Chapter 1. The labour market situation of youth in Peru

This chapter provides an overview of labour market outcomes of Peruvian youth and lays out the key challenges ahead for employment policy in the country. Despite favourable economic conditions the Peruvian labour market has long had difficulties with absorbing large and increasingly educated cohorts of young people. One in five Peruvians aged 15-29 is not in employment, education or training (NEET), and nearly three quarters of economically active youth work informally. Informality greatly contributes to the prevalence of poor quality jobs. The incidence of low-pay among youth is common and appears compounded by labour market insecurity and pervasiveness of intermittent work arrangements. Females, early school leavers and youth coming from economically disadvantaged backgrounds are the most vulnerable groups in the labour market.

    

1.1. Introduction

The Peruvian labour market is beset by high rates of youth neither in employment, nor in education or training (NEET) and high rates of informality. 21% of youth (aged 15-29) are NEETs and even where youth are in employment the quality of their jobs is weak since the majority of them continue to work under informal conditions. Economic growth will be essential for job creation, but growth alone will not solve all the difficulties that the youth generations face in gaining access to productive and rewarding jobs. This chapter provides an overview of the labour market outcomes for young people in Peru, covering key demographic and labour market indicators, alongside measures of the quality of jobs, a detailed profiling of the NEETs and school-to-work transitions. The chapter takes an internationally comparative perspective, with OECD countries as well as selected LAC countries as comparators. The main findings of the chapter are:

  • While high, the share of youth in the working age population in Peru is expected to fall in the years to come. Although this will release some of the pressure on the youth labour market, the share of the working age population will correspondingly shrink. As a result, there will be fewer opportunities to benefit from the growth dividend associated with the demographic transition.

  • The Peruvian youth labour market is characterised by relatively low inactivity rates (39.2%), alongside low unemployment rates (8.7%). However, the overall youth employment figures hide significant differences between groups. Young women have lower employment rates than young men (52.1% compared to 60%). In addition, employment outcomes are far worse for young people living in the more deprived inland areas of the South, the Andean highlands and the Amazon regions.

  • One key issue for Peru is not the lack of jobs, as such, since open unemployment tends to be low. Rather, it is the lack of quality jobs that raises the greatest concerns. The inadequacy of social security forces workers to accept subsistence-level occupations.

  • Young people from vulnerable families, particularly in the poor rural areas, the least educated youth and young women are more likely to have an informal occupation. NEET rates vary significantly across groups of young people with more than one in four young women in Peru being NEETs, compared to less than one in five for men.

  • Although unemployment rates are high among university graduates, youth with a university degree are significantly less likely to be NEET. These youth represent a relatively small share of the total unemployed youth.

  • By age 15, roughly one fifth of the youth population has dropped out from formal education and by age 17-18 at least half has left. Both figures are high in the comparison with the OECD countries and other LAC countries.

  • Estimate analysis suggests that in 2014 the extent of the forgone productivity associated to the NEETs ranges between 1.5 and 2.5% of Peru’s GDP. By comparison, the estimate for the OECD ranges between 0.9 and 1.5% of the OECD GDP in the same year.

1.2. Youth make up for a large but rapidly declining share of the working age population

Over a quarter of Peru’s working age population (ages 15-64) is young (15-24), compared to under a fifth on average among OECD countries (Figure 1.1). While the share of youth in the working age population in Peru is expected to fall to just above 20% by 2050, this will still be relatively high by OECD standards and will place Peru towards the high end of what is forecast for the selected LAC countries included in Figure 1.1.

Figure 1.1. Share of youth in the working age population, Peru, OECD and selected Latin American countries
Population aged 15-24 as a percentage of the population aged 15-64
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Source: United Nations, Department of Economic and Social Affairs, Population Division (2017), World Population Prospects: The 2017 Revision.

Peru’s rapid demographic transition can be traced back to the country’s dramatic declines in average fertility and mortality. Campaigns and actions in relation to family planning from the early 1960s (Velikoff, 2011) resulted in the average crude birth rate falling from 47 in 1960 to 20 in 2015 – although it is important to underline that regional and urban-rural variations are significant within Peru. At the same time, advances in health and education, led the life expectancy to rise from 48 to 75 years over the same period. Reflecting these patterns, the proportion of youth in the working age population reached a peak in the first half of the 1980s and has been trending down since 1985 (Figure 1.2, Panel A). In perspective, this proportion is expected to fall incessantly throughout the first half of the 21st century, with the youth population also beginning to contract in absolute terms by around 2040 (Figure 1.2, Panel B). In turn, the fall in the share (and number) of youth in the working age population will ease some of the pressure on the labour market for youth in the decades to come. At the same time, it will mean that the share of the working age population will shrink, thus lessening the opportunities to benefit from the growth dividend associated with the demographic transition.

Figure 1.2. Youth population in Peru, 1950-2050
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Source: United Nations, Department of Economic and Social Affairs, Population Division (2017), World Population Prospects: The 2017 Revision.

