4. Skills and economic outcomes in Latin America

The purpose of this chapter is to investigate the role that education and skills can play in fostering economic growth in Latin America. Most Latin American countries are middle-income countries that are struggling to shift their economic structures towards more skill-intensive and higher value-added activities, which would allow them to position themselves better in global value chains. Previous analysis conducted by the OECD (OECD/CAF/ECLAC, 2014[1]) already highlighted education and skills as key factors in promoting more dynamic and inclusive growth and escaping the so-called “middle-income trap”, characterised by economic stagnation and the inability to complete the process of convergence with the most developed economies.

The chapter draws on data from the Survey of Adult Skills, a component of the Programme for the International Assessment of Adult Competencies (PIAAC). These data provide new insights and strengthen the results of previous analysis as they contain precise measures of the cognitive skills (literacy and numeracy) of the adult population, as well as a wealth of information on how skills are used in the workplace and the actual tasks workers engage with in their jobs. Four Latin American countries have participated in PIAAC to date: Chile in 2014-15, and Ecuador, Mexico and Peru in 2017. In-depth results from those two rounds of data collection, including a detailed portrait of adults’ proficiency in literacy, numeracy and problem-solving in technology-rich environments, were published in OECD (2016[2]) and OECD (2019[3]).

In 2012, Bolivia and Colombia took part in the World Bank Skills Towards Employability and Productivity (STEP) programme, a household survey that contained an assessment of literacy comparable with the one administered in PIAAC (Pierre et al., 2014[4]). This chapter includes results from the STEP programme, although care must be taken in comparing data from the two surveys. STEP was only administered in urban areas, and its results are therefore not fully comparable with PIAAC because they are not representative of the entire adult population living in the participating countries. For an in-depth discussion of the differences between PIAAC and STEP, see Keslair and Paccagnella (2020[5]).

This chapter is structured as follows. First, it presents data on the economic structure of the Latin American countries that participated in PIAAC and STEP. It shows that a relatively small share of workers are employed in knowledge-intensive occupations and industries for which high levels of skills are required. This might be either due to limited demand for skilled workers, or to constraints in the supply of skills that prevent the creation of more high-skilled jobs. To shed light on which of the two explanations is more likely to apply, the chapter then looks at how skills and education are rewarded in the labour market, as high returns to skills would signal tensions between demand and supply. Finally, the chapter looks at the extent to which adults engage in lifelong learning and training activities, which are important policy levers for updating and upgrading the skills of people who are now too old to go back to formal education.

One reason for the inability of Latin American countries to escape the middle-income trap is the difficulty in transitioning to an economic structure featuring more innovative and high value-added industries and jobs. This section therefore looks at the economic structure of Latin American countries that participated in PIAAC and STEP. It examines the prevalence of different types of industries and occupations, drawing on unique information about the actual tasks performed on the job to highlight how the skills content of similar jobs can differ between countries. Finally, the analysis considers another important structural characteristic of Latin American economies: the pervasiveness of the informal sector.

Research and development (R&D) activities are a particularly important ingredient in the transition towards higher economic development, as they lead to innovations that increase productivity and allow countries to position themselves higher up in global value chains, thus capturing a larger share of value-added. OECD/CAF/ECLAC (2014[1]) already noted that in Latin American countries the share of gross domestic product (GDP) devoted to investments in R&D between 1990 and 2010 remained low compared to other regions. Moreover, it noted how in Latin America, in contrast to most other developed countries, the lion’s share of R&D investment is done by the public, rather than by the private sector.

Data from PIAAC confirm this picture, although indirectly, and also provide evidence suggesting that insufficient levels of skills are an important constraint to scaling up investments in R&D. PIAAC data do not contain direct information on R&D activities, but contain detailed information on respondents’ jobs, which can be used to codify them using international classifications of either occupation or industry.

Industries can then be classified according to their R&D intensity: Galindo-Rueda and Verger (2016[6]) propose a taxonomy based on the ratio between investment in R&D and value-added, using balance-sheet data from 27 OECD countries and 2 partner economies. The taxonomy divides manufacturing and non-manufacturing industries into five categories: high, medium-high, medium, medium-low and low R&D intensity. For example, the high R&D intensity category includes the air and spacecraft industries, pharmaceuticals, and computer manufacturers, as well as software publishing firms (from the non-manufacturing sector). At the bottom of the scale (low R&D intensity) there are only non-manufacturing industries: accommodation and food services, waste management, electricity supply, agriculture, and construction and real estate activities.

