This research focuses on the evolution of the Gender Wage Gap (GWG) and its components (endowment efect and coeficients efect) over the entire wage distribution in Ecuador between 2010 and 2021. It was used Melly's decomposition method based on quantile regressions along the wage distribution with Data from Ecuador's National Survey of Employment, Unemployment, and Underemployment (ENEMDU for its name in Spanish). There was found strong evidence of a Sticky Floor (a larger GWG at the tenth percentile than the median) and of a Glass Ceiling (a larger GWG at the ninetieth percentile than the median) for Ecuador. The results show that women with low and high income have limited mobility and invisible barriers which prevent them from getting better positions and salaries. The ninety-nine (99) regressions also suggest a GWG favoring men, which is explained by a discrimination factor (coeficient efect). Public policy must be geared towards fighting discrimination against women to reduce gender wage inequality.
2019 and 2020. However, women already had a significantly lower employment rate than men by 2019 (45.2% against 69.4%), and by 2022 there have not been any relevant changes in rates.
Gender inequality in Latin America has had a long concerning story with data, resulting in old
multiple academic pieces of research aiming at analyzing the wage gap3. Ecuador is no stranger
to this matter4. As seen in the following part of this work, factual evidence shows a wage gap
in Ecuador, considerably persistent benefiting men. Most empiric research on the determiners
of this gap in Ecuador has been based on the Blinder [
On the other hand, we have those whose root is discrimination5. Based on this method, the bibliography used for the national analysis tends to conclude that the gender gap results from discrimination and not due to employees’ diferent characteristics. Likewise, part of the literature finds that the wage gap shows a downtrend, although most papers only analyze short terms.
This study analyzes and identifies critical factors in the evolution of the wage gap along with its observable and non-observable components in Ecuador for the period 2010-2021. This research contributes to the empirical literature in two fundamental aspects. First, we analyze the gap for all wage distribution (quantiles 1 to 99). This is especially relevant since it helps approach the "Sticky Floor" and "Glass Ceiling" issues6. It also provides evidence of Covid-19’s impact on the evolution of the wage gap and its components.
On top of this introduction, this article is built as follows. In the first section, we will conduct
relevant research on gender wage inequality in Ecuador. In the second section, we present
the traditionally used Blinder [
Amid the first research on the gender wage gap in Ecuador, we find the one conducted by Ñopo et
al. [
Pérez et al. [
Aligned with the works that used Blinder-Oaxaca’s methodology with bias selection
corrections, we have Linthon-Delgado et al. [
So far, the analysis for literature in Ecuador had as its primary source the ENEMDU. Antón
et al. [
The research analyzing the wage gap over diferent distribution points shows the coexistence of the ’Sticky Floor’ and ’Glass Ceiling’ patterns, although there is a wider discrepancy in its magnitude and evolution. Some say that ’Sticky Floor’ is similar in size to the ’Glass Ceiling,’ and others think the ’Glass Ceiling’ is a much bigger phenomenon than the ’Sticky Floor.’ The main conclusion of all this research is that the gender wage gap is due to discrimination, not human capital diferences.
The most used methodology to decompose wage gaps is Blinder [
This method consists of estimating Mincer’s regressions [
=1
Where subindices “” and “” represent employees and coeficients, respectively. ln is the
natural logarithm of wages, represents the human capital component for employees, are
the regressors, and reflect the returns from the labor market to the characteristics of employees
or the ’prices’ of services associated with them [
Stressing equations 1 and 2, the Blinder-Oaxaca’s decomposition results in the following expression: ∑︁ ¯ − ∑︁ ¯ = ∑︁ (︁ ¯ −
¯)︁ + ∑︁ ¯ (︀ − )︀
Where the first term, ∑︀ (︁ ¯ − ¯)︁ represents the observable component, meaning the part of the wage gap that can be explained by the diferences observed in the characteristics of groups A and B, and the second term ∑︀ ¯ (︁ − )︁ expresses the non-observable component, in other words, the part of the wage gap that can be explained by the diferences in the coeficients related to each of the estimations of the equations for employees’ wages in groups A and B, typically considered as the discrimination efect.
Blinder-Oaxaca’s methodology is strongly present in the field; however, it only allows the
calculation of the mean and not of all the wage distribution [
We used Melly’s methodology [
Melly’s decomposition methodology allows us to obtain a numerically equivalent estimator
when the number of simulations used in Machado et al. [
Therefore, Melly [
tional distribution function. • Step 1. Estimate through quantile linear regression all the conditional distributions. • Step 2. Integrate the conditional function over the covariable range to obtain the uncon• Step 3. Obtain the unconditional quantile function of interest by inverting the uncondi• Step 4. Invert the counterfactual distribution of interest to obtain the counterfactual quantile of interest , = −1 ( ) with women’s wage structure, indicated as follows: ∫︁ , ( ) =
, | ( |) ()
Decompose the changes in wage density [
Equation 5 shows the decomposition of the gender wage gap for diferent quantiles, where the first term on the right of the equation represents the efect of the characteristics (observable components) and the second the efect of the coeficients (non-observable components), commonly understood as the discrimination efect. (4) (5)
Data used for this research were taken from the ENEMDU, which is made on a quarterly basis by INEC. We used figures for September within the 2010-2021 period. We limited the sample to people between 18- and 65 years old living in an urban area. Table 1 shows the main statistics for the total sample and Table 2 shows the main statistics for employees.
