metadata: authors: 0: author: Davalos,Maria Eugenia
1: author: Londono Aguirre,Diana Isabel
2: author: Medina Gaspar,Daniel Santiago
count: Colombia
docna: 0: docna: How Have Gender Gaps in the Colombian Labor
            Market Changed during the Economic Recovery?
docty: Policy Research Working Paper
abstracts: cdata!: This paper analyzes gender gaps in the
            Colombian labor market, with a particular focus on the
            impact of the COVID-19 shock and the economic recovery.
            Using household survey and administrative data, the analysis
            finds significant and persistent gender gaps in favor of men
            in terms of participation, unemployment, and income. These
            gaps are heterogeneous at the regional level in Colombia,
            and are exacerbated among women with children, particularly
            young children, and those with low levels of education. The
            COVID-19 pandemic widened these gaps, including from higher
            female concentration in the most affected sectors and
            potentially associated with the disproportionate burden of
            care on women. Moreover, using decomposition techniques, the
            paper finds that the decline observed in the hourly gender
            wage gap results from two offsetting effects: the explained
            gap, which favors women due to their endowments, primarily
            education level, and the unexplained gap, which favors men
            and may be associated with discriminatory biases. Moreover,
            the gender wage gap widens significantly when considering
            monthly income, showing variations in hours worked between
            men and women. Given the poorer labor market outcomes among
            women with children, policies toward the reduction and
            redistribution of care work within households could
            contribute to increase women&#x2019;s opportunities in
            the labor market.
display_title: How Have Gender Gaps in the
            Colombian Labor Market Changed during the Economic Recovery?
listing_relative_url: /research/2023/11/34196317/gender-gaps-colombian-labor-market-changed-during-economic-recovery
topicv3: Gender m1327621 615,Female Labor Market m1504222 2950,Wage Gap m1582439 4439,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2023-11-14T00:00:00Z
dois: 10.1596/1813-9450-10605
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 38
author_keys: 0: author_key: 1127099
1: author_key: 1869960
2: author_key: 2030173
model_extractions: Policy Research Working Paper 10605 How Have Gender Gaps in the Colombian Labor Market Changed during the Economic Recovery?

_María E. Dávalos Diana Londoño Daniel Medina_ Poverty and Equity Global Practice November 2023 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10605

# **Abstract**

This paper analyzes gender gaps in the Colombian labor market, with a particular focus on the impact of the COVID-19 shock and the economic recovery. Using household survey and administrative data, the analysis finds significant and persistent gender gaps in favor of men in terms of participation, unemployment, and income. These gaps are heterogeneous at the regional level in Colombia, and are exacerbated among women with children, particularly young children, and those with low levels of education. The COVID-19 pandemic widened these gaps, including from higher female concentration in the most affected sectors and potentially associated with the disproportionate burden of care on women. Moreover, using decomposition techniques, the paper finds that the decline observed in the hourly gender wage gap results from two offsetting effects: the explained gap, which favors women due to their endowments, primarily education level, and the unexplained gap, which favors men and may be associated with discriminatory biases. Moreover, the gender wage gap widens significantly when considering monthly income, showing variations in hours worked between men and women. Given the poorer labor market outcomes among women with children, policies toward the reduction and redistribution of care work within households could contribute to increase women’s opportunities in the labor market.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mdavalos@ worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **How Have Gender Gaps in the Colombian Labor Market Changed during the Economic Recovery?**

María E. Dávalos, Diana Londoño and Daniel Medina-Gaspar JEL code: J16, J21, J31.

Keywords: Gender wage gap, COVID-19, gap decomposition, female labor force participation, care policy.
# **1. Introduction**

During 2020, the COVID-19 crisis had a significant and unprecedented negative impact on the global labor market. This was due to an economic and social shock that resulted in a 3.3%<sup>1</sup> global economic contraction. The labor market served as the main economic transmission channel to households. The International Labor Organization (ILO) estimated a reduction of 258 million hours worked for full-time jobs worldwide, equivalent to an 8.9% reduction in employment levels, affecting 135 million people (4%). The ILO study also found a sharp decline in the world's labor force participation, with over 100 million people leaving the workforce due to lockdowns and becoming economically inactive. Despite the economic recovery that started in 2021, the ILO forecasted that even in 2023 the employment recovery would still be underway.

The impact of the COVID-19 crisis on employment and participation has been uneven globally, affecting certain sociodemographic groups disproportionately. A cross-country study conducted by the International Monetary Fund (IMF) across 38 advanced and developing economies found that 50% and 66% of them experienced significant decreases in women's employment levels compared to men (Bluedorn et al., 2021). This is due to unfavorable changes in sectoral composition, which disproportionately affect women. A more recent study by the World Bank (2023) documents the impact of the shock in the Latin America and Caribbean region, showing that job losses had a significant negative impact on youth, low-educated individuals, informal workers and female workers, and that the recovery has been slower for these groups than for the rest.

This paper analyzes gender gaps in unemployment, labor participation, income, and hours worked in Colombia, considering key features of the pandemic and women's endowments. The study uses probabilistic models and standard decomposition methodologies to assess gender gaps and their drivers. It also offers a non-comprehensive view of national policies aimed at closing gender gaps in the labor market.

These are the five key conclusions drawn from the research. First, the economic recovery in 2021 did not close the pre-existing gender gaps, which were further exacerbated by the pandemic. This suggests that there are underlying structural factors that need to be addressed. Second, while the gender gap in labor income and hours worked narrowed during the pandemic, this was due to a sharp decline in the levels for men rather than significant progress for women. Third, the formal sector showed more recovery in labor demand for women than the informal sector, but women are still largely concentrated in the informal sector. Fourth, women with children and lower levels of education had worse indicators and wider gaps compared to men and women without children or with higher levels of education, and this situation worsened during the pandemic. Finally, although there are programs in Colombia aimed at reducing gender gaps, such as those promoting economic opportunities for women, there is a need to increase the focus on addressing barriers related to 1 Based on information by the World Bank from 80% of the economies in 2020.
care, including the promotion of the care economy, that disproportionately affect women and particularly women with lower levels of education.

# **2. Literature Review**

Historically, women have experienced labor gaps in the workforce. These gaps are often attributed to social norms and negative biases that have limited their participation in productive activities (Argys & Averett, 2022). Some of the primary determinants of these gaps include women's role as housewives or caregivers (Blau & Winkler, 2017; Cortés & Pan, 2020), occupational segregation (Blau et al., 2013; Dolado et al., 2003), and differences in education and non-cognitive skills (Grove et al., 2011). Additionally, employers often view mothers as a potential productivity loss due to maternity leave, resulting in hiring biases. While progress has been made in recent years, significant gaps still exist in income (17%) and labor force participation (66% compared to men) in Latin America and the Caribbean. Despite women achieving higher levels of education and reduced birth rates, income and participation gaps persist, especially among low-income households, self-employed individuals, non-formal workers, and rural areas (ILO, 2019). Recent studies from the World Bank (2020) and the ILO (2019) have highlighted the need for continued efforts to reduce these gaps.

The COVID-19 economic shock had a significant impact on the labor market, and across many countries the international literature has documented more severe negative effects for women than for men. For example, the Republic of Korea recorded a higher rate of absence among women, particularly due to their role in care work, as well as high rates of part-time employment and an unexplained gap attributed to care-related roles (Ham, 2021). Similarly, women in Japan faced lower demand due to their high participation in fixed-term jobs and concentration in the service sector (Kikuchi et al., 2021). In the United States, the situation was similar, with a more significant contraction in participation and employment levels for married women with children, partly due to restrictions on childcare services (Albanesi & Kim, 2021). The impact of children on labor participation and employment was also found to be significant by other authors in the United States, particularly for women with children between 0-6 years and 6-12 years, in low-income jobs, and among Latinas and Blacks (Lim et al., 2021; Pitts, 2021; Fairlie et al., 2021). Comparable effects on participation were also seen in the United Kingdom<sup>2</sup> (Cattan et al., 2020), Australia (Churchill, 2021), and Canada (Quian & Fuller, 2021).

