Women's participation in the labor market and children's educational progress in Senegal

Ndoye, Mamadou Laye; Atchade, Touwédé Bénédicte Article Women's participation in the labor market and children's educational progress in Senegal Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Ndoye, Mamadou Laye; Atchade, Touwédé Bénédicte (2025) : Women's participation in the labor market and children's educational progress in Senegal, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 5, pp. 1-18, https://doi.org/10.3390/economies13050132 This Version is available at: https://hdl.handle.net/10419/329412 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Academic Editor: Lourenco Paz Received: 6 March 2025 Revised: 27 April 2025 Accepted: 29 April 2025 Published: 13 May 2025 Citation: Ndoye, M. L., & Atchade, T. B. (2025). Women’s Participation in the Labor Market and Children’s Educational Progress in Senegal. Economies,13(5), 132. https://doi.org/ 10.3390/economies13050132 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Women’s Participation in the Labor Market and Children’s Educational Progress in Senegal Mamadou Laye Ndoye 1,* and Touwédé Bénédicte Atchade 2 1 Laboratoire d’Economie Publique (LEP), Faculty of Economics and Management (FASEG), Cheikh Anta Diop University (UCAD), Dakar 10700, Senegal 2Faculty of Economics and Management (FASEG), University of Abomey Calavi (UAC), Abomey-Calavi 01 BP 526, Benin; [email protected] *Correspondence: [email protected] Abstract: This research aimed to better understand the impact of a mother’s work on girls’ and boys’ school progression at the end of primary school in Senegal. The observed correlations between a child’s educational success and the mother’s labor market involvement may not indicate causation but could instead result from other shared factors influencing both variables (an endogeneity issue). To address this issue, we estimated a bivariate model with two equations, one explaining the mother’s participation in the labor market and the other explaining the child’s educational survival, applied to data from the Integrated Regional Survey on Employment and the Informal Sector (ERI-ESI-2018). We discovered that certain individual characteristics, such as age, education level, and marital status, as well as family characteristics, including household size and parents’ social background, play significant roles in maintaining women’s labor market activity. Furthermore, we concluded that mothers’ participation in the labor market has a positive and significant effect (at 10%) on boys’ success in primary school exit exams, while the impact on girls is negative and not statistically significant. When controlling for various factors, we found that children whose mothers possess higher levels of education are more likely to pass their primary school exams. The results highlight the significance of women’s education, underscoring its role in not only integrating women into the labor market, but also in fostering their children’s academic success. In terms of economic policy implications, the study suggests that state authorities should continue to invest more in improving women’s literacy rates and in strengthening their academic and professional capacities, thereby enabling them to achieve advanced levels of education and higher qualifications. Keywords: labor market; educational progression; instrumental variable; Senegal 1. Introduction Children’s educational success has become a major social concern, giving rise to widespread mobilization (Hill et al.,2005;Tong et al.,2009;Kasiwa,2018). Most of the works reviewed maintain that, in addition to the so-called disciplinary skills associated with the acquisition of knowledge in traditionally taught subjects (Tulk,2013), the family remains the anchor point in the development of young people and their success at school (Belsky et al.,2005). However, the weakening of marital unions, the diversification of family structures, and the active participation of mothers in the labor market are all factors that increase the complexity of family organization and, incidentally, the parental role in children’s education (Parent & Brousseau,2008;Tulk,2013). In this research, we were particularly interested in the effect of the mother’s work on child rearing in a context Economies 2025,13, 132 https://doi.org/10.3390/economies13050132 Economies 2025,13, 132 2 of 18 where women are increasingly engaged in the labor market and face serious difficulties in reconciling their professional role with their maternal obligations. In the economic literature, the mother’s participation in the labor market as a direct determinant of children’s educational success has been little explored. However, according to Afridi et al. (2016), there are three main channels through which maternal employment affects children’s education. Firstly, overall household