Selected Issues


Selected Issues

Gender Gaps in Senegal: From Education to Labor Market1

A. The Economic and Social Context of Gender Inequality in Senegal

1. Senegal has made progress in reducing poverty and inequalities since the early 2000s. The share of population living under $1.90 a day dropped from 49 percent in 2001 to 38 percent in 2011, close to the average fall of 12 percentage points in Sub-Saharan Africa (SSA) over the same period. Income inequality as measured by the Gini coefficient was reduced marginally, from 41.2 in 2001 to 40.3 in 2011.2 In terms of gender equality, there were improvements in some areas, notably in primary education, labor force participation and unemployment. Nonetheless, according to UNDP, Senegal ranked only124th out of 160 countries in terms of gender equality in 2017.

2. Gender gaps in primary education have reversed and female employment and participation have increased. Gender gaps in primary education in both enrollment and completion rates have closed and have now even reversed (meaning girls have now better outcomes than boys). According to UNESCO, from 1999 to 2016 gross enrollment rates in primary education jumped from 59 percent to 88 percent for girls while boys’ improved from 71 percent to 78 percent. Primary education completion rates rose from 33 percent for girls and 43 percent for boys in 2000 to 64 percent and 54 percent, respectively, in 2016. Authorities claim that a cash transfer program conditional on kids being in primary school was a key factor in increasing school attendance. Meanwhile, in the labor market, female labor force participation increased from 34 percent of the total labor force in 2000 to 41 percent in 2016. Furthermore, according to the International Labor Organization (ILO), the ratio of female to male unemployment rates of young people (from 15 years to 24 years old) dropped from 1.73 to 1.13 between 2000 and 2017.

3. Despite these improvements, education levels are low and gender gaps remain high in Senegal. Average years of education in Senegal were only 2.8 in 2015 (according to UNDP), lower than the average of WAEMU (3.0 years) and SSA (5.1 years)—see Figure 1. Girls’ completion rates in secondary education and enrollment in tertiary education are still substantially lower than those of boys. The Demographic and Health Survey program (DHS) reported that, in 2012, the average female completion rate in secondary education was only 13 percent, compared to 21 percent for boys. Secondary education is especially important as it provides crucial skills for the job market. In tertiary education the gender gap is also wide, as the female completion rate doubled from 4 percent in 2006 to 8 percent in 2016, while the male rate increased from 8 percent to 13 percent. These gender gaps in secondary and tertiary education can be seen in all quintiles of households’ income distribution. Figure 2 provides a summary of the evolution of education gaps in Senegal.

Figure 1:
Figure 1:

Senegal: Education in Senegal Relative to Peers

Citation: IMF Staff Country Reports 2019, 028; 10.5089/9781484396292.002.A002

Source: UNDP, Human development reports
Figure 2.
Figure 2.

Senegal: Education Gaps in Senegal—Enrollment and Completion Rates

Citation: IMF Staff Country Reports 2019, 028; 10.5089/9781484396292.002.A002

Source: DHS, Unesco, World Development Indicators

4. Gender gaps in secondary education are linked to social and economic factors. Some social factors can play important roles in women’s economic participation. In Senegal, early marriage and early pregnancy are still relatively common: in 2016, 31 percent of women of age 20 to 24 were first married by the age of 18, and 8 percent by the age of only 15.3 Early marriage is one of the main causes of girls’ dropping out of school, and this happens during secondary education. Having children at a young age, not only force girls to drop out of school, but also sharply increases the chances of maternal mortality: 629 deaths per 100,000 for mothers aged 15–19, compared to 371 deaths per 100,000 births for mothers aged 20–24.4 Authorities also note that, in terms of financial incentives, it usually makes more sense for a poor family to marry their daughters than to continue to incur costs—including the costs of sending them to school. In Senegal, even though school is supposedly free and mandatory, there are hidden costs such as buying school material and transportation to school. Furthermore, as seen below, prospects for well-paying jobs are weak for Senegalese girls.

