1. Gender gaps in Peru: An international and sub-national comparison

Abstract

This chapter reviews the evidence on gender gaps in economic outcomes in Peru and discusses the drivers of these gaps. It starts with an overview of gender gaps in educational and labour market outcomes across different dimensions (enrolment and out-of-school rates, skills outcomes, along with labour market participation, gender pay gaps and the interactions between motherhood and access to better quality jobs). It then discusses the main contributing factors to these gaps (unbalanced unpaid care and domestic work, the access to care facilities, attitudes and gender-based stereotypes, and legal barriers). In addition to comparing Peru with other Latin American and OECD countries, the chapter addresses the articulation of gender differences across socio-economic groups (urban vis-à-vis rural differences, along with differences across levels of education, age cohorts and levels of incomes).

In Peru, just as elsewhere in Latin America and all around the world, men and women do not share paid and unpaid work equally. Women are less likely to work, and to work full-time hours, for pay. Instead, they typically spend more hours looking after children and elderly relatives, doing housework, shopping for food, and so on. When they work, they tend to be overrepresented in the informal labour market. One important trait of Peru is the strong polarisation across women and men of the time undertaken for paid and unpaid work activities. On the one hand, on average Peruvian women spend 24 more hours per week on unpaid task than men do. On the other, Peruvian men devote 21 more hours to paid work activities per week than women do. Shedding light on the driving forces for this marked divergence in the gender distribution of paid and unpaid work in Peru is the main objective of Chapter 1.

Across the OECD but even in Peru and Latin America at large, the unequal partition of working hours and of categories of work is reconcilable to a broad set of interdependent forces. Key examples of interactions are with the relative distribution of educational and labour market outcomes between men and women. Such economic outcomes are influenced, in turn, by a complex set of attitudes and gender stereotypes. Furthermore, the intersectional character of the drivers is very important, given that gender inequalities vary widely across socio economic groups – between younger and older generations, between urban and rural areas, between indigenous and non-indigenous populations and between couples and single parents.

The chapter provides a systematic review of these interactions placing Peru in the international comparative perspective. It starts with a review of gender gaps in educational and labour outcomes, along with a discussion of time-sharing and earning patterns, seeking to distinguish areas where there has been progress, from those where achievements have been less visible or hardly visible at all. The chapter then looks at international indicators of well-being and gender gaps that capture the influence of stereotypes and discrimination.

In recent decades, gender gaps in educational and labour market outcomes have shrunk in Peru. Some of these differences, such as the share of university graduates in STEM degrees and the employment rate, are nowadays smaller than in other Latin American countries and across the OECD. On other metrics, such as the median labour income, for example, they are larger, which points to less favourable outcomes in paid employment among women than among men. The main drivers of these developments are women’s higher unpaid work burden and still widespread conservative attitudes concerning the respective roles of men and women for a relatively high share of the population. Although reforms have largely eliminated legal barriers to women’s equal participation in the labour market in Peru, they remain strongly represented in the informal labour market.

This section first presents gender gaps in educational and labour market outcomes across different dimensions and then discusses the contribution of unpaid work, attitudes, the legal environment and other factors to these gaps. In addition to comparing the outcomes and drivers in Peru with other Latin American countries and the OECD, it also discusses sub-national differences whenever possible.

Gender gaps in education and labour market outcomes

Education

There is an extensive body of research and literature focussing on the importance of education for individuals and society: individuals with higher levels of education typically have a higher probability of being employed, earning a higher income (OECD, 2019[1]) and being healthier (Conti, Heckman and Urzua, 2010[2]; Dávila-Cervantes and Agudelo-Botero, 2019[3]). At the societal level, the return on the investment in education reflects mainly the enhanced contribution to productivity growth generated by a more educated labour force (Mincer, 1984[4]).

In the case of women, these benefits are even greater, reflecting the double effect of education on earnings. On the one hand, education increases skills, productivity and income opportunities, as it does for men (Woodhall, 1973[5]; Montenegro and Patrinos, 2014[6]). Additionally, it contributes to reducing the gap in earnings between men and women that is attributable to discrimination (Dougherty, 2005[7]). Beyond these beneficial effects, gains also materialise in terms of decreased child mortality and unwanted pregnancy. Importantly, inter-generational redistribution will improve, since the increased education of mothers will typically lead to improve health and educational outcomes of their children, even when taking into account the father’s education and household income (Schultz, 1993[8]). Furthermore, by making women feel more empowered to speak out to affirm their needs and aspirations, higher levels of education for girls are a cornerstone of a stronger political voice and representation (Marcus and Page, 2016[9]).

Evidence for Peru corroborates these patterns by showing that the returns to education are higher for women than men, including for completing primary education (Montenegro and Patrinos, 2014[6]). In addition, recent analysis using qualitative longitudinal data from the Young Lives study in Peru finds that increased access to higher education acts as a key driver to stronger personal identity as well as the economic independence of single young women without children (Guerrero and Rojas, 2020[10]). Qualified young women, holding a professional certificate, can support themselves and have a greater sense of autonomy compared to their mothers at a similar age, who generally devoted more time to traditional domestic and care occupations. Nevertheless, the analysis also concludes that education does not significantly challenges the prevalent gender norms that define girls and young women as natural born caregivers.

Box 1.1. The benefits of a more equitable division of paid and unpaid labour between men and women

Individuals working outside the home generally have a higher degree of economic independence from their partners and other family members than those who do not. Unpaid care and domestic work is also valuable, but in general does not garner the same respect as other activities do. In countries where women carry out a disproportionally large share of the unpaid work burden, they are also more frequently employed in part-time or vulnerable jobs, which are often poorly paid (Ferrant, Pesando and Nowacka, 2014[11]). This is because high unpaid care and domestic work burdens often imply that women cannot find an occupation corresponding to their qualification level on a part-time basis, decreasing their job quality and earnings (Connolly and Gregory, 2008[12]).

By contrast, an equal division of unpaid work responsibilities can be beneficial to the entire family. To the extent that a more equal sharing of unpaid work reduces women’s overall work hours – in particular regarding tasks that are considered less desirable, namely housework and care of the elderly – it can reduce their stress levels (MacDonald, Phipps and Lethbridge, 2005[13]). A study of British families suggests that couples in which men do more unpaid care and other housework are less likely to divorce (Sigle-Rushton, 2010[14]). The negative effects of an unequal division of unpaid work on marital quality are particularly strong when couples disagree about how egalitarian a marriage should be (Ogolsky, Dennison and Monk, 2014[15]). Men who spend more time with their children may have higher life satisfaction, and their children may have better mental and physical health and cognitive development. However, it is unclear whether these differences are driven by confounding factors that these studies do not account for (WHO, 2007[16]).

Individuals’ well-being may be boosted even more if overall unpaid work hours can be reduced. When an increasing share of the population is able to access stable utilities (such as running water and electricity) and labour-saving appliances (such as washing machines), and thus needs fewer hours for housework, this reduces time-poverty and increases choices and well-being. As a result, in countries with higher GDP levels, the number of hours that need to be devoted to unpaid work decrease, benefitting women in particular (Ferrant and Thim, 2019[17]).