income is likely to increase when the mother starts earning an income. This can lead to greater investment in children’s human capital. Secondly, when the mother is involved in outside work, the children may be required to take on household chores and other domestic obligations, which can worsen their school performance. Thirdly, earning an income can give the mother the power to make decisions about the allocation of resources within the household. This high decision-making power of the woman positively affects the human capital of the children in the household. All three channels may be at play, making the direction of the effect of women’s participation in the labor market on children’s human capital ambiguous. Indeed, if a woman’s participation in the labor market is due to an exogenous shock putting the household in a precarious situation, this would have no effect on the children’s human capital, among other things. Similarly, in the absence of a woman’s high decision-making power within the household, an increase in household income due to her participation in the labor market may not be beneficial for improving children’s human capital. This suggests that a greater weighting of working mothers’ preferences in household decisionmaking could be a key factor in improving their children’s educational attainment. The existing literature has shown that when a mother works, she generally has more influence over household decisions, which can encourage children to attend school. However, in some cultures, and in the patriarchal regimes that prevail in Africa, even if a woman works, she does not control her income, which reduces her impact on children’s schooling. In Senegal, more and more women are active in the job market. Between 2005 and 2020, the female labor force participation rate rose significantly, from 36.9% to 48.6% (ANSD,2021). The employment rate also changed considerably over the same period. It rose from 25.5% in 2005 to 31.1% in 2020 (ANSD,2021). However, although they are economically active, they operate mainly in the subsistence farming sector and in marginal activities of the informal economy, with low added value and insufficient economic profitability (BIT,2007). Senegal is characterized by a highly informal labor market, which may influence the effects of women’s employment on children’s schooling differently from countries where formal employment is more widespread. Gender norms play an important role in the division of domestic and professional labor, which could affect the effect of women’s participation in the labor market on children’s educational follow-up. The prevalence of extended families in Senegal could be a factor mitigating or amplifying the impact of women’s employment on children’s education. In certain situations, other family members (grandparents, aunts) can compensate for maternal absence and thus reduce the potential negative impact on school supervision. Conversely, in a context of heavy domestic burden for women, the lack of alternative childcare options could hamper their educational progress. On the political level, Senegal has been pursuing a bold policy of universal enrollment for almost two decades, with significant results in terms of access to schooling and the reduction of inequalities between girls and boys in compulsory education cycles. An analysis of the academic progression 1 of primary school pupils shows that, for each primary level, the country recorded more than 94% of pupils progressing to the next level between 2016 and 2017 (ANSD & AFRISTAT,2019). Analysis by gender shows that the proportion of girls moving on to the next grade is generally higher than that of boys, except for the last grades of primary, secondary 1, and secondary 2 schools. In fact, at these levels, boys are Economies 2025,13, 132 3 of 18 more likely to succeed and move on to the next level (89.2% of girls and 86% of boys pass the primary level and move on to secondary level 1). These statistics raise a central question: what is the potential effect of mothers’ participation in the labor market on the school survival of pupils (girls and boys) at the end of primary school in Senegal? Drawing on data from (ANSD & AFRISTAT,2019)’s Integrated Regional Survey on Employment and the Informal Sector (ERI-ESI), the overall objective of this research was to examine the effect of mothers’ labor market participation on children’s education in Senegal. Specifically, it aimed to (i) identify the factors determining mothers’ participation in the labor market and (ii) analyze the effect of mothers’ participation in the labor market on the academic success of children (girls and boys) at the end of primary school. Table 1 below shows elementary school survival rates by area of residence and gender. Table 1. Primary school survival rate according to the area of residence and the gender of the pupils. Area of