## wpiea2019112

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---

### I. Introduction and scope
- Women are disproportionately overrepresented in the informal economy in more than 90 percent of countries in sub-Saharan Africa (SSA).
- Average shares of informal employment in SSA:
  - Non-agricultural sector: women 83 percent; men 72 percent.
  - Including agricultural sector: women 94 percent; men 89 percent.
- Key characteristics of informal employment: less stability, lack of social protection, lower earnings, and higher gender gaps.
- Gender wage gap reported by UN Women (2016):
  - Informal sector in SSA: 28 percent.
  - Formal sector in SSA: 6 percent.
- Paper objectives and approaches:
  - Cross-country analysis of association between female overrepresentation in informality and gender gaps (education, social norms, legal framework).
  - Micro-level empirical analysis using probit models on Senegal household/survey data.
- Rationale for focus on Senegal:
  - Employment in informal sector: Senegal 91 percent vs SSA average 92 percent (ILO).
  - Informal sector share of GDP: Senegal 40 percent vs SSA 38 percent (IMF Regional Economic Outlook, Sub-Saharan Africa, April 2017).
  - Availability of good quality micro-level survey data for Senegal.

### II. Cross-country associations and key empirical findings
- Associations across countries:
  - Higher female presence in the informal sector associated with larger gender gaps in education, fewer family planning needs being satisfied, and higher rates of early marriage.
  - Higher Global Gender Gap Index (GGI) values associated with lower relative rates of women in informal employment.
- Informality levels and composition:
  - ILO (2018) estimates: 70 percent of employment in developing and emerging economies is informal; 18 percent in developed economies.
  - Informal work in SSA: 92 percent of total employment (ILO, 2018).
  - IMF (2017): informal sector in SSA accounted for 38 percent of GDP between 2010 and 2014.
  - Cross-country variation: Mauritius ~20 percent of GDP; Nigeria >60 percent of GDP in informal economy.
  - Informal jobs concentrated in agriculture; formal jobs concentrated in services.
  - Occupational types: most informal workers in SSA are own account workers; male informal workers more often employees (32 percent); female informal workers more often contributing family workers (24 percent).
- Education and returns:
  - More than 90 percent of low-skilled workers are employed in the informal economy in SSA.
  - Among workers with no education, 95 percent are in the informal sector; with only primary education, 90 percent; with tertiary education, 27 percent.
  - Average female years of schooling is 70 percent that of male years of schooling in SSA (with ratios around 30 percent in Chad and Guinea).
  - Returns to education (Psacharopoulos & Patrinos, 2004): female return on secondary education 18.4 percent; primary 12.8 percent; post-secondary 10.8 percent.
- Family planning, early marriage, and education:
  - Early childbearing increases probability of dropping out (e.g., Herrera & Sahn (2014) for Madagascar: +42 percent dropouts; −44 percent chances of completing secondary).
  - Cross-country correlation: negative correlation of 0.53 between gender gaps in informal employment and female-to-male ratio in net secondary school enrollment (14 SSA countries).
  - Early marriage correlated with higher female informal employment relative to men (correlation 0.54, 57-country sample with 24 SSA).

### III. Micro-evidence from Senegal (probit estimations)
- Data and definitions:
  - Data: ESPS II, 2011; workers aged 15–64.
  - Formal worker defined as paid worker with formal contract (11.9 percent) and/or declared affiliation through employer to social security (7.7 percent); total formal share under classification: 14.3 percent.
- Sample sizes (Table 2):
  - All workers: 47,169
  - Female workers (all areas): 19,367
  - Male workers (all areas): 27,802
  - Urban workers: 19,069
  - Urban female workers: 7,583
  - Urban male workers: 11,486
  - Rural workers: 28,100
  - Rural female workers: 11,783
  - Rural male workers: 16,316
- Model fit (Pseudo R2, Table 2):
  - All workers: 0.2435
  - Female workers: 0.2209
  - Male workers: 0.2588
  - Urban workers: 0.2212
  - Urban female workers: 0.2326
  - Urban male workers: 0.2159
  - Rural workers: 0.1021
  - Rural female workers: 0.0919
  - Rural male workers: 0.1218

Key marginal effects and summary (preserve exact reported values):
- Gender:
  - Being female increases probability of being informal by 0.0340 (3.4 percentage points)*** for all workers.
  - Urban: being female increases probability by 0.0850 (8.5 percentage points)***.
  - Rural: being female increases probability by 0.0088 (0.88 percentage points)*.
- Education (negative marginal effects imply lower probability of informality):
  - No education: All workers 0.0336***.
  - Primary Education:
    - All workers: -0.1640*** (16.40 percentage points).
    - Urban workers: -0.2547*** (25.47 percentage points).
    - Urban female workers: -0.3120*** (31.20 percentage points).
    - Urban male workers: -0.2248*** (22.48 percentage points).
    - Rural male workers: -0.0720*** (7.20 percentage points).
    - Rural female workers: -0.0461** (4.61 percentage points).
  - Secondary Education:
    - All workers: -0.5553*** (55.53 percentage points).
    - Female workers (all areas): -0.6098*** (60.98 percentage points).
    - Male workers (all areas): -0.5384*** (53.84 percentage points).
    - Urban workers: -0.6044*** (60.44 percentage points).
    - Urban female workers: -0.6643*** (66.43 percentage points).
    - Urban male workers: -0.5707*** (57.07 percentage points).
    - Rural workers: -0.4577*** (45.77 percentage points).
  - Tertiary Education:
    - All workers: -0.7285*** (72.85 percentage points).
    - Female workers (all areas): -0.6551*** (65.51 percentage points).
    - Male workers (all areas): -0.7625*** (76.25 percentage points).
    - Urban female workers: -0.6990*** (69.90 percentage points).
    - Urban male workers: -0.6939*** (69.39 percentage points).
    - Rural tertiary coefficients include -0.7793*** and other large negatives.
    - Note: tertiary educated paid workers are 0.5 percent of paid workers; only 22 observations correspond to female workers with tertiary education in sample of 163,490 observations.
- Household headship:
  - Being head of household reduces probability of being informal by -0.0249*** (2.49 percentage points) for all workers.
  - Urban headship effect: -0.0530*** (5.30 percentage points).
  - Female subsample: head of household improves probability of being formal by 0.028 (2.8 percentage points) (significant at 5 percent).
- Marital status and children:
  - Married men: -0.0213*** (2.13 percentage points) lower probability of being informal (All workers); men specifically: -0.0392*** (3.92 percentage points).
  - Urban married men: -0.0477*** (4.77 percentage points).
  - Number of kids:
    - Male samples: each additional child reduces probability of being informal by -0.0059** (0.59 percentage points).
    - Urban females: each additional child increases probability of being informal by 0.0139** (1.39 percentage points).
    - Average fertility: women in Senegal have on average five children.
- Income decile (1 to 10):
  - All workers: -0.0116*** (1.16 percentage points) per decile.
  - Female workers: -0.0060*** (0.60 percentage points) per decile.
  - Male workers: -0.0161*** (1.61 percentage points) per decile.
  - Urban male workers: -0.0310*** (3.10 percentage points) per decile.
  - Wealth improves probability of being formal faster for men than for women (men: 1.6 percent per decile; urban men: 3.1 percent per decile; women: 0.6 percent per decile; urban women: 1.6 percent per decile as summarized in text).
- Age groups and regional effects:
  - Age dummies: older age groups generally show substantially negative marginal effects (example: Age 30-34 All workers -0.0910***).
  - Regional coefficients reported for Dakar, Diourbel, Kaffrine, Kaolack, Kedougou, Louga, Saint Louis, Sedhiou, Thies with negative and significant marginal effects in many cases (e.g., Dakar -0.0344*** for All workers).