The increased participation of women in paid work will likely increase economic growth. The impact will go far beyond an accounting ‘trick’ of simply substituting unpaid by paid work: estimates based on the 2010 time-use survey suggests that in Peru, unpaid domestic work contributes 20,4% of a modified GDP measure (INEI, 2016[18]). Women’s increased participation in the labour market would substitute lower- for higher-added value activities and increase the stock of human capital employed. Since young female university graduates now outnumber their male counterparts, using their human capital fully has become more urgent. Moreover, firm-level research suggests that teams that are more diverse may be more cohesive and innovative. Therefore, bringing more women into the labour market, including into management positions, could strengthen productivity growth.

Educational attainment levels in Peru have risen strongly from one cohort to the next, with particularly large gains among women. In 2017, close to 37% of men and 50% of women aged 55 to 64 years in Peru just had less than an upper secondary degree (Figure 1.1). Among young adults that had gone to school three decades later, the same share had dropped to about 15% for men and around 21% for women. Concomitantly, the share of high-school achievers (those having competed an upper and post-secondary education curriculum) had increased by close to 19 percentage points among women, whereas the increase approximated 17 percentage points among men. The share of tertiary graduates had increased by 5 percentage points for men and 11 points for women in the 25-34 age bracket, compared to the 55-64 age group.

Figure 1.1. The educational attainment of women has been constantly rising

Nevertheless, results from a qualitative study suggest that the average figures hide important differences between income groups. Particularly, the tendency to prioritise the post-secondary education of boys remains dominant among low-income families in Peru, whereas the bulk of the domestic chores continues to fall on the shoulders of girls and women (Rojas, Guerrero and Vargas, 2016[19]). This signals that in contexts of high poverty many parents still see education as a cause of concern, attending school can divert their daughters away from duties within the households.

Today, at all levels of pre-primary to upper secondary education Peruvian rates of enrolment for boys and girls are essentially the same (Figure 1.2). Illiteracy among older teenagers of either sex has practically disappeared; for boys and girls of 15-19 years-old, the illiteracy rate approximates half a percentage point. Among older cohorts, by contrast, women tend to be more frequently illiterate, with the gap rising up to 18.7 percentage points among 60+ year olds (INEI, 2019[20]).

Figure 1.2. Enrolment rates in Peru are similar between boys and girls

However, although the share of children who attend primary school is similar across Peru’s regions, for secondary school there are important differences. In 2018, about 94% of 6 to 11-year-olds in both urban and rural areas were enrolled in primary school (INEI, 2019[21]). In contrast, the enrolment rate of 12 to 16-year-olds was around 10% higher in urban than in rural areas (87.2% compared to 79.1%). Similarly, 87.2% were enrolled in coastal areas compared to 84.6% and 77.1% in mountainous and forest areas, respectively (INEI, 2019[22]). Despite relatively similar net enrolment rates, among upper secondary school age teenagers, a higher share of girls in Peru are out of school (Figure 1.3). In contrast, in many other Latin American countries, the out-of-school rate is higher among boys.1

One of the contributing factors for school dropout among girls in Peru are teenage pregnancies (UNESCO, 2014[23]; OECD, 2019[24]). In Peru, 6.8% of girls aged 15-17 were pregnant or mothers in 2018. The rate is more than twice as high in rural than in urban areas (11.9%, compared to 5.3%), ranging from 3.0% in Cusco to 22.4% in Loreto. By age 18-19, nearly one in two girls (47%) living in rural areas are mothers or are pregnant for the first time, which compares with a national average of nearly one in four teenage girls (22.5%) (MINEDU, 2019[25]). Positively, the teenage fertility rate in Peru is below the Latin American and Caribbean average; and is the second lowest in South America after Chile. However, it is also noteworthy that the regional rate is second only to the Sub-Saharan African region (PAHO, UNFPA and UNICEF, 2017[26]).

Another factor that can influence educational attainment relates to the work obligations of teenagers outside of school. In Peru, regardless of their sex, slightly more than one in four children and teenagers aged 5 to 17 years work. In rural areas, the share is nearly four times higher (57.7%) than in urban areas (14.7%) (MINEDU, 2019[25]). As discussed below, the total (paid and unpaid) work burden of female teenagers exceeds that of male teenagers. This has important implications on school attainments in turn, as shown by the fact that sixth-grade Peruvian students who worked performed worse on a standardised test in both reading and mathematics, starting from one hour of work per day. Elsewhere in the Latina American region, the negative effects are for the most part only apparent from three hours of work per day (Chile, Colombia and Ecuador, for example) (Post, 2011[27]).

Figure 1.3. A higher share of older teenage girls than boys are out of school in Peru

Surveys among students and adults depict the presence of gender skill gaps. According to the OECD Survey of Adult Skills (PIAAC), women in Peru have slightly higher average literacy skills than men. The opposite is true for numeracy skills (OECD, 2019[28]). These results mirror findings based on the Programme for International Student Assessment (PISA) survey: for 15-year-old girls the share of low achievers was lower in the reading and higher in the mathematics and science sections than observed for men (Figure 1.4). Across different subjects, the average score for Peruvian students is slightly less than 90 points lower than the OECD average (OECD, 2018[29]), a value which corresponds to the skills typically gained during around three school years, according to some studies (OECD, 2019[30]). In part, this gap is not surprising, since per-capita GDP accounts for 44% of the variation in average PISA reading scores. Nevertheless, Peruvian students also underperform relative to several countries with similar levels of GDP per capita, such as Costa Rica and Colombia (OECD, 2019[30]). Improving the quality of schooling and as a result, cognitive skills would be a boon to economic growth (Hanushek, 2013[31]).

Figure 1.4. In Peru gender differences exist in the share of low performers but not top performers

The skill gap in maths and sciences among teenagers is reflected in the later education and occupation choices of women. One quarter of female tertiary graduates in Peru has a STEM degree compared to well above a third of male tertiary graduates (Figure 1.5). It is important to signal, however, that while noticeable the extent of the gap is markedly lower than in some other Latin American countries and across the OECD on average. There is a multitude of explanations for performance differences in quantitative subjects between girls and boys and for the lesser orientation of women towards STEM occupations. Yet, the fact that score differences in math tests are negligible among young children (Kahn and Ginther, 2018[32]), is an indicator that suggests that gender stereotypes play an important role in shaping observed differences later on during the school curriculum, in both outcomes and preferences (Nollenberger, Rodríguez-Planas and Sevilla, 2016[33]). Chapter 2 of this report discusses how gender-sensitive education can help the reduction of gender stereotypes.

Figure 1.5. Women in Peru are under-represented among STEM graduates, though less so than across the OECD

Employment

A high and increasing share of adults in Peru work for a pay. In 2020, close to six out of ten women and more than seven out of ten men aged 15-64 years were employed (Figure 1.6). These rates are higher than in other Latin American, although still somewhat below the OECD countries, and represent increases of 9.4 and 3.7 percentage points, respectively, compared to 2002. The gender gap in labour force participation differs across age groups and education levels. It rises with age, from 10.2 percentage points among 14-24 year-olds, to 17.7 percentage points among 25-44 year-olds and 26.7 percentage points among 45-64 year-olds, according to figures for 2018 (INEI, 2020[34]). However, this increasing gap across age group is only observable in urban areas, while it remains constant at around 15 percentage points in rural areas. Among women who graduated from upper secondary school and university, 80% and 90%, respectively, participate in the labour market, while the same share among those with lower levels of formal education is below 50%. This pattern is similar to that in other Latin American countries (ECLAC/ILO, 2019[35]).