1.3. Prima facie evidence suggests that Peruvian youth do not perform badly in the labour market

Broadly speaking, the youth labour market in Peru has remained fairly robust and resilient since the early 2000s, notwithstanding the effects of the global financial crises (Franco and Ñopo, 2018). This outcome was helped by an overall stronger economic climate than observed across both the OECD and LAC countries (Figure 1.3). As of 2017, close to 56% of youth were in employment, compared to about 40% across the OECD (Figure 1.4, Panel A). In the comparison with the regional countries, Paraguay, Bolivia, Ecuador, and Argentina, countries with shares of youth in the working age population not too dissimilar to that of Peru, had youth employment rates of about 50%, 39%, 41% and 31%, respectively in the same year. Peruvian youth labour market is characterised by low inactivity rates (39.2%, versus 53.7% across the OECD), alongside low unemployment rates (8.7%, compared to 15.1% across the OECD). On both accounts Peru performs comparatively well also by LAC standards.

Figure 1.3. Annual real GDP growth, Peru, OECD and Latin American and Caribbean countries
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Source: World Bank, World Development Indicators and OECD.

Figure 1.4. Labour force status of youth (15-24), Peru, OECD and selected Latin American countries, 2017
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Source: OECD Employment Database and ILO.

1.4. However, some youth fare much worse than others

The above relatively favourable aggregate pattern masks important labour market differences across groups of Peruvian youth. Young women have significantly lower employment rates than young men (52.1% compared to 60%, Figure 1.5; Atal, Ñopo and Winder, 2012, and OECD, CAF and ECLAC, 2017 and 2016 provide regional discussions across LAC countries). Such a marked gap in employment by gender is almost entirely explained by the higher inactivity rates of young women than young men (43%, compared to 34.6%), given that the unemployment rates for the two groups differ only marginally (8.5% as compared to 8.4%, respectively). In addition, labour market outcomes vary significantly by region, with employment outcomes being worse for young people living in the more deprived inland areas of the South, the Andean highlands and the Amazon regions. The role played by local circumstances is important when addressing youth challenges in Peru. For example, non-wage employment is more pervasive in rural areas. At the same time, the large majority of Peruvian youth is urban, amounting to about 78% of those aged 15-29 (SENAJU, 2015).

Interestingly, the employment outcomes of youth with tertiary education are not necessarily better than their peers with lower educational outcomes in Peru. If anything, the most recent figures show that high skilled youth (i.e. graduates) face an even higher risk of unemployment. Specifically, in 2017 their unemployment rate was 14.6%, compared to 8.7% for medium skilled (i.e. with at most finished secondary education) and 7.3% for unskilled youth. This comparatively high unemployment rate reflects the fact that the number of students graduating with tertiary education qualifications has considerably increased over time in Peru. An over-supply of university graduates in relation to demand may be expected to lead to a considerable degree of over-qualification as highly educated young people are forced to accept positions that require less education - at least in the short-run, before firms have the chance to adapt their productive processes to make the best use of the available human capital. Moreover, the quality of education seems to have deteriorated overtime (Box 1).

Box 1.1. Challenges related to the quality of education in Peru

Recent OECD analysis suggests that the expansion in access to tertiary education in Peru may have come to the detriment of quality. This finding partly reflects the fact that a large part of the many universities that have been created in the past 15 years rely on part-time lecturers and few full-time professors (Castro and Yamada, 2013; Brunner and Hurtado, 2011). An excessive fragmentation, may have affected both teaching quality and course content.

In addition, private financial incentives and funding allocation formulas that prioritise higher enrolment numbers over responsiveness to labour market demands have prompted higher education institutions (HEIs) to expand their programs in popular subjects (e.g. business administration and accounting and finance), which has generated growing mismatches in qualifications and field of studies. Furthermore, students may not be adequately prepared to align their field of study choices with the needs of the labour market.

All in all the skills possessed by Peruvian youth graduating with a higher education degree do not always adequately reflect their level of formal qualification, which slows the transition to quality jobs and fuels widespread sentiments of occupational inadequacy. These issues will be further discussed in Chapter 3, which provides an in-depth analysis of the supply and demand for skills in Peruvian labour market. In particular, the chapter suggests that the limited demand for skills may largely inflate the misalignment between skill supply and needs of the labour market.

Source: OECD, 2016.

In order to set this discussion in context, it is important to recall that university graduates represent a minority of the young unemployed: in 2017, only about 6% of unemployed youth had a tertiary qualification, compared to 65% and 29% with at most secondary and primary qualifications, respectively. As shown below, despite the high unemployment rate university graduates are less likely to be neither in employment, nor in education or training (NEET) than those with less education. Furthermore, research on the returns to education indicates that obtaining a tertiary qualification in Peru is worth the investment, although this evidence varies depending upon the field of study and the quality of education institution (Espinoza and Urzùa, 2015). This suggests that the unemployment rate is probably higher for those with low-quality education.