Extensive evidence also exists for developing countries. Using High-Frequency Phone Surveys (HFPS) conducted in 13 Latin American and Caribbean (LAC) countries, Cucagna & Romero (2021) analyzed the gendered impact at the onset of the COVID-19 crisis. They found that female workers were 44% more likely than male workers to lose their jobs, with a persistence in the job loss gap as the crisis continued. Furthermore, they found that 56% of job losses were explained by highly female-intensive sectors such as trade, personal services, education, and hospitality, and

> 2 However, Witteveen et al. (2020) find that in the same country, men suffered more layoffs during the pandemic’s early part than women.
that school-age children at home were linked to a rise in job losses among females but not males. Berniell et al. (2021) also used the same surveys to estimate fixed effect models and confirmed that women's labor outcomes were harder hit, with more job losses, income reductions, and fewer job entries. They also discovered that working from home strongly decreased the negative effects, mainly in women with children.

Using HFPS in African countries, Contreras-Gonzalez et al. (2022) found a higher probability of job loss for women, urban workers, and youth, with an even greater likelihood for urban workers among women with children. Viollaz et al. (2022) investigated four Latin American countries and found a significant impact on preexisting gaps before the pandemic, with women being less likely to work and more likely to have informal jobs. The authors suggested that a driving factor was the disparity in the share of children, particularly those with school-age children.<sup>3</sup> On the flip side, the pandemic also brought about changes in the gender income gap, although the impacts varied depending on women’s occupational choices. Contreras-Gonzalez et al. (2022) found that African non-agricultural companies experienced a greater income loss among women and youth. Doorley et al. (2021) found negative effects on women's wages during the first wave of COVID-19 in Ireland but also interestingly observed a shift in occupational segregation that previously favored men to now favor women's earnings. Additionally, fiscal benefits had a multiplier effect that reduced the gap, which was twice as large as the pre-pandemic reduction. In Canada, Singh et al. (2022) found negative effects on women's income and employment, which increased gender disparities and related to sectoral segregation. Bonacini et al. (2021) used decomposition methods in Italy and discovered that the income gap was greater for women working from home (WFH)<sup>4</sup> - something not expected - with effects on older and married women and those working in the private sector.

On the contrary, in the United Kingdom, Kan M-Y (2021) found that women experienced a smaller reduction in their income given their high participation in critical sectors such as the health and care industries. A similar result was found by Aina et al. (2021) in Italy. On the other hand, in Estonia, the pandemic reduced the gaps in employment, income, and hours worked. Again, women with children and those from more affected sectors are penalized with possible long-term effects on inequality (Verdostup, 2021). Hill & Köhler (2021) analyzed gender wage inequality in South Africa during the pandemic and found that the gender wage gap increased significantly by 37%, with a more pronounced effect on the poorest quintile, among which the gap increased by as much as 2.7 times.

In Colombia, studies (World Bank, 2019; Garcia-Rojas, 2020 and Hernandez, 2022) show a significant reduction in female participation and employment level, labor formality, and increases

> 3 Some independent studies also find negative effects on employment outcomes for women: Brazil (Masri et al., 2021), Mexico (Hoehn-Velasco et al., 2021; Juarez & Villaseñor, 2022) and Chile (Egana-del Sol et al., 2020).

> 4 In the UK, increases in self-perceived productivity for those working at home were also found, except for mothers with children, who also work longer hours (Deole et al., 2021).
in the burden of care. The country has smaller wage gaps than the OECD average, but this gap is associated with the unexplained component of the observables and is greater at the extremes of the income and education distributions.

# **3. Data**

# **Sources and definitions**

We use the Great Integrated Household Survey (GEIH), a probabilistic sampling survey that gathers data on individuals' employment status, including their occupation, income, access to social security, and job search activities, as well as their demographic characteristics such as gender, age, education level, and sources of income. The GEIH is nationally representative and uses a self-weighted sampling method stratified by unequal clusters. The survey's primary aim is to address the factors that affect labor participation and estimate gender-based labor gaps. Additionally, the survey includes information at the regional, departmental, and department capital levels.

The GEIH's sample frame was updated in 2022 with information from the 2018 National Population and Housing Census (CNPV), resulting in a structural change in trend series compared to the previous sample frame collected until December 2021. Because our work requires comparing results over time since 2015 and using information from the Mission for the Splicing of the Employment, Poverty, and Inequality Series (MESEP) as a source for labor income variables, we use the 2005 frame GEIH data between 2015 and 2021.<sup>5</sup> The labor income variable comprises five categories of income that vary depending on the type of employment relationship, including First Activity Monetary Income (IMPA) for wage earners and self-employed, Income in Kind (IE) for wage earners only, Second Activity Income (ISA) for wage earners, self-employed, and family workers, Monetary Income of the Unemployed and Inactive (IMDI) for the unemployed and inactive, and Income from other sources (IOF) available to everyone. From 2009 onwards, the survey covers 437 municipalities and annually visits an average of 248,000 households concentrated in 22,548 segments.

There are limitations to the survey data collected in 2020. Due to government-imposed restrictions in the first few months of the pandemic, data collection in cities was done over the phone. From March to April, the survey was restricted to 39 questions from the usual 200 to reduce respondent rejection. Moreover, data collection restrictions were only relaxed from June onwards, making it unclear how these measures affected data collection and comparability. Therefore, we only make comparisons with 2020 when analyzing primary labor indicators such as unemployment, participation, and employment, as we have information for March and April. When including 2020

> 5 Nevertheless, the previous section made a descriptive reference to the main indicators for the years 2021-2022 with the information from the new sample framework of 2018.
data elsewhere, we do so for informational purposes only, and any comparisons must consider this caveat.

# **Data description**

Table 2 presents key descriptive statistics of the survey data. Between 2015 and 2019, the survey collected an average of 204,000 observations annually for men and 240,000 for women. However, in 2020, the year most impacted by the pandemic, the number of observations for both groups dropped sharply to 123,000 and 142,000, respectively. By 2021, the data collected had partially recovered, but the observations remained below pre-pandemic levels (188,000 for men and 219,000 for women).

Demographic variables such as average age, marital status, and residential area show a slight difference between the groups, while poverty is more prevalent among women. For both groups, the average age is 36 years, and 22% of men live in rural areas, while on average, 19% of women do. Additionally, 56% of men are married or living in a union, and 58% of women are. Finally, 31% of men live in low-income households, while 37% of women do. A further breakdown of these categories reveals that 21% of the population are young people between 18-24 years old, approximately 42.6% are between 25-44 years old, and 36.5% are between 45-59 years old.

In terms of children in the household, women live with more children than men. On average, for every man, there are 0.3 children aged 0-3 years in the household, 0.2 children aged +3-6 years, and 0.40 children aged +6-12 years. In comparison, women have, on average, 0.3 children aged 0- 3 years in the household, 0.2 children aged +3-6 years, and 0.45 children aged +6-12 years. Moreover, 21% of men live in households with children aged 0-3 years, while 26% of women do. For +6-12 years, the numbers are 17% for men and 21% for women. Finally, in households with children aged +6-12 years, the numbers are 25% for men and 30% for women.