Just as elsewhere, one of the factors that pushes many women to withdraw (at least temporarily) from the labour force is motherhood. In Peru, the employment rate of women with children under the age of three is approximately 10 percentage points lower than that of women with older toddlers, which in turn is around 8 percentage points lower than the rate for mothers with school-aged children (Figure 1.7). Recent cross country assessment of the links between motherhood and labour market outcomes in four Latin American countries – Chile, Mexico, Peru and Honduras – finds that motherhood lowers women labour supply and tilts occupational choices towards more flexible jobs, such as part-time jobs, self-employment, and informal work arrangements (Berniell et al., 2021[36]). The authors underline that these effects occur right after childbirth and tend to persists in the medium or long term. Since fathers’ labour outcomes remain unaffected, these findings reveals that motherhood triggers the polarisation of labour markets, with quality jobs more likely to be a prerogative of men and women more likely to work in low quality jobs.

Figure 1.7. Mothers of young children have low employment rates

Although the female employment rate is higher in Peru than observable in Chile, Colombia and Costa Rica, women are often employed in low-quality jobs. For example, the level of part-time employment rate, which in Peru far exceeds the shares seen in other Latin American countries and across the OECD, is particularly pronounced among women (Figure 1.8, Panel A). Correspondingly, only half of women workers work full-time in Peru. In addition, the vast majority of women and men work informally in Peru. At 67% for men and 75% for women workers, respectively, the rates of informal employment are significantly higher in Peru than observed in Chile and Costa Rica and (for women) in Colombia (Figure 1.8, Panel B).

Informality is more widespread outside metropolitan Lima and in rural areas. In Lima, around five in ten men and six in ten women workers are informally employed, compared to around eight to ten in the rest of the country. In rural areas, almost all workers are informally employed: 94.8% of male and 96.6% of female workers (INEI, 2020[34]). Despite these high levels, a trend towards formalisation can be observed, with the national average of Peru showing that in 2018 the share of men and women employed informally were some 8-9 percentage points lower than in 2009 (ILO, 2020[37]). This continues a pattern observable since the early 2000s and that reversed some of the increases that took place in the previous 20 years (Chong, Galdo and Saavedra, 2008[38]; Centro Nacional de Planeamiento Estratégico, 2016[39]).

Figure 1.8. A high share of female employees in Peru work part-time and informally

In Peru, the rate of self-employment is lower among women than among men, although the gap is very small and smaller than in other countries in the region (Figure 1.9, Panel A). This being said, in Peru the share among women exceeds the regional average by 10 percentage points. Moreover, the share of employers among employed women is half the share among men (Figure 1.9, Panel B).

In 2018, 73.6% of registered Peruvian enterprises were in the judicial form of natural persons, which excludes other registered enterprises such as anonymous societies and limited liability companies, as well as unregistered enterprises. Among this subset virtually all are microenterprises, whether they are led by a man or by a woman (INEI, 2019[40]). In Peru, the ratio between those who start a business because they have identified a good opportunity to those who did so out of necessity is larger among male than among female entrepreneurs (Serida et al., 2017[41]). 4.7% of CEOs or managing directors are women (CENTRUM PUCP, WomenCEO Perú and PwC Perú, 2018[42]).

Figure 1.9. A smaller share of female workers are employers

Occupations characterised by a higher percentage of women workers generally pay less than occupations with a lower percentage, even controlling for education and skill demands. Empirical analysis of the reasons that stand behind this association has focussed on two different socio-economic perspectives: devaluation and queuing both of which point to a type of discrimination by the employers (Levanon, England and Allison, 2009[43]). According to the former, the pay offered in an occupation affects its women proportion, due to employers’ preference for men. The latter argues that the proportion of females in an occupation affects pay, owing to devaluation of work done by women.

In Peru, full-time women workers are around 1.5 times more likely than male workers to earn less than two-thirds of the median wage (Figure 1.10). This is similar to the ratio across the OECD but below that observed in other countries in the region, in particular Costa Rica. It is important to underline, however, that the general share of low-income workers in Peru is considerably higher than in other regional comparator countries. Moreover, given that a larger share of workers in Peru work part-time, the overall share of low-income workers is likely to be proportionally more important.

Figure 1.10. In Peru as elsewhere, women are more likely to be low-paid

Different methodologies exist to estimate the gender pay gap, the difference in labour incomes of male and female workers. These range from the simple comparison between the mean or median wages of men and women to complex approaches that control for the characteristics of workers and their jobs that typically influence pay, such as their education level and the sector they work in. These different approaches are best thought of as complementary, rather than substitutes, since they fundamentally measure different things. The simpler approach provides evidence on how much less money the average female worker earns – and thus for example the extent of the incentive couples have for a husband rather than a wife to work full-time. The more complex approach seeks to highlight what part of the pay gap cannot be explained by the characteristics of workers and their jobs, thus pointing towards the extent of discrimination of female workers, for example.2

According to different measures, women in Peru earn approximately a sixth less than men do. First, the median female full-time worker earns 17% less than the median male full-time worker (Figure 1.11, OECD estimate). This difference exceeds that observed in Chile, Colombia and Costa Rica, as well as the OECD average. Second, according to a factor-adjusted approach that includes both part- and full-time workers, the Peruvian estimate remains at 16% (Figure 1.11, ILO factor-weighted estimate). For other countries, however, the two estimates are not so aligned. In particular, according to the factor-weighted estimate the pay gap is still larger in Peru than in Costa Rica, but it is smaller than in Chile and the region overall.

Figure 1.11. Using the OECD definition, the gender pay gap in Peru is particularly high

Drivers of gender gaps in outcomes

Various economic theories explain the causes of the gender gaps in economic outcomes. The approaches that put the accent on human capital factors emphasise the characteristics of workers and their jobs, as captured by the level of education, the work experience and the skills required to fulfil specific tasks and responsibilities. However, human capital characteristics are not enough to capture the wide range of factors explaining gender gaps. As highlighted in the previous sections, although education certainly represents an important factor in explaining female employment outcomes, also other drivers matter, which relate more intrinsically to the fact of being a woman (Bertrand, 2020[44]; Ciminelli Gabriele and Schwellnus Cyrille, 2021[45]). Indeed, despite important educational gains, women continue to make educational choices likely to result in lower labour market earnings than men. One evidence of this is the underrepresentation of women in STEM disciplines. Likewise, motherhood can lead women to change labour market decisions in ways that permanently alter careers and undermine earning prospects. Moreover, when launching a new business activity, this will more likely be out of necessity, rather than responding to an innovative entrepreneurial opportunity with a potential for further expansion in the future. Much of these patterns in choices, preferences and opportunities are endogenous to the presence of sticky stereotypes about gender-specific skills, professions and roles.

In addition, the nature of such factors as lack of qualifications, which preclude the access to good job opportunities, alongside the struggle with personal and social problems, for example, are typically intersectional. In other words, they tend to associate with other disadvantages, like being in a young age, living in a rural area, coming from a poor household, or belonging to an indigenous population group.

