Abstract

We assess whether online data on vacancies and applications to a job board are a suitable source for studying skills dynamics outside of Europe and the United States, where a rich literature has examined skills dynamics using online vacancy data. Yet, the knowledge on skills dynamics is scarce for other countries, irrespective of their level of development. We first propose a taxonomy that systematically aggregates three broad categories of skills – cognitive, socioemotional and manual – and fourteen commonly observed and recognizable skills sub-categories, which we define based on unique skills identified through keywords and expressions. Our aim is to develop a taxonomy that is comprehensive but succinct, suitable for the labour market realities of developing and emerging economies and adapted to online vacancies and applicants’ data. Using machine-learning techniques, we then develop a methodology that allows implementing the skills taxonomy in online vacancy and applicants’ data, thus capturing both the supply and the demand side. Implementing the methodology with Uruguayan data from the job board BuscoJobs, we assign skills to 64 per cent of applicants’ employment spells and 94 per cent of vacancies. We consider this a successful implementation since the exploited text information often does not follow a standardized format. The advantage of our approach is its reliance on data that is currently available in many countries across the world, thereby allowing for country-specific analysis that does not need to assume that occupational skills bundles are the same across countries. To the best of our knowledge, we are the first to explore this approach in the context of emerging economies.

About the authors

Fidel Bennett is an economist from the University of Chile and currently is the Head of Bolsa Nacional de Empleo (National Employment Agency) at the Chilean Ministry of Labour. He has also worked as an economic specialist at the University of Chile and the Chilean Ministry of Education, and has collaborated in different research projects as a consultant for the International Labour Organization. His research focuses on labour economics, economics of education and impact of public interventions.

Verónica Escudero joined the Research Department of the International Labour Organization in 2008 and today she is Head of the Labour Market Trends and Policy Evaluation Unit. Since March 2021, she is serving as a Visiting Scholar with CEGA (Center for Effective Global Action) at the University of California Berkeley. She is a Ph.D economist specialized on the analysis and evaluation of labour market and social policies. Her current research focuses on assessing the effectiveness of labour market and social policies on job quality and social conditions and unveiling whether leveraging complementarities between policies can foster their beneficial effect. More recently, she has been exploring topics related to the skills necessary to foster effective transitions to decent work with a focus on low- and middle-income countries, through the use of online data on vacancies and applications to labour portals. She holds a Ph.D. in Economics from Paris School of Economics and the École des Hautes Études en Sciences Sociales (EHESS).

Hannah Liepmann is an Economist in the Research Department of the ILO. Her research interests are in labour economics and applied microeconomics, with a focus on studying how labour market policies and structural changes affect the integration of marginalized groups into quality employment. She obtained her PhD from Humboldt-University Berlin, where she has also worked for the Collaborative Research Centre “Rationality and Competition”. She is an IZA Research Affiliate and has been a visiting researcher at the Institute for Research and Employment at the University of California Berkeley and the Institute for Employment Research (IAB) in Nuremberg.

Ana Podjanin is a Technical Officer at the Enterprises Department of the International Labour Organization in Geneva. She joined the ILO in 2015 and previously worked with the Research, Statistics and Employment Policy Departments, focusing on different topics, such as monetary and non-monetary poverty, labour market statistics and the identification of new methods for skills needs anticipation.

Introduction

Major transformative phenomena, such as technological progress and trade, are shaping labour markets. Skills are an important factor in this process. They influence how labour market transformations change the relative demand for different jobs and occupations. For example, the demand for a given job or occupation rises if it relies heavily on skills that are complementary to a newly introduced technology. That same demand declines if instead a job or occupation requires skills that are substitutes of technological innovations. As a result, skills affect the comparative resilience of some groups of workers in contemporary labour markets, and the relative vulnerability of other groups.