Residence Percentage of Children Who Were in Their First Year in 2016 and Their Second Year in 2017 (%) Percentage of Children Who Were in Their Second Year in 2016 and Their Third Year in 2017 (%) Percentage of Children Who Were in Their Third Year in 2016 and Their Fourth Year in 2017 (%) Percentage of Children Who Were in Their Fourth Year in 2016 and Their Fifth Year in 2017 (%) Percentage of Children Who Were in Their Fifth Year in 2016 and Their Sixth Year in 2017 (%) Percentage of Children Who Were in Their Seventh Year in 2016 and Their Seventh Year in 2017 (%) Dakar 95.5 98.9 94.6 96.6 92.4 77.6 Other towns 95.8 97 94.9 96.3 96.3 92.7 Rural 96.5 96.9 95.3 96.3 95.6 91.4 Sex Boys 95.7 97.3 95 96 94.6 89.2 Girls 96.5 97.3 95 96.6 95.2 86 Senegal 96.1 97.3 95 96.3 94.9 87.5 Source: ANSD, ERI-ESI 2018. This research responds to a question of international interest, and from this point of view, it is relevant to both scientific research and policy-making. As far as scientific research is concerned, we are unaware of any works in West Africa, particularly in Senegal, focusing on identifying the causal effect of mothers’ participation in the labor market on their children’s school performance. It is this gap that the present research sought to fill. From a policy point of view, this research contributes to inclusive growth and fits in perfectly with the achievement of sustainable development goals, notably quality education, decent work and economic growth, and gender equality. This article is organized as follows. Section 2presents the literature review, Section 3 details the research methodology and describes the data used, Section 4provides the results and their economic interpretations, and Section 5describes the conclusions. 2. Literature Review The conflict between the roles of mother and worker is considered to stem from the separation of home and workplace, the nature of employment, and social norms regarding the roles of men and women (Mason & Palan,1981;Rindfuss & Brewster,1996). However, this conflict could be attenuated under certain circumstances. First, there are some jobs with characteristics that allow for the simultaneous fulfilment of worker and mother roles, thereby reducing the incompatibility between the two. For example, women occupied in agriculture, working at home or on a family farm, are largely able to combine their working and mothering roles. For women working outside the home, particularly in the modern sector, it is more difficult to combine parenting and worker roles. Economies 2025,13, 132 4 of 18 Results from studies by Luke and Munshi (2011) focusing on plantations in southern India where women are permanently employed show that a relative increase in women’s income has a positive impact on their children’s education. Using data from the Young Lives Study (YLS) in India, Afridi et al. (2016) found that higher maternal labor market participation is associated with more time spent in school by children in their households. Moreover, they found that the impact was more pronounced in poorer households. In fact, almost half of the increase in time spent in school by students could be explained by an increase in the probability of a child attending school while the mother is at work. Moreover, this increase in school attendance translates into better educational outcomes for the students. A study conducted in Canada showed that a woman’s employment has a positive impact on her child’s academic performance and well-being (Tulk et al.,2016). In fact, the more involved a mother is in her work, the more likely her child is to do well in school. However, changes in career direction, promotions, or irregular working hours could affect family life and cause anxiety for children. This would undoubtedly deprive them of valuable time that could have been spent playing together, reading, doing homework, and going on outings (Tulk et al.,2016). These findings are consistent with those of Marchant et al. (2001), who showed that the relationship between students and their parents and the support they receive from their parents contribute significantly to academic success. Cawley and Liu (2012), in a study conducted in the United States, found that working outside the home is associated with less time spent with children to provide cognitive stimulation, but the magnitude of the effect remains small. Mothers who worked outside the home spent only 12 min less per day with their children and 37 min less on direct child care than mothers who did not work outside the home. Ruhm (2004) showed that maternal employment during the first three years of a child’s life has a small negative effect on the estimated verbal ability of three-year-olds and a larger negative effect on reading and achievement of fiveand six-year-olds. Nelen et al. (2013) found that there is no negative relationship between maternal working