Robustness checks — Probit with interactions (Table 4: All Workers sample):
- Model statistics:
  - Prob > chi2 = 0.000.
  - Pseudo R2 = 0.2486.
  - Log likelihood = -1252692.1.
  - Number of Observations = 47,169.
- Selected interaction coefficients (exact reported values):
  - male with primary education: -0.617; female with primary education: -0.887.
  - male with secondary education: -1.612; female with secondary education: -1.932.
  - male . married: -0.259; female . married: 0.074.
  - male . number of kids: -0.036; female . number of kids: 0.031.
  - male . decile: -0.082; female . decile: -0.041.
- Hypothesis tests reported (exact values):
  - Gender × primary education: Chi2 = 10.49 P-value = 0.0006.
  - Gender × secondary education: Chi2 = 2.86 P-value = 0.0454.
  - Gender × decile: Chi2 = 81.24 P-value = 0.0000.

Robustness checks — Urban Workers sample (Table 5) and Rural Workers sample (Table 6):
- Urban (19,069 observations):
  - Prob > chi2 = 0.000; Pseudo R2 = 0.2248; Log likelihood = -720720.94.
  - male with primary education: -0.621; female with primary education: -0.988.
  - male with secondary education: -1.586; female with secondary education: -1.912.
  - male . decile: -0.100; female . decile: -0.063.
  - Gender × primary education: Chi2 = 12.74 P-value = 0.0002.
  - Gender × decile: Chi2 = 64.90 P-value = 0.0000.
- Rural (28,099 observations):
  - Prob > chi2 = 0.000; Pseudo R2 = 0.1052; Log likelihood = -517337.67.
  - male with primary education: -0.481; female with primary education: -0.398.
  - male with secondary education: -1.568; female with secondary education: -2.156.
  - male . decile: -0.070; female . decile: -0.026.
  - Gender × decile: Chi2 = 24.89 P-value = 0.0000.

### IV. Interpretation and mechanisms
- Informal sector roles:
  - Informal jobs can be both a poverty-trap (last-resort strategy) and reflect worker choice (voluntary-choice), consistent with mixture view (Fields, 1990; Perry et al., 2007).
  - Informal jobs offer proximity and flexibility but often yield lower skills and earnings, lowering incentives to invest in girls’ education and perpetuating gender gaps.
- Identified drivers of women’s overrepresentation:
  - Lower education levels for women (primary and secondary completion strongly reduce informality, especially for women).
  - Traditional gender roles and social norms: time allocation data show women spend substantially more time on unpaid care (e.g., Senegal: women spend on average six times more time than men taking care of family and household chores).
  - Labor market discrimination and legal barriers (detailed legal constraints across SSA and in Senegal).
  - Early marriage and early pregnancy: 23 percent of female adolescents aged 15–19 are married in Senegal; married female adolescents average 2.6 years of education vs 3.9 years for unmarried female adolescents.

### V. Policy recommendations
- Increase girls’ educational attainment:
  - Focus on completion of primary and secondary education.
  - Improve access, quality, and effectiveness of education systems.
  - Use targeted cash transfers to incentivize parents to keep daughters in school until completion of primary and secondary education where social barriers exist.
  - Prohibit child marriage and disseminate information on women’s health to reduce early dropouts.
- Change legal framework to ensure equality before the law:
  - Remove legal differences that restrict women’s access to institutions, assets, credit, mobility, and choice.
  - Enforce property rights including inheritance.
  - Combat domestic violence, sexual harassment and child marriage.
- Family planning and reproductive health:
  - Run campaigns and provide quality health care, sexual education, and modern contraceptives to meet unmet demand and reduce early pregnancy.
- Invest in infrastructure:
  - Reduce time spent on home production by facilitating access to water and energy.
  - Provide safe transportation to workplaces and schools to expand women’s labor market participation.
- Address social norms and enforcement:
  - Enforce civil laws where customary laws reduce women’s freedom and power.
  - Promote equal division of household labor through education campaigns and paternity leave.
  - Government to act as role model within administrations.
- Labor market and fiscal policies:
  - Enact laws against gender discrimination and sexual harassment.
  - Provide childcare subsidies or organize childcare facilities and parental leave (government-provided).
  - Use fiscal policies (tax breaks, subsidies) for families with young children and ensure income tax does not penalize secondary wage earners.
  - Improve access to credit and assets to promote equal entrepreneurship opportunities.
- Methodological caveat:
  - Lack of full causal proof for some channels noted, but many recommendations rest on strong empirical evidence (e.g., more education → better salaries and living standards).

### VI. Contribution to literature and related findings
- Places analysis within two strands:
  - Dual hypothesis: informality as last-resort strategy (Lewis, 1954; Harris and Todaro, 1970; Rauch, 1991; Magnac, 1991).
  - Voluntary-choice hypothesis: informality as worker’s choice (Maloney 1999, 2004; Levy 2007).
  - Paper aligns with view that informality is a combination of last-resort strategy and voluntary choice (Fields, 1990; Perry et al., 2007).
- Related empirical findings cited:
  - Gunther and Launov (2012): returns to education and experience higher in formal sector; wider gender wage gap in informal sector (Cote d’Ivoire).
  - McCaig and Pavcnik (2015): educated male urban workers more likely to switch to formal sector (Vietnam).
  - De Mel et al. (2008): female education more important determinant than male education for choosing formal wage work vs informal own-account work (Sri Lanka).
  - Ahn et al. (2019): female workers more likely to work informally; younger and less educated workers more likely to be employed in informal sector (57 countries).
  - IMF (2018a) and Marzo & Atuesta (forthcoming): analyses of Senegal on gender gaps in education, participation, productivity, and earnings.