Figure 1.5. Youth labour market outcomes by socio-demographic characteristics, Peru, 2017
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Note: Low-skilled refers to education lower than upper level secondary school; Medium-skilled refers to education lower than a bachelor equivalent tertiary degree; High-skilled corresponds to a bachelor equivalent tertiary degree or higher. Tertiary education refers to University and non-University tertiary education.

Source: OECD calculations based on Encuesta Nacional de Hogares (ENAHO, National Household Survey), 2017.

1.5. Heterogeneous labour market outcomes are associated with low levels of well-being

The lack of equal labour market opportunities across groups of Peruvian youth is closely related to their financial vulnerability and their perception of economic risk (OECD, 2017). 33.9% of Peruvian youth affirm that they find it difficult, or very difficult, to get by with their present household income -- as opposed to living comfortably, or getting by (Figure 1.6). This evidence compares to an OECD average of about 20% and places Peruvian youth towards the worse-off end of the LAC countries shown in Figure 1.6. As a counterpart of the difficulty to cope financially, the indicator of self-reported well-being is in Peru about the same of the least satisfied OECD countries and the lowest among a selection of regional comparators (Figure 1.7).

Figure 1.6. Youth perceptions about household income, Peru, OECD and selected Latin American countries, 2015-16
Percentage share of those finding it difficult or very difficult to live comfortably on present income
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Note: The indicator is calculated based on the following question: Which one of these phrases comes closest to your own feelings about your household income these days? Including possible responses: i) Living comfortably on present income; ii) Getting by on present income; iii) Finding it difficult on present income; iv) Finding it very difficult on present income. Data refer to the 15-29 age bracket and the average between 2016 and 2015. For Australia the average is calculated for 2013-2014; Finland 2014-2015; New Zealand and Switzerland 2014 and 2016; for Iceland data refer to 2015. OECD average is an unweighted average of 34 countries.

Source: OECD calculations based on Gallup World Poll.

Figure 1.7. Youth self-reported perception of life satisfaction, Peru, OECD and selected Latin American countries, 2015-16
Average score on a scale 0 to 10
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Note: The indicator is calculated based on question: “Please imagine a ladder with steps numbered from 0 at the bottom to 10 at the top. Suppose we say that the top of the ladder represents the best possible life for you, and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time, assuming that the higher the step the better you feel about your life, and the lower the step the worse you feel about it? Which step comes closest to the way you feel?” Data refer to 15-29 age bracket and the average between 2016 and 2015. For Australia the average is calculated for 2013-14; Finland 2014-15; New Zealand and Switzerland 2014 and 2016; for Iceland data refer to 2015. OECD average is an unweighted average of 34 countries.

Source: OECD calculations based on Gallup World Poll.

Beyond self-reporting, other socio-economic indicators point to tensions in the economic and social situation of Peruvian youth. The Gini index, a standard measure of income inequality that ranges from 0 (when everybody has identical incomes) to 1 (when all income goes to one person) has declined from 0.51 in 2005 to 0.44 in 2015, thus placing Peru at the low-end of the LAC comparison. However, it remains high compared to the OECD average and it has stagnated in recent years. Almost two decades of remarkable economic growth have enabled the country to reduce the poverty headcount ratio (measured at USD 3.2 a day) from about 30% to 10%, a comparatively stronger decline than across the LAC countries (Figure 1.8, Panel A).Despite this positive dynamic, recent data using the benchmark of the national poverty level, show that the share of individuals living below 338 PEN per month, corresponding to the level at which the benchmark is fixed (approximately equal to USD 103), has increased by one percentage point in 2017 (Figure 1.8, Panel B). This was the first rise in a decade. Poverty remains high for the rural population and particularly among children. In rural areas, 25% of the population is poor, which is more than five times higher than the corresponding share in urban areas (4.5%). Most worrying, the distribution of poverty is concentrated on the youngest age brackets in Peru -- 12% of children aged 0-14 live in poor households compared to 7.2% of working age population 15-64. In 2017 more than one and a half million Peruvian youth 15-29 lived in conditions of poverty (about 1.3 million), or extreme poverty (about 257 thousands).

Figure 1.8. Evolution of poverty
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Note: In panel A Data for the LAC average for 2000, 2001, 2003, 2004, 2006, 2007 and 2009 are interpolated through linear interpolation techniques; data for 1998 and 2014 are extrapolated through linear extrapolation technique. Data for 2015 are not available and extrapolation cannot be carried out with confidence.

Source: WB, World Development Indicators for Panel A and Instituto Nacional de la Estadistica e Informatica (INEI, National Statistical and Informatics Institute, 2018) for Panel B.

1.6. The quality of jobs for youth is poor

Much like what can be observed in most emerging economies, the main challenge in Peru is not the lack of jobs, since open unemployment tends to be relatively low. Rather, it is the lack of quality jobs that raises greatest concern. Job quality is difficult to measure given that it encompasses a range of dimensions that can be complex to identify, particularly in the context of the emerging economies. The OECD Job Quality Framework (OECD, 2014) measures job quality along three dimensions: earnings quality (the level and distribution of earnings); labour market security; and the quality of the work environment. The main dimensions of the framework have been adapted to reflect the key features of the labour markets of emerging economies. Box 1.2 provides a detailed discussion of the OECD Job Quality Framework.