The skills requirements in the different industries can be assessed by the average literacy proficiency of adults employed in those industries. On average across OECD countries participating in PIAAC (excluding Latin American countries), workers in high and medium-high R&D intensity industries score around 290 points on the PIAAC literacy scale, which is about 20 points (or half of a standard deviation) higher than the overall OECD average. The average literacy scores of workers in other industries are significantly lower: 271 score points in the medium R&D intensity category, 282 in the medium-low category and 274 in the low category. In Latin American countries participating in PIAAC, the share of employment in industries with high and medium-high R&D intensity is significantly lower than in the average OECD country (Figure 4.1).

While the industrial structure is a useful indicator of an economy’s potential to generate value and undertake a sustained growth path, industries are defined in terms of the output they produce rather than in terms of the activities workers engage in, and so they necessarily bundle together jobs requiring different types and levels of skills. Occupations are defined as a bundle of tasks that a worker must complete, and as successful completion of such tasks normally require a more precise set of skills, occupations rather than industries are probably a better level of analysis to investigate demand for skills in an economy.

The International Labour Organization (ILO) classifies occupations on the basis of their required skill level and skill specialisation using the International Standard Classification of Occupations (ISCO). Skill level is “a function of the complexity and range of tasks and duties to be performed in an occupation”, while skill specialisation is considered in terms of the field of knowledge required, the tools and machinery used, the materials worked on or with, and the kinds of goods and services produced (ILO, 2012[8]). At the highest level, occupations are classified in four groups: skilled occupations, semi-skilled white-collar occupations, semi-skilled blue-collar occupations and elementary occupations. Skilled occupations include managers and professionals while semi-skilled white-collar jobs include clerks and service and sale workers. Semi-skilled blue-collar workers include craft workers and skilled agricultural workers, while elementary occupations include cleaners, street vendors, and labourers in agriculture, transport or construction.

As expected, differences in skills requirements between different occupations (using the average literacy scores of adults working in those jobs as a proxy) are much more pronounced than was the case for industries: across OECD countries participating in PIAAC (and excluding Latin American countries), the average literacy proficiency of workers in skilled occupations is 294 points, falling to 243 points in elementary occupations. Average literacy proficiency for those in semi-skilled occupations is 270 points in white-collar jobs and 258 points in blue-collar jobs. Figure 4.2 shows that in Latin American countries much smaller shares of the adult population are employed in skilled occupations, and much larger shares in elementary occupations, than in the average OECD country.

To classify occupations in an internationally comparable way, as done by the ILO, some assumptions need to be made, which inevitably means simplifying a reality that is much more complex. Occupations are therefore defined as a “set of jobs whose main tasks and duties are characterized by a high degree of similarity” (whereby jobs are “a set of tasks and duties performed, or meant to be performed, by one person”). In reality, there is ample evidence of substantial variability in the actual tasks carried out by workers working in the same occupation (Autor and Handel, 2013[10]). This is because “jobs”, which are just a collection of tasks, are shaped by the particular match between the skills and capabilities of the workers doing them, and the technological and organisational capital of the firm where they work. One of the distinctive features of PIAAC (and of STEP, although to a lesser extent) is that it collects information about the frequency with which workers undertake certain tasks at work. This information can be used to gain more insights about how jobs are organised, and about the type of skills that are required of adults on the workplace.

Figure 4.3 displays the share of workers who need to read or use a computer in their work. Reflecting the differences in the occupational structures discussed above, the share of workers engaging in such activities is lower in Latin American countries than on average across the OECD, especially as far as computer use is concerned.

These two rough measures of skills use at work, however, are not sufficient to draw an accurate picture of what workers do in their jobs. PIAAC contains a battery of other questions about tasks performed on the job, which allow indices to be constructed measuring the degree of engagement in tasks requiring both information-processing (or cognitive) skills and other generic skills (often called non-cognitive or socio-emotional skills). Information-processing skills include reading, writing, numeracy, information and communications technology (ICT) (based on the frequency of use of a wide range of software), and problem solving. Generic skills include task discretion (whether workers are able to choose the sequence or the speed at which they perform tasks), learning at work, influencing skills (when dealing with colleagues or customers), co-operative skills, self-organising skills, physical skills, and dexterity. Indices have been derived to capture the degree of involvement in these activities. All indices are standardised to have a mean value of 2 and a standard deviation of 1 across all countries participating in PIAAC. For more information about how such indices are constructed, the reader is referred to OECD (2019[11]).