hours and child outcomes, as is often found for preschool-aged children. Instead, they found that children’s sorting test scores were higher if their mother worked part-time (girls) or full-time (boys). At the same time, working may benefit children, for example, by increasing family income. De Hoop et al. (2017) found that women’s participation in the labor market increases household income and the demand for children’s education and hobbies, which in turn reduces children’s labor supply. This finding is in line with previous studies that showed that an increase in maternal income activity, which translates into higher investment in children’s health, leads to an improvement in children’s human capital (Haddad et al.,1997). Hill et al. (2005) estimated the impact of maternal employment on children’s cognitive outcomes (measured for children aged 3 to 8). They found that estimating the true impact of women’s labor market participation on children’s development is complicated by selection bias and the endemic lack of data in most studies of such policies. To overcome these problems, researchers have resorted to the use of propensity score matching and multiple imputation. Hill et al. (2005) compared the results of four methods of maternal employment status: not working in the first three years after childbirth, working only after the first year, working part-time in the first year, and working full-time in the first year. The results highlighted the small and negative, but significant, effects of maternal labor market participation on children’s cognitive outcomes in the case of full-time work in the first year after childbirth, compared to delaying work until the end of the first year after childbirth. Multiple imputations produced estimates for the different measures that differed markedly from a case-by-case approach. These researchers also found that the differences between the results of propensity score matching and regression modeling were often minimal. Economies 2025,13, 132 5 of 18 Relatively few empirical studies have focused on the impact of mothers’ labor market participation on their children’s well-being in general and on children’s human capital in particular, especially with respect to developing countries. The impact of women’s participation in the labor market on children’s survival at school in West Africa is highly dependent on the type of employment held by mothers. While stable, well-paid work for women improves school enrolment rates, informal and insecure work can lead to economic constraints that increase the risk of dropping out of school (Kpadonou,2022). Debela et al. (2019) used panel data estimated using the fixed effects model from a study conducted in Tanzania. They concluded that maternal employment has a nonlinear effect on children’s weight-for-age z-scores. Moore and Schmidt (2004) used the instrumental variables (IV) method and found that maternal employment has a negative effect in the first year of children’s lives and a potentially positive and definite effect in the second year. The net effect over the first three or four years is significant. Diagne (2006) showed that in rural Senegal, economic constraints force children from families where the mother works to drop out of school early to contribute to the household income. The results of Evans and Acosta (2021) are in line with these findings, showing that an economically active mother improves her children’s chances of survival at school, provided that her income is stable and sufficient to cover educational costs. Filmer and Schady (2009) showed that in households where women contribute to income, school enrolment rates are higher. At the same time, if the mother’s employment is unstable or informal, the positive impact may be limited (Tsimpo & Wodon,2016). There is, therefore, no consensus in the literature on the impact of women’s labor market participation on child well-being in general and on human capital in particular. The impact of women’s employment on child well-being is usually captured by the socioeconomic status of the household, which, in turn, operates through a number of ‘proximate determinants’ of child health and education. In this context, child health and education outcomes depend on a combination of social, economic, biological, and environmental forces. Women’s participation in the labor market contributes to household income, which generally provides access to more and better-quality food, housing, protection from repeated school absenteeism, and improved school performance. 