Abstract
This paper examines gender inequality focusing on two critical spheres in which gender inequality is generated: education and work. Our objective is to provide a snapshot of gender inequality across key indicators as well as a dynamic perspective that highlights successes and failures. We also facilitate a cross-country comparison by grouping countries within Latin America by their level of economic development and drawing comparisons with countries outside the region. Finally, we reflect on differences in the ways that gender inequalities play out across different socio-economic groups, particularly those that highlight other sources of inequality.
Introduction
This chapter examines gender inequality focusing on two critical spheres in which gender inequality is generated: education and work. Our objective is to provide a current snapshot of gender inequality across key indicators as well as a dynamic perspective that highlights successes and failures. We also facilitate a cross-country comparison by grouping countries within Latin America by their level of economics development and drawing comparisons with countries outside the region. Finally, we reflect on differences in the ways that gender inequalities play out across different socio-economic groups, particularly those that highlight other sources of inequality.
Following a life-cycle approach, we start by examining gender inequalities in both quantity of education and performance at different levels of schooling—from pre-school to higher education. The picture that emerges is mixed. In some important dimensions girls are more advantaged than boys. Girls are more likely to complete secondary and higher education and throughout schooling outperform boys in reading. A key dimension in which they continue to be disadvantaged, however, is performance in math and the likelihood of completing a degree in a STEM subject. Instead, women are over-represented in fields such as health and education. This is significant because labor market returns to STEM subjects are much higher than in the latter. Importantly, gender gaps in math are not evident at early stages of primary school and the cross-country variation in gender gaps suggests that environmental factors are at play. This is consistent with evidence we present of gender gaps in confidence in math ability in favor of boys, even controlling for differences in achievement, as well as a large gender gap in aspirations for STEM occupations.1
The second part of the chapter focuses on the work sphere. Here, we document significant increases in female labor force participation (LFP) over the last 20 years, especially among the least-educated women (those with incomplete secondary education). Progress has not been equal across all the countries in the region, however, and has been slowest among the least economically developed countries in the region. These are also the countries where a significant proportion of the adult working population, especially among men, continue to hold highly conservative norms about women’s participation in work. Honing in on the working population, we continue to see persistent and significant gender differences in job quality, wages and the distribution of hours and total time spent on paid market and unpaid non-market work. Even after conditioning on education, employment sector and occupation, there is a significant gender wage gap in favor of men in almost all countries in the region which has remained constant or even increased over the last 20 years. Furthermore, gender inequalities interact with socio-economic inequalities in the work sphere. For example, gender gaps in indicators of job quality (working in the informal sector and working for a larger firm) are significantly greater among less-educated women, but also including those with a secondary school education. Finally, across all of the countries in the region it continues to be the case that only women’s labor market trajectories (not men’s) are hugely impacted by the arrival of children, resulting in declines in LFP of up to 40%, with little evidence of recovery in the medium term.
This chapter proceeds as follows. Section 2 focuses on the education sphere, starting with access to education from the pre-primary to tertiary levels and moving on to consider quality of education and education-related attitudes and expectations. The focus of Section 3 is on the work sphere. Here we consider LFP, time spent on work, job quality, wages, as well as factors that drive the gender wage gaps. We also examine attitudes towards women’s work and their perceived competence in the business and political realms. Section 4 concludes.
Education
This part of the paper focuses on gender inequality in education at each of the key stages—pre-primary, primary, secondary and tertiary. We consider several dimensions of education including quantity, achievement and attitudes which may shape educational choices. We utilize GenLAC–CEDLAS harmonized microdata from national household surveys from more than 300 household surveys in the region to compute statistics as well as several sources of internationally comparable assessment data including SERCE, TERCE, PISA and ECAF.2 SERCE (Second Regional Comparative and Explanatory Study, 2006) and TERCE (Third Regional Comparative and Explanatory Study) are regional exams for primary education (third and sixth grades) produced by UNESCO. Nearly every country in Latin America took part in TERCE. PISA is the OECD’s Program for International Student Assessment. It evaluates students’ knowledge and skills as they approach the end of their compulsory schooling (at 15 years of age). The CAF Survey (ECAF) 2015 was carried out by CAF-development bank of Latin America, and has information about adult’s skills in 10 major cities in 10 Latin American (LA) countries. Table B.1 in Appendix B.2 lists the surveys used in this chapter for 17 Latin American countries. For more details, see Appendix B. Throughout this chapter we focus on the period up to 2019 for two reasons. First, this is the most recent year for which much of the data used in the analysis is available. Second, in the cases where more recent data is available there is a concern about the differential effects that the COVID-19 pandemic may have had by country and across gender.
To facilitate comparisons, we classified countries into three broad income groups based on their GNI per capita (in US$) for the period 2010–2020 and the corresponding World Bank Analytical Classifications: (i) Lower-middle income (LMI), which we defined as countries that were considered LMI at least once in 2010–2020; (ii) Upper-middle income (UMI), which we defined as countries that were considered UMI during the entire 2010–2020 period; (iii) High income (HI), which we defined as countries that were considered HI at least once in 2010–2020.3 Note that whenever we show trends for a country group, it consists of the simple (unweighted) average across countries in that group.
The gender gap in education: quantity
This section examines the gender gap in educational attainment as measured by completion of primary, secondary and tertiary education as well as by attendance of a pre-school program.
Pre-school
We start with a snapshot of pre-primary educational attainment, which we measure as the proportion of children age 5 attending pre-school in 2019. There are stark differences in enrollment rates across LA countries: at around 95%, enrollment rates in HI countries are comparable to OECD enrollment rates, but they are significantly lower in LMI countries, at around 70%. Irrespective of levels of enrollment, however, in the great majority of LA countries, as in the OECD, boys and girls are equally likely to be attending pre-school (Fig. 1).4 This has been the case for some time, especially in the wealthier countries. Figure 2 shows the evolution of pre-school enrollment rates for boys and girls over the last 20 years; with the exception of a small gender gap in favor of girls in LMIs in the early 2000’s, we see gender parity across the period.
Pre-school attendance. Note: The figure shows, by gender, the share of children age 5 attending pre-school in 2019 or the latest year prior to that (see Table B.1 in Appendix B.2). The average bars show unweighted means. The OECD average is the simple average over the OECD countries’ enrollment rates for individuals aged 5 years old in 2019. Source: Authors’ own calculations based on household surveys (GenLAC) and education at a glance for the OECD
Evolution of pre-school attendance 2000–2019. Note: The figure shows, by gender and country income group, the evolution of the share of children age 5 attending pre-school. each dot represents the (unweighted) cross-country average of their 5-year average. All countries with available data in the corresponding sub-periods are included (the panel is unbalanced for LMI countries). See Table B.1 in Appendix B.2). Source: Authors’ own calculations based on household surveys (GenLAC)
Primary school
In the majority of LA countries there is also little evidence of gender differences in the completion of primary-school education. Figure 3 shows primary school completion rates for individuals who were between 20 and 30 years old in 2019. The absence of gender gaps in primary school enrollment is similar to that of OECD countries. Where there are gender gaps, they tend to be small and in favor of girls—e.g. in Brazil, Dominican Republic, Nicaragua and Honduras. Guatemala stands out as an exception with an 8 percentage point gap in favor of boys. As with pre-primary education, the picture has remained fairly constant over the last 20 years, especially in the wealthier countries (Fig. 4). In LMI countries the average showed a small gender gap in favor of boys in the early 2000’s, which closed entirely over the subsequent years.
Primary school completion. Note: This figure shows, by gender, the primary school completion rates. For LAC, the sample is restricted to individuals aged 20–30 years old. The average bars show unweighted means. The OECD average is the simple average over the OECD countries’ primary completion rates for individuals aged 14–16 years old. The year is 2019 or the latest available up to that year (see Table B.1 in Appendix B.2.) Source: Authors’ own calculations based on household surveys (GenLAC) and UIS–UNESCO for the OECD
Evolution of primary school completion rates 2000–2019. Note: The figure shows, by gender and country income group, the evolution of the primary school completion rates. Each dot represents the (unweighted) cross-country average of their 5-year average. Source: See note to Fig. 2. Only countries with available data in the corresponding periods are included (unbalanced panel in the case of LMI countries). See Table B.1 in Appendix B.2
Secondary school
The gender parity in pre and primary-school completion rates does not extend to secondary education. Figure 5 shows secondary school completion rates for individuals age 20–30 in 2019. Across the large majority of countries the gaps favor women. These are largest in the high income countries; on average women here are 8 percentage points more likely to have completed secondary school than men, significantly greater than the 3.4 percentage points gap of OECD countries. By far the largest gap is in Uruguay at 15 percentage points. Despite being in the HI country group, Uruguay also has among the lowest secondary school completion rates in the region, especially for men, comparable to much poorer countries such as Guatemala, Nicaragua and Honduras.
Secondary school completion (2019). Note: This figure shows, by gender, the secondary school completion rates. The sample is restricted to individuals aged 20–30 years old, except in the case of OECD where it is restricted to individuals aged 25–34 years old. The average bars show unweighted means. Source: Authors’ own calculations based on household surveys (GenLAC) and OECD STATS. The year used in the calculations is 2021 in the OECD and 2019 or the latest available up to 2019 in LA (see Table B.1 in Appendix B.2)
The gaps in favor of women are not a recent phenomenon in the wealthier countries; Fig. 6a shows that they were already present in HI and UMI countries 20 years earlier. Furthermore, there is no sign of them narrowing over this period—if anything, they have widened slightly (Fig. 6c), a similar trend to the one observed in the OECD (Fig. 6b). In the poorer countries, the likelihood of completing secondary school was similarly low for women and men in the early 2000’s. Over the last 20 years, however, women have experienced slightly faster-paced improvement than men so that by 2019 these countries are starting to look more similar to the wealthier countries in terms of the gender gap, though not in terms of completion levels.
Evolution of secondary school completion rates. Note: Panels (a) and (b) show the evolution of secondary school completion rates. The sample is restricted to individuals aged 20–30 years old (except for the OECD, where it is restricted to individuals aged 25–34 years old). Panel (c) shows the evolution of the gender gap (F-M) measured in percentage points in secondary school completion rates. In panels (a) and (c), each dot represents the (unweighted) cross-country average of their 5-year average. In panel (b), each dot represents the (unweighted) annual average among the OECD countries. Source: See note to Fig. 5. In LAC, all countries with available data in the corresponding periods are included (unbalanced panel in the case of LMI countries). See Table B.1 in Appendix B.2
Higher education completion
Women’s higher rate of secondary-school completion translates into even larger gaps in higher education completion rates. These gaps are presented in Fig. 7 for individuals age 30–40 in 2019. As at the secondary-school level, these gaps are largest in the wealthiest countries: on average men in these countries are 6 percentage points less likely to have completed higher education; the size of this gap is equivalent to 30% of men’s higher education completion rate in these countries. The gap is smaller than the OECD average which is 10 percentage points. There are several countries in the LMI group where completion rates are the same for men and women but these tend to also be the countries with some of the lowest completion rates in the region. In the UMI group, Mexico and Peru are distinguished by their degree of gender parity. It is worth noting that the gender gap in higher education completion is not a direct consequence of the aforementioned gender differences in secondary school completion rates. Even conditional on having completed secondary education, women have higher rates of higher education graduation across most countries. This gender gap (F-M) in the conditional probability of higher education completion stands at 5 percentage points in HI countries and 3 percentage points in UMI and LMI countries. Exceptions include Mexico and El Salvador, where this probability is 2 percentage points higher among men, and Peru, Bolivia and Guatemala, where no gender differences are observed.
Higher education completion. Note: This figure shows, by gender, the higher education completion rates (%). The sample is restricted to individuals aged 30–40 years old, except in the case of OECD where it is restricted to individuals aged 35–44 years old. The average bars show unweighted means. Data is from 2019. Source: See note to Fig. 5
While gender differences in primary and, for the most part, secondary school completion rates have been fairly stable over the last 20 years, there is clear evidence of a widening gap in higher education completion in favor of women across the region (Fig. 8a). In the lower and middle income countries, higher-education completion rates were roughly the same among men than women in the early 2000’s. In both sets of countries growth in completion rates among women has been faster than that among men over the subsequent period. This has also been the case in the high income countries, though there a gap already existed in the early 2000’s. The widening of the gender gap in tertiary education in LA is likely to keep on increasing, judging by the experience of the OECD as shown in Fig. 8b.
Evolution of higher education completion rates. Note: The figure shows the evolution of higher education completion rates (%). The sample is restricted to individuals aged 30–40 years old (except for the OECD, where it is restricted to individuals aged 35–44 years old). In panel (a), each dot represents the (unweighted) cross-country average of their 5-year average. In panel (b), each dot represents the (unweighted) annual average among the OECD countries. Source: See note to Fig. 6
The gender gap in education: achievement
Thus far, the analysis has shown that gender gaps in the level of education emerge at the secondary school level, widen at the tertiary level and favor women. These gaps have been widening slightly over time and are largest in the highest income countries. We now examine whether and how the knowledge and skills acquired in the education system vary by gender. In order to make comparisons across countries, we use data from international assessment programs implemented in Latin America including SERCE and TERCE for primary school students, which is available for children in 3rd grade (age 7–8) and children in 6th grade (age 10–11), PISA for secondary school students (age 15), and ECAF data for adults (see Appendix B for descriptions of each of these data sets). While we do not have internationally comparable assessment data at the higher education level, data on subject choice provides an indication of gender differences at this stage.
Primary school
At the primary school level we use data from regional assessments for primary education produced by UNESCO. We use data from the Second and Third Comparative and Explanatory Studies (SERCE and TERCE, respectively) of 3rd and 6th graders in most LA countries conducted in 2006 and 2013. We cannot benchmark the results here against countries outside the region as these assessments were conducted only in LAC.
Figure 9 shows gender gaps in TERCE reading and math scores in 2013 for children in 3rd and 6th grades.5 In the great majority of countries in the region 3rd-grade girls do at least as well or better than boys in reading, but the picture in math is already more nuanced. Girls in HI countries tend to do better in math whereas the split is more or less half-half in the UMI countries and favors boys in the LMI countries. Comparing the gender gap in 2006 to that of 2013 (the 2 years for which we have data) in Fig. 10a (which shows the gender gap in 2006 on the x-axis vs that in 2013 on the y-axis), the fact that most countries are over the 45 degree line in math indicates that on average girls improved relative to boys. Boys are on average likewise catching up to girls in reading: most of the points in Fig. 10c are either close to or below the 45 degree line indicating that the advantage girls have in reading relative to boys in 3rd grade has either remained constant or declined between 2006 and 2013.6 The female reading advantage persists in 6th grade and is larger than in 3rd grade for several HI and LMI countries (Fig. 9a), but tends to decrease in the UMI countries. Comparing 2013 with 2006 in Fig. 10d, there is a mixed picture in reading with girls decreasing their reading advantage in some countries but increasing it in others. In math, on the other hand, girls lose any advantage they had in 3rd grade relative to boys. In the vast majority of countries the gender gap in math favors boys in 6th grade. Furthermore, the math gender gap grew between 2006 and 2013 in almost all countries as reflected by the fact that almost all countries are below the 45 degree line in Fig. 10b. This is in stark contrast to the patterns of improvement in girls’ math performance relative to boys in 3rd grade over the same period.
Gender gaps (F-M) in TERCE scores. Note: The figure shows the gender gaps (F-M) in reading scores (panel a) and math scores (panel b) in 2013, for children in 3rd (the bar on the left, ochre bars) and 6th grades (the bar on the right, green bars). The test score scale has a standard deviation of 100 points. We use the average score of boys as the base from which these differences are expressed. boys’ scores in reading (3rd grade, 6th grade): Argentina (507500), Chile (567551), Panama (486475), Uruguay (517525), Brazil (513518), Colombia (512523), Costa Rica (539544), Dominican Republic (445450), Ecuador (509489), Mexico (514526), Peru (521505), Guatemala (496490), Honduras (495475), Nicaragua (478472) and Paraguay (475463). Boys’ scores in math (3rd grade, 6th grade): Argentina (530534), Chile (583583), Panama (491460), Uruguay (545571), Brazil (537525), Colombia (518523), Costa Rica (560540), Dominican Republic (446439), Ecuador (526518), Mexico (548569), Peru (537540), Guatemala (503498), Honduras (507485), Nicaragua (489466) and Paraguay (490457). Source: Authors’ own calculations based on TERCE; 2013
Change in gender gaps (F-M) 2006–2013. Note: The figure shows changes in test score gender gaps (F-M) between 2006 and 2013. Points above the 45 degree line show that the gap became more favorable to girls and points below show the opposite. The test score scale has a standard deviation of 100 points. HI countries are in orange, UMI countries are in green and LMI countries are in violet. Source: Authors’ own calculations based on the second and the third regional comparative and explanatory study (SERCE 2006 and TERCE)
Secondary-school performance
We use PISA data in order to study gender inequalities in school achievement at the secondary-school level (at age 15). The sample of countries in the analysis is restricted by the fact that PISA assessments have only been administered in a sub-set of LA countries, excluding all the countries in the LMI group. Furthermore, not all countries participated in all rounds.
Figure 11 shows gender gaps in the PISA math assessment (F-M) in 2018 across the LA countries for which these data are available.7 We also include (in black) the average gender gap in the OECD as a benchmark. In all countries with the exception of the Dominican Republic, there is a gap in favor of boys in the math score. In most HI countries, this gap is relatively small and comparable to that in OECD countries. Argentina is an exception with a gap of around 15% of a standard deviation (PISA is standardized to have a standard deviation of 100). The gaps tend to be larger in UMI countries—above 10% of a standard deviation in most and as high as 20% in Colombia. Part of the male–female gap in math test performance in secondary school may be attributed to sample selection. Lower-performing male students tend to drop out at higher rates than their lower- performing female counterparts due to greater incentives to leave school and enter the labor market, particularly in developing countries where dropout rates are higher.8
Gender gaps in Pisa scores in math in 2018 (F-M). Note: The figure shows the gender gaps (F-M) in math scores in 2018, for 15 years old children. The test score scale has a standard deviation of 100 points. We use boy’s scores as the base from which these differences are expressed: 387 Argentina, 421 Chile, 357 Panama, 422 Uruguay, 388 Brazil, 401 Colombia, 411 Costa Rica, 324 Dominican Republic, 415 Mexico, 408 Peru. The OECD average bar shows unweighted means. Source: Authors’ own calculations based on PISA 2018 (program for international student assessment, OECD)
Gender gaps in Pisa scores in reading in 2018 (F-M). Note: The figure shows the gender gaps (F-M) in reading scores in 2018, for 15 years old children. The test score scale has a standard deviation of 100 points. We use boy’s scores as the base from which these differences are expressed: 393 Argentina, 442 Chile, 370 Panama, 415 Uruguay, 400 Brazil, 407 Colombia, 419 Costa Rica, 326 Dominican Republic, 415 Mexico, 395 Peru. The OECD average bar shows unweighted means. Source: Authors’ own calculations based on PISA 2018 (program for international student assessment, OECD)
In contrast to the patterns in math, girls outperform boys in PISA reading assessment in most LA countries (Fig. 12). These gender gaps tend to be larger than the ones in math in both HI and UMI countries, reaching as high as nearly a third of a standard deviation in the Dominican Republic. This is also similar to what is seen in OECD countries, where on average girls outperform boys on the PISA reading assessment by 30% of a standard deviation.9
Figures 13a and 13b show the changes in the gender gaps in math and reading PISA scores between 2009 and 2018 for those countries for which data is available for both years. In Fig. 13a the gaps are negative in both periods, indicating that boys outperformed girls in math in both years. As all points (other than Argentina) lie above the 45-degree line, they indicate a decrease in the gap, i.e. the average performance of girls relative to boys has improved over time. This is most evident in Colombia and Chile where the math gender gap shrunk by over 10 points (10% of a standard deviation). OECD countries also narrowed their math gender gap over the same time period. Argentina is an exception, with the gender gap in math widening over this time period.
Change in gender gaps in PISA scores, 2009–2018. Note: The figure shows changes in test score gender gaps (F-M) between 2009 and 2018. The test score scale has a standard deviation of 100 points. LA countries are in orange and the OECD average is in green. Source: Authors’ own calculations based on PISA 2009, 2018
While girls are catching up with boys in math, boys are catching up with girls in reading. Figure 13b shows changes in reading gender gaps. These are positive in both periods indicating a persistent girl advantage over that time-span. However, all of the points are on or below the 45 degree line, showing that the female advantage in reading either remained constant or shrunk in all seven countries for which data are available, as in the OECD. Furthermore, the catch up in reading was, on average, of a larger magnitude than the catch-up in math; in four out of the seven countries in the analysis the reading gap shrunk by 12–20% of a standard deviation.
Overall the picture over time appears to be one in which gender gaps are shrinking in both math and reading, but boys have decreased their disadvantage in reading more than girls have in math in absolute terms as well as relative to the size of the gap that existed.
Higher education
We do not have cross-country measures of achievement in higher education in Latin America. We can, however, examine gender differences in the choice of field of study. To do this we use data available from Our World in Data to document the female shares of graduates across main fields of study.10
Figure 14 considers Science, Technology, Engineering and Mathematics (STEM) subjects only and shows the female proportion of STEM graduates by country. As in many countries around the world, across the region women make up the minority of STEM graduates. Chile stands out as the country with by far the lowest proportion of women graduates in STEM, at around 18%, especially compared to the other three HI countries in this analysis where this proportion is between 40 and 45%. This figure compares favorably with the OECD average of 31%. Across the majority of UMI and LMI countries, women make up between a third to two-fifths of STEM graduates.11
Female share of STEM graduates. Note: The figure shows the female share of STEM graduates. The OECD average bar shows unweighted means. Source: Our world In data. Argentina (2013), Brazil (2014), Chile (2014), Colombia (2014), Dominican Republic (2014), Ecuador (2013), El Salvador (2014), Guatemala (2007), Honduras (2014), Mexico (2012), Panama (2013) and Uruguay (2010)
A more general analysis of female representation across all fields of study (presented in Appendix Figs. A.3, A.4 and A.5) shows that, throughout the region. Education and Health stand out as the two fields of study where women are consistently over-represented, making up between 70 and 80% of the graduates across all of the countries included in the analysis.
Adult skills
Lastly, we examine measures of skills in adulthood which capture skills accumulated through the entire education system as well as subsequent work experience. This data was collected by the Development Bank of Latin America (CAF) in 2015 for representative samples of individuals age 15–55 in capital cities of 10 Latin American countries. The survey included assessments of several dimensions of adult skills. Here we study gender differences in measures of ‘basic cognitive functioning’ verbal skills, and numerical skills. “Basic cognitive functioning” is measured using the Ravens Progressive Matrices Test (Raven, 1936; Raven, 2000). Further details about the data and assessments used in the analysis can be found in Appendix B.5. It should be noted that this data set is only representative of capital cities of a select group of LA countries. There is a clear need for more data in this area.
Figure 15 shows, for each gender, the percent of a standard deviation by which the group on average scores differently than the mean for that variable. Each variable is standardized to have mean of 0 and a standard deviation equal to 1. The standardization was done by CAF using the entire sample of the capital cities of 10 LA countries for which this data is available.12 Thus, the numbers in the figures show the percentage of one standard deviation by which the average score for that gender in that specific country is different from the mean of the entire cross-country sample.13 Figure 15a shows standardized scores in the Ravens assessment of ‘basic cognitive functioning’. In seven out of nine countries included in this analysis, men outperform women in this assessment but this difference is statistically significantly different from zero only in Bolivia, Brazil and Peru.14 The largest gaps are in Peru and Bolivia, where they are 16 and 21% of a standard deviation, respectively.15 The exceptions are two countries in the HI group—Argentina and Uruguay—where on average women score slightly higher than men, although again these differences are not statistically significant at conventional levels.
Adult’s skills. Note: The figure shows the average performance in three measures of skills in adulthood, computed for individuals aged 25 to 55 years old in 2015. Each variable is standardized (mean of 0 and a standard deviation equal to 1) for the entire sample as explained in the text Source: Authors’ own calculations based on ECAF 2015 (CAF-development bank of Latin America)
The gender gap in math, already evident in the PISA math scores at age 15, is also present among adults across most of the countries in the region as shown in Fig. 15b for the CAF assessment of numerical skills. While the largest gap in the math PISA scores was around a fifth of a standard deviation (Fig. 11), among adults the gap in numerical skills are 44% of a standard deviation in Bolivia, 33% for Panama, and 32% for Mexico. These gender gaps are statistically significant in all but one of the countries included in CAF; the exception is Argentina—despite being the country with the largest math gender gap among HI countries in the PISA test. The gender gap in adult math skills is likely to reflect gender differences in occupations which would then magnify any initial differences in math skills.
The most pronounced gender gaps in PISA scores are in reading and favors girls (Fig. 12). Among adults, however, the pattern of measured verbal skills—which may test different skills than those assessed in the PISA exam—is less clear-cut (Fig. 15c). In most countries women perform slightly worse than men in the CAF verbal conceptualization assessment, although the difference is statistically significant only in Bolivia, Colombia and Mexico. Again Bolivia stands out as the country with the largest gap in favor of men (41% of a standard deviation). At the other end of the spectrum, Argentina is distinguished by its low levels of gender inequality in adult skills.
The gender gap in self-confidence and expectations
How does the gender gap in mathematics relate to each gender’s perception of their ability in that field? Several studies, using different country data-sets, find that math self-perception is strongly and positively associated with subsequent achievement in math at all levels of the achievement distribution, even controlling for earlier attainment in math and various child characteristics (Marsh and Martin, 2011; Susperreguy et al., 2018). We use PISA data to examine whether there is evidence of gender differences in math self-perception and ask whether this perception is correct relative to performance.
In the 2012 round of PISA, students were asked to indicate how strongly they agreed with five statements relating to their math competence.16 The statements included the following:
I am good at mathematics.
I get good grades in mathematics.
I learn mathematics quickly.
I have always believed that mathematics is one of my best subjects.
In my mathematics class, I understand even the most difficult work.
The possible answers to these questions are: ‘very confident’, ‘confident’, ‘a little confident’ and ‘not confident at all’. We code the response of ‘not confident at all’ as zero, ‘a little confident’ as one, ‘confident’ as two, and ‘very confident’ as three. We use an individual’s responses to construct a math self-perception index by summing over the response scores and dividing that sum by 3 × 5 = 15. This results in an index with a minimum value of zero and a maximum value of one. Figure 16 shows the distribution of the math self-perception index separately for boys and girls. In both HI and UMI countries it is clear that across the distribution, boys have higher math self-perception than girls. This is consistent with the pattern in the OECD and is not surprising given that, as discussed previously and can be seen in Fig. 17, boys at this age outperform girls in math.
Math self-concept index distribution. Note: The figure shows, by gender, the distribution of the math self-perception index. the index is constructed based on each student’s responses to five questions related to their math competence, as defined in the text. the density functions depicted in panels (a) and (b) are obtained by pooling individual responses for all LA countries included in the analysis in the corresponding income group. Individual weights are reweighted to give equal weight to all countries in the same income group. The same procedure was followed to compute the density function for the OECD (panel c). Source: Authors’ own calculations based on PISA 2012
Math score distribution. Note: The figure shows, by gender, the distribution of the PISA math test score for the LA countries in the sample in (a) and (b), and the OECD countries in (c). The standard deviation of the test score distribution is 100 points. In (a) and (b) the density function is obtained by pooling individual responses for all countries included in the analysis in the corresponding income group. Individual weights are re-weighted to give equal weight to all nations in the same income group. The same procedure was followed to compute the density function for the OECD (panel c). Source: Authors’ own calculations based on PISA 2012
Figure 18a shows, however, that the math self-perception of girls is lower than that of boys even conditional on actual performance in mathematics. This figure plots the average self-perception index (multiplied by 100) for each percentile of the PISA score separately for boys and girls. Thus, each dot represents a cell defined by gender, and percentile of the PISA math score. The orange points (girls) tend to be below the blue points (boys) indicating that for each percentile of the score in PISA mathematics assessment, on average girls have lower math self-perception than boys across the region. This trend is highlighted by the line of best fit for girls being below than for boys (Lowess regression curves). Regression analysis further confirms that these differences are statistically significant. Appendix Table A.10 shows the results of regressing the math self-concept score on the math PISA score, a female dummy, and the interaction between the two for the whole of the region as well as for each of the countries separately. With the exception of Peru, in all LA countries the sum of the female dummy and the interaction effect is negative and statistically significant across the entire math score distribution. The positive coefficient on the interaction term, which is significant in the specification which pools all of the countries (column 1), further indicates that the gender gap in self-perception is narrower at higher levels of attainment. This can be seen in Fig. 18a panel a: at the bottom of the PISA math score distribution on average there is roughly a 10 point gender gap (equivalent to 21% of the mean), while among the highest achievers this gap is less than half that number.17 As can be seen in Fig. 18, a similar pattern holds for the OECD.
Math self-perception by percentile of PISA mathematics score distribution. Note: The figure graphs the average math self-perception index (as defined in the text and multiplied by 100) against each percentile of the PISA score distribution (x-axis). The relationship is shown separately for boys (blue) and girls (orange) in LA (Figure a), and in the OECD (Figure b). The blue (top) and orange (bottom) lines depict the Lowess regression curves illustrating the relationships between the self-perception index and the PISA score percentile for boys and girls, respectively
Self-perceptions of secondary school students matter as these may affect outcomes not only through impacts on achievement but also by shaping young people’s expectations and subsequent career choices, as shown by a growing eeeeiterature (Elsner and Isphording, 2017; Wiswall and Zafar, 2021). PISA data contain information on pupils’ occupational expectations, which we now turn to.
Figure 19 shows gender gaps in the share of 15 year-old students who report that they expect to work in a STEM occupation at age 30 (which includes working in science, engineering and information and communication technology). Across the LA countries included in PISA, only a small minority of girls (between around 10 and 20% in most countries) report that they expect to work in a STEM-related occupation. Boys are more than twice as likely as girls to report this expectation. A similar expectations gender gap exists in OECD countries. The gender gap in expectations is especially large in UMI countries; e.g. in Colombia, Mexico and Peru boys are around three times more likely to report that they expect to work in a STEM occupation than girls.