Figure 4.4 and Figure 4.5 illustrate the intensity of use of two generic and two information-processing skills (or, more precisely, the frequency of engagement with certain tasks), within the four different types of occupation analysed above. Interestingly, according to these metrics, jobs in Latin American countries do not seem to differ much from jobs in OECD countries on average. If anything, workers in Latin American countries engage to a greater extent in tasks involving instructing, teaching or training people; making speeches or presentations; selling products or services; advising people; and persuading or influencing others – all activities that require influencing skills. Similarly, they report that they have to engage in learning-by-doing, or in keeping up to date with new products of services more frequently than on average in OECD countries.

As far as information-processing skills are concerned, although a lower share of workers in Latin American countries use a computer at work (Figure 4.3), those that do so engage with a wide range of computer software and applications at similar rates to the average across OECD countries, for all types of occupations. Similarly, they engage at similar frequencies with tasks drawing on their numeracy skills. The picture for the use of reading and writing skills (not shown here) deliver very similar results.

Despite differences in their economic structures, OECD and Latin American countries do not appear to differ very much, in terms of sectoral and occupational composition, in the tasks performed by (and the skills required of) workers. On the one hand, this could be seen as a positive thing, as it provides workers with the incentive to acquire the skills that will be demanded on the job. On the other hand, it could be problematic to the extent that results from PIAAC highlight that adults in Latin America have lower proficiency in literacy, numeracy and problem-solving than adults in other participating countries (see Chapter 1). As a consequence, workers in Latin America might not be fully equipped to adequately meet the demands of their jobs. This could be one of the reasons behind the large gap between demand for and supply of skills highlighted in OECD/CAF/ECLAC (2014[1]). According to that report, this gap is one factor behind the high degree of labour-market informality observed in Latin America.

The high incidence of informal labour markets is indeed also evident looking at data from PIAAC and STEP. In order to identify workers in the informal economy, the PIAAC and STEP questionnaires ask workers who report working as employees whether or not they have a formal labour contract. This provides a first approximation of the size of the informal sector.

Figure 4.6 clearly shows how Latin American countries (with the partial exception of Chile) are characterised by larger shares of workers who have no formal employment contract, or are self-employed. In the case of self-employed workers, it is not possible to tell whether or not they work in the formal or in the informal sector. However, both self-employed workers and those without a formal employment contract are most likely have a lower degree of social protection. Information on earnings collected in the PIAAC and STEP questionnaires, can be used to show that workers who do not have a formal contract incur a wage penalty: compared to workers with a regular contract, hourly wages in the informal sector are 60% lower in Colombia; 35% lower in Bolivia, Mexico and Peru; and 25% lower in Ecuador. In Chile and in OECD countries, where a much lower share of workers are employed in the informal sector, these wage penalties are much smaller: 2% in Chile and 4% on average across the OECD. Moreover, both in Latin America and in OECD countries, workers in the informal economy tend to work fewer hours in a typical week. In Latin America this is also true for self-employed workers (while in OECD countries self-employed workers work on average four hours more per week than employees with a formal contract).

If low levels of skills are a constraint to economic development, in the sense that they are preventing the creation or expansion of firms in more remunerative sectors, this should be reflected in higher returns for the relatively few individuals who do possess skills in high demand.

OECD/CAF/ECLAC (2014[1]) notes how employers in Latin America are more likely than those in more advanced OECD economies to report that they struggle to find workers with high enough skills. At the same time, educational attainment has increased significantly in the past decades, so that the gap with respect to advanced economies in terms of the share of the population with secondary or tertiary education has significantly narrowed. This increased supply of educated workers might be one factor behind observations that returns to education have decreased, although it is a bit at odds with the observation that many firms struggle to find adequately skilled workers.

Data from PIAAC and STEP can help to shed some light on this issue, as their assessments of adults’ proficiency in literacy, numeracy and problem solving can be used as a proxy for the quality of the formal education received by adults. OECD (2019[3]) has already shown that the performance of adults living in Latin American countries was consistently lower than in the average OECD country for each completed level of schooling.

Low levels of skills do not seem to keep adults out of employment. Employment rates are normally higher in Latin American countries than in more advanced OECD economies, and this is true irrespective of the level of proficiency in literacy. Figure 4.7 shows that the difference in the probability of not being in employment between adults scoring at Level 3 or above in literacy and adults scoring below Level 1 is higher in the average OECD country than in Latin American countries participating in either PIAAC or STEP.

The low returns to literacy skills in terms of employment rates are likely to be linked to the low degree of social protection and the high prevalence of the informal economy in Latin America. These tend to favour the creation of low-quality jobs, often characterised by low levels of productivity and poor working conditions.