5. In this context, authorities can play an important role to encourage girls to continue their studies. Measures to achieve this objective include: (i) diminishing indirect costs of studying; (ii) investing in safe transportation so that kids can go to school; (iii) targeting transfers to families that keep their teenage daughters in secondary school up until completion; (iv) campaigns for prevention of child marriage and pregnancy; and (v) enforcing civil laws, rather than customary laws. Corroborating international empirical evidence, the 2011 Senegal’s Household Survey5 shows a stark negative correlation between education and fertility rates among women (Figure 3). Women with more years of education have lower fertility rates and higher earnings from labor, therefore allowing them to provide better life conditions and a better future for each of their kids. The literature shows that gender gaps in education can have negative consequences for economic growth, development, and diversification (see e.g. King and Hill, 1991).

Figure 3.
Figure 3.

Senegal: Number of Kids and Females’ Wages per Years of Education

Citation: IMF Staff Country Reports 2019, 028; 10.5089/9781484396292.002.A002

6. Sizable gender gaps in earnings from labor persist, as women face larger barriers to enter and advance in the labor market and in entrepreneurial activities. Besides gender gaps in labor force participation, wage gaps and lower access to land, durable goods and credit impose constraints to women’s economic participation.

Senegalese women have less access to assets, especially land, mainly due to customary laws. According to Senegalese authorities, 50 percent of Senegalese households are in the agriculture sector, where only 16.4 percent of farms are headed by women. Men control 93.6 percent of cultivated areas and use an average of 1.3 hectares, while women’s plots rarely exceed 0.4 hectare6. Lower female access to land is strongly related to patrilineal inheritance practices. Marzo and Atuesta (2018) provides extensive research on gender differences in access to economic opportunities in Senegal, finding evidence of strong job segregation, as well as differences in access to land, credit, and labor (employees). It also emphasizes the relevance of secular and customary laws, as gender norms remain a drag on the trajectory of women in the labor market. Figure 4 shows some key statistics of labor gaps in Senegal.

Figure 4.
Figure 4.

Senegal: Gender Gaps in the Labor Market

Citation: IMF Staff Country Reports 2019, 028; 10.5089/9781484396292.002.A002

Source: World Development Indicators, ILO, calculations using the 2.011 household survey.

7. Estimations point to a gender wage gap of 47 percent in Senegal. Using the 2011 Household Survey, men’s and women’s log hourly wages are modeled as functions of education, experience, sector, localization, type of contract, type of activity, gender, age, and ethnicity. Applying the Blinder-Oaxaca decomposition on these regressions results in a predicted and statistically significant average gender pay gap in Senegal of 47 percent. One third of this gap can be explained by differences in male and female endowments of the observables characteristics. For instance, the fact that women have less years of education accounts for more than one fourth of the explained wage gap, and the fact that women work relatively less than men in the formal sector (where wages are higher) explains one fifth of it.

8. Two thirds of the gender wage gap are “unexplained.” The “unexplained gender wage gap” emerges from differences in the estimated parameters of the regressions.7 For Senegal, the Blinder-Oaxaca decomposition reveals two facts: (i) women are subjected to lower returns from experience than men; and (ii) women start from an overall lower pay level than men, evidenced by a much inferior and statistically significant regression intercept on the women’s wages equation. Since the unexplained wage gap is often linked to gender discrimination against working women, this second fact would reflect widely spread discrimination in the labor market. The regressions also show that education plays an important role in women’s salaries and helps them close the wage gap—women earn 5.5 percent more for every additional year of education (compared to 4.1 percent for men), reflecting that women are on average less educated than men and thus face higher returns on education at the margin.

9. Social barriers create obstacles, requiring stronger efforts to address gender gaps. Even though the fertility rate is high (at 4.8) and 88 percent of the population lives under US$5.50 a day8, family planning discussions are reportedly not common. In 2016, contraceptive use among women between 15 and 49 years of age was only 25 percent9. According to the DHS, in 2014 only 6.6 percent of 15–49-year-old Senegalese women were in charge of decisions regarding their own health care (husbands were in charge 76 percent of the time). Some of these facts are linked to the Senegalese Family Code,10 which was passed into law in 1973. According to Articles 152 and 153, the husband has the power to make all the decisions of the household, in the interest of his wife and kids.

