***, –0.0975***, –0.0539***, –0.0202*** (standard errors reported in the source).
  - UNDP Gender Inequality Index (GII): –0.1120**, –0.1131**, –0.3818***, –0.0885* (standard errors reported in the source).
- Visual correlations shown in figures:
  - Income inequality (Net Gini) and re-estimated GII: R² (HIC) = 0.4711; R² (LIC) = 0.0863; R² (MIC) = 0.0757; overall R² = 0.3323.
  - Income share of poorest 10 percent and GII: R² (HIC) = 0.3679; R² (LIC) = 0.3074; R² (MIC) = 0.2196; overall R² = 0.1337.
  - Poverty headcount ratio at US$2 a day (PPP) and GII: R² (HIC) = 0.104; R² (LIC) = 0.3057; R² (MIC) = 0.1871.
  - Poverty headcount ratio at US$1.25 a day (PPP) and GII: R² = 0.104 (as reported in figures).

### Mechanisms linking gender inequality to macro outcomes
- Inequality of economic outcomes:
  - Gender wage gaps and participation gaps raise earnings dispersion.
  - Higher female incidence in informal sector (with lower earnings) widens gender earnings gap and overall income inequality.
- Inequality of opportunities:
  - Gender gaps in education reduce equality of opportunity, lowering expected future incomes for excluded groups and increasing income inequality.
  - Lower financial inclusion among women constrains entrepreneurship and investment in human capital, exacerbating income inequality.
- Macroeconomic channels:
  - Gender gaps in economic participation can reduce total factor productivity and GDP growth.
  - Raising female economic participation can mitigate labor force shrinkage effects and support pension system stability.

### Policy implications and recommendations
- Level the economic playing field between men and women to reduce overall income inequality and support growth.
- Prioritize reforms according to country context:
  - Advanced countries: focus on reducing gender gaps in economic participation (labor market policies, barriers to full employment, childcare support, retirement and pension design where relevant).
  - Emerging markets and low-income countries: prioritize equalizing opportunities in education, health, political empowerment, and financial inclusion.
- Remove legal and other obstacles that prevent women from reaching their full economic potential and exercising equal choice to participate in the economy.
- Recognize gender equity as both a development objective and a contributor to more favorable development outcomes and lower income inequality.

*EXECUTIVE SUMMARY — INTERNATIONAL MONETARY FUND*

### Box 2. Measuring Gender Inequality

### Box 2. Measuring Gender Inequality

### Overview of the United Nations Gender Inequality Index (GII)
- The GII is a composite measure of gender inequality in:
  - reproductive health (maternal mortality ratios and adolescent fertility rates),
  - empowerment (share of parliamentary seats and education attainment at the secondary level for both males and females),
  - economic opportunity (labor force participation rates by sex).
- Higher values of the GII can be interpreted to be a loss in human development.
- Drawbacks: complicated functional form; combines indicators that compare men and women with indicators that pertain only to women.
- Preferred over alternatives such as the GDI because one of the GDI’s main components is not observed and is imputed.
- Regressions in the note are also run on subcomponents of the GII; findings are robust to inclusion of these subcomponents.

### Construction, extension, and data coverage
- Original UNDP GII available for 2008, and from 2011 to 2013.
- Underlying data are available from 1990 onward.
- This paper extended the GII from 1990 to 2010.
- Data available only every five years were linearly interpolated.
- Interpolation was not a major concern for five-year panel regressions, though it could limit other analyses.
- Close relationship between the actual and constructed GII: correlation of 0.97.

