Maternity Leave Reform and Women’s Labor Outcomes in Colombia: A Synthetic Control Analysis

Abstract

This article examines the effects of maternity leave (Law 1822 of 2017) on the Colombian women’s labor market. Using biannual cohorts during the working life cycle of women (18 to 57 years old) reveals that the law’s implementation reduced the hours worked and the real hourly wage for younger women compared to older women. Average treatment effects show that the difference between the hours worked after 2017 was 0.917 (treatment vs. control), and before, it was 1.714 h worked (treatment vs. control). Differences show a reduction of 41 h per cohort and year (approximately one week worked). Synthetic control analysis shows that young cohort experienced a reduction of 0.007 U$ cents in 2017 and a reduction of 2.2 h worked in 2017. Our results highlight the importance of differential policies related to maternity leave by age (cohort) when analyzing the incorporation of women into the labor market.

1. Introduction

Recent studies indicate that half of the women in developing countries participate in the labor market and face wage disparities based on their gender or motherhood (Botello & López, 2014; Botello & Guerrero, 2020; Dussuet & Gasté, 2024; Arando Lasagabaster & Lekuona Agirretxe, 2025; Castro Núñez et al., 2025). In 2018, Costa Rica had a 48.5% labor-female participation rate. Meanwhile, Colombia had 53.2%, Chile 49.3%, Argentina 49.1%, Ecuador 54.6%, and the average female labor participation in America Latina was 51.79%; male labor participation was 77.8% (Internacional Labor Organization [ILO], 2018). With respect to the pay penalty, in Colombia, Gamboa and Zuluaga (2013) found a −1.8% pay penalty for motherhood, and Uribe et al. (2019) adapted the estimates to 6.73% on average for 2014. Agüero and Marks (2011) found a −42% penalty for motherhood in less developed countries.

Maternity leave arrangements vary substantially across Latin America. In Chile, for instance, paid maternity and postnatal leave was extended to 24 weeks in 2011; evidence links this longer leave to higher rates of exclusive breastfeeding and sustained maternal attachment to the labor market (Caro et al., 2021; Navarro-Rosenblatt et al., 2023). In Brazil, the law guarantees 120 days of paid leave, and the Empresa Cidadã program extends this period to 180 days through tax-favored employer participation. Research shows that maternity leave in Brazil is associated with higher rates of exclusive breastfeeding (Monteiro et al., 2017). In Mexico, women are entitled to 12 weeks of paid maternity leave, provided through the Mexican Social Security Institute (Instituto Mexicano del Seguro Social, IMSS) (Goodman et al., 2024). The Women, Business and the Law database documents these de jure parameters and related supportive frameworks (World Bank, 2024). Taken together, these cases suggest that longer, insurance-financed leave—when combined with childcare provision and credible enforcement—can mitigate adverse employment effects while improving maternal and child health outcomes.

In addition to ensuring employment by guaranteeing the continuity of mothers in the labor market, maternity leave improves the well-being of women and their newborns and reduces the costs of fertility, poverty, and gender disparities associated with motherhood (Thévenon, 2011). Maternity leave therefore provides women with job security and, in some cases, remuneration, thus reducing stress during pregnancy (Rönsen & Sundström, 1996; Rossin, 2011). Maternity leave also provides the needed time for the mother’s postpartum physical and mental health to recover (Waldfogel, 1998; Staehelin et al., 2007; Dagher et al., 2014; Van Niel et al., 2020), resulting in long-term increases in female labor productivity (Liu & Buzzanell, 2004; Ma et al., 2021). Furthermore, maternity leave benefits children’s health by allowing for extended breastfeeding and improvements in the quantity and quality of parenting (Baker & Milligan, 2008a, 2008b; Van Niel et al., 2020).

Maternity leave is “time off from a job given to a mother to take care of a newborn child” (Merriam-Webster.com Dictionary, 2025). Maternity and family leave are policies that enable mothers to take time off work to prepare for and recover from childbirth and to care for their new children while being protected from dismissal (Rossin-Slater, 2017). Maternity leave could reduce women’s labor market participation through an income effect or increase it through job protection (Lalive et al., 2013).

In Colombia, maternity leave developed out of Law 53 of 1938, which established an 8-week period of paid leave, beginning on the day indicated by the interested party and granting a maternity leave of 3 months before and after childbirth based on medical opinion. Law 50 of 1990 extended the leave to 12 paid weeks and prohibited dismissal on the basis of pregnancy or breastfeeding, while also granting the same provisions and guarantees to adoptive mothers of children under the age of 7. For its part, Law 1468 of 2011 increased maternity leave to 14 paid weeks, and Law 1822 of 2017 extended it by 4 more weeks.

Currently, the main characteristics of maternity leave include: Every pregnant worker is entitled to an 18-week paid leave at the time of childbirth. She is paid the salary she earned at the time of starting her leave or the average salary if it is not fixed. All provisions and guarantees established for the biological mother are extended to an adoptive mother on the same terms. Finally, the paid leave will be in charge of the Health Promoting Entity (hereafter EPS).

Despite the virtue of maternity leave, it can have an impact on company hiring decisions and the improvement of mothers’ working conditions (Romero, 2018). Accordingly, maternity leave constitutes a public policy instrument that regulates the participation of women of child-bearing age in the labor market and influences fertility decisions. In the private sector, noncompliance with maternity-leave regulations remains common: some employers pressure women to exit their jobs to provide infant care, with adverse effects on income and career continuity. By contrast, such noncompliance appears less prevalent in the public sector, where statutory protections are enforced more consistently (Odunga et al., 2024).