10. Authorities acknowledge these social barriers as well as the need for additional budget to address gender gaps. Authorities have drafted the “National Strategy for Equity and Gender Equality in Senegal: 2016—2026”, a publication containing guidelines to reduce gender inequality in the country. However, they recognize that there has not been sufficient effort to operationalize the strategy and that social and financial barriers create implementation challenges. Efforts to improve the quality of spending should be addressed—for instance, education spending per student (as percentage of GDP) is higher than SSA average both in primary and secondary education.

11. A model calibrated to Senegal is used to show the macro and distribution impacts of some policies that address gender inequalities. The section below describes a model that was built to analyze gender inequalities in Senegal—such as education gaps, difficulties in the labor market, and barriers preventing women from joining the labor force—from a macroeconomic perspective. Different scenarios are developed to better understand the economic benefits of diminishing these gaps.

B. Aggregate and Distributional Impacts of Reducing Gender Gaps in Senegal

12. A general equilibrium framework is developed to simulate gap reductions. A micro-founded overlapping generations model11 is used to analyze the impact of policies on both aggregate and distributional levels of income and gender. In this framework, households decide how much to consume and save (if they don’t have financial markets constraints), and how much labor to supply in the formal and informal labor markets. Females in the household face different barriers to their development over the life-cycle, including early education, costs of taking care of the home and the family, and discrimination in the labor market.

13. The model quantifies the impact of distinct fiscal and gender targeted policies. The model is calibrated to the Senegalese economy using micro level data.12 This allows it to replicate key features of the Senegalese economy, such as size of formal vs informal sector on GDP and on labor shares, taxes (income tax rates, VAT and corporate income tax), government spending (on education and on other goods and services), returns on wages from experience and from education, female labor force participation (relative to male’s), wage gaps in the formal and informal sectors, and inequality, as measured by the Gini coefficient.13

14. There are three sources of gender inequality in the theoretical model. The first source is the different education levels for men and women for each income level of the distribution—derived from the micro data on years of education. The second source is a utility cost the family incurs when a woman supplies labor, which comes from the difficulty of coordinating multiple household activities, such as home production and rearing children, as well as social and cultural factors that result in lower female labor force participation. The third source is the discrimination faced by women in the labor market. These sources of gender inequality create different outcomes for men and women in terms of labor force participation, types of jobs (formal versus informal) and earnings. Table 1 provides a summary of these three sources, indicates the empirical justification for adding them into the model and presents examples of policies that can address each of them.

Table 1.

Senegal: Gender Inequalities in the Micro-Founded General Equilibrium Framework

article image

15. The model simulates the impact of increasing years of education and reducing education gaps. Replicating the 2011 Household Survey data, the model starts from a benchmark where girls receive on average 75 percent of the years of education received by boys14: 3.4 years vs 4.5 years. Gender gaps are higher in the low-income population: for instance, the gap in years of education is 50 percent when one considers solely the bottom 10 percent of the income distribution, while it is 1 percent when considering only the top 10 percent of the distribution. Two exercises are then performed: (i) an increase in years of education so that all percentiles of the income distribution receive at least 5 years of education15; and (ii) a more ambitious target of 10 years of education for everyone, as suggested by Senegalese laws, which guarantee free and mandatory education up until 16 years of age.16

16. Making sure everyone receives at least 5 years of education promotes growth and equity. This policy generates GDP gains of 8.2 percent after a single generation, improves female labor force participation by 11 percentage points and reduces inequality (as measured by the Gini coefficient) by 3 percentage points. Average wages would increase for both men (3.4 percent) and women (9.9 percent), as both would benefit from higher human capital formation. Income equality would improve as low-income households would be more affected by the policy, since average years of education are lower for them.

17. Costs of this measure could be mitigated by higher government revenues. Total government revenues (from taxes on wages, corporations and VAT) would increase by 1.1 percent of GDP, driven mostly by an increase in VAT collection.17 In this context, the government would raise education spending from 6.1 percent of GDP to 7.8 percent of GDP, implying a net cost of 0.6 percent of GDP with this policy. It is worth noting that, if this measure was accompanied by an increase in the formal sector of 10 percent of GDP (from 55 percent to 65 percent of GDP), then the increase in government revenue would triple to 3.2 percent of GDP, implying that the policy would generate a net budget surplus of 1.5 percent of GDP.