One very synthetic but telling manifestation of these intersectional disadvantages is provided by Figure 1.12, which depicts the international comparison of the rates for women and men Not in Employment, Education or Training (NEET) as a percentage of the youth population. In Peru, young women are 1.7 times more likely to be NEETs than young men are. This sizeable gap compares with an OECD-wide average of 1.4 times, although there are variations from country to country.

Figure 1.12. Women are more likely to be NEETs than men are

As noted, the reasons behind this situation are multiple. They trace back to the traditional gender-related assignment of roles, whereby women do most of the unpaid domestic work, the caring for children and other family members. Other reasons reflect the influence of inherited cultural factors, gender stereotypes and attitudes and their interplay in influencing attitudes and behaviours. Another driver relates to the interplay with the role of laws and institutions. Finally, access to the infrastructure also matters, with the availability of quality care facilities and the supporting physical infrastructure representing one example. The remainder of this section provides a review of these forces, which integrate the role played by human capital factors in shaping gender economic outcomes.

Unpaid work

One key factor that contributes to the lower labour force participation and higher part-time employment rate of women is the higher number of hours spent on unpaid care and housework activities. Women in Peru spend 24 more hours per week on these tasks than men do, which corresponds to a slightly more pronounced gap than observed in Chile (23 hours), Colombia (22 hours) and Costa Rica (23 hours), although significantly more pronounced than the average of the OECD countries (15 hours) (Figure 1.13, Panel A). At the same time, Peruvian men work 21 more hours in paid work per week than women do, which makes for a sizeable gap, both compared with Chile and Costa Rica (15 hours in both countries) and the OECD average (12 hours).

The pattern according to which girls do more unpaid work and boys more paid work also exists among teenagers (Figure 1.13, Panel B). On average, girls in Peru spend on unpaid work 8 hours per week more than boys, which are essentially dedicated to household chores. Conversely, boys spend on paid work 6 hours per week more than girls. As noted above, work adversely affects educational performance on a standardised test in Peru (Post, 2011[27]). A study based on 20 countries found that regardless of whether it is paid or unpaid, work performed outside of the school, affects maths scores negatively, even when family resource and school effects are taken into account (Post and Pong, 2009[46]).

Figure 1.13. Women and girls in Peru work longer hours than men and boys

The time spent on unpaid work differs between socio-economic groups in Peru:

Among working age population, indigenous men and women who grew up speaking an indigenous language spend an additional six and ten hours more on paid and unpaid work per week, respectively, compared to those that grew up speaking Castilian (Instituto Nacional de Estadística y Informatica, n.d.[47]).
Similarly, in rural areas, the time spent on domestic non-remunerated activities is six and 11 hours longer than in urban areas, for men and women, respectively. The observed difference in the total workload becomes even more drastic when considering the indigenous-non-indigenous divide. Men in urban areas work slightly more hours than men in rural areas do, while the opposite is true among women.
In addition, there is evidence that the gender gap in total work hours is far larger for those with lower educational attainments. It falls from 11 hours among those with primary education to three hours among those with a tertiary degree (Figure 1.14). Women with a university degree on average work 11 hours less than women who at most completed primary school (Instituto Nacional de Estadística y Informatica, n.d.[47]).

Figure 1.14. In Peru, the gender gap in total working hours falls with education

Even in countries where egalitarian attitudes are more prevalent and where there are small or no gaps in the labour market outcomes of young men and women, the paid and unpaid work distribution often starts diverging when the individuals become parents. New mothers often stay home initially after the birth and the patterns that emerge during this time may significantly alter the division of work within the couple, becoming permanent thereafter. However, the extent of this shift will depend upon the attitudes of parents (also see the following section) and their relative labour income (Schober, 2011[48]; Sanchez and Thomson, 1997[49]).

In Peru, for slightly more than one-third of couples with children under the age of 15 both parents work full-time. A further third have one person working full- and one working part-time with the person working part-time almost systematically being the woman (Table 1.1., Panel A). Interestingly, the share of couples that are split between full- and part-time work is considerably bigger in Peru than in Chile and Costa Rica and an average across 29 OECD countries. The reasons behind this imbalance can be practical, for example if a mother is still breast-feeding. However, it often reflects cultural attitudes, according to which care and homework duties are ‘women’s prerogatives’, along with financial reasons, if the male partner earns more than the female partner.

On the other hand, the division of labour whereby one partner works full-time and the other does not work at all for pay occurs in fewer than one in four couples with children in Peru, which is below the OECD average, as well as Chile and Costa Rica. In the Peruvian context, characterised by a high incidence of low-quality and low-paid jobs, this attitude may be the outcome of economic necessity, rather than preferences. The low prevalence of non-working single mothers (around 11% in Peru, compared to 29-35% in the comparison countries and the OECD average) seems to support this presumption (Table 1.1., Panel B).

Table 1.1. The employment rates of parents in Peru are comparatively high
A. Distribution (%) of employment patterns in couples with at least one child aged 0-14
	Proportion (%) of couple households with:
	Both partners full-time	One partner full-time, one partner part-time		One partner full-time, one partner not working		Both partners not working		Other
Chile	39.0	9.6		44.5		3.0		3.9
Costa Rica	21.1	12.7		36.4		1.2		28.6
Peru	35.2	32.0		22.8		1.2		8.7
OECD average	45.6	16.9		25.8		3.4		8.3
B. Distribution (%) of single parents with at least one child aged 0-14 by employment status
	Proportion (%) of single parents with at least one child:
	Working full-time		Working part-time		Working – no information on hours		Not working

Chile	57.2		13.5		0.1		29.3
Costa Rica	44.2		21.1				34.7
Peru	64.7		24.8				10.5
OECD average	57.3		15.5		1.0		26.1
Note: Data refer to 2019 except for Chile (2017) and Peru (2018). For Chile, the distinction between part-time and full-time work is based on actual hours worked in the main job during the survey reference week, rather than usual weekly working hours. For Peru, working hours were imputed when responses were missing. For Costa Rica, data refer to the employment status of the two parents in 'two parent households' or 'couple families' with at least one child aged 0-14, rather than to couples themselves. For Costa Rica data cover households where at least one child (aged 0-14) shares a relationship with the reported 'head of household' only. For more information please refer to the OECD Database on Gender gaps in Latin America and the Caribbean under https://www.oecd.org/latin-america/regional-programme/gender/.
Source: OECD (n.d.[50]), “LMF2.2 Patterns of employment and the distribution of working hours for couples with children” and “LMF2.3 Patterns of employment and the distribution of working hours for single parents ”, OECD Family Database, http://www.oecd.org/social/family/database.htm accessed on March 2022; and own estimations based on the INE (2019[51]), Encuesta Nacional de Hogares.

Gender-based stereotypes and attitudes

Gender stereotypes can influence female employment in multiple ways. Everything else equal, women who believe that their role is in the home will likely feel less inclined to seek outside employment (Christiansen et al., 2016[52]). This supply effect often appears compounded by the attitude of the partners, if they not only hold the same view but also believe that it is their right to impose it on the wives. In addition, the views about gender roles in the labour market can also influence the demand for female jobs: employers who believe that certain jobs should go to men, rather than women, are less likely to hire women or to pay them the same wage. In fact, in countries where more men than women believe that scarce jobs should go to men first, the gender pay gap tends to be larger (Fortin, 2005[53]). On the other hand, women’s employment itself will likely have feedback effects on gender attitudes, changing them over time (Seguino, 2007[54]).