Expectations: Work in STEM-related occupations. Note: The figure shows the percentage of 15 year old students (PISA) who expect to work in a STEM-related occupation at the age of 30. STEM occupations are defined using the following 2-digit categories of the ISCO-08: 21 (science and engineering professionals) and 25 (information and communications technology professionals). The OECD average shows unweighted means. Source: Authors’ own calculations based on PISA 2018
How correlated might these expectations be with future occupation choices? In Fig. 20 we use CEDLAS household survey data from 2018 to document gender gaps in actual proportions of young workers (age 30–40) working in STEM occupations in 2018 in the countries included in PISA. The results indicate that the proportions of 30–40 year-old women working in STEM occupations are broadly similar to those found in the expectations data around the same period and does not exceed a fifth of working women in this age group, while the proportion of men is 2–3 times larger in many countries. Furthermore, the countries where the expectations gender gap is largest, such as Chile, Colombia, Peru and Mexico are also the countries where the gender gap in STEM occupations is largest.
Adults working in STEM-related occupations. Note: The figure shows the percentage of the employed population aged 30–40 years old who work in a STEM-related occupation. STEM occupations are defined using the following 2-digit categories of the ISCO-08: 21 (science and engineering professionals) and 25 (information and communications technology professionals). The year used in the calculations is 2018 (2017 for Chile). Source: Authors’ own calculations based on household surveys (GenLAC)
Women have the same capacity for science and technology as men. Note: Individuals aged 25–55 years old. This figure shows the percentage of individuals who agree or strongly agree with the statement ‘Women have the same capacity for science and technology as men.’ the average bars show unweighted means. Source: Authors’ own calculations based on Latinobarometro, 2018
Despite the pronounced gender gaps in math self-confidence and expectations regarding working in STEM occupations, as well as actual patterns of work in STEM occupations, there is widespread agreement in LA countries that women and men have the same capacity for science and technology. Using data from Latinobarometro, Fig. 21 shows that across the region in 2018 in the majority of countries, over 90% of respondents agreed or strongly agreed with the statement that ‘Women have the same capacity for science and technology as men.’ The Dominican Republic and Ecuador have the lowest levels of agreement in the region but even there the agreement rate is around 80%. Figure 21 also shows that this high level of agreement extends to both women and men with only small gender differences. There is also little evidence of systematic differences in this view either by education or age cohort (see Fig. A.6 in the Appendix).
Work
This section focuses on gender inequalities in the work sphere, including LFP, employment structure and wages. We assess how gender inequalities in the work sphere differ by education and how the presence of children affects women and men differentially. We also study the distribution of paid work hours and unpaid work hours spent on care and domestic chores. As in the prior section, we primarily utilize GenLAC–CEDLAS harmonized microdata from national household surveys and study the state in 2019 (the most recent year in our analysis) as well as the evolution of trends over the last 20 years. We benchmark our analysis against several countries outside of Latin America, including the US and, for some indicators, France and Spain.18
Labor force participation
Across all countries in LAC, over 90% of men between the ages of 25 and 55 were economically active, i.e. either working or actively looking for work in 2019 (Fig. 22). As in the rest of the world, the proportion of women in this category is significantly smaller (World Bank, 2011; Gasparini and Marchionni, 2015; Chioda and Verdú, 2016), ranging between an average of 63% in the LMI countries and 74% in the HI countries in the region. Comparing these rates to the U.S., although the rates of female labor force participation rates (FLFP) are similar to those in the US for the HI and UMI countries (but significantly lower in the LMI countries), men’s LFP is notably higher in LA. As a result there is a larger LFP gender gap throughout the region than in the USA.
Labor force participation in LA circa 2019. Note: This figure shows, by gender, the share of the population aged 25–55 years old that is economically active, as defined in the text. the average bars show unweighted means. Source: Authors’ own calculations based on LA household surveys (GenLAC) and the American community survey. Survey year is 2019 or the latest year available up to 2019 (see Table B.1 in Appendix B)
There has been a large increase in FLFP over the last 20 years, especially in the HI and UMI countries in the region (Fig. 23a). Since men’s LFP has remained constant over this period, the LFP gender gap has decreased significantly, particularly in the HI and UMI countries in the region where, on average, the gap has shrunk by 10 percentage points compared to the 5 percentage points decrease in LMI countries. In the US, on the other hand, the change has been much smaller (but women started at a higher level) and the gap has narrowed due both to male LFP falling and female LFP rising (Fig. 23).
Evolution of LFP in LA and USA. Note: These figures show the evolution of the share of the population aged 25–55 years old that is economically active, as defined in the text. In panel (a), each dot represents the (unweighted) cross-country average of their 5-year average. In panel (b), each dot represents the 5-year average for the US. Source: See note to Fig. 22. Only countries with available data in the corresponding periods are included (unbalanced panel in the case of LMI countries. See Table B.1 in Appendix B)
The aggregate patterns mask significant heterogeneity: across all LA countries there is a pronounced education gradient in FLFP, with much higher LFP among women with higher education levels. Figure 24 shows how these differ by country and education, comparing LFP across women with incomplete secondary schooling, complete secondary schooling and complete higher education. This gradient is similar across HI, UMI, and LMI countries in LAC (with the exception of Bolivia). The average gap of around 25 percentage points between the most and least educated women is comparable in magnitude to the LFP gender gap in HI and UMI countries. It is also comparable to (though slightly lower than) the equivalent gap between less and more educated women in the USA. The steep education gradient in FLFP is mirrored by a similarly steep education gradient in LFP gender gaps which are above 40 percentage points among the least educated group in the majority of countries, compared to a maximum of 16 percentage points among the most educated group (Fig. 25). The similarity in FLFP education gradient between LA countries and the USA is less evident in the education LFP gender-gap gradient: the difference in the size of the gap between the least and most educated groups is 2–3 times smaller in the USA than in most LA countries. It is interesting to note that at all levels of education, women in HI countries in LA have higher LFP than women in the USA. Thus, the difference in the average LFP stems from fewer women in LA, on average, obtaining higher levels of education than in the US.
Female LFP by education. Note: This figure shows, by education level, the share of the female population aged 25–55 years old that is economically active, as defined in the text. low refers to less than complete high-school; medium denotes high school graduates without higher education; and high indicates completed tertiary education. The average bars show unweighted means. Source: See the note to Fig. 22
Gender gaps in LFP (F-M) by education. Note: This figure shows, by education level, the gender gaps (F-M) measured in percentage points in the share of the population aged 25–55 years old that is economically active, as defined in the text. Low refers to less than complete high-school; medium denotes high school graduates without higher education; and high indicates completed tertiary education. The average bars show unweighted means. Source: See the note to Fig. 22
Evolution of female LFP by education. Note: These figures show, by education level, the evolution of the share of the female population aged 25–55 years old that is economically active, as defined in the text. low refers to less than complete high-school; medium refers to high school graduates without higher education; and high refers to completed tertiary education. In panel (a), each dot represents the (unweighted) cross-country average of their 5-year average. In panel (b), each dot represents the 5-year average for the USA. Source: See the note to Fig. 23
Disaggregating trends in FLFP over the last 20 years by women’s education shows that in HI and UMI countries, FLFP increased in all education groups, although slightly faster for women with less than tertiary education, which is as expected given the already high level at the beginning of the period for the latter (Fig. 26a). For LMI countries, it is notable that LFP has decreased among women with complete secondary schooling: they have gone from having a higher LFP than similarly educated women in LA 20 years ago to a lower level than these. For the least-educated women (those with incomplete secondary schooling), however, there is a clear positive gradient. In fact, the increase in the rate of FLFP in LMI countries in the last 5 years in Fig. 23a is driven entirely by improvements among women with the least-education, compensating for the relatively flat profiles of more educated women. Nevertheless, even among these women the rate of growth of LFP is lower than those of similar education in HI countries in the region. The patterns in the US are different: LFP among the most educated women increased during this period whereas the LFP of less educated women fell (Fig. 26). It is worth noting that while in the early 2000’s LFP among the least educated women in LA and US were comparable, the differential trends over the last 20 years have resulted in a higher LFP in this group even in the poorest LA countries than in the USA.
The analysis above pools women born in different years as each five year period considers women between the ages of 25 and 55. An alternative is to trace out the evolution of LFP across the life cycle of different cohorts. Figure 27 shows the life-cycle evolution of LFP for cohorts of women born between 1930 and 1989 in HI countries, pooling women born in the same decade.19 In line with the findings in Fig. 23a, there is a general pattern of higher LFP for more recent cohorts. For example, in Argentina, LFP for women born in 1980–89 is over 20 percentage points higher than that of the 1950–59 cohort at the age of 30–34. The increase in LFP across cohorts is broadly similar across UMI countries, with the exception of Panama where it has been noticeably less pronounced and recently in Chile where the most recent cohort has seen a very large increase in its LFP at young ages, allowing it to catch up to the higher levels reached by Argentina and Uruguay.
Female LFP over the life cycle across cohorts in high income countries. Note: This figure shows the share of the female population that is economically active, as defined in the text, over the life cycle and for six different cohorts (individuals born in 1930–39, in 1940–49, in 1950–59, in 1960–69, in 1970–79 and in 1980–89). Source: Authors’ own calculations based on IPUMS International’s harmonized census microdata. We use censuses from the 1960s up to the 2010s. The years of the censuses differ by country (see Table B.4 in Appendix Section B.7)
Broadly, there are similar trends in the UMI countries as in the HI ones, though starting from much lower LFP rates for the older cohorts (Fig. 28). There is some important variation across countries, however, with evidence of larger increases in Brazil and Ecuador than in the other countries. Colombia and Mexico stand out for the very small changes in LFP for younger cohorts and, in the Dominican Republic there is a pronounced decrease in LFP rates among women born in more recent decades.
Female LFP over the life cycle across cohorts in upper middle income countries. Note: This figure shows the share of the population that is economically active, as defined in the text, over the life cycle and for six different cohorts (individuals born in 1930–39, 1940–49, 1950–59, 1960–69, 1970–79 and 1980–89). Source: See note to Fig. 27
In line with trends in Fig. 23a, LFP across cohorts in the poorest countries in the region (Fig. 29) show smaller increases in LFP, with the marked exception of Bolivia. For example, in Guatemala LFP among those born in 1970s at age 30–34 was 15 percentage points higher than the 1940’s cohort; in Brazil and Argentina this gap is 40 percentage points. Honduras, El Salvador and Nicaragua also stand out for the lack of significant change and indeed decrease in LFP for the younger cohorts.
Female LFP over the life cycle across cohorts in lower middle income countries. Note: This figure shows the share of the population that is economically active, as defined in the text, over the life cycle and for six different cohorts (individuals born in 1930–39, 1940–49, 1950–59, 1960–69, 1970–79 and in 1980–89). Source: See note to Fig. 27
The Appendix shows these trends disaggregated by education (Figs. A.7–A.10). Across the region, in most countries FLFP is increasing most for the least educated women, especially in the HI and UMI countries. For example, in Argentina FLFP among least educated women in the 35–39 age group increased by around 35 percentage points between the 1940’s and 1970’s cohorts, compared to less than 20 percentage points for women in these cohorts with complete secondary education and to 15 percentage points for those with higher education.
How do the trends in LA countries compare to other countries around the world? Figure 30 shows the evolution of FLFP across cohorts in US, France and Spain. Although FLFP in the USA among earlier cohorts was significantly higher than in any of the LA countries, the gap between the US and especially HI countries in LA has shrunk considerably due both to the increase in FLFP in the latter and its stagnation in the US. In contrast, both in France and Spain, FLFP has continued to grow, evidencing a 20 percentage points higher participation rate than even those in highest FLFP LA countries. The case of Spain is particularly interesting since LFP among the earlier cohorts were comparable to those in some of the poorest LA countries. Spain then saw larger increases in FLFP between each subsequent cohort than in any other LA country, overtaking all of them, as well as the USA.
Female LFP over the life cycle across cohorts in comparison countries. Note: This figure shows the share of the female population that is economically active, as defined in the text, over the life cycle and for six different cohorts (individuals born in 1930–39, 1940–49, 1950–59, 1960–69, 1970–79 and in 1980–89). Source: See note to Fig. 27
Hours worked (2019). Note: Individuals aged 25–55 years old. The figure shows, by gender, the average weekly number of hours worked in the market, including all jobs, conditional on working. The average bars show unweighted means. Source: See note to Fig. 22
Evolution of the number of hours worked. Note: Individuals aged 25–55 years old. These figures show the evolution of the average weekly number of hours worked in the market, including all jobs, conditional on working. In panel (a), each dot represents the (unweighted) cross-country average of their 5-year average. In panel (b), each dot represents the 5-year average for the USA. Source: See note to Fig. 22. Only countries with available data in the corresponding periods are included (unbalanced panel in the case of LMI countries. See Table B.1 in Appendix B)
Gender gaps in the number of hours worked (F-M) by education. Note: Individuals aged 25–55 years old. This figure shows, by education level, the gender gap (F-M) in the weekly number of hours worked in the market, including all jobs. Low refers to less than high-school education; medium denotes high school graduates without higher education; and high indicates completed tertiary education. The average bars show unweighted means. Source: See the note to Fig. 22
Weekly non-market working hours. Note: Individuals in couples aged 25–45 years old. The letter ‘E’ over a bar indicates average values for employed women or, in the case of men, for those men whose partner is employed. time spent on care activities and household chores is derived from time use surveys. The value of the variable is set equal to zero when the individual does not do participate in an activity. Source: Authors’ own calculations based on time use surveys (GenLAC). The year of the time use surveys ranges from 2010 to 2017 (see tables B.4 and B.1 in appendix B, respectively)
Market and non-market weekly working hours (ratio F/M). Note: Individuals in couples aged 25–45 years old. Non-market hours include care activities and household chores. Bars in dark colors show values for all individuals. Bars in lighter colors show the ratio for employed women relative to men whose partners are employed. The value of the variable is set equal to zero when the individual does not do participate in an activity. Source: See note to Fig. 34
The profile of work
In this section we examine gender gaps in the intensive margin (time worked) in market work, in work outside the market and total work hours. We also examine the gender gap in whether work is classified as wage employee, employer, unpaid worker or self-employed. Lastly, we document the gender gap in employment in the formal versus informal sector as well as firm size, as these are dimensions that correlate with job quality.
Several of the outcomes analyzed in this section are conditional on working. The women who select into working may be different from the men who do so. For example, we show in 25 that while female LFP is higher among more educated women, this is not the case for men. The analysis in this section does not allow to infer exactly how selection may be affecting the patterns that we present as selection may be driven by both observable and unobservable characteristics. Nevertheless, it is important to bear in mind that gender differences in who selects into working are likely to be contributing to some of the patterns shown in this section.
Hours worked in the market
Among those age 25–55 who were employed in 2019, men work longer hours on average than women in all LA countries (Fig. 31). There has been a decline in the number of hours worked over the last 20 years (since 2000), for both men and women. This can be seen most starkly for men in UMI and HI countries, where the gender gap in work hours has shrunk slightly as a result (Fig. 32a). The gender gaps in the average number of hours worked range from a maximum of 12.3 h in Guatemala to 4.7 h in El Salvador. While work hours are on average higher in poorer countries, gender gaps are similar in magnitude across poorer and richer LA countries (Fig. 31). Both the average number of hours worked and the gender gap in the HI LA countries are similar to those in the USA.
As in the case of LFP, there is a steep education gradient in the gender gaps in hours worked: across HI, UMI and LMI countries, the gender gap in working hours among those with tertiary education is around half of that among those who have not completed high school. This education gradient is steeper than in the USA (see Fig. 33).
Hours worked outside the market
The preceding subsection examined hours worked in the market, excluding time spent on non-market work such as care activities and household chores. As is well documented in the literature, around the world the burden of these activities falls disproportionately on women (Charmes, 2019). Thus patterns of gender differences in hours worked are likely to differ significantly depending on whether work includes non-market work. We use harmonized data from time-use surveys available for a subset of countries processed by GenLAC to document patterns in hours worked once non-market work is included in work hours.20 In this analysis we focus on married or cohabiting individuals age 25–45.21 We refer to this group as married individuals throughout this section for brevity.
Figure 34 shows total hours spent on non-market work in housework and care activities by married men and women in each of the LA countries for which data is available and the US. We restrict the sample to individuals between the ages of 25–45.22 Bars with the letter ‘E’ show the total hours spent by employed married women on non-market work. The bars without the ‘E’ are for all married women regardless of their employment status. Men’s average hours spent on non-market work are shown in the last two bars on the right for each country. For men, the ‘E’ indicates that their female partner is employed.
As can be seen in the figure, married women in LA spend from around 40 to 70 h a week on unpaid non-market work. This is comparable and, for many countries even higher, than the time that men spend on market work as shown in Fig. 31. Employed women tend to spend slightly less time on non-market work, with hours ranging between 30 in El Salvador and 60 in Mexico. With the exception of Paraguay, at least half of the non-market work time is spent on housework across the region. On the whole, women tend to spend more time on non-market work in HI and UMI countries compared to LMIs countries. The experience of married women across LA contrasts with that of their US counterparts who, on average, spend much less time on non-market work at just under 30 h per week (21 h among employed women).
In all countries, men in couples spend less than half the time on housework and care activities than women. In most countries this proportion is closer to a third ranging between 10 h in Ecuador and Guatemala and around 27 h in Chile. Across the region, the time spent on non-market work by women exceeds that spent on these activities by men by significantly more than the excess number of hours that men spend on market work compared to women shown previously (Fig. 31), i.e. the total number of hours worked is greater for women. Furthermore, though employed women spend less time on non-market work than women who are not employed, their male partners do not compensate by increasing their non-market work hours as can be seen by comparing the bar without an ‘E’ to that with an ‘E’ for men. The difference in non-market work hours between men in LA versus in the US is much smaller than that for LA women compared to US women.
This analysis clearly shows that focusing on time spent on market work only provides a partial view of gender inequality in work time. In order to paint a more complete picture, Fig. 35 aggregates time-use survey data on both market and non-market activities to show how women fare relative to men in each of these individually and the two combined. A ratio of less than one indicates that women spend less time on a given activity than men. Darker columns show ratios for all married men and women, while lighter ones include only employed women (in couples) and men whose female partner works.
In line with results presented above in Figs. 31 and 34, the gender ratio in market work is below one, indicating that women spend less time in market work, whereas the ratios are considerably higher than one for non-market work time. The difference in the two ratios tend to be smaller for employed women and men with employed women as partners. In virtually all countries in the region, the combination of women spending less time on paid work and a lot more time on non-market work than men translates into a ratio above one for total hours worked. Appendix Table A.11 shows that across the region this difference is statistically significant. The ratio is highest in Chile where women spend around 75% more total time on work than men and lowest in El Salvador and Peru. The ratios increase slightly when conditioning on women being employed. The ratio of market work hours in the USA is similar to that of LA countries in the HI group, but household chores and care activities are more evenly distributed, with women spending double the hours that men do. Overall, the total hours ratio is below one indicating that, unlike in LA, US women spend less time on the sum of market and non-market work than men.
Figure 36 shows how patterns differ by education. Panels (a), (c) and (e) show results for the sample of individuals in couples aged 25–45, as before. Across the region, non-market work times are highest among individuals in the lowest education group. Whereas women in this group tend to spend somewhere between 3 and 6 times as much time on non-market activities as men, the difference is 2–3 times among those in the most educated group. The exception to this is Guatemala where women with higher education spend over 6 times as much time on non-market activities as men. In line with Fig. 33, the gradient goes in the opposite direction for market work: in all countries the hours that women spend on market work are closer to those done by men for those with higher education relative to those with less education.
Market and non-market weekly working hours, by education (ratio F/M). Note: Individuals in couples aged 25–45 years old. Figures (a), (c) and (e) show values for all individuals. Figures (b), (d) and (f) show the ratio for employed women to men whose partners are employed. Low refers to less than high school; medium denotes high school graduates without higher education; and high indicates completed tertiary education. Non-market hours include care activities and household chores. The value of the variable is set equal to zero when the individual does not do participate in an activity. Source: See note to Fig. 34
Share of employed in each type of job. Note: Individuals aged 25–55 years old. For each sex, the figures show the share of employed individuals in each of the following four categories, as defined in the text and depending on what the individuals report their main job to be: (a) wage employees, (b) employers, (c) unpaid workers, (d) self-employed. For the USA, we only present statistics for wage employees and those in self-employment as the ACS data we use does not have a code for employers and the share of unpaid workers is nearly negligible (less than 0.002 within our sample). In the US, the self-employed category encompasses both incorporated (4.1% for males & 2.4% for females) and not incorporated self-employment (6% for males & 4.9% for females). The average bars show unweighted means. Source: See note to Fig. 22
Share of employed in informal sector. Note: Individuals aged 25–55 years old. The figures show the share of employed individuals working in the informal sector, as defined in the text. In panel (b), low refers to less than high-school education; medium denotes high school graduates without higher education; and high indicates completed tertiary education. The average bars show unweighted means. Source: Authors’ own calculations based on LA household surveys (GenLAC). Survey year is 2019 or the latest year available up to 2019 (see Table B.1 in Appendix B)
Gender gap in informality by education (F-M). Note: Individuals aged 25–55 years old. The figures show, by education level, the gender gap (F-M) measured in percentage points in the share of employed individuals who are working in the informal sector, as defined in the text. low refers to less than high-school education; medium denotes high school graduates without higher education; and high indicates completed tertiary education. In panel (a) the average bars show unweighted means. In panel (b), each dot represents the (unweighted) cross-country average of their 5-year average. In panel (a), the survey year is 2019 or the latest year available up to 2019. In panel (b), only countries with available data in the corresponding periods are included (unbalanced panel in the case of LMI countries). See Table B.1 in Appendix B). Source: See note to Fig. 38
Share of employed who work in large firms. Note: Individuals aged 25–55 years old. The figures show the share of employed individuals who work in large firms, as defined in the text. In panel (b), low refers to less than high-school education; medium denotes high school graduates without higher education; and high indicates completed tertiary education. The average bars show unweighted means. Source: See the note to Fig. 38
Combining paid and unpaid hours, there is much less evidence of systematic variation in the F/M ratio in total hours worked (blue column in Fig. 36) across education groups. This is consistent with education gradients that go in opposite directions for paid and unpaid work. In the countries where we see some differences, it tends to be the case that the ratio of total hours women spend on work relative to men is highest among the least-educated group. This is the case in Argentina and Chile, e.g.
Education gradients in female to male ratios of time spent on paid and unpaid work are much less pronounced in the sub-sample of employed women and men whose partners are employed (panels b, d, f in Fig. 36). This is especially true for time spent on market work. As before, overall, in most countries we do not see large differences in female to male ratio of total time spent on market and non-market work between education groups.
Job categories
There are some important differences in the types of jobs that men and women do. We categorize employed individuals into four types, depending on what they report their main job to be: wage employees, employers, unpaid workers or self-employed. Unpaid workers include individuals working on a family farm or business (mostly in retail) without receiving a wage. Figures 37a–37d show that, in all countries, women are less likely to be employers than men and much more likely to be unpaid workers. In some countries, such as Peru, Bolivia and Ecuador, nearly a fifth of working women have unpaid jobs compared to less than 3% of men whereas in most HI countries there are few unpaid workers of either sex.
There is a less consistent pattern of gender differences in wage employment and self-employment across countries. In HI countries and about half of the UMI countries, women are more likely than men to be wage employees and men are more likely than women to be self-employed. Across both categories, in the majority of these countries the gender gaps are fairly small. This is also the pattern that we see outside of LAC, for the USA. This pattern is reversed, however, in LMI countries, as well as in Ecuador, Mexico and Peru from the UMI country group. On average, in LMI countries, the proportion of women is 10 percentage points lower than that of men in wage employment and ~7 percentage points higher in self-employment.
Job quality: informality and firm size
An important dimension of jobs in LA is whether they are in the formal or informal sector. Those in the informal sector tend to have less employment protection, fewer formal rights and fewer entitlements to in-work benefits. In this analysis we define wage workers without pension rights, non-professional self-employed and all unpaid workers as working in the informal sector (ILO, 2013).
Overall, informality is much more widespread in the poorer countries in LA (see Perry et al., 2007; Gasparini and Tornarolli, 2009) for thorough descriptions of labor informality in LAC). Across the LMI countries, around two-thirds of men and women work in the informal sector as their main job, compared to around a third in the HI countries. The largest gender gap in the informal sector is in Peru where 71% of working women report their main job to be in the informal sector, compared to 59% of working men (Fig. 38a).
There is a great deal of variation in informal sector employment by education. As shown in Fig. 38b, less-educated women are much more likely to work in the informal sector than women with a tertiary education. In most LA countries, the main job of the majority of working women with incomplete secondary school education was in the informal sector in 2019, compared to no more than a quarter of women with complete higher education. We also see much larger gender gaps in informality rates among the less-educated workers compared to those with higher education (see Fig. 39a). Indeed, in most countries, for those individuals with a tertiary education the share of men in the informal sector is greater than that of women. On the whole, the gender ratio in the informal sector has been stable for the last 20 years, though there was a period in the early 2000’s when in the UMI countries there was a big decline in this ratio among the least-educated group; that trend has reversed in the more recent years bringing the gap back up to the level of those with complete secondary education (Fig. 39b).
Another dimension that is likely to capture variation in job quality is firm size. Working for larger firms may provide several benefits, including more opportunities for progression within the firm and a lower risk of losing one’s job. Larger firms tend to also be more productive in LA and thus pay higher wages (Eslava et al., 2021). We define a ‘large’ firm as one with more than five employees as this is the measure available for all of the countries in the harmonized data-set. Overall, we see that both men and women are much more likely to be working in a large firm in the richer than the poorer countries in LA (Fig. 40a). With the exception of the Dominican Republic, a greater proportion of employed men report working in a large firm than employed women. On the whole, the size of the gender gap in this dimension is similar across poorer and richer countries. There is a steep education gradient in all of the countries; in most countries over half of working women with tertiary education are employed in large firms compared to between a tenth (in LMI countries) and a quarter (in HI countries) of women without a high school degree (see Fig. 40b). This is also the group with the largest gender gap as shown in Fig. 41a. For example, in high income countries, on average, the proportion of women without a secondary school degree working in large firms is 20 percentage points lower than that of men in this education group, whereas women with higher education are as likely to work in large firms as men. Figure 41b shows that over the last 20 years gender gaps in the likelihood of working for a large firm have, on the whole remained stable or declined slightly.
Gender gap in working for larger firms (F-M), by education. Note: Individuals aged 25–55 years old. The figures show, by education level, the gender gap (F-M) measured in percentage points in the share of employed individuals who work in larger firms (i.e. those with at least five employees). Low refers to less than high-school education; medium denotes high school graduates without higher education; and high indicates completed tertiary education. In panel (a) the average bars show unweighted means. In panel (b), each dot represents the (unweighted) cross-country average of their 5-year average. Source: See the note to Fig. 38
Wages
Next we turn to remuneration. We start by examining gender differences in hourly wages of workers (including those who are self-employed) between the ages of 25 and 55. Figure 42 shows that on average in LAC, as in virtually everywhere, women earn a lower hourly wage than men. For example, in HI LA countries the average hourly wage of a woman is 90% of a man’s; in the USA, it is 84%. There are, however, several countries where the average hourly wage for men and women is roughly equal (Argentina, Colombia, Guatemala and Nicaragua) or even slightly higher for women (Costa Rica and Honduras).
Figure 43 shows how female/male wage ratios differ by levels of education. We do not see a strong consistent trend. In several countries the gender wage gap is constant across education levels. These include Colombia, Costa Rica, Peru, Honduras and Nicaragua. This is similar to what we see for the USA. In Brazil, Chile, Guatemala and El Salvador the gender wage gap increases with education. For example, while there is no gender wage gap among workers with incomplete high school education in Guatemala, working women with completed tertiary education earn only 70% of what men with this level of education earn. We see the reverse pattern in Argentina, the Dominican Republic, Bolivia and Paraguay where the gender wage gap is higher among the less educated workers.
In order to explore which characteristics might be contributing to the gender wage gaps across the region, we decompose the gaps by estimating wage models which include controls for (i) basic individual characteristics of workers: age, region of residence and whether living in a rural area; (ii) education; (iii) employment sector and occupation, as well as whether in full time work and whether working in the informal sector (see notes for Fig. 44 for exact definitions). We use the Oaxaca-Blinder decomposition method in order to assess the role that these characteristics play in explaining the gender gap, as well as how much of the gap remains even after taking differences in these characteristics between working men and women into account (Oaxaca, 1973).23
Figure 44 shows female to male log wage ratios unadjusted and adjusted for the characteristics listed above (basic characteristics, education, job characteristics). Mirroring the patterns in Fig. 42, the unadjusted log wage ratio is below 100% in most countries, with lowest ratios in Peru, the Dominican Republic and Bolivia. In almost all countries the adjusted female to male log wage ratio is lower than the unadjusted ratio. Across the majority of countries the difference ranges between around 4.5 and 17 percentage points. The clear outlier is Honduras where the difference is 57 percentage points; the unadjusted ratio here suggests that working women’s wages are 30% higher than men’s, while adjusting for covariates reduces that by 57 percentage points to 26% lower than men’s, which is the highest adjusted gender wage gap in the region. Countries in which the unadjusted and adjusted ratios are closest include Chile, Dominican Republic and Bolivia. In addition to Honduras, countries with some of the biggest decreases in the ratio after adjustment for covariates include Nicaragua and Panama. The exception is Peru where adjusting for covariates does not change the ratio.
The striking finding that, with the exception of one country, the gender wage gap increases when characteristics of workers are included indicates that far from explaining the gender wage gap, differences in these characteristics mask the much larger wage gaps that would exist if working women were more similar to working men along these dimensions.24 Although the USA has similar gender wage gaps as HI countries in LA, adjusting for covariates in the USA results in a reduction in the gender wage gap (albeit a very small one) indicating that in the USA at least some of the gap is due to women differing in characteristics that yield higher wages.