This article’s main contribution is an analysis of the effects of Colombia’s adoption of Law 1822 of 2017—which extends female maternity leave from 14 to 18 paid weeks (28.5% of the leave)—on the female employment rate, and on the real hourly wage. The results show that the implementation of the law significantly reduced the female occupation rate for younger women (18 to 25 years of age) relative to the rate of the comparable synthetic group. However, for women over 34, the female occupation rate is higher than that estimated in its synthetic version. This is the quantitative effect of the law. The law’s effect on wages was also evaluated. We found significant effects of the law on the wages of younger women (18 to 31 years old) relative to their comparable synthetic group. This difference can be seen as the opportunity cost of the law for younger women. This analysis is based on a comparison of biannual synthetic cohorts for women residing in Colombian urban areas during their working life cycle.

The rest of this article is structured as follows: Section 2 reviews the scientific literature linking fertility and female labor-force participation. Section 3 presents the empirical identification strategy, which links maternity leave with female labor participation and real hourly wages and a description of the data used. Section 4 presents and discusses our main empirical results. Finally, Section 5 concludes with policy recommendations.

2. Literature Review

Female labor participation and fertility are inextricably linked. The presence of children at home necessitates a trade-off for parents between work and childcare. This task is traditionally performed by women and causes them to interrupt or give up their jobs (Rosenfeld, 1996; Klerman & Leibowitz, 1999; Morgan & Zippel, 2003; Rege & Solli, 2013).

The International Labour Organization (2018) states that mothers of children under 5 years old face an employment penalty compared with fathers, men who are not fathers, and women who are not mothers, that is evident on a global scale. Likewise, maternity leave encourages traditionally gendered occupational specialization, thus increasing inequality between men and women in the labor force (Pettit & Hook, 2009).

Women who have children face significant labor market consequences (Gornick et al., 1970). In particular, the arrival of children creates a long-term gender gap in earnings that is driven by labor-force participation, hours worked, wage rates, occupation, and sector (Kleven et al., 2019). Jones and Wilcher (2023) provide evidence that well-designed paid family leave programs not only offer direct income protection but also reduce maternal labor-market detachment and generate private benefits—including higher breastfeeding rates, improved maternal and infant health, and stronger early bonding.

The empirical literature on the impact of maternity leave on female labor market outcomes is inconclusive. Some authors argued that leave has a positive impact on women’s employability, particularly those of childbearing age, but a negative impact on their hourly earnings in the long run (Ruhm, 1998). According to several cross-country studies, paid leave is associated with higher female employment rates (Jaumotte, 2003; Pettit & Hook, 2005; Gregg et al., 2007). Furthermore, maternity leave can help reduce the negative employment effects of childbearing, such as the potential withdrawal of women from the labor force following the birth of a child (Misra et al., 2011). However, some authors find negative impacts of these policies: extended leave significantly reduces the return to work and postpartum employment rates of mothers in the short term (Baker & Milligan, 2008a; Hanratty & Trzcinski, 2009; Lalive & Zweimüller, 2009; Schönberg & Ludsteck, 2014). Similarly, empirical evidence demonstrates that leaves with an extended period of labor protection may translate into higher labor costs for firms, thus generating a decrease in the hiring of women of childbearing age or a reduction in their wages (Hegewisch & Gornick, 2011; Ramírez et al., 2015; Blau & Kahn, 2017). Canaan et al. (2022) discuss the effects of extension of parental leave in high-income countries.

These results are inconclusive for several reasons: Eberhard et al. (2023) highlight the gender composition of the labor force because these differences can impact labor market decisions across genders; Rossin-Slater (2017) explains four key policy levers such as the duration of the leave, the rights to payments, the job protection entitlement, and the financing of benefits, these are the four most important main differences across countries, but there are others as well such as paternity, parental or family leave; Berniell et al. (2021, 2023) and Galván et al. (2024) bring the attention to the differences in the levels of labor informality, coverage of childcare, conservative social norms, and weaker labor market institutions because developing countries differ from these aspects concerning high-income countries family leaves “could led to different conclusions to those found in high-income countries.” (Galván et al., 2024, p. 389).

Studies focusing on Latin America have found heterogeneities in place among countries. Galván et al. (2024) explore a panel of 15 Latin American countries for the period from 2000 to 2019 and find a positive impact on countries with limited initial coverage and no significant results on those countries with fairer perceptions. Additionally, Berniell et al. (2021) focus on the impacts of parenthood in Chile and emphasize the differences between formal and informal jobs. Furthermore, Eberhard et al. (2023) also focus on Chile but bring their attention to the Quality of Employment Index (QoE) to capture the multidimensionality of the impact of motherhood, and their estimations show that the impact is stronger for less educated women. Berniell et al. (2023) delve into the similar impact across Chile, Mexico, Peru, and Uruguay; their findings show that motherhood reduces women’s employment and changes their occupational structure towards part-time jobs, self-employment, and informal work. Machado and Pinho Neto (2018) and Machado et al. (2024) analyze the case of the change in the extension of the maternity leave policy in Brazil from 120 days to 180 days and the impact on the formal labor market. The authors find an inverted U-shaped employment pattern that peaks at the time of leave-taking but remains stable until four months after the leave period begins and falls thereafter.