Human capital variables such as years of education and experience show slight differences between the groups. On average, men have 9.6 years of schooling, women have 10.2 years, and both groups have over 20 years of experience. A disaggregation of these categories shows a difference in the higher education component, with approximately 20% of men having higher education while around 25% of women do. However, in non-education or just elementary school, men account for 35%, while women account for 30%. Experience is strongly centered on the group with +15 years of experience, where 57% of both groups are.

The survey reveals significant heterogeneity between men and women in the labor component. Between 2015 and 2021, for the population aged 18-59 years, 82% of men were employed, 8.4% were unemployed, and 9% were inactive. In contrast, only 56% of women were employed, 11% were unemployed, and 33% were inactive. Additionally, women have a higher rate of part-time employment (32%) compared to men (12%), indicating that women work almost two times fewer hours per week (23 hours) on average than men (40.3 hours).
Salaried workers dominate in both groups, with 49% of men in the private sector and 3.6% in the public sector, while women have higher rates of 51.5% in the private sector and 4.8% in the public sector. Self-employment is more common among men (48%) than women (44%), and both groups have a similar rate of formal employment with social security contributions (around 40%).

Regarding the sectoral composition of the occupation, men have a significantly higher relative participation in transportation (13%), construction (10%), and public services (2.7%), while women have a more significant presence in accommodation and food services (12%), education and health services (14%), and other services (14%). Women also have a higher proportion of employees in commerce (21%) and professional services (9%) compared to men (17% and 5%, respectively). In the agricultural sector, only 6% of women work compared to 20% of men. Both groups have a similar representation of around 12% in the industry sector. Finally, women have only 3% representation in the financial and public sectors.

# **4. Existing gaps and negative impacts of the pandemic**

# **Despite progress in narrowing income gaps, gender gaps in employment remain unresolved.**

The COVID-19 pandemic reinforced pre-existing gender gaps in labor participation and unemployment, resulting in a significant increase in the gender gap from 23.4 to 25.7 percentage points (p.p.) between 2019 and 2020 (Figure 1). The gender gap in unemployment also grew by 2.1 p.p. in the same period (Figure 2). Although both groups experienced a recovery in 2021, the participation level grew faster in men, widening the gender gap by 0.3 p.p. However, the unemployment rate gap remained unchanged. This suggests that the gaps have widened in the pandemic due to worse indicators for women. Across regions, there is significant heterogeneity in the indicators and gaps between the main cities, with some able to reduce the gap in 2021 compared to the pre-pandemic level (Figure 3-5).

On the other hand, the wage gap reduced significantly in 2020 due to a greater drop in men's labor income but increased slightly in 2021 as men's income recovered faster.<sup>6</sup> In terms of hours worked (Figure 6), men have been reducing their weekly working hours since 2015, and the gap has narrowed following the pandemic. However, there is still a high heterogeneity in the gap across regions (figure 7- 8). In general, all cities achieved a reduction in their labor income gaps during the pandemic, but some cities exceeded pre-pandemic levels in 2021.

# **The sectors that experienced an employment recovery in 2021 had low female participation. However, there are indications of progress in 2022.**

The sectors with high female participation, such as education, health, accommodation, and food service, experienced a slow-paced recovery of labor demand in 2021, which slowed the adjustment of the pandemic-led gap between men and women. Figure 9 shows a negative relationship between 6 Estimated gap not controlled by sociodemographic characteristics.
female sectoral participation (Zone I) in 2019 and the growth of employed persons between 2019 and 2021, indicating that sectors with high female participation were more affected by the contraction in labor demand than sectors with a high concentration of men (Zone II). This pattern is also evident in the job vacancies offered by the public employment services, even though formal demand for the health and financial sectors increased during the same period (Figure 10).

In 2022, DANE changed its expansion factors, making it difficult to compare information for years before 2021. However, a review of the trend of participation and unemployment for the 1st semester of 2021 and 2022 (with the 2018 factors) shows that the recovery trend is positive for both men and women (Table 1).<sup>7</sup> The participation rate has increased in both groups, especially for women, and the unemployment rate has contracted in both groups,<sup>8</sup> with the reduction being more significant in women. Therefore, it can be deduced that there was a gap reduction for both indicators. In 2022, the growth of women's labor demand has been driven by growth in the service sector, such as professional services, accommodation, food and entertainment, commerce, and industry, with more than 10% growth rates pared to 2021. Among these groups, only the industry grew more rapidly among men.

Table 1. Labor Market Indicators 2021-2022

||||1er semester<br>||Chang<br>|e (p.p.)<br>|
|---|---|---|---|---|---|---|
||Indicator|2021<br>(1)|2021<br>(2)|2022<br>(3)|(3)-(1)|(3) - (2)|
|Mn|GPR|72.0%|75.7%|76.6%|4.6|0.9|
|e|UR|12.0%|12.9%|9.6%|-2.4|-3.3|
|Women|GPR|49.1%|48.2%|51.5%|2.4|3.3|
||UR|20.1%|19.4%|15.6%|-4.5|-3.8|
|Expansi|on factors|2005|2018|2018|||
|||S|ource: GEIH.||||

# **The lack of recovery can be attributed to persistent structural barriers that impede access for women with children and those with lower educational levels, who face fewer economic opportunities.**

Women with children under 12 years of age and those with lower levels of education face persistent structural barriers that limit their economic opportunities. Higher education is a protective factor against job losses for women compared to those without it, although there is still a significant gap between women with and without higher education (as shown in Figure 11). In terms of labor participation (Figure 12), there is a wide gap between women with and without higher education, which can be attributed to the latter being more concentrated in the informal sector (Figure 13).

> 7 Columns (1) and (2) compare the result of the 1st semester of 2021 with both expansion factors, where the gap generated by the change in the projections can be seen. It is particularly strong over the GPR given the change in the working-age population decision (10 to 15 years).

> 8 In this comparison, all ages are used because the aggregate annex of the GEIH is used.
Vulnerability to unemployment increased during the pandemic for people living in urban areas, with ethnic self-identification, and aged between 18-28 years (Figures 14 - 15).

Furthermore, there are significant differences in labor incomes and hours worked between men and women. Those without higher education and those with children are particularly vulnerable to larger gaps and worse levels in these labor market indicators (Figure 16). Rural areas and ethnic populations have low labor incomes, below 50%, which is critical. However, higher education can significantly reduce the gap, up to 80 percentage points for women (Figure 18). Finally, the number of women engaged in unpaid family work has declined, while men with children experienced a rise in this type of work in 2020 (Figure 17).

The low labor income in rural areas and among the ethnic population is especially critical, with levels below 50%. Higher education improves the income for both groups, with or without children, and for women, it can structurally reduce the gaps up to 80 p.p. (Figure 18). In hours worked per week, the differences are like the previous ones, but proportionally of a smaller magnitude (Figures 19- 20).

# **5. Methodology**

The objective of this paper is to assess the influence of the pandemic on preexisting disparities in labor market outcomes by utilizing two methodologies that enable us to approximate the genderspecific impacts of the pandemic on labor participation and income.

# **Estimation of the probability of participating in the labor market**

Labor participation between men and women presents a high structural gap, as was observed in Section 1. To identify the factors that determine this gap and quantify its importance, we use a probabilistic model to estimate the probability for each group to participate in the labor market based on their characteristics. Given the type of survey, we do not have information that follows individuals over time which limits causal inference. Therefore, we conduct separate estimates _(cross-section)_ for the years 2015, 2017, 2019, and 2021 with the following specification:
From the estimation, we obtain the i and i coefficients s � i �, inputs to jj kk jj predict the marginal effects on the probabilities evaluated over the mean of the independent ii ββ αα �∆(zz−s pp) ∆xx variables (CME) and the average of the marginal effects (AME). The goal of estimating both effects is to show whether the results are sensitive to the benchmark of the independent variables when evaluating the impact of only changes in one of them. Thus, these estimators allow us to identify the gap between the estimated probabilities for men and women according to specific characteristics and their response to changes in the level of one of the predictor variables. Finally, we estimate the effect of the pandemic as of 2021 on the gaps existing in 2019 in terms of labor participation.