Although literacy proficiency is not closely related to the probability of being employed, it does appear to be linked to the type of jobs adults have access to. Figure 4.8 displays the results from a series of linear regressions of the probability of being employed without a formal contract on measures of human capital, in particular levels of literacy proficiency (the omitted category in this case being Level 1) and years of completed formal education (asked of PIAAC and STEP respondents). The regression also controls for gender, parental education, age and age squared. In Mexico, Peru and urban Bolivia, adults scoring at Level 3 or above in literacy have a much lower probability (between 10 and 15 percentage points) of being employed in the informal sector than adults scoring at Level 1. In Ecuador and urban Colombia, the only significant effect seems to come from additional years of schooling. In Chile and OECD countries overall, where informal employment is much less widespread, literacy skills and education do not appear to be strongly related to the probability of working in the informal sector.

A stronger association is found between years of schooling, literacy proficiency and the probability of working in a skilled occupation, as defined by the ILO. Figure 4.9 shows the results of a similar regression, where the dependent variable is whether the respondent is employed in a skilled occupation. In all countries, both literacy proficiency and years of schooling are positively associated with the chance of being employed in a skilled occupation. For literacy proficiency, the relationship is particularly strong in Peru and Bolivia, where adults scoring at Level 3 or above are about 25 and 20 percentage points more likely to be employed in a skilled occupation than adults scoring at Level 1.

Ultimately, as skills are related to better occupational outcomes, they also lead to higher wages. Table 4.1 shows the results of a series of Mincerian wage regressions, where the dependent variable is always the logarithm of hourly wages. It presents three regression models, all of which include gender, age, age squared and parental education. Model 1 additionally controls for years of schooling, Model 2 controls for levels of proficiency in literacy and Model 3 controls for both.

Looking jointly at Model 1 and 2 lends support to the idea that the observed decline in returns to schooling is partly due to the lower quality of the additional education younger cohorts have been able to acquire. Across Latin America, returns to education are broadly in line with the OECD average (about 7% for each additional year of schooling). However, returns to literacy skills, which are likely to be in shorter supply in Latin America, are much higher: close to 50% in most Latin American countries, while averaging 34% in OECD countries.

When controlling for both years of schooling and literacy proficiency in Model 3, returns to schooling are mostly unchanged, while returns to literacy decrease substantially and converge towards the OECD average (with the exceptions of Peru, when they remain much higher, and Bolivia, where they are much lower). In other words, in Latin American countries a larger part of the variation in literacy proficiency is captured by variation in years of schooling, while schooling provides distinctive skills, on top of literacy, that remain highly valued in the labour market, in Latin America as in more advanced OECD countries.

In the long run, skill levels will only be raised by investment in improving the quality of education, with the goal of equipping students with the skills the market requires and that could help Latin American economies to upgrade their production structure and improve their position in global value chains. This strategy, however, will not address the problems of adults already in the labour market who are struggling to find good jobs because of a lack of skills and are having to resort to self-employment or the informal sector.

Investing in adult training can be an effective way to help the current labour force to improve their economic situation. Moreover, the benefits would not only be felt in the short term. Future generations will be increasingly subject to technological change and longer working lives. Inevitably, at some point in their careers, they will need to engage in training in order to keep their competencies up to date and meet the demands of the labour market and of society more broadly.

As discussed above, adults in Latin American countries report that they engage in on-the-job learning to a similar extent as adults in more economically advanced OECD countries. However, this does not seem to translate into a similar rate of participation in organised training and learning activities. PIAAC collects detailed information about training activity undertaken by respondents in the 12 months prior to the interview, distinguishing between formal activities (those leading to a formal qualification) and non-formal activities, as well as identifying activities that are not job-related. STEP asks a single question about participation in any kind of training activity, explicitly excluding those that are part of the formal education system.

Figure 4.10 displays rates of participation in different forms of training, showing that adults in Latin American countries tend to participate less frequently than the OECD average. More than half of adults in OECD countries reported they had participated in any kind of training activity, formal or non-formal, with similar rates in Chile, but the share does not reach 40% in Ecuador, Mexico and Peru. The vast majority of training is non-formal. Data for Bolivia and Colombia are not fully comparable with other countries, because of the way the question is asked, but seem to indicate even lower rates of participation, at around 20%. Finally, among countries that participate in PIAAC, job-related activities are by far the most common form of training.

Latin American countries do not differ much from the OECD average in what determines participation in training. Participation is more common for those in employment and for those with higher levels of skills and education. The degree of association between these individual traits and the probability of participating in any kind of training activities is very similar in Latin American and in other OECD countries (with the exception of Mexico, as far as literacy proficiency is concerned), as shown in Table 4.2.