Several studies focused on the impact on the informal sector. Berniell et al. (2021) find that women’s employment falls by 4% of hours worked, and monthly earnings decrease by 28%. Additionally, the informality rate increases by 38% among working women. Furthermore, Berniell et al. (2023) provide evidence that motherhood brings in higher labor informality, more self-employment, and part-time jobs in Chile, Mexico, Peru, and Uruguay.

Several studies have analyzed the effects of maternity leave on female labor market outcomes in Colombia. For instance, Forero de Saade et al. (1991) and Molinos (2012) found a decrease in women’s labor participation because of maternity leaves and the prohibition of dismissals of pregnant workers. By contrast, Olarte and Peña (2010) and Botello and López (2014) confirmed a substantial wage penalty for maternity. Furthermore, Ramírez et al. (2015) discovered that increasing maternity leave from 12 to 14 weeks worsened the working conditions of women of childbearing age. The increased leave resulted in higher probabilities of being unemployed or working informally and earning lower wages than men. According to Romero (2018), leave protects mothers’ employment in the short term, and the increase from 12 to 14 weeks had no effect on the probability of continuing with the same job. However, it did decrease the probability of re-engaging in new jobs and increased the probability of leaving formal employment in the long term. Botello and Guerrero (2020) found that extending maternity leave from 14 to 18 weeks increased the likelihood of women with high fertility rates becoming informally employed and inactive. Martínez-Cárdenas et al. (2017) suggested that telecommuting might be an option that could increase maternity leave by allowing employees to take care of their families at home while receiving compensation without suspending the labor contract. At the same time, it allows the employer to keep the services of the employee in an extended leave. Finally, Uribe et al. (2019) find that the extension of the maternity leave increased the probability of unemployment, informality, and self-employment and decreased women’s wages compared to men in Colombia.

3. Empirical Strategy and Data

3.1. Empirical Strategy

This article uses the synthetic control methodology (Abadie et al., 2010) to analyze the effect of the extension of maternity leave on the female employment rate and the real hourly wage. Let $D_{jt}$ be an indicator of treatment for cohort $j$ at time $t$. Next, let the employment rate (or real hourly wage) of the cohort, $Y_{jt}$, be the sum of a time-varying treatment effect, ${\alpha }_{jt}{D}_{jt}$, and the no-treatment counterfactual $Y_{jt}^N$, which is specified using a factor model,

$$Y_{jt}={\alpha }_{jt}{D}_{jt}+Y_{jt}^N={\alpha }_{jt}{D}_{jt}+({\delta }_t+{\theta }_t{Z}_j+{\lambda }_t{\mu }_j+{\epsilon }_{jt})$$

(1)

where ${\delta }_t$ is an unknown time factor, $Z_j$ is an $(r \times 1)$ vector of observed covariates unaffected by treatment, ${\theta }_t$ is a $(1 \times r)$ vector of unknown parameters, ${\lambda }_t$ is a $(1 \times F)$ vector of unknown factors, ${\mu }_j$ is an $(F \times 1)$ vector of unknown factor loadings, and the error ${\epsilon }_{jt}$ is independent across cohorts and time with zero mean. Letting the first unit be the treated cohort, we estimated the treatment effect by approximating the unknown $Y_{1t}^N$ with a weighted average of untreated cohorts.

$$\widehat{{\alpha }_{1t}}=Y_{1t}-\sum _{j\ge 2}{w}_j{Y}_{jt}$$

(2)

Equation (1) simplifies the traditional fixed-effects equation if ${\lambda }_t{\mu }_j={\varphi }_j$. The fixed-effects model allows for unobserved heterogeneity that is only time-invariant. The factor model employed using the synthetic control method (SCM) generalizes this to allow for the existence of nonparallel trends between treated and untreated units after controlling for observables (Abadie, 2021).

3.2. Data

The data were obtained from the National Quality of Life Survey (ENVC), which was provided by the Colombia’s National Administrative Department of Statistics (DANE) from 2013 to 2020. The analysis was performed at the level of biannual age cohorts for a total of 20 cohorts, aged between 18 and 57 years. The interval corresponds to the working life cycle of women in Colombia1.

We selected the period 2013–2020 for the following reasons. First, in 2011, the Colombian government extended the leave period from 12 to 14 weeks, a 17% increase (Law 1468), and we consider 2012 a relevant period for adapting to Law 1468. Second, in 2021, the Colombian government expanded paternity leave provisions and introduced paid shared parental leave and flexible parental leave for new and adopting parents in Law No. 2114 (2021). The new law also expands nondiscrimination/equality provisions related to pregnancy in the workplace and could change the effects of income on the household.

The treatment group corresponded to women between the ages of 18 and 43 years, the time during which they are usually fertile (World Health Organization [WHO], 2018). The control group was composed of women between the ages of 44 and 57 years, the average years of education, the proportion of female heads of household, the proportion of women residing in a household with children under the age of two years, the proportion of informally employed women, and dummies by economic sector. Also, we included the proportion of women whose mothers have a higher education. This last control variable was used in the cohorts of women under age 30 because the mother’s education influences the human capital formation decisions made by these women. Assumedly, women over 30 have already made their higher education decisions.