The international comparison across the above three dimensions shows that Peruvian youth score low on average in terms of job quality, both compared to the average of the OECD countries and the levels observed in other emerging economies (see Figure 1.9). Specifically, Peru has one of the lowest earnings quality across the eleven emerging economies reported in the figure, reflecting a gap in average earnings and higher levels of earnings inequality. Labour market insecurity due to the risk of unemployment is below the OECD average in Peru and the lowest across the emerging economies that are sampled in this analysis (Figure 1.9, Panel B). In part, this evidence reflects the weakness of social insurance scheme, which makes unemployment unaffordable and pushes many youth workers into jobs of ‘last resort’. Indeed, as concerns unemployment insurance, the average effective replacement rate is much lower in Peru than in the average OECD country. In many other emerging economies the low unemployment risk reflects the sheer unaffordability of unemployment when social insurance is inadequate (Figure 1.9).

Box 1.2. The OECD Job Quality Framework

Job quality refers to multiple aspects of employment that contribute to the well-being of workers. The multi-dimensional character of job quality is discussed in the chapter “How good is your job? Measuring and assessing job quality” of the 2014 OECD Employment Outlook (OECD, 2014), which proposes to evaluate job quality along three key dimensions that have been shown in the existing literature to be particularly relevant for workers’ well-being. These are earnings quality, labour market security, and quality of the working environment. The chapter “Enhancing Job Quality in Emerging Economies” of the 2015 OECD Employment Outlook (OECD, 2015) adapts this framework to the context of emerging economies.

Earnings quality is characterised in terms of the level of earnings and its distribution. The need to take into account these two aspects reflects their importance for well-being. First, levels of earnings and subjective well-being, as measured by life satisfaction, are positively correlated across countries as well as between individuals within countries. Second, for a given level of average earnings, overall well-being tends to be higher the more equal its distribution. This reflects the evidence pointing to life satisfaction rising at a decreasing rate as earnings rise and that people tend to display an intrinsic dislike of high inequality in society (inequality aversion).

Labour market security is defined in terms of unemployment risk as well as the probability of falling into extremely low pay. Unemployment risk encompasses both the probability of becoming unemployed and the average expected duration of unemployment spells. As such, it gives an indication of the expected amount of time an average person is likely to spend in unemployment in a given year. Insurance against the risk of unemployment is captured in terms of both unemployment benefit coverage and benefit generosity. New evidence suggests that the perception of unemployment risk and insurance protection are important determinants of life satisfaction among the employed. However, the lack of a widespread social protection system in many emerging economies means that unemployment is unaffordable in these countries since workers have to take up jobs of last resort to mitigate the drop in consumption. Thus a useful complementary dimension of labour market insecurity is the risk of falling into undesirable jobs out of necessity. The Job Quality Framework 2015 defines this dimension as a threshold of extreme low pay.

The quality of the working environment relates to the nature and intensity of work performed, the organisation of work and the working atmosphere. This is an important driver of individual well-being and depends crucially on whether workers have autonomy in their job, are given learning opportunities and well-defined work objectives, and also receive constructive feedback. Good relationships with colleagues are also important. When jobs and workplaces combine these factors, people are more apt to manage work pressure and difficult tasks, while also tend to be healthier, more satisfied with their job and more productive. In the Job Quality Framework 2015 this dimension is approximated by the incidence of long working hours, i.e., the probability of working more than 60 hours a week. This adjustment allows broadening the coverage of emerging economies, as well as facilitating the breakdown between formal and informal jobs. Available evidence supports the validity of this approach and points towards a strong positive correlation between job strain and long working hours across a broad group of countries where both measures are available.

Source: OECD (2014), OECD Employment Outlook 2014, OECD Publishing, Paris, http://dx.doi.org/10.1787/empl_outlook-2014-en.

OECD (2015), OECD Employment Outlook 2015, OECD Publishing, Paris, http://www.oecd-ilibrary.org/employment/oecd-employment-outlook-2015_empl_outlook-2015-en.

Figure 1.9. Job quality outcomes for youth (15-29), Peru and selected emerging economies in the comparison with the OECD average
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Note: The OECD Job Quality Framework identifies three pillars of job quality: (1) gross hourly earnings in USD, adjusted for inequality; (2) expected monetary loss associated with becoming and staying unemployed as a share of previous earnings; (3) incidence of very long working hours, measured as the percentage of employed people working more than 60 hours in an average week. Data refer to 2010, except for Brazil (2009), Chile (2009), India (2011) and Peru (2014).

Source: OECD calculations based on national household and labour force surveys (OECD Employment Outlook, Chapter 5. http://www.oecd-ilibrary.org/employment/oecd-employment-outlook-2015_empl_outlook-2015-en). Data for Peru come from ENAHO survey for 2014.