18. Increasing years of education to 10 to all individuals would result in substantial economic gains in a single generation. This simulation is consistent with existing laws that make education free and mandatory until 16 years of age and would result in a 26 percent increase in GDP. Figure 5 compares the impact of this more ambitious policy with the previous simulation, showing that this one provides larger benefits in terms of wages per hour – 20 percent higher for women and 30 percent higher for men. It is worth noting that there would not be extra gains in female labor force participation. The reason is that female labor force participation is measured relative to males’ and in high-income levels—where this policy affects gender equality (relative to the previous policy simulation—there are no gender gaps in labor force participation.

Figure 5.
Figure 5.

Senegal: Comparing Education Policies

Citation: IMF Staff Country Reports 2019, 028; 10.5089/9781484396292.002.A002

Source: IMF’s calculations.

19. Equalizing marginal gains from experience boosts GDP without hurting males’ wages. As noted in paragraph 8, the marginal impact of experience on wages is lower for women than men. Equalizing females to males’ returns from experience would boost GDP by 4.7 percent and would not affect males’ wages (while females’ earnings would jump 6.2 percent). The policy would insert more low-middle-income female workers in the formal sector, while poorer working women would remain in the informal sector. As a result, as seen in Figure 6, the policy would generate higher gains for the bottom 50 percent (+14 percent) than the upper 50 percent (+2.1 percent).

Figure 6.
Figure 6.

Senegal: Reducing Gender Gaps in the Labor Market

Citation: IMF Staff Country Reports 2019, 028; 10.5089/9781484396292.002.A002

Source: IMF’s calculations.

20. Reducing gender discrimination in the labor market reduces income inequality and generates economic growth. The residual discrimination in the labor market is calibrated endogenously in the model so that, in the baseline scenario, average female to male wage ratio matches the household survey data: 0.74 in the formal sector and 0.64 in the informal sector. If the government enforces anti-discrimination policies that can drop the average wage gap by 5 percentage points, then female labor force participation would increase by 8.6 percentage points. Furthermore, GDP would increase by 5 percent and tax revenues by 0.8 percent of GDP. The measure would affect low-income women who would join the labor force, while upper-middle-income women would spend more time in the formal labor market (+16 percent in intensive margin). These two factors would push up female workers’ average earnings by 7 percentage points. When all the effects are taken into account, the policy would slightly diminish males’ average earnings (by 0.4 percent) due to higher competition in both the formal and informal sectors. As seen in Figure 6, the policy would also generate much larger earnings for the bottom 50 percent of the income distribution (+15 percent) than for the upper 50 percent (+2.0 percent). The Figure also compares the impact relative to a previous scenario.

21. Policies to reduce the cost of women going to work would provide a significant boost to female labor force participation. The only disutility cost from labor supply in the model comes from women working outside. This cost is calibrated endogenously so that female labor force participation (in relation to males’) in the model matches the data and takes into account different fertility rates in the income pyramid (which can also be seen in the data). The rational is that having more children increases the mother’s responsibilities inside the house, thus diminishing female labor force participation. For instance, a simulation that reduces this utility cost to zero will equalize labor force participation for men and women. Under this scenario, GDP would grow 5.3 percent and the Gini coefficient would fall 4 percentage points, reflecting the fact that lower skilled women will also join the labor market. However, without policies to address gender gaps in human capital formation and in labor opportunities, wage gaps would only marginally change.

C. Conclusions and Recommendations

22. For Senegal to meet its goal of reaching emerging market status by 2035, reforms should address development challenges, including gender inequality. Gender inequality is associated with lower economic growth (IMF 2015, Hakura and others 2016; Gonzales and others 2015), higher income inequality (Gonzales and others 2015, IMF 2016), lower economic diversification (Kazandjian and others 2016), and less bank stability (Sahay and others 2017), while it worsens other development indicators.

23. Senegal still has large gender gaps in both education access and labor opportunities. Authorities should improve incentives for girls to continue their studies, by diminishing indirect costs of studying (such as those in transportation and in school supplies); enforcing civil laws and campaigning against child marriage and early pregnancy; targeting areas with higher gender gaps (especially rural areas); and reducing discrimination in the labor market (thus increasing the financial returns from studying). To improve outcomes in the labor market, authorities should address gender gaps in access to assets, especially credit and land, and employment segregation.