The previous estimation also allows us to estimate the Heckman correction equation to correct the self-selection of participation in the labor market over the decomposition of the labor income gaps.

# **Decomposition of the labor income gaps**

Now, our focus shifts towards estimating the income gaps in labor through two standard decomposition methodologies: the Oaxaca & Blinder (1973) method and the Ñopo (2008) method. These methodologies play a vital role in improving the accuracy of the unadjusted calculation of the gap. They not only address the issue of sampling bias but also break down the gap into components that can be explained by differences in observable characteristics between the two groups, as well as an unexplained component attributed to factors that are not directly observable.

To conduct a comprehensive analysis, we incorporated both the Oaxaca-Blinder and Ñopo decomposition methodologies. The Oaxaca-Blinder approach, a seminal parametric method, provides an overview of the average unexplained gender wage gap between male and female workers in the dataset. However, it has limitations in terms of robustness, as it does not account for differences in individual characteristics across the entire distribution, thus hindering comparability between groups.

To address this limitation, we employed the non-parametric Ñopo decomposition approach (2008). This approach allows us to estimate gender wage gaps across the entire distribution of individual characteristics and further disentangles the gap into groups that share common characteristics and those that do not. The latter approach is particularly robust in identifying the gap, especially given the significance of gender differences in observable characteristics in Latin American countries, as highlighted by Ñopo (2008), citing the work of Blau and Ferber (1992).

By combining both methodologies, we were able to identify gender wage gaps within the common support and outside the common support for Colombia. This provides crucial evidence for making meaningful cross-country comparisons and deepening our understanding of the factors contributing to income disparities between genders.

_Oaxaca Blinder decomposition_
<!-- Start of picture text --> yy<br>( DB )(B ) 3)<br>(x )<br>= (E(X 6 ) x “ )<br>= (EX ) B ) +(6 -6 )@<br><!-- End of picture text -->
Ñopo WGt = (Δ + Δ + Δ ) + Δ0 (5) XX HH MM _Unexplained_ (∆0) _:_ this component is comparable to the unexplained component of the OaxacaBlinder method.

_Explained_ (∆ ) _:_ the income difference with the observable characteristics (∆ ) in the common support. This component corresponds to the Oaxaca-Blinder explained component. XX XX _Explained outside of the common support_ (∆ + ∆ ) _:_ The second and third component is the part of the gap that can be explained by the differences between two groups of men HH MM (∆ and two groups of women (∆ ), (M : Those with characteristics that can be matched (common support, group 1) and those that cannot (group 2). HH MM ), (CCEECC ppe ppssee) MMDD ppe ppssee) The estimations use the following categorical variables in the specification (see the table 2 for category division): Union or married, formal job, rural housing, Part-time job (<40 hours per week), # Children 0-6 years old at household, experience (see table 2), age, area, sector, education and labor relation.

# **6. Results**

# **Probability of participating in the labor market**

In this section, we present the results of the estimation of the probabilistic model using the transformation of coefficients known as conditional marginal effect at mean (CME) and average marginal effect (AME). Figures 21 to 24 describe these indicators for the number of children in two age categories: 0-3 years old and +3-6 years old. It is evident that in 2019, as the number of children in the household increases, the probability of women's participation in the labor market significantly decreases in households with children aged 0-3 years (Figures 21 and 22). The probability can drop by up to 40 percentage points, regardless of whether the CMEs are evaluated over the mean or the AME. In households with children aged 3-6 years, the estimated probability also decreases with the number of children in 2019 (Figures 23 and 24). However, the confidence intervals do not indicate statistical significance for this decrease.

In 2021, the post-pandemic year, the probability of women participating in the labor market dropped sharply in all estimates, including the case of children aged 3-6 years, where the reduction in probability becomes significant in both CME and AME calculations. These results provide important evidence of women's role as caregivers at home and the implications this has on their labor market participation.

Another variable considered a determinant in the literature on women's labor participation is household per capita income (see Goldin, 1994). Our estimates reveal that the gender gap is significant in the poorest households, but as income increases, this gap exponentially narrows. Figure 25 shows the CMEs for the mean of this relationship. We observe that in households where
per capita income is at the extreme poverty line (161,099 COP), women have a probability of participating in the labor market of 75% in 2019, compared to 95% for men, resulting in a 22percentage point difference. At the moderate poverty line (350,000 COP), the gap narrows to 17 percentage points, and in households with a per capita income of 1 million COP, the gap is reduced to 13 percentage points. Beyond that, the gap stabilizes at 10 percentage points. This finding is particularly significant as it highlights that in situations of poverty or vulnerability, men tend to assume income-generating roles while women's responsibilities revolve around caring for the home and family. Consequently, women acquire fewer or no skills and experience in productive sectors compared to men, leading to greater economic dependence for partnered women. Again, the pandemic exacerbated this gap, but only in lower-income households.

Regarding schooling, the results at the mean in 2019 indicate a probability of approximately 70% for women with no education, implying a gender gap of around 30 percentage points compared to men in the same group (Figures 27-28). As years of education increase, the probability rises and the gap shrinks to around 30 percentage points. However, men consistently have higher participation rates at any level of education. During the pandemic, women with less than 15 years of education were less likely to participate, with activity levels decreasing as the level of education approaches zero.

When examining participation gaps by age groups, both the CMEs (Figure 29) and AMEs (Figure 30) reveal higher gaps in labor market participation for older age groups (+20 percentage points), while for individuals in their 20s, the gap is around 10%. Although it is positive that the intergenerational gap has reduced, it is concerning that it still persists. Older women were particularly impacted during the pandemic, likely due to their predominant roles in caregiving activities and household chores.

For women residing in rural areas (Figure 31), the AME indicates an 8-percentage point decrease in the likelihood of labor market participation in 2019 compared to urban areas. In 2021, this probability also declined for women in urban areas. Furthermore, being married or in a union (Figure 31) leads to a drop of approximately 13 percentage points in the likelihood of women's labor market participation, while slightly increasing it for men. This dynamic contributes to a structural expansion of the gender gap. These findings highlight the persistence of gender disparities in labor market participation, with certain factors such as age, rural residence, and marital status exacerbating these gaps. Efforts to address these inequalities and promote gender equity in the labor market remain crucial.

# **Gender labor income gaps**

First, we analyze the Oaxaca-Blinder decomposition results. Figure 33 illustrates the decomposition of real hourly labor income using the Heckman correction from 2015 to 2021. The total income gap for women in 2015 and 2017 was 11% and 9%, respectively. However, the gap sharply decreased to 2% in 2019 (non-comparable result) and returned to 3% in 2021. The
unexplained component plays a crucial role in the overall gap, decreasing from 37% in 2015 to 25% in 2019. This reduction can be attributed to differences in discriminatory biases,<sup>9</sup> changes in the population group composition, and measurement issues. Segmenting the data by age groups, it is evident that the high gap is primarily driven by the unexplained component in the 29-59 years old group. For this group, the gap decreased from 15% in 2015 to 5% in 2019, while no significant differences were observed in the 18-29 years old group. The coefficients of age, schooling, and experience exhibited notable variations, suggesting changes in the composition of the labor force that led to lower biases against women.