Table 1. Statistics of the variables for the treatment cohorts after the Law 1822 of 2017.

Year	Variable	Cohort [Age Group]
		1 [18–19]	2 [20–21]	3 [22–23]	4 [24–25]	5 [26–27]	6 [28–29]	7 [30–31]	8 [32–33]	9 [34–35]	10 [36–37]	11 [38–39]	12 [40–41]	13 [42–43]
2017	Hours worked	35.64	38.59	43.77	44.47	42.47	43.00	42.20	43.43	41.85	45.04	42.35	42.93	40.23
	Real hourly wage (COP)	3804.71	4436.57	4856.97	4826.67	5776.16	6140.37	6678.99	5962.12	5689.99	6965.06	8083.04	7694.95	9559.75
	Real hourly wage (USD)	0.84	0.98	1.07	1.07	1.28	1.36	1.48	1.32	1.26	1.54	1.79	1.70	2.11
	Years of education	10.61	11.35	12.26	12.71	12.3	12.63	12.21	11.83	11.52	12.2	12.23	10.99	11.73
	Head household (%)	4.13	11.24	14.55	18.37	22.57	26.15	29.93	36.2	38.97	35.55	32.6	37.41	38.84
	Children under 2 (%)	29.61	30.04	31.22	27.02	30.51	24.49	20.40	17.14	19.70	18.81	14.51	14.09	13.05
	Informality (%)	76.45	64.14	48.46	46.89	49.9	43.48	53.24	54.83	71.66	54.75	51.83	56.87	54.12
	Economic activity—Manufacturing Industries (%)	9.65	11.31	5.84	18.13	7.73	5.05	13.61	10.36	16.60	7.01	9.70	4.82	4.77
	Economic activity—Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles (%)	8.00	13.77	13.77	7.84	10.80	12.88	13.57	17.76	18.43	16.73	11.23	12.80	11.70
	Economic activity—Accommodation and Food Services (%)	10.11	17.93	6.21	3.02	7.32	11.28	6.17	9.92	5.41	5.85	11.29	10.02	4.24
	Economic activity—Activities of Extraterritorial Organizations and Entities (%)	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	4.35
	Mother educ. (%)	1.76	5.08	4.35	5.96	6.69	7.27	6.77	6.38	3.78	6.14	8.16	5.29	3.38
2018	Hours worked	39.20	39.73	43.58	43.46	43.75	42.57	42.12	43.54	42.81	42.45	42.88	41.21	42.87
	Real hourly wage (COP)	4020.91	4716.88	4805.36	5241.21	6129.10	7232.31	6985.96	9157.73	10,500.17	8648.16	10,859.32	9360.07	9767.59
	Real hourly wage (USD)	0.89	1.04	1.06	1.16	1.36	1.60	1.54	2.02	2.32	1.91	2.40	2.07	2.16
	Years of education	10.15	11.03	11.87	12.69	12.84	12.7	12.48	12.61	12.37	12.33	12.06	11.22	11.17
	Head household (%)	4.38	11.82	13.59	16.84	22.4	24.95	26.48	29.72	27.8	31.35	33.81	34.08	36.66
	Children under 2 (%)	26.30	30.76	32.18	28.84	29.28	28.01	25.79	21.74	17.68	18.98	16.22	15.69	11.81
	Informality (%)	84.5	64.26	47.94	44.78	42.79	47.13	52.78	50.18	50.14	47.23	51.07	55.02	55.12
	Economic activity—Manufacturing Industries (%)	7.46	9.76	10.12	10.81	9.78	9.81	10.04	9.21	9.56	10.86	10.23	10.17	9.67
	Economic activity—Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles (%)	21.95	21.85	19.76	20.14	19.76	19.95	19.29	18.32	17.94	17.37	17.51	16.58	16.39
	Economic activity—Accommodation and Food Services (%)	11.20	10.15	7.46	6.55	6.27	6.15	5.71	5.93	5.39	6.24	6.04	5.47	5.76
	Economic activity—Activities of Extraterritorial Organizations and Entities (%)	0.00	0.00	0.01	0.02	0.03	0.01	0.00	0.00	0.00	0.00	0.00	0.17	0.00
	Mother educ. (%)	2.68	5.08	5.22	5.16	6.88	6.7	6.18	7.98	5.78	6.57	7.28	6.52	5.33
2019	Hours worked	37.05	41.39	43.76	43.12	43.12	42.20	42.37	42.04	42.17	41.73	42.23	43.75	42.92
	Real hourly wage (COP)	3970.69	4671.58	5330.08	6377.38	7496.60	7954.10	9195.22	8724.54	11,055.75	9780.15	9762.07	8484.73	10,421.61
	Real hourly wage (USD)	0.88	1.03	1.18	1.41	1.66	1.76	2.03	1.93	2.44	2.16	2.16	1.88	2.30
	Years of education	10.41	11.41	12.22	12.69	13.24	12.57	13.06	12.71	12.37	12.3	12.2	11.44	11.43
	Head household (%)	7.36	10.19	14.38	18.81	22.19	27.05	29.34	29.36	29.33	33.85	34.97	34.97	41.84
	Children under 2 (%)	22.64	28.38	28.01	27.30	25.92	23.62	22.47	22.87	18.14	15.44	15.66	13.15	8.99
	Informality (%)	80.45	57.03	49.29	45.7	41.62	48.87	45.74	48.81	52.83	52.79	50.71	57.27	52.68
	Economic activity—Manufacturing Industries (%)	7.82	10.50	10.64	11.20	11.22	11.88	12.57	10.99	10.49	10.47	10.58	9.58	12.65
	Economic activity—Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles (%)	20.74	20.95	21.31	19.88	19.33	18.99	15.82	15.12	18.12	17.93	18.22	16.96	16.04
	Economic activity—Accommodation and Food Services (%)	10.64	9.74	8.34	9.28	6.45	6.63	5.77	5.67	6.51	5.88	5.79	6.80	5.40
	Economic activity—Activities of Extraterritorial Organizations and Entities (%)	0.00	0.16	0.11	0.00	0.02	0.01	0.00	0.38	0.10	0.00	0.10	0.00	0.00
	Mother educ. (%)	4.44	3.71	4.45	7.32	5.33	6.72	8.16	6.62	7.01	5.84	6.55	4.02	3.7
2020	Hours worked	38.83	39.98	40.97	40.95	42.27	41.35	42.56	41.82	42.39	41.01	41.10	40.64	41.00
	Real hourly wage (COP)	5076.55	5877.60	6480.46	7792.41	8011.05	9472.22	9216.70	12,513.85	10,293.68	13,938.34	14,379.21	10,157.28	11,453.20
	Real hourly wage (USD)	1.12	1.30	1.43	1.72	1.77	2.09	2.04	2.77	2.28	3.08	3.18	2.25	2.53
	Years of education	10.36	11.54	12.41	12.9	13.12	12.98	12.87	12.62	12.52	12.46	11.89	12.18	11.54
	Head household (%)	4.37	9.84	12.84	20.26	20.75	26.6	32.01	32.05	32.47	33.64	37.82	40.31	40.83
	Children under 2 (%)	19.85	26.27	26.33	24.86	28.07	25.77	20.64	20.76	16.39	13.55	12.63	12.73	8.95
	Informality (%)	86.93	57.80	52.81	49.46	45.34	40.73	51.18	49.28	47.3	49.85	52.92	57.19	55.85
	Economic activity—Manufacturing Industries (%)	10.88	10.10	10.39	9.68	9.40	10.98	10.13	11.46	11.42	11.09	9.09	11.06	9.99
	Economic activity—Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles (%)	20.01	22.43	21.18	19.98	20.16	18.36	20.61	18.00	17.65	17.41	16.77	19.11	17.28
	Economic activity—Accommodation and Food Services (%)	7.69	6.33	7.02	5.98	6.06	5.20	5.52	4.89	5.10	6.08	5.03	5.14	5.40
	Economic activity—Activities of Extraterritorial Organizations and Entities (%)	0.00	0.01	0.00	0.03	0.00	0.00	0.00	0.00	0.16	0.00	0.04	0.00	0.00
	Mother educ. (%)	2.31	3.32	5.23	5.4	3.84	5.2	6.05	5.94	6.55	5.21	7.04	5.14	4.53