### VII. Conclusion (synthesis)
- Women are disproportionately overrepresented in the informal economy in SSA and Senegal.
- Key drivers: lower female educational attainment, social norms and unpaid care burdens, legal barriers, early marriage and early pregnancy, and unequal wealth gains from formalization.
- Feedback loop identified: low expected returns for daughters → less parental investment in girls’ education → lower female education → higher likelihood of remaining in informal sector → perpetuation of gender inequality.
- Policy package recommended: expand girls’ education (especially secondary), remove legal discrimination, provide family planning and reproductive health services, invest in infrastructure and childcare, enforce anti-discrimination and anti-harassment laws, and use fiscal measures to remove penalties on secondary earners; timely policy action urged despite limitations in causal identification.

*Source: wpiea2019112 - REFERENCES*

### REFERENCES .............................................................................................................

### wpiea2019112 - REFERENCES

### I. Introduction and scope
- Women are disproportionately overrepresented in the informal economy in more than 90 percent of countries in sub-Saharan Africa (SSA).
- Average shares of informal employment:
  - Non-agricultural sector in SSA: women 83 percent; men 72 percent.
  - Including agricultural sector in SSA: women 94 percent; men 89 percent.
- Key characteristics of informal employment: less stability, lack of social protection, lower earnings, and higher gender gaps.
- Gender wage gap reported by UN Women (2016):
  - Informal sector in SSA: 28 percent.
  - Formal sector in SSA: 6 percent.
- Paper objectives and approaches:
  - Cross-country analysis of association between female overrepresentation in informality and gender gaps (education, social norms, legal framework).
  - Micro-level empirical analysis using probit models on Senegal household/survey data.
- Rationale for focus on Senegal:
  - Employment in informal sector: Senegal 91 percent vs SSA average 92 percent (ILO).
  - Informal sector share of GDP: Senegal 40 percent vs SSA 38 percent (IMF Regional Economic Outlook, Sub-Saharan Africa, April 2017).
  - Ratio of female-to-male employment in informal sector close to SSA average (ILO).
  - Availability of good quality micro-level survey data for Senegal.

### II. Cross-country associations and key empirical findings
- Associations found across countries:
  - Higher female presence in the informal sector is associated with larger gender gaps in education.
  - Higher female presence is associated with fewer family planning needs being satisfied.
  - Higher female presence is associated with higher rates of early marriage.
- Role of education:
  - Women tend to receive less education than men.
  - Formal jobs often require more skills and education than informal jobs, making education a key factor in formal-sector access.
- Legal and normative barriers:
  - Several laws in SSA restrict women’s economic possibilities: inability to get a job without husband’s permission, limited decision-making for the household, travel restrictions, inability to administer marital property, inability to perform same jobs as men, or inability to open a bank account.
  - In more than half of SSA countries, women’s access to finance is not protected by law; in several countries inheritance and property rights differ from men’s (World Bank, 2018).

### III. Micro-evidence from Senegal (probit estimations)
- Gender differential in informality (controlling for observables):
  - Women in urban areas are 8.5 percentage points more likely to work in the informal sector than men.
- Education effects on informality (gender-differentiated):
  - Completing secondary education decreases a working woman’s chances of being in the informal sector by 61 percentage points.
  - Completing secondary education decreases a working man’s chances of being in the informal sector by 54 percentage points.
  - Attaining primary and secondary education is generally more important for women than for men in reducing informality probability.
- Demographic effects:
  - Marriage and children reduce a man’s probability of being in the informal sector.
  - For each additional child in the household:
    - Working man: probability of being in the informal sector decreases by 0.6 percent.
    - Female worker in urban areas: probability of being in the informal sector increases by 1.4 percent.
- Wealth effects:
  - Male workers enjoy a lower probability of being in the informal sector the wealthier they become at a faster pace than female counterparts.

### IV. Interpretation and mechanisms
- Informal sector can be both a poverty trap and reflect choice:
  - Informal jobs may offer proximity to home and flexibility but often yield lower skills and earnings, reducing incentives to invest in girls’ education and perpetuating gender gaps.
- Drivers of women’s overrepresentation in informality include:
  - Lower levels of education for women.
  - Traditional gender roles and social norms.
  - Labor market discrimination.
  - Gender-biased laws and legal barriers.

### V. Policy recommendations
- Governments have policy levers to reduce discrimination and women’s overrepresentation in the informal economy:
  - Invest in physical and human capital needed for quality education.
  - Remove discriminatory barriers from the legal framework.
  - Provide family planning to women and families that desire it.
  - Improve infrastructure to facilitate access to formal employment.
- Goal: reduce gender gaps in education and employment to raise female transitions to formal-sector work and improve long-run growth.

### VI. Contribution to literature and related findings
- Places the analysis within two strands on informality:
  - Dual hypothesis: informal employment as last-resort strategy (Lewis, 1954; Harris and Todaro, 1970; Rauch, 1991; Magnac, 1991).
  - Voluntary-choice hypothesis: informal employment as worker’s choice (Maloney 1999, 2004; Levy 2007).
  - Paper aligns with view that informality is a combination of last-resort strategy and voluntary choice (Fields, 1990; Perry et al., 2007).
- Related empirical findings from other studies:
  - Gunther and Launov (2012): returns to education and experience higher in formal sector; wider gender wage gap in informal sector (Cote d’Ivoire).
  - McCaig and Pavcnik (2015): educated male urban workers more likely to switch to formal sector (Vietnam).
  - De Mel et al. (2008): female education more important determinant than male education when choosing formal wage work vs informal own-account work (Sri Lanka).
  - Ahn et al. (2019): female workers more likely to work informally; younger and less educated workers more likely to be employed in informal sector (57 countries).
  - IMF (2018a) and Marzo & Atuesta (forthcoming): analyses of Senegal on gender gaps in education, participation, productivity, and earnings.

### VII. Structure of the paper (remaining sections)
- Section II: Cross-country evidence of gender gaps in informality and other dimensions.
  - Section II.A: Differences in educational attainment between boys and girls in SSA.
  - Section II.B: Importance of social norms and family planning.
  - Section II.C: Gender gaps in the legal framework of SSA countries.
- Section III: Micro-data estimations for Senegal—impact of gender, education level, and demographic characteristics on probability of informal employment.