The explained component (Table 3) has an average value of -23.5%, with a slight increase of 1.6 percentage points in the pandemic year of 2021. In terms of endowments, women could have a real hourly labor income that is 20% higher than men's. Table 3 highlights the key determinants contributing to the gender gap in 2021, including differences in schooling (-9%), the proportion of people living in rural areas (-3.3%), occupation in formal positions (-2.3%), type of employment relationship (-2.4%), and part-time work (-3.8%, a decrease compared to 2019). For men, only the economic sector shows a favorable explained gap of 1%. Among these factors, schooling has become the most important determinant that contributes to the gender gap in favor of women, increasing in significance over time.

The Ñopo methodology yields smaller estimates for the gender income gap but maintains a decreasing trend over time (Table 4). Interestingly, in 2020-2021, the gap turned favorable to women. This difference can be explained by the growth of the men CEO effect, capturing a gap component outside the common support. Furthermore, the unexplained gap experiences a 1- percentage-point reduction between 2019 and 2021, contributing to gap narrowing. The common support remains around 60% in each period, a significant proportion that supports the exercise in calculating gaps between comparable populations.

While estimates of hourly labor income are standard in the literature, capturing the returns of the production process unaffected by part-time work, we also examined the dynamics of the gap with monthly earnings. The Oaxaca-Blinder monthly estimates reveal a substantial growth in the gap compared to hourly estimations (29.3% on average, 2019-2021), primarily driven by the unexplained component (Figure 34). This gap consistently decreased since 2015, even during the pandemic. Notably, the reduction in the gap has been mainly due to a decrease in men's labor income rather than favorable reasons. Reviewing differences in coefficients, a significant gap estimated for the number of hours worked closes over time, aligning with the overall gap narrowing. In terms of the explained component (Table 5), it is evident that hours worked is an endowment favoring the gap for men (19% in 2015, 14% in 2021), while for women, education,

> 9 Changing social norms may not effectively address gender bias. Research by Tribin et al. (2022) using Colombian data shows that 70% believe women are suited for domestic work, despite women spending more time on caregiving. Improved working conditions, education, and income correlate with greater autonomy for women.
experience, and type of employment relationship serve as endowments that would explain the gap in their favor in the absence of unobservable factors.

Under the Ñopo approach (Table 3), the monthly income gap is reduced by 50% by 2015, consistent with the Oaxaca-Blinder estimate. Once again, the unexplained component is the key factor explaining this gap, tending to align between the methodologies from 2017 onwards (with a difference of 1.4 percentage points in 2019). The explained component plays a smaller role in reducing the overall gap, but it consistently favors women (15% on average). The _CEO_ and _MAID_ effects do not significantly impact the aggregate results as they tend to counteract each other. The common support does not undergo significant changes in the monthly exercise. Following the impact of the COVID-19 pandemic in 2020, the decrease in labor income disparity persisted under the Ñopo decomposition method. Specifically, the reduction in monthly labor income was 10%, while the hourly labor income decreased by -2.4%. The decline in the wage gap can be attributed to a combination of factors, including the reduction of unobserved issues and improvements in women's relative endowments compared to men.

# **7. Public programs to close gaps**

Public policy in Colombia, through various programs and strategies, aims at addressing gender gaps. These initiatives aim to improve the conditions of women in the labor market, enhance their demand, and promote their presence in occupations with better prospects and rewards. Table 6 presents a classification of recent measures.<sup>10</sup> These policies address areas such as health, education, social protection, and access to better employment opportunities. The strategies include promoting gender equality training, flexible education models, and financing for women's access to higher education, particularly in non-traditional fields like STEM. Other measures focus on removing barriers for women in the job market, such as raising awareness among companies, providing payroll subsidies, and offering tax incentives. Additionally, efforts are made to empower women by improving access to resources, providing technical advice, and facilitating networking opportunities. Measures also aim to enhance women's participation in decision-making processes, political empowerment, and the prevention of violence against women in politics, through initiatives like the _Mujer Emprende_ Fund and the _Casas de las Mujeres Empoderadas_ .

Finally, we examine some cross-sectional policies related to the first four axes aimed at improving women's working conditions through targeted resource allocation and incentives to close gender gaps. The "Pact for Equity for Women" is an initiative with the objective of promoting equal access and participation of women in the labor market, enabling them to improve their economic status in an environment free from gender-based violence. One of its expected outcomes is a 4- percentage-point reduction in the income gap below the global level. The " _Mecanismo de Protección al Cesante_ " (Unemployed Protection Mechanism) serves as an instrument to promote 10 This categorization is based on the World Bank's four key axes for gender equality promotion (World Bank Group, 2015).
social protection for workers by ensuring access to social security for the unemployed through various funds and solidarity mechanisms.

A notable finding from this review of programs is the lack of national measures addressing barriers to accessing care and redistributing care work within households, including the promotion of the care economy, which disproportionately falls on women with lower levels of education. Existing programs, managed by the Colombian Institute of Family Welfare (ICBF), primarily focus on childcare and time release, without providing options for elderly care or complementary measures that facilitate the effective integration of caregivers into the care and labor sectors.

# **8. Conclusions**

The gender gap in labor markets presents a significant challenge, particularly impacting vulnerable women, and Colombia is no exception, with the pandemic exacerbating this issue. This study examines recent trends in Colombian labor outcomes within the context of COVID-19. Our focus is primarily on gender gaps in the labor market and the factors influencing them, particularly in sectors where women are more vulnerable, such as home care.

Utilizing data from the GEIH, decomposition methodologies, and probabilistic models, we find that the economic recovery observed in 2021 did not close the gender gaps, indicating underlying structural factors. Moreover, the pre-pandemic gender gaps in terms of participation and unemployment were intensified by the crisis. Interestingly, gender gaps in labor income and hours worked decreased during the crisis, but this was primarily driven by a significant decline in men's levels rather than improved conditions for women. Another contributing factor to women's slow recovery in the labor market is the moderate recovery of the informal sector in 2021, in which women have a significant presence. However, there are indications of a more favorable labor market recovery for women in 2022.

The analysis reveals that several factors disproportionately affect women and impact their probability of participating in the labor market. Having children in the household, low levels of schooling, and household income are among the key determinants. Women with children can experience a significant decline in the probability of labor market participation, up to 40 percentage points, whereas this remains unchanged for men. Women at the poverty line have a 22-percentage point difference compared to men, and among women with lower levels of education the gender gap widens. These gaps tend to narrow with fewer children, higher income, and higher education levels. Other factors such as age, rurality, and marital status also contribute to significant gaps between men and women, which widened during the pandemic without signs of recovery in 2021. Access to quality childcare appears to be a barrier for these groups.

Using the Oaxaca-Blinder approach for gap decomposition, the average hourly income gap decreased from 11% to 3% between 2017 and 2019, a level that remained in 2021. However, further analysis reveals that this result is a net outcome of two key factors: the explained gap, which favors women due to their endowments, primarily education level, and the unexplained gap,
which favors men and may be associated with discriminatory biases. Reducing the unexplained component could result in higher hourly labor income for women. The Ñopo (2008) results align with the previous findings but with lower values in the components. When considering monthly labor income, the gap increases significantly for women, averaging 30% between 2019 and 2021. This can be attributed to the importance of hours worked, where women tend to have a higher proportion of part-time jobs. This suggests that the number of hours worked (extensive margin) is a critical determinant of the labor income gap, rather than the hourly income (intensive margin).