Mean hourly wage in LA (2019). Note: Individuals aged 25–55 years old. This figure shows, by gender, the mean hourly wage of workers (2005 PPP USD). The average bars show unweighted means. Source: See note to Fig. 22
Gender wage gap in mean hourly wages (F/M) by education. Note: Individuals aged 25–55 years old. This figure shows, by education level, the F/M ratio of mean hourly wage (2005 PPP USD). The average bars show unweighted means. Low refers to less than high-school education; medium denotes high school graduates without higher education; and high indicates completed tertiary education. The average bars show unweighted means. Source: See note to Fig. 22
Oaxaca-blinder decomposition: Female to male log wage ratio (%). Note: Workers aged 25–55 years old, working at least 20 h a week. The model used for the Oaxaca-blinder decomposition controls for age, age squared, region of residence, an indicator for living in a rural area, education, sector, occupation (2-digits codes ISCO), an indicator for full-time worker (35+ h a week) and another for working in the informal sector. The unadjusted gender wage gap represents the female-to-male log wage ratio, multiplied by 100, and it is calculated as the inverse of the exponential of the log-point values shown in Table A.12 in Appendix, Column 6. Lighter bars show the adjusted female-to-male log wage ratio after accounting for covariates and are calculated as the inverse of the exponential of the values shown in column 5. The values shown in this figure are also displayed in columns 13 and 14 of Table A.12 in Appendix. The average bars display unweighted means. Source: See note to Fig. 22
Evolution of the unadjusted and the adjusted female to male log wage ratio (%). Note: The figure shows the evolution of the unadjusted and adjusted female-to-male log wage ratios (%) computed from the Oaxaca-blinder decomposition, as explained in the note to Fig. 44. Each bar represents the (unweighted) cross- country average of their 5-year average. Only countries with available data in the corresponding periods are included (unbalanced panel in the case of LMI countries and missing data on occupation in UMI countries before 2010. See Table B.1 in Appendix B). Source: See note to Fig. 22
In order to examine these findings further, Appendix Table A.12 shows the contribution of different characteristics to the size of gender wage gap, based on the Oaxaca-Blinder decomposition. Column 6 presents the raw differences in log wages between males and females, while Column 4 shows the log points that are explained by all the gender differences in characteristics considered in the model. Column 5 represents the log point differences that remain unexplained. Columns 1–3 show how male–female differences in age and location (Column 1), education (Column 2) and job characteristics (Column 3) translate into differences in their log wages. For each column, the values are computed by multiplying the male–female mean difference in the characteristic by the respective male coefficients from the wage regression (refer to footnote 21 for more details). Columns 7–9 then show the proportion of the total gender log wage gap that is explained by these differences.25 For example, in the case of Bolivia (first row), we see that differences in age and location between working men and women contributes 0.01 log points to the gender wage gap (column 1), which is equivalent to 5% of this gap (Column 7). In line with Fig. 44, most entries in this table are negative numbers showing that women have higher levels of the characteristics that contribute positively to wages. For example, the results in column 2 for Chile show that the difference in men and women’s education is responsible for −0.03 log points in wages. The negative value is due to the fact that, on average, working men have less education than working women. Column 8 then shows that if Chilean women had the same level of education as men on average, then at current wages the gender wage gap would widen further by 23%.
The fact that the great majority of the values in all cells in Columns 1–3 and 7–9 are negative indicates that in most countries if working women had the same characteristics as working men, there would be a further widening of the wage gap. Overall, with the same characteristics, Column (11) shows that, in most countries, the gender wage gap would increase by at least 50%. Thus the gender wage gap in LA countries is driven by the ‘unexplained’ component i.e. by differences in the wages paid to men and women with the same characteristics. Of course, our data does not allow us to observe all characteristics of the individual or the job/occupation and factors such as experience in the workplace or finer occupational categories could decrease the unexplained portion of the wage gap significantly.
The case is different in the USA. Although there, as in Latin America, women are more educated than men, differences in job characteristics account for nearly a quarter of the US gender wage gap. This is why, for the USA, Fig. 44 shows a slightly higher adjusted than unadjusted F/M log wage ratio, but not for LA countries. Figure 45 shows that there has been little change in the unadjusted and adjusted female to male log wage ratios over the 20 years between 2000 and 2019 in LAC. Both the adjusted and unadjusted ratios have remained remarkably stable over this period across the LA region. If anything, the unadjusted gap has grown slightly in LMIs while for most of the period the adjusted gap has remained constant. We see more evidence of a trend over time in the US. Here both the unadjusted and adjusted ratios have grown slightly over time indicating a gradual narrowing of the gender wage gap. The unexplained component has remained constant as there has been little change in the difference between the unadjusted and adjusted gender wage gap.
Children
In our preceding analysis of wages and LFP, we did not include either marital status or the number of children as potential explanatory variables as they are likely to be endogenous to these outcomes. Parenthood has been found to be associated with a widening of gender gaps in the labor market globally. There are strong negative associations between having children and labor market outcomes for women which are absent for men. This finding holds even in countries considered to have some of the most gender-equal norms and strong public provision of child-care, such as Denmark (Kleven et al., 2019). Here, we present some descriptive analysis of heterogeneity in LFP and wages across women with and without children, as well as between mothers and fathers.
Panel a of Figure shows FLFP in 2019 across LA countries among 25–55 year olds, distinguishing between women without children, those with children age 0–5 and those with older children (age 6–15). Across the region we see the expected pattern of lower LFP among women with children than without, with by far the lowest rates among women with young children. On average, in LMI countries in the region there is a 17 percentage points gap between the LFP of women without children and those with young children. This gap is a bit smaller, but comparable, in the wealthier countries. In half of the LMI countries, a strikingly low proportion (less than 50%) of women with small children were in the labor force in 2019.
While there is some, though not complete recovery, in LFP as children get older, a parallel recovery is not observed in hourly wages for most countries. Panel b of Fig. 46 shows that on average, across LAC, hourly wages among working women without children are higher than those of working women with children. In many of the countries, especially richer ones, working women with young children have a higher wage than working women with older children. This may reflect various factors: women with younger children are themselves younger, on average, and thus likely to have more education and be in more highly-paid occupations. This is not the pattern that we see in the poorer countries: there, women with older children have the same or a higher wage than women with younger children and in Paraguay and El Salvador there is almost no difference in the hourly wages of the three groups of women.
Female LFP, wages, and children. Note: Women aged 25–55 years old. Lighter olive bars show the LFP rate (panel a) and average wage (panel b) of women without children; the medium olive bars show LFP (panel a) and wages (panel b) of women with children aged 0 to 5 years old; the darker olive bars show the LFP (panel a) and wages (panel b) of women with children aged 6 to 15 years old. Wages are measured in 2005 PPP USD. The average bars show unweighted means. Source: Authors’ own calculations based on household surveys (GenLAC). Survey year is 2019 or the latest year available up to 2019 (see Table B.1 in Appendix B)
Parenthood and LFP. Note: These figures show, for men and women, the estimated impact of children on LFP, from equation 1 given the matching methodology described in the text. As the omitted category is τ = −1, the scaled coefficients show the impact of children as a percentage of the counterfactual relative to the year preceding the birth of the first child. Controls include calendar year and age fixed effects. Sample restriction: Age for first birth is 25 to 45. The figure displays the 90% confidence intervals. Source: Authors’ own calculations based on household surveys (GenLAC). Years 2003–2019
Parenthood and earnings. Note: These figures show, for men and women, the estimated impact of children on labor market earnings, from equation 1. As the omitted category is τ = −1, the scaled coefficients show the impact of children as a percentage of the counterfactual relative to the year preceding the birth of the first child. Controls include calendar year and age fixed effects. Sample restriction: Age for first birth is 25 to 45. The figure displays the 90% confidence intervals. Source: See note to Fig. 47
A woman should work only if her husband doesn’t earn enough (% disagreeing). Note: Individuals aged 25–55 years old. This figure shows the percentage of individuals who disagree or strongly disagree with the statement ‘A woman should work only if her husband doesn’t earn enough’ in 2008 and 2015. The different colors refer to the different country groups: HI, UMI and LMI. Source: Authors’ own calculations based on Latinobarometro, 2008 and 2015
A woman should work only if her husband doesn’t earn enough: % disagreeing. Note: Individuals aged 25–55 years old. This figure shows the percentage of individuals who disagree or strongly disagree with the statement ‘A woman should work only if her husband doesn’t earn enough’. The average bars show unweighted means. Source: Authors’ own calculations based on Latinobarometro 2015
Clearly across most countries in the region there are significant differences in wages of women with and without children. Next, we explore how women with children fare in the labor market relative to men with children. We adopt an event-study methodology following the approach taken in several recent studies of the impact of children on the gender wage gap (Fig. 46) to quantify the gaps that emerge between men and women after the birth of the first child and how these evolve over the years following that.
In order to do this we create pseudo-panels (Kleven et al., 2024) for each of the countries in our analysis using multiple rounds of cross-sectional data from household surveys.26 We build the pseudo panels by matching individuals on age and location: A parent observed at time t is matched to a childless individual in the same region who is p years younger and was observed in the cross section data p years before.27 This yields the proxy observations for t = −p.28
Using this pseudo-panel we estimate the following model separately for men and women:
where yit is the outcome of interest (LFP and earnings) for individual i at time t. The first term on the right-hand side is a set of event time dummies, τ = kit, which indicate the years relative to the birth of the first child of individual i at time t. The events τ ≥ 0 capture the post-child effects relative to the base year that is τ = −1, i.e. 1 year before the first child was born. The second and third term are a full set of age-in-years dummies and calendar year dummies to control non-parametrically for life-cycle trends and time trends. We scale βτ to show our results as a percentage effect compared to the counterfactual outcome without children predicted by the estimated model (see Kleven et al., 2019).
Figures 47a–47c plot the results for LFP. In all of the countries we see very similar trends in LFP for men and women in the 3 years before the birth of the first child, controlling for life-cycle and time-trends, followed by a sharp divergence after the birth of the first child. This divergence is driven entirely by a sharp drop in women’s LFP, which is between 20–30% in HI countries (similar to the 25% estimated for the US in Kleven, 2022) and 30–40% in UMI and LMI countries. There is evidence of some recovery over the 5 years following the birth of the child in the majority of UMI and LMI countries but not in the HI countries.29 At the end of the 5-year period, therefore, the decline in the LFP of mothers looks more similar across HI, UMI and LMI countries. There is no evidence of any decline in men’s LFP around the time of the birth of their first child or in the 5 years after that.
In line with the findings for LFP, Figs. 48a–48c show similar evolution in earnings among men and women before the birth of the first child, followed by a sharp drop for women but not for men after this event.30 In fact, in several of the countries, such as Argentina and Bolivia, we see an increase in male earnings in the first few years following the birth of the first child. The drop in women’s earnings varies in magnitude across countries, ranging between around 50% in El Salvador and Bolivia to around 20% in Argentina and Colombia; as with LFP, it tends to be larger in poorer countries. Although there is some recovery in LFP, especially in the poorer countries, this is not reflected in earnings. In several countries including Argentina, Brazil and Mexico, women’s earnings decline further over this period. There are a few exceptions; some recovery of around 10 percentage points is evident in Ecuador, the Dominican Republic, Peru and El Salvador. In most countries, the gap between men and women remains constant over the 5 years after the birth of the child as there is also a slight reduction in male earnings.
It is important to interpret what has been called the ‘motherhood penalty’ correctly. The fall in women’s earnings does not necessarily reflect discrimination on part of the employer, though this may account for some of it. A significant part is driven by women, and not men, deciding to leave the labor force once they become mothers, or by reducing their work hours, or by switching to a more flexible (and often less well-paid) job. These decisions themselves are driven by factors such as the availability, affordability and quality of childcare as well as by expectations (and individual preferences) regarding mothers’ versus fathers’ roles in the care of their child.
Gender roles
Beliefs about how men and women should behave in various environments both within and outside the home affects how individuals act, their aspirations and the opportunities that are open to them, with important consequences for gender equality and the economy (see Fernández and Fogli, 2009; Fernández et al., 2021). Here we use nationally representative polls from Latinobarometro to examine on how women and men are viewed in several spheres related to work.
Latinobarometro asks respondents to indicate the extent to which they agree with several statements regarding beliefs about the appropriateness of women working and their competence in business and politics relative to men. Specifically respondents are asked to indicate whether they strongly agree, agree, disagree or strongly disagree that ‘A woman should work only if her husband does not earn enough’; ‘Men make better business executives than women’; and ‘Men make better political leaders than women.’
We start by documenting variation in responses to these statements across countries and over time. We then examine cross-country patterns in gender gaps in the responses, as well as how these gender gaps differ across education groups and cohorts. We end by showing a strong negative correlation between the gender gap in LFP (M-F) and the extent to which social norms regarding women’s work are gender-equal.
Figure 49 plots the proportion of respondents age 25–55 who disagreed that a woman should work only if her husband does not earn enough in 2008 and 2015.31 Note that a higher level of disagreement with this statement indicates more gender-equal the norms. Turning first to the more recent year (2015), we see that countries vary significantly in their degree of disagreement, ranging from around 33% in Guatemala to over 80% in Brazil and Chile. There is a clear pattern of more gender-equal norms in higher-income countries. Whereas in LMI countries between around 35 and 60% of individuals disagree with this statement, in HI countries this range is between just under 70 and around 85%. Panama is an outlier among the HI countries with significantly less gender-equal norms as indicated by only 45% disagreeing. Over time (between 2008 and 2015), beliefs in most countries have become more gender-equal as demonstrated by the fact that most data points lie above the 45 degree line. This trend is evident across poorer and higher-income countries. For example, in Brazil the degree of disagreement increased from just over 60% to nearly 85%, whereas in Nicaragua it went from around 45 to nearly 60%. There are exceptions to this trend in each of the income groups. In Guatemala, Panama and Mexico the degree of disagreement has decreased over time. Panama stands out again as the biggest outlier with a very substantial decrease of around 20 percentage points.
Figure 50 disaggregates the aggregate proportion that disagrees in 2015 by gender. On average, men have less gender-equal views than women (lower disagreement rate). The gender gap is largest in HI countries, with the exception of Chile, and on average the proportion of men disagreeing with the statement is around 10 percentage points lower than among women. The average gender gaps in opinion are smaller in LMI and UMI countries and there is a sizable group of countries, including four out of the seven UMI countries, where the degree of disagreement is either very similar among men and women or even lower for women.
Throughout the region, without exception, there is a steep education gradient in attitudes towards women working outside the home (Fig. 51). Those with more education—completed tertiary—have more gender-equal norms compared to those in the least educated group (incomplete secondary). On average this education gap is largest in LMI countries at over 20 percentage points compared to close to half of that in HI countries. Several countries in the UMI group have gaps closer to the average for LMI countries. These include Colombia, Costa Rica and Mexico. A number of countries across the income groups stand out for having especially gender-unequal beliefs (low rates of disagreement) among the less educated group. In Panama, Mexico, Guatemala, Honduras and El Salvador, less than half of those with incomplete secondary education disagreed with the statement that a woman should only work if her husband does not earn enough. In Guatemala this proportion is around a third.
A woman should work only if her husband doesn’t earn enough: Cohort and education. Note: Individuals aged 25–55 years old. This figure shows, by education level and cohort, the percentage of individuals who disagree or strongly disagree with the statement ‘A woman should work only if her husband doesn’t earn enough’. ‘Low edu’ refers to individuals with less than a high school education; ‘medium edu’ denotes high school graduates without higher education; and ‘high edu’ indicates those who have completed tertiary education. Source: See note to Fig. 50
Gender gap in LFP (M-F) and percent disagreeing with statement ‘A woman should work only if husband doesn’t earn enough’. Note: Individuals aged 25–55 years old. This figure shows the correlation between the percentage of individuals who disagree or strongly disagree with the statement ‘A woman should work only if her husband doesn’t earn enough’ and the gender gap in LFP (M-F) measured in percentage points). Source: Authors’ own calculations based on LA household and time use surveys (GenLAC, several years) and Latinobarometro 2015
Men make better business executives than women (% disagreeing). Note: Individuals aged 25–55 years old. This figure shows the percentage of individuals who disagree or strongly disagree with the statement ‘Men make better business executives than women.’ Source: Authors’ own calculations based on Latinobarometro, 2012 and 2019
Men make better political leaders than women (% disagreeing). Note: Individuals aged 25–55 years old. This figure shows the percentage of individuals who disagree or strongly disagree with the statement ‘Men make better political leaders than women.’ the average bars show unweighted means. Source: See note to Fig. 53
Men make better business executives than women: % disagreeing. Note: Individuals aged 25–55 years old. This figure shows the percentage of individuals who disagree or strongly disagree with the statement ‘Men make better business executives than women.’ the average bars show unweighted means. Source: Authors’ own calculations based on Latinobarometro circa 2019
Men make better political leaders than women: % disagreeing. Note: Individuals aged 25–55 years old. This figure shows the percentage of individuals who disagree or strongly disagree with the statement ‘Men make better political leaders than women.’ the average bars show unweighted means. Source: See note to Fig. 55
Men make better business executives than women: % disagreeing. Note: Individuals aged 25–55 years old. This figure shows, by education level and cohort, the percentage of individuals who disagree or strongly disagree with the statement ‘Men make better business executives than women.’ ‘Low edu’ refers to individuals with less than a high school education; ‘medium edu’ denotes high school graduates without higher education; and ‘high edu’ indicates those who have completed tertiary education. Source: See note to Fig. 55
Men make better political leaders than women: % disagreeing. Note: Individuals aged 25–55 years old. This figure shows, by education level and cohort, the percentage of individuals who disagree or strongly disagree with the statement ‘Men make better political leaders than women.’ ‘Low edu’ refers to individuals with less than a high school education; ‘medium edu’ denotes high school graduates without higher education; and ‘high edu’ indicates those who have completed tertiary education. Source: See note to Fig. 55
Figure 51 also shows how views on women’s work differs across cohorts, distinguishing between individuals between the ages of 25–34, 35–44 and 45–54, all in 2015. On the whole there is less of a gradient in how gender-equal these views are by cohort than by education. In some countries, younger cohorts hold more gender-equal views (e.g. Honduras and Panama), whereas in others (e.g. Argentina and Chile), older cohorts are more gender-equal.
Figure 52 shows the correlation between average opinions regarding women’s work and the size of the gender gap in LFP. As can be seen in the figure, those countries in which a higher proportion of the population has more gender-equal views tend to have a smaller gender gap in LFP.
Next we turn to beliefs regarding the aptitudes of women versus men in various roles, focusing on leadership in business and politics. Figure 53 plots the proportion of respondents age 25–55 who disagree (coded as before) with the statement that men make better business executives than women in 2012 and 2019; Fig. 54 plots the same for the statement that men make better political leaders than women. On the whole, in both periods the majority of respondents disagreed with these statements. Again, note that a higher the level of disagreement with this statement indicates more gender-equal norms.
With respect to attitudes towards leadership in business, there is significantly less variation across countries in 2019 than in 2012. Whereas nearly a third of the respondents agreed with the statement in Brazil in 2012, in Peru this proportion was around 14%. By 2019 the gap between Brazil and Peru had shrunk by 7 percentage points, or 47% of the original gap. Attitudes towards political leadership also became more gender-equal during this time period. This is especially the case in Uruguay, where by 2019 nearly 100% of respondents disagreed compared to 80% 7 years earlier and in Brazil where the level of disagreement went up by over 10 percentage points, from 72% in 2012 (Fig. 54). Chile is a clear outlier relative to the general trend of increasingly gender-equal views over time. Both with respect to business and political leadership, the proportion of individuals who disagreed dropped sharply over this time period.
Throughout the region men are more likely to agree than women with the propositions that men make better business and political leaders than women (Figs. 55 and 56). The pattern in gender differences is similar for the two statements with the largest gender gaps of close to 20 percentage points observed in Chile.
As in the case of attitudes towards women’s LFP discussed above, there are clear education gradients in views on women’s capacity for business and political leadership roles (Figs. 57 and 58); those with less education tend to hold less gender-equal views. For example, in Colombia, which has one of the steeper education gradients in responses to both statements, there is a gap of around 20 percentage points in the proportion of individuals disagreeing between those in the least and most educated groups. In contrast to education, there is less of a clear-cut pattern by cohort. In some countries, including Argentina, Colombia, Ecuador and Nicaragua, there is a somewhat higher level of disagreement with both statements among those in the youngest cohort (age 25–34) than those in the older cohorts as we would expect if more recent cohorts have more gender-equal norms. However, there are also several countries with the opposite pattern or with essentially no differences across cohorts.
Conclusion
This chapter examined gender inequality in Latin America focusing on education and work as two critical spheres in which gender inequality is generated and providing a current snapshot as well as a historical perspective. The picture that emerges is mixed. On the one hand, girls and women continue to be disadvantaged relative to boys and men in several key indicators. There are, however, important dimensions in which the reverse is true and areas in which there have been significant reductions in inequality over time. Moreover, cross-country comparisons, analysis of interactions between gender and socio-economic inequalities and the life-cycle perspective yield insights into promising directions for future research on the mechanisms underlying the trends that we document. In this concluding section we draw out some of the key findings as well as suggestions for future work.
Starting with areas in which women continue to perform worse than men, in the sphere of education adolescent girls lag behind in attainment and confidence in mathematics. They are also much less likely to expect to work in STEM and, consistently, women are strongly underrepresented in STEM subjects at the tertiary education level and are much less likely to work in STEM in adulthood. This matters because labor-market returns to STEM subjects are significantly higher than in health or education, areas in which women tend to be over-represented at tertiary level. Strikingly, these large disparities exist in spite of widespread agreement among the adult population that women have the same capacity for science and technology as men.
In the work sphere, in Latin America as in many other parts of the world, women are significantly less likely to work for pay and, irrespective of whether they are in the labor market, they spend much more time on unpaid, non-market work including household chores and care than men. In market work, there are persistent differences in the quality of jobs men and women do, especially among those with less education, and large wage gaps persist especially after conditioning on individual and occupational characteristics. If working women had the same characteristics as working men with respect to education, location of residence, sector of employment and occupation, then at constant wages the gender wage gap would have been at least 50% larger in 2019; there has been no improvement in this feature over the last 20 year. A key factor impacting women’s labor market trajectories but not men’s is becoming a parent. This results in declines in LFP of mothers of up to 40%, with little evidence of recovery in the medium term. Gender gaps in LFP are strongly correlated with work-related gender norms. Poorer countries and individuals with lower levels of education have greater gender gaps in labor market participation as well as more conservative gender norms. For example, over half of the adult population in several of the poorest countries in the region subscribe to the view that a woman should work only if her husband does not earn enough.
There are some grounds for optimism, however, and there are several important areas in which there have been significant improvements over the last 20 years. Notably, among secondary school students the male–female gap in mathematics has fallen as has the male disadvantage in reading. In the work sphere, there have been significant increases in female LFP. Furthermore, although steep education gradients in FLFP remain, women with low education levels have seen the largest increases over the last 20 years, especially in the poorest group of LA countries.
The objective of this chapter is to document key trends in gender inequality. While understanding the mechanisms underlying these is beyond the scope of our work, our work can be useful in highlighting some potential avenues for future research.
For example, the life-cycle perspective in the analysis allows us to clearly see that gender gaps in mathematics are not evident at early stages of primary school and start to emerge between 3rd and 6th grade (ages 8/9–11/12). Currently, ‘middle childhood’ is a relatively neglected life-cycle stage in research compared to early childhood and adolescence (Voss et al., 2023). A fruitful avenue in future research on gender inequalities in STEM subjects may, therefore, be to better understand the process of acquisition of math skills in middle childhood, including learning from interventions that work to boost math skills in this age-group, as well as how gender inequalities interact with key inputs such as teachers.32
Another potential avenue links to our findings on gender inequalities in the completion of secondary and tertiary education. Here boys tend to be at a disadvantage relative to girls, especially in the wealthier countries in the region and similar to what is found in the vast majority of countries. An in-depth understanding of drivers of this trend could help design effective interventions to prevent these gaps opening up or widening as the poorer countries in the region become wealthier. Peru is an interesting context for further research into these inequalities. Despite having higher secondary and tertiary education completion rates than many wealthier countries, there is near gender parity in completion of both.
Turning to adulthood, there is a clear pattern of larger increases in FLFP across cohorts in wealthier countries than in poorer ones. However, comparisons with countries in the region as well as with countries outside LA suggest that economic development alone is insufficient to achieve improvements in this domain. For example, while there is significant stagnation in women’s LFP in recent cohorts in the USA, Spain has seen very large increases. Similarly within LA, among the UMI countries, Brazil and Ecuador have experienced steady increases in FLFP for each subsequent cohort whereas Colombia stagnated with increases in its FLFP ending with the 1960’s cohort. What insights can be gained from the drivers of these different experiences that might inform policy in contexts where there has been little progress? Across all the countries in the region, the gender gap in LFP is largest for individuals without a tertiary education, indicating that policies should be targeted primarily towards these women. Similarly, especially for the lowest income group of countries, these women have the least gender-equal attitudes towards women’s work, suggesting that policies that enhance their LFP and change their views would have a high payoff.
These are just some examples of the ways in which this chapter provides not only a snapshot of the current situation and recent trends, but also can help motivate future research into drivers of gender inequality in Latin America and policy tools that could be used to tackle it. Advancement of research in this area depends critically on data availability. Here there are several challenges. The first is lack of up-to-date information on some key indicators such as adult skills, domestic violence and social norms. While in this paper we set out to present analysis using the most recent comparable data available, for some key indicators this dates back to as much as a decade ago. The second challenge is comparability of data which is being collected. There are examples of impressive efforts to harmonize existing microdata over the recent years. However, these efforts are hindered by persistence of substantive differences in the ways that data on some key indicators, such as time-use and occupations, is collected across Latin America. Greater coordination between countries in data collection methodologies could significantly enhance cross-country comparisons. In addition, improving the availability and access to administrative data for research, as well as investing in linkages between administrative and survey data-sets has the potential to greatly expand capacity to produce up to date analysis of the evolution of gender inequality in the region. Lastly, particularly with respect to policy design, more experimentation with policies that enhance, e.g. greater LFP of mothers would benefit the region as a whole. While there are a few papers in this area, more work is needed especially in the poorer countries of the region, to understand what works, what does not and why.33
Our work has necessarily focused only on a few key areas of gender inequality. Other important areas, some of which are studied in other chapters of the LACIR project, include dimensions of intersectionality between women and ethnicity and race (Telles et al., 2023) or women’s health and reproductive rights (Bancalari et al., 2023). We also have examined gender through a binary lens given data availability, but recognize the need for more work focused on LGBTQ+ and inequality. An important step in this direction is the set of papers on Latin America in a recent special issue in the journal Labour Economics dedicated to ‘The Economic and Labour Market Position of Sexual Minorities in Developing Countries’ (Plug et al., 2024) but more work and data is needed.
To conclude, as we have documented, LA has seen important progress with respect to several key aspects of gender equality. Progress has not been uniformed across countries nor among all dimensions, however, and there are important cases of stagnation and even regression in various areas. The material presented here is intended to inspire and inform policy actions in these spheres, spur further research in the critical areas that we have examined, as well as highlight the need for greater investment in relevant up-to-date comparable data. Finally, we have demonstrated how a cross-country, dynamic and multifaceted perspective can enrich the analysis highlighting the great potential for learning from the wealth of different experiences in LA across space and time.
ACKNOWLEDGEMENTS
We are grateful to seminar participants at the LACIR workshop for helpful discussions and to our discussant Florencia Torche for her suggestions. We also thank our two referees for their comments. Jessica Bracco, Florencia Pinto and Julián Pedrazzi contributed excellent research assistance.
CONFLICT OF INTEREST
The authors have no conflicts of interest to declare.
STUDY FUNDING and APC FUNDING
This article was written for the Latin American and Caribbean Inequality Review, funded by the International Inequalities Institute at the London School of Economics and Political Science, the Inter-American Development Bank, Yale University, and the Institute for Fiscal Studies. The views expressed are those of the authors and not necessarily of the funders. Krutikova acknowledges funding from the Economic and Social Research Council Centre for Microeconomic Analysis of Public Policy at the Institute for Fiscal Studies.
DATA AVAILABILITY
Instructions on how to access the data underlying this article are available in the appendix.
Footnotes
Interestingly, however, the broad consensus around the region among adults is that men and women have the same capacity for science and technology.
GenLAC is the CEDLAS (Center for Distributive, Labor and Social Studies) initiative to promote gender equity in Latin America and the Caribbean.
Lower-middle income countries include Bolivia, Guatemala, Honduras, Nicaragua, Paraguay and El Salvador; upper- middle-income countries include Brazil, Colombia, Costa Rica, the Dominican Republic, Ecuador, Mexico and Peru; high-income countries include Argentina, Chile, Panama and Uruguay. Note that the Dominican Republic is the only Caribbean country that we were able to include in the analysis due to data limitations.
Exceptions to this gender parity are the Dominican Republic, Ecuador and Bolivia. In Colombia, the gap favors girls.
See Tables A.1 through A.6 in the Appendix for a regression analysis.
The fact that boys are catching up with girls in reading is not due to girls performing worse, but rather boys improving their reading skills more rapidly. In almost all countries, except Argentina, Costa Rica, Mexico, and Uruguay, reading scores for both genders increased between 2006 and 2013.
See Tables A.7 and A.8 in the Appendix for a regression analysis.
Muñoz (2018) shows that in Colombia boys outperforming girls in math tests may be explained by sample selection driven by gender differences in the opportunity costs of schooling. These differences result in lower-achieving boys dropping out at a higher rate than lower-achieving girls. By analyzing an exogenous policy change—the introduction of a conditional cash transfer program in Colombia—he estimates that sample selection accounts for between 50% and 60% of the observed gap. His findings indicate that selection effects in the lower quantiles of the male distribution contribute significantly to the performance disparity.
The gender gaps are similar when examining PISA 2022 data. Appendix Figures A.1 and A.2 show the gender gaps in reading and math scores for 2022. We use the 2018 data for the main analysis due to concerns about potentially differential impacts of COVID-19 pandemic across countries and gender.
See Our World in Data.
The under-representation of women in STEM occupations is shown in figure 20.
These 10 countries include Venezuela. However, since Venezuela is not included in the analysis presented in the rest of the paper, it is also not included in Figure 15.
Our analysis differs from the CAF sample in that we exclude Venezuela (since it is not included in the rest of the paper) and restrict our analysis to individuals of age 25–55 years old (the CAF sample also included individuals age 15–24).
See Table A.9 in the Appendix for a full analysis.
The number for the gender gap is obtained by subtracting the score for men from the score for women (F-M), e.g. the gender gap for Peru is −.19 - -.03 = −.16
For more details about the surveys, see Appendix, Section B.3.