There are reasons to maintain that the pressure to accept low quality jobs may be comparatively strong in Peru. Figure 1.10 plots the risk of extreme low-pay estimated using the methodology proposed by Dang et al. (2011) and extended by Dang and Lanjouw (2013). This methodology allows for deriving an estimate of upward mobility (the probability of transitioning out of low pay) and an estimate of downward mobility (the probability of transitioning into low pay; see OECD (2015) for a detailed discussion of the methodology). The two can be combined to obtain a measure of the risk that a “random” youth worker in the economy will be in a low-paying occupation at a given point in time. The results show substantial variation in the risk of extreme low-pay among the countries analysed. Interestingly, however, Peruvian youth stand out for scoring towards the high end of the risk spectrum, reflecting a combination of high downward mobility and low upward mobility.

Figure 1.10. Labour market insecurity due to extreme low pay, Peru and selected emerging economies
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Note: The low pay threshold is set at USD PPP 1 in terms of net hourly earnings and corresponds to a disposable income per capita of USD PPP 2 per day in a typical household of five members with a single earner working full time.

The probabilities of entering and exiting low pay status are calculated using the methodology proposed by Dang and Lanjouw (2013). The risk of extreme shows the likelihood that an individual’s earnings are below the low pay threshold at any given time. Data refer to 2010, except for Brazil (2009), Chile (2009), India (2011) and Peru (2014).

Source: OECD calculations based on national household and labour force surveys (OECD Employment Outlook, Chapter 5. http://www.oecd-ilibrary.org/employment/oecd-employment-outlook-2015_empl_outlook-2015-en). Data for Peru come from ENAHO survey for 2014.

The quality of the working environment can be captured by the incidence of very long working hours. The choice of this approximation reflects the fact that more detailed information on working conditions is typically scarce and limited in scope in emerging economies. Figure 1.9 Panel C, displays the incidence of working more than 60 hours a week, which is the maximum authorised in the countries with the most permissive working time legislation among those included in this chapter (i.e. Colombia and Costa Rica). In Peru, roughly 10% of young workers in a typical week spend more than 60 hours at work in their principal occupation, which places the country in the middle of the distribution among other emerging market economies. When considering the overall time of work in the principal and secondary occupation the proportion of young people who work more than 60 hours is higher. Nevertheless, young Peruvians are less likely to work excessive hours than prime age and older populations, which may reflect the fact that they are more likely to be underemployed and to work part-time.

Looking at how different groups of youth fare within Peru, allows shedding additional light on the labour market inequalities discussed in the previous section. To this end, Figure 1.11 presents the break-down of the principal job quality indices and employment rates by gender, age and level of education for Peruvian youth. The results show that some socio-demographic groups cumulate many disadvantages, while other groups show a good performance in all dimensions.

Figure 1.11. Job quality by socio-demographic characteristics in Peru, 2014
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Note: Calculations for earnings quality correspond to high inequality aversion scenario (α= - 3) and average earnings correspond to net hourly earnings. Inequality is measured by Atkinson Index.

Source: OECD calculations based on ENAHO survey for 2014.

The most disadvantaged youth in terms of job quality are women and low-skilled workers. The above discussed low employment rates for these two groups are compounded by poor outcomes along the different dimensions of job quality. On average, for example, young men earn 10% higher hourly wages than young women and face lower wage inequality. This gap among youth at early stage of careers is non-negligible, in light of the fact that wage differences typically tend to increase over the work life. Young males are also at an advantage in terms of labour market security, since their exposure to both risks of unemployment and low earnings appears to be lower. Notably, young females are 25% more likely to fall below the extreme low pay threshold than young males and face a 1.4 percentage points higher risk of being unemployed. Young men also have a higher propensity than young women to work long hours. Notably, they work on average five more hours a week than women, which may reflect differences in caring responsibilities and the fact that men have higher chances to be in full-time employment than women.

Educational differences have a strong impact on the job quality of Peruvian youth. Education has a clear pay-off in relation to youth earnings, with the average hourly wage for individual youth with tertiary education being twice as high as for those with only primary education. Whereas over 25% of low-skilled workers are trapped in occupations with wages that do not allow meeting even basic needs, the risk of the extreme low pay among individuals with tertiary education is drastically reduced (to a small 3%). The unemployment risk for individuals with tertiary education is low, compared to medium–skilled individuals, although it is even lower for the low-skilled youth. Nevertheless, the low-skilled workers typically face the biggest problems of finding full-time employment, whereas most individuals with tertiary education work full time, with even a propensity to work over-time.

Youngest workers aged 15-19, who typically have the lowest skills and earn the lowest wages, face particularly high risk of low pay and of unemployment. The youngest workers typically are underemployed and tend to work only part-time -- some 25 hours on average per week. With age, as levels of education and/or work experience increase, hourly wages and earnings quality also increase, along with steadily decreasing labour market risks, while opportunities to find better quality occupations on a full time basis improve.