Note: 1 USD = 4522.39 Colombian Pesos (COP).

presents the hours worked, the real wage per hour, and the control variables considered in the study. First, an increase in the hours worked is observed across the cohorts (excluding cohorts one and two). Second, real hourly wages consistently increased among cohorts during the study period, peaking between cohorts 11 and 13.

Table 1 also shows a sustained increase in the mean number of years of education for women between 18 and 27 years. This age range is characterized by decisions to pursue a higher education. This is consistent with a higher proportion of women whose mothers had higher educations.

The female single-parent structure is the most common new family configuration in Colombia (Peña et al., 2013). In turn, the proportion of women living in households with children under the age of two is decreasing across cohorts. This is consistent with the sustained increase in labor occupation rates and higher qualification. In this regard, the proportion of women in households with children under the age of two is higher in younger cohorts (ages 18–29 years) and lower in older cohorts.

Finally, in terms of female labor informality, this proportion is significantly higher in the first two cohorts studied, with at least 6 out of 10 women working informally, which is explained by the young population’s greater vulnerability in the labor market (Mora et al., 2021).

Colombia has a high level of informal employment, especially for women, and informality affects especially young cohorts (Mora & Muro, 2017). Also, Uribe et al. (2019) found that the 2011 law increased informality in women between 18 and 30 years old. For these reasons, we control the informality rates of women. (We have a density function upon request).

To explore the effects of the 2017 Law, we computed average hours worked and average log wages before and after the law by cohort.

Table 2. Log of wages and hours worked by cohort (after and before of the Law 1822 of 2017).