The structure of the age groups is very similar between men and women; about 50% of the
people in the sample are between 25-49 years old. This percentage grows by about 5% for all
employees. Almost 56% of people from the sample are married or live in Free Union, but when
it comes to the employees’ group, the percentage for women in either of these marital statuses
is, on average, 13% lower than for men. Concerning the head of household, the percentage of
men is on average 33% higher than women for the total sample; this percentage reaches up
to 38% when only employees are considered; this suggests there is a gender social division
of household roles in Ecuador just as the literature pinpointed [
In the labor context, we can see a rise in the average hourly wage from 0.81 in 2010 to 1.11 in 2021 for men and from 0.81 in 2010 to 1.15 in 2021 for women; meaning that by the last year of the sample, there is a wage gap benefiting women. This wage gap in favor of women could be connected to the fact that the percentage of women with higher education is, on average, 13% higher than their male peers. the years 2010, 2015, and 2021. We can see a U shape, which means men have higher salaries than women in the low part of the wage distribution (’Sticky Floor’) and the high part too (’Glass Ceiling’). Firstly, the ’Glass Ceiling’ phenomenon shows a higher magnitude, suggesting that women with higher salaries face the largest inequality compared with their male peers. Secondly, women have better compensation in the middle part of the wage distribution, which can result from better human capital skills for women during the last years. Thirdly, excluding 2021 (a pandemic period), we can see a practically constant uptrend for ’Sticky Floor’ and ’Glass Ceiling’; in other words, between 2010 and 2015, there was a wage inequality process between men and women with the lowest income, while women with the highest wages did not experience higher inequality with men between 2010 and 2015. Covid-19 seems to have afected the gender wage gap since 2021, showing a more even U shape than the previous two periods; this confirms the existence of a ’Sticky Floor’ and ’Glass Ceiling.’
In summarizing, the descriptive statistics analysis of the gender wage gap in Ecuador for the 2010-2021 period showed the following fundamentals: i) an uptrend in the ’Sticky Floor’ and ’Glass Ceiling’ patterns, ii) women have better income in a great part of the wage distribution, iii) women with low school levels are in an evident disadvantage compared to men, iv) regarding employees in the higher education group, men have better salaries in the low and the high side of the distribution, v) Covid-19 seems to have increased the total female labor participation, but it dropped the participation of married women, vi)the period afected by Covid-19, the ’Sticky Floor’ and ’Glass Ceiling’ phenomena show a drop.
We will go through the results of the decomposition of the gender wage gap in observable factors (endowment efect) and non-observable (coeficients efect) after having applied Melly’s methodology.
Figure 1 shows that during the three studied years, 2010, 2015, and 2021, the relevance of the observable components (characteristics) and the non-observable (coeficients) for the gender wage gap varies through the quantiles. In 2010, there is no wage gap for people with the lowest income (until quantile 19), while after quantile 20 and up to the 80th, women’s salaries are slightly higher than men’s, and then forward the gap expands in favor of men. In other words, it can be said that in 2010 there was an ongoing ’Glass Ceiling’ phenomenon. More importantly, results show that if the characteristics of employees had determined wages, the wage gap would have benefitted women along the entire wage distribution. In fact, the observed wage gap had such a trend due to discrimination.
Discrimination mainly afected women in the high part of the distribution. In 2015, the observed wage gap showed a U trend, meaning it favored men in the high and low part of the distribution; these phenomena are known as the ’Sticky Floor’ and ’Glass Ceiling’. Likewise, in 2010, the observed wage gap in 2015 is mainly explained by discrimination; in other words, men’s human capital had better compensation than women. Finally, in 2021, a year impacted by Covid-19, we can see more even wages, the result of a drop in the ’Sticky Floor’ and ’Glass Ceiling’ patterns compared with previous years (2010 and 2015). In the last mean of the wage distribution, women have better average wages than men, but this is due to them having broader human capital.