1.7. Informality acts as a driver to inequality

In a pattern common to most LAC countries, informality for wage and salary workers has declined in the past decade in Peru. Nevertheless, it still affects more than 50% of workers (taking into consideration dependent workers), somewhat higher than the region’s average (Figure 1.12). Youth are more likely than adults to work in the informal sector or in other unprotected work in the formal sector. Although youth informality has decreased by almost fifteen percentage points over the past ten years -- a decline similar to that of the overall rate of informality -- around 65% of Peruvian youth employees continue to work under informal conditions (Figure 1.13). Individuals from vulnerable populations, particularly in the poor rural areas, the least educated, women and teenagers are more likely to have an informal occupation.

Figure 1.12. Informality rates, Peru and selected Latin America countries
As a percentage of all dependent workers (legal definition) and of all workers (productive definition) aged 15-64, 2015
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Note: Legal definition of informality: a worker is considered informal if (s)he does not have the right to a pension when retired. For cross country comparability, rates are calculated for wage and salary workers only. Productive definition of informality: a worker is considered informal if (s)he is a salaried worker in a small firm, a non-professional self-employed, or a zero-income worker. The LAC average is the unweighted average of the 15 countries shown in the figures. Data for Argentina are only representative of urban areas and wage workers.

Source: SEDLAC Database by CEDLAS and the World Bank.

Figure 1.13. Informality by socio-demographic characteristics in Peru
As a percentage of relevant subpopulations of dependent workers, 2015
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Note: The figure depicts the rates of informality according to legal definition. A worker is considered informal if (s)he does not have the right to a pension when retired. Decompositions by Gender, Education and Location refer to population of prime-age dependent workers aged 25-64. Decomposition by age groups refers to relevant subpopulations of dependent workers.

Source: SEDLAC Database by CEDLAS and the World Bank.

Starting in the informal sector can result in very different labour market outcomes. Using the OECD Job Quality Framework, Figure 1.14 suggests that informal jobs are worse along all dimensions of job quality:

  • Informal workers earn significantly less on average than formal workers and earnings inequality is also relatively higher among informal workers. Accordingly, the level of earnings quality is substantially lower for informal workers than for formal workers. Lower wages for informal workers are consistent with the perception that informal jobs are less productive (OECD, 2015b, 2016b).

  • Informal jobs tend to be associated with a significantly higher incidence of extreme low pay. The risk of falling below the extremely low pay threshold is roughly six times higher for informal workers than for formal workers. Moreover, the analysis of upward and downward earnings mobility reveals that downward mobility is generally higher in informal jobs, whereas upward mobility is significantly larger in formal jobs.

  • The share of employees working very long hours is somewhat lower for workers in informal jobs. This may reflect the fact that they often do part-time jobs, which might compound the problem of low wages, inflating the discrepancy in monthly earnings between formal and informal workers and supposedly also between men and women.

All in all, the evidence provided using the prism of the job quality approach suggests that the paths taken by the many Peruvian youth who work in the informal sector are likely to be very different from what happens to the youth working in the formal sector. These contrasting dynamics compound the segmentation of the Peruvian labour market (see Chapter 2).

Figure 1.14. Job quality among formal and informal workers in Peru, 2014
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Note: Informality is defined using the legal definition, i.e. a worker is considered informal if (s)he does not contribute to any pension scheme nor accumulates rights to a retirement pension in the old age. Only dependent workers are considered. Labour market insecurity due to unemployment is not estimated, given lack of unemployment insurance for informal workers by construction.

Source: OECD calculations based on ENAHO survey for 2014.

1.8. Portraits of young people at high risk of becoming disconnected from the labour market

Additional insights about the drivers to strongly unequal youth labour markets in Peru are provided by the analysis of the youth who are neither in employment, nor in education, or training -- the so-called NEET group, or ninis using the Spanish acronym. These youth form a group at high risk of labour and social marginalisation, especially the longer they remain outside the world of work.

This analysis adopts a wide definition of youth, including all 15-29 year-olds, to allow for the fact that young people remain in education for longer and to include the beginning of family formation. On this basis, in 2016 more than one in five Peruvian youth were NEET, a figure that compares to 13.9% for the OECD taken as an average (Figure 1.15). Within the OECD, only five countries (Turkey, Italy, Greece, Spain and Mexico) have higher NEET rates. In addition, most other LAC countries fare better than Peru. Furthermore, unlike the pattern observed in many other regional and OECD countries, in Peru the NEET rate has increased since 2010.

Figure 1.15. NEET rates, Peru, OECD and selected Latin American countries
As a percentage of the population aged 15-29
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Note: NEET rates refer to young people who are neither employed nor in education or training. Data are for 2016 or the most recent year available (2014 for Bolivia, Ecuador, Paraguay and Uruguay and 2013 for Argentina). For Argentina data cover selected urban areas only. OECD average is an unweighted average of 34 OECD countries. Ranking of LAC countries may be affected by methodological issues associated with using data from different data sources.

Source: OECD Education Statistics for OECD countries, Brazil, Colombia and Costa Rica; CEDLAS and World Bank for Bolivia, Ecuador, Paraguay and Uruguay. Figures for Peru are based on ENAHO surveys for 2010 and 2016.