A rich literature has thus examined skills dynamics in Europe and, in particular, the United States. Computer technology is found to replace work that can be routinized and to complement non-routine analytical and interactive work (Autor, Levy, and Murnane 2003; Goos, Manning, and Salomons 2014). More recently, however, the rising demand for non-routine analytical work has been reversed in the United States (Beaudry, Green, and Sand 2016). Some studies predict that artificial intelligence and robotics will also replace non-routine analytical labour and lead to large-scale job destruction (Frey and Osborne, 2017). Other analyses point to the importance and non-replicability of interactive skills, projecting smaller net job losses (Arntz, Gregory, and Zierahn 2016) or highlight possible job-creation potentials (Nübler 2016). Given systematic differences in labour markets, it would be misleading to extrapolate the findings for the United States and Europe directly to other countries. Yet, the knowledge on skills dynamics is scarce for emerging and developing economies. This is largely driven by the absence of adequate sources of data. At the same time, such knowledge would contribute to understanding which policy responses are needed to better prepare workers and respond to employers’ needs in contemporary labour markets.

In this paper, we assess whether online data on vacancies and applications to a job board (or job portal) are a suitable source for studying skills dynamics outside of the high-income economies that the literature has so far focused on. The advantage of this approach is its reliance on data that is currently available in many countries across the world, thereby allowing for country-specific analysis that does not need to assume that occupational skills are the same across countries. Another distinguishable feature is the ability to study detailed skills dynamics across time, representative of both labour demand and supply. This results from the panel nature and granularity of the data, which is distinct from currently available survey data from emerging and developing economies. Finally, to the best of our knowledge, we are the first to explore this approach in the context of an emerging economy.

This study relates to a growing literature that uses online vacancy data from the United States to study questions related to skills dynamics, as we use similar classification methods and big data in the context of an emerging economy. Deming and Kahn (2018) investigate the central role of cognitive and social skills in predicting occupational wage differences across local labour markets. Their results suggest that the variation across firms in workers’ pay and firm performance is indeed related to the demand of these two skills. Three other studies show that recessions accelerate changes in the demand for skills as they provide an opportunity for firms to upgrade skill requirements in response to new technologies. These effects are more pronounced in routine-cognitive occupations, which also exhibit relative wage growth (Hershbein and Kahn 2018), in states and occupations that experience greater upsurge in the supply of available workers (Modestino, Shoag, and Ballance 2020) and in higher-wage cities and occupations (Blair and Deming 2020). Finally, Deming and Noray (2020) use online vacancy data to study the skill requirements and skill returns in Science, Technology, Engineering and Math (STEM) professions. They find that the earnings premium of college graduates majoring in STEM fields is highest at labour market entry but declines rapidly, which pushes these graduates to move out of technology-intensive fields as they gain experience. We build on these studies when devising our taxonomy, in addition to other references from the social sciences (in particular, the literature on skill-biased technological change, see for example Acemoglu and Autor 2011) and psychology (see for example Almlund et al. 2011).

In more detail, our taxonomy systematically aggregates three broad categories of skills – cognitive, socioemotional and manual – and fourteen commonly observed and recognizable skills sub-categories, which we define based on unique skills identified through keywords and expressions.[1] Our aim is to obtain a taxonomy that is comprehensive but succinct, suitable for the labour market realities of developing and emerging economies and adapted to online vacancies and applicants’ data. We then develop a methodology that allows implementing this skills taxonomy in big online vacancy and applicants’ data. For this purpose, we take our taxonomy to the data, exploiting information from the Uruguayan job board BuscoJobs. BuscoJobs is the biggest private job-search portal in Uruguay. [2] Our data include the entire content uploaded between 2010 and 2020, capturing both the supply side (through applicants sharing their current profile and labour market biographies) and the demand side (in the form of vacancies posted by firms). Exploiting rich open-text descriptions of job ads posted by firms and employment histories of applicants, we pre-process the text, distil and analyze the keywords and expressions that pertain to a given skill. For this, we use machine-learning techniques (i.e., a natural language processing (NLP) model). To test our methodology, we use three types of metrics: first, the share of observations (vacancies and applicants’ job spells) classified in terms of their skills content; second, the degree of representativeness and bias of these data in comparison to the overall labour market, and how stable this is across time; and, finally, the possibility of capturing a broader set of skills vis-à-vis existing studies that have analyzed similar data in a high-income context and neglected, in particular, manual skills.