		Log of Real Wages		Hours Worked
Cohort	Age	2013–2016	2017–2020	2013–2016	2017–2020
1	18–19	8.0230	8.3410	38.0120	37.6800
2	20–21	8.1843	8.4962	42.9282	39.9210
3	22–23	8.3268	8.5808	41.9479	43.0199
4	24–25	8.5920	8.6919	43.4233	43.0009
5	26–27	8.6056	8.8232	43.7651	42.9007
6	28–29	8.6511	8.9366	42.9163	42.2802
7	30–31	8.6971	8.9784	42.2426	42.3132
8	32–33	8.7755	9.0810	42.3560	42.7063
9	34–35	8.7919	91.139	42.8523	42.3057
10	36–37	8.8617	9.1612	42.2982	42.5594
11	38–39	8.7849	9.2625	42.9875	42.1393
12	40–41	8.7437	9.0911	42.7723	42.1335
13	42–43	8.9123	9.2375	43.1095	41.7553
14	44–45	8.8348	9.2205	41.9483	42.1629
15	46–47	8.8592	9.1288	41.8642	41.4870
16	48–49	8.8666	9.2042	41.0828	41.4302
17	50–51	8.9348	9.2373	41.0108	41.0708
18	52–53	8.7926	9.2840	40.3901	40.5872
19	54–55	8.8324	9.3856	39.6880	39.3070
20	56–57	8.8384	9.3348	37.7330	38.6866
Average Treatment Effects	ATE1	−0.239 *** (0.045)	−0.349 *** (0.055)	1.901 *** (0.446)	1.225 *** (0.375)
	ATE2	−0.172 *** (0.048)	−0.304 *** (0.056)	1.714 *** (0.523)	0.917 ** (0.390)

Note: Standard errors are shown in parentheses. Significance levels: *** p < 0.01; ** p < 0.05.

shows that younger cohorts (18–32 years old) earn less than older cohorts (34–43 years old). Also, we observed control cohorts earn more treatment cohorts before the Law in 2017. Concern to hours worked, treatment cohorts work more than control cohorts before the Law. To deeply explore these differences, we computed the average treatment effects, ATE, for treated (1 to 13 cohort) and control (14 to 20 cohort) and before and after the law in wages and hours worked. In ATE1, we use inverse-probability weighting (IPW); in ATE2, we use nearest-neighbor matching (NNM) “NNM estimators impute the missing potential outcome for each subject by using an average of the outcomes of similar subjects that receive the other treatment level” (StataCorp, 2023, p. 432) and control by informality, years of education, child under two, and economic sector dummies.

In wages, we observe a statistically significant negative difference between control and treatment cohorts. Control cohorts have higher wages than treated control cohorts before and after the law, and the difference increased using both methods to estimate the ATE. Differences is approximately U$ 0.14 cents per year and cohort.

With respect to the hours, treated cohorts work more than control cohorts before and after the law. However, there is a penalty after the law. Treated cohorts worked 0.68 h less (1.901–1.225) after the law. In Colombia, the number of weeks worked is 52, and the results imply that the year-treated cohort worked 35 and 41 h less (approximately one week in the year as a result of the law).

4. Results

To evaluate the impact of Law 1822 of 2017 on female employment in Colombia, we must consider how the female hours worked and the real hourly wage would have evolved after 2017 if the Law had not been passed using the cohorts in Table 2. This counterfactual can be estimated systemically using the SCM (Synthetic cohort).

4.1. Quantity Effects

The effect of Law 1822 of 2017 on female employment in Colombia is estimated to be the difference between the female hours worked and its synthetic version after the implementation of the law.

Figure 1 depicts the actual and synthetic female hours worked for the 1–7 (18–31 years old). In 2017, results show that for the first cohort, there was a reduction of 2.2 h worked compared with what could be worked in the absence of the law and 2.9 h for cohort two. In the general cohort one and two (18 to 21 years old), experienced a reduction in the hours worked in all periods considered. However, the reduction of the hours worked is not standard by the cohort. Cohorts three and nine show an increase in the hours worked between 2017 and 2020 respect the synthetic version and this increase is statistically significant.

The preceding highlights the difficult situation that young women face in the labor market: characterized by lower qualifications, high rates of informality, and a higher proportion of breastfeeding dependents. These findings are consistent with those of Botello and Guerrero (2020) and Uribe et al. (2019) who’s contend that the extension of maternity leave in Colombia increased rates of informality and inactivity among women aged 15 to 30.

Figure 2 shows the effects of the Law in cohorts 8–13 (32–43 years old). The Law does not seem to affect the hours worked in all years (Increase or decrease). Some years increase the hours worked and some years decrease.

To quantify the effects, we computed the differences in the real and synthetic hours worked by cohort in

Table 3. Differences in actual and synthetic hours worked by cohort (quantity effects).

Cohort	Year
	2017	2018	2019	2020
1 (18–19 years old)	−2.2074 ***	−0.9356 ***	−2.5064 ***	−0.8843 ***
2 (20–21 years old)	−2.9160 ***	−2.6856 ***	0.8116	−1.6531 ***
3 (22–23 years old)	1.1142 **	1.3612 ***	2.3670 ***	−1.1844 *
4 (24–25 years old)	1.6378 **	1.3466 **	1.6385 **	−1.2708 *
5 (26–27 years old)	0.1495 **	1.3197 **	1.9039 ***	0.2432 ***
6 (28–29 years old)	0.1621 **	0.4590 **	0.7205 **	−0.8725 *
7 (30–31 years old)	0.0525 **	−0.4155	1.2504 **	0.6061
8 (32–33 years old)	0.5943 **	1.4262 **	0.5546 **	−0.4013
9 (34–35 years old)	−0.9836 **	0.7038 **	0.6850 **	0.1660 **
10 (36–37 years old)	2.2075 ***	0.3430 *	0.2500 *	−1.2145 **
11 (38–39 years old)	−0.2697 **	0.6380 *	0.8563 *	−1.0459 **
12 (40–41 years old)	0.4319 *	−1.1033 **	2.4415 ***	−1.4553 **
13 (42–43 years old)	−2.568 ***	0.7369 **	1.4557 **	−1.2063 **

Note: Each column reports average differences between female hours worked and their synthetic cohort. Significance levels: *** p < 0.01; ** p < 0.05; * p < 0.1.