Figure 1.16 shows the separation between inactive and unemployed NEET rates, which is essential to gauge the extent to which the NEET problem is structural. In Peru, the rate of inactive NEET who do not look for a job accounts for the bulk of the overall NEET rate. Also the OECD countries have a high rate of inactive NEET. However, unlike Peru the split between the two groups is more balanced in the OECD countries. Moreover, in Peru the rate of inactive NEET has trended upwardly in the recent past -- since 2013, it has increased by around 2 percentage points. Thus overall, while in both Peru and the OECD countries the NEET problem has an important structural connotation, the extent of the challenge seems to be more pronounced in Peru.

Figure 1.16. Unemployed and inactive NEET rates, Peru and the average of the OECD countries
As a percentage of the youth population aged 15-29, 2010-16
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Note: Unemployed NEET refers to jobless youth who are available to take up employment and actively search for a job. Inactive NEET refers to the jobless youth who do not look for work.

Source: OECD Education Statistics. Figures for Peru are OECD calculations based on ENAHO surveys for 2010 and 2016.

Unsurprisingly, NEET rates also vary significantly across groups of young people (Figure 1.17 and 1.18):

  • First, more than one-in-four young women in Peru is NEET (Figure 1.17). Moreover, unlike the share of male NEETs, which decreases steadily and markedly with age, the same share for women is remarkably stable across ages (Figure 1.18). The decline does not begin until around age 25 and is of a considerable less sizeable magnitude than observed for men in the same age. It is important to stress that traditional gender-related division of tasks and family responsibilities, which place the brunt of domestic work and childcare on women, is not the only source of concern (OECD, CAF and ECLAC, 2016). By age 18-19, one in every five females in Peru has already at least one child and is already married or cohabiting. Not only do teenage mothers have lower educational attainments compared to adult mothers, they are frequently marginalised and as a result are much less likely to complete their high school curricula and to continue to study afterwards. Only a tiny portion of the women with small children looks for a job. Their children typically face a higher mortality rate and a higher risk of nutritional deficiencies than the children of adult mothers. Since arguably the educational achievements of these children are low, their own risks of becoming inactive are particularly high, which contributes to perpetuate the structural component of the NEET rate (Favara, Lavado and Sanchez, 2016; see also Chapter 4 for a discussion).

  • Second, the bulk of the NEET population appears concentrated in Amazonia (especially in the Northern regions with 25.5% rate) and urban Coast areas (with 26.4% and 22.3% rates, respectively in Callao and Lima, for example).

  • Finally, among youth individuals aged 25-29 who have completed education around one-quarter of the low-skilled are NEETs, compared to less than one-fifth among the medium- and high-skilled. The higher probability that early school leavers remain excluded from the labour market is compounded by the fact that they often come from disadvantaged backgrounds. As shown in Figure 1.17, originating from a low income household increases the propensity of becoming NEET by almost 50%.

Figure 1.17. NEET rates by socio-demographic characteristics in Peru
As a percentage of population aged 15-29, 2016
picture

Note: Rates are calculated as shares of population aged 15-29 with the exception of the breakdown by education attainments which refers to population aged 25-29 (i.e. with completed education). The breakdown by levels of income refers to individuals who live with their parents. Low-skilled refers to education lower than upper level secondary school; Medium-skilled refers to education lower than a bachelor equivalent tertiary degree; High-skilled corresponds to a bachelor equivalent tertiary degree or higher. Ethnicity is approximated by the mother language learnt in childhood. Indigenous languages comprise Quechua, Aymara or other native languages. Quartiles of income refer to equivalised household income (i.e., income adjusted for the number of household members).

Source: OECD calculations based on ENAHO survey for 2016.

Figure 1.18. NEET rates by gender, age, and status in Peru
As a percentage of the relevant population, 15-29
picture

Note: Shares are rounded to 1.

Source: OECD calculations based on ENAHO survey for 2016.

1.9. Early school leaving is an issue for concerns

Figure 1.19 illustrates the activity status of youth (15-29) by age, for a selection of advanced economies and LAC countries. Although the years used for the comparison vary across countries, the figure distinguishes between five groups: i) education only; ii) work and study; iii) work only as employee; iv) work only as self-employed, unpaid worker or other atypical employment relationships (notably, casual work); and v) neither in work nor in education (Quintini and Martin, 2014). The vertical bar shows the median age of leaving education, that is, the age at which at least 50% of youth have left the education system. Peruvian youth tend to stay in education shorter than in both advanced economies and most LAC countries taken into account for Figure 1.19. The median age of school leaving is around 17-18 in Peru (as well in Brazil and Colombia), which compares to 21-22 in the advanced economies and Chile; 19-20 in Costa Rica; and 18-19 in Mexico.