24. Net costs of policies can be mitigated through an enlargement of the formal sector and an improvement of spending efficiency. As shown in the model simulations, increasing average years of education to 5, combined with increasing the formal sector share of GDP by 10 percentage points can boost government tax revenues to more than cover the costs, generating a net surplus for the government budget. Furthermore, improving education spending efficiency (for instance as pointed out by the experiments in Senegal by Carneiro and others, 2016) would reduce the government’s overall cost of education.

25. Mixed policies are necessary to tackle all sources of macro-critical gender inequalities. The framework presented is a valuable tool to show how gender gaps should be tackled from different angles simultaneously to end gender gaps in economic opportunities. For instance, although higher expected returns from labor expands female labor force participation (as seen in Figure 6), it is difficult to close the participation gap entirely if policies to address family costs for women to work outside the house (such as those in Table 1) are not implemented. Similarly, wage gaps cannot be closed if authorities address education gaps but ignore gaps in the labor market.


  • Carneiro, P., Koussihouèdé, O., Lahire, N., Meghir, C. and Mommaerts, C. 2016, “School Grants and education quality: experimental evidence from Senegal.” The World Bank.

    • Search Google Scholar
    • Export Citation
  • 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).

    • Search Google Scholar
    • Export Citation
  • IMF. 2015, “Catalyst for change: empowering women and tackling income inequality.” Staff Discussion Note, SDN/15/20, (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Hakura, M.D.S., Hussain, M. M., Newiak, M. M., Thakoor, V. and Yang, M.F. 2016, “Inequality, gender gaps and economic growth: Comparative evidence for sub-Saharan Africa”. IMF Working Paper, WP/16/111, (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Kazandjian, R., Kolovich, M. L., Kochhar, M.K. and Newiak, M.M. 2016, “Gender equality and economic diversification.” IMF Working Paper, WP/16/140, (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Marzo, F. and Atuesta, B. (forthcoming)—“Breaking Out of the Productivity Trap: How gender inequalities lock Senegal’s women into lifetimes of lower income.” The World Bank.

    • Search Google Scholar
    • Export Citation
  • Sahay, M. R., Cihak, M. M., N’Diaye, M.P.M., Barajas, M. A., Kyobe, A., Mitra, M. S., Mooi, M.N. and Yousefi, M.R. 2017, “Banking on Women Leaders: A Case for More?IMF Working Paper, WP/17/199, (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

Prepared by Vivian Malta and Marina Mendes Tavares.


Source of poverty and inequality statistics: World Bank.


Source: 2016 DHS in Senegal.


Source: Save The Children, “Child Marriage in Senegal.” Available at


“Enquête de Suivi de la Pauvreté au Senegal—ESPS II, 2011,” which is the latest available comprehensive household survey containing individual and household level data on social and economic characteristics (including earnings).


“National Strategy for Equity and Gender Equality in Senegal: 2016–2026,” available at


Note however that the estimations depend largely on the available dataset. More observed variables could enlarge the explained portion of the wage gap.


According to the World Bank, in 2011, using 2011 PPP US dollars.


Source: World Bank Data, which uses UNICEF’s State of the World’s Children Reports, United Nations Population Division’s World Contraceptive Use, household surveys including DHS and Multiple Indicator Cluster Surveys.


“Code de la Famille Sénégalais.” Available at


Detailed in Malta, Mendes Tavares, Martinez, and Kolovich (IMF Working Paper, forthcoming).


Micro level data comes from the 2011 Household Survey: “Enquête de Suivi de la Pauvreté Au Senegal—ESPS II, 2011,” which is the latest available comprehensive household survey containing individual and household level data on both social and economic characteristics (including earnings).


We calculate the income Gini coefficient using the 2011 Household Survey.


If one considers all working-age population, this average would drop to 67 percent.


Percentiles in which years of education for boys or girls are already higher than 5 years are not affected by the measure.


This target is also supported by USAID (


For simplicity we assume that costs of increasing years of education are perfectly linear to years of schooling.

Senegal: Selected Issues
Author: International Monetary Fund. African Dept.