### Correlation with other gender-related indices
- Other indices include: Economist Intelligence Unit’s Women’s Economic Opportunity Index (WEOI), OECD’s Social Institution and Gender Index (SIGI), World Bank’s Country Policy and Institutional Assessments (CPIA) Gender Equality Rating, World Economic Forum’s Global Gender Gap Index.
- Most other indices were created recently, limiting time coverage for empirical work.
- For overlapping years, the extended GII is highly correlated with other gender-related indices.
- Correlations as presented:
  - SIGI ( 0.89)                        (0.88)
  - WEOI ( 0.66)                        (0.67)
  - Gender CPIA 0.50                          0.60
  - GII (constructed) 1.00                          0.97
  - UNDP GII (original) 1.00
- Note: Negative signs are not an issue because higher values for some indices represent higher inequality, while others represent higher equality.
- Time Coverage by Index (as presented): SIGI(2009, 2012, 2014), WEOI (2010 and 2012), CPIA(2005-2014), GII Constructed (1990-2010), GII Original (2008, 2011-2013).

### Role of the GII in the paper’s empirical analysis
- The paper augments standard determinants of income inequality to include different dimensions of gender inequality using the GII.
- Key empirical finding linking the GII and income inequality:
  - An increase in the GII from 0 (perfect gender equality) to 1 (perfect gender inequality) is associated with an increase in net inequality by almost 10 points.
  - If the GII falls from 0.7 (highest level in the sample, seen in Yemen) to the median level of 0.4 (seen in Peru), the net Gini decreases by 3.4 points (comparable to the difference in net Gini between Mali and Switzerland).

### Limitations and practical considerations
- Time coverage limitations for other indices constrain empirical work.
- Linear interpolation of five-year data was used to create the constructed GII series (1990–2010); suitable for five-year panel regressions but potentially limiting for other analyses.
- The GII’s construction mixes indicators that are sex-comparative and indicators pertaining only to women, which is a methodological caveat.

*Source: Box 2. Measuring Gender Inequality (IMF).*

### 15.      We also find that gender inequality has a strong association with the actual

### _sdn1520 - 15.      We also find that gender inequality has a strong association with the actual

### Gender inequality and income distribution: key findings
- Higher gender inequality is strongly associated with higher income shares in the top 10 percent income group.
- If the GII index increases from the median to the highest levels, the income share of the top 10 percent increases by 5.8 percentage points (difference between Norway and Greece).
- Gender inequality is associated with lower income shares at the bottom of the income distribution.
- If the GII index increases from the median to the highest levels, the income share of the bottom 20 percent declines by 2 percentage points (similar to the difference between Estonia and Uganda).
- Table 2 regression highlights (selected coefficients and significance):
  - United Nations Gender Inequality Index (GII): 9.761* (Net GINI), 16.81* (Top 10), 10.09** (Top 60), –9.367** (Bottom 40), –5.934** (Bottom 20).
  - Financial Openness: 0.0422*** (Net GINI), 0.0310*** (Top 10), 0.0347*** (Top 60), –0.0291*** (Bottom 40), –0.0141** (Bottom 20).
  - Financial Deepening: 0.0233** (Net GINI), 0.0230*** (Top 10), 0.0208** (Top 60), –0.0200** (Bottom 40), –0.00876** (Bottom 20).
  - Educational Attainment: –0.793** (Net GINI), –0.504 (Top 10), –0.481** (Top 60), 0.546*** (Bottom 40), 0.292*** (Bottom 20).
  - Government Spending: –0.320*** (Net GINI), –0.356*** (Top 10), –0.112** (Top 60), 0.132** (Bottom 40), 0.0660** (Bottom 20).
  - Observations (five-year averages): 338 (Net GINI), 208 (Top 10), 244 (Top 60), 244 (Bottom 40), 244 (Bottom 20).
  - Countries: 97 (Net GINI), 66 (Top 10), 89 (Top 60), 89 (Bottom 40), 89 (Bottom 20).
  - Adjusted R-squared: 0.236 (Net GINI), 0.421 (Top 10), 0.359 (Top 60), 0.345 (Bottom 40), 0.305 (Bottom 20).