. To generate p-values, we used placebo permutations2. To do so, we divided the estimated effects by the pretreatment period root mean squared predictor error to standardize the effects based on pretreatment match quality.

Table 3 shows, for the first cohort, a reduction between 0.8 and 2.5 h worked per week between 2017 and 2020 after applying the norm. Cohort two also showed a reduction in hours that could be worked: 2.9 h in 2017 and 1.6 in 2020. Cohorts 3 and 8 experiments showed an increase in the treated compared to the synthetic version. That is, the real number of hours worked is higher than in the absence of the Law. As previously mentioned, these results are mainly due to the effects of labor informality and, secondly, to the degree of qualification that can be observed from potential experience3.

The results of the effect on quantities align with those found by Uribe et al. (2019) as the effect of Law 1468 of 2011. Who found an increase in the rate of informality and self-employment. In our case, there is an increase in the hours worked, which is also consistent with a higher likelihood of being informal.

The negative effect on younger women’s hours worked is partly due to the difficulty of combining motherhood and work in an informal labor market. Gender discrimination against young mothers combined with gender roles could explain why younger women face more significant employment challenges following the extension of maternity leave. Also, we observed that older women benefit more from the policy because of their greater attachment to formal employment with more human capital, which could explain why younger women are more concentrated in informal sectors (with low human capital requirements).

4.2. Price Effects

In the same way, the effect of Law 1822 of 2017 on female wages in Colombia is the difference between the log hourly wage4 and its synthetic version after the implementation of the law. We use the same controls (see Table 1) and hourly wages, employment and unemployment rates (2013 to 2016) pretreatment Law.

Figure 3 shows the real hourly wage of women and the synthetic version for the first seven cohorts (18–31 years old). As shown in

Table 4. Differences in actual and synthetic hourly wage by cohort (price effects).

Cohort	Year
	2017	2018	2019	2020
1 (18–19 years old)	−1.1934 ***	−1.1732 ***	−0.9322 ***	−0.8812 ***
2 (20–21 years old)	−0.6226 ***	−0.6419 ***	−0.8348 ***	−0.7981 ***
3 (22–23 years old)	−0.8709 ***	−0.6924 ***	−0.6611 ***	−0.5826 ***
4 (24–25 years old)	−0.7134 ***	−0.5722 ***	−0.5019 ***	−0.4552 ***
5 (26–27 years old)	−0.3587 ***	−0.3797 ***	−0.3618 ***	−0.4884 ***
6 (28–29 years old)	−0.4623 ***	−0.2481 ***	−0.2823 ***	−0.2636 ***
7 (30–31 years old)	−0.4022 ***	−0.2876 ***	−0.1343 ***	−0.2826 ***
8 (32–33 years old)	−0.2981 ***	0.0466	−0.2199 ***	0.0220
9 (34–35 years old)	−0.6255	0.0990	0.0435	−0.1227
10 (36–37 years old)	−0.1043	0.0226	−0.1186	0.2150
11 (38–39 years old)	0.0057	0.2113	−0.1085	0.1683
12 (40–41 years old)	−0.3164	−0.0064	−0.2078 **	−0.1660 ***
13 (42–43 years old)	0.1581	0.0271	−0.0113	−0.0834

Note: Each column reports average differences between female hourly wage and their synthetic cohort. Significance levels: *** p < 0.01; ** p < 0.05.

, the discrepancy between the two lines in these cohorts suggests—with high statistical significance—a negative effect of the law on women’s real hourly wages.

On the other hand, as shown in Figure 4, the law does not seem to affect female wages for older cohorts 8–13 (32–43 years old). The differences between the real and synthetic hourly wages per cohort are detailed in Table 4.

The results of Table 4 show that the main differences between salaries, as a product of the extension of the law, occur in the cohorts of young women up to 31 years of age. This result shows that the opportunity cost is much higher for younger women (under 31 years of age) with respect to the other cohorts (over 31 years of age) because what they could earn compared to what a man earns is higher for women under 31 years of age. As can be seen from Departamento Administrativo Nacional de Estadística [DANE] (2020), the wage gap between younger men and women, 15–24 years old, was −6.1% for 2019; while for the age group between 25 and 34 years it was −3.5%, and for individuals between 35 and 44 years it was −0.3%5.

Finally, regarding the price effect, unlike Uribe et al. (2019). we do find that younger women earn less than they could have earned in the absence of the law. Regarding women aged between 32 and 43, our results are inconclusive about an effect on wages, similar to Uribe et al. (2019) for Law 1468 of 2011. In the cohorts aged between 32 and 43 years, the effect on prices is not statistically different between the synthetic and real cohorts. The reasons may be due to fertility rates. Note that for cohorts 1 and 2, the average proportion of children under 2 years old is 25.9% and 30.6%, while in cohorts 12 and 13, the proportion is 13.9% and 11.7% (almost half). In this way, given that the cohorts after the age of 32 have fewer children on average than the cohorts before the age of 32, they will be less affected by the increase in costs for companies and/or discriminatory practices due to the probability of becoming pregnant.