3. Methodology and Data To analyze student behavior, specific statistical methods are necessary to isolate the effects of particular explanatory variables. Impact evaluation models, especially the instrumental variable method, help correct for these effects. This study aimed to examine the impact of mothers’ labor market participation on primary school completion rates in Senegal, with a focus on gender differences. The observed correlations between a child’s educational success and the mother’s labor market involvement may not indicate causation but could instead result from other shared factors influencing both variables (an endogeneity issue). For example, an unobserved factor, such as the mother’s talent, might simultaneously lead to her career advancement and her child’s improved academic performance. Similarly, household tensions could both impact the mother’s employment status—potentially leading to unemployment—and negatively affect the child’s educational outcomes (Duée,2005). To address this issue, we estimated a bivariate model with two equations, one explaining the mother’s participation in the labor market and the other explaining the child’s educational survival. Note the following: Economies 2025,13, 132 6 of 18 -y1 —the mother’s participation in the labor market is captured here by the occupation of any job. This variable is the implementation of an unobserved variable: y1=1(y∗ 1>0)(1) -y2 —the school survival of the child (transition to a higher grade in two consecutive years). This variable is the fulfilment of unobserved variable y∗ 2: y2=1(y∗ 2>0)(2) The model is, therefore, written as follows: y∗ 1i=X1iα+ε1i y∗ 2i=X2iβ+δy1i+ε2i (3) where X1i and X2i are the individual characteristics of the mothers and their pupils, respectively. The model incorporates explanatory variables linked to students’ sociodemographic characteristics (such as gender and area of residence), maternal characteristics (including age, qualifications, education type, employment type, and social background), and household attributes (notably, area of residence). For the model to be identified, it is preferable that at least one variable, called the instrument, be excluded. We, therefore, needed to find a variable that directly determined the mother’s participation in the labor market but had no direct impact on the student’s survival in school. Instruments used in the literature include capital income (Mayer,1997) or unionization in the company where the parent works, which are thought to explain part of the household income without influencing children’s educational success (Shea, 2000;Duée,2005). Other instruments, such as grandparent characteristics (socioeconomic status or socio-professional category), have been used (Maurin,2002;Duée,2005). The assumption is that these variables have no direct effect on the child’s education, but that their effect is taken into account by the various control variables describing the parents’ level of education, father’s and mother’s qualifications, etc. In this research, the existence of a trade union in the sector in which the mother is employed was used as an instrument for women’s participation in the labor market. This approach is justified by the structuring role that trade unions have played in promoting gender equality and improving working conditions in several developing countries (ILO, 2018). In these countries, union action has helped to reduce structural barriers to women’s economic participation, justifying the relevance of unionization as an instrument. The underlying assumption is that the presence of unions improves working conditions (better job security, more flexible working hours, higher wages), making women’s participation in the labor market more attractive. In the Senegalese context, trade unions, although often dominated by men, have gradually integrated the gender issue into their demands, notably by promoting equal pay, fighting occupational discrimination, reorganizing working hours, and defending the rights of women workers in the informal sector (ILO,2020). We postulate that the existence of a union in a given sector can affect an individual’s decision to work in that sector. If an individual is unionized, this can have a direct effect on the household environment through channels such as job stability, social benefits, and improved household income. Thus, we assume that the impact of the mother’s unionization on the children’s educational attainment is solely through its effects on the mother’s employment conditions, job security, improved income, and legal recognition of the mother’s work—thus satisfying the exclusion condition. This approach follows the logic of Card (1996) and Angrist and Krueger (2001), who recommended the use of aggregate variables to capture structural effects without introducing biases related to individual characteristics. Economies 2025,13, 132 7 of 18 The instrumental variable method used here allowed us to deal with the endogeneity of the “mother’s participation in the labor market” variable, which was our variable of interest. It should be noted that other variables in our model (control variables) may also be suspected of endogeneity. In the case of our research, we started