*Source: Excerpt from wpiea2019112 - REFERENCES*

### Section IV concludes the paper and offers policy recommendations based on the cross-country

### Section IV concludes the paper and offers policy recommendations based on the cross-country

### II. INFORMAL SECTOR AND GENDER GAPS
- The informal economy is large globally, particularly in developing and emerging economies.
- ILO (2018) estimates: 70 percent of employment in developing and emerging economies is informal; 18 percent in developed economies.
- Informal work in sub-Saharan Africa (SSA): 92 percent of total employment (ILO, 2018).
- IMF (2017) estimates the informal sector in SSA accounted for 38 percent of GDP between 2010 and 2014.
- Cross-country variation in SSA:
  - Mauritius: informal sector around 20 percent of GDP.
  - Nigeria: informal economy more than 60 percent of GDP.
- Sectoral composition in SSA:
  - Informal jobs concentrated in agriculture.
  - Formal jobs concentrated in services.
- Gender composition:
  - In half of SSA countries the share of female workers in the informal sector is larger than 95 percent.
  - No SSA country has male share in informal sector larger than 95 percent.
  - Even excluding agriculture, informality dominates in all SSA countries; women work on average more often in the informal sector (ILO, 2018).
- Occupational types:
  - Most informal workers in SSA are own account workers (both men and women).
  - After own account workers: male informal workers tend to be employees (32 percent); female informal workers tend to be contributing family workers (24 percent).
    - Contributing family workers: “hold self-employment jobs in an establishment operated by a related person, with a too limited degree of involvement in its operation to be considered a partner.”
- Cross-country association:
  - Higher gender gaps in the informal sector are associated with higher levels of gender inequality (as measured by the World Economic Forum’s 2018 Global Gender Gap Index, GGI).
  - Higher GGI values (more overall gender equality) associated with lower relative rates of women in informal employment.

### A. Women are less educated than men
- Informal jobs are disproportionately held by low-skilled workers with zero or little formal education.
- ILO (2018) estimates:
  - More than 90 percent of low-skilled workers are employed in the informal economy in SSA.
  - Among workers with no education, 95 percent are employed in the informal sector.
  - Among workers with only primary education, 90 percent are in the informal sector.
  - Only 27 percent of workers with tertiary education are in the informal economy.
- Gender education gaps in SSA:
  - Average female years of schooling is 70 percent that of male years of schooling.
  - In countries like Chad and Guinea, the ratio is around 30 percent.
- GDP losses from incomplete education (Patrinos, 2008, analysis of 13 countries including nine in SSA):
  - Investing in girls to ensure completion of the level they dropped out from can lead to lifetime earnings equivalent to up to 68 percent of annual GDP (the 68 percent case is secondary education in Burundi).
  - Estimated GDP loss due to secondary school dropouts: 48 percent for Kenya, 32 percent for Tanzania, 35 percent for Uganda, 24 percent for Senegal.
- Returns to education (Psacharopoulos & Patrinos, 2004):
  - Women’s return on secondary education: 18.4 percent.
  - Women’s return on primary education: 12.8 percent.
  - Women’s return on post-secondary education: 10.8 percent.
  - Latin American and sub-Saharan African countries have the highest returns on education in the world.
- Early marriage and early childbearing:
  - Early marriage is one of the main reasons for school dropouts.
  - Herrera & Sahn (2014) estimate for Madagascar: early childbearing increases probability of dropping out of school by 42 percent and decreases chances of completing secondary school by 44 percent.
- Family planning and secondary school enrollment:
  - Countries with more family planning needs being attended to have more girls in secondary school.
  - Figure 4 shows a strong correlation between the female-to-male secondary education enrollment ratio and the percentage of women reporting their family planning needs are attended by modern methods (including 14 SSA countries).
- Urban-rural divide in education (Senegal example):
  - Boys and girls ages 10-14 in rural Senegal have approximately 1.5 years less education than urban counterparts (ESPS II, 2011).
  - For ages 15-19, urban students have completed approximately twice as many years of education as rural counterparts.
  - Gender gaps in years of education are larger in urban areas than rural areas in absolute years (1.3 vs 0.7 years), but in percentage terms women in rural areas complete much less education: urban difference averages 31 percent; rural increases to 57 percent.
- Cross-country relationship:
  - Negative correlation of 0.53 between gender gaps in informal employment (female-to-male ratio of non-agricultural informal employment) and female-to-male ratio in net secondary school enrollment (14 SSA countries).
- Senegal specific education statistics:
  - Women working in the formal sector: average 6.0 years of education; men: 6.5 years.
  - In the informal sector: female workers average 1.3 years; male workers average 1.9 years.

### B. Social norms and lack of family planning curtail women’s competitiveness
- Traditional gender roles and social norms:
  - Reduce women’s competitiveness in the formal labor market.
  - Impose higher unpaid care work and household responsibilities on women and girls (UNICEF, 2016).
- Time allocation examples:
  - Senegal (calculation using ESPS II, 2011): women spend on average six times more time than men taking care of the family and doing household chores.
  - Wodon & Blackden (2006) examples:
    - Benin: working women spend 208 minutes/day on household chores; men 67 minutes.
    - South Africa: women 228 minutes/day; men 75 minutes.
    - Mauritius: women 277 minutes/day; men 73 minutes.
- Consequences:
  - Larger domestic time burdens diminish women’s productivity at work and competitiveness in the labor market.
- Early marriage, early childbearing, lack of family planning:
  - Constrain women’s ability to compete in the labor market.
  - Early childbearing reduces human capital formation and increases probability of working in low-quality informal jobs (Herrera et al, 2016).
  - Figure 8: unattended family planning needs associated with more women working in informal sector relative to men in SSA.
  - Figure 9: high fertility rates associated with low levels of income.
- Cross-country association:
  - Early marriage associated with higher levels of informal employment for women relative to men (correlation 0.54, data from 57 countries including 24 SSA).