### Empirical strategy and controls
- Estimation approach: five-year averages in a country and time fixed-effects panel regression over 1980 to 2010.
- Dependent variables: inequality measures (Net GINI) and different percentiles/percentile groups of the income distribution.
- Primary gender inequality measures: United Nations Gender Inequality Index (GII) and its dimensions (labor force participation gaps; measures of empowerment such as share of women in parliament and gender gaps in educational attainment; health indicators such as adolescent fertility and risk of maternal death).
- Controls included:
  - Trade openness (sum of exports and imports relative to GDP).
  - Educational attainment (average years of schooling).
  - Financial openness (sum of foreign assets and liabilities as a share of GDP).
  - Government spending relative to GDP.
  - Technology (share of information and communications technology capital in total capital stock).
  - Financial deepening (ratio of private credit to GDP).
  - Share of population over 65.
  - Labor market institutions (extent of regulations on hiring/firing, minimum wages, collective bargaining).
- Country and time dummies included; residual factors captured by error term.

### Heterogeneity by country group (Table 3 and Figure 10)
- Across all countries:
  - Labor Force Participation Gender Gap: 0.150* (Net GINI).
  - Education Attainment Gender Gap: 2.520*** (Net GINI).
- Advanced markets (AM):
  - Labor Force Participation Gender Gap: 0.239* (Net GINI).
  - Education Attainment Gender Gap: 0.549 (Net GINI).
  - Interpretation: gender gap in labor force participation is the key aspect affecting income inequality in advanced countries.
- Emerging markets and developing countries (EMDC / EM):
  - Education Attainment Gender Gap: 4.423*** (All countries EMDC/EM subset), 3.997** (EM).
  - Maternal Mortality and adolescent fertility show varied significance across groups.
  - Interpretation: in emerging markets and low-income countries, gender gaps in opportunities (education and health) are important drivers of income inequality; in low-income countries women’s health is an important driver.
- Table 3 sample sizes and fit:
  - Observations (five-year averages): 338 (All), 139 (AM), 199 (EMDC), 162 (EM).
  - Number of Countries: 97 (All), 32 (AM), 65 (EMDC), 48 (EM).
  - Adjusted R-squared: 0.279 (All), 0.336 (AM), 0.367 (EMDC), 0.357 (EM).

### Causality, instruments, and robustness (Table 4)
- Concern: potential reverse causality between income inequality and gender inequality.
- Instrumental strategy:
  - Instruments for the gender gap in labor force participation include legal restrictions on women’s economic participation (guaranteed equality under the law; daughter’s inheritance rights) and other instruments such as lag of share of female tertiary teachers.
  - Rationale: legal rights affect income inequality only indirectly through labor force participation gap.
- Table 4 highlights:
  - First-stage: Guaranteed Equality = –6.219**; Daughter Inheritance = –2.203** (both strong instruments).
  - Second-stage: Labor Force Participation Gender Gap coefficient = 0.257* (First Stage reported), and education attainment gender gap in second stage = 2.646***.
  - Observations (five-year averages): 241 (both stages).
  - Number of Countries: 64.
  - Angrist-Pischke F-test (P-value): [0.0008].
  - Hansen J-test (P-value): [0.877].
- Conclusion from IV results: widening of the gender gap in labor force participation leads to greater income inequality; results robust to alternative instruments.

### Synthetic Control Method application: Chile (Box 5 and Figure 11)
- Method: Synthetic Control Method (SCM) constructs counterfactual (“synthetic” country) using weighted combination of donor countries that best match pre-intervention trajectory.
- Chile case:
  - Intervention: 1999 constitutional amendments (articles 1 and 19) legally guaranteeing equality between men and women.
  - Prior evidence: such a change associated with a decrease of 1.3 percent in the labor force participation (LFP) gender gap in emerging markets.
  - SCM results:
    - Chile’s LFP gap decreased a further almost three percent after five years relative to synthetic Chile.
    - The constitutional change induced a reduction in the LFP gap and, using the relationship between LFP gaps and income inequality, had an effect in lowering income inequality relative to synthetic Chile.
- Caveats: results should be interpreted with caution due to potential unobserved confounders (e.g., concurrent redistributive policy changes).