Where Latin American countries differ from the OECD average is in the role played by employment status and by the sector in which adults are employed. As Figure 4.11 shows, rates of participation in training for adults with a regular employment contract do not differ greatly across countries, and are even higher in Chile, Ecuador and Peru than in the average OECD country participating in PIAAC. On the other hand, Latin American adults without a contract or who are self-employed participate much less in training than in other OECD countries.

Similarly, workers in industries characterised as having high or medium-high R&D intensity have higher rates of participation in training in Chile, Ecuador and Peru than in the average OECD country. This advantage disappears for adults working in sectors with less R&D, however. In industries with low R&D intensity, 56% of workers in the average OECD country report having participated in training of some kind, compared to only 49% of workers in Chile and about 30% of workers in Ecuador, Mexico and Peru (Figure 4.12). These results also hold in regression models that explicitly take into account the fact that workers in high-intensity R&D industries are also more likely to possess characteristics that are positively correlated with participation in training, such as higher levels of education and skills.

Investment in human capital can help Latin American countries to transition out of the “middle-income trap” and complete the process of convergence towards more advanced economies. This structural trap is evident in the distribution of jobs in Latin American economies, which is skewed towards occupations and sectors with a lower skill content and that invest less in R&D. These are typically low value-added jobs that signal the disadvantageous position of Latin American economies in global value chains.

Data from international skills assessments like PIAAC confirm that adults in Latin American countries lag significantly behind the average OECD country in proficiency, in spite of the large increases in formal educational attainment in recent decades. Data from PIAAC also contain useful information about the skills content of occupations and the economic outcomes associated with greater proficiency. The type of tasks that are required of workers in Latin American economies do not differ much from those required in more advanced economies in the OECD, signalling that demand for skills is high. This is confirmed by the large economic returns associated with literacy proficiency, not only in terms of wages, but also in terms of the likelihood of finding work in the formal economy – a large informal sector remains a worrying structural feature of Latin American economies, constituting a barrier to social and economic development and contribute to the high level of inequality observed in the region.

The high prevalence of the informal sector is also one factor that explains why Latin American countries lag behind in participation in adult training and lifelong learning activities, which could be an important means of upgrading the skills of the adult population and facilitating the transition to a different and more productive mix of economic activities.


[10] Autor, D. and M. Handel (2013), “Putting tasks to the test: Human capital, job tasks, and wages”, Journal of Labor Economics, Vol. 31/S1, pp. S59-S96, https://doi.org/10.1086/669332.

[6] Galindo-Rueda, F. and F. Verger (2016), “OECD taxonomy of economic activities based on R&D intensity”, OECD Science, Technology and Industry Working Papers, No. 4, OECD Publishing, Paris, https://doi.org/10.1787/5jlv73sqqp8r-en.

[8] ILO (2012), International Standard Classification of Occupations: ISCO-08, International Labour Organization, Geneva, http://www.ilo.org/public/english/bureau/stat/isco/index.htm (accessed on 8 February 2018).

[5] Keslair, F. and M. Paccagnella (2020), “Assessing adults’ skills on a global scale: A joint analysis of results from PIAAC and STEP”, Education Working Paper, No. 230, OECD Publishing, Paris, https://doi.org/10.1787/ae2f95d5-en.

[3] 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.

[11] OECD (2019), The Survey of Adult Skills: Reader’s Companion, Third Edition, OECD Skills Studies, OECD Publishing, Paris, https://doi.org/10.1787/f70238c7-en.

[2] OECD (2016), Skills Matter: Further Results from the Survey of Adult Skills, OECD Skills Studies, OECD Publishing, Paris, https://doi.org/10.1787/9789264258051-en.

[7] OECD (n.d.), Survey of Adult Skills (PIAAC), http://www.oecd.org/skills/piaac/data/ (accessed on 13 September 2022).

[1] OECD/CAF/ECLAC (2014), Latin American Economic Outlook 2015: Education, Skills and Innovation for Development, OECD Publishing, Paris, https://doi.org/10.1787/leo-2015-en.

[4] Pierre, G. et al. (2014), “STEP Skills Measurement Surveys: Innovative tools for assessing skills”, Social Protection & Labor Discussion Paper, No. 1421, World Bank, https://documents.worldbank.org/en/publication/documents-reports/documentdetail/516741468178736065/step-skills-measurement-surveys-innovative-tools-for-assessing-skills.

[9] World Bank (n.d.), STEP Skills Measurement Program, https://microdata.worldbank.org/index.php/catalog/step.

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