5. Conclusions

This article examines the impact of Law 1822 of 2017, which increased maternity leave from 14 to 18 weeks (28.5% of the leave), on the female employment rate and real hourly wages in Colombia. Our results show that the norm has an ambivalent effect on the employment rate, which means that several cohorts exhibit negative results, and others have positive results. We find that the effects estimated for younger cohorts are negative, and for older cohorts are positive. On the other hand, the estimated hourly wage by cohort results shows a negative and significant effect on the hourly wage for younger cohorts and no significant impact for older cohorts. These findings are mainly explained by the cohort ranges, sectoral composition differences, and labor informality, which hinder inspection and compliance control mechanisms with the norm (Eberhard et al., 2023; Rossin-Slater, 2017). The different results can also be explained by the degree of qualification of women in their labor stages. Also, younger women exhibit more vulnerability in the labor market. Therefore, such conditions affect their labor outcomes.

These findings emphasize the importance of designing public policy instruments that are not applied uniformly across the entire population. Instead, these policies should account for different age groups’ unique characteristics and opportunity costs (Rossin-Slater, 2017; Machado et al., 2024). Our results indicate that the impact of these policies varies by age group, favoring or disadvantaging women based on their qualifications, level of labor formality, and reproductive life cycle. Consequently, these differences lead to policies that benefit workers with higher levels of human capital in the formal labor market while negatively affecting younger and informal workers. Such variations influence their decisions to participate in the labor market and should be considered when establishing economic policy measures.

Three instruments can improve outcomes without reducing women’s attachment to work (Addati et al., 2014; World Bank, 2024; ECLAC et al., 2025): (1) Childcare support: expanding affordable, high-quality childcare (through greater coverage, standards, and alignment with breastfeeding breaks) can ease time constraints and strengthen early human-capital investments (Addati et al., 2014); (2) Employer incentives: neutral financing of paid leave via social insurance schemes, retention bonuses, or payroll tax credits for firms that maintain women’s employment after childbirth, along with subsidies for temporary replacements, can help reduce statistical discrimination (World Bank, 2024); (3) Monitoring and enforcement: targeted inspections, hotlines or anonymous reporting mechanisms, and audits linked to payroll and benefit records—monitored through transparent rule-of-law and supportive-framework indicators—can help translate legal entitlements into effective protection (World Bank, 2024; ECLAC et al., 2025).

Because regional health is a core component of human capital and a driver of economic growth (Gumbau Albert, 2021), and because children’s human capital rises with maternal time in childcare while greater maternal market work can depress it (Hashimzade, 2020), our findings suggest clear policy avenues for Colombia. In particular, family- and maternity-friendly measures—such as paid leave, flexible work arrangements, and high-quality childcare—could bolster children’s human capital without reducing women’s labor-force participation, thereby supporting long-run growth. Tailoring these instruments to Colombia’s institutional realities and regional heterogeneity would maximize both efficiency and equity.

Maternity leave can help reduce the negative employment effects of childbearing, such as the potential withdrawal of women from the labor force following the birth of a child. Extents the maternity leave is significant because it increases the benefits in the mental health of mothers and children (including a decrease in postpartum maternal depression and intimate partner violence, and improved infant attachment and child development), a decrease in infant mortality and mother and infant rehospitalizations, and increase the pediatric visit attendance and timely administration of infant immunizations (Van Niel et al., 2020). The men’s contribution to unpaid activities such as child care in Colombia are poor compared to women “Although Colombians perceive that the economic contribution of men and women should be equal, they leave women with the most significant responsibility for life-sustaining activities in the home” (Fajardo Hoyos & Mora Rodríguez, 2024, p. 114) more time help to assure the below benefits to the women.

It is important to underscore the Colombian context, where institutional capacity remains weak and the justice system often underperforms (Galván et al., 2024). As a result, the system may fail to deter and sanction employment discrimination—particularly against pregnant workers and women of reproductive age. Strengthening enforcement will require measurable benchmarks and transparent monitoring; adopting internationally comparable rule-of-law metrics, such as those developed by the World Justice Project, can enhance regulatory effectiveness and accountability (World Justice Project, 2024). The current protection system may discourage employers from hiring these women (Uribe et al., 2019). Consequently, the enforcement of laws against illegal hiring practices, such as requiring pregnancy tests, is ineffective due to high levels of informality, limited institutional capacity, a lack of awareness about the illegality of such tests, and the normalization of these practices as job requirements.

In the Colombian context, an ineffectiveness of the legal system may exist to prevent and punish discrimination in access to employment, where the current protection system generates a disincentive for employers to hire pregnant women and women of reproductive age (Ramírez, 2019). Therefore, the control of illegal hiring practices, such as the request for pregnancy tests, is ineffective due to high informality, low institutional capacity, ignorance of the illegality of these tests, and the naturalization of this practice as a requirement to obtain a job.

We can observe that in the two extensions of the maternity laws (2011) and (2017), the law’s overall effect increased the demand for women’s labor, but only in low-quality jobs (informal). The 2017 law, unlike the 2011 one, did have the effect of reducing the wages of cohorts aged between 18 and 32 years.

One limitation is that our article does not discuss whether the effects of the first child are the same as those of the second child and how the extension of the leave affects the second child’s choice. Also, we do not have information about whether the law in 2017 will impact effective dismissal by pregnancy. One important limitation is how the law affects the time between paid and unpaid tasks (time women have for leisure and rest during the day) and increases discrimination against women.