A notable outlier in this analysis is Peru where the coefficient on the interaction is negative and statistically significant (Table A.10, Col 8), suggesting that the self-perception gender gap widens rather than narrows at higher match achievement levels.
These countries were chosen for a variety of reasons. Spain because of its ex-colonial status in LAC, and France as an OECD country
These figures are constructed using census data. LFP rates calculated using household surveys differ from those derived using census data. These discrepancies primarily stem from coverage years and LFP definitions. The census data in IPUMS tends to come from earlier years (e.g. 2001–2007 for Guatemala, Honduras, Nicaragua, Paraguay, El Salvador and Colombia). Furthermore, the questions pertinent to LFP tend to be less detailed in the censuses than in the household surveys, with some censuses use a single question to determine work or job-seeking status, whereas household surveys employ a battery of questions to identify whether a person worked (paid or unpaid) for at least an hour, or actively sought employment. See Appendix B for more details and refer to IPUMS for precise LFP definitions.
There is some variation in the year for which this data is available across the countries. We use survey years that are closest to each other in timing and conducted before 2020 to align with the rest of our analysis that goes up to 2019. The resulting set includes surveys among which the earliest is 2010 for Peru and latest is 2017 for Costa Rica and El Salvador.
The data does not allow us to identify who is married/cohabiting with whom so we cannot conduct a couple level analysis.
Although we are using harmonized time use data, cross-country comparability remains limited and should be approached cautiously. This limitation stems from significant heterogeneity in the methodologies and protocols employed in Latin American time use surveys for different countries, as well as variations in the years they were conducted. Refer to the GenLAC Methodology section for more information about the data harmonization process undertaken to achieve comparability of time use indicators across countries.
The Oaxaca-Blinder decomposition is a widely employed technique to study wage gender differences. From the estimation of linear regression models, this decomposition shows the proportion of the wage gap between men and women that can be attributed to (i) differences in their characteristics; and (ii) differences in the size of regression coefficients. These two components are referred to as the ‘explained’ and the ‘unexplained’ parts of the gender wage gap. To compute the decomposition, for each year we estimate separate male (m) and female (f) ordinary least squares log wage regressions (Y), using a set of explanatory variables X (i.e. age, location, education and job characteristics): Ym = Xmβm + um, and Yf = Xf βf + uf. Using the estimated parameters βˆm and βˆf, and the mean values Y- and X-, we decompose the gender differences in log wages into two terms: Y-m − Y-f = βˆmX-m − βˆf X-f = βˆm(X-m − X-f) + X-f (βˆm − βˆf). The first term accounts for the portion of the gender difference in log wages that is explained by gender differences in the explanatory variables. It is evaluated using the male coefficients (βˆm). The second term shows the unexplained portion. Following Blau & Kahn (2017), we exponentiate this unexplained component to obtain the adjusted female/male wage ratio after accounting for the explanatory variables included in the model.
This finding is in line with existing evidence for Latin America (e.g. Atal et al., 2009).
Note that some numbers in columns 8–11 are very large, e.g. Panama and El Salvador. This is due to the fact that these countries have very small unadjusted wage gaps.
Results presented here computed by using pseudo-panels in Chile are in line with those shown in Berniell et al. (2021) using Chilean panel data.
While Kleven et al. (2024) matches on education as well, we think it is potentially problematic to do so as women may not have finished their education 5 years before giving birth to their first child. Note furthermore that any age restriction (here we start at age 25 for first birth), inevitably introduces a degree of selection.
In the cross-section we can identify whether the individuals are parents and the year in which their first child was born. Let τ = kit indicate the period relative to the birth of the first child of individual i at time t, and let τ = 0 identify the year that the first child was born. The data allows us to observe individuals once they become parents, but not before, i.e. we do not have information for τ < 0. In order to overcome this problem, we match a parent i of age a observed in year t and τ years relative to the first childbirth to a non-parent individual observed in year t − τ − p, at age a − τ − p, living in the same region. We do this to trace a pseudo-history of 5 years prior to becoming a parent (i.e. with p = 1, ..., 5). If there is more than one observation that could be a match, we collapse the observations using sampling weights.
Berniell et al. (2021) shows that after the first child is born, the probability of employed mothers having an informal job increases substantially but not for fathers. The availability of informal jobs—characterized by more flexible working hours, but also lower wages and weaker social protection—might serve as a buffer against the drop in female employment.
Note that the earnings include zeroes for women who are not participating in market work.
An individual was said to disagree if they choose either ‘strongly disagree’ or ‘disagree’ as their response.
A recent paper by Lavy & Megalokonomou (2024) shows that teachers that are biased against boys or girls in high school affects the probability that they enroll later in university and, for girls, also their choice of major conditional on enrollment.
In the area of childcare and women’s work, for example, Berlinski et al. (2011) find that large-scale construction of pre-primary school increased maternal employment in Argentina, Martínez and Perticará (2017) found that large-scale expansion of after-school care significantly increased employment rates of eligible mothers in Chile and Padilla-Romo and Cabrera-Hernández (2019) evaluation of Mexico’s Full-Time Schools Program which extended the school day from 4.5 to 8 hours, concluded that it increased the LFP of mothers with young children.
REFERENCES
A. Appendix
Evolution of Female LFP Across Cohorts, by Education.
Test score regressions using TERCE for 3rd grade students (2013)
| High income countries | ||||||||
|---|---|---|---|---|---|---|---|---|
| Country | ARG | CHL | PAN | URY | ||||
| Reading | ||||||||
| Female | 10.92*** | 11.81*** | 9.606*** | 10.36*** | 8.139*** | 8.902*** | 14.19*** | 12.60*** |
| (2.838) | (2.948) | (2.527) | (2.717) | (3.059) | (3.154) | (3.334) | (3.423) | |
| College parents | 36.86*** | 41.06*** | 56.76*** | 47.89*** | ||||
| (6.566) | (4.621) | (6.194) | (7.563) | |||||
| Female × College parents | 0.0112 | −7.494 | 5.601 | 15.02 | ||||
| (9.768) | (6.570) | (9.032) | (10.88) | |||||
| 2589 | 2589 | 2955 | 2955 | 2372 | 2372 | 1937 | 1937 | |
| Math | ||||||||
| Female | 5.612* | 6.323* | −1.382 | −1.861 | 6.858** | 8.168** | 12.46*** | 12.94*** |
| (3.121) | (3.240) | (2.646) | (2.833) | (3.066) | (3.174) | (3.906) | (3.992) | |
| College parents | 42.68*** | 45.52*** | 60.99*** | 74.94*** | ||||
| (7.234) | (4.761) | (6.115) | (8.273) | |||||
| Female × College parents | 1.136 | −0.327 | −7.805 | −3.955 | ||||
| (10.59) | (6.784) | (8.934) | (12.03) | |||||
| Observations | 2608 | 2608 | 3110 | 3110 | 2400 | 2400 | 1925 | 1925 |
| High income countries | ||||||||
|---|---|---|---|---|---|---|---|---|
| Country | ARG | CHL | PAN | URY | ||||
| Reading | ||||||||
| Female | 10.92*** | 11.81*** | 9.606*** | 10.36*** | 8.139*** | 8.902*** | 14.19*** | 12.60*** |
| (2.838) | (2.948) | (2.527) | (2.717) | (3.059) | (3.154) | (3.334) | (3.423) | |
| College parents | 36.86*** | 41.06*** | 56.76*** | 47.89*** | ||||
| (6.566) | (4.621) | (6.194) | (7.563) | |||||
| Female × College parents | 0.0112 | −7.494 | 5.601 | 15.02 | ||||
| (9.768) | (6.570) | (9.032) | (10.88) | |||||
| 2589 | 2589 | 2955 | 2955 | 2372 | 2372 | 1937 | 1937 | |
| Math | ||||||||
| Female | 5.612* | 6.323* | −1.382 | −1.861 | 6.858** | 8.168** | 12.46*** | 12.94*** |
| (3.121) | (3.240) | (2.646) | (2.833) | (3.066) | (3.174) | (3.906) | (3.992) | |
| College parents | 42.68*** | 45.52*** | 60.99*** | 74.94*** | ||||
| (7.234) | (4.761) | (6.115) | (8.273) | |||||
| Female × College parents | 1.136 | −0.327 | −7.805 | −3.955 | ||||
| (10.59) | (6.784) | (8.934) | (12.03) | |||||
| Observations | 2608 | 2608 | 3110 | 3110 | 2400 | 2400 | 1925 | 1925 |
Robust standard errors are reported in parentheses. Calculations based on TERCE 2013. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using TERCE for 3rd grade students (2013)
| High income countries | ||||||||
|---|---|---|---|---|---|---|---|---|
| Country | ARG | CHL | PAN | URY | ||||
| Reading | ||||||||
| Female | 10.92*** | 11.81*** | 9.606*** | 10.36*** | 8.139*** | 8.902*** | 14.19*** | 12.60*** |
| (2.838) | (2.948) | (2.527) | (2.717) | (3.059) | (3.154) | (3.334) | (3.423) | |
| College parents | 36.86*** | 41.06*** | 56.76*** | 47.89*** | ||||
| (6.566) | (4.621) | (6.194) | (7.563) | |||||
| Female × College parents | 0.0112 | −7.494 | 5.601 | 15.02 | ||||
| (9.768) | (6.570) | (9.032) | (10.88) | |||||
| 2589 | 2589 | 2955 | 2955 | 2372 | 2372 | 1937 | 1937 | |
| Math | ||||||||
| Female | 5.612* | 6.323* | −1.382 | −1.861 | 6.858** | 8.168** | 12.46*** | 12.94*** |
| (3.121) | (3.240) | (2.646) | (2.833) | (3.066) | (3.174) | (3.906) | (3.992) | |
| College parents | 42.68*** | 45.52*** | 60.99*** | 74.94*** | ||||
| (7.234) | (4.761) | (6.115) | (8.273) | |||||
| Female × College parents | 1.136 | −0.327 | −7.805 | −3.955 | ||||
| (10.59) | (6.784) | (8.934) | (12.03) | |||||
| Observations | 2608 | 2608 | 3110 | 3110 | 2400 | 2400 | 1925 | 1925 |
| High income countries | ||||||||
|---|---|---|---|---|---|---|---|---|
| Country | ARG | CHL | PAN | URY | ||||
| Reading | ||||||||
| Female | 10.92*** | 11.81*** | 9.606*** | 10.36*** | 8.139*** | 8.902*** | 14.19*** | 12.60*** |
| (2.838) | (2.948) | (2.527) | (2.717) | (3.059) | (3.154) | (3.334) | (3.423) | |
| College parents | 36.86*** | 41.06*** | 56.76*** | 47.89*** | ||||
| (6.566) | (4.621) | (6.194) | (7.563) | |||||
| Female × College parents | 0.0112 | −7.494 | 5.601 | 15.02 | ||||
| (9.768) | (6.570) | (9.032) | (10.88) | |||||
| 2589 | 2589 | 2955 | 2955 | 2372 | 2372 | 1937 | 1937 | |
| Math | ||||||||
| Female | 5.612* | 6.323* | −1.382 | −1.861 | 6.858** | 8.168** | 12.46*** | 12.94*** |
| (3.121) | (3.240) | (2.646) | (2.833) | (3.066) | (3.174) | (3.906) | (3.992) | |
| College parents | 42.68*** | 45.52*** | 60.99*** | 74.94*** | ||||
| (7.234) | (4.761) | (6.115) | (8.273) | |||||
| Female × College parents | 1.136 | −0.327 | −7.805 | −3.955 | ||||
| (10.59) | (6.784) | (8.934) | (12.03) | |||||
| Observations | 2608 | 2608 | 3110 | 3110 | 2400 | 2400 | 1925 | 1925 |
Robust standard errors are reported in parentheses. Calculations based on TERCE 2013. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using TERCE for 3rd grade students (2013)
| Upper-middle income countries | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | BRA | COL | CRI | DOM | MEX | PER | ||||||
| Reading | ||||||||||||
| Female | 13.08*** | 12.69*** | 13.53*** | 14.55*** | 8.147*** | 8.077*** | 16.71*** | 16.82*** | 11.04*** | 11.74*** | 0.491 | −0.463 |
| (2.936) | (3.045) | (2.486) | (2.562) | (2.546) | (2.650) | (3.181) | (3.545) | (2.894) | (3.029) | (2.483) | (2.560) | |
| College parents | 41.42*** | 63.55*** | 37.67*** | 29.09*** | 49.98*** | 52.47*** | ||||||
| (6.723) | (5.064) | (5.555) | (5.424) | (5.815) | (5.370) | |||||||
| Female × College parents | 5.199 | −13.33* | 1.922 | 2.387 | −3.412 | 4.522 | ||||||
| (9.586) | (6.962) | (8.066) | (7.603) | (8.297) | (7.698) | |||||||
| 2405 | 2405 | 2977 | 2977 | 2552 | 2552 | 2090 | 2090 | 2488 | 2488 | 3458 | 3458 | |
| Math | ||||||||||||
| Female | 5.245 | 4.278 | 1.928 | 3.670 | −4.127 | −5.514** | 4.107 | 2.702 | 2.802 | 2.010 | −8.136*** | −9.307*** |
| (3.466) | (3.533) | (2.783) | (2.919) | (2.649) | (2.754) | (3.070) | (3.383) | (3.185) | (3.338) | (2.738) | (2.823) | |
| College parents | 66.30*** | 54.62*** | 37.24*** | 30.92*** | 50.08*** | 58.82*** | ||||||
| (8.196) | (5.423) | (5.971) | (5.223) | (6.485) | (5.823) | |||||||
| Female × College parents | 9.433 | −7.736 | 7.921 | 10.60 | 4.470 | 5.555 | ||||||
| (11.47) | (7.741) | (8.388) | (7.390) | (9.097) | (8.327) | |||||||
| Observations | 2412 | 2412 | 2869 | 2869 | 2551 | 2551 | 2254 | 2254 | 2493 | 2493 | 3451 | 3451 |
| Upper-middle income countries | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | BRA | COL | CRI | DOM | MEX | PER | ||||||
| Reading | ||||||||||||
| Female | 13.08*** | 12.69*** | 13.53*** | 14.55*** | 8.147*** | 8.077*** | 16.71*** | 16.82*** | 11.04*** | 11.74*** | 0.491 | −0.463 |
| (2.936) | (3.045) | (2.486) | (2.562) | (2.546) | (2.650) | (3.181) | (3.545) | (2.894) | (3.029) | (2.483) | (2.560) | |
| College parents | 41.42*** | 63.55*** | 37.67*** | 29.09*** | 49.98*** | 52.47*** | ||||||
| (6.723) | (5.064) | (5.555) | (5.424) | (5.815) | (5.370) | |||||||
| Female × College parents | 5.199 | −13.33* | 1.922 | 2.387 | −3.412 | 4.522 | ||||||
| (9.586) | (6.962) | (8.066) | (7.603) | (8.297) | (7.698) | |||||||
| 2405 | 2405 | 2977 | 2977 | 2552 | 2552 | 2090 | 2090 | 2488 | 2488 | 3458 | 3458 | |
| Math | ||||||||||||
| Female | 5.245 | 4.278 | 1.928 | 3.670 | −4.127 | −5.514** | 4.107 | 2.702 | 2.802 | 2.010 | −8.136*** | −9.307*** |
| (3.466) | (3.533) | (2.783) | (2.919) | (2.649) | (2.754) | (3.070) | (3.383) | (3.185) | (3.338) | (2.738) | (2.823) | |
| College parents | 66.30*** | 54.62*** | 37.24*** | 30.92*** | 50.08*** | 58.82*** | ||||||
| (8.196) | (5.423) | (5.971) | (5.223) | (6.485) | (5.823) | |||||||
| Female × College parents | 9.433 | −7.736 | 7.921 | 10.60 | 4.470 | 5.555 | ||||||
| (11.47) | (7.741) | (8.388) | (7.390) | (9.097) | (8.327) | |||||||
| Observations | 2412 | 2412 | 2869 | 2869 | 2551 | 2551 | 2254 | 2254 | 2493 | 2493 | 3451 | 3451 |
Robust standard errors are reported in parentheses. Calculations based on TERCE 2013. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using TERCE for 3rd grade students (2013)
| Upper-middle income countries | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | BRA | COL | CRI | DOM | MEX | PER | ||||||
| Reading | ||||||||||||
| Female | 13.08*** | 12.69*** | 13.53*** | 14.55*** | 8.147*** | 8.077*** | 16.71*** | 16.82*** | 11.04*** | 11.74*** | 0.491 | −0.463 |
| (2.936) | (3.045) | (2.486) | (2.562) | (2.546) | (2.650) | (3.181) | (3.545) | (2.894) | (3.029) | (2.483) | (2.560) | |
| College parents | 41.42*** | 63.55*** | 37.67*** | 29.09*** | 49.98*** | 52.47*** | ||||||
| (6.723) | (5.064) | (5.555) | (5.424) | (5.815) | (5.370) | |||||||
| Female × College parents | 5.199 | −13.33* | 1.922 | 2.387 | −3.412 | 4.522 | ||||||
| (9.586) | (6.962) | (8.066) | (7.603) | (8.297) | (7.698) | |||||||
| 2405 | 2405 | 2977 | 2977 | 2552 | 2552 | 2090 | 2090 | 2488 | 2488 | 3458 | 3458 | |
| Math | ||||||||||||
| Female | 5.245 | 4.278 | 1.928 | 3.670 | −4.127 | −5.514** | 4.107 | 2.702 | 2.802 | 2.010 | −8.136*** | −9.307*** |
| (3.466) | (3.533) | (2.783) | (2.919) | (2.649) | (2.754) | (3.070) | (3.383) | (3.185) | (3.338) | (2.738) | (2.823) | |
| College parents | 66.30*** | 54.62*** | 37.24*** | 30.92*** | 50.08*** | 58.82*** | ||||||
| (8.196) | (5.423) | (5.971) | (5.223) | (6.485) | (5.823) | |||||||
| Female × College parents | 9.433 | −7.736 | 7.921 | 10.60 | 4.470 | 5.555 | ||||||
| (11.47) | (7.741) | (8.388) | (7.390) | (9.097) | (8.327) | |||||||
| Observations | 2412 | 2412 | 2869 | 2869 | 2551 | 2551 | 2254 | 2254 | 2493 | 2493 | 3451 | 3451 |
| Upper-middle income countries | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | BRA | COL | CRI | DOM | MEX | PER | ||||||
| Reading | ||||||||||||
| Female | 13.08*** | 12.69*** | 13.53*** | 14.55*** | 8.147*** | 8.077*** | 16.71*** | 16.82*** | 11.04*** | 11.74*** | 0.491 | −0.463 |
| (2.936) | (3.045) | (2.486) | (2.562) | (2.546) | (2.650) | (3.181) | (3.545) | (2.894) | (3.029) | (2.483) | (2.560) | |
| College parents | 41.42*** | 63.55*** | 37.67*** | 29.09*** | 49.98*** | 52.47*** | ||||||
| (6.723) | (5.064) | (5.555) | (5.424) | (5.815) | (5.370) | |||||||
| Female × College parents | 5.199 | −13.33* | 1.922 | 2.387 | −3.412 | 4.522 | ||||||
| (9.586) | (6.962) | (8.066) | (7.603) | (8.297) | (7.698) | |||||||
| 2405 | 2405 | 2977 | 2977 | 2552 | 2552 | 2090 | 2090 | 2488 | 2488 | 3458 | 3458 | |
| Math | ||||||||||||
| Female | 5.245 | 4.278 | 1.928 | 3.670 | −4.127 | −5.514** | 4.107 | 2.702 | 2.802 | 2.010 | −8.136*** | −9.307*** |
| (3.466) | (3.533) | (2.783) | (2.919) | (2.649) | (2.754) | (3.070) | (3.383) | (3.185) | (3.338) | (2.738) | (2.823) | |
| College parents | 66.30*** | 54.62*** | 37.24*** | 30.92*** | 50.08*** | 58.82*** | ||||||
| (8.196) | (5.423) | (5.971) | (5.223) | (6.485) | (5.823) | |||||||
| Female × College parents | 9.433 | −7.736 | 7.921 | 10.60 | 4.470 | 5.555 | ||||||
| (11.47) | (7.741) | (8.388) | (7.390) | (9.097) | (8.327) | |||||||
| Observations | 2412 | 2412 | 2869 | 2869 | 2551 | 2551 | 2254 | 2254 | 2493 | 2493 | 3451 | 3451 |
Robust standard errors are reported in parentheses. Calculations based on TERCE 2013. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using TERCE for 3rd grade students (2013)
| Lower-middle income countries | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Country | ECU | GTM | HND | NIC | PRY | |||||
| Reading | ||||||||||
| Female | −0.661 | −0.619 | −1.540 | −2.643 | 3.165 | 1.777 | 0.883 | 0.899 | 11.25*** | 11.94*** |
| (2.372) | (2.498) | (2.559) | (2.566) | (2.588) | (2.660) | (2.798) | (2.951) | (3.309) | (3.511) | |
| College parents | 41.05*** | 57.56*** | 44.65*** | 37.13*** | 42.81*** | |||||
| (4.555) | (8.720) | (7.005) | (5.707) | (6.145) | ||||||
| Female × College parents | 6.026 | 21.25* | 1.317 | 5.032 | 1.688 | |||||
| (6.675) | (12.29) | (9.212) | (8.114) | (9.109) | ||||||
| 3499 | 3499 | 3122 | 3122 | 2797 | 2797 | 2555 | 2555 | 2056 | 2056 | |
| Math | ||||||||||
| Female | −3.157 | −0.350 | −5.519** | −6.336** | 1.706 | −1.317 | −7.883*** | −5.927** | −4.034 | −4.329 |
| (2.696) | (2.851) | (2.642) | (2.632) | (2.990) | (3.066) | (2.716) | (2.862) | (3.676) | (3.897) | |
| College parents | 51.06*** | 73.87*** | 41.06*** | 45.42*** | 40.31*** | |||||
| (5.069) | (8.429) | (8.247) | (5.572) | (7.194) | ||||||
| Female × College parents | −12.21 | 14.92 | 20.10* | −12.33 | 7.054 | |||||
| (7.587) | (12.02) | (10.89) | (7.884) | (10.55) | ||||||
| Observations | 3461 | 3461 | 3152 | 3152 | 2717 | 2717 | 2670 | 2670 | 2144 | 2144 |
| Lower-middle income countries | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Country | ECU | GTM | HND | NIC | PRY | |||||
| Reading | ||||||||||
| Female | −0.661 | −0.619 | −1.540 | −2.643 | 3.165 | 1.777 | 0.883 | 0.899 | 11.25*** | 11.94*** |
| (2.372) | (2.498) | (2.559) | (2.566) | (2.588) | (2.660) | (2.798) | (2.951) | (3.309) | (3.511) | |
| College parents | 41.05*** | 57.56*** | 44.65*** | 37.13*** | 42.81*** | |||||
| (4.555) | (8.720) | (7.005) | (5.707) | (6.145) | ||||||
| Female × College parents | 6.026 | 21.25* | 1.317 | 5.032 | 1.688 | |||||
| (6.675) | (12.29) | (9.212) | (8.114) | (9.109) | ||||||
| 3499 | 3499 | 3122 | 3122 | 2797 | 2797 | 2555 | 2555 | 2056 | 2056 | |
| Math | ||||||||||
| Female | −3.157 | −0.350 | −5.519** | −6.336** | 1.706 | −1.317 | −7.883*** | −5.927** | −4.034 | −4.329 |
| (2.696) | (2.851) | (2.642) | (2.632) | (2.990) | (3.066) | (2.716) | (2.862) | (3.676) | (3.897) | |
| College parents | 51.06*** | 73.87*** | 41.06*** | 45.42*** | 40.31*** | |||||
| (5.069) | (8.429) | (8.247) | (5.572) | (7.194) | ||||||
| Female × College parents | −12.21 | 14.92 | 20.10* | −12.33 | 7.054 | |||||
| (7.587) | (12.02) | (10.89) | (7.884) | (10.55) | ||||||
| Observations | 3461 | 3461 | 3152 | 3152 | 2717 | 2717 | 2670 | 2670 | 2144 | 2144 |
Robust standard errors are reported in parentheses. Calculations based on TERCE 2013. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using TERCE for 3rd grade students (2013)
| Lower-middle income countries | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Country | ECU | GTM | HND | NIC | PRY | |||||
| Reading | ||||||||||
| Female | −0.661 | −0.619 | −1.540 | −2.643 | 3.165 | 1.777 | 0.883 | 0.899 | 11.25*** | 11.94*** |
| (2.372) | (2.498) | (2.559) | (2.566) | (2.588) | (2.660) | (2.798) | (2.951) | (3.309) | (3.511) | |
| College parents | 41.05*** | 57.56*** | 44.65*** | 37.13*** | 42.81*** | |||||
| (4.555) | (8.720) | (7.005) | (5.707) | (6.145) | ||||||
| Female × College parents | 6.026 | 21.25* | 1.317 | 5.032 | 1.688 | |||||
| (6.675) | (12.29) | (9.212) | (8.114) | (9.109) | ||||||
| 3499 | 3499 | 3122 | 3122 | 2797 | 2797 | 2555 | 2555 | 2056 | 2056 | |
| Math | ||||||||||
| Female | −3.157 | −0.350 | −5.519** | −6.336** | 1.706 | −1.317 | −7.883*** | −5.927** | −4.034 | −4.329 |
| (2.696) | (2.851) | (2.642) | (2.632) | (2.990) | (3.066) | (2.716) | (2.862) | (3.676) | (3.897) | |
| College parents | 51.06*** | 73.87*** | 41.06*** | 45.42*** | 40.31*** | |||||
| (5.069) | (8.429) | (8.247) | (5.572) | (7.194) | ||||||
| Female × College parents | −12.21 | 14.92 | 20.10* | −12.33 | 7.054 | |||||
| (7.587) | (12.02) | (10.89) | (7.884) | (10.55) | ||||||
| Observations | 3461 | 3461 | 3152 | 3152 | 2717 | 2717 | 2670 | 2670 | 2144 | 2144 |
| Lower-middle income countries | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Country | ECU | GTM | HND | NIC | PRY | |||||
| Reading | ||||||||||
| Female | −0.661 | −0.619 | −1.540 | −2.643 | 3.165 | 1.777 | 0.883 | 0.899 | 11.25*** | 11.94*** |
| (2.372) | (2.498) | (2.559) | (2.566) | (2.588) | (2.660) | (2.798) | (2.951) | (3.309) | (3.511) | |
| College parents | 41.05*** | 57.56*** | 44.65*** | 37.13*** | 42.81*** | |||||
| (4.555) | (8.720) | (7.005) | (5.707) | (6.145) | ||||||
| Female × College parents | 6.026 | 21.25* | 1.317 | 5.032 | 1.688 | |||||
| (6.675) | (12.29) | (9.212) | (8.114) | (9.109) | ||||||
| 3499 | 3499 | 3122 | 3122 | 2797 | 2797 | 2555 | 2555 | 2056 | 2056 | |
| Math | ||||||||||
| Female | −3.157 | −0.350 | −5.519** | −6.336** | 1.706 | −1.317 | −7.883*** | −5.927** | −4.034 | −4.329 |
| (2.696) | (2.851) | (2.642) | (2.632) | (2.990) | (3.066) | (2.716) | (2.862) | (3.676) | (3.897) | |
| College parents | 51.06*** | 73.87*** | 41.06*** | 45.42*** | 40.31*** | |||||
| (5.069) | (8.429) | (8.247) | (5.572) | (7.194) | ||||||
| Female × College parents | −12.21 | 14.92 | 20.10* | −12.33 | 7.054 | |||||
| (7.587) | (12.02) | (10.89) | (7.884) | (10.55) | ||||||
| Observations | 3461 | 3461 | 3152 | 3152 | 2717 | 2717 | 2670 | 2670 | 2144 | 2144 |
Robust standard errors are reported in parentheses. Calculations based on TERCE 2013. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using TERCE for 6th grade students (2013)
| High income countries | ||||||||
|---|---|---|---|---|---|---|---|---|
| Country | ARG | CHL | PAN | URY | ||||
| Reading | ||||||||
| Female | 17.63*** | 17.13*** | 13.06*** | 12.16*** | 14.06*** | 13.58*** | 14.18*** | 11.75*** |
| (3.154) | (3.250) | (2.869) | (3.102) | (3.224) | (3.389) | (3.900) | (3.959) | |
| College parents | 41.29*** | 34.73*** | 58.78*** | 62.19*** | ||||
| (8.306) | (5.355) | (6.296) | (9.876) | |||||
| Female × College parents | 6.603 | 2.504 | −1.354 | 22.12 | ||||
| (11.71) | (7.621) | (8.649) | (13.68) | |||||
| 2766 | 2766 | 3384 | 3384 | 2583 | 2583 | 1979 | 1979 | |
| Math | ||||||||
| Female | −7.785** | −8.928*** | −5.727** | −6.981** | 2.318 | 1.737 | −8.197* | −11.50*** |
| (3.153) | (3.274) | (2.913) | (3.129) | (3.057) | (3.246) | (4.287) | (4.353) | |
| College parents | 28.25*** | 56.31*** | 45.33*** | 68.79*** | ||||
| (8.041) | (5.313) | (5.923) | (10.56) | |||||
| Female × College parents | 12.11 | 0.723 | 2.239 | 35.08** | ||||
| (11.31) | (7.345) | (8.286) | (14.60) | |||||
| Observations | 2677 | 2677 | 3261 | 3261 | 2671 | 2671 | 1924 | 1924 |
| High income countries | ||||||||
|---|---|---|---|---|---|---|---|---|
| Country | ARG | CHL | PAN | URY | ||||
| Reading | ||||||||
| Female | 17.63*** | 17.13*** | 13.06*** | 12.16*** | 14.06*** | 13.58*** | 14.18*** | 11.75*** |
| (3.154) | (3.250) | (2.869) | (3.102) | (3.224) | (3.389) | (3.900) | (3.959) | |
| College parents | 41.29*** | 34.73*** | 58.78*** | 62.19*** | ||||
| (8.306) | (5.355) | (6.296) | (9.876) | |||||
| Female × College parents | 6.603 | 2.504 | −1.354 | 22.12 | ||||
| (11.71) | (7.621) | (8.649) | (13.68) | |||||
| 2766 | 2766 | 3384 | 3384 | 2583 | 2583 | 1979 | 1979 | |
| Math | ||||||||
| Female | −7.785** | −8.928*** | −5.727** | −6.981** | 2.318 | 1.737 | −8.197* | −11.50*** |
| (3.153) | (3.274) | (2.913) | (3.129) | (3.057) | (3.246) | (4.287) | (4.353) | |
| College parents | 28.25*** | 56.31*** | 45.33*** | 68.79*** | ||||
| (8.041) | (5.313) | (5.923) | (10.56) | |||||
| Female × College parents | 12.11 | 0.723 | 2.239 | 35.08** | ||||
| (11.31) | (7.345) | (8.286) | (14.60) | |||||
| Observations | 2677 | 2677 | 3261 | 3261 | 2671 | 2671 | 1924 | 1924 |
Robust standard errors are reported in parentheses. Calculations based on TERCE 2013. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using TERCE for 6th grade students (2013)
| High income countries | ||||||||
|---|---|---|---|---|---|---|---|---|
| Country | ARG | CHL | PAN | URY | ||||
| Reading | ||||||||
| Female | 17.63*** | 17.13*** | 13.06*** | 12.16*** | 14.06*** | 13.58*** | 14.18*** | 11.75*** |
| (3.154) | (3.250) | (2.869) | (3.102) | (3.224) | (3.389) | (3.900) | (3.959) | |
| College parents | 41.29*** | 34.73*** | 58.78*** | 62.19*** | ||||
| (8.306) | (5.355) | (6.296) | (9.876) | |||||
| Female × College parents | 6.603 | 2.504 | −1.354 | 22.12 | ||||
| (11.71) | (7.621) | (8.649) | (13.68) | |||||
| 2766 | 2766 | 3384 | 3384 | 2583 | 2583 | 1979 | 1979 | |
| Math | ||||||||
| Female | −7.785** | −8.928*** | −5.727** | −6.981** | 2.318 | 1.737 | −8.197* | −11.50*** |
| (3.153) | (3.274) | (2.913) | (3.129) | (3.057) | (3.246) | (4.287) | (4.353) | |
| College parents | 28.25*** | 56.31*** | 45.33*** | 68.79*** | ||||
