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.