Major differences in school enrolment are already evident at younger ages in Peru, as suggested by the fact that by age 15, roughly 20% of the youth population have already left education. Although this situation is common to other LAC countries, it compares with close to 100% enrolment among 15-year-olds in advanced economies (and Chile). These figures suggest that the median young person enters the labour market with, at most, a high-school diploma in Peru and most LAC countries, whereas the same person does so with a few years of tertiary education in advanced economies. In other words, the share of youth leaving education before the typical age of completion of upper secondary education – a proxy for school drop-outs and an education level that experts consider essential to embark on a promising career path – is higher in Peru (and many LAC countries) than it is in the advanced economies.

Data on activity status by single year of age allow getting more insights about what young people do after leaving education. In Peru, Brazil and Mexico, at the median age of school leaving, 25-30% of young people are working as employees or are self-employed or in unpaid work. This is comparable to employment shares at the median age of school leaving in Italy but well below employment shares of 40% or over in Canada and Germany. In addition, Peru with Colombia and, to a lesser extent, Brazil and Mexico have much higher incidences of self-employment and unpaid work among youth than advanced economies, suggesting that under-employment is an issue in these countries. In turn, differences between employment shares are reflected in the observed shares for inactive and unemployed youth not enrolled in education (NEET) at the median age of school leaving.

It is at the level of the youth who combine work with study that the picture portrayed by Peruvian youth is particularly striking. Across different ages, both before and after the age at which at least 50% of youth have left the education system, many are the youth who opt to combine work and study in Peru. It is interesting that in general the countries in Figure 1.19 with the largest proportion of youth who combine work and study also show a low proportion of youth who have a NEET status and/or are under-employed after leaving education. This correlation is consistent with the available evidence, according to which country models that combine study and work are better suited to enable the youth generations achieve a smoother transition from school to work than those taking a study first, then work approach (OECD, 2010). The fact that a similar correlation is not discernible in Peru, suggests that many of the youth who work during their studies chose this option out of necessity. It could also suggest that the services provided by the institutions in charge of combining work and study are of low quality.

Figure 1.19. Activity status by year of age, Peru, selected OECD and LAC countries
picture

Note: The vertical line shows the median age of leaving education, i.e., the age at which at least 50% of youth have left the education system.

Source: OECD calculations from micro data. Estimates for Peru are based on ENAHO survey for 2014.

1.10. Assessing the costs of youth labour market marginalisation

In concluding the chapter, it is useful to get a sense of the macroeconomic losses incurred by Peru due to the high number of NEETs. Applying the methodology used by the latest edition of the OECD publication Society at a Glance, NEET costs can be defined as the gross labour income the NEETs could command if they were employed - measured as the gross labour cost, including social security contributions (OECD, 2016c). This provides an approximation of the forgone productivity of NEETs, with three being the estimates used for the analysis: upper and lower bound estimates, as well as a point estimate. The upper bound estimate assumes that if employed, NEETs would on average receive the same wages and would choose to work the same hours as employed youth of the same gender and age. The lower bound estimate assumes that the NEETs could only command a “low-wage”, specified as two-thirds of the median wage among youth of the same gender and age-group. Between these two boundaries, the intermediate point estimate accounts for the fact that jobless young people may have a lower earnings potential than young people in employment. This reflects the fact that the NEETs generally have a lower educational background than other youth, for example, and are more likely to have care responsibilities.

Estimate results suggest that the gross labour cost that could have been generated by the NEETs in Peru in 2014 - roughly the measure of the forgone productivity associated to this particular group - ranges between 1.5 and 2.5% of Peru’s GDP. By comparison, the same estimates for the OECD range between 0.9 and 1.5% of the OECD GDP in the same year. Figure 1.20 depicts the lower bound estimate for Peru, taken in the comparison with the OECD countries. As expected, it shows that the highest costs are associated to countries with the highest NEET: Turkey at 3.4% of GDP, Greece at 2% and Peru at 1.5%. However, in light of the fact that the total costs of NEETs are affected by both NEET rates and wage levels, significant costs can also be suffered by countries combining relatively moderate NEET rates with high wage levels, with Belgium being an example.

Figure 1.20. Macroeconomic costs of NEET
Annual NEET rate and estimated cost of NEETs as a percentage of GDP, 2014
picture

Note: Data refer to the lower bound estimate illustrating the most optimistic scenario. Upper bound and the point estimates suggest higher economic costs of NEETS. For Peru where roughly 85% of working youth are engaged in informal economy the gross earnings are approximated by the net earnings.

Source: OECD calculations based on the EU-SILC, HILDA (Australia), SLID (Canada), CASEN (Chile), SOEP (Germany), ENIGH (Mexico), SILC (Turkey), the CPS (United States) and ENAHO (Peru). Data are for 2014 except for Chile and Switzerland (2013), Turkey (2012) and Canada (2011).

These estimates only provide a partial indication of the social cost of NEET rates. For example, they do not take into account the extra costs for individuals and their communities of prolonged spells of unemployment, which typically include skills depreciation and a loss of self-worth and motivation. When the outcome of long term unemployment is a permanent marginalisation from the labour market, these costs appear compounded by the effects of enhanced risks of poverty, worsened health conditions, higher school failures for the children of the affected workers and rising violent crime. The next chapters will review the key policy requirements to reduce these risks.

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