To preview our empirical results, we find that our NLP model performs well with regard to the number of observations (vacancies and applicants’ job spells) that we can characterize in terms of skills requirements and the skills that people have. The results significantly improve once we consider synonyms in addition to the keywords and expressions included in our pre-defined skills taxonomy. Then, 64 per cent of applicants’ job spells and 94 per cent of vacancies are assigned at least one of our 14 skills-subcategories. This means that we can classify a meaningful number of observations, which is especially notable considering the substantial heterogeneity in the quality of information available in the free-text descriptions we use to code the skills variables. This gives hope to the possibility of replicating our methodology in similar data sources of other countries. Further improvements to our methodology might be possible by training a prediction model. For example, we discuss the potential of using as a benchmark the skills classification of the Occupational Information Network (O-NET) Uruguay, which is currently in its pilot phase.

We also find that our data are not fully representative of the country’s labour force, based on the Uruguayan household survey. For example, applicants tend to be younger, more educated, and more likely to live in the capital, compared to the labour force. They are also more likely to work in clerical support occupations and are less represented in craft and other trades. This is inherent to online vacancy and applicants’ data and may require focusing the analysis on specific segments of the labour market and/or using weighting techniques to account for specific biases (Fabo and Kureková 2022). At the same time, we show that in addition to highly qualified work, the data includes meaningful numbers of vacancies requiring and applicants having intermediate and even lower qualification levels. We also establish that the bias in the BuscoJobs data remains stable across time for both vacancies and applicants’ job spells, since the discrepancy between the occupational distribution in the BuscoJobs data and the household survey data does change significantly from 2010 through 2020. This means that the data are suitable for studying dynamics over time (for a similar argument see Deming and Noray 2020; Hershbein and Kahn 2018).

Finally, we demonstrate the importance of capturing a mixture of different skills, which might be especially relevant when analyzing skills dynamics outside of Europe and the United States. A large share of classified skills (61 per cent in the case of the applicants’ data) is attributable to keywords and expressions from studies analyzing online data, which neglect manual skills. However, additional sources also play a meaningful role. For the applicants’ data, 31 per cent of classified skills thus relate to keywords and expressions from studies using non-online sources and 9 per cent to complementary keywords and expressions from the pilot version of O-NET Uruguay. Likewise, within occupations we find considerable differences when comparing our findings to what we would have obtained, had we used O-NET data from the United States.

This study advances the literature on skills dynamics in emerging and developing countries. While the relative scarcity in empirical evidence in these economies is in large part due to the absence of appropriate data, some studies have addressed this data challenge in innovative ways. One strand of the literature studies skills dynamics in low- and middle-income countries by imputing occupational task information from the United States’ O-NET data (Almeida, Corseuil, and Poole 2017; Bhorat et al. 2018; Reijnders and de Vries 2018). This requires making the strong assumption that the task content of occupations is invariant across countries, which we do not need to make in our approach. A second strand of the literature measures tasks directly in the countries studied, combining survey data from the Programme for the International Assessment of Adult Competencies (PIAAC) and the Skills Measurement Program (STEP) with other longitudinal data sources. These studies identify significant differences in the skill composition of jobs and occupations across countries and highlight the importance of analyzing these questions using country-specific data (Caunedo, Keller, and Shin 2021; Carbonero et al. 2021; Lewandowski, Park, and Schotte 2020; Lewandowski et al. 2019; Lo Bello, Sanchez Puerta, and Winkler 2019). For example, Lewandowski, Park, and Schotte (2020) show that even within the same occupations, emerging and developing countries rely more strongly on routine work than developed economies. Moreover, Zapata-Román (2021) documents, in contrast to advanced economies, increases in earnings for routine occupations in Chile, while Xing (2021) finds the opposite for China.[3] Unfortunately, for developing and emerging economies, PIAAC and STEP surveys are currently available as single cross-sections only.[4] In contrast to our study, this strand of the literature therefore neglects skills dynamics that occur within occupations over time, although findings for high-income countries show that within-occupational skills changes can be significant (Atalay et al. 2020; Spitz‐Oener 2006). Moreover, skills variables in the PIAAC and STEP surveys are limited in number, making it difficult to assess skills in a comprehensive manner. This stands in contrast to the granularity of the data that we propose.

This paper is structured as follows. Chapter 1 develops the skills taxonomy and explains how we classify unique skills within categories and subcategories. Chapter 2 describes the BuscoJobs data. Chapter 3 summarizes our methodology to implement the skills taxonomy in the BuscoJobs data. It describes the process we follow to create skills variables through the identification of keywords and expressions and the use of machine-learning techniques. The paper concludes with a brief discussion of how online job data could be used in the future to analyze a range of research questions for emerging and developing countries.