### Conclusions and policy implications
- Main contribution: documents strong association between gender-based economic inequalities and a more unequal overall income distribution.
- Policy implications:
  - Aspiring to equality of opportunities and removing obstacles to women’s full economic participation would increase female economic participation and is associated with higher growth, more favorable development outcomes, and lower income inequality.
  - Redistribution complements but is not a substitute for gender-specific policies aimed at reducing gender and income inequality; redistributive policies can lower income inequality directly and, if not excessive, be pro-growth.
  - To address deeper inequality of opportunities (labor force access, health, education, financial access), targeted gender-specific interventions are required alongside redistribution.
  - A significant decrease in gender gaps requires integrated policies across many dimensions, including anti-discrimination laws and revision of tax policies; some policies require collaboration with other organizations.
- Examples of policy measures discussed:
  - Anti-discrimination laws.
  - Legal reforms guaranteeing equality (example: Chile constitutional change).
  - Policies addressing educational and health gaps and barriers to female labor force participation.

*International Monetary Fund — "Catalyst for Change: Empowering Women and Tackling Income Inequality" (excerpt).*

### 22.      Remove gender-based legal restrictions. Equalizing laws boost female labor force

### 22.      Remove gender-based legal restrictions. Equalizing laws boost female labor force

### Legal restrictions and female labor force participation (LFP)
- Restrictions on women’s rights to inheritance and property, and legal impediments to undertaking economic activities (opening a bank account, pursuing a profession, signing a contract, initiating legal proceedings without husband’s permission) are strongly associated with larger gender gaps in labor force participation (Gonzales and others, 2015).
- Country examples and observed impacts:
  - Namibia equalized property rights for married women and granted women the right to sign a contract, head a household, pursue a profession, open a bank account, and initiate legal proceedings without the husband’s permission in 1996. In the decade that followed, Namibia experienced a 10 percentage point increase in its female labor force participation rate.
  - Peru and Malawi invalidated customary law in 1993 and 1994 respectively, and both experienced significant increases in their female LFP rates.
- Conclusion: Equal access to productive resources for men and women would go a long way to increase productivity and growth in many low-income developing countries (LIDCs).

### Create fiscal space for priority expenditures
- Foster education:
  - Policies to equalize enrollment rates for boys and girls would significantly boost overall education levels in LIDCs.
  - Das and others (2015) find that female labor force participation in India would rise by 2 percentage points with an increase in spending on education of 1 percent of GDP of Indian states.
  - Cash transfers could be designed to be conditional on sending daughters to school to increase girls’ participation in education and thus the human capital stock.
- Develop infrastructure (World Bank, 2012b):
  - Investments in infrastructure and transportation services reduce costs related to work outside the home.
  - In India, poor infrastructure dampens female labor force participation; women living in states with greater access to roads are more likely to be in the labor force (Das and others, 2015).
  - In rural Bangladesh, upgrading and expanding the road network increased female labor supply and incomes.
  - Access to electricity and water sources closer to home frees up women’s time for work outside the house and allows integration into the formal economy.
  - In rural South Africa, electrification was found to have increased women’s labor market participation by 9 percent.
- Improve access to health services:
  - Adequate health care reduces women’s obligations to time-consuming informal health care.
  - In Bangladesh, female labor participation benefited from the government’s Health and Community Services, and the participation rate of young women almost doubled in the late 1990s (World Bank, 2012b).

### Revise tax policies
- Reducing the tax burden for (predominantly female) secondary earners by replacing family taxation with individual taxation or other measures aimed at reducing marginal taxes on second earners can potentially generate large efficiency gains and improve aggregate labor market outcomes.
- Tax credits or benefits for low-wage earners can be used to stimulate labor force participation.

### Implement well-designed family benefits
- Better access to parental leave and high-quality, affordable child care will make it easier for women to seek employment.
- Greater parity between paternity and maternity leave could enable women to share the burden with their partners and return to the labor market at an earlier stage.
- Offering flexible work arrangements and breaking down the barriers between part-time and full-time work would also help.