### C. Legal Frameworks Impose Barriers for Working Women
- World Bank’s Women, Business and the Law indicators highlight constraints across 189 countries (47 in SSA); four emphasized areas: access to institutions, use of property, ability to build credit, ability to go to court.
- SSA regional ranking:
  - SSA ranks sixth out of seven groups for women’s ability to access institutions.
- Specific legal constraints in SSA examples:
  - Women may need husband’s permission to sign a contract in Equatorial Guinea.
  - Women may need husband’s permission to open a bank account in Chad, Guinea-Bissau, and Niger.
  - Women cannot register a business the same way as men in Guinea-Bissau.
  - Women may not travel outside the country as men can in Sudan.
  - In Benin, Cameroon, Republic of Congo, Mauritius, and Namibia, women cannot apply for a national identification card the same way as men.
  - In nine countries (Cameroon, Chad, Comoros, Gabon, Guinea, Guinea-Bissau, Mauritania, Niger, Sudan) women cannot get a job without their husband’s permission.
  - In 15 countries (Burundi, Cameroon, Central African Republic, Chad, Comoros, Democratic Republic of Congo, Gabon, Guinea, Guinea-Bissau, Madagascar, Mali, Mauritania, Niger, Senegal, Sudan) women cannot be the head of household the same way as men.
  - In another set of 15 countries (Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Comoros, Republic of Congo, Equatorial Guinea, Gabon, Guinea, Guinea-Bissau, Mali, Niger, Senegal, Sudan) women cannot choose where to live.
  - In 11 countries (Benin, Botswana, Cameroon, Republic of Congo, Gabon, Malawi, Mali, Nigeria, Sudan, Uganda, Zambia) women cannot apply for a passport the same way as men.
- Property and inheritance:
  - In eight SSA countries (Cameroon, Chad, Democratic Republic of Congo, Republic of Congo, Côte d'Ivoire, Equatorial Guinea, Gabon, Guinea-Bissau), only husbands can legally administer marital property.
  - In nine countries (Cameroon, Chad, Democratic Republic of Congo, Republic of Congo, Côte d'Ivoire, Equatorial Guinea, Gabon, Guinea-Bissau, Mauritania) married women may not have equal ownership rights.
  - Female and male surviving spouses do not have equal inheritance rights in nine countries (Comoros, Gambia, Guinea, Kenya, Mauritania, Senegal, Sudan, Tanzania, Uganda).
- Access to finance:
  - In the vast majority of SSA countries discrimination based on gender or marital status is not prohibited in access to finance.
  - Listed countries where such discrimination is not prohibited include: Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Comoros, Republic of Congo, Democratic Republic of Congo, Côte d'Ivoire, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Namibia, Niger, Nigeria, Rwanda, São Tomé and Príncipe, Senegal, Seychelles, Sierra Leone, South Sudan, Sudan, Tanzania, Togo, Uganda, Zimbabwe.
- Labor market legal restrictions:
  - In 27 SSA countries (Angola, Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Democratic Republic of Congo, Republic of Congo, Côte d'Ivoire, Equatorial Guinea, Eswatini, Ethiopia, Gabon, Ghana, Guinea, Guinea-Bissau, Lesotho, Madagascar, Mali, Mauritania, Mozambique, Niger, Nigeria, São Tomé and Príncipe, Senegal, South Sudan, Sudan) women are legally barred from performing the same jobs as men (examples: night work restrictions).
- Other legal protections lacking in many SSA countries:
  - 19 SSA countries do not have laws prohibiting or invalidating child or early marriage.
  - 21 countries have no legislation on domestic violence.
  - Nine countries do not have legislation specifically addressing sexual harassment.
- Legal improvements:
  - Since 2009, 31 out of the 47 SSA countries covered have seen some improvement in gender equality in their legal frameworks.
  - Democratic Republic of Congo recorded substantial progress by 2018 across multiple economic-related areas: pursuit of jobs, signing contracts, opening bank accounts, registering businesses, nondiscrimination by creditors; married women no longer legally required to obey husbands; nondiscrimination laws in employment.
  - Other countries with considerable advancements in the last ten years: Guinea, Mauritius, Rwanda, São Tomé and Príncipe, Zambia.

### III. CASE STUDY: SENEGAL
- Purpose:
  - Investigates relationship between gender and informal employment in Senegal.
  - Uses probit regression models to estimate determinants of probability of being employed in the informal sector, with focus on women.
  - Data: 2011 household survey in Senegal (Enquete de Suivi De La Pauvrete Au Senegal – ESPS II, 2011).
- Definition of formal worker (survey-based):
  - Formal worker: a paid worker who declares having a formal contract with the employer (11.9 percent of workers) and/or a paid worker who declares having affiliation, through the employer, to a social security system (7.7 percent of workers).
  - Total formal workers under this classification: 14.3 percent of all workers.
  - Note: international standards separate informal worker and informal sector; in this analysis both terms used interchangeably.
- A. The Context of Senegal
  - Sectoral employment:
    - Employment concentrated in agriculture (almost half of total employment), then industry, then services.
    - Senegal’s economic structure resembles a country at the beginning of structural transformation.
  - Education and gender gaps:
    - Gender gaps in primary education enrollment and completion have closed and reversed (IMF 2018a).
    - UNESCO: 1999–2016 gross enrollment in primary education for girls rose from 59 percent to 88 percent; for boys from 71 percent to 78 percent.
    - Primary completion rates: girls rose from 33 percent (2000) to 64 percent (2016); boys from 43 percent to 54 percent.
    - Average years of education in Senegal: 2.8 in 2015 (UNDP), lower than WAEMU average 3.0 and SSA average 5.1.
    - Secondary completion and tertiary enrollment remain substantially lower for girls:
      - 2012 DHS: female secondary completion rate 13 percent vs male 21 percent.
      - Female tertiary completion doubled from 4 percent (2006) to 8 percent (2016); male tertiary from 8 percent to 13 percent.
    - Despite overall gender gaps, women in formal sector in Senegal are on average almost as educated as men.
  - Labor force participation and part-time work:
    - Female labor force participation increased from 34 percent of total labor force in 2000 to 41 percent in 2016.
    - ILO: ratio of female-to-male unemployment rates of young people (15–24) dropped from 1.7 to 1.1 between 2000 and 2017.
    - Part-time work:
      - Senegalese women more likely to work part-time than men: probability 20 percent for women versus 7 percent for men.
      - Majority of men and women work full-time.
      - Urban sector: women work 43 hours/week in formal urban sector and 48 hours/week in informal urban sector.
      - It does not seem that Senegalese women are benefiting from part-time flexibility more often given by informal sector.
  - Gender gaps across income distribution:
    - Female workers in top income groups more often informal than male counterparts.
      - Top 40 percent of income distribution: female informal share 82 percent vs male 73 percent (difference 9.0 percentage points).
      - Bottom 40 percent: the average gender gap in informality falls from 9.0 percent to zero.
    - Education gaps vary by income group:
      - Top 40 percent of income distribution: gender gap in years of education 0.92 years.
      - Bottom 30 percent: (text truncated; available content ends here).

*Source: wpiea2019112 - Section IV concludes the paper and offers policy recommendations based on the cross-country*

### 0.60 years.

### 0.60 years.

### Legal framework and gender constraints
- The overall legal framework index for women in Senegal is below the SSA average.
- Senegal scores below the SSA average on World Bank (2018) indicators for “using property”, “getting a job”, “building credit” and “accessing institutions”.
- Legal restrictions:
  - Non-pregnant, non-nursing women are prevented by law from performing the same job as men in some cases.
  - Sexual harassment is not recognized as a criminal offense.
  - According to Marzo & Atuesta (forthcoming), 18 percent of working Senegalese women declared they were asked to have sexual relations to get a promotion; 16 percent claimed they were refused a work contract for having rejected sexual advances.
  - The constitution does not formally recognize, nor prohibit, discrimination against women that may result from customary laws.
- Family and inheritance law:
  - The Senegalese Family Code (1974) sets minimum age of marriage at 16 and allows for early marriage.
  - 23 percent of female adolescents aged 15 to 19 are already married; 2 percent of males in the same age group are married.
  - Married female adolescents have on average 2.6 years of education vs 3.9 years for unmarried female adolescents (one-third fewer years).
  - The Family Code gives husbands power to make household decisions and advantages to men in inheritance, hindering women’s asset ownership.
- Positive legal components:
  - Senegal scores above the SSA average in “providing incentives to work” and “protecting women from violence”.
  - Women in the formal sector have legal guarantee of an equivalent position after maternity leave; government provides child allowances to parents who work.
  - Progress in legislation against domestic violence has improved the “protecting women from violence” index component.