[1]  Our taxonomy is complementary to the global skills framework in ILO (2021a). Targeting practitioners, that framework identifies “core skills” that improve workers’ resilience vis-à-vis transformative changes in contemporary labour markets. In contrast, our taxonomy is designed for research purposes, and also captures those skills with declining or stagnating demand. We nevertheless discuss the overlap and complementarities between the global skills framework and our taxonomy.

[2]  In fact, Uruguay is classified as a high-income country by the World Bank. While it is wealthier than Latin American countries on average, Uruguay tends to share important features with other labour markets in Latin America and its occupational skills distribution systematically differs from the one of the United States (see Sections 3.1 and 4.3 for details).

[3]  Additional studies that exploit country-level survey data on skills include Almeida, Fernandez, and Viollaz (2020); Ballon and Dávalos (2020); Bidisha, Mahmood, and Rahman (2021); Bustelo, Flabbi, and Viollaz (2019); Davies and van Seventer (2020); Khurana and Mahajan (2020); Marouani, Le Minh, and Marshalian (2020); Maurizio and Monsalvo (2021); Valerio et al. (2016); Yusuf and Halim (2021), among others.

[4]  In addition, the STEP data, which tend to cover less wealthy countries, include urban populations only.

Assessing skills outside of Europe and the United States: A skills taxonomy for research purposes

Our taxonomy has to strike a balance between different objectives. To begin with, we want to categorize skills in a comprehensive way, capturing broadly defined skills with a view to understanding major labour market trends. In addition, we want these broadly defined skills categories to be suitable for a Latin American context and, more generally, to be representative of an emerging and developing-country labour market. This requires adjustments in comparison to approaches that were developed for a North American or European country context. Finally, our taxonomy needs to be suitable for an implementation using online data on job vacancies and applicants. This requires adapting the taxonomy to the mode of expression and vocabularies specific to this type of data.

With these objectives in mind, our taxonomy consists of the three broad categories of cognitive skills, socioemotional skills, and manual skills that we disaggregate into 14 sub-categories (Table 1). Recent research confirms that these three broad skills categories represent vastly different attributes. Across workers’ employment biographies, each category entails unique learning and adjustment patterns and therefore produces distinct returns (Lise and Postel-Vinay 2020). This motivates our choice to organize the taxonomy around these three categories.

We deliberately focus on skills, rather than occupations. This is based on the realisation that, first, skills are a central dimension for understanding how major transformative phenomena, such as technological progress or trade, affect employment opportunities (e.g., Acemoglu and Autor 2011; Autor, Levy, and Murnane 2003). Second, occupations are characterized by complex bundles of skills that change across time (e.g., Arntz, Gregory, and Zierahn 2016; Atalay et al. 2020; Spitz‐Oener 2006). This implies that workers – across qualification levels[1] – perform a combination of cognitive, socioemotional, and manual skills. The composition of this skills combination is expected to shape how mega-trends (like technology and trade) affect workers’ labour market situations. In a similar vein, ongoing policy debates emphasize the importance of skills that are portable across jobs and occupations (see ILO 2021a).[2]

As shown in Table 1, our taxonomy builds on Deming and Kahn (2018), who augment the task-based approach of Autor, Levy, and Murnane (2003) to adapt it to the specific characteristics of online job data (BurningGlass data for the United States in their case). We have extended the approach of Deming and Kahn (2018) by adding information provided by a range of different studies (Autor, Levy, and Murnane 2003; Atalay et al. 2020; Spitz‐Oener 2006; Deming and Noray 2020; Kureková et al. 2016; Heckman and Kautz 2012; Hershbein and Kahn 2018) and O-NET Uruguay.[3] Specifically, within the categories of cognitive and socioemotional skills, we include additional keywords that are meant to broaden the scope of descriptions used. This also allows us to capture terms whose popularity changed over time, but which refer to the same skill (see Deming and Noray 2020).[4] In addition, we add the entire category of “manual skills”. Manual skills were not the focus of Deming and Kahn (2018) but are important for our goal of obtaining a comprehensive skills taxonomy. Manual skills are, moreover, particularly relevant in the context of an emerging-country’s labour market, such as that of Uruguay. Finally, we have identified keywords that are associated with “routine tasks”. As we explain below, based on these keywords we define a cross-cutting category reflecting whether tasks can be automated. Overall, we make use of the various keywords used by seminal papers to be the most comprehensive possible in the definition of categories and subcategories already while devising our conceptual framework.[5]