| (8.041) | (5.313) | (5.923) | (10.56) | |||||
| Female × College parents | 12.11 | 0.723 | 2.239 | 35.08** | ||||
| (11.31) | (7.345) | (8.286) | (14.60) | |||||
| Observations | 2677 | 2677 | 3261 | 3261 | 2671 | 2671 | 1924 | 1924 |
| High income countries | ||||||||
|---|---|---|---|---|---|---|---|---|
| Country | ARG | CHL | PAN | URY | ||||
| Reading | ||||||||
| Female | 17.63*** | 17.13*** | 13.06*** | 12.16*** | 14.06*** | 13.58*** | 14.18*** | 11.75*** |
| (3.154) | (3.250) | (2.869) | (3.102) | (3.224) | (3.389) | (3.900) | (3.959) | |
| College parents | 41.29*** | 34.73*** | 58.78*** | 62.19*** | ||||
| (8.306) | (5.355) | (6.296) | (9.876) | |||||
| Female × College parents | 6.603 | 2.504 | −1.354 | 22.12 | ||||
| (11.71) | (7.621) | (8.649) | (13.68) | |||||
| 2766 | 2766 | 3384 | 3384 | 2583 | 2583 | 1979 | 1979 | |
| Math | ||||||||
| Female | −7.785** | −8.928*** | −5.727** | −6.981** | 2.318 | 1.737 | −8.197* | −11.50*** |
| (3.153) | (3.274) | (2.913) | (3.129) | (3.057) | (3.246) | (4.287) | (4.353) | |
| College parents | 28.25*** | 56.31*** | 45.33*** | 68.79*** | ||||
| (8.041) | (5.313) | (5.923) | (10.56) | |||||
| Female × College parents | 12.11 | 0.723 | 2.239 | 35.08** | ||||
| (11.31) | (7.345) | (8.286) | (14.60) | |||||
| Observations | 2677 | 2677 | 3261 | 3261 | 2671 | 2671 | 1924 | 1924 |
Robust standard errors are reported in parentheses. Calculations based on TERCE 2013. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using TERCE for 6th grade students (2013)
| Upper-middle income countries | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | BRA | COL | CRI | DOM | MEX | PER | ||||||
| Reading | ||||||||||||
| Female | 11.67*** | 12.02*** | 6.564** | 4.468 | 4.058 | 2.811 | 10.79*** | 9.075*** | 5.789* | 6.865** | 0.313 | −2.265 |
| (3.537) | (3.660) | (2.876) | (2.984) | (2.973) | (3.097) | (2.874) | (3.246) | (3.286) | (3.404) | (2.932) | (3.038) | |
| College parents | 43.48*** | 49.99*** | 38.49*** | 27.42*** | 69.89*** | 63.61*** | ||||||
| (8.341) | (6.166) | (6.561) | (4.663) | (6.483) | (6.157) | |||||||
| Female × College parents | 10.01 | 12.30 | 9.685 | 8.681 | −8.844 | 4.138 | ||||||
| (12.29) | (8.854) | (9.424) | (6.590) | (9.260) | (8.486) | |||||||
| 2236 | 2236 | 3287 | 3287 | 2540 | 2540 | 2656 | 2656 | 2566 | 2566 | 3394 | 3394 | |
| Math | ||||||||||||
| Female | −10.20*** | −11.24*** | −17.02*** | −18.77*** | −10.43*** | −11.42*** | −4.738* | −6.114** | −7.475** | −6.027* | −24.90*** | −27.74*** |
| (3.272) | (3.374) | (2.598) | (2.691) | (2.807) | (2.920) | (2.687) | (3.017) | (3.371) | (3.529) | (3.383) | (3.502) | |
| College parents | 39.45*** | 60.27*** | 43.29*** | 26.78*** | 65.80*** | 76.45*** | ||||||
| (7.830) | (5.712) | (6.300) | (4.389) | (6.618) | (6.964) | |||||||
| Female × College parents | 21.55* | −4.200 | 6.325 | 4.973 | −6.619 | 9.883 | ||||||
| (11.48) | (7.809) | (8.893) | (6.281) | (9.517) | (9.633) | |||||||
| Observations | 2210 | 2210 | 3243 | 3243 | 2509 | 2509 | 2764 | 2764 | 2681 | 2681 | 3711 | 3711 |
| Upper-middle income countries | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | BRA | COL | CRI | DOM | MEX | PER | ||||||
| Reading | ||||||||||||
| Female | 11.67*** | 12.02*** | 6.564** | 4.468 | 4.058 | 2.811 | 10.79*** | 9.075*** | 5.789* | 6.865** | 0.313 | −2.265 |
| (3.537) | (3.660) | (2.876) | (2.984) | (2.973) | (3.097) | (2.874) | (3.246) | (3.286) | (3.404) | (2.932) | (3.038) | |
| College parents | 43.48*** | 49.99*** | 38.49*** | 27.42*** | 69.89*** | 63.61*** | ||||||
| (8.341) | (6.166) | (6.561) | (4.663) | (6.483) | (6.157) | |||||||
| Female × College parents | 10.01 | 12.30 | 9.685 | 8.681 | −8.844 | 4.138 | ||||||
| (12.29) | (8.854) | (9.424) | (6.590) | (9.260) | (8.486) | |||||||
| 2236 | 2236 | 3287 | 3287 | 2540 | 2540 | 2656 | 2656 | 2566 | 2566 | 3394 | 3394 | |
| Math | ||||||||||||
| Female | −10.20*** | −11.24*** | −17.02*** | −18.77*** | −10.43*** | −11.42*** | −4.738* | −6.114** | −7.475** | −6.027* | −24.90*** | −27.74*** |
| (3.272) | (3.374) | (2.598) | (2.691) | (2.807) | (2.920) | (2.687) | (3.017) | (3.371) | (3.529) | (3.383) | (3.502) | |
| College parents | 39.45*** | 60.27*** | 43.29*** | 26.78*** | 65.80*** | 76.45*** | ||||||
| (7.830) | (5.712) | (6.300) | (4.389) | (6.618) | (6.964) | |||||||
| Female × College parents | 21.55* | −4.200 | 6.325 | 4.973 | −6.619 | 9.883 | ||||||
| (11.48) | (7.809) | (8.893) | (6.281) | (9.517) | (9.633) | |||||||
| Observations | 2210 | 2210 | 3243 | 3243 | 2509 | 2509 | 2764 | 2764 | 2681 | 2681 | 3711 | 3711 |
Robust standard errors are reported in parentheses. Calculations based on TERCE 2013. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using TERCE for 6th grade students (2013)
| Upper-middle income countries | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | BRA | COL | CRI | DOM | MEX | PER | ||||||
| Reading | ||||||||||||
| Female | 11.67*** | 12.02*** | 6.564** | 4.468 | 4.058 | 2.811 | 10.79*** | 9.075*** | 5.789* | 6.865** | 0.313 | −2.265 |
| (3.537) | (3.660) | (2.876) | (2.984) | (2.973) | (3.097) | (2.874) | (3.246) | (3.286) | (3.404) | (2.932) | (3.038) | |
| College parents | 43.48*** | 49.99*** | 38.49*** | 27.42*** | 69.89*** | 63.61*** | ||||||
| (8.341) | (6.166) | (6.561) | (4.663) | (6.483) | (6.157) | |||||||
| Female × College parents | 10.01 | 12.30 | 9.685 | 8.681 | −8.844 | 4.138 | ||||||
| (12.29) | (8.854) | (9.424) | (6.590) | (9.260) | (8.486) | |||||||
| 2236 | 2236 | 3287 | 3287 | 2540 | 2540 | 2656 | 2656 | 2566 | 2566 | 3394 | 3394 | |
| Math | ||||||||||||
| Female | −10.20*** | −11.24*** | −17.02*** | −18.77*** | −10.43*** | −11.42*** | −4.738* | −6.114** | −7.475** | −6.027* | −24.90*** | −27.74*** |
| (3.272) | (3.374) | (2.598) | (2.691) | (2.807) | (2.920) | (2.687) | (3.017) | (3.371) | (3.529) | (3.383) | (3.502) | |
| College parents | 39.45*** | 60.27*** | 43.29*** | 26.78*** | 65.80*** | 76.45*** | ||||||
| (7.830) | (5.712) | (6.300) | (4.389) | (6.618) | (6.964) | |||||||
| Female × College parents | 21.55* | −4.200 | 6.325 | 4.973 | −6.619 | 9.883 | ||||||
| (11.48) | (7.809) | (8.893) | (6.281) | (9.517) | (9.633) | |||||||
| Observations | 2210 | 2210 | 3243 | 3243 | 2509 | 2509 | 2764 | 2764 | 2681 | 2681 | 3711 | 3711 |
| Upper-middle income countries | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | BRA | COL | CRI | DOM | MEX | PER | ||||||
| Reading | ||||||||||||
| Female | 11.67*** | 12.02*** | 6.564** | 4.468 | 4.058 | 2.811 | 10.79*** | 9.075*** | 5.789* | 6.865** | 0.313 | −2.265 |
| (3.537) | (3.660) | (2.876) | (2.984) | (2.973) | (3.097) | (2.874) | (3.246) | (3.286) | (3.404) | (2.932) | (3.038) | |
| College parents | 43.48*** | 49.99*** | 38.49*** | 27.42*** | 69.89*** | 63.61*** | ||||||
| (8.341) | (6.166) | (6.561) | (4.663) | (6.483) | (6.157) | |||||||
| Female × College parents | 10.01 | 12.30 | 9.685 | 8.681 | −8.844 | 4.138 | ||||||
| (12.29) | (8.854) | (9.424) | (6.590) | (9.260) | (8.486) | |||||||
| 2236 | 2236 | 3287 | 3287 | 2540 | 2540 | 2656 | 2656 | 2566 | 2566 | 3394 | 3394 | |
| Math | ||||||||||||
| Female | −10.20*** | −11.24*** | −17.02*** | −18.77*** | −10.43*** | −11.42*** | −4.738* | −6.114** | −7.475** | −6.027* | −24.90*** | −27.74*** |
| (3.272) | (3.374) | (2.598) | (2.691) | (2.807) | (2.920) | (2.687) | (3.017) | (3.371) | (3.529) | (3.383) | (3.502) | |
| College parents | 39.45*** | 60.27*** | 43.29*** | 26.78*** | 65.80*** | 76.45*** | ||||||
| (7.830) | (5.712) | (6.300) | (4.389) | (6.618) | (6.964) | |||||||
| Female × College parents | 21.55* | −4.200 | 6.325 | 4.973 | −6.619 | 9.883 | ||||||
| (11.48) | (7.809) | (8.893) | (6.281) | (9.517) | (9.633) | |||||||
| Observations | 2210 | 2210 | 3243 | 3243 | 2509 | 2509 | 2764 | 2764 | 2681 | 2681 | 3711 | 3711 |
Robust standard errors are reported in parentheses. Calculations based on TERCE 2013. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using TERCE for 6th grade students (2013)
| Lower-middle income countries | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Country | ECU | GTM | HND | NIC | PRY | |||||
| Reading | ||||||||||
| Female | 3.447 | 5.111* | −2.150 | −1.129 | 7.959*** | 8.195*** | 12.54*** | 12.59*** | 12.32*** | 17.71*** |
| (2.730) | (2.855) | (2.729) | (2.725) | (2.713) | (2.756) | (2.766) | (2.971) | (3.472) | (3.655) | |
| College parents | 66.37*** | 73.07*** | 62.39*** | 41.66*** | 65.49*** | |||||
| (5.051) | (7.575) | (6.490) | (5.267) | (6.337) | ||||||
| Female × College parents | −11.09 | 19.58 | 2.266 | −3.940 | −24.27** | |||||
| (7.432) | (12.13) | (9.367) | (7.209) | (9.542) | ||||||
| 3765 | 3765 | 3118 | 3118 | 2990 | 2990 | 2703 | 2703 | 2302 | 2302 | |
| Math | ||||||||||
| Female | −10.51*** | −10.97*** | −20.15*** | −18.12*** | −10.03*** | −9.895*** | −7.853*** | −9.456*** | −2.971 | −0.881 |
| (2.693) | (2.864) | (2.714) | (2.705) | (2.850) | (2.920) | (2.524) | (2.695) | (3.582) | (3.820) | |
| College parents | 47.70*** | 89.02*** | 60.18*** | 34.21*** | 51.38*** | |||||
| (4.979) | (7.688) | (6.747) | (4.905) | (6.818) | ||||||
| Female × College parents | 2.623 | −9.924 | −0.564 | 6.608 | −13.17 | |||||
| (7.311) | (11.94) | (9.563) | (6.769) | (9.880) | ||||||
| Observations | 3893 | 3893 | 3118 | 3118 | 2958 | 2958 | 2829 | 2829 | 2394 | 2394 |
| Lower-middle income countries | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Country | ECU | GTM | HND | NIC | PRY | |||||
| Reading | ||||||||||
| Female | 3.447 | 5.111* | −2.150 | −1.129 | 7.959*** | 8.195*** | 12.54*** | 12.59*** | 12.32*** | 17.71*** |
| (2.730) | (2.855) | (2.729) | (2.725) | (2.713) | (2.756) | (2.766) | (2.971) | (3.472) | (3.655) | |
| College parents | 66.37*** | 73.07*** | 62.39*** | 41.66*** | 65.49*** | |||||
| (5.051) | (7.575) | (6.490) | (5.267) | (6.337) | ||||||
| Female × College parents | −11.09 | 19.58 | 2.266 | −3.940 | −24.27** | |||||
| (7.432) | (12.13) | (9.367) | (7.209) | (9.542) | ||||||
| 3765 | 3765 | 3118 | 3118 | 2990 | 2990 | 2703 | 2703 | 2302 | 2302 | |
| Math | ||||||||||
| Female | −10.51*** | −10.97*** | −20.15*** | −18.12*** | −10.03*** | −9.895*** | −7.853*** | −9.456*** | −2.971 | −0.881 |
| (2.693) | (2.864) | (2.714) | (2.705) | (2.850) | (2.920) | (2.524) | (2.695) | (3.582) | (3.820) | |
| College parents | 47.70*** | 89.02*** | 60.18*** | 34.21*** | 51.38*** | |||||
| (4.979) | (7.688) | (6.747) | (4.905) | (6.818) | ||||||
| Female × College parents | 2.623 | −9.924 | −0.564 | 6.608 | −13.17 | |||||
| (7.311) | (11.94) | (9.563) | (6.769) | (9.880) | ||||||
| Observations | 3893 | 3893 | 3118 | 3118 | 2958 | 2958 | 2829 | 2829 | 2394 | 2394 |
Robust standard errors are reported in parentheses. Calculations based on TERCE 2013. ***P < 0.01,** P < 0.05, *P < 0.1
Test score regressions using TERCE for 6th grade students (2013)
| Lower-middle income countries | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Country | ECU | GTM | HND | NIC | PRY | |||||
| Reading | ||||||||||
| Female | 3.447 | 5.111* | −2.150 | −1.129 | 7.959*** | 8.195*** | 12.54*** | 12.59*** | 12.32*** | 17.71*** |
| (2.730) | (2.855) | (2.729) | (2.725) | (2.713) | (2.756) | (2.766) | (2.971) | (3.472) | (3.655) | |
| College parents | 66.37*** | 73.07*** | 62.39*** | 41.66*** | 65.49*** | |||||
| (5.051) | (7.575) | (6.490) | (5.267) | (6.337) | ||||||
| Female × College parents | −11.09 | 19.58 | 2.266 | −3.940 | −24.27** | |||||
| (7.432) | (12.13) | (9.367) | (7.209) | (9.542) | ||||||
| 3765 | 3765 | 3118 | 3118 | 2990 | 2990 | 2703 | 2703 | 2302 | 2302 | |
| Math | ||||||||||
| Female | −10.51*** | −10.97*** | −20.15*** | −18.12*** | −10.03*** | −9.895*** | −7.853*** | −9.456*** | −2.971 | −0.881 |
| (2.693) | (2.864) | (2.714) | (2.705) | (2.850) | (2.920) | (2.524) | (2.695) | (3.582) | (3.820) | |
| College parents | 47.70*** | 89.02*** | 60.18*** | 34.21*** | 51.38*** | |||||
| (4.979) | (7.688) | (6.747) | (4.905) | (6.818) | ||||||
| Female × College parents | 2.623 | −9.924 | −0.564 | 6.608 | −13.17 | |||||
| (7.311) | (11.94) | (9.563) | (6.769) | (9.880) | ||||||
| Observations | 3893 | 3893 | 3118 | 3118 | 2958 | 2958 | 2829 | 2829 | 2394 | 2394 |
| Lower-middle income countries | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Country | ECU | GTM | HND | NIC | PRY | |||||
| Reading | ||||||||||
| Female | 3.447 | 5.111* | −2.150 | −1.129 | 7.959*** | 8.195*** | 12.54*** | 12.59*** | 12.32*** | 17.71*** |
| (2.730) | (2.855) | (2.729) | (2.725) | (2.713) | (2.756) | (2.766) | (2.971) | (3.472) | (3.655) | |
| College parents | 66.37*** | 73.07*** | 62.39*** | 41.66*** | 65.49*** | |||||
| (5.051) | (7.575) | (6.490) | (5.267) | (6.337) | ||||||
| Female × College parents | −11.09 | 19.58 | 2.266 | −3.940 | −24.27** | |||||
| (7.432) | (12.13) | (9.367) | (7.209) | (9.542) | ||||||
| 3765 | 3765 | 3118 | 3118 | 2990 | 2990 | 2703 | 2703 | 2302 | 2302 | |
| Math | ||||||||||
| Female | −10.51*** | −10.97*** | −20.15*** | −18.12*** | −10.03*** | −9.895*** | −7.853*** | −9.456*** | −2.971 | −0.881 |
| (2.693) | (2.864) | (2.714) | (2.705) | (2.850) | (2.920) | (2.524) | (2.695) | (3.582) | (3.820) | |
| College parents | 47.70*** | 89.02*** | 60.18*** | 34.21*** | 51.38*** | |||||
| (4.979) | (7.688) | (6.747) | (4.905) | (6.818) | ||||||
| Female × College parents | 2.623 | −9.924 | −0.564 | 6.608 | −13.17 | |||||
| (7.311) | (11.94) | (9.563) | (6.769) | (9.880) | ||||||
| Observations | 3893 | 3893 | 3118 | 3118 | 2958 | 2958 | 2829 | 2829 | 2394 | 2394 |
Robust standard errors are reported in parentheses. Calculations based on TERCE 2013. ***P < 0.01,** P < 0.05, *P < 0.1
Test score regressions using PISA for 15 year-old students (2018)
| High income countries | ||||||||
|---|---|---|---|---|---|---|---|---|
| Country | ARG | CHL | PAN | URY | ||||
| Reading | ||||||||
| Female | 16.05*** | 12.68*** | 19.81*** | 23.58*** | 14.37*** | 16.2*** | 23*** | 23.41*** |
| (2.57) | (3.1) | (3.64) | (4.1) | (2.7) | (3.18) | (3.24) | (3.92) | |
| College parents | 53.85*** | 47.65*** | 60.95*** | 59.71*** | ||||
| (5.24) | (4.24) | (6.4) | (5.59) | |||||
| Female × College parents | 11.92** | −5.85 | −4.52 | 2.86 | ||||
| (5.69) | (5.28) | (5.46) | (6.49) | |||||
| Math | ||||||||
| Female | −15.43** | −16.33*** | −7.47** | −3.79 | 7.66** | 5.23 | −8.33** | −6.73* |
| (2.25) | (2.82) | (3.65) | (3.99) | (3.32) | (3.83) | (3.31) | (3.71) | |
| College parents | 50.55*** | 46.6*** | 51.84*** | 56.7*** | ||||
| 4.84 | (3.81) | (5.54) | (5.43) | |||||
| Female × College parents | 5.32 | −5.69 | −6.35 | −2.01 | ||||
| (5.12) | (4.42) | (5.54) | (6.1) | |||||
| Observations | 11 975 | 11 975 | 7621 | 7621 | 6270 | 6270 | 5263 | 5263 |
| High income countries | ||||||||
|---|---|---|---|---|---|---|---|---|
| Country | ARG | CHL | PAN | URY | ||||
| Reading | ||||||||
| Female | 16.05*** | 12.68*** | 19.81*** | 23.58*** | 14.37*** | 16.2*** | 23*** | 23.41*** |
| (2.57) | (3.1) | (3.64) | (4.1) | (2.7) | (3.18) | (3.24) | (3.92) | |
| College parents | 53.85*** | 47.65*** | 60.95*** | 59.71*** | ||||
| (5.24) | (4.24) | (6.4) | (5.59) | |||||
| Female × College parents | 11.92** | −5.85 | −4.52 | 2.86 | ||||
| (5.69) | (5.28) | (5.46) | (6.49) | |||||
| Math | ||||||||
| Female | −15.43** | −16.33*** | −7.47** | −3.79 | 7.66** | 5.23 | −8.33** | −6.73* |
| (2.25) | (2.82) | (3.65) | (3.99) | (3.32) | (3.83) | (3.31) | (3.71) | |
| College parents | 50.55*** | 46.6*** | 51.84*** | 56.7*** | ||||
| 4.84 | (3.81) | (5.54) | (5.43) | |||||
| Female × College parents | 5.32 | −5.69 | −6.35 | −2.01 | ||||
| (5.12) | (4.42) | (5.54) | (6.1) | |||||
| Observations | 11 975 | 11 975 | 7621 | 7621 | 6270 | 6270 | 5263 | 5263 |
Robust standard errors are reported in parentheses. Calculations based on PISA 2018. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using PISA for 15 year-old students (2018)
| High income countries | ||||||||
|---|---|---|---|---|---|---|---|---|
| Country | ARG | CHL | PAN | URY | ||||
| Reading | ||||||||
| Female | 16.05*** | 12.68*** | 19.81*** | 23.58*** | 14.37*** | 16.2*** | 23*** | 23.41*** |
| (2.57) | (3.1) | (3.64) | (4.1) | (2.7) | (3.18) | (3.24) | (3.92) | |
| College parents | 53.85*** | 47.65*** | 60.95*** | 59.71*** | ||||
| (5.24) | (4.24) | (6.4) | (5.59) | |||||
| Female × College parents | 11.92** | −5.85 | −4.52 | 2.86 | ||||
| (5.69) | (5.28) | (5.46) | (6.49) | |||||
| Math | ||||||||
| Female | −15.43** | −16.33*** | −7.47** | −3.79 | 7.66** | 5.23 | −8.33** | −6.73* |
| (2.25) | (2.82) | (3.65) | (3.99) | (3.32) | (3.83) | (3.31) | (3.71) | |
| College parents | 50.55*** | 46.6*** | 51.84*** | 56.7*** | ||||
| 4.84 | (3.81) | (5.54) | (5.43) | |||||
| Female × College parents | 5.32 | −5.69 | −6.35 | −2.01 | ||||
| (5.12) | (4.42) | (5.54) | (6.1) | |||||
| Observations | 11 975 | 11 975 | 7621 | 7621 | 6270 | 6270 | 5263 | 5263 |
| High income countries | ||||||||
|---|---|---|---|---|---|---|---|---|
| Country | ARG | CHL | PAN | URY | ||||
| Reading | ||||||||
| Female | 16.05*** | 12.68*** | 19.81*** | 23.58*** | 14.37*** | 16.2*** | 23*** | 23.41*** |
| (2.57) | (3.1) | (3.64) | (4.1) | (2.7) | (3.18) | (3.24) | (3.92) | |
| College parents | 53.85*** | 47.65*** | 60.95*** | 59.71*** | ||||
| (5.24) | (4.24) | (6.4) | (5.59) | |||||
| Female × College parents | 11.92** | −5.85 | −4.52 | 2.86 | ||||
| (5.69) | (5.28) | (5.46) | (6.49) | |||||
| Math | ||||||||
| Female | −15.43** | −16.33*** | −7.47** | −3.79 | 7.66** | 5.23 | −8.33** | −6.73* |
| (2.25) | (2.82) | (3.65) | (3.99) | (3.32) | (3.83) | (3.31) | (3.71) | |
| College parents | 50.55*** | 46.6*** | 51.84*** | 56.7*** | ||||
| 4.84 | (3.81) | (5.54) | (5.43) | |||||
| Female × College parents | 5.32 | −5.69 | −6.35 | −2.01 | ||||
| (5.12) | (4.42) | (5.54) | (6.1) | |||||
| Observations | 11 975 | 11 975 | 7621 | 7621 | 6270 | 6270 | 5263 | 5263 |
Robust standard errors are reported in parentheses. Calculations based on PISA 2018. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using PISA for 15 year-old students (2018)
| Upper-middle income countries | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | BRA | COL | CRI | DOM | MEX | PER | ||||||
| Reading Female | 25.68*** | 22.76*** | 10.33*** | 10.37*** | 14.38*** | 18.28*** | 31.08*** | 27.63*** | 11.12*** | 9.79*** | 10.52*** | 11.59*** |
| (2.11) | (2.6) | (3.29) | (3.15) | (3.34) | (3.42) | (2.41) | (2.86) | (2.52) | (2.92) | (3.04) | (2.98) | |
| College parents | 50.83*** | 49.25*** | 50.4*** | 24.35*** | 39.09*** | 55.12*** | ||||||
| (4.22) | (5.53) | (4.18) | (4.83) | (4.88) | (4.61) | |||||||
| Female × College parents | 7.54* | 6.33 | −4.22 | 11.72** | 6.96 | −4.46 | ||||||
| Math | (4.35) | (5.55) | (4.95) | (4.92) | (5.08) | (5.02) | ||||||
| Female | −8.6*** | −9.65*** | −19.52*** | −18.85*** | −17.67*** | −16.2*** | 3.15 | 2.43*** | −11.75*** | −10.26*** | −16.33*** | −14.14*** |
| (2.19) | (2.43) | (3.47) | (3.41) | (3.93) | (3.48) | (2.76) | (3.05) | (2.58) | (2.97) | (2.85) | (3.17) | |
| College parents | 49.32*** | 43.04*** | 39.65*** | 26.45*** | 35.03*** | 51.07*** | ||||||
| (4.08) | (6.11) | (4.63) | (5.12) | (4.78) | (4.24) | |||||||
| Female × College parents | 2.43 | 3.25 | 0.53 | 4.8 | −2.36 | −6.85 | ||||||
| (4.1) | (5.55) | (4.99) | (4.8) | (4.52) | (4.78) | |||||||
| Observations | 10 691 | 10 691 | 7522 | 7522 | 7221 | 7221 | 5674 | 5674 | 7299 | 7299 | 6086 | 6086 |
| Upper-middle income countries | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | BRA | COL | CRI | DOM | MEX | PER | ||||||
| Reading Female | 25.68*** | 22.76*** | 10.33*** | 10.37*** | 14.38*** | 18.28*** | 31.08*** | 27.63*** | 11.12*** | 9.79*** | 10.52*** | 11.59*** |
| (2.11) | (2.6) | (3.29) | (3.15) | (3.34) | (3.42) | (2.41) | (2.86) | (2.52) | (2.92) | (3.04) | (2.98) | |
| College parents | 50.83*** | 49.25*** | 50.4*** | 24.35*** | 39.09*** | 55.12*** | ||||||
| (4.22) | (5.53) | (4.18) | (4.83) | (4.88) | (4.61) | |||||||
| Female × College parents | 7.54* | 6.33 | −4.22 | 11.72** | 6.96 | −4.46 | ||||||
| Math | (4.35) | (5.55) | (4.95) | (4.92) | (5.08) | (5.02) | ||||||
| Female | −8.6*** | −9.65*** | −19.52*** | −18.85*** | −17.67*** | −16.2*** | 3.15 | 2.43*** | −11.75*** | −10.26*** | −16.33*** | −14.14*** |
| (2.19) | (2.43) | (3.47) | (3.41) | (3.93) | (3.48) | (2.76) | (3.05) | (2.58) | (2.97) | (2.85) | (3.17) | |
| College parents | 49.32*** | 43.04*** | 39.65*** | 26.45*** | 35.03*** | 51.07*** | ||||||
| (4.08) | (6.11) | (4.63) | (5.12) | (4.78) | (4.24) | |||||||
| Female × College parents | 2.43 | 3.25 | 0.53 | 4.8 | −2.36 | −6.85 | ||||||
| (4.1) | (5.55) | (4.99) | (4.8) | (4.52) | (4.78) | |||||||
| Observations | 10 691 | 10 691 | 7522 | 7522 | 7221 | 7221 | 5674 | 5674 | 7299 | 7299 | 6086 | 6086 |
Robust standard errors are reported in parentheses. Calculations based on PISA 2018. ***P < 0.01, **P < 0.05, *P < 0.1
Test score regressions using PISA for 15 year-old students (2018)
| Upper-middle income countries | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | BRA | COL | CRI | DOM | MEX | PER | ||||||
| Reading Female | 25.68*** | 22.76*** | 10.33*** | 10.37*** | 14.38*** | 18.28*** | 31.08*** | 27.63*** | 11.12*** | 9.79*** | 10.52*** | 11.59*** |
| (2.11) | (2.6) | (3.29) | (3.15) | (3.34) | (3.42) | (2.41) | (2.86) | (2.52) | (2.92) | (3.04) | (2.98) | |
| College parents | 50.83*** | 49.25*** | 50.4*** | 24.35*** | 39.09*** | 55.12*** | ||||||
| (4.22) | (5.53) | (4.18) | (4.83) | (4.88) | (4.61) | |||||||
| Female × College parents | 7.54* | 6.33 | −4.22 | 11.72** | 6.96 | −4.46 | ||||||
| Math | (4.35) | (5.55) | (4.95) | (4.92) | (5.08) | (5.02) | ||||||
| Female | −8.6*** | −9.65*** | −19.52*** | −18.85*** | −17.67*** | −16.2*** | 3.15 | 2.43*** | −11.75*** | −10.26*** | −16.33*** | −14.14*** |
| (2.19) | (2.43) | (3.47) | (3.41) | (3.93) | (3.48) | (2.76) | (3.05) | (2.58) | (2.97) | (2.85) | (3.17) | |
| College parents | 49.32*** | 43.04*** | 39.65*** | 26.45*** | 35.03*** | 51.07*** | ||||||
| (4.08) | (6.11) | (4.63) | (5.12) | (4.78) | (4.24) | |||||||
| Female × College parents | 2.43 | 3.25 | 0.53 | 4.8 | −2.36 | −6.85 | ||||||
| (4.1) | (5.55) | (4.99) | (4.8) | (4.52) | (4.78) | |||||||
| Observations | 10 691 | 10 691 | 7522 | 7522 | 7221 | 7221 | 5674 | 5674 | 7299 | 7299 | 6086 | 6086 |
| Upper-middle income countries | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | BRA | COL | CRI | DOM | MEX | PER | ||||||
| Reading Female | 25.68*** | 22.76*** | 10.33*** | 10.37*** | 14.38*** | 18.28*** | 31.08*** | 27.63*** | 11.12*** | 9.79*** | 10.52*** | 11.59*** |
| (2.11) | (2.6) | (3.29) | (3.15) | (3.34) | (3.42) | (2.41) | (2.86) | (2.52) | (2.92) | (3.04) | (2.98) | |
| College parents | 50.83*** | 49.25*** | 50.4*** | 24.35*** | 39.09*** | 55.12*** | ||||||
| (4.22) | (5.53) | (4.18) | (4.83) | (4.88) | (4.61) | |||||||
| Female × College parents | 7.54* | 6.33 | −4.22 | 11.72** | 6.96 | −4.46 | ||||||
| Math | (4.35) | (5.55) | (4.95) | (4.92) | (5.08) | (5.02) | ||||||
| Female | −8.6*** | −9.65*** | −19.52*** | −18.85*** | −17.67*** | −16.2*** | 3.15 | 2.43*** | −11.75*** | −10.26*** | −16.33*** | −14.14*** |
| (2.19) | (2.43) | (3.47) | (3.41) | (3.93) | (3.48) | (2.76) | (3.05) | (2.58) | (2.97) | (2.85) | (3.17) | |
| College parents | 49.32*** | 43.04*** | 39.65*** | 26.45*** | 35.03*** | 51.07*** | ||||||
| (4.08) | (6.11) | (4.63) | (5.12) | (4.78) | (4.24) | |||||||
| Female × College parents | 2.43 | 3.25 | 0.53 | 4.8 | −2.36 | −6.85 | ||||||
| (4.1) | (5.55) | (4.99) | (4.8) | (4.52) | (4.78) | |||||||
| Observations | 10 691 | 10 691 | 7522 | 7522 | 7221 | 7221 | 5674 | 5674 | 7299 | 7299 | 6086 | 6086 |
Robust standard errors are reported in parentheses. Calculations based on PISA 2018. ***P < 0.01, **P < 0.05, *P < 0.1
Adult’s skills
| ARG | BOL | BRA | COL | ECU | MEX | PAN | PER | URY | |
|---|---|---|---|---|---|---|---|---|---|
| Panel A | (1) | (2) | (3) | (4) | (5) Raven PMT | (6) | (7) | (8) | (9) |
| Women | 0.120 | −0.211 | −0.131 | −0.071 | −0.081 | −0.091 | −0.096 | −0.166 | 0.083 |
| (0.074) | (0.077)*** | (0.075)* | (0.073) | (0.069) | (0.070) | (0.098) | (0.075)** | (0.075) | |
| Observations | 721 | 670 | 731 | 730 | 672 | 737 | 437 | 693 | 742 |
| Panel B | (1) | (2) | (3) | (4) | (5) Numerical skills | (6) | (7) | (8) | (9) |
| Women | −0.036 | −0.436 | −0.239 | −0.234 | −0.228 | −0.315 | −0.326 | −0.151 | −0.163 |
| (0.074) | (0.085)*** | (0.084)*** | (0.073)*** | (0.078)*** | (0.074)*** | (0.100)*** | (0.087)* | (0.078)** | |
| Observations | 702 | 536 | 570 | 709 | 612 | 701 | 386 | 533 | 657 |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
| Panel C | Verbal conceptualization | ||||||||
| Women | −0.030 | −0.408 | −0.125 | 0.230 | −0.059 | −0.243 | −0.081 | −0.048 | 0.071 |
| (0.077) | (0.080)*** | (0.077) | (0.075)*** | (0.077) | (0.075)*** | (0.094) | (0.077) | (0.075) | |
| Observations | 721 | 670 | 731 | 729 | 672 | 709 | 437 | 676 | 739 |
| ARG | BOL | BRA | COL | ECU | MEX | PAN | PER | URY | |
|---|---|---|---|---|---|---|---|---|---|
| Panel A | (1) | (2) | (3) | (4) | (5) Raven PMT | (6) | (7) | (8) | (9) |
| Women | 0.120 | −0.211 | −0.131 | −0.071 | −0.081 | −0.091 | −0.096 | −0.166 | 0.083 |
| (0.074) | (0.077)*** | (0.075)* | (0.073) | (0.069) | (0.070) | (0.098) | (0.075)** | (0.075) | |
| Observations | 721 | 670 | 731 | 730 | 672 | 737 | 437 | 693 | 742 |
| Panel B | (1) | (2) | (3) | (4) | (5) Numerical skills | (6) | (7) | (8) | (9) |
| Women | −0.036 | −0.436 | −0.239 | −0.234 | −0.228 | −0.315 | −0.326 | −0.151 | −0.163 |
| (0.074) | (0.085)*** | (0.084)*** | (0.073)*** | (0.078)*** | (0.074)*** | (0.100)*** | (0.087)* | (0.078)** | |
| Observations | 702 | 536 | 570 | 709 | 612 | 701 | 386 | 533 | 657 |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
| Panel C | Verbal conceptualization | ||||||||
| Women | −0.030 | −0.408 | −0.125 | 0.230 | −0.059 | −0.243 | −0.081 | −0.048 | 0.071 |
| (0.077) | (0.080)*** | (0.077) | (0.075)*** | (0.077) | (0.075)*** | (0.094) | (0.077) | (0.075) | |
| Observations | 721 | 670 | 731 | 729 | 672 | 709 | 437 | 676 | 739 |
Note: All these variables are standardized (mean 0, and SD 1). The standardization was done taking into account the whole sample (ages 15 to 55 and the 10 major cities of the nine countries depicted plus Venezuela). The regression shown in this table was computed for people aged 25 to 55 years old. Robust standard errors are reported in parentheses. Calculations based on PISA 2018. ***P < 0.01, **P < 0.05, *P < 0.1. Source: authors’ own calculations based on ECAF 2015 (CAF-development bank of Latin America).