### Empirical analysis: data and methods
- Data: Senegal’s 2011 household survey; workers aged 15–64.
- Method: probit models estimating marginal effects on probability of being an informal worker.
- Regressors of interest: gender; educational attainment (no education, primary, secondary, tertiary); head of household; married; number of kids; income decile (1 to 10); age group; controls for geographic regions.
- Samples: entire household sample, urban only, rural only; for each sample, regressions run for all workers, female workers only, male workers only.
- Number of observations (from Table 2):
  - All workers: 47,169
  - Female workers (all areas): 19,367
  - Male workers (all areas): 27,802
  - Urban workers: 19,069
  - Urban female workers: 7,583
  - Urban male workers: 11,486
  - Rural workers: 28,100
  - Rural female workers: 11,783
  - Rural male workers: 16,316
- Pseudo R2 of the probit model (from Table 2):
  - All workers: 0.2435
  - Female workers: 0.2209
  - Male workers: 0.2588
  - Urban workers: 0.2212
  - Urban female workers: 0.2326
  - Urban male workers: 0.2159
  - Rural workers: 0.1021
  - Rural female workers: 0.0919
  - Rural male workers: 0.1218

### Key empirical findings (marginal effects and summary)
- Gender:
  - Being female increases the probability of being employed in the informal sector by 0.0340 (3.4 percentage points)*** for all workers.
  - Urban: being female increases probability by 0.0850 (8.5 percentage points)***.
  - Rural: being female increases probability by 0.0088 (0.88 percentage points)*.
- Education (marginal effects from Table 2; negative values indicate lower probability of informality):
  - No education: positive marginal effects in several samples (e.g., All workers 0.0336***).
  - Primary Education:
    - All workers: -0.1640*** (16.40 percentage points less likely to be informal).
    - Urban workers: -0.2547*** (25.47 percentage points).
    - Urban female workers: -0.3120*** (31.20 percentage points).
    - Urban male workers: -0.2248*** (22.48 percentage points).
    - Rural male workers: -0.0720*** (7.20 percentage points).
    - Rural female workers: -0.0461** (4.61 percentage points).
    - Rural note: only 6.7 percent of rural workers have primary diploma; urban: 21.7 percent.
  - Secondary Education:
    - All workers: -0.5553*** (55.53 percentage points).
    - Urban workers: -0.6044*** (60.44 percentage points).
    - Female workers (all areas): -0.6098*** (60.98 percentage points).
    - Urban female workers: -0.6643*** (66.43 percentage points).
    - Male workers (all areas): -0.5384*** (53.84 percentage points).
    - Urban male workers: -0.5707*** (57.07 percentage points).
    - Rural workers: -0.4577*** (45.77 percentage points).
    - Note: Secondary degree holders are 2.4 percent of paid workers.
  - Tertiary Education:
    - All workers: -0.7285*** (72.85 percentage points).
    - Female workers (all areas): -0.6551*** (65.51 percentage points).
    - Male workers (all areas): -0.7625*** (76.25 percentage points).
    - Urban female workers: -0.6990*** (69.90 percentage points).
    - Urban male workers: -0.6939*** (69.39 percentage points).
    - Rural tertiary coefficients reported as large negative values (e.g., -0.7793***).
    - Note: tertiary educated paid workers are 0.5 percent of paid workers; only 22 observations correspond to female workers with tertiary education in sample of 163,490 observations (small sample bias warning).
- Household headship:
  - Being head of household reduces probability of being informal by 0.0249 (2.49 percentage points)*** for all workers.
  - Urban headship effect: -0.0530*** (5.30 percentage points).
  - Female subsample: head of household improves probability of being formal by 0.028 (2.8 percentage points) (significant at 5 percent).
- Marital status and children:
  - Married men: 0.0213*** (2.13 percentage points) lower probability of being informal (All workers). For men specifically: married reduces probability by 0.0392*** (3.92 percentage points).
  - Urban married men: -0.0477*** (4.77 percentage points); urban female and rural subsamples marriage variable not significant in some specifications.
  - Number of kids:
    - For men, each additional child reduces probability of being informal by 0.0059** (0.59 percentage points) in male samples.
    - For urban females, each additional child increases probability of being informal by 0.0139** (1.39 percentage points).
    - Average fertility: women in Senegal have on average five children.
- Income decile (1 to 10):
  - Each decile increase reduces probability of being informal:
    - All workers: -0.0116*** (1.16 percentage points per decile).
    - Female workers: -0.0060*** (0.60 percentage points per decile).
    - Male workers: -0.0161*** (1.61 percentage points per decile).
    - Urban male workers: -0.0310*** (3.10 percentage points per decile).
  - Wealth improves probability of being formal faster for men than for women (men: 1.6 percent per decile; urban men: 3.1 percent per decile; women: 0.6 percent per decile; urban women: 1.6 percent per decile as summarized in text).
- Age groups:
  - Age dummies generally show older age groups have lower probability of being informal relative to baseline; many coefficients are large and statistically significant across age bands (e.g., Age: 30-34 All workers -0.0910***).

### Synthesis of mechanisms
- Women are disproportionately overrepresented in the informal economy in sub-Saharan Africa and Senegal.
- Key drivers identified:
  - Lower educational attainment among women (primary and secondary completion strongly reduce informality for women).
  - Traditional gender roles and household responsibilities (children increase women’s informality in urban areas; parenthood reduces men’s probability of informality).
  - Legal constraints on access to property, jobs, credit, and institutions.
  - Early marriage and early pregnancy (23 percent of female adolescents 15–19 married; early marriage linked to substantially lower education).
  - Wealth/income benefits men more than women in shifting from informal to formal employment.
- Feedback loop (trap): low expected returns for daughters → parents invest less in girls’ education → lower female education → higher likelihood of remaining in informal sector → perpetuation of gender inequality.

### Policy recommendations (as presented)
- Increase girls’ educational attainment:
  - Focus on completion of primary and secondary education.
  - Improve access, quality, and effectiveness of education systems.
  - Use targeted cash transfers to incentivize parents to keep daughters in school until completion of primary and secondary education where social barriers exist.
  - Prohibit child marriage and disseminate information on women’s health to reduce early dropouts.
- Change legal framework to ensure equality before the law:
  - Expunge legal differences that restrict women’s access to institutions, assets, credit, mobility, and choice.
  - Enforce property rights including inheritance.
  - Combat domestic violence, sexual harassment and child marriage.
- Educate population on family planning:
  - Run campaigns and provide quality health care, sexual education, and modern contraceptives to meet unmet demand and reduce early pregnancy.
- Invest in infrastructure:
  - Reduce time spent on home production by facilitating access to water and energy.
  - Provide safe transportation to workplaces and schools to expand women’s labor market participation.
- Address social norms and enforcement:
  - Enforce civil laws where customary laws reduce women’s freedom and power.
  - Promote equal division of household labor through education campaigns and paternity leave.
  - Act as role model within government administrations.
- Address unequal opportunities in the labor market:
  - Enact laws against gender discrimination and sexual harassment.
  - Provide childcare subsidies or organize childcare facilities and parental leave (government-provided).
  - Use fiscal policies (tax breaks, subsidies) for families with young children and ensure income tax does not penalize secondary wage earners.
  - Improve access to credit and assets to promote equal entrepreneurship opportunities.
- Methodological note: lack of full causal proof for some channels should not delay policy action; many recommendations have strong empirical support (e.g., more education → better salaries and living standards).