The review of current government programs aimed at closing the gender gaps highlights a strong focus on promoting economic opportunities for women, particularly in the context of the pandemic. Strategies such as payroll subsidies, hiring incentives, and entrepreneurship funds have been implemented. However, there is a gap in addressing barriers to access to care and redistributing care work within households, which disproportionately affects women with lower levels of education. Existing programs primarily focus on childcare without complementarities to promote the labor market integration of caregivers.

Based on the analysis, several key recommendations for improving public policies in this area emerge:

1. Implement a National Care System that integrates benefits and social protection programs to enhance the quality of life for individuals with high dependency levels and their caregivers. This system should also promote women's participation in education and the labor market.

2. Address the correlation between having children and labor gaps by promoting policies that ensure access to care, family planning, advice, and mentoring. Addressing these issues can help break the cycle of poverty and mitigate the impact of gender norms.

3. Advance the mandate of the Intersectoral Commission for Public Care Policy to facilitate coordination and implementation of care policies at the national level. This coordination can lead to more efficient resource utilization and better outcomes.

4. Recognize the importance of local leadership in narrowing gender gaps. Local and regional knowledge and engagement are crucial for effectively challenging culturally entrenched discriminatory norms and driving change<sup>11</sup> from within communities. Consequently, local leaders can have a significant role in promoting change.

5. Enhance public information systems with gender breakdowns to enable better diagnosis and design of public policies.

The findings also emphasize the vital role of education in improving working conditions for women. Higher levels of education are associated with reduced income gaps, increased labor

> 11 Lachapelle (2020) provides empirical evidence, based on self-reported data, which demonstrates the significance of training programs for leaders in enhancing their engagement with their community and objectives. Furthermore, Nwaze and Mohammed (2013) present both quantitative and qualitative evidence supporting the crucial role of traditional and religious leaders in promoting initiatives towards the eradication of polio in Nigeria.
market participation, and lower unemployment rates. However, occupational segregation remains a challenge, resulting in lower remuneration for jobs predominantly held by women.
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Figure 20. Average hours worked by week, ages 25-59 Figure 21. CME at mean, children in the household ages 0-3 <!-- Start of picture text --> No children Children<br>51<br>50 50 49<br>48 48 48<br>47<br>47<br>45 45<br>43 44<br>43<br>41 41<br>40<br>39 39<br>35<br>2019 2021 2019 2021 2019 2021 2019 2021<br>No higher education Higher education No higher education Higher education<br>Men Women<br>Figure 22. AME, children in the household<br>ages 0-3, ages 15-59<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0 2 4 6 8<br>Children in the household 0-3 years<br>Men 2019 Men 2021 Women 2019 Women 2021<br>Figure 24. AME, children in the household<br>ages +3-6, ages 15-59<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0 2 4 6 8<br>Children in the household +3-6 years<br>Men 2019 Men 2021 Women 2019 Women 2021<br>Pr(labour force=1)<br>Pr(labour force=1)<br><!-- End of picture text --> <!-- Start of picture text -->
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Figure 23. CME at mean, children in the household ages +3-6, ages 15-59 <!-- Start of picture text -->
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Figure 25. CME at mean, Household per capita income, ages 15-59
Note: Colombia’s extreme poverty line 161,000 pesos in 2021 and 147,404 pesos in 2019 (2021 prices).

Figure 26. AME, Household per capita income, ages 15-59 <!-- Start of picture text -->
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Note: Colombia’s extreme poverty line 161,000 pesos in 2021 and 147,404 pesos in 2019 (2021 prices).

Figure 28. AME, year of schooling, ages 15-59 <!-- Start of picture text -->
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Figure 27. CME at mean, year of schooling, ages 15-59 <!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>0 5 10 15 20 25<br>Years of schooling<br>Men 2019 Men  2021 Women 2019 Women 2021<br>Pr(labour force=1)<br><!-- End of picture text -->

Figure 29. CME at mean, age, ages 15-59 <!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>20 30 40 50 60<br>Age<br>Men 2019 Men  2021 Women 2019 Women 2021<br>Pr(labour force=1)<br><!-- End of picture text -->
Figure 30. AME, age <!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>20 30 40 50 60<br>Age<br>Men 2019 Men  2021 Women 2019 Women 2021<br>Pr(labour force=1)<br><!-- End of picture text -->

Figure 32. AME, Marital status, ages 15-59 <!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>Single In union<br>Marital status<br>Men 2019 Men  2021 Women 2019 Women 2021<br>Pr(Labor force=1)<br><!-- End of picture text -->

Figure 31. AME, household zone <!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>Urban Rural<br>Housing zone<br>Men 2019 Men  2021 Women 2019 Women 2021<br>Pr(Labor force=1)<br><!-- End of picture text -->