Adult’s skills
| ARG | BOL | BRA | COL | ECU | MEX | PAN | PER | URY | |
|---|---|---|---|---|---|---|---|---|---|
| Panel A | (1) | (2) | (3) | (4) | (5) Raven PMT | (6) | (7) | (8) | (9) |
| Women | 0.120 | −0.211 | −0.131 | −0.071 | −0.081 | −0.091 | −0.096 | −0.166 | 0.083 |
| (0.074) | (0.077)*** | (0.075)* | (0.073) | (0.069) | (0.070) | (0.098) | (0.075)** | (0.075) | |
| Observations | 721 | 670 | 731 | 730 | 672 | 737 | 437 | 693 | 742 |
| Panel B | (1) | (2) | (3) | (4) | (5) Numerical skills | (6) | (7) | (8) | (9) |
| Women | −0.036 | −0.436 | −0.239 | −0.234 | −0.228 | −0.315 | −0.326 | −0.151 | −0.163 |
| (0.074) | (0.085)*** | (0.084)*** | (0.073)*** | (0.078)*** | (0.074)*** | (0.100)*** | (0.087)* | (0.078)** | |
| Observations | 702 | 536 | 570 | 709 | 612 | 701 | 386 | 533 | 657 |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
| Panel C | Verbal conceptualization | ||||||||
| Women | −0.030 | −0.408 | −0.125 | 0.230 | −0.059 | −0.243 | −0.081 | −0.048 | 0.071 |
| (0.077) | (0.080)*** | (0.077) | (0.075)*** | (0.077) | (0.075)*** | (0.094) | (0.077) | (0.075) | |
| Observations | 721 | 670 | 731 | 729 | 672 | 709 | 437 | 676 | 739 |
| ARG | BOL | BRA | COL | ECU | MEX | PAN | PER | URY | |
|---|---|---|---|---|---|---|---|---|---|
| Panel A | (1) | (2) | (3) | (4) | (5) Raven PMT | (6) | (7) | (8) | (9) |
| Women | 0.120 | −0.211 | −0.131 | −0.071 | −0.081 | −0.091 | −0.096 | −0.166 | 0.083 |
| (0.074) | (0.077)*** | (0.075)* | (0.073) | (0.069) | (0.070) | (0.098) | (0.075)** | (0.075) | |
| Observations | 721 | 670 | 731 | 730 | 672 | 737 | 437 | 693 | 742 |
| Panel B | (1) | (2) | (3) | (4) | (5) Numerical skills | (6) | (7) | (8) | (9) |
| Women | −0.036 | −0.436 | −0.239 | −0.234 | −0.228 | −0.315 | −0.326 | −0.151 | −0.163 |
| (0.074) | (0.085)*** | (0.084)*** | (0.073)*** | (0.078)*** | (0.074)*** | (0.100)*** | (0.087)* | (0.078)** | |
| Observations | 702 | 536 | 570 | 709 | 612 | 701 | 386 | 533 | 657 |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
| Panel C | Verbal conceptualization | ||||||||
| Women | −0.030 | −0.408 | −0.125 | 0.230 | −0.059 | −0.243 | −0.081 | −0.048 | 0.071 |
| (0.077) | (0.080)*** | (0.077) | (0.075)*** | (0.077) | (0.075)*** | (0.094) | (0.077) | (0.075) | |
| Observations | 721 | 670 | 731 | 729 | 672 | 709 | 437 | 676 | 739 |
Note: All these variables are standardized (mean 0, and SD 1). The standardization was done taking into account the whole sample (ages 15 to 55 and the 10 major cities of the nine countries depicted plus Venezuela). The regression shown in this table was computed for people aged 25 to 55 years old. Robust standard errors are reported in parentheses. Calculations based on PISA 2018. ***P < 0.01, **P < 0.05, *P < 0.1. Source: authors’ own calculations based on ECAF 2015 (CAF-development bank of Latin America).
Math self-perception and PISA mathematics score
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
|---|---|---|---|---|---|---|---|---|---|
| LA | ARG | BRA | CHL | COL | CRI | MEX | PER | URY | |
| Math score | 0.0657*** | 0.0620*** | 0.0451*** | 0.1159*** | 0.0563*** | 0.0972*** | 0.0984*** | 0.0496*** | 0.0948*** |
| (0.0017) | (0.0075) | (0.0037) | (0.0064) | (0.0056) | (0.0093) | (0.0026) | (0.0058) | (0.0074) | |
| Female | −8.1301*** | −13.3829*** | −9.9063*** | −9.3920** | −16.6369*** | −11.8803** | −4.8521*** | 0.6756 | −12.5151*** |
| (0.9713) | (4.2537) | (2.1325) | (3.9487) | (3.1385) | (5.5784) | (1.6205) | (3.1447) | (4.3967) | |
| Math score * female | 0.0044* | 0.0155 | 0.0070 | 0.0029 | 0.0306*** | 0.0109 | −0.0004 | −0.0165** | 0.0110 |
| (0.0024) | (0.0106) | (0.0053) | (0.0092) | (0.0080) | (0.0135) | (0.0038) | (0.0082) | (0.0103) | |
| Constant | 23.6258*** | 25.6125*** | 32.3870*** | −1.0126 | 34.5165*** | 16.0082*** | 11.2341*** | 37.0945*** | 12.9666*** |
| (0.7497) | (3.0833) | (1.5339) | (2.8327) | (2.2789) | (3.9883) | (1.1354) | (2.2785) | (3.2164) | |
| Observations | 56 528 | 3584 | 11 581 | 4395 | 5443 | 2819 | 21 881 | 3624 | 3201 |
| Mean dep. Var. | 48.25 | 46.15 | 46.32 | 43.96 | 53.39 | 51.53 | 49.53 | 52.94 | 48.09 |
| Country FE | Yes | No | No | No | No | No | No | No | No |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
|---|---|---|---|---|---|---|---|---|---|
| LA | ARG | BRA | CHL | COL | CRI | MEX | PER | URY | |
| Math score | 0.0657*** | 0.0620*** | 0.0451*** | 0.1159*** | 0.0563*** | 0.0972*** | 0.0984*** | 0.0496*** | 0.0948*** |
| (0.0017) | (0.0075) | (0.0037) | (0.0064) | (0.0056) | (0.0093) | (0.0026) | (0.0058) | (0.0074) | |
| Female | −8.1301*** | −13.3829*** | −9.9063*** | −9.3920** | −16.6369*** | −11.8803** | −4.8521*** | 0.6756 | −12.5151*** |
| (0.9713) | (4.2537) | (2.1325) | (3.9487) | (3.1385) | (5.5784) | (1.6205) | (3.1447) | (4.3967) | |
| Math score * female | 0.0044* | 0.0155 | 0.0070 | 0.0029 | 0.0306*** | 0.0109 | −0.0004 | −0.0165** | 0.0110 |
| (0.0024) | (0.0106) | (0.0053) | (0.0092) | (0.0080) | (0.0135) | (0.0038) | (0.0082) | (0.0103) | |
| Constant | 23.6258*** | 25.6125*** | 32.3870*** | −1.0126 | 34.5165*** | 16.0082*** | 11.2341*** | 37.0945*** | 12.9666*** |
| (0.7497) | (3.0833) | (1.5339) | (2.8327) | (2.2789) | (3.9883) | (1.1354) | (2.2785) | (3.2164) | |
| Observations | 56 528 | 3584 | 11 581 | 4395 | 5443 | 2819 | 21 881 | 3624 | 3201 |
| Mean dep. Var. | 48.25 | 46.15 | 46.32 | 43.96 | 53.39 | 51.53 | 49.53 | 52.94 | 48.09 |
| Country FE | Yes | No | No | No | No | No | No | No | No |
Note: The self-perception index is derived from each student’s responses to five questions about their level of agreement with specific statements related to their math competence, as defined in the text. Robust standard errors are reported in parentheses. ***P < 0.01, **P < 0.05, *P < 0.1. Source: authors’ own calculations based on PISA 2012.
Math self-perception and PISA mathematics score
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
|---|---|---|---|---|---|---|---|---|---|
| LA | ARG | BRA | CHL | COL | CRI | MEX | PER | URY | |
| Math score | 0.0657*** | 0.0620*** | 0.0451*** | 0.1159*** | 0.0563*** | 0.0972*** | 0.0984*** | 0.0496*** | 0.0948*** |
| (0.0017) | (0.0075) | (0.0037) | (0.0064) | (0.0056) | (0.0093) | (0.0026) | (0.0058) | (0.0074) | |
| Female | −8.1301*** | −13.3829*** | −9.9063*** | −9.3920** | −16.6369*** | −11.8803** | −4.8521*** | 0.6756 | −12.5151*** |
| (0.9713) | (4.2537) | (2.1325) | (3.9487) | (3.1385) | (5.5784) | (1.6205) | (3.1447) | (4.3967) | |
| Math score * female | 0.0044* | 0.0155 | 0.0070 | 0.0029 | 0.0306*** | 0.0109 | −0.0004 | −0.0165** | 0.0110 |
| (0.0024) | (0.0106) | (0.0053) | (0.0092) | (0.0080) | (0.0135) | (0.0038) | (0.0082) | (0.0103) | |
| Constant | 23.6258*** | 25.6125*** | 32.3870*** | −1.0126 | 34.5165*** | 16.0082*** | 11.2341*** | 37.0945*** | 12.9666*** |
| (0.7497) | (3.0833) | (1.5339) | (2.8327) | (2.2789) | (3.9883) | (1.1354) | (2.2785) | (3.2164) | |
| Observations | 56 528 | 3584 | 11 581 | 4395 | 5443 | 2819 | 21 881 | 3624 | 3201 |
| Mean dep. Var. | 48.25 | 46.15 | 46.32 | 43.96 | 53.39 | 51.53 | 49.53 | 52.94 | 48.09 |
| Country FE | Yes | No | No | No | No | No | No | No | No |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
|---|---|---|---|---|---|---|---|---|---|
| LA | ARG | BRA | CHL | COL | CRI | MEX | PER | URY | |
| Math score | 0.0657*** | 0.0620*** | 0.0451*** | 0.1159*** | 0.0563*** | 0.0972*** | 0.0984*** | 0.0496*** | 0.0948*** |
| (0.0017) | (0.0075) | (0.0037) | (0.0064) | (0.0056) | (0.0093) | (0.0026) | (0.0058) | (0.0074) | |
| Female | −8.1301*** | −13.3829*** | −9.9063*** | −9.3920** | −16.6369*** | −11.8803** | −4.8521*** | 0.6756 | −12.5151*** |
| (0.9713) | (4.2537) | (2.1325) | (3.9487) | (3.1385) | (5.5784) | (1.6205) | (3.1447) | (4.3967) | |
| Math score * female | 0.0044* | 0.0155 | 0.0070 | 0.0029 | 0.0306*** | 0.0109 | −0.0004 | −0.0165** | 0.0110 |
| (0.0024) | (0.0106) | (0.0053) | (0.0092) | (0.0080) | (0.0135) | (0.0038) | (0.0082) | (0.0103) | |
| Constant | 23.6258*** | 25.6125*** | 32.3870*** | −1.0126 | 34.5165*** | 16.0082*** | 11.2341*** | 37.0945*** | 12.9666*** |
| (0.7497) | (3.0833) | (1.5339) | (2.8327) | (2.2789) | (3.9883) | (1.1354) | (2.2785) | (3.2164) | |
| Observations | 56 528 | 3584 | 11 581 | 4395 | 5443 | 2819 | 21 881 | 3624 | 3201 |
| Mean dep. Var. | 48.25 | 46.15 | 46.32 | 43.96 | 53.39 | 51.53 | 49.53 | 52.94 | 48.09 |
| Country FE | Yes | No | No | No | No | No | No | No | No |
Note: The self-perception index is derived from each student’s responses to five questions about their level of agreement with specific statements related to their math competence, as defined in the text. Robust standard errors are reported in parentheses. ***P < 0.01, **P < 0.05, *P < 0.1. Source: authors’ own calculations based on PISA 2012.
Market and non-market weekly working hours
| All | Employed | |||||
|---|---|---|---|---|---|---|
| Market | Non Market | Total | Market | Non Market | Total | |
| Panel A: Low-middle income countries | ||||||
| GTM | ||||||
| Women | −30.52*** | 39.50*** | 8.98*** | −17.59*** | 27.20*** | 9.61*** |
| Constant | 38.06*** | 9.65*** | 47.71*** | 35.65*** | 11.51*** | 47.17*** |
| PRY | ||||||
| Women | −25.35*** | 37.55*** | 12.20*** | −13.35*** | 29.36*** | 16.02*** |
| Constant | 43.04*** | 17.64*** | 60.68*** | 43.29*** | 18.03*** | 61.32*** |
| SLV | ||||||
| Women | −27.62*** | 31.02*** | 3.40*** | −4.84** | 15.78*** | 10.94*** |
| Constant | 48.17*** | 12.49*** | 60.66*** | 47.53*** | 14.61*** | 62.13*** |
| Panel B: Upper-middle income countries | ||||||
| COL | ||||||
| Women | −26.93*** | 44.52*** | 17.59*** | −2.81*** | 23.55*** | 20.74*** |
| Constant | 47.31*** | 20.26*** | 67.57*** | 46.44*** | 22.33*** | 68.77*** |
| CRI | ||||||
| Women | −28.97*** | 36.72*** | 7.75*** | −8.75*** | 21.35*** | 12.59*** |
| Constant | 44.93*** | 20.13*** | 65.06*** | 45.11*** | 23.03*** | 68.14*** |
| ECU | ||||||
| Women | −29.48*** | 43.41*** | 13.48*** | −8.47*** | 34.21*** | 25.09*** |
| Constant | 47.20*** | 10.51*** | 57.39*** | 48.56*** | 12.99*** | 61.06*** |
| MEX | ||||||
| Women | −33.40*** | 49.67*** | 16.27*** | −16.04*** | 38.99*** | 22.95*** |
| Constant | 51.44*** | 19.31*** | 70.75*** | 52.29*** | 21.37*** | 73.66*** |
| PER | ||||||
| Women | −19.86*** | 34.63*** | 4.75*** | −18.04*** | 26.89*** | 9.67*** |
| Constant | 50.66*** | 15.37*** | 65.27*** | 50.98*** | 15.52*** | 65.68*** |
| Panel C: High income countries | ||||||
| ARG | ||||||
| Women | −26.70*** | 39.58*** | 12.90*** | −13.43*** | 27.67*** | 14.24*** |
| Constant | 44.83*** | 19.92*** | 64.74*** | 44.44*** | 21.79*** | 66.22*** |
| CHL | ||||||
| Women | −5.63*** | 35.03*** | 29.40*** | −0.10 | 28.66*** | 28.56*** |
| Constant | 16.51*** | 27.00*** | 43.51*** | 16.58*** | 27.70*** | 44.28*** |
| URY | ||||||
| Women | −9.69*** | 26.14*** | 6.72*** | −10.24*** | 19.43*** | 10.18*** |
| Constant | 46.62*** | 19.67*** | 65.20*** | 47.17*** | 20.20*** | 66.39*** |
| USA | ||||||
| Women | −16.29*** | 12.40*** | −3.89*** | −7.97*** | 6.90*** | −1.07 |
| Constant | 39.77*** | 14.68*** | 54.45*** | 39.46*** | 14.94*** | 54.40*** |
| All | Employed | |||||
|---|---|---|---|---|---|---|
| Market | Non Market | Total | Market | Non Market | Total | |
| Panel A: Low-middle income countries | ||||||
| GTM | ||||||
| Women | −30.52*** | 39.50*** | 8.98*** | −17.59*** | 27.20*** | 9.61*** |
| Constant | 38.06*** | 9.65*** | 47.71*** | 35.65*** | 11.51*** | 47.17*** |
| PRY | ||||||
| Women | −25.35*** | 37.55*** | 12.20*** | −13.35*** | 29.36*** | 16.02*** |
| Constant | 43.04*** | 17.64*** | 60.68*** | 43.29*** | 18.03*** | 61.32*** |
| SLV | ||||||
| Women | −27.62*** | 31.02*** | 3.40*** | −4.84** | 15.78*** | 10.94*** |
| Constant | 48.17*** | 12.49*** | 60.66*** | 47.53*** | 14.61*** | 62.13*** |
| Panel B: Upper-middle income countries | ||||||
| COL | ||||||
| Women | −26.93*** | 44.52*** | 17.59*** | −2.81*** | 23.55*** | 20.74*** |
| Constant | 47.31*** | 20.26*** | 67.57*** | 46.44*** | 22.33*** | 68.77*** |
| CRI | ||||||
| Women | −28.97*** | 36.72*** | 7.75*** | −8.75*** | 21.35*** | 12.59*** |
| Constant | 44.93*** | 20.13*** | 65.06*** | 45.11*** | 23.03*** | 68.14*** |
| ECU | ||||||
| Women | −29.48*** | 43.41*** | 13.48*** | −8.47*** | 34.21*** | 25.09*** |
| Constant | 47.20*** | 10.51*** | 57.39*** | 48.56*** | 12.99*** | 61.06*** |
| MEX | ||||||
| Women | −33.40*** | 49.67*** | 16.27*** | −16.04*** | 38.99*** | 22.95*** |
| Constant | 51.44*** | 19.31*** | 70.75*** | 52.29*** | 21.37*** | 73.66*** |
| PER | ||||||
| Women | −19.86*** | 34.63*** | 4.75*** | −18.04*** | 26.89*** | 9.67*** |
| Constant | 50.66*** | 15.37*** | 65.27*** | 50.98*** | 15.52*** | 65.68*** |
| Panel C: High income countries | ||||||
| ARG | ||||||
| Women | −26.70*** | 39.58*** | 12.90*** | −13.43*** | 27.67*** | 14.24*** |
| Constant | 44.83*** | 19.92*** | 64.74*** | 44.44*** | 21.79*** | 66.22*** |
| CHL | ||||||
| Women | −5.63*** | 35.03*** | 29.40*** | −0.10 | 28.66*** | 28.56*** |
| Constant | 16.51*** | 27.00*** | 43.51*** | 16.58*** | 27.70*** | 44.28*** |
| URY | ||||||
| Women | −9.69*** | 26.14*** | 6.72*** | −10.24*** | 19.43*** | 10.18*** |
| Constant | 46.62*** | 19.67*** | 65.20*** | 47.17*** | 20.20*** | 66.39*** |
| USA | ||||||
| Women | −16.29*** | 12.40*** | −3.89*** | −7.97*** | 6.90*** | −1.07 |
| Constant | 39.77*** | 14.68*** | 54.45*** | 39.46*** | 14.94*** | 54.40*** |
Note: Married individuals aged 25–45 years old. Columns 1 to 3 show values for the sample of married individuals. Columns 4 to 6 show values for the sample of employed married women or men whose partners are employed. Non-market hours include care activities and household chores. Each variable is equal to zero when the individual does not do such an activity. Source: authors’ own calculations based on time use surveys (GenLAC). The year of the time use surveys ranges from 2010 to 2017 (see Tables B.4 and B.1 in Appendix B, respectively).