*Source: wpiea2019112 - 0.60 years.*

### REFERENCES

### wpiea2019112 - REFERENCES

### Key empirical findings from Table 3 (Marginal Effects of Probit Regressions)
- Dependent variable: informal worker (binary variable).
- Female: marginal effects reported as 0.0340***, 0.0850***, 0.0088* with std error(0.0050)(0.0113)(0.0049); z 6.76 7.51 1.82; P>z 0.000 0.000 0.069.
- No education: marginal effects reported as 0.0336*** 0.0197** 0.0376*** 0.0634*** 0.0269 0.0803*** 0.0186*** 0.0175* 0.0153** with std error(0.0062)(0.0090)(0.0084)(0.0135)(0.0189)(0.0187)(0.0056)(0.0095)(0.0066); z 5.42 2.19 4.50 4.70 1.42 4.30 3.32 1.85 2.31; P>z 0.000 0.028 0.000 0.000 0.155 0.000 0.001 0.065 0.021.
- Primary Education: marginal effects reported as -0.1640*** -0.2010*** -0.1506*** -0.2547*** -0.3120*** -0.2248*** -0.0656*** -0.0461** -0.0720*** with std error(0.0115)(0.0216)(0.0138)(0.0177)(0.0301)(0.0221)(0.0111)(0.0191)(0.0132); z -14.26 -9.31 -10.91 -14.41 -10.37 -10.18 -5.92 -2.41 -5.46; P>z 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.016 0.000.
- Secondary Education: marginal effects reported as -0.5553*** -0.6098*** -0.5384*** -0.6044*** -0.6643*** -0.5707*** -0.4577*** -0.6073*** -0.4364*** with std error(0.0311)(0.0582)(0.0365)(0.0267)(0.0478)(0.0314)(0.0645)(0.1530)(0.0664); z -17.88 -10.48 -14.74 -22.64 -13.90 -18.15 -7.09 -3.97 -6.57; P>z 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000.
- Tertiary Education: marginal effects reported as -0.7285*** -0.6551*** -0.7625*** -0.6990*** -0.6939*** -0.6862*** -0.7793*** -0.7899*** with std error(0.0611)(0.1610)(0.0415)(0.0367)(0.1131)(0.0220)(0.1503)(0.1504); z -11.92 -4.07 -18.37 -19.05 -6.13 -31.12 -5.18 -5.25; P>z 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000.
- Head of the Household: marginal effects include -0.0249*** -0.0279** -0.0063 -0.0530*** -0.0361 -0.0292 -0.0190** -0.0239 -0.0031 with std error(0.0071)(0.0131)(0.0087)(0.0155)(0.0230)(0.0210)(0.0074)(0.0177)(0.0071); z -3.54 -2.13 -0.72 -3.42 -1.57 -1.39 -2.56 -1.35 -0.44; P>z 0.000 0.033 0.472 0.001 0.117 0.165 0.010 0.176 0.660.
- Married: marginal effects include -0.0213*** 0.0071 -0.0392*** -0.0477*** 0.0173 -0.1016*** -0.0000 0.0079 -0.0013 with std error(0.0062)(0.0082)(0.0090)(0.0134)(0.0166)(0.0212)(0.0059)(0.0088)(0.0069); z -3.42 0.87 -4.34 -3.57 1.05 -4.80 -0.01 0.90 -0.20; P>z 0.001 0.383 0.000 0.000 0.295 0.000 0.994 0.370 0.845.
- Number of Kids: coefficients reported as -0.00200 0.0036 -0.0059** -0.0007 0.0139** -0.0084 -0.0033** -0.0011 -0.0049*** with std error(0.0019)(0.0027)(0.0025)(0.0047)(0.0067)(0.0064)(0.0014)(0.0022)(0.0018); z -1.06 1.37 -2.32 -0.14 2.07 -1.31 -2.29 -0.52 -2.69; P>z 0.289 0.171 0.020 0.886 0.038 0.189 0.022 0.604 0.007.
- Income Decile (1 to 10): marginal effects reported as -0.0116*** -0.0060*** -0.0161*** -0.0254*** -0.0165*** -0.0310*** -0.0055*** -0.0020 -0.0081*** with std error(0.0011)(0.0016)(0.0014)(0.0029)(0.0039)(0.0042)(0.0009)(0.0015)(0.0010); z -10.74 -3.68 -11.55 -8.60 -4.21 -7.45 -6.45 -1.32 -8.47; P>z 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.186 0.000.
- Urban indicator: marginal effects reported as -0.0735*** -0.0560*** -0.0836*** with std error(0.0052)(0.0076)(0.0070); z -14.25 -7.33 -11.96; P>z 0.000 0.000 0.000.

### Age-group marginal effects (Table 3 continued)
- Age: 15-19: coefficients include -0.0054 -0.0090 -0.0002 -0.0272 -0.0258 -0.0367 -0.0123 -0.0167 -0.0065; std error(0.0116)(0.0171)(0.0147)(0.0403)(0.0465)(0.0593)(0.0088)(0.0159)(0.0096); P>z values include 0.637 0.600 0.988 0.499 0.580 0.536 0.165 0.293 0.495.
- Age: 20-24: -0.0353*** -0.0372* -0.0338* -0.0840** -0.0614 -0.1109* -0.0380*** -0.0395* -0.0337**; P>z include 0.008 0.058 0.055 0.038 0.199 0.058 0.002 0.053 0.028.
- Age: 25-29: -0.0669*** -0.0806*** -0.0529*** -0.1354*** -0.1183** -0.1531*** -0.0624*** -0.0720*** -0.0446***; P>z include 0.000 0.001 0.002 0.001 0.017 0.008 0.000 0.004 0.001.
- Age: 30-34 through Age: 60-64: detailed negative marginal effects reported for each group with associated std errors, z-statistics, and P>z values (see Table 3 continued).