In Peru, the share of men and women whose beliefs align strongly to the traditional “male breadwinner”-“female homemaker” model divide is smaller than seen in other countries. For several decades, the World Value survey has analysed these attitudes by inviting feedbacks on:

i) the ‘right’ of women to participate in the labour market and education (“When jobs are scarce, men should have more right to a job than women” and “A university education is more important for a boy than a girl”);
ii) the leadership potential of both genders (“On the whole, men make better political leaders than women do”); and
iii) the compatibility of being a mother and working (“When a mother works for pay, children suffer”).

The share of people who agree with the more traditional norms has declined overtime in many countries (Seguino, 2007[54]). In Peru, fewer individuals support the conservative attitudes compared to individuals in other Latin American and a selection of OECD countries (Figure 1.15). This lower prevalence may reflect the higher labour force participation of women in Peru. One interesting piece of evidence is that the views of men and women appear to be broadly aligned, while almost everywhere else, a higher share of men agree with the socially conservative statements (Seguino, 2007[54]). In addition to influencing labour market participation, gender attitudes may also influence the division of paid and unpaid work. An analysis of Ecuador, Mexico and Peru shows that countries with more egalitarian attitudes have lower gaps in the total work burdens between men and women (Campaña, Giménez-Nadal and Molina, 2018[55]).

Figure 1.15. In Peru, the share of men and women with traditional views on women’s role in economic life varies less than elsewhere

Despite these comparatively more egalitarian social norms, in Peru one in ten women declares to have experienced economic violence during the previous 12 months. In 2015, 28.9% of women 18 and older in Peru reported that they experienced economic violence at some point in their life. Economic violence is defined as denying them money for household expenses, taking away their salary, preventing them from knowing what the family income was, forbidding them from working, and other similar acts. Evidence from other countries suggests that women who experience economic violence from their partners often also experience physical violence – with the latter being an important issue for concerns in Peru, particularly among middle-lower income, urban and less educated women, and among those who have witnessed or experienced domestic violence in childhood (Muller and Paz, 2018[56]).

More than a third and a quarter of adult respondents agreed that a woman who leaves the house neglects her domestic duties and that a woman should not work if her partner did not want her to, respectively. More than half agreed that women needed to first fulfil their role as mother, wife or housewife before realising other aspirations (INEI, 2016[57]). Economic violence might make women more reluctant to take up and retain employment because the violence might escalate when women have a job (Bettio and Ticci, 2017[58]).

Institutions and laws

An analysis across developing and emerging economies suggests that equality under the law, the respect of the right of equal inheritance and of the right for women to be head of a household are associated with a decline in the gender gap in labour force participation of around 4.6 percentage points (Gonzales et al., 2015[59]). Over the past three decades, Peru has made great strides in reducing discriminatory laws and regulations that can limit the ability of women to choose any profession they want, start a business and be paid equally. The adoption of a new Constitution in 1993 established the equality of men and women under the law and paved the way to the elimination of many customary laws, which prevented women from working outside the home, having a bank account, and getting loans, owning and inheriting assets. In addition to the elimination of customary laws, Peru adopted a range of national policies to combat all forms of discrimination based on gender. Among others, in 2000 Peru introduced a law to outlaw discrimination based on gender and in 2007 a law on equal opportunities between women and men. More recently, in 2019 the Peruvian State reaffirmed its commitment to combat all form of discrimination against women, while strengthening the participation of women in social and political life and granting equitable access to productive resources and employment (State Policy Nº 11 of the National Agreement). Within ten years since the launch of these reforms, women’s labour force participation increased by 15 percentage points, with beneficial effects also accruing to the women from the indigenous population and rural communities (Council on Foreign Relations, 2022[60]).

Recent international surveys detect the progress achieved by Peru to improve the regulatory framework against gender discrimination. Specifically, the OECD’s Social Institutions and Gender Index (OECD, 2019b) rates the level of gender discrimination in Peru’s national laws as low (Table 1.2.), although gender discrimination persists in practice, in particular with regard to women’s land rights. At the same time, the World Bank Women, Business and the Law index scores Peru 95 out of 100, which is the highest among Latin American countries and close to the average OECD score (Table 1.3). According to expert judgement, women in Peru have the same rights to mobility in the workplace, for pay, in entrepreneurship and regarding assets and pensions as men have. The two important components for which Peru does not receive a full score are marriage (reflecting the fact that women do not have the same legal rights to remarry as men do) and parenthood (reflecting the lack of mandatory paid parental leave).

Table 1.2. The OECD Social Institutions and Gender Index suggests gender discrimination in Peru is comparatively low
	SIGI		Discrimination in the family		Restricted physical integrity		Restricted access to productive and financial resources		Restricted civil liberties
	Score	Cat.	Score	Cat.	Score	Cat.	Score	Cat.	Score	Cat.
Colombia	15	Very low	9.6	Very low	14.9	Low	14.5	Low	20.6	Low
Peru	24.5	Low	47.7	Medium	26.6	Medium	5.5	Very low	12.9	Low
Costa Rica	27.9	Low	45.7	Medium	24.8	Low	27.5	Medium	10.5	Low
Chile	36.1	Medium	36.4	Medium	18.8	Low	64.8	High	16.6	Low
Latin America	25.4		31.2		21.8		22.9		20.2
OECD	17.2		25.1		12.6		13.4		17.3
Note: The Latin American and OECD averages are unweighted means. The Latin American and the Caribbean average of the SIGI is based on Bolivia, Brazil, Colombia, Chile, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Haiti, Guatemala, Jamaica, Honduras, Mexico, Nicaragua, Paraguay, Peru, Trinidad and Tobago and Uruguay. The discrimination in the family indicator is in addition based on Antigua and Barbuda, Bahamas, Barbados, Belise, Cuba, Dominica, Grenada, Guyana, Argentina, Panama and Venezuela (the latter three also for the productive and financial resources and civil liberties dimensions). For more information please refer to the OECD Database on Gender gaps in Latin America and the Caribbean under https://www.oecd.org/latin-america/regional-programme/gender/.
Source: OECD (2020[61]), SIGI 2020 Regional Report for Latin America and the Caribbean, https://dx.doi.org/10.1787/cb7d45d1-en.

Table 1.3. Peru scores high in the Women, Business and the Law Index of the World Bank
	WBL INDEX	Mobility	Workplace	Pay	Marriage	Parenthood	Entrepreneurship	Assets	Pension
Chile	80	100	75	75	80	100	75	60	75
Colombia	82	100	100	50	100	80	75	100	50
Costa Rica	83	100	100	50	100	40	75	100	100
Peru	95	100	100	100	80	80	100	100	100
LAC	86	100	95	75	93	69	84	96	77
OECD	94	100	98	88	96	89	95	99	86
Note: The LAC and OECD averages are unweighted. For the index, 35 questions are scored across the eight indicators based on laws and regulations that were in force at the time of the development of the index. Overall scores were calculated by taking the average of each indicator, with 100 representing the highest possible score. For more information please refer to the OECD Database on Gender gaps in Latin America and the Caribbean under https://www.oecd.org/latin-america/regional-programme/gender.
Source: World Bank (2021[62]), Women, Business and the Law data 1970-2021.