### Budget gender-responsively
- Gender-responsive budgeting examines the gender impact of government expenditures, policies, and programs and can reduce gender inequalities in education, employment, and health outcomes.
- Bangladesh: incorporation of gender issues in the national budget began in 2005; 20 ministries now compile gender budgeting reports (World Bank, 2012a).
- Morocco: has published a gender report since 2006 with involvement from more than 25 ministries and departments.

### Make finance accessible to women
- In many LIDCs, the availability of microfinance has helped to reduce the gender productivity gap (Kabeer 2005), with higher credit repayment rates among women than among men.

### Definitions and data sources (selected variables)
- Account at financial institution (female and male): percentage age 15+ with an account at a bank, credit union, another financial institution, or post office including respondents who reported having a debit card. Source: World Bank, Global Findex.
- Adolescent fertility rate: number of births per 1,000 women ages 15–19. Source: World Bank, WDI.
- Daughter inheritance: Do sons and daughters have equal inheritance rights to property from their parents? Source: World Bank, Women, Business, and the Law (WBL).
- Guaranteed equality: Does the constitution guarantee equality before the law? Source: World Bank, WBL.
- Labor force participation rates: proportion of the population ages 15 and older that is economically active (female and male). Source: World Bank, WDI.
- Literacy rate: Adult (15+) literacy rate (percent). Source: World Bank, WDI.
- Maternal mortality ratio: number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. Source: World Bank, WDI.
- Proportion of seats held by women in national parliaments: percentage of parliamentary seats held by women. Source: World Bank, WDI.
- Secondary education: Percentage completed at the secondary level (female and male). Source: Barro-Lee Educational Attainment Data.

### Econometric methodology and robustness
- Fixed effects regression model estimated over the period from 1980 to 2012 for a global sample (advanced, emerging, developing countries).
- Country fixed effects used to control for country-specific drivers of income inequality.
- Hausman test indicates fixed effects model is appropriate for panel regressions.
- Robustness checks include:
  - Alternatives to net Gini (Luxembourg Income Study Gini, market Gini).
  - Human capital from Penn World Tables as alternative to total years of education.
  - Education spending and social spending as alternatives to government spending.
  - Controls for female mortality and education Gini.
  - Inclusion of gender wage gap in OECD regressions.
- Instrumental variables (IV) regressions to address causality concerns:
  - Instruments: legal restrictions on women’s economic participation (guaranteed equality under law and daughter’s inheritance rights are strongest instruments).
  - Using these instruments, widening gender gap in LFP leads to greater income inequality. Tests for instrument validity and over identification suggest validity.
  - Alternative instruments used: lag of the share of female tertiary teachers; lag of the LFP gap. Both were significant in first stage and valid by Hansen test; both significant in second stage.
- Synthetic Control Method (SCM) applied where feasible (data limitations: LFP gap data available starting in 1990; few changes in guaranteed equality since 1990). Donor pool restricted to 45 emerging market economies for comparability; explanatory variables included average values of fertility and education for LFP gaps.
- Results are robust to placebo and sensitivity tests; sensitivity tests excluding individual countries did not dramatically change results for LFP gaps, though small donor samples for net GINI could produce poor fit if key countries removed.

*Source: _sdn1520 - 22.      Remove gender-based legal restrictions. Equalizing laws boost female labor force (IMF PDF).*