The fourteen subcategories of our taxonomy capture both skills that pertain to tasks that workers perform on the job[6] and skills that refer to individuals’ personal attributes. Both types of skills are important to provide a consistent representation for labour market developments in a wide range of contexts. In combination, they encompass skills that employers demand in vacancies and skills that workers supply as presented in workers’ online profiles. It is important to note that even if some skills subcategories are closely related, the words used to categorize them are mutually exclusive.[7] This allows for the unique identification of skills categories in our granular data.

Our categorization has the advantage of being both complete and succinct. The comprehensiveness of this approach is necessary for it to be relevant to different countries’ realities and is possible thanks to the granularity of online data. Meanwhile, its compactness into three broad categories and only 14 subcategories is tailored to research purposes, where overly detailed taxonomies or uncategorized lists of skills may add complexity that would be difficult to exploit in a meaningful way.

Table 1. Categorization of skills, keywords, and sources

Category

Source of the category

Keywords/expressions

Source of keywords/expressions

Cognitive skills

Cognitive skills (narrow sense)

DK (2018)

Problem solving, research, analytical, critical thinking, math, statistics

DK (2018)

Mathematics, adaptability, direction, control, planning

ALM (2003) (from nonroutine analytic tasks)

Data analysis, data engineering, data modelling, data visualization, data mining, data science, predictive analytics, predictive models

DN (2020)

Analyse, design, devising rule, evaluate, interpreting rule, sketch

S-O (2006), APST (2020) (from nonroutine analytic tasks)

Calculation

ALM (2003) (from routine analytic tasks)

Bookkeeping, correcting, measurement

S-O (2006), APST (2020) (from routine cognitive tasks)

Information processing, decision making, generation of ideas, memory

O-NET Uruguay

Computer (general) skills

DK (2018)

Computer, spreadsheets, common software, Excel, PowerPoint

DK (2018)

Computer literacy, Internet skills, Word, Outlook, Office, Windows

DN (2020)

Software (specific) skills and technical support

DK (2018) & DN (2020)

Programming language or specialized software, Java, SQL, Python

DK (2018)

Computer installation, computer repair, computer maintenance, computer troubleshooting, web development, site design

DN (2020)

Machine Learning and Artificial Intelligence

DK (2018)

DN (2020)

Artificial intelligence, machine learning, decision trees, apache hadoop, Bayesian Networks, Automation Tools, Neural Networks, Support Vector Machines (SVM), Supervised learning, TensorFlow, MapReduce, Splunk, Convolutional Neural Network (CNN), Cluster Analysis

DN (2020)

Financial skills

DK (2018)

Budgeting, accounting, finance, cost

DK (2018)

Writing skills

DK (2018)

Writing

DK (2018)

Editing, reports, proposals

DN (2020)

Project management skills

DK (2018)

Project management

DK (2018)

Socioemotional skills

Character skills (conscientiousness, emotional stability and openness to experience)

DK (2018)

Organized, detail oriented, multitasking, time management, meeting deadlines, energetic

DK (2018)

Self-starter, initiative, self-motivated

DN (2020)

Competent, achieving, hardworking, reliable, punctual, resistant to stress, creative, independent

KBHT (2016), HK (2012)

Social skills (including agreeableness and extraversion)

DK (2018)

Communication, teamwork, collaboration, negotiation, presentation

DK (2018)

Team, persuasion, listening

DN (2020)

Flexibility, empathy, assertiveness

KBHT (2016), HK (2012)

Advice, entertain, lobby, teaching

S-O (2006), APST (2020) (from non-routine interactive tasks)

Interact with others, verbal abilities

O-NET Uruguay

People management skills

DK (2018)

Supervisory, leadership, management (not project), mentoring, staff

DK (2018)

Staff supervision, staff development, performance management, personnel management

DN (2020)

Customer service skills

DK (2018)

Customer, sales, client, patient

DK (2018)

Persuading, selling

ALM (2003) (from nonroutine analytic and interactive tasks)