Care and physical infrastructure

One additional factor that can contribute to differences in economic outcomes between men and women is the access to the physical and social infrastructure and, related to this, the availability of labour-saving household technology. On the one hand, the availability of reliable and affordable local transports, child and elderly care facilities, and electricity and running water, determine how many hours adult household members need to spend on commuting, looking after children, cooking and cleaning and hence how many hours they can spend in paid work. As discussed, given prevailing gender stereotypes and gender pay gaps, women usually end up doing a disproportionate share of unpaid work in the household. On the other hand, access to public infrastructure affects how safe people feel and hence their perception about what activities they can pursue. For example, when girls and women have to walk throughout poorly lit areas to get to school or work, or if sexual harassment is common on public transport, they may avoid going out when it is dark or taking the bus. This can limit the range of educational and economic options open to them.

Although the infrastructure plays an important role in facilitating women’s active participation in the labour market and public life in general, it likely varies strongly by geographic areas, as well as by households’ income levels. Well-off households generally live in areas where different types of infrastructure are available and of better quality. Moreover, even if certain infrastructures are not available in these areas, richer citizens can more easily compensate for this absence. For example, instead of using public transport, higher-income women will opt in favour of driving a car; and instead of sending their children to a public day-care centre, they will hire a nanny or pay for a private day care centre.

Access to affordable and quality formal or informal childcare acts as a decisive factor in allowing women to increase their participation in the labour market (Mateo Díaz and Rodriguez-Chamussy, 2016[63]). In Peru, pre-school education for children aged three to five is mandatory. Correspondingly, nine out of ten toddlers enrol in childcare (Figure 1.16). However, the share of younger children that are in formal out-of-home care remains unknown. In addition to childcare, many people – and women in middle age, in particular – also have to provide care for their elderly relatives. Compared to childcare, care of the elderly can be even more problematic to plan, increasing the difficulty of combining care and work (Laczko and Noden, 2007[64]). The current state of the Peruvian care system and suggestions for further improvements are addressed in the following chapter on the policies to support equal sharing of paid and unpaid work.

Figure 1.16. Peruvian enrolment rates in pre-school education are high

Long and onerous commutes negatively affect the well-being and economic opportunities of men and women alike. However, it is important to take into account that the transport needs of women on average differ from those of men. Across many countries, men tend to spend more time commuting to and from work. Women more frequently make short or multi-stop trips that consist of dropping a child at school before work, for example, and stopping by the market on the way home from work. They are more likely to walk and take public transport and are less likely to drive (Duchène, 2011[65]). These patterns also characterise different Latin American cities, including Lima (Dominguez Gonzalez et al., 2020[66]). Women who regularly need to move between different areas of the outskirts generally lack access to the service, which reflects the incapacity of the system to ferry passengers from the periphery to the centre.

Even if transport options are available, women may be reluctant to take them if they are afraid of being a victim of robbery, sexual harassment or otherwise attacked. In a 2014 survey of 15 of the 20 largest capitals around the world, women in the Latin American cities (in particular in Bogota, followed by Mexico City and Lima) felt the most unsafe (Boros, 2014[67]). With perceived safety conditions affecting the transport choices of women, in Lima, for example, as in other Latin American cities, women often reported that they prefer to use minibuses than other public transport, even though they are more expensive and slower compared to the metro. The stated reason was that they have their seat in the minibus, which makes them feel safer from harassment. Women who take busses often wait for less crowded ones. A lack of security also arises from having to walk on poorly maintained and lit sidewalks and having to wait for a long time at bus stops in isolated locations (Dominguez Gonzalez et al., 2020[66]).

Finally, the amount of work required for maintaining a household in good conditions and the hours available for other activities also depends on the access to electricity and labour-saving technology. Appliances such as the washing machine have massively reduced the physical and time effort needed to wash clothes, clean the home and cook. The timesaving effects of household appliances are so massive that some economists believe that they have changed the world more than the internet (Chang, 2012[68]). In Peru in 2018, 95.2% of households had access to electricity, strongly up from 72.5% in 2000 and 88.1% in 2010. However, in rural areas nearly one in five still do not have access. In Chile, Costa Rica and Colombia, in contrast, access is close to universal, while the average access around Latin America is 98.3% (World Bank, 2022[69]). Access to electricity does not necessarily entail ownership of labour-saving appliances: in 2017, 49% had a refrigerator or freezer and 30.3% had a washing machine. In rural areas, only 7.7% had a refrigerator and 1.5% had a washing machine (INEI, 2018[70]). Still, some urban inhabitants without a washing machine may be able to access laundromats, which is unlikely to be the case in rural areas or in poor urban areas. In many households, people need to spend significantly more time shopping (since they cannot keep food fresh for a long time), washing, cooking and cleaning than they would have to if they owned household appliances.

References

[36] Berniell, I. et al. (2021), Motherhood and flexible jobs: Evidence from Latin American countries, UNU-WIDER, https://doi.org/10.35188/UNU-WIDER/2021/971-6.

[44] Bertrand, M. (2020), “Gender in the Twenty-First Century”, AEA Papers and Proceedings, Vol. 110, pp. 1-24, https://doi.org/10.1257/pandp.20201126.

[58] Bettio, F. and E. Ticci (2017), Violence against Women and Economic Independence, Publishing Office of the European Commission , Luxembourg, http://knjiznica.sabor.hr/pdf/E_publikacije/Violence%20against%20women%20and%20economic%20independence.pdf.

[67] Boros, C. (2014), EXCLUSIVE-POLL: Latin American cities have most dangerous transport for women, NYC best, Reuters, London, https://uk.reuters.com/article/women-poll/exclusive-poll-latin-american-cities-have-most-dangerous-transport-for-women-nyc-best-idUKL6N0S32MQ20141029.

[55] Campaña, J., J. Giménez-Nadal and J. Molina (2018), “Gender Norms and the Gendered Distribution of Total Work in Latin American Households”, Feminist Economics, doi: 10.1080/13545701.2017.1390320, pp. 35-62, https://doi.org/10.1080/13545701.2017.1390320.

[39] Centro Nacional de Planeamiento Estratégico (2016), Economía informal en Perú: Situación actual y perspectivas, Centro Nacional de Planeamiento Estratégico, Lima, https://perureports.com/wp-content/uploads/2016/08/Economia-informal-en-Peru-situacion-actual-perspectivas-15-03-2016.pdf.

[42] CENTRUM PUCP, WomenCEO Perú and PwC Perú (2018), Primer Estudio sobre Mujeres Miembros de Directorios de las Empresas en el Mercado de Valores Peruano 2018, Centro de Negocios de la Pontificia Universidad Católica del Perú , Lima, https://womenceoperu.org/wp-content/uploads/2018/11/womanceo_estudio_vf-baja.pdf.

[68] Chang, H. (2012), 23 things they don’t tell you about capitalism, Bloomsbury Publishing, London.

[38] Chong, A., J. Galdo and J. Saavedra (2008), “Informality and productivity in the labor market in Peru”, Journal of Economic Policy Reform, doi: 10.1080/17487870802543480, pp. 229-245, https://doi.org/10.1080/17487870802543480.