### References

### _sdn1520 - References

### Methodology, Econometrics, and Empirical Techniques
- Abadie, Alberto, Alexis Diamond, and Jens Hainmuller, 2010, “Synthetic Control Method for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program.” Journal of the American Statistical Association 105 (490): 493–505.  
- Lazear, E., and S. Rosen, 1981, “Rank-Order Tournaments as Optimum Labor Contracts.” Journal of Political Economy 89 (5): 841–64.  
- Mincer, J., 1958, “Investment in Human Capital and Personal Income Distribution.” Journal of Political Economy 66 (2): 281–302.  
- Becker, G. S., and B. R. Chiswick, 1966, “Education and the Distribution of Earnings.” American Economic Review 56 (1/2): 358–69.  
- Das, Sonali, Sonali Jain-Chandra, Kalpana Kochhar, and Naresh Kumar, 2015, “Women Workers in India: Why So Few among So Many?” Working Paper 15/55 (Washington: International Monetary Fund).  
- Demirguc-Kunt, Asli, and others, 2015, The Global Findex Database 2014 (Findex 2014): measuring financial inclusion around the world (English)., 2015. Policy Research Working Pape; no. WPS 7255 (Washington: World Bank).  

### Inequality, Distribution, and Growth
- Aghion, P., E. Caroli, and C. Garcia-Penalosa, 1999, “Inequality and Economic Growth: The Perspective of the New Growth Theories.” Journal of Economic Literature 37 (4): 1615–60.  
- Barro, R. J., 2000, “Inequality and Growth in a Panel of Countries.” Journal of Economic Growth 5 (1): 5–32.  
- Galor, O., and O. Moav, 2004, “From Physical to Human Capital Accumulation: Inequality and the Process of Development.” Review of Economic Studies 71 (4): 1001–26.  
- Galor, O., and J. Zeira, 1993, “Income Distribution and Macroeconomics.” The Review of Economic Studies 60 (1): 35–52.  
- Carvalho, L., and A. Rezai, 2014, “Personal Income Inequality and Aggregate Demand.” Working Paper 2014–23, Department of Economics, University of São Paulo, São Paulo.  
- Castello-Climent, A., and R. Domenech, 2014, “Capital and Income Inequality: Some Facts and Some Puzzles (Update of WP 12/28 published in October 2012).” Working Paper 1228, Economic Research Department, (Madrid: BBVA Bank).  
- Van der Weide, R., and B. Milanovic, 2014, “Inequality Is Bad for Growth of the Poor (But Not For That of the Rich).” Policy Research Working Paper 6963 (Washington: World Bank).  
- Ostry, Jonathan, Andrew Berg, and Charalambos Tsangarides, 2014, “Redistribution, Inequality, and Growth.” Staff Discussion Note, SDN/14/02 (Washington” International Monetary Fund).  

### Gender, Labor Markets, and Development
- Duflo, E., 2012, “Women Empowerment and Economic Development.” Journal of Economic Literature 50 (4): 1051–79.  
- Elborgh-Woytek, Katrin, Monique Newiak, Kalpana Kochhar, Stefania Fabrizio, Kangni Kpodar, Philippe Wingender, Benedict Clements, and Gert Schwartz, 2013, “Women, Work, and the Economy: Macroeconomic Gains from Gender Equity.” Staff Discussion Note, SDN/13/10, (Washington: International Monetary Fund).  
- Esteve-Volart, Berta, 2004, “Gender Discrimination and Growth: Theory and Evidence from India,” LSE STICERD Research Paper DEDPS 42, (London).  
- Klasen, S., 1999, “Does Gender Inequality Reduce Growth and Development? Evidence from Cross-Country Regressions.” Policy Research Report, Engendering Development Working Paper No. 7 (Washington: World Bank).  
- Klasen, Stephan, and Francesca Lamanna, 2009, “The Impact of Gender Inequality in Education and Employment on Economic Growth: New Evidence for a Panel of Countries.” Feminist Economics, Vol. 15(3) pp. 91–132.  
- Cuberes, David, and Marc Teignier, 2015, “Aggregate Costs of Gender Gaps in the Labor Market: A Quantitative Estimate.”: Journal of Human Capital, forthcoming.  
- Cuberes, David, and Marc Teignier, 2012, “Gender Gaps in the Labor Market and Aggregate Productivity.”: Sheffield Economic Research Paper 2012017, Sheffield University.  
- Gonzales, Christian, Sonali Jain-Chandra, Kalpana Kochhar, and Monique Newiak. 2015, “Fair Play: More Equal Laws Boost Female Labor Force Participation.” Staff Discussion Note, SDN/15/02, (Washington: International Monetary Fund).  
- Steinberg, Chad, and Masato Nakane, 2012, “Can Women Save Japan?” Working Paper 12/48 (Washington: International Monetary Fund).  
- Rubalcava, L., G. Teruel, and D. Thomas, 2004, “Spending, Saving and Public Transfers to Women.” California Center of Population Research UCLA, On-Line Working Paper Series, CCPR-024-04.  
- Kabeer, Naila, 2005, “Is Microfinance a ‘Magic Bullet’ for Women’s Empowerment? Analysis of Findings from South Asia.” Economic and Political Weekly, October 29, pp. 4709–18.  
- Miller, Grant, 2008, “Women’s Suffrage, Political Responsiveness, and Child Survival in American History.” The Quarterly Journal of Economics (August): 1287–326.  
- Thomas, D., 1990, “Intra-Household Resource Allocation. An Inferential Approach.” The Journal of Human Resources 25 (4) 635–64.  