Advertise, sell, buy, purchase

S-O (2006), APST (2020) (from non-routine interactive tasks)

Repetitive customer service

ALM (2003) (from routine analytic and interactive tasks)

Manual skills

Finger-dexterity skills

ALM (2003), under routine manual tasks

Picking, sorting, repetitive assembly, mixing ingredients, baking ingredients, sewing and decorative trimming, operating tabulating machines, packing agricultural produce

ALM (2003)

Control, equip, operate

S-O (2006), APST (2020)

Repetitive movements

O-NET Uruguay

Hand-foot-eye coordination skills

ALM (2003), under nonroutine manual tasks

Attending cattle, attending other animals, driving to transport passengers, driving to transport charge, piloting airplanes, pruning and treating ornamental and shade trees, performing gymnastic feats, performing other sports requiring skill and balance

ALM (2003)

Accommodate, renovate, repair, restore, serving, cleaning

S-O (2006), APST (2020)

Reaction on time, fine manipulations

O-NET Uruguay

Physical skills

O-NET Uruguay

Resistance, time dedicated to walking and running, carrying heavy loads

O-NET Uruguay

Notes: ALM (2003) stands for Autor, Levy, and Murnane (2003), APST (2020) for Atalay et al. (2020), DK (2018) for Deming and Kahn (2018), DN (2020) for Deming and Noray (2020), HK (2018) for Hershbein and Kahn (2018), HK (2012) for Heckman and Kautz (2012), KBHT (2016) for Kureková et al. (2016) and S-O (2006) for Spitz‐Oener (2006). The O-NET Uruguay pilot project, which so far captures 22 selected occupations only, is detailed in Ministerio de Trabajo y Seguridad Social (2020) and Velardez (2021). The keywords used are meant to be the most comprehensive possible to provide appropriate definitions for categories and subcategories, but synonyms are not yet included at this stage. Even if some skill subcategories are closely related, the words used to categorize them are mutually exclusive. This allows for a unique identification of skills categories. Note also that within sub-categories, some keywords are redundant (like “math” and “mathematics”). This simply means that more than one of the sources used that word to categorize the subcategory and has no repercussion for the implementation of our taxonomy as long as the repetition occurs within subcategories. Words in italics indicate keywords included by the authors of this paper to complete existing definitions.

Within the category of cognitive skills, we define the subcategory ‘cognitive skills (narrow sense)’ as the abilities or qualities needed to perform tasks that require analysis and calculation, problem-solving, intuition, flexibility and creativity (Acemoglu and Autor 2011, 1076; Autor, Levy, and Murnane 2003, 1284). A large literature has studied the relative demand for cognitive skills and their returns in high-income countries, and cognitive skills are recognized in policy debates as being central for workers’ resilience to labour market transformations (ILO 2021a).

One strand of this literature thus looks at the polarization of occupations by skill level vis-à-vis technological change and automation.[8] This literature finds that occupations largely requiring cognitive skills have thrived with the development of computer technology, both in terms of job creation and wage growth. This is driven by cognitive skills that are difficult to routinize (notably, those associated with non-repetitive cognitive tasks), where occupations relying on such tasks have been more resilient to technological shocks. Meanwhile, low-skilled occupations (also non substitutable by technology, as discussed below) have likewise witnessed a broad-based increase in employment, relative to middle-skilled occupations. More recent studies suggest that the rise of high-skilled occupations has evolved, documenting a reversal in the demand for cognitive skills in the United States in 2000 (Beaudry, Green, and Sand 2016). While the share of highly educated college graduates has increased, occupations intensive in cognitive skills have seen little wage and employment growth after 2000. The finding is consistent with the Information Technology (IT) revolution, and hence the introduction of IT as a general-purpose technology, having reached a maturity stage. This implied that high-skilled workers moved into occupations relying less strongly on cognitive skills (Beaudry, Green, and Sand 2016). The reversal in the demand for cognitive skills therefore has consequences for the wage structure of occupations (see also Roys and Taber 2019).