[52] Christiansen, L. et al. (2016), “Individual Choice or Policies? Drivers of Female Employment in Europe”, IMF Working Paper, No. 16/49.

[45] Ciminelli Gabriele and Schwellnus Cyrille (2021), “Sticky floors or glass ceilings? The role of humancapital, working time flexibility and discriminationin the gender wage gap”, VOX, CEPR Policy Portal, https://voxeu.org/article/human-capital-working-time-flexibility-and-discrimination-gender-wage-gap.

[12] Connolly, S. and M. Gregory (2008), “Moving Down: Women’s Part-Time Work and Occupational Change in Britain 1991–2001*”, The Economic Journal, Vol. 118/526, pp. F52-F76, https://doi.org/10.1111/j.1468-0297.2007.02116.x.

[2] Conti, G., J. Heckman and S. Urzua (2010), “The Education-Health Gradient”, American Economic Review, Vol. 100/2, pp. 234-238, https://doi.org/10.1257/aer.100.2.234.

[60] Council on Foreign Relations (2022), Peru’s legal reforms effectively increased female labor force participation, https://static-live-backend.cfr.org/womens-participation-in-global-economy/case-studies/peru/.

[3] Dávila-Cervantes, C. and M. Agudelo-Botero (2019), “Health inequalities in Latin America: persistent gaps in life expectancy”, The Lancet Planetary Health, Vol. 3/12, pp. e492-e493, https://doi.org/10.1016/S2542-5196(19)30244-X.

[66] Dominguez Gonzalez, K. et al. (2020), Why does she move? A Study of Women’s Mobility in Latin American Cities, World Bank, Washington, D.C., http://documents.worldbank.org/curated/en/276931583534671806/pdf/Why-Does-She-Move-A-Study-of-Womens-Mobility-in-Latin-American-Cities.pdf.

[7] Dougherty, C. (2005), “Why Are the Returns to Schooling Higher for Women than for Men?”, The Journal of Human Resources, Vol. 40/4, pp. 969-988, http://www.jstor.org/stable/4129547.

[65] Duchène, C. (2011), “Gender and Transport”, Discussion Paper, No. 11, International Transport Forum, Paris.

[35] ECLAC/ILO (2019), “Evolution of and prospects for women’s labour participation in Latin America”, Employment Situation in Latin America and the Caribbean, No. 21, Economic Commission for Latin America and the Caribbean /International Labour Organization, Santiago, https://repositorio.cepal.org/bitstream/handle/11362/44917/1/S1900832_en.pdf.

[11] Ferrant, G., L. Pesando and K. Nowacka (2014), “Unpaid Care Work: The missing link in the analysis of gender gaps in labour outcomes”, OECD, Paris, http://www.oecd.org/dev/development-gender/unpaid_care_work.pdf.

[17] Ferrant, G. and A. Thim (2019), “Measuring Women’s Economic Empowerment: Time Use Data and Gender Inequality”, OECD Development Policy Papers, No. 16, OECD, Paris, https://www.oecd.org/dev/development-gender/MEASURING-WOMENS-ECONOMIC-EMPOWERMENT-Gender-Policy-Paper-No-16.pdf.

[53] Fortin, N. (2005), “Gender Role Attitudes and the Labour-market Outcomes of Women across OECD Countries”, Oxford Review of Economic Policy, Vol. 21/3, pp. 416-438, https://doi.org/10.1093/oxrep/gri024.

[59] Gonzales, C. et al. (2015), “Fair Play: More Equal Laws Boost Female Labor Force Participation”, IMF Staff Discussion Paper, No. 15/02, International Monetary Fund, Washington, D.C.

[10] Guerrero, G. and V. Rojas (2020), “Young women and higher education in Peru: how does gender shape their educational trajectories?”, https://doi.org/10.1080/09540253.2018.1562055.

[31] Hanushek, E. (2013), “Economic growth in developing countries: The role of human capital”, Economics of Education Review, Vol. 37, pp. 204-212, https://doi.org/10.1016/j.econedurev.2013.04.005.

[37] ILO (2020), ILOSTAT, https://ilostat.ilo.org/ (accessed on 29 April 2020).

[51] INE (2019), Encuesta Nacional de Hogares, Instituto Nacional de Estadística e InformáticaA, Lima, https://webinei.inei.gob.pe/anda_inei/index.php/catalog/613/.

[34] INEI (2020), Estadísticas - Empleo, Instituto Nacional de Estadística e Informatica, Lima, http://m.inei.gob.pe/estadisticas/indice-tematico/ocupacion-y-vivienda/ (accessed on 27 May 2020).

[40] INEI (2019), Perú: Estructura empresarial, 2018, Instituto Nacional de Estadística e Informática, Lima, https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1703/libro.pdf.

[20] INEI (2019), Tasa de analfebetismo de la población de 15 y más años de edad, según grupos de edad y sexo, 2008-2018, Instituto Nacional de Estadistica e Informatica, Lima.

[21] INEI (2019), Tasa neta de matricula escolar a educación primaria de la población de 6 a 11 años, Instituto Nacional de Estadistica e Informatica, Peru, https://www.inei.gob.pe/estadisticas/indice-tematico/education/.

[22] INEI (2019), Tasa neta de matricula escolar a educación secundaria de la población de 12 a 16 años de edad, Instituto Nacional de Estadistica e Informatica, Peru, https://www.inei.gob.pe/estadisticas/indice-tematico/education/.

[70] INEI (2018), Perú: Perfil Sociodemográfico, 2017, Instituto Nacional de Estadística e Informática, Lima, https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1539/.

[18] INEI (2016), Cuenta Satélite del Trabajo Doméstico No Remunerado, nstituto Nacional de Estadística e Informática, Lima, https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1358/libro.pdf.

[57] INEI (2016), Encuesta Nacional sobre Relaciones Sociales ENARES 2013 y 2015 - Principales Resultados, Instituto Nacional de Estadística e Informática, Lima.

[47] Instituto Nacional de Estadística y Informatica (n.d.), Indicadores de Género, https://www.inei.gob.pe/estadisticas/indice-tematico/brechas-de-genero-7913/ (accessed on 29 April 2020).

[32] Kahn, S. and D. Ginther (2018), “Women and Science, Technology, Engineering, and Mathematics (STEM): Are Differences in Education and Careers Due to Stereotypes, Interests, or Family?”, in Averett, S., L. Argys and S. Hoffman (eds.), The Oxford Handbook of Women and the Economy, Oxford University Press, Oxford, https://doi.org/10.1093/oxfordhb/9780190628963.013.13.

[64] Laczko, F. and S. Noden (2007), “Combining paid work with eldercare: the implications for social policy”, Health & Social Care in the Community, Vol. 1/2, pp. 81-89, https://doi.org/10.1111/j.1365-2524.1993.tb00200.x.

[43] Levanon, A., P. England and P. Allison (2009), “Occupational Feminization and Pay: Assessing Causal Dynamics Using 1950-2000 U.S. Census Data”, Social Forces, Vol. 88/2, pp. 865-891, https://doi.org/10.1353/sof.0.0264.

[13] MacDonald, M., S. Phipps and L. Lethbridge (2005), “Taking Its Toll: The Influence of Paid and Unpaid Work on Women’s Well-Being”, Feminist Economics, Vol. 11/1, pp. 63-94, https://doi.org/10.1080/1354570042000332597.