Figure 33. Oaxaca-Blinder decomposition of real labor income per hour, ages 18-59 <!-- Start of picture text --> 40%<br>37%<br>30%<br>31% 28%<br>20% 25%<br>25%<br>10% 11% 9% 3%<br>3% 2%<br>0%<br>-10% -21% -21% -20% -24% -21%<br>-20%<br>2015 2017 2019 2020 2021<br>Explained Unexplained Estimated gap<br><!-- End of picture text --> Note: gap is estimated as a proportion of women's income. All estimates are significant at 1% (except 2020, which is 10%).
Figure 34. Oaxaca-Blinder decomposition of real monthly labor income, ages 18-59 <!-- Start of picture text --> 52%<br>50%<br>40% 40%<br>33% 28%<br>30%<br>53% 27%<br>20% 39%<br>32% 34% 31%<br>10%<br>0% 0% -2% -1%<br>-8% -6%<br>-10%<br>2015 2017 2019 2020 2021<br>Explained Unexplained Estimated gap<br><!-- End of picture text --> Note: gap is estimated as a proportion of women's income. All estimates are significant at 1% (except 2020 which is not significant).
|**Table 2. Descriptive**|**Statistics b**<br>**M**|**y Gender and**<br>**en**|**Year, ages**|**15 - 59**|**Wo**|**men**||
|---|---|---|---|---|---|---|---|
|**Variable**<br>**2015**|**2019**|**2020**|**2021**|**2015**|**2019**|**2020**|**2021**|
|**Continuous (mean)**||||||||
|Age<br>36.1|36.2|36.2|36.1|36.3|36.3|36.3|<br>36.4|
|# Children 0-3 years old at household<br>0.30|0.30|0.30|0.20|0.30|0.30|0.30|<br>0.30|
|# Children +3-6 years old at household<br>0.20|0.20|0.20|0.20|0.20|0.20|0.20|<br>0.20|
|# Children +6-12 years old at household<br>0.40|0.40|0.40|0.40|0.50|0.50|0.40|<br>0.40|
|Years of education<br>9.2|9.6|9.7|9.9|9.6|10.2|10.2|<br>10.4|
|Years of experience<br>20.2|20.0|19.9|19.8|20.1|19.8|19.6|<br>19.7|
|Real monthly labor income - imputed<br>1,239,760|1,225,853|1,088,381|1,137,485|986,352|1,029,838|989,716|<br>1,031,265|
|Real hourly labor income - imputed<br>6,671|6,615|5,914|6,119|6,313|6,539|6,032|<br>6,272|
|Hours worked per week<br>43.0|41.0|36.6|38.7|24.9|23.5|20.0|<br>21.4|
|**Categoricals (%, total)**||||||||
|Rural<br>22.3|21.9|32.8|22.1|19.1|19.0|29.0|<br>19.3|
|Union or married<br>56.9|56.5|56.9|55.2|58.5|58.6|59.5|<br>57.2|
|Poor<br>20.6|28.4|32.5|32.1|24.5|32.8|37.3|<br>37.4|
|Age category||||||||
|_18-24 years old_<br>21.6|20.8|20.8|20.4|21.3|21.0|21.5|<br>20.7|
|_+25-44 years old_<br>41.5|42.6|42.6|43.4|41.1|42.1|41.9|<br>42.0|
|_+45-59 years old_<br>36.9|36.6|36.5|36.2|37.6|37.0|36.6|<br>37.3|
|Children at household (Yes=1)||||||||
|_Children 0-3 years_<br>22.1|21.7|22.1|19.8|26.9|26.2|26.5|<br>24.2|
|_Children +3-6 years_<br>17.5|17.3|18.0|16.7|21.2|21.3|22.1|<br>20.9|
|_Children +6-12 years_<br>27.0|25.5|25.3|24.5|31.2|30.4|30.3|<br>29.7|
|Education category||||||||
|_No education_<br>15.5|12.8|13.7|11.1|13.5|10.2|10.6|<br>8.9|
|_Elementary_<br>24.5|22.5|23.2|21.1|22.0|19.5|20.0|<br>18.1|
|_Secondary_<br>6.6|6.4|6.2|6.3|6.6|5.9|6.1|<br>5.7|
|_High School_<br>35.4|38.7|38.7|40.9|35.2|39.5|39.6|<br>41.5|
|_Technical and technological_<br>9.4|9.6|8.8|9.8|12.6|12.7|12.0|<br>13.0|
|_University_<br>6.1|7.1|6.7|7.9|7.2|8.9|8.5|<br>9.6|
|_Postgraduate_|2.5|2.9<br>|2.6|2.9|2.9|3.3<br>|3.1|3.2|
|---|---|---|---|---|---|---|---|---|
|Experience|||||||||
|_No experience_|0.4|0.4<br>|0.3|0.3|0.7|0.6<br>|0.6|0.5|
|_+0-3 years_|7.2|7.1<br>|7.0|7.2|8.0|8.1<br>|8.0|8.1|
|_+3-6 years_|10.0|10.0<br>|9.8|10.2|9.8|10.0<br>|10.3|10.4|
|_+7-10 years_|11.7|12.3<br>|12.0|12.3|11.3|12.1<br>|12.3|12.1|
|_+10-15 years_|12.9|13.1<br>|13.5|13.4|12.7|12.9<br>|13.1|13.1|
|_+15 years_|57.8|57.2<br>|57.5|56.6|57.5|56.1<br>|55.6|55.8|
|Part-time job (<40 hours per week)|11.9|12.6<br>|13.3|12.1|34.1|33.2<br>|33.3|30.4|
|<br>Formal|39.9|41.3<br>|36.5|38.7|38.7|41.1<br>|40.1|41.4|
|Labor status|||||||||
|_Employed_|86.0|83.6<br>|80.3|80.4|60.8|58.1<br>|49.7|52.0|
|_Unemployed_|6.2|7.6<br>|9.6|9.7|8.5|9.7<br>|12.2|12.1|
|_Inactive_|7.8|8.8<br>|10.1|9.9|30.7|32.2<br>|38.1|35.9|
|Labor relation|||||||||
|_Private employee_|48.6|50.2<br>|47.7|47.8|51.5|52.6<br>|49.6|51.1|
|_Government employee_|3.8|3.6<br>|3.3|3.3|5.0|4.4<br>|4.8|4.8|
|_Self-employed_|47.7|46.2<br>|49.0|48.9|43.5|42.9<br>|45.6|44.1|
|Sector|||||||||
|_Agriculture_|19.8|20.1<br>|27.0|20.2|6.3|6.1<br>|9.0|6.1|
|_Electricity, gas, water and mining_|2.6|2.6<br>|2.9|2.9|0.8|0.8<br>|1.0|1.0|
|_Industry_|12.4|11.3<br>|11.0|11.3|12.2|11.4<br>|10.9|10.4|
|_Construction_|12.1|11.6<br>|10.6|11.7|1.1|1.0<br>|1.1|1.1|
|_Trade and vehicle repair_|16.9|17.3<br>|15.6|17.4|20.9|21.0<br>|21.2|22.1|
|_Transportation, storage and communications_|13.1|12.8<br>|11.8|13.2|3.7|3.1<br>|2.9|3.1|
|_Accommodation and food services_|3.8|4.1<br>|3.3|3.8|11.4|12.0<br>|11.9|12.5|
|_Financial and real estate activities_|2.7|2.7<br>|2.2|2.7|3.0|3.0<br>|2.8|3.0|
|_Professional and administrative activities_|4.3|4.7<br>|4.3|4.9|7.9|8.7<br>|8.3|9.6|
|_Public administration_|3.2|3.2<br>|2.8|3.0|3.0|3.1<br>|3.2|3.5|
|_Education and human health_|4.6|4.5<br>|3.8|4.3|14.5|14.9<br>|13.6|13.9|
|_Other activities_|4.4|4.9<br>|4.6|4.7|15.3|14.9<br>|14.0|13.7|
|Observations(thousands)|207,102|201,801|123,872|188,251|243,894|235,040|142,129|219,796|
|Table 3. Explainedgapshare in real labor<br>|incomeper hour(percenta<br>|gepoint<br>|s),ages<br>|18-59<br>||
|---|---|---|---|---|---|
|_Variable_|_2015_|_2017_|_2019_|_2020_|_2021_|
|Total explained gap|**-22.6**|**-23.1**|**-21.8**|**-26.7**|**-23.4**|
|**Demographics**||||||
|Age|**-1.1**|**-0.6**|0.0|**-0.4**|**-0.1**|
|Marital status: married|**-0.1**|**-0.1**|**0.1**|**0.0**|**-0.1**|
|Rural area|**-3.0**|**-2.4**|**-2.8**|**-4.1**|**-3.3**|
|City|**-1.9**|**-1.9**|**-2.0**|**-2.0**|**-1.7**|
|**Human capital**||||||
|Schooling|**-6.8**|**-7.5**|**-7.2**|**-8.4**|**-9.0**|
|Experience|**-2.4**|**-2.4**|**-3.0**|**-3.0**|**-1.7**|
|**Work**||||||
|Part-time|**-5.2**|**-5.5**|**-4.5**|**-3.8**|**-3.8**|
|Formality|**-0.2**|**-0.6**|**-0.7**|**-3.2**|**-2.3**|
|Economic sector|**-0.2**|**-0.1**|**-0.4**|**0.5**|**1.0**|
|Employment relationship|**-1.7**|**-2.0**|**-1.3**|**-2.3**|**-2.4**|
|Total unexplained gap|**36.8**|**31.4**|**25.3**|**27.9**|**25.4**|
|Totalgap|**11.1**|**8.5**|**3.2**|**2.2**|**3.3**|
|Source: GEIH, own calculations.||||||