from the strong assumption that the control variables used were exogenous, or that their possible endogeneity would not significantly bias the estimate of the effect of a mother’s participation in the labor market on pupils’ school results at the end of primary school. The variables used in the different models are presented in Table 2below. Table 2. Summary description of the variables. Number Variables Explanation Categories 1Y1Participation of women in the labor market 0 = Do not participate 1 = Participate 2Y2Child’s academic achievements 0 = Not promoted to the next grade 1 = Promoted to the next grade 3x1Child’s sex 0 = Boy 1 = Girl 4x2Mother’s age (ref.: 15–24 years) 0 = 15–24 years 1 = 25–34 years 2 = 35–64 years 3 = 65 years and over 5x3Area of residence (ref.: Dakar) 0 = Dakar 1 = Other urban areas 2 = Rural 6x4Mother’s degree (ref.: no degree) 0 = No degree 1 = CFE 2 = BFEM 3 = CAP 4 = BEP 5 = BAC 6 = DEUG, DUT, BTS 7 = Undergraduate degree 8 = Master’s, DESS, DEA, BSc in Engineering 7x5Marital status (ref.: Single) 0 = Single 1 = Married 2 = Divorced 3 = Widow 8x6Household size (ref.: 1–6 people) 0 = 1–6 people 1 = 7–10 people 2 = 11–15 people 3 = 16 and more 9x7Type of apprenticeship (ref.: academic training) 0 = Academic training 1 = Simple (praticals with no theory) 2 = Dual (theory and practice) 10 x8Parents’ social category (ref.: children of managers) 0 = Children of managers 1 = Children of the employees 2 = Children of a self-employed worker 3 = Children of another social category of parents Economies 2025,13, 132 8 of 18 Table 2. Cont. Number Variables Explanation Categories 11 x9Mother’s group job (ref.: highly qualified) 0 = Highly qualified 1 = Low-skilled worker 2 = Qualified employee 3 = Unqualified employment 12 x10 Existence of a trade union in the sector in which the mother is employed (ref.: not unionized) 0 = Not unionized 1 = Unionized Source: authors’ own construction. The data used were extracted from the Integrated Regional Survey on Employment and the Informal Sector (ERI-ESI, 2017–2018), which covers two components: the first component collects data on the socioeconomic characteristics of the population (education, health, employment, demography, etc.), and the second component relates to the collection of data from the informal non-agricultural production units identified during the first component. We focused mainly on the first phase, during which two questionnaires were used: a household questionnaire used to collect information on all household members, the household, and the dwelling, and an employment questionnaire administered in each household to all individuals aged 10 and over. We performed a descriptive analysis of the data according to certain characteristics of the mothers (see Appendix B, Table A1). 4. Results and Discussion In this section, we first present the results of the binary logit model used to identify the determinants of women’s participation in the labor market. Secondly, using the instrumental variables method, we discuss the results of the impact of mothers’ participation in the labor market on pupils’ academic success at the end of primary school, distinguishing between boys and girls. -Determinants of women’s participation in the labor market The determinants of women’s participation in the labor market were obtained by estimating a binary logit model (Table 3). Estimates were made by applying White’s correction to obtain unbiased parameters in the presence of heteroscedasticity. The probabilities associated with the Wald statistic were zero, indicating that the overall quality of the model was satisfactory. In other words, the variables selected contribute to explaining women’s participation in the labor market. Moreover, the percentage of correct predictions is 77.34%. We estimated the odds ratios in order to obtain a precise estimate of the probability of participating in the labor market according to women’s individual characteristics. Table 3. Determinants of women’s labor force participation. Women’s Participation in the Labor Market Variables Odds Ratios Robust Std. Err. Age (ref.: 15–24 years) 25–34 years 1.246 ** 0.114 35–64 years 1.627 *** 0.159 65 years and over 0.647 * 0.144 Economies 2025,13, 132 15 of 18 Appendix B Table A1. Descriptive statistics of the variables used in the estimated models. Employed Women Non-Employed Women Variables NAverage/ Proportion Standard Deviation Min Max N Average/ Proportion Standard Deviation Min Max Children at the end of their primary school years Promoted to the next grade 109 0.733945 0.4439345 0 1 1163 0.9243336 0.2645773 0 1 Not promoted to the next grade 109 0.266055 0.4439345 0 1 1163 0.0756664 0.2645773 0 1 Mother’s age 15–24 years 12,392 0.2293415 0.4204263 0 1 50,885 0.482657 0.499704 0 1 25–34 years 12,392 0.2503228 0.4332164 0 1 50,885 0.2239363 0.416884 0 1 35–64 years 12,392 0.4713525 0.4991988 0 