[9] Marcus, R. and E. Page (2016), Girls’ Learning and Empowerment - The Role of School Environments, United Nations Girls’ Education Initiative, http://www.ungei.org/Policy_Brief_-_School_Environments-v2.pdf.

[63] Mateo Díaz, M. and L. Rodriguez-Chamussy (2016), Cashing in on Education - Women, Childcare, and Prosperity in Latin America and the Caribbean, International Bank for Reconstruction and Development / The World Bank, Washington, D.C.

[4] Mincer, J. (1984), “Human capital and economic growth”, Economics of Education Review, Vol. 3/3, pp. 195-205, https://doi.org/10.1016/0272-7757(84)90032-3.

[25] MINEDU (2019), Estadistica de la Calidad Educativa, http://escale.minedu.gob.pe/ueetendencias2016 (accessed on 26 May 2020).

[6] Montenegro, C. and H. Patrinos (2014), “Comparable estimates of returns to schooling around the world”, Policy Research Working Paper, No. 7020, World Bank, Washington, D.C., http://documents.worldbank.org/curated/en/830831468147839247/Comparable-estimates-of-returns-to-schooling-around-the-world.

[56] Muller, M. and C. Paz (2018), Gender Gaps in Peru : An Overview, World Bank, Washington, DC, https://openknowledge.worldbank.org/handle/10986/31820.

[33] Nollenberger, N., N. Rodríguez-Planas and A. Sevilla (2016), “The Math Gender Gap: The Role of Culture”, American Economic Review, Vol. 106/5, pp. 257-261, https://doi.org/10.1257/aer.p20161121.

[61] OECD (2020), SIGI 2020 Regional Report for Latin America and the Caribbean, Social Institutions and Gender Index, OECD Publishing, Paris, https://doi.org/10.1787/cb7d45d1-en.

[1] OECD (2019), Education at a Glance 2019: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/f8d7880d-en.

[24] OECD (2019), Investing in Youth: Peru, Investing in Youth, OECD Publishing, Paris, https://doi.org/10.1787/9789264305823-en.

[30] OECD (2019), PISA 2018 Results (Volume II): Where All Students Can Succeed, PISA, OECD Publishing, Paris, https://doi.org/10.1787/b5fd1b8f-en.

[28] OECD (2019), Skills Matter: Additional Results from the Survey of Adult Skills, OECD Skills Studies, OECD Publishing, Paris, https://doi.org/10.1787/1f029d8f-en.

[29] OECD (2018), Education GPS, https://gpseducation.oecd.org/ (accessed on 26 May 2020).

[50] OECD (n.d.), OECD Family Database, http://www.oecd.org/social/family/database.htm.

[15] Ogolsky, B., R. Dennison and J. Monk (2014), “The Role of Couple Discrepancies in Cognitive and Behavioral Egalitarianism in Marital Quality”, Sex Roles, Vol. 70/7, pp. 329-342, https://doi.org/10.1007/s11199-014-0365-9.

[26] PAHO, UNFPA and UNICEF (2017), Accelerating progress toward the reduction of adolescent pregnancy in Latin America and the Caribbean, Pan American Health Organization / World Health Organization, Washington, D.C., https://iris.paho.org/handle/10665.2/34493.

[27] Post, D. (2011), “Primary school student employment and academic achievement in Chile, Colombia, Ecuador and Peru”, International Labour Review, Vol. 150/3-4, pp. 255-278, https://doi.org/10.1111/j.1564-913X.2011.00116.x.

[46] Post, D. and S. Pong (2009), “The academic effects of after‐school paid and unpaid work among 14‐year‐old students in TIMSS countries”, Compare: A Journal of Comparative and International Education, Vol. 39/6, pp. 799-818, https://doi.org/10.1080/03057920802681804.

[19] Rojas, V., G. Guerrero and J. Vargas (2016), Gendered Trajectories Through Education, Work and Parenthood in Peru, Young Lives, Oxford Department of International Development, https://www.younglives.org.uk/sites/www.younglives.org.uk/files/YL-WP157-Gendered-Trajectories-in-Peru.pdf.

[49] Sanchez, L. and E. Thomson (1997), “Becoming Mothers and Fathers: Parenthood, Gender, and the Division of Labor”, Gender & Society, Vol. 11/6, pp. 747-772, https://doi.org/10.1177/089124397011006003.

[48] Schober, P. (2011), “The Parenthood Effect on Gender Inequality: Explaining the Change in Paid and Domestic Work When British Couples Become Parents”, European Sociological Review, Vol. 29/1, pp. 74-85, https://doi.org/10.1093/esr/jcr041.

[8] Schultz, T. (1993), “Returns to Women’s Education”, in King, E. and M. Hill (eds.), Women’s Education in Developing Countries: Barriers, Benefits and Policies.

[71] Schultz, T. (1993), “Returns to Women’s Education”, in King, E. and M. Hill (eds.), Women’s Education in Developing Countries: Barriers, Benefits and Policies, World Bank, Washington, D.C., https://agris.fao.org/agris-search/search.do?recordID=US2012420719.

[54] Seguino, S. (2007), “Plus ça Change? Evidence on Global Trends in Gender Norms and Stereotypes”, Feminist Economics, Vol. 13/2, pp. 1-28, https://doi.org/10.1080/13545700601184880.

[41] Serida, J. et al. (2017), Global Entrepreneurship Monitor: Perú 2016-2017, Universidad ESAN, Lima, https://www.researchgate.net/publication/320161398_Global_Entrepreneurship_Monitor_Peru_2016-2017.

[14] Sigle-Rushton, W. (2010), “Men’s Unpaid Work and Divorce: Reassessing Specialization and Trade in British Families”, Feminist Economics, Vol. 16/2, pp. 1-26, https://doi.org/10.1080/13545700903448801.

[23] UNESCO (2014), Developing an education sector response to early and unintended pregnancy - Discussion document for a global consultation, UNESCO, Paris, https://unesdoc.unesco.org/ark:/48223/pf0000230510.

[16] WHO (2007), Fatherhood and health outcomes in Europe, World Health Organization , Copenhagen, http://www.euro.who.int/__data/assets/pdf_file/0017/69011/E91129.pdf.

[5] Woodhall, M. (1973), “The economic returns to investment in women’s education”, Higher Education, Vol. 2/3, pp. 275-299, https://doi.org/10.1007/BF00138806.

[69] World Bank (2022), World Development Indicators, World Bank, Washington, D.C., https://data.worldbank.org.

[62] World Bank (2021), Women, Business and the Law data for 1971-2021, World Bank, Washington, D.C., https://wbl.worldbank.org/en/wbl.

Notes

← 1. A possible explanation for the discrepancy between the enrolment rate and the share of out-of-work children is that a higher share of boys who are of the theoretical age to be in upper secondary school are still in lower secondary school, thus not contributing to a higher upper secondary net enrolment rate but not being among out-of-school children.

← 2. An adjusted gender gap that controls for too many variables can obscure other systematic disadvantages women face. For example, pay may differ across sectors. If the sector is controlled for but women face disadvantages in hiring in this sector, the wage gap understates the pay disadvantage women face.

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