Another strand of the literature complements this knowledge by focusing on skill changes within occupations.[9] These studies take advantage of richer data sources with greater granularity, such as vacancy data. They find substantial variation in skill requirements within occupations. While cognitive skills are an important driver of this variation, socioemotional skills are taking on a key role (Kureková et al. 2016). In this context, Deming and Kahn (2018) show that a greater emphasis on cognitive skills, especially when they are demanded in combination with socioemotional skills, is associated with higher wages and firm productivity. Therefore, the considerable within-occupational variation in the demand for such skills contributes to explaining a significant share of existing patterns of wage inequality. In addition, changes in skills over time play a decisive role also within occupations (Atalay et al. 2020; Spitz‐Oener 2006). As one example, STEM occupations have seen pronounced changes in cognitive skills requirements – e.g., STEM vacancies requiring skills linked to machine learning and artificial intelligence rose by 460 per cent in the decade before 2017 – which can be attributed to the rapid diffusion of technological innovation in STEM (Deming and Noray 2020). Against this background, the words we choose to identify cognitive skills aim to match the analytical job tasks defined by Autor, Levy, and Murnane (2003) that are used by most papers studying routine-biased technological change and employment polarization. We add additional words suggested by Atalay et al. (2020), Deming and Kahn (2018), Deming and Noray (2020), Spitz‐Oener (2006), and O-NET Uruguay.

Finally, we complete cognitive skills by adding five additional sub-categories from Deming and Kahn (2018): computer skills, software skills, writing skills, financial skills and project management skills. We also add one additional category suggested by Deming and Noray (2020), which refers to machine learning and artificial intelligence. These sub-categories are intricately linked to the cognitive skills described before and represent topical sub-categories that organize cognitive skills around themes. We categorize them separately because they are commonly listed in a wide range of online job vacancies and applicants’ work experiences. The list of topical sub-categories targets in particular white-collar jobs (Deming and Kahn 2018), which means that this list can be consulted at the detailed level or condensed, depending on the focus of a given study on a particular segment of the labour market.

Our second broad category of socioemotional skills adds to the categorization a set of personal attributes that involve intellect, but more indirectly and less consciously than cognitive skills. The literature uses a range of expressions to refer to such skills, including non-cognitive skills, soft skills, socioemotional skills, or personality traits.[10] These different terms suggest different properties. First, we avoid the use of the term ‘non-cognitive skills’, as all skills require some sort of cognition. Second, while ‘traits’ gives a sense of immutability, ‘skills’ connotes the possibility of learning (Kautz et al. 2014). A long debate in the psychology literature has discussed whether socioemotional skills are stable across different situations at a fixed point in time, as well as the degree of malleability of these skills over the life course (see Almlund et al. 2011, sec. 2). Today, most psychologists and economists support the notion of a stable personality across situations,[11] which is supported by a large body of evidence showing that stable socioemotional skills exist and predict a variety of behaviours (Kautz et al. 2014). However, as pointed out by Heckman and Kautz (2012) “while traits are relatively stable across situations, they are not set in stone. They change over the life cycle.” The consensus that socioemotional skills are malleable and can be learned, is key in terms of policy. If this were not the case, there would be little room for policy. Another decisive question is whether socioemotional skills can still significantly change during adult life. Disciplines diverge on this question. Economists like Carneiro and Heckman (2005) and more recent publications by Heckman and co-authors put emphasis on the malleability of these skills until adolescence and early adulthood. From this perspective, parenthood, education policies and workplace-based internships and apprenticeships are the central means for developing socioemotional skills. Meanwhile, social psychologists argue that at least some of these attributes (e.g., emotional intelligence) can be learned at any age, which places greater emphasis on training, learning and education policies for adults.[12] Sociologists and management scholars agree, emphasizing the indispensability of experiential learning for some socioemotional skills such as problem solving, teamwork and other social skills (see Green, Ashton, and Felstead 2001, chapter 1).