|Table 4.|Ñopo(20|08)deco|mpositio|n - Real l|abor inco|me(per|hour and|month),a|ges 18-5|9|
|---|---|---|---|---|---|---|---|---|---|---|
|_Variable_|_20_|_15_<br>|_20_|_17_<br>|_20_|_19_<br>|_20_|_20_<br>|_20_|_21_<br>|
||_Hour_|_Month_|_Hour_|_Month_|_Hour_|_Month_|_Hour_|_Month_|_Hour_|_Month_|
|Total gap Δ|**5.7%**|**25.7%**|**3.4%**|**21.2%**|**1.2%**|**19.0%**|**-2.0%**|**10.0%**|**-2.4%**|**10.3%**|
|Not explained Δ0|**34.4%**|**39.2%**|**27.9%**|**33.4%**|**23.7%**|**31.0%**|**16.6%**|**22.7%**|**22.7%**|**27.1%**|
|CEO effect ΔH|**1.8%**|**0.9%**|**0.0%**|**-1.1%**|**-1.5%**|**-3.0%**|**-1.3%**|**-1.6%**|**-4.2%**|**-5.5%**|
|MAID effect ΔM|**-5.1%**|**1.3%**|**-2.3%**|**3.7%**|**-1.3%**|**4.7%**|**-1.9%**|**1.2%**|**-0.8%**|**4.7%**|
|Explained ΔX|**-25.3%**|**-15.7%**|**-22.2%**|**-14.7%**|**-19.7%**|**-13.6%**|**-15.4%**|**-12.3%**|**-20.1%**|**-15.9%**|
|% Men|65%|65%|65%|65%|63%|63%|70%|70%|61%|61%|
|% Women|66%|66%|66%|66%|65%|65%|72%|72%|66%|66%|
|Standard error Δ0|0.008|0.009|0.008|0.008|0.009|0.008|0.009|0.009|0.009|0.009|
|Table 5. Explained gap share in<br>|real monthly labor in<br>|come (percen<br>|tage points), <br>|ages 18-59<br>||
|---|---|---|---|---|---|
|_Variable_|2015|2017|2019|2020|2021|
|Total explained gap|**1.6**|**-0.2**|**0.5**|**-6.1**|**-5.2**|
|**Demographics**||||||
|Age|**-1.1**|**-0.7**|0.0|**-0.5**|**-0.1**|
|Marital status: married|**-0.2**|**-0.2**|0.0|**-0.1**|**-0.2**|
|Rural area|**-3.1**|**-2.5**|**-3.0**|**-4.4.**|**-3.5**|
|City|**-1.9**|**-1.9**|**-2.0**|**-1.9**|**-1.6**|
|**Human capital**||||||
|Schooling|**-6.6**|**-7.3**|**-7.0**|**-8.1**|**-8.9**|
|Experience|**-2.4**|**-2.4**|**-3.1**|**-3.2**|**-1.8**|
|**Work**||||||
|Part-time|**19.1**|**17.7**|**18.3**|**17.1**|**14.8**|
|Formality|**-0.2**|**-0.6**|**-0.7**|**-3.3**|**-2.4**|
|Economic sector|**-0.2**|**-0.2**|**-0.5**|**0.7**|**1.0**|
|Employment relationship|**-1.8**|**-2.1**|**-1.5**|**-2.4**|**-2.5**|
|Total unexplained gap|**52.7**|**39.0**|**32.4**|**33.5**|**30.5**|
|Totalgap|**52.3**|**39.7**|**32.8**|**28.0**|**26.6**|
|Source: GEIH, own calculations.||||||
|Table 6. Public p<br>|olicies that promote the reductio<br>|n of gender gaps<br>**Active programs and policies of**|
|---|---|---|
|**Strategy**|**Description**|<br>**the national government**|
|1. Improving<br>human<br>endowments<br>in<br>health,<br>education,<br>and<br>social<br>protection.|Closing the remaining gender gaps<br>in health, which includes reducing<br>maternal mortality and improving<br>women's access to health care<br>services, for instance, in sexual and<br>reproductive health; in education,<br>which involves getting girls to<br>complete<br>secondary<br>school,<br>reducing boys' secondary school<br>dropout rates and improving the<br>quality of learning among girls and<br>boys, as well as expanding social<br>security networks.|_Elementary and secondary school:_<br>1. Under the framework of the<br>strategy Entornos para la Vida, la<br>Convivencia<br>y<br>la<br>Ciudadanía,<br>several programs promote gender<br>equality.<br>2. Literacy strategies focus on<br>gender equality and targeting.<br>3. Modelos Educativos Flexibles.<br>_Higher education:_<br>1. Promotion strategy to offer<br>professional<br>training<br>in<br>non-<br>traditional programs for women.<br>2. Financing strategy to promote<br>access and retention in higher<br>education programs that are less<br>traditional for women through the<br>new program Generación E.<br>3. Por TIC Mujer program.|
|2. Eliminate restrictions so<br>women can access more<br>and better jobs.|Focusing on aspects such as safe<br>transportation to and from the<br>workplace;<br>care<br>services<br>for<br>children and other family members;<br>training on digital and technological<br>skills needed to access the world of<br>work and economic opportunities;<br>and the reduction of employment<br>discrimination based on gender.|_Signaling:_<br>1. Iniciativa de Paridad de Género<br>Colombia (IPG).<br>2. Programa Nacional de Equidad<br>Laboral con Enfoque de Género<br>(PNELG).<br>- Raising awareness on labor<br>equality.<br>- Gender equality seals "Equipares".<br>3.<br>SPE<br>inclusive<br>paths<br>to<br>employment.|
|||_Payroll subsidies:_<br>4. Programa de Apoyo al Empleo<br>Formal (PAEF).<br>5. Apoyo a Empresas Afectadas por<br>el Paro Nacional (AEAP).<br>6. Incentives to create new jobs.<br>_Tax incentives:_<br>7. Tax reliefs for creating jobs for<br>vulnerable groups of people.<br>8. (PNELG) promotion of the<br>Decree 2733 of 2012: tax reliefs for<br>hiring women victims of gender-<br>based violence.|
|---|---|---|
|3. Remove the obstacles that<br>deprive<br>women<br>of<br>ownership and control of<br>assets.|Such as land, house, and bank<br>accounts, and facilitate access to<br>financing,<br>technology,<br>and<br>insurance services to make those<br>assets profitable.|1. Fondo Mujer Emprende.<br>2. Regalías Mujer.<br>3.<br>ALDEA<br>–<br>Fondo<br>Mujer<br>Emprende.<br>4. Generando Equidad.<br>5. Autonomous assets.<br>6. Public purchases with a gender<br>perspective.|
|4. Fostering the ability and<br>the women's participation<br>in<br>the<br>decision-making<br>process.|Along with the engagement and<br>support of men and boys, address<br>challenges related to child marriage,<br>gender-based<br>violence,<br>social<br>norms, and women's representation,<br>participation and decision-making in<br>the local structures of utilities,<br>especially water and energy supply<br>in schools, management committees<br>of health care centers and local<br>development committees.|_Instruction:_<br>1. School for instruction in politics<br>for women.<br>2. Promotion of non-stereotyped<br>activities<br>and<br>equitable<br>relationships.<br>3. Más mujeres Más democracia.<br>_Participation and inclusion:_<br>4. Peer lists.<br>5. Women's empowerment in peace<br>building.|
|||6. Strengthening of territorial gender<br>mechanisms.<br>7. Pacto por la Equidad de las<br>Mujeres Rurales.<br>_Places and protection:_<br>8. Casa de Mujeres Empoderadas<br>program.<br>9. Protocol for the prevention of<br>violence against women in politics.|
|---|---|---|
|5. Cross-sectional to the prior<br>strategies.|Policies and regulations that outline<br>comprehensive<br>institutional<br>frameworks to promote actions in<br>favor of gender equality.|1. Pacto XIV “Pacto de Equidad<br>para las Mujeres”.<br>2. Mecanismo de Protección al<br>Cesante - SPE y FOSFEC.<br>3. Resolution 758 of March 07,<br>2016:<br>creates<br>the<br>Gender<br>Subcommittee, which reports to the<br>Comisión<br>Permanente<br>de<br>Concertación de Políticas Salariales<br>y Laborales of the Ministry of<br>Labor.|

Notes:* Categories from the four key areas of the World Bank's strategies on gender equality (World Bank Group, 2015).

Source: Own elaboration based on information from the official websites of the Ministry of National Education, Ministry of Labor, Ministry of Agriculture and Rural Development, National Planning Department, and Presidential Advisor's Office for Women's Equality.