### Regional effects (Table 3 continued)
- Dakar: coefficients reported as -0.0344*** -0.0194* -0.0407*** -0.0378*** -0.0022 -0.0568*** -0.1024*** -0.1284*** -0.0903*** with std error(0.0078)(0.0110)(0.0105)(0.0132)(0.0186)(0.0177)(0.0179)(0.0320)(0.0218); P>z 0.000 0.077 0.000 0.004 0.904 0.001 0.000 0.000 0.000.
- Diourbel: coefficients include -0.0476*** -0.0747*** -0.0066 -0.0986*** -0.0894*** -0.0984*** -0.0243** -0.0477** 0.0029; P>z 0.006 0.003 0.722 0.000 0.001 0.000 0.041 0.011 0.815.
- Kaffrine: coefficients include -0.2032*** -0.1858*** -0.2086*** -0.1481*** -0.1413*** -0.1506*** with P>z 0.000 across shown cells.
- Kaolack, Kedougou, Louga, Saint Louis, Sedhiou, Thies: coefficients and exact std errors, z, and P>z values reported in Table 3 continued.

### Model fit and sample sizes (Table 3)
- Pseudo R2 of the probit model reported as 0.2435, 0.2209, 0.2588, 0.2212, 0.2326, 0.2159, 0.1021, 0.0919, 0.1218 (by sample columns).
- Number of Observations reported as 47,169; 19,367; 27,802; 19,069; 7,583; 11,486; 28,100; 11,783; 16,316.
- Significance notation: *** p<0.01, ** p<0.05, * p<0.1.

### Robustness checks — Probit with interactions (Table 4: All Workers sample)
- Prob > chi2 = 0.000.
- Pseudo R2 = 0.2486.
- Log likelihood = -1252692.1.
- Number of Observations = 47,169.
- Coefficients and statistics:
  - male with primary education: coefficient -0.617; Std. Err. 0.0465; z -13.28; P>z 0.000.
  - female with primary education: coefficient -0.887; Std. Err. 0.0695; z -12.76; P>z 0.000.
  - male with secondary education: coefficient -1.612; Std. Err. 0.0988; z -16.32; P>z 0.000.
  - female with secondary education: coefficient -1.932; Std. Err. 0.1621; z -11.92; P>z 0.000.
  - male . married: coefficient -0.259; Std. Err. 0.0442; z -5.86; P>z 0.000; H0 test: Chi2 = 34.31 P-value = 0.0000.
  - female . married: coefficient 0.074; Std. Err. 0.0545; z 1.35; P>z 0.176; H0 test: Chi2 = 1.83 P-value = 0.0882.
  - male . number of kids: coefficient -0.036; Std. Err. 0.0128; z -2.8; P>z 0.005; H0 test: Chi2 = 7.85 P-value = 0.0025.
  - female . number of kids: coefficient 0.031; Std. Err. 0.0190; z 1.63; P>z 0.104; H0 test: Chi2 = 2.65 P-value = 0.0519.
  - male . decile: coefficient -0.082; Std. Err. 0.0070; z -11.8; P>z 0.000.
  - female . decile: coefficient -0.041; Std. Err. 0.0115; z -3.61; P>z 0.000.
- Hypothesis tests reported:
  - Gender interacted with "primary education": H0: Female coefficient is larger (in absolute terms) than male's. Chi2 = 10.49 P-value = 0.0006.
  - Gender interacted with "secondary education": Chi2 = 2.86 P-value = 0.0454.
  - Gender interacted with "decile": H0: Female coefficient is smaller (in absolute terms) than male's. Chi2 = 81.24 P-value = 0.0000.

### Robustness checks — Probit with interactions (Table 5: Urban Workers sample)
- Prob > chi2 = 0.000.
- Pseudo R2 = 0.2248.
- Log likelihood = -720720.94.
- Number of Observations = 19,069.
- Coefficients and statistics:
  - male with primary education: -0.621; Std. Err. 0.0590; z -10.54; P>z 0.000.
  - female with primary education: -0.988; Std. Err. 0.0840; z -11.75; P>z 0.000.
  - male with secondary education: -1.586; Std. Err. 0.1188; z -13.35; P>z 0.000.
  - female with secondary education: -1.912; Std. Err. 0.1725; z -11.09; P>z 0.000.
  - male . married: -0.346; Std. Err. 0.0593; z -5.84; P>z 0.000; H0: Chi2 = 34.12 P-value = 0.0000.
  - female . married: 0.090; Std. Err. 0.0641; z 1.4; P>z 0.161; H0: Chi2 = 1.97 P-value = 0.0804.
  - male . number of kids: -0.028; Std. Err. 0.0189; z -1.5; P>z 0.135; H0: Chi2 = 2.24 P-value = 0.0673.
  - female . number of kids: 0.053; Std. Err. 0.0271; z 1.94; P>z 0.052; H0: Chi2 = 3.77 P-value = 0.0261.
  - male . decile: -0.100; Std. Err. 0.0121; z -8.23; P>z 0.000.
  - female . decile: -0.063; Std. Err. 0.0155; z -4.04; P>z 0.000.
- Hypothesis tests:
  - Gender interacted with "primary education": Chi2 = 12.74 P-value = 0.0002.
  - Gender interacted with "secondary education": Chi2 = 2.45 P-value = 0.0588.
  - Gender interacted with "decile": Chi2 = 64.90 P-value = 0.0000.

### Robustness checks — Probit with interactions (Table 6: Rural Workers sample)
- Prob > chi2 = 0.000.
- Pseudo R2 = 0.1052.
- Log likelihood = -517337.67.
- Number of Observations = 28,099.
- Coefficients and statistics:
  - male with primary education: -0.481; Std. Err. 0.0687; z -7; P>z 0.000.
  - female with primary education: -0.398; Std. Err. 0.1207; z -3.3; P>z 0.001.
  - male with secondary education: -1.568; Std. Err. 0.1727; z -9.08; P>z 0.000.
  - female with secondary education: -2.156; Std. Err. 0.4162; z -5.18; P>z 0.000.
  - male . married: -0.111; Std. Err. 0.0636; z -1.75; P>z 0.080; H0: Chi2 = 3.06 P-value = 0.0401.
  - female . married: 0.127; Std. Err. 0.0929; z 1.37; P>z 0.172; H0: Chi2 = 1.87 P-value = 0.0860.
  - male . number of kids: -0.052; Std. Err. 0.0165; z -3.15; P>z 0.002; H0: Chi2 = 9.90 P-value = 0.0008.
  - female . number of kids: 0.000; Std. Err. 0.0261; z -0.01; P>z 0.994; H0: Chi2 = 0.00 P-value = 0.5030.
  - male . decile: -0.070; Std. Err. 0.0083; z -8.43; P>z 0.000.
  - female . decile: -0.026; Std. Err. 0.0170; z -1.52; P>z 0.128.
- Hypothesis tests:
  - Gender interacted with "primary education": Chi2 = 0.37 P-value = 0.7277.
  - Gender interacted with "secondary education": Chi2 = 1.71 P-value = 0.0958.
  - Gender interacted with "decile": Chi2 = 24.89 P-value = 0.0000.

*Italic: Source — wpiea2019112 - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/wp/2019/wpiea2019112.pdf_