***P < 0.01, **P < 0.05, *P < 0.1.
Market and non-market weekly working hours
| All | Employed | |||||
|---|---|---|---|---|---|---|
| Market | Non Market | Total | Market | Non Market | Total | |
| Panel A: Low-middle income countries | ||||||
| GTM | ||||||
| Women | −30.52*** | 39.50*** | 8.98*** | −17.59*** | 27.20*** | 9.61*** |
| Constant | 38.06*** | 9.65*** | 47.71*** | 35.65*** | 11.51*** | 47.17*** |
| PRY | ||||||
| Women | −25.35*** | 37.55*** | 12.20*** | −13.35*** | 29.36*** | 16.02*** |
| Constant | 43.04*** | 17.64*** | 60.68*** | 43.29*** | 18.03*** | 61.32*** |
| SLV | ||||||
| Women | −27.62*** | 31.02*** | 3.40*** | −4.84** | 15.78*** | 10.94*** |
| Constant | 48.17*** | 12.49*** | 60.66*** | 47.53*** | 14.61*** | 62.13*** |
| Panel B: Upper-middle income countries | ||||||
| COL | ||||||
| Women | −26.93*** | 44.52*** | 17.59*** | −2.81*** | 23.55*** | 20.74*** |
| Constant | 47.31*** | 20.26*** | 67.57*** | 46.44*** | 22.33*** | 68.77*** |
| CRI | ||||||
| Women | −28.97*** | 36.72*** | 7.75*** | −8.75*** | 21.35*** | 12.59*** |
| Constant | 44.93*** | 20.13*** | 65.06*** | 45.11*** | 23.03*** | 68.14*** |
| ECU | ||||||
| Women | −29.48*** | 43.41*** | 13.48*** | −8.47*** | 34.21*** | 25.09*** |
| Constant | 47.20*** | 10.51*** | 57.39*** | 48.56*** | 12.99*** | 61.06*** |
| MEX | ||||||
| Women | −33.40*** | 49.67*** | 16.27*** | −16.04*** | 38.99*** | 22.95*** |
| Constant | 51.44*** | 19.31*** | 70.75*** | 52.29*** | 21.37*** | 73.66*** |
| PER | ||||||
| Women | −19.86*** | 34.63*** | 4.75*** | −18.04*** | 26.89*** | 9.67*** |
| Constant | 50.66*** | 15.37*** | 65.27*** | 50.98*** | 15.52*** | 65.68*** |
| Panel C: High income countries | ||||||
| ARG | ||||||
| Women | −26.70*** | 39.58*** | 12.90*** | −13.43*** | 27.67*** | 14.24*** |
| Constant | 44.83*** | 19.92*** | 64.74*** | 44.44*** | 21.79*** | 66.22*** |
| CHL | ||||||
| Women | −5.63*** | 35.03*** | 29.40*** | −0.10 | 28.66*** | 28.56*** |
| Constant | 16.51*** | 27.00*** | 43.51*** | 16.58*** | 27.70*** | 44.28*** |
| URY | ||||||
| Women | −9.69*** | 26.14*** | 6.72*** | −10.24*** | 19.43*** | 10.18*** |
| Constant | 46.62*** | 19.67*** | 65.20*** | 47.17*** | 20.20*** | 66.39*** |
| USA | ||||||
| Women | −16.29*** | 12.40*** | −3.89*** | −7.97*** | 6.90*** | −1.07 |
| Constant | 39.77*** | 14.68*** | 54.45*** | 39.46*** | 14.94*** | 54.40*** |
| All | Employed | |||||
|---|---|---|---|---|---|---|
| Market | Non Market | Total | Market | Non Market | Total | |
| Panel A: Low-middle income countries | ||||||
| GTM | ||||||
| Women | −30.52*** | 39.50*** | 8.98*** | −17.59*** | 27.20*** | 9.61*** |
| Constant | 38.06*** | 9.65*** | 47.71*** | 35.65*** | 11.51*** | 47.17*** |
| PRY | ||||||
| Women | −25.35*** | 37.55*** | 12.20*** | −13.35*** | 29.36*** | 16.02*** |
| Constant | 43.04*** | 17.64*** | 60.68*** | 43.29*** | 18.03*** | 61.32*** |
| SLV | ||||||
| Women | −27.62*** | 31.02*** | 3.40*** | −4.84** | 15.78*** | 10.94*** |
| Constant | 48.17*** | 12.49*** | 60.66*** | 47.53*** | 14.61*** | 62.13*** |
| Panel B: Upper-middle income countries | ||||||
| COL | ||||||
| Women | −26.93*** | 44.52*** | 17.59*** | −2.81*** | 23.55*** | 20.74*** |
| Constant | 47.31*** | 20.26*** | 67.57*** | 46.44*** | 22.33*** | 68.77*** |
| CRI | ||||||
| Women | −28.97*** | 36.72*** | 7.75*** | −8.75*** | 21.35*** | 12.59*** |
| Constant | 44.93*** | 20.13*** | 65.06*** | 45.11*** | 23.03*** | 68.14*** |
| ECU | ||||||
| Women | −29.48*** | 43.41*** | 13.48*** | −8.47*** | 34.21*** | 25.09*** |
| Constant | 47.20*** | 10.51*** | 57.39*** | 48.56*** | 12.99*** | 61.06*** |
| MEX | ||||||
| Women | −33.40*** | 49.67*** | 16.27*** | −16.04*** | 38.99*** | 22.95*** |
| Constant | 51.44*** | 19.31*** | 70.75*** | 52.29*** | 21.37*** | 73.66*** |
| PER | ||||||
| Women | −19.86*** | 34.63*** | 4.75*** | −18.04*** | 26.89*** | 9.67*** |
| Constant | 50.66*** | 15.37*** | 65.27*** | 50.98*** | 15.52*** | 65.68*** |
| Panel C: High income countries | ||||||
| ARG | ||||||
| Women | −26.70*** | 39.58*** | 12.90*** | −13.43*** | 27.67*** | 14.24*** |
| Constant | 44.83*** | 19.92*** | 64.74*** | 44.44*** | 21.79*** | 66.22*** |
| CHL | ||||||
| Women | −5.63*** | 35.03*** | 29.40*** | −0.10 | 28.66*** | 28.56*** |
| Constant | 16.51*** | 27.00*** | 43.51*** | 16.58*** | 27.70*** | 44.28*** |
| URY | ||||||
| Women | −9.69*** | 26.14*** | 6.72*** | −10.24*** | 19.43*** | 10.18*** |
| Constant | 46.62*** | 19.67*** | 65.20*** | 47.17*** | 20.20*** | 66.39*** |
| USA | ||||||
| Women | −16.29*** | 12.40*** | −3.89*** | −7.97*** | 6.90*** | −1.07 |
| Constant | 39.77*** | 14.68*** | 54.45*** | 39.46*** | 14.94*** | 54.40*** |
Note: Married individuals aged 25–45 years old. Columns 1 to 3 show values for the sample of married individuals. Columns 4 to 6 show values for the sample of employed married women or men whose partners are employed. Non-market hours include care activities and household chores. Each variable is equal to zero when the individual does not do such an activity. Source: authors’ own calculations based on time use surveys (GenLAC). The year of the time use surveys ranges from 2010 to 2017 (see Tables B.4 and B.1 in Appendix B, respectively).
***P < 0.01, **P < 0.05, *P < 0.1.
Oaxaca-Blinder Decomposition
| Contribution of Explanatory Variables to the Gender Wage Gap | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Log points | Percent of gender gap explained | F/M log wage | ||||||||||||
| Age + location | Educ. | Job charact. | Total expl. Gap | Total unexpl. Gap | Total pay gap | Age + location | Educ. | Job charact. | Total expl. Gap | Total unexpl. Gap | Total pay gap | Unadjusted | Adjusted | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | |
| Panel A: LMI BOL | 0.0098 | −0.0044 | −0.1042 | −0.0988 | 0.2978 | 0.1990 | 4.9% | −2.2% | −52.4% | −49.7% | 149.7% | 100% | 82.0% | 74.2% |
| NIC | 0.0224 | −0.0388 | −0.1633 | −0.1796 | 0.2171 | 0.0375 | 59.9% | −103.5% | −435.8% | −479.4% | 579.4% | 100% | 96.3% | 80.5% |
| SLV | 0.0003 | −0.0148 | −0.1456 | −0.1602 | 0.1592 | −0.0010 | −27.6% | 1477.0% | 14 487.7% | 15 937.1% | −15 837.1% | 100% | 100.1% | 85.3% |
| HND | −0.0728 | −0.0952 | −0.4035 | −0.5715 | 0.3075 | −0.2640 | 27.6% | 36.1% | 152.8% | 216.5% | −116.5% | 100% | 130.2% | 73.5% |
| Panel B: UMI PER | −0.0140 | 0.0005 | 0.0113 | −0.0022 | 0.2673 | 0.2651 | −5.3% | 0.2% | 4.3% | −0.8% | 100.8% | 100% | 76.7% | 76.5% |
| DOM | −0.0004 | −0.0493 | −0.0296 | −0.0792 | 0.2770 | 0.1978 | −0.2% | −24.9% | −14.9% | −40.0% | 140.0% | 100% | 82.1% | 75.8% |
| MEX | −0.0064 | −0.0306 | −0.1256 | −0.1626 | 0.3104 | 0.1478 | −4.3% | −20.7% | −85.0% | −110.0% | 210.0% | 100% | 86.3% | 73.3% |
| ECU | 0.0028 | −0.0240 | −0.0949 | −0.1160 | 0.1996 | 0.0836 | 3.4% | −28.7% | −113.5% | −138.8% | 238.8% | 100% | 92.0% | 81.9% |
| BRA | −0.0093 | −0.0699 | −0.0500 | −0.1292 | 0.2110 | 0.0818 | −11.4% | −85.5% | −61.1% | −158.1% | 258.1% | 100% | 92.1% | 81.0% |
| COL | −0.0139 | −0.0674 | −0.0848 | −0.1662 | 0.2128 | 0.0465 | −29.9% | −144.9% | −182.3% | −357.2% | 457.2% | 100% | 95.5% | 80.8% |
| CRI | 0.0061 | −0.0571 | −0.1273 | −0.1784 | 0.1676 | −0.0107 | −56.6% | 531.6% | 1185.5% | 1660.5% | −1560.5% | 100% | 101.1% | 84.6% |
| Panel C: HI ARG | −0.0039 | −0.0651 | −0.0734 | −0.1424 | 0.1897 | 0.0473 | −8.3% | −137.8% | −155.2% | −301.3% | 401.3% | 100% | 95.4% | 82.7% |
| CHL | 0.0003 | −0.0298 | −0.0234 | −0.0529 | 0.1827 | 0.1298 | 0.2% | −22.9% | −18.0% | −40.7% | 140.7% | 100% | 87.8% | 83.3% |
| PAN | −0.0124 | −0.0854 | −0.0859 | −0.1837 | 0.1816 | −0.0020 | 611.0% | 4218.6% | 4242.4% | 9072.0% | −8972.0% | 100% | 100.2% | 83.4% |
| URY | −0.0030 | −0.0478 | −0.0675 | −0.1184 | 0.1838 | 0.0655 | −4.6% | −73.0% | −103.2% | −180.8% | 280.8% | 100% | 93.7% | 83.2% |
| US | 0.0004 | −0.0226 | 0.0400 | 0.0178 | 0.1597 | 0.1775 | 0.2% | −12.7% | 22.5% | 10.0% | 90.0% | 100% | 83.7% | 85.2% |
| Contribution of Explanatory Variables to the Gender Wage Gap | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Log points | Percent of gender gap explained | F/M log wage | ||||||||||||
| Age + location | Educ. | Job charact. | Total expl. Gap | Total unexpl. Gap | Total pay gap | Age + location | Educ. | Job charact. | Total expl. Gap | Total unexpl. Gap | Total pay gap | Unadjusted | Adjusted | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | |
| Panel A: LMI BOL | 0.0098 | −0.0044 | −0.1042 | −0.0988 | 0.2978 | 0.1990 | 4.9% | −2.2% | −52.4% | −49.7% | 149.7% | 100% | 82.0% | 74.2% |
| NIC | 0.0224 | −0.0388 | −0.1633 | −0.1796 | 0.2171 | 0.0375 | 59.9% | −103.5% | −435.8% | −479.4% | 579.4% | 100% | 96.3% | 80.5% |
| SLV | 0.0003 | −0.0148 | −0.1456 | −0.1602 | 0.1592 | −0.0010 | −27.6% | 1477.0% | 14 487.7% | 15 937.1% | −15 837.1% | 100% | 100.1% | 85.3% |
| HND | −0.0728 | −0.0952 | −0.4035 | −0.5715 | 0.3075 | −0.2640 | 27.6% | 36.1% | 152.8% | 216.5% | −116.5% | 100% | 130.2% | 73.5% |
| Panel B: UMI PER | −0.0140 | 0.0005 | 0.0113 | −0.0022 | 0.2673 | 0.2651 | −5.3% | 0.2% | 4.3% | −0.8% | 100.8% | 100% | 76.7% | 76.5% |
| DOM | −0.0004 | −0.0493 | −0.0296 | −0.0792 | 0.2770 | 0.1978 | −0.2% | −24.9% | −14.9% | −40.0% | 140.0% | 100% | 82.1% | 75.8% |
| MEX | −0.0064 | −0.0306 | −0.1256 | −0.1626 | 0.3104 | 0.1478 | −4.3% | −20.7% | −85.0% | −110.0% | 210.0% | 100% | 86.3% | 73.3% |
| ECU | 0.0028 | −0.0240 | −0.0949 | −0.1160 | 0.1996 | 0.0836 | 3.4% | −28.7% | −113.5% | −138.8% | 238.8% | 100% | 92.0% | 81.9% |
| BRA | −0.0093 | −0.0699 | −0.0500 | −0.1292 | 0.2110 | 0.0818 | −11.4% | −85.5% | −61.1% | −158.1% | 258.1% | 100% | 92.1% | 81.0% |
| COL | −0.0139 | −0.0674 | −0.0848 | −0.1662 | 0.2128 | 0.0465 | −29.9% | −144.9% | −182.3% | −357.2% | 457.2% | 100% | 95.5% | 80.8% |
| CRI | 0.0061 | −0.0571 | −0.1273 | −0.1784 | 0.1676 | −0.0107 | −56.6% | 531.6% | 1185.5% | 1660.5% | −1560.5% | 100% | 101.1% | 84.6% |
| Panel C: HI ARG | −0.0039 | −0.0651 | −0.0734 | −0.1424 | 0.1897 | 0.0473 | −8.3% | −137.8% | −155.2% | −301.3% | 401.3% | 100% | 95.4% | 82.7% |
| CHL | 0.0003 | −0.0298 | −0.0234 | −0.0529 | 0.1827 | 0.1298 | 0.2% | −22.9% | −18.0% | −40.7% | 140.7% | 100% | 87.8% | 83.3% |
| PAN | −0.0124 | −0.0854 | −0.0859 | −0.1837 | 0.1816 | −0.0020 | 611.0% | 4218.6% | 4242.4% | 9072.0% | −8972.0% | 100% | 100.2% | 83.4% |
| URY | −0.0030 | −0.0478 | −0.0675 | −0.1184 | 0.1838 | 0.0655 | −4.6% | −73.0% | −103.2% | −180.8% | 280.8% | 100% | 93.7% | 83.2% |
| US | 0.0004 | −0.0226 | 0.0400 | 0.0178 | 0.1597 | 0.1775 | 0.2% | −12.7% | 22.5% | 10.0% | 90.0% | 100% | 83.7% | 85.2% |
Note: Workers aged 25–55 years old, working at least 20 h a week. The model used for the Oaxaca-Blinder decomposition controls for age, age squared, region of residence, an indicator for living in a rural area, education, sector, occupation (2-digits codes ISCO), an indicator for full-time worker (35+ h a week), and another for working in the informal sector. Columns 1 to 3 show the result of multiplying the male–female difference in the specified set of variables by the male log wage coefficients associated with those variables. ‘Age + location’ combines the coefficients of age, region and urban/rural areas. ‘Educ.’ aggregates the education level dummies, while ‘job characteristics’ sums up the explanatory contribution of sector, occupation, full-time and informal dummies. Column 4 displays the total explained part in the Oaxaca-Blinder decomposition, and Column 5 presents the unexplained part. Columns 7 to 12 show the ratio—multiplied by 100—of the respective values shown in Columns 1 to 6 to the total pay gap (i.e. column 6). The unadjusted gender wage gap (column 13) is calculated as the inverse of the exponential of the values shown in column 6, and the adjusted gender gap (column 14) is calculated as the inverse of the exponential of the values shown in column 5. Source: authors’ own calculations based on LA household surveys (GenLAC) and the American Community Survey. Survey year is 2019 or the latest year available up to 2019 (see Table B.1 in Appendix B).
Oaxaca-Blinder Decomposition
| Contribution of Explanatory Variables to the Gender Wage Gap | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Log points | Percent of gender gap explained | F/M log wage | ||||||||||||
| Age + location | Educ. | Job charact. | Total expl. Gap | Total unexpl. Gap | Total pay gap | Age + location | Educ. | Job charact. | Total expl. Gap | Total unexpl. Gap | Total pay gap | Unadjusted | Adjusted | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | |
| Panel A: LMI BOL | 0.0098 | −0.0044 | −0.1042 | −0.0988 | 0.2978 | 0.1990 | 4.9% | −2.2% | −52.4% | −49.7% | 149.7% | 100% | 82.0% | 74.2% |
| NIC | 0.0224 | −0.0388 | −0.1633 | −0.1796 | 0.2171 | 0.0375 | 59.9% | −103.5% | −435.8% | −479.4% | 579.4% | 100% | 96.3% | 80.5% |
| SLV | 0.0003 | −0.0148 | −0.1456 | −0.1602 | 0.1592 | −0.0010 | −27.6% | 1477.0% | 14 487.7% | 15 937.1% | −15 837.1% | 100% | 100.1% | 85.3% |
| HND | −0.0728 | −0.0952 | −0.4035 | −0.5715 | 0.3075 | −0.2640 | 27.6% | 36.1% | 152.8% | 216.5% | −116.5% | 100% | 130.2% | 73.5% |
| Panel B: UMI PER | −0.0140 | 0.0005 | 0.0113 | −0.0022 | 0.2673 | 0.2651 | −5.3% | 0.2% | 4.3% | −0.8% | 100.8% | 100% | 76.7% | 76.5% |
| DOM | −0.0004 | −0.0493 | −0.0296 | −0.0792 | 0.2770 | 0.1978 | −0.2% | −24.9% | −14.9% | −40.0% | 140.0% | 100% | 82.1% | 75.8% |
| MEX | −0.0064 | −0.0306 | −0.1256 | −0.1626 | 0.3104 | 0.1478 | −4.3% | −20.7% | −85.0% | −110.0% | 210.0% | 100% | 86.3% | 73.3% |
| ECU | 0.0028 | −0.0240 | −0.0949 | −0.1160 | 0.1996 | 0.0836 | 3.4% | −28.7% | −113.5% | −138.8% | 238.8% | 100% | 92.0% | 81.9% |
| BRA | −0.0093 | −0.0699 | −0.0500 | −0.1292 | 0.2110 | 0.0818 | −11.4% | −85.5% | −61.1% | −158.1% | 258.1% | 100% | 92.1% | 81.0% |
| COL | −0.0139 | −0.0674 | −0.0848 | −0.1662 | 0.2128 | 0.0465 | −29.9% | −144.9% | −182.3% | −357.2% | 457.2% | 100% | 95.5% | 80.8% |
| CRI | 0.0061 | −0.0571 | −0.1273 | −0.1784 | 0.1676 | −0.0107 | −56.6% | 531.6% | 1185.5% | 1660.5% | −1560.5% | 100% | 101.1% | 84.6% |
| Panel C: HI ARG | −0.0039 | −0.0651 | −0.0734 | −0.1424 | 0.1897 | 0.0473 | −8.3% | −137.8% | −155.2% | −301.3% | 401.3% | 100% | 95.4% | 82.7% |
| CHL | 0.0003 | −0.0298 | −0.0234 | −0.0529 | 0.1827 | 0.1298 | 0.2% | −22.9% | −18.0% | −40.7% | 140.7% | 100% | 87.8% | 83.3% |
| PAN | −0.0124 | −0.0854 | −0.0859 | −0.1837 | 0.1816 | −0.0020 | 611.0% | 4218.6% | 4242.4% | 9072.0% | −8972.0% | 100% | 100.2% | 83.4% |
| URY | −0.0030 | −0.0478 | −0.0675 | −0.1184 | 0.1838 | 0.0655 | −4.6% | −73.0% | −103.2% | −180.8% | 280.8% | 100% | 93.7% | 83.2% |
| US | 0.0004 | −0.0226 | 0.0400 | 0.0178 | 0.1597 | 0.1775 | 0.2% | −12.7% | 22.5% | 10.0% | 90.0% | 100% | 83.7% | 85.2% |
| Contribution of Explanatory Variables to the Gender Wage Gap | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Log points | Percent of gender gap explained | F/M log wage | ||||||||||||
| Age + location | Educ. | Job charact. | Total expl. Gap | Total unexpl. Gap | Total pay gap | Age + location | Educ. | Job charact. | Total expl. Gap | Total unexpl. Gap | Total pay gap | Unadjusted | Adjusted | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | |
| Panel A: LMI BOL | 0.0098 | −0.0044 | −0.1042 | −0.0988 | 0.2978 | 0.1990 | 4.9% | −2.2% | −52.4% | −49.7% | 149.7% | 100% | 82.0% | 74.2% |
| NIC | 0.0224 | −0.0388 | −0.1633 | −0.1796 | 0.2171 | 0.0375 | 59.9% | −103.5% | −435.8% | −479.4% | 579.4% | 100% | 96.3% | 80.5% |
| SLV | 0.0003 | −0.0148 | −0.1456 | −0.1602 | 0.1592 | −0.0010 | −27.6% | 1477.0% | 14 487.7% | 15 937.1% | −15 837.1% | 100% | 100.1% | 85.3% |
| HND | −0.0728 | −0.0952 | −0.4035 | −0.5715 | 0.3075 | −0.2640 | 27.6% | 36.1% | 152.8% | 216.5% | −116.5% | 100% | 130.2% | 73.5% |
| Panel B: UMI PER | −0.0140 | 0.0005 | 0.0113 | −0.0022 | 0.2673 | 0.2651 | −5.3% | 0.2% | 4.3% | −0.8% | 100.8% | 100% | 76.7% | 76.5% |
| DOM | −0.0004 | −0.0493 | −0.0296 | −0.0792 | 0.2770 | 0.1978 | −0.2% | −24.9% | −14.9% | −40.0% | 140.0% | 100% | 82.1% | 75.8% |
| MEX | −0.0064 | −0.0306 | −0.1256 | −0.1626 | 0.3104 | 0.1478 | −4.3% | −20.7% | −85.0% | −110.0% | 210.0% | 100% | 86.3% | 73.3% |
| ECU | 0.0028 | −0.0240 | −0.0949 | −0.1160 | 0.1996 | 0.0836 | 3.4% | −28.7% | −113.5% | −138.8% | 238.8% | 100% | 92.0% | 81.9% |
| BRA | −0.0093 | −0.0699 | −0.0500 | −0.1292 | 0.2110 | 0.0818 | −11.4% | −85.5% | −61.1% | −158.1% | 258.1% | 100% | 92.1% | 81.0% |
| COL | −0.0139 | −0.0674 | −0.0848 | −0.1662 | 0.2128 | 0.0465 | −29.9% | −144.9% | −182.3% | −357.2% | 457.2% | 100% | 95.5% | 80.8% |
| CRI | 0.0061 | −0.0571 | −0.1273 | −0.1784 | 0.1676 | −0.0107 | −56.6% | 531.6% | 1185.5% | 1660.5% | −1560.5% | 100% | 101.1% | 84.6% |
| Panel C: HI ARG | −0.0039 | −0.0651 | −0.0734 | −0.1424 | 0.1897 | 0.0473 | −8.3% | −137.8% | −155.2% | −301.3% | 401.3% | 100% | 95.4% | 82.7% |
| CHL | 0.0003 | −0.0298 | −0.0234 | −0.0529 | 0.1827 | 0.1298 | 0.2% | −22.9% | −18.0% | −40.7% | 140.7% | 100% | 87.8% | 83.3% |
| PAN | −0.0124 | −0.0854 | −0.0859 | −0.1837 | 0.1816 | −0.0020 | 611.0% | 4218.6% | 4242.4% | 9072.0% | −8972.0% | 100% | 100.2% | 83.4% |
| URY | −0.0030 | −0.0478 | −0.0675 | −0.1184 | 0.1838 | 0.0655 | −4.6% | −73.0% | −103.2% | −180.8% | 280.8% | 100% | 93.7% | 83.2% |
| US | 0.0004 | −0.0226 | 0.0400 | 0.0178 | 0.1597 | 0.1775 | 0.2% | −12.7% | 22.5% | 10.0% | 90.0% | 100% | 83.7% | 85.2% |
Note: Workers aged 25–55 years old, working at least 20 h a week. The model used for the Oaxaca-Blinder decomposition controls for age, age squared, region of residence, an indicator for living in a rural area, education, sector, occupation (2-digits codes ISCO), an indicator for full-time worker (35+ h a week), and another for working in the informal sector. Columns 1 to 3 show the result of multiplying the male–female difference in the specified set of variables by the male log wage coefficients associated with those variables. ‘Age + location’ combines the coefficients of age, region and urban/rural areas. ‘Educ.’ aggregates the education level dummies, while ‘job characteristics’ sums up the explanatory contribution of sector, occupation, full-time and informal dummies. Column 4 displays the total explained part in the Oaxaca-Blinder decomposition, and Column 5 presents the unexplained part. Columns 7 to 12 show the ratio—multiplied by 100—of the respective values shown in Columns 1 to 6 to the total pay gap (i.e. column 6). The unadjusted gender wage gap (column 13) is calculated as the inverse of the exponential of the values shown in column 6, and the adjusted gender gap (column 14) is calculated as the inverse of the exponential of the values shown in column 5. Source: authors’ own calculations based on LA household surveys (GenLAC) and the American Community Survey. Survey year is 2019 or the latest year available up to 2019 (see Table B.1 in Appendix B).
Gender gaps in Pisa scores in math in 2022 (F-M). Note: The figure shows the gender gaps (F-M) in math scores in 2022, for 15 years old children. The test score scale has a standard deviation of 100 points. Source: Authors’ own calculations based on PISA 2022 (program for international student assessment, OECD)
Gender gaps in Pisa scores in reading in 2022 (F-M). Note: The figure shows the gender gaps (F-M) in reading scores in 2022, for 15 years old children. The test score scale has a standard deviation of 100 points. Source: Authors’ own calculations based on PISA 2022 (program for international student assessment, OECD)
Female share of graduates in a given field of education: High income countries. Source: World Bank. Argentina (2011), Chile (2017), Panama (2016) and Uruguay (2017)
Female share of graduates in a given field of education: Upper middle income countries. Source: World Bank. Brazil (2017), Colombia (2018), Dominican Republic (2017), Ecuador (2016), Mexico (2017) and Peru (2017)
Female share of graduates in a given field of education: Lower middle income countries. Source: World Bank. El Salvador (2018), Guatemala (2015) and Honduras (2018)
Women have the same capacity for science and technology as men: % of individuals agreeing, by education and age. Note: Individuals aged 25–55 years old. This figure shows, by education level and cohort, the percentage of individuals who agree or strongly agree with the statement ‘Women have the same capacity for science and technology as men.’ ‘low edu’ refers to individuals with less than a high school education; ‘medium edu’ denotes high school graduates without higher education; and ‘high edu’ indicates those who have completed tertiary education. The bars show unweighted means. Source: Authors’ own calculations based on Latinobarometro, 2018
Evolution of female LFP across cohorts in high income countries, by education. Note: These figures show the share of the population that is economically active, categorized by education level, over the life cycle and for six different cohorts: 1930–39, 1940–49, 1950–59, 1960–69, 1970–79 and 1980–89. Education levels are defined as follows: ‘Low’ refers to less than high-school education; ‘medium’ denotes high school graduates without higher education; and ‘high’ indicates completed tertiary education. Source: Authors’ own calculations based on IPUMS International’s harmonized census microdata. We use censuses from the 1960s up to the 2010s. The years of the censuses differ by country (see Table B.4 in Appendix Section B.7)
Evolution of female LFP across cohorts in upper middle income countries, by education. Note: See note to Fig. A.7
Evolution of female LFP across cohorts in lower middle income countries, by education. Note: See note to Fig. A.7
Evolution of female LFP across cohorts in US, France and Spain, by education. Note: See note to Fig. A.7
B. Appendix: Data Sources
Definitions of countries income groups and education categories
Countries income groups
Countries were classified into different income groups based on their GNI per capita (in US$) for the period 2010–2020 and the corresponding World Bank Analytical Classifications. Each country was assigned to one of the following three groups:
Lower-middle income (LMI): Countries that were considered LMI at least once in 2010–2020.
Upper-middle income (UMI): Countries that were considered UMI during the entire 2010–2020 period.
High income (HI): Countries that were considered HI at least once in 2010–2020.
Education categories
Low: less than high-school education.
Medium: high school graduates without higher education.
High: higher education completed.
GenLAC–CEDLAS household surveys data
GenLAC is the CEDLAS (Center for Distributive, Labor and Social Studies) initiative to promote gender equity through the generation, analysis and dissemination of evidence for Latin America and the Caribbean. For more information about GenLAC, visit www.genlac.econo.unlp.edu.ar.
In this chapter we use their processed microdata, from more than 300 household surveys conducted in Latin American countries and in the Dominican Republic, to compute statistics. Table B.1 lists the surveys used in this chapter for 17 LA countries. Surveys are nationally representative (with the exception of Uruguay before to 2006 and Argentina, where surveys include mainly the urban population, which accounts for around 85% of the total population of both countries).
Household surveys included in the analysis
| Country | Survey |
|---|---|
| Argentina | Encuesta Permanente de Hogares Continua (yearly, from 2003 to 2019) |
| Bolivia | Encuesta de Hogares (yearly, from 2001 to 2019) |
| Brazil | Pesquisa Nacional por Amostra de Domicilios—Continua (yearly, from 2001 to 2019) Chile Encuesta de Caracterizaci’on Socioecon’omica Nacional (2000 & biyearly in 2003–2017) |
| Colombia | Gran Encuesta Integrada de Hogares (yearly, from 2001 to 2019) |
| Costa Rica | Encuesta Nacional de Hogares (yearly, from 2000 to 2019) |
| Dom. Republic | Encuesta Continua Nacional de la Fuerza de Trabajo (yearly, from 2000 to 2019) |
| Ecuador | Encuesta de Empleo, Desempleo y Subempleo (yearly, from 2003 to 2019) |
| Guatemala | Encuesta Nacional de Condiciones de Vida (2004, 2006, 2011 & 2014) |
| Honduras | Encuesta Permanente de Hogares de Prop’ositos Múltiples (yearly, from 2001 to 2019) |
| Mexico | Encuesta Nacional de Ingresos y Gastos de los hogares (biyearly from 2000 to 2018 Nicaragua Encuesta Nacional de Hogares sobre Medici’on de Nivel de Vida (2001, 2005, 2009, 2014) |
| Panama | Encuesta de Hogares (yearly, from 2000 to 2019) |
| Peru | Encuesta Nacional de Hogares (yearly, from 2000 to 2019) |
| Paraguay | Encuesta Permanente de Hogares (yearly, from 2001 to 2019) |
| El Salvador | Encuesta de Hogares de Prop’ositos Múltiples (yearly, from 2000 to 2019) |
| Uruguay | Encuesta Continua de Hogares (yearly, from 2000 to 2019) |
| Country | Survey |
|---|---|
| Argentina | Encuesta Permanente de Hogares Continua (yearly, from 2003 to 2019) |
| Bolivia | Encuesta de Hogares (yearly, from 2001 to 2019) |
| Brazil | Pesquisa Nacional por Amostra de Domicilios—Continua (yearly, from 2001 to 2019) Chile Encuesta de Caracterizaci’on Socioecon’omica Nacional (2000 & biyearly in 2003–2017) |
| Colombia | Gran Encuesta Integrada de Hogares (yearly, from 2001 to 2019) |
| Costa Rica | Encuesta Nacional de Hogares (yearly, from 2000 to 2019) |
| Dom. Republic | Encuesta Continua Nacional de la Fuerza de Trabajo (yearly, from 2000 to 2019) |
| Ecuador | Encuesta de Empleo, Desempleo y Subempleo (yearly, from 2003 to 2019) |
| Guatemala | Encuesta Nacional de Condiciones de Vida (2004, 2006, 2011 & 2014) |
| Honduras | Encuesta Permanente de Hogares de Prop’ositos Múltiples (yearly, from 2001 to 2019) |
| Mexico | Encuesta Nacional de Ingresos y Gastos de los hogares (biyearly from 2000 to 2018 Nicaragua Encuesta Nacional de Hogares sobre Medici’on de Nivel de Vida (2001, 2005, 2009, 2014) |
| Panama | Encuesta de Hogares (yearly, from 2000 to 2019) |
| Peru | Encuesta Nacional de Hogares (yearly, from 2000 to 2019) |
| Paraguay | Encuesta Permanente de Hogares (yearly, from 2001 to 2019) |
| El Salvador | Encuesta de Hogares de Prop’ositos Múltiples (yearly, from 2000 to 2019) |
| Uruguay | Encuesta Continua de Hogares (yearly, from 2000 to 2019) |
Note: All samples are restricted to individuals aged 25–55. Source: GenLAC—Evidence for gender equity in Latin America and the Caribbean (CEDLAS, 2022).