1 50,885 0.2226786 0.4160484 0 1 65 years and over 12,392 0.0489832 0.2158417 0 1 50,885 0.0707281 0.2563727 0 1 Mother’s place of residence Dakar 12,392 0.1265332 0.3324628 0 1 50,885 0.0743245 0.2623008 0 1 Other urban areas 12,392 0.2997095 0.4581492 0 1 50,885 0.2730274 0.4455192 0 1 Rural 12,392 0.5737573 0.4945499 0 1 50,885 0.6526481 0.4761334 0 1 Mother’s level of education No level 12,392 0.6792285 0.4667919 0 1 50,885 0.5638579 0.4959107 0 1 Primary 12,392 0.1964977 0.3973652 0 1 50,885 0.3075471 0.4614829 0 1 Secondary 12,392 0.1112815 0.3144931 0 1 50,885 0.1226114 0.3279941 0 1 Higher 12,392 0.0129923 0.1132453 0 1 50,885 0.0059837 0.0771232 0 1 Mother’s diploma obtained Without diploma 12,392 0.6381256 0.4806017 0 1 50,885 0.7178749 0.4500446 0 1 CFEE 12,392 0.1687929 0.3746149 0 1 50,885 0.1636836 0.3699969 0 1 BFEM 12,392 0.1177625 0.3223662 0 1 50,885 0.0742385 0.2621648 0 1 CAP 12,392 0.0068695 0.0826073 0 1 50,885 0.0042975 0.065416 0 1 BEP 12,392 0.0206084 0.1420869 0 1 50,885 0.0223849 0.1479352 0 1 BAC 12,392 0.0252699 0.1569629 0 1 50,885 0.0126092 0.1115832 0 1 DEUG, BTS, DUT 12,392 0.0039254 0.0625377 0 1 50,885 0.0011806 0.0343409 0 1 License 12,392 0.0103042 0.1009977 0 1 50,885 0.0020307 0.0450185 0 1 Master’s degree 12,392 00034347 0.0585131 0 1 50,885 0.0006139 0.0247706 0 1 Marital status Single 12,392 0.1475145 0.3546324 0 1 50,885 0.4136249 0.4924914 0 1 Married 12,392 0.7307133 0.4850656 0 1 50,885 0.4868353 0.4952862 0 1 Divorced 12,392 0.0323596 0.1769604 0 1 50,885 0.0158807 0.1250162 0 1 Widowed 12,392 0.0894125 0.2853498 0 1 50,885 0.0836591 0.2768807 0 1 Household size 1–6 persons 12,392 0.2169948 0.4122157 0 1 50,885 0.156274 0.3631184 0 1 7–10 persons 12,392 0.2883312 0.4530043 0 1 50,885 0.2932691 0.4552652 0 1 11–15 persons 12,392 0.2458844 0.4306277 0 1 50,885 0.2607841 0.4390667 0 1 16 and over 12,392 0.2487895 0.432329 0 1 50,885 0.2896728 0.4536149 0 1 Vocational training Yes 12,392 0.3289219 0.4698405 0 1 50,885 0.5037289 0.4999923 0 1 No 12,392 0.6710781 0.4698405 0 1 50,885 0.4962711 0.4999923 0 1 Mother’s group job Highly qualified 12,392 0.138525 0.3454657 0 1 50,885 0.1317143 0.3381973 0 1 Low-skilled workers 12,392 0.3161672 0.4649999 0 1 50,885 0.2026137 0.4019677 0 1 Qualified employees 12,392 0.2848556 0.4513659 0 1 50,885 0.4277629 0.4947797 0 1 Unqualified employment 12,392 0.2604522 0.438901 0 1 50,885 0.237909 0.4258251 0 1 Parents’ social origin Children of executives 12,392 0.0249485 0.1559841 0 1 50,885 0.0278065 0.1644238 0 1 Children of employees 12,392 0.0981443 0.2975404 0 1 50,885 0.0852136 0.2792088 0 1 Children of self-employed people 12,392 0.7948454 0.4038562 0 1 50,885 0.7584351 0.4280467 0 1 Children of parents with other CSP 12,392 0.0820619 0.2744872 0 1 50,885 0.1285448 0.334707 0 1 Economies 2025,13, 132 16 of 18 Appendix C Table A2. Result of the first stage of the IV regression. Participation in the Labor Market Variables Coefficient Standard Error p-syndica 0.357 *** 0.017 Age (ref.: 15–24 years) 25–34 years 0.084 *** 0.006 35–64 years 0.143 *** 0.006 65 years and over −0.101 ** 0.011 Diploma (ref.: without diploma) CFEE 0.013 ** 0.005 BFEM 0.033 *** 0.006 CAP −0.019 0.025 BEP 0.005 0.012 BAC −0.076 *** 0.132 DEUG, DUT, BTS −0.040 0.031 License 0.049 * 0.253 Master’s, DESS, DEA, degree in Engineering 0.011 0.035 Type of apprenticeship (ref.: theoretical training) Simple (practice without theory) 0.334 *** 0.005 Dual (theoretical and practical) 0.394 *** 0.009 Marital status (ref.: Single) Married 0.113 *** 0.006 Divorced 0.082 *** 0.009 Widowed 0.023 0.018 Household size (ref.: 1–6 people) 7–10 people −0.026 *** 0.006 11–15 people −0.024 *** 0.006 16 and over −0.029 *** 0.006 Place of residence (ref.: Dakar) Other urban centers −0.020 *** 0.006 Rural −0.023 *** 0.006 Constant 0.048 *** 0.008 Note: The endogenous variable represents the participation of women in the labor market (holding any employment position). Signs ***, **, and * indicate the significance of variables to their respective thresholds of 1%, 5% and 10%. Source: authors’ own computation. CFEE: Certificat de Fin d’Etudes Elémentaires, BFEM: Brevet de Fin d’Etudes Moyennes, CAP: Certificat d’Aptitude Professionnelle, BEP: Brevet d’Etudes Professionnelles, BAC: Baccalauréat, DEUG: Diplôme d’Etudes Universitaires Générales, DUT: Diplôme Universitaire de Technologie, BTS: Brevet de Technicien Supérieur, DESS: Diplôme d’Etudes Supérieures Spécialisées, DEA: Diplôme d’Etudes Approfondies. F test of exclued instruments F(1, 35716) = 441.52 Prob > F = 0.000 Sanderson-Windmeijer multivariate F test of excluded instruments: F (1, 35716) = 441.52 Prob > F = 0.000 Underidentification test Anderson canon. Corr. LM statistic Chi-sq (1) = 436.44 p-val = 0.000 Note 1 Progress measures the level at which pupils move from one year to the next. 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