Furthermore, results show that the coeficients efect is higher than the endowment efect along the distribution. This allows us to reinforce the gender discrimination hypothesis present in the Ecuadorian labor market. However, the discrimination efect is higher for women in the high part of the wage distribution, meaning that for the best-paid employees’ group, women’s human capital is worth less in the labor market than men’s. This was particularly true in 2010 and 2015 since, in 2021, discrimination afected both women in the high part and women in the low part of the distribution. These results also mean that if women were to bring the same human capital as men to the labor market, the wage gap benefitting men would be way higher than observed. In a non-discriminatory scenario, the wage gap would benefit women along the entire wage distribution.
We can see in Figure 1 two key aspects: 1) men’s wages have benefitted from discrimination, and 2) women have broader human capital than men. When we compare 2021 with 2010, we can see a slight upturn in the ’Sticky Floor’ and an essential downturn in the ’Glass Ceiling’ phenomena. We can say that when it comes to gender wage equality matters, women with the lowest income are worse than ten years ago, and women with the highest income have experienced an improvement. Finally, women were paid in the middle part of the distribution in the three studied years.
In the same sense, Figure 2 shows the gender wage decomposition for people with low school levels (primary or less). In 2010 the gender wage gap (observed) slightly benefitted men in quantiles 1 to 83; ever since it has benefitted women. Moreover, this gap is mainly explained by the coeficients efect, suggesting positive discrimination against women. In 2015, the wage gap mostly favored men over women in all quantiles; although the gap was higher in the higher part of the distribution, this uneven wage gap was also the result of discrimination. In 202, the wage gap trend is like the one in 2010, suggesting that gender wage inequality dropped during the Covid-19 pandemic.
On the other hand, the decomposition of the gender wage gap showed that the coeficients efect prevails for the higher education employees’ group (Figure 3). In 2010, the coeficient efects grew significantly right after the 70th quantile, meaning that discrimination afects women with higher education and high income. In 2015, the pattern was similar to 2010, where the main diference is that it also afected women in the low quantiles. In 2021, we can see a trend toward wage equality, although the ’Glass Ceiling’ phenomenon persists due to discrimination.
In summary, gender wage inequality is significantly diferent for lower and higher-education employees. The main factor preventing gender wage equality is discrimination against women. Without it, the gender gap in the lower education level employees’ group would be indiscernible for almost all earnings’ quantiles; furthermore, wage inequality would significantly drop for men and women with higher education. Finally, Covid.19 seems to have dropped gender wage inequality.
This research aimed to analyze the evolution of the gender wage gap and its components (observed and non-observed) in Ecuador during the period between 2010 and 2021. We used Melly’s process, which allows the decomposition of the gender wage gap per quantiles in observed factors (characteristics efect) and non-observed (coeficients efect).
This research results showed that the gender wage gap is widely diferent over most quantiles, which means that studies that only analyze the changes in the wage mean or in some points of the distribution (quantiles 10, 50, and 90, for most cases), using Blinder-Oaxaca’s method or methodologies based on quantile regressions present important limitations.
This study has proven that women in quantiles between 30 and 80 are better paid than men and that during the analyzed period, this gap widened, even during the pandemic. However, this is due to women having better school levels than men.
On the low part of the distribution, we found an evident pattern of ’Sticky Floor,’ which rose between 2010 and 2015. Still, it slightly dropped in 2021, which leads to conclude that less educated women engaged in low-productivity jobs face working more challenging than men with similar characteristics. The problem with this gap is that it results from discrimination, meaning the coeficients efect is the one that explains the wage gap benefitting men and not the diferences in the human capital (characteristics efect). Furthermore, although this research results coincide with most of the investigation, many quantiles difer. Therefore, when we consider the entire distribution, we can infer that the ’Sticky Floor’ mainly afects 20 percent of the distribution (below quantile 20) and not only those in quantile 10, as it has usually been studied.
Moreover, we found conclusive evidence showing ’Glass Ceilings,’ which is also the result of discrimination. Nevertheless, during the Covid-19 pandemic period, there was an evident downturn in this phenomenon, meaning there was more wage equality between men and women with the highest income. This pattern is also more apparent after quantile 90. This last quantile is the one that most studies consider analyzing the gap between employees with better wages.
During the Covid-19 pandemic, we observed a change toward gender wage equality, especially for women with low school levels; however, discrimination continues to be an essential obstacle to wage parity for men and women with higher education.
This research provides a broad perspective on evaluating the gender wage in Ecuador during the last decade. However, for further investigation, we recommend examining the gender wage gap in more specific groups, such as employees in the public and private sectors, urban and rural areas, and formal and informal sectors, amongst others. This will undoubtedly bring a broader understanding of the domestic trends for wage gaps. Future research should also consider bias correction over sample selection since this would lead to more precise estimations.
Concerning public policy, this research shows that policies should focus on fighting discrimination and women exclusion, mainly in jobs with low and high productivity. Results prove that women have more than enough human capital to hold such positions and that granting access to those jobs will significantly contribute to dropping the gender wage gap and to the seeking of a more egalitarian society.