Household surveys included in the analysis
| Country | Survey |
|---|---|
| Argentina | Encuesta Permanente de Hogares Continua (yearly, from 2003 to 2019) |
| Bolivia | Encuesta de Hogares (yearly, from 2001 to 2019) |
| Brazil | Pesquisa Nacional por Amostra de Domicilios—Continua (yearly, from 2001 to 2019) Chile Encuesta de Caracterizaci’on Socioecon’omica Nacional (2000 & biyearly in 2003–2017) |
| Colombia | Gran Encuesta Integrada de Hogares (yearly, from 2001 to 2019) |
| Costa Rica | Encuesta Nacional de Hogares (yearly, from 2000 to 2019) |
| Dom. Republic | Encuesta Continua Nacional de la Fuerza de Trabajo (yearly, from 2000 to 2019) |
| Ecuador | Encuesta de Empleo, Desempleo y Subempleo (yearly, from 2003 to 2019) |
| Guatemala | Encuesta Nacional de Condiciones de Vida (2004, 2006, 2011 & 2014) |
| Honduras | Encuesta Permanente de Hogares de Prop’ositos Múltiples (yearly, from 2001 to 2019) |
| Mexico | Encuesta Nacional de Ingresos y Gastos de los hogares (biyearly from 2000 to 2018 Nicaragua Encuesta Nacional de Hogares sobre Medici’on de Nivel de Vida (2001, 2005, 2009, 2014) |
| Panama | Encuesta de Hogares (yearly, from 2000 to 2019) |
| Peru | Encuesta Nacional de Hogares (yearly, from 2000 to 2019) |
| Paraguay | Encuesta Permanente de Hogares (yearly, from 2001 to 2019) |
| El Salvador | Encuesta de Hogares de Prop’ositos Múltiples (yearly, from 2000 to 2019) |
| Uruguay | Encuesta Continua de Hogares (yearly, from 2000 to 2019) |
| Country | Survey |
|---|---|
| Argentina | Encuesta Permanente de Hogares Continua (yearly, from 2003 to 2019) |
| Bolivia | Encuesta de Hogares (yearly, from 2001 to 2019) |
| Brazil | Pesquisa Nacional por Amostra de Domicilios—Continua (yearly, from 2001 to 2019) Chile Encuesta de Caracterizaci’on Socioecon’omica Nacional (2000 & biyearly in 2003–2017) |
| Colombia | Gran Encuesta Integrada de Hogares (yearly, from 2001 to 2019) |
| Costa Rica | Encuesta Nacional de Hogares (yearly, from 2000 to 2019) |
| Dom. Republic | Encuesta Continua Nacional de la Fuerza de Trabajo (yearly, from 2000 to 2019) |
| Ecuador | Encuesta de Empleo, Desempleo y Subempleo (yearly, from 2003 to 2019) |
| Guatemala | Encuesta Nacional de Condiciones de Vida (2004, 2006, 2011 & 2014) |
| Honduras | Encuesta Permanente de Hogares de Prop’ositos Múltiples (yearly, from 2001 to 2019) |
| Mexico | Encuesta Nacional de Ingresos y Gastos de los hogares (biyearly from 2000 to 2018 Nicaragua Encuesta Nacional de Hogares sobre Medici’on de Nivel de Vida (2001, 2005, 2009, 2014) |
| Panama | Encuesta de Hogares (yearly, from 2000 to 2019) |
| Peru | Encuesta Nacional de Hogares (yearly, from 2000 to 2019) |
| Paraguay | Encuesta Permanente de Hogares (yearly, from 2001 to 2019) |
| El Salvador | Encuesta de Hogares de Prop’ositos Múltiples (yearly, from 2000 to 2019) |
| Uruguay | Encuesta Continua de Hogares (yearly, from 2000 to 2019) |
Note: All samples are restricted to individuals aged 25–55. Source: GenLAC—Evidence for gender equity in Latin America and the Caribbean (CEDLAS, 2022).
Definitions of labor market variables
| Variable | Definition |
|---|---|
| Labor force participation | Economically active population as a percentage of the population aged 25–55. A person is considered economically active if she is either employed or unemployed. |
| Hours worked | Weekly hours worked in a paid job for workers aged 25–55, including all jobs. |
| Hourly wage | Average hourly wage in main occupation (in 2005 PPP USD), for workers aged 25–55 with positive earnings and positive hours worked. |
| Informality | Workers in informal jobs as a percentage of the employed population aged 25–55. Informal workers include wage workers without access to social security, self-employed workers who have not completed higher education and zero-income workers. |
| Employer | Employers as a percentage of the employed population aged 25–55. |
| Wage employee | Wage employees as a percentage of the employed population aged 25–55. |
| Self-employment | Self-employed workers as a percentage of the employed population aged 25–55. |
| Unpaid workers | Unpaid workers (mainly family workers or apprentices) as a percentage of the employed population aged 25–55. |
| Variable | Definition |
| Large-firm workers Occupation | Workers employed in large firms (with five or more employees) as a percentage of the employed population aged 25–55. Occupation held in a worker’s main job, based on the 2-digit International Standard Classification of Occupations of 2008 (ISCO-08). Not all surveys in the region allow for this classification. |
| Sector | Sector of activity of a worker’s main job, based on the 1-digit International Standard Industrial Classification (ISIC-Revision 3). |
| Variable | Definition |
|---|---|
| Labor force participation | Economically active population as a percentage of the population aged 25–55. A person is considered economically active if she is either employed or unemployed. |
| Hours worked | Weekly hours worked in a paid job for workers aged 25–55, including all jobs. |
| Hourly wage | Average hourly wage in main occupation (in 2005 PPP USD), for workers aged 25–55 with positive earnings and positive hours worked. |
| Informality | Workers in informal jobs as a percentage of the employed population aged 25–55. Informal workers include wage workers without access to social security, self-employed workers who have not completed higher education and zero-income workers. |
| Employer | Employers as a percentage of the employed population aged 25–55. |
| Wage employee | Wage employees as a percentage of the employed population aged 25–55. |
| Self-employment | Self-employed workers as a percentage of the employed population aged 25–55. |
| Unpaid workers | Unpaid workers (mainly family workers or apprentices) as a percentage of the employed population aged 25–55. |
| Variable | Definition |
| Large-firm workers Occupation | Workers employed in large firms (with five or more employees) as a percentage of the employed population aged 25–55. Occupation held in a worker’s main job, based on the 2-digit International Standard Classification of Occupations of 2008 (ISCO-08). Not all surveys in the region allow for this classification. |
| Sector | Sector of activity of a worker’s main job, based on the 1-digit International Standard Industrial Classification (ISIC-Revision 3). |
Note: For more details about variables’ construction and definitions refer to GenLAC methodological documents.
Definitions of labor market variables
| Variable | Definition |
|---|---|
| Labor force participation | Economically active population as a percentage of the population aged 25–55. A person is considered economically active if she is either employed or unemployed. |
| Hours worked | Weekly hours worked in a paid job for workers aged 25–55, including all jobs. |
| Hourly wage | Average hourly wage in main occupation (in 2005 PPP USD), for workers aged 25–55 with positive earnings and positive hours worked. |
| Informality | Workers in informal jobs as a percentage of the employed population aged 25–55. Informal workers include wage workers without access to social security, self-employed workers who have not completed higher education and zero-income workers. |
| Employer | Employers as a percentage of the employed population aged 25–55. |
| Wage employee | Wage employees as a percentage of the employed population aged 25–55. |
| Self-employment | Self-employed workers as a percentage of the employed population aged 25–55. |
| Unpaid workers | Unpaid workers (mainly family workers or apprentices) as a percentage of the employed population aged 25–55. |
| Variable | Definition |
| Large-firm workers Occupation | Workers employed in large firms (with five or more employees) as a percentage of the employed population aged 25–55. Occupation held in a worker’s main job, based on the 2-digit International Standard Classification of Occupations of 2008 (ISCO-08). Not all surveys in the region allow for this classification. |
| Sector | Sector of activity of a worker’s main job, based on the 1-digit International Standard Industrial Classification (ISIC-Revision 3). |
| Variable | Definition |
|---|---|
| Labor force participation | Economically active population as a percentage of the population aged 25–55. A person is considered economically active if she is either employed or unemployed. |
| Hours worked | Weekly hours worked in a paid job for workers aged 25–55, including all jobs. |
| Hourly wage | Average hourly wage in main occupation (in 2005 PPP USD), for workers aged 25–55 with positive earnings and positive hours worked. |
| Informality | Workers in informal jobs as a percentage of the employed population aged 25–55. Informal workers include wage workers without access to social security, self-employed workers who have not completed higher education and zero-income workers. |
| Employer | Employers as a percentage of the employed population aged 25–55. |
| Wage employee | Wage employees as a percentage of the employed population aged 25–55. |
| Self-employment | Self-employed workers as a percentage of the employed population aged 25–55. |
| Unpaid workers | Unpaid workers (mainly family workers or apprentices) as a percentage of the employed population aged 25–55. |
| Variable | Definition |
| Large-firm workers Occupation | Workers employed in large firms (with five or more employees) as a percentage of the employed population aged 25–55. Occupation held in a worker’s main job, based on the 2-digit International Standard Classification of Occupations of 2008 (ISCO-08). Not all surveys in the region allow for this classification. |
| Sector | Sector of activity of a worker’s main job, based on the 1-digit International Standard Industrial Classification (ISIC-Revision 3). |
Note: For more details about variables’ construction and definitions refer to GenLAC methodological documents.
GenLAC-CEDLAS made every effort to make statistics comparable across countries and over time by utilizing similar variable definitions in each country/year. The indicators are constructed according to SEDLAC’s processing protocol (CEDLAS and The World Bank). For the specific details about variables’ construction and definitions, refer to GenLAC methodological documents.
Definitions of education completion rates
Pre-primary enrollment rate (5 years old): Percentage of five-year-old children enrolled in an educational institution.
Primary completion rate (20–30 years old): Individuals who completed at least primary education as a percentage of individuals aged 20–30.
Secondary completion rate (20–30 years old): Individuals who completed at least secondary education as a percentage of individuals aged 20–30.
Tertiary completion rate (30–40 years old): Individuals who completed at tertiary education as a percentage of individuals aged 30–40.
Definition of location
It is a variable that captures the geographical macro-regions in which a country is organized. These macro-regions do not usually have a main authority, but instead result from the aggregation of other geographical areas with their own government. For example, each of the macro-regions of Argentina (Greater Buenos Aires, Pampeana, Patagonia, Cuyo, Northwest and Northeast) result from the aggregation of various provinces and each of the macro-regions of Brazil (North, Northeast, Southeast, South, Central -West) result from the aggregation of various federation units. In the case of Peru, the macro-regions (Urban Coast, Urban Sierra, Urban Jungle, Rural Coast, Rural Sierra, Rural Jungle and Metropolitan Lima) result from the combination of regions (Coast, Sierra, Jungle, Lima) and areas (urban and rural).
PISA evaluations
PISA is the OECD’s Program for International Student Assessment. It evaluates students’ knowledge and skills as they approach the end of their compulsory schooling (at 15 years of age). Since their initial administration in 2000, the PISA examinations have been conducted every 3 years in a number of countries. The tests evaluate students’ abilities in reading, math, and science (and in certain countries, additional topics). In Latin America, the countries that participated in, for instance, the 2018 edition of PISA are Argentina, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Mexico, Panama, Peru and Uruguay. For more details on this survey see OECD (2018).
In this chapter, we use PISA data from the years 2009, 2012 and 2018. We analyze tests scores in math and reading, math self-concept, and expectations to work in a STEM occupation at age 30.
Tests scores: The standard deviation of the test score distribution is 100 points. Therefore, a 10-point difference on the PISA scale indicates an effect size of 0.10 SD.
Math self-concept index: Index constructed based on each student’s responses to five questions about how strongly they agree with a given statement related to their math competence. The five statements considered are: ‘I am good at mathematics’; ‘I get good grades in mathematics’; ‘I learn mathematics quickly’; ‘I have always believed that mathematics is one of my best subjects’; and ‘In my mathematics class, I understand even the most difficult work’. The possible answers to these questions are: ‘very confident’, ‘confident’, ‘little confident’ and ‘not confident at all’. We code the response of ‘not confident at all’ as zero, ‘little confident’ as one, ‘confident’ as two and ‘very confident’ as three. We use their responses to construct a math self-perception index by summing over the scores of an individual’s responses and dividing that sum by 3x5 = 15. This results in an index with a minimum value of zero and a maximum value of one. Available in 2012 only.
Expectation to work in a STEM occupation at age 30: Indicator equal to one if a student expects to work in a STEM-related occupation at the age of 30. STEM-related occupations include science and engineering professionals, and information and communications technology professionals. Available in 2018 and in 2015 only for some countries.
SERCE and TERCE evaluations
These regional exams for primary education are produced by UNESCO. This chapter makes use of the Second Regional Comparative and Explanatory Study (SERCE; 2006) and the Third Regional Comparative and Explanatory Study (TERCE; 2013). These assessments evaluate the learning achievements of third- and sixth-grade students in reading, Math and Science. With the exception of Bolivia, Cuba, Honduras and Venezuela, nearly every nation in Latin America took part in the TERCE testing (see Table B.3). The test score scale has a standard deviation of 100 points.
Adult’s skills (ECAF 2015)
The ECAF 2015 was carried out by CAF-development bank of Latin America, and has information about adult’s skills in 10 major cities in 10 LA countries. In this chapter we analyze the data for nine countries (all except Venezuela).
Raven Progressive Matrices Test—Raven PMT—(Raven, 2000, 1936): It is a nonverbal assessment of basic cognitive functioning that gauges abstract reasoning using 60 items. The person is asked to find the missing component that completes a certain pattern in each of the 60 items by comparing various forms and using analogies. A brief test with eight items is utilized in the 2015 CAF Survey.
Test of Verbal Conceptualization: This test measures the capacity for inductively producing linguistic concepts. The task entails inferring the relationship or rule that unites two concepts—in this case, ‘table-chair’—based on the presentation of the stimuli and verbally expressing it (answer: ‘They are both furniture’). It also requires putting into practice the three fundamental steps of inductive reasoning: codification, inference and mapping. The exam consists of a sample of questions from the WAIS III’s subtest ‘Analogies’. The first and last items, which are deemed to be easy, the first two items of medium difficulty and the first two items of maximum difficulty were used to choose the items (Berniell et al., 2017).
Index of numerical skills: This index is created by summing the results of a test and three questions requiring basic mathematical computations. The exam asks the respondent to count backward from 20 to 0; if they do it properly in the allotted time, they receive 1 point; if not, they receive no points. The respondent is asked to answer real-world mathematics issues. They receive 1 point for each accurate response. There is no credit for wrong responses. The numeral skills index ranges from 0 to 4, with each question taking the value 1 if the respondent answers correctly and 0 otherwise.
All these variables are standardized (expressed in standard deviations with respect to the measure). The standardization was done taking into account the whole sample (ages 15 to 55 and 10 cities). The statistics shown in this chapter were computed for people aged 25 to 55 years old.
For more details on this survey and the indicators used here, please see Chapter 1’s Appendix in Berniell et al. (2017).
UNESCO Institute for Statistics (UIS) data
To compute the OECD indicator shown in Fig. 3, we use the UIS, UNESCO data, SDG Indicator 4.1.2: Percentage of a cohort of children or young people aged 3–5 years above the intended age for the last grade of each level of education who have completed that grade (i.e. 14–16 years old is the reference age group for calculation of the primary completion rate in the OECD).
Countries and years included in the SERCE/TERCE analysis
| Country | Years |
|---|---|
| Argentina | 2006, 2013 |
| Brazil | 2006, 2013 |
| Chile | 2006, 2013 |
| Colombia | 2006, 2013 |
| Costa Rica | 2006, 2013 |
| Dominican Republic | 2006, 2013 |
| Ecuador | 2006, 2013 |
| El Salvador | 2006 |
| Guatemala | 2006, 2013 |
| Honduras | 2013 |
| Mexico | 2006, 2013 |
| Nicaragua | 2006, 2013 |
| Panama | 2006, 2013 |
| Paraguay | 2006, 2013 |
| Peru | 2006, 2013 |
| Uruguay | 2006, 2013 |
| Country | Years |
|---|---|
| Argentina | 2006, 2013 |
| Brazil | 2006, 2013 |
| Chile | 2006, 2013 |
| Colombia | 2006, 2013 |
| Costa Rica | 2006, 2013 |
| Dominican Republic | 2006, 2013 |
| Ecuador | 2006, 2013 |
| El Salvador | 2006 |
| Guatemala | 2006, 2013 |
| Honduras | 2013 |
| Mexico | 2006, 2013 |
| Nicaragua | 2006, 2013 |
| Panama | 2006, 2013 |
| Paraguay | 2006, 2013 |
| Peru | 2006, 2013 |
| Uruguay | 2006, 2013 |
Countries and years included in the SERCE/TERCE analysis
| Country | Years |
|---|---|
| Argentina | 2006, 2013 |
| Brazil | 2006, 2013 |
| Chile | 2006, 2013 |
| Colombia | 2006, 2013 |
| Costa Rica | 2006, 2013 |
| Dominican Republic | 2006, 2013 |
| Ecuador | 2006, 2013 |
| El Salvador | 2006 |
| Guatemala | 2006, 2013 |
| Honduras | 2013 |
| Mexico | 2006, 2013 |
| Nicaragua | 2006, 2013 |
| Panama | 2006, 2013 |
| Paraguay | 2006, 2013 |
| Peru | 2006, 2013 |
| Uruguay | 2006, 2013 |
| Country | Years |
|---|---|
| Argentina | 2006, 2013 |
| Brazil | 2006, 2013 |
| Chile | 2006, 2013 |
| Colombia | 2006, 2013 |
| Costa Rica | 2006, 2013 |
| Dominican Republic | 2006, 2013 |
| Ecuador | 2006, 2013 |
| El Salvador | 2006 |
| Guatemala | 2006, 2013 |
| Honduras | 2013 |
| Mexico | 2006, 2013 |
| Nicaragua | 2006, 2013 |
| Panama | 2006, 2013 |
| Paraguay | 2006, 2013 |
| Peru | 2006, 2013 |
| Uruguay | 2006, 2013 |
IPUMS
American Community Survey
The American Community Survey (ACS) is a project of the United States Census Bureau that has supplanted the decennial census as the primary source of information about the US population. We utilize the ACS data extracted from IPUMS for the years 2000–2019.
IPUMS international
In the cohort analysis, we rely on IPUMS International’s harmonized census microdata from Latin American countries, France, Spain and the United States. To facilitate comparative research, IPUMS codes the data consistently across countries and over time.
LFP rates in the IPUMS data are not necessarily fully comparable to the CEDLAS (household survey) data for all LA countries. For example, there is likely to be some variation in the treatment of unpaid work. However, IPUMS invests significant effort into standardizing measures across years and countries so these data are suitable for cross-country and cross-cohort comparisons.
There is some variation in the years for which census data are available across LA countries; e.g. while in Chile census data are available for every decade from 2017 to 1960, in Peru there are only data for 2007 and 1993. In particular, Peru is excluded from the cohort analysis in this chapter because IPUMS has data for only two Peruvian censuses, and because there were relevant changes in the definitions of key labor market outcomes between those two censuses. In Bolivia and El Salvador, there were also some changes in the definition of labor market variables across censuses. However, IPUMS argues that the categories are generally comparable over time, so we keep those two countries in our cohort analysis.
Table B.4 lists the censuses analyzed in this chapter. For more details about this data, refer to inter- national.ipums.org
Census data (Source: IPUMS International)
| 2010s | 2000s | 1990s | 1980s | 1970s | 1960s | |
|---|---|---|---|---|---|---|
| Bolivia | 2012 | 2001 | 1992 | 1976 | ||
| Brazil | 2010 | 2000 | 1991 | 1980 | 1970 | 1960 |
| Chile | 2017 | 2002 | 1992 | 1982 | 1970 | 1960 |
| Colombia | 2005 | 1993 | 1985 | 1973 | 1964 | |
| Costa Rica | 2011 | 2000 | 1984 | 1973 | 1963 | |
| Dominican Republic | 2010 | 2002 | 1981 | 1970 | 1960 | |
| Ecuador | 2010 | 2001 | 1990 | 1982 | 1974 | 1962 |
| El Salvador | 2007 | 1992 | ||||
| Guatemala | 2002 | 1994 | 1981 | 1973 | 1964 | |
| Honduras | 2001 | 1988 | 1974 | 1961 | ||
| Mexico | 2015 & 2010 | 2005& 2000 | 1995& 1990 | 1970 | 1960 | |
| Nicaragua | 2005 | 1995 | 1971 | |||
| Panama | 2010 | 2000 | 1990 | 1980 | 1970 | 1960 |
| Paraguay | 2002 | 1992 | 1982 | 1972 | 1962 |
| 2010s | 2000s | 1990s | 1980s | 1970s | 1960s | |
|---|---|---|---|---|---|---|
| Bolivia | 2012 | 2001 | 1992 | 1976 | ||
| Brazil | 2010 | 2000 | 1991 | 1980 | 1970 | 1960 |
| Chile | 2017 | 2002 | 1992 | 1982 | 1970 | 1960 |
| Colombia | 2005 | 1993 | 1985 | 1973 | 1964 | |
| Costa Rica | 2011 | 2000 | 1984 | 1973 | 1963 | |
| Dominican Republic | 2010 | 2002 | 1981 | 1970 | 1960 | |
| Ecuador | 2010 | 2001 | 1990 | 1982 | 1974 | 1962 |
| El Salvador | 2007 | 1992 | ||||
| Guatemala | 2002 | 1994 | 1981 | 1973 | 1964 | |
| Honduras | 2001 | 1988 | 1974 | 1961 | ||
| Mexico | 2015 & 2010 | 2005& 2000 | 1995& 1990 | 1970 | 1960 | |
| Nicaragua | 2005 | 1995 | 1971 | |||
| Panama | 2010 | 2000 | 1990 | 1980 | 1970 | 1960 |
| Paraguay | 2002 | 1992 | 1982 | 1972 | 1962 |
Census data (Source: IPUMS International)
| 2010s | 2000s | 1990s | 1980s | 1970s | 1960s | |
|---|---|---|---|---|---|---|
| Bolivia | 2012 | 2001 | 1992 | 1976 | ||
| Brazil | 2010 | 2000 | 1991 | 1980 | 1970 | 1960 |
| Chile | 2017 | 2002 | 1992 | 1982 | 1970 | 1960 |
| Colombia | 2005 | 1993 | 1985 | 1973 | 1964 | |
| Costa Rica | 2011 | 2000 | 1984 | 1973 | 1963 | |
| Dominican Republic | 2010 | 2002 | 1981 | 1970 | 1960 | |
| Ecuador | 2010 | 2001 | 1990 | 1982 | 1974 | 1962 |
| El Salvador | 2007 | 1992 | ||||
| Guatemala | 2002 | 1994 | 1981 | 1973 | 1964 | |
| Honduras | 2001 | 1988 | 1974 | 1961 | ||
| Mexico | 2015 & 2010 | 2005& 2000 | 1995& 1990 | 1970 | 1960 | |
| Nicaragua | 2005 | 1995 | 1971 | |||
| Panama | 2010 | 2000 | 1990 | 1980 | 1970 | 1960 |
| Paraguay | 2002 | 1992 | 1982 | 1972 | 1962 |
| 2010s | 2000s | 1990s | 1980s | 1970s | 1960s | |
|---|---|---|---|---|---|---|
| Bolivia | 2012 | 2001 | 1992 | 1976 | ||
| Brazil | 2010 | 2000 | 1991 | 1980 | 1970 | 1960 |
| Chile | 2017 | 2002 | 1992 | 1982 | 1970 | 1960 |
| Colombia | 2005 | 1993 | 1985 | 1973 | 1964 | |
| Costa Rica | 2011 | 2000 | 1984 | 1973 | 1963 | |
| Dominican Republic | 2010 | 2002 | 1981 | 1970 | 1960 | |
| Ecuador | 2010 | 2001 | 1990 | 1982 | 1974 | 1962 |
| El Salvador | 2007 | 1992 | ||||
| Guatemala | 2002 | 1994 | 1981 | 1973 | 1964 | |
| Honduras | 2001 | 1988 | 1974 | 1961 | ||
| Mexico | 2015 & 2010 | 2005& 2000 | 1995& 1990 | 1970 | 1960 | |
| Nicaragua | 2005 | 1995 | 1971 | |||
| Panama | 2010 | 2000 | 1990 | 1980 | 1970 | 1960 |
| Paraguay | 2002 | 1992 | 1982 | 1972 | 1962 |
Time use surveys
Table B.5 provides the list of the time-use surveys used in this chapter, employed to compute market and non-market hours. Non-market hours include care activities and household chores. Household chores are grouped into seven categories, following the guidelines established by the CAUTAL (CEPAL): preparing and serving food, cleaning the house, cleaning and maintenance of clothes and shoes, maintenance and minor reparations in the house, house administration, shopping for the household (including commuting time), caring for plants and pets. In all cases, the commuting and waiting time is included. Indicators related to the use of time are computed for married individuals aged 25–45. For more details about variables’ construction and limitations of the time use data, refer to GenLAC methodological documents.
Time use surveys included in the analysis
| Country | Survey | Year |
|---|---|---|
| Argentina | Encuesta sobre Trabajo No Remunerado y Uso del Tiempo | 2013 |
| Chile | Encuesta Nacional sobre Uso del Tiempo (ENUT) | 2015 |
| Colombia | Encuesta Nacional de Uso del Tiempo (ENUT) | 2016 |
| Costa Rica | Encuesta Nacional de Uso del Tiempo (ENUT) | 2017 |
| Ecuador | Encuesta Especifica de Uso del Tiempo | 2012 |
| El Salvador | Encuesta Nacional de Uso del Tiempo (ENUT) | 2017 |
| Guatemala | Modulo de Uso del Tiempo de la ENCOVI | 2014 |
| Mexico | Encuesta Nacional sobre Uso del Tiempo | 2014 |
| Paraguay | Encuesta sobre Actividades Remuneradas y No Remuneradas (EUT) | 2016 |
| Peru | Encuesta Nacional de Uso del Tiempo (ENUT) | 2010 |
| Uruguay | Encuesta de Uso del Tiempo y del Trabajo no Remunerado (EUT) | 2013 |
| Country | Survey | Year |
|---|---|---|
| Argentina | Encuesta sobre Trabajo No Remunerado y Uso del Tiempo | 2013 |
| Chile | Encuesta Nacional sobre Uso del Tiempo (ENUT) | 2015 |
| Colombia | Encuesta Nacional de Uso del Tiempo (ENUT) | 2016 |
| Costa Rica | Encuesta Nacional de Uso del Tiempo (ENUT) | 2017 |
| Ecuador | Encuesta Especifica de Uso del Tiempo | 2012 |
| El Salvador | Encuesta Nacional de Uso del Tiempo (ENUT) | 2017 |
| Guatemala | Modulo de Uso del Tiempo de la ENCOVI | 2014 |
| Mexico | Encuesta Nacional sobre Uso del Tiempo | 2014 |
| Paraguay | Encuesta sobre Actividades Remuneradas y No Remuneradas (EUT) | 2016 |
| Peru | Encuesta Nacional de Uso del Tiempo (ENUT) | 2010 |
| Uruguay | Encuesta de Uso del Tiempo y del Trabajo no Remunerado (EUT) | 2013 |
Note: All samples are restricted to individuals aged 25–45. For more details about variables’ construction and definitions refer to GenLAC methodological documents.
Time use surveys included in the analysis
| Country | Survey | Year |
|---|---|---|
| Argentina | Encuesta sobre Trabajo No Remunerado y Uso del Tiempo | 2013 |
| Chile | Encuesta Nacional sobre Uso del Tiempo (ENUT) | 2015 |
| Colombia | Encuesta Nacional de Uso del Tiempo (ENUT) | 2016 |
| Costa Rica | Encuesta Nacional de Uso del Tiempo (ENUT) | 2017 |
| Ecuador | Encuesta Especifica de Uso del Tiempo | 2012 |
| El Salvador | Encuesta Nacional de Uso del Tiempo (ENUT) | 2017 |
| Guatemala | Modulo de Uso del Tiempo de la ENCOVI | 2014 |
| Mexico | Encuesta Nacional sobre Uso del Tiempo | 2014 |
| Paraguay | Encuesta sobre Actividades Remuneradas y No Remuneradas (EUT) | 2016 |
| Peru | Encuesta Nacional de Uso del Tiempo (ENUT) | 2010 |
| Uruguay | Encuesta de Uso del Tiempo y del Trabajo no Remunerado (EUT) | 2013 |
| Country | Survey | Year |
|---|---|---|
| Argentina | Encuesta sobre Trabajo No Remunerado y Uso del Tiempo | 2013 |
| Chile | Encuesta Nacional sobre Uso del Tiempo (ENUT) | 2015 |
| Colombia | Encuesta Nacional de Uso del Tiempo (ENUT) | 2016 |
| Costa Rica | Encuesta Nacional de Uso del Tiempo (ENUT) | 2017 |
| Ecuador | Encuesta Especifica de Uso del Tiempo | 2012 |
| El Salvador | Encuesta Nacional de Uso del Tiempo (ENUT) | 2017 |
| Guatemala | Modulo de Uso del Tiempo de la ENCOVI | 2014 |
| Mexico | Encuesta Nacional sobre Uso del Tiempo | 2014 |
| Paraguay | Encuesta sobre Actividades Remuneradas y No Remuneradas (EUT) | 2016 |
| Peru | Encuesta Nacional de Uso del Tiempo (ENUT) | 2010 |
| Uruguay | Encuesta de Uso del Tiempo y del Trabajo no Remunerado (EUT) | 2013 |
Note: All samples are restricted to individuals aged 25–45. For more details about variables’ construction and definitions refer to GenLAC methodological documents.