### Policy, Institutions, and International Reports
- International Monetary Fund, 2013, “Jobs and Growth—Analytical and Operational Considerations for the Fund.” Policy Paper SM/13/73.  
- International Monetary Fund, 2015, “Inequality and Economic Outcomes in Sub-Saharan Africa.” October 2015 Regional Economic Outlook: Sub-Saharan Africa. Forthcoming.  
- International Monetary Fund, 2015, “Does Inequality Constrain Economic Outcomes in Sub-Saharan Africa?” Sub-Saharan Africa Regional Economic Outlook, forthcoming, Washington.  
- Jaumotte, Florence, and Carolina Osorio Buitron, 2015, “Inequality and Labor Market Institutions.” Staff Discussion Note, SDN/15/14 (Washington: International Monetary Fund)  
- Dabla-Norris, Era, Kalpana Kochhar, Nujin Suphaphiphat, Frantisek Ricka, and Evridiki Tsounta. 2015a. “Causes and Consequences of Inequality: A Global Perspective.” Staff Discussion Note, SDN/15/13, (Washington: International Monetary Fund).  
- Honohan, P., 2007, “Cross-Country Variation in Household Access to Financial Services.”  
- World Bank, 2012a, Accelerating Progress on Gender Mainstreaming and Gender-Related MDGs: A Progress Report. IDA 16 Mid-term Review. (Washington).  
- World Bank, 2012b, World Development Report 2013: Jobs. (Washington).  
- World Bank, 2013, Women, Business and the Law 2014 Removing Restrictions to Enhance Gender Equality (Washington).  
- World Bank, 2015, World Development Indicators. (Washington).  
- Organization of Economic Cooperation and Development, 2012, Closing the Gender Gap: Act Now: OECD Publishing, 2012.  
- Organization of Economic Cooperation and Development, 2015, “In It Together: Why Less Inequality Benefits All, OECD Publishing”. Paris. DOI: http://dx.doi.org/10.1787/9789264235120-en  
- World Economic Forum, 2014, The Global Gender Gap Report 2014. (Geneva: WEF).  
- Murray, C., A. Lopez, and M. Alvarado, 2013, “The State of US Health, 1990–2010: Burden of Diseases, Injuries, and Risk Factors.” Journal of the American Medical Association 310 (6): 591–606.  

### Country and Regional Studies
- Das, Sonali, Sonali Jain-Chandra, Kalpana Kochhar, and Naresh Kumar, 2015, “Women Workers in India: Why So Few among So Many?” Working Paper 15/55 (Washington: International Monetary Fund).  
- Esteve-Volart, Berta, 2004, “Gender Discrimination and Growth: Theory and Evidence from India,” LSE STICERD Research Paper DEDPS 42, (London).  

*Source: _sdn1520 - References*

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