Measuring and classifying socioemotional skills is a challenge. Yet, the psychology literature has arrived at a well-accepted categorization of these skills, called the “five-factor personality model” or “big five” (McCrae and Costa 2008). It includes agreeableness, conscientiousness, emotional stability, extraversion and autonomy, and openness to experience. A large literature on the labour market returns of non-cognitive skills has established that different non-cognitive skills are important predictors of life outcomes, such as education and labour market success (Heckman and Kautz 2012). Some of these personal attributes are correlated with cognitive skills (Kureková et al. 2016), but their explanatory power on labour market and education outcomes goes beyond this correlation.[13] A study for the United States found that improvements in non-cognitive skills are much more important for future earnings and employment than similar improvements in cognitive skills (Heckman, Stixrud, and Urzua 2006). Similarly, a study for Sweden showed that both cognitive and non-cognitive skills are strong predictors of future earnings, but that non-cognitive skills have a stronger effect for people at the low-end of the earnings distribution (Lindqvist and Vestman 2011). In the emerging and developing world, four studies have assessed the causal effects of socioemotional skills on labour market outcomes, by experimentally evaluating the effect of job training programmes focused solely on socioemotional skills and different categories of workers. The evidence is more mixed, with two studies finding positive effects in India and Togo (Adhvaryu, Kala, and Nyshadham 2018; Campos et al. 2017), another study from the Dominican Republic finding positive effects for women but not men (Acevedo et al. 2017), and a final study for Jordan finding null effects (Groh et al. 2016).

In terms of our taxonomy, we divide the five-factor model between the subcategories ‘character skills’ and ‘social skills’. Our sub-category ‘character skills’ follows Deming and Kahn (2018), deviating somewhat from their definition (see Table 1). It includes conscientiousness (i.e. "the tendency to be organized, responsible, and hardworking”, American Psychological Association 2020), openness to experience (i.e. "the tendency to be open to new aesthetic, cultural, or intellectual experiences”, ibid), and emotional stability (i.e. this is the contrary to ‘neuroticism’. “Emotional stability is predictability and consistency in emotional reactions, with absence of rapid mood changes.”, ibid). It also encompasses dimensions such as being relaxed, independent, self-confident and the degree of vulnerability to stress (Brunello and Schlotter 2011; Heckman and Kautz 2012).

There is no single agreed way to aggregate the five-factor personality model into broader categories. Most researchers bundle together various socioemotional skills depending on their specific research questions. We aggregate them into ‘character skills’ and ‘social skills’, as distinguishing between all five categories separately would increase the probability of making mistakes at this finer level of aggregation, without necessarily adding considerable value to the analysis. Instead, the two groups of ‘character skills’ and ‘social skills’ are based on the similarities in their predictive nature that have been found by the empirical literature. As such, we bundle conscientiousness, emotional stability and openness to experience together within our subcategory ‘character skills’, because these three factors appear to share the ability to predict education and labour market outcomes, although not with equal strength (Almlund et al. 2011; Brunello and Schlotter 2011). For example, conscientiousness stands out as the most predictive trait of the five-factor model on future outcomes. It has been found to predict educational attainment, health, and labour market outcomes, in some cases, as strongly as measures of cognitive ability.[14] While conscientiousness predicts performance and wages across a wide spectrum of jobs, the predictive power of cognitive skills decreases with job complexity (Almlund et al. 2011; Kautz et al. 2014). Meanwhile, attributes related to emotional stability ‒ especially internal locus of control, or the belief that one can determine one’s success as opposed to believing that outcomes are the result of fate or luck ‒ positively predict earnings (Brunello and Schlotter 2011) and job search effort (Almlund et al. 2011). Finally, openness to experience predicts finer measures of educational attainment, such as class attendance (Almlund et al. 2011) but also years of education (Borghans et al. 2008). It has also been associated with transversal competencies, such as sense of initiative and entrepreneurship, which are important factors for education and labour market success (Brunello and Schlotter 2011). The words we use to define such ‘character skills’ come from Deming and Kahn (2018), but also from Deming and Noray (2020), Kureková et al. (2016) and Heckman and Kautz (2012).

We complete the category of socioemotional skills with three of Deming and Kahn (2018)’s sub-skills groups, which capture social skills (as mentioned before), people management skills and customer service skills. Within ‘social skills’ we include the original words suggested by the authors (e.g., communication, teamwork, collaboration). We add the remaining two categories from the five-factor personality model, namely agreeableness and extraversion. Agreeableness is the tendency to be cooperative towards others (American Psychological Association 2020). Extraversion is the orientation people show toward the outer world and social contact (ibid.). In our taxonomy, we characterize these two personal attributes with words such as cooperation, flexibility, empathy and assertiveness.[15] We also add the sub-categories of ‘people management skills’ and ‘customer service skills’, which are determined by a large set of socioemotional skills, but usually listed separately in online job vacancies and workers’ job experiences. As before, we use various references as sources for key words with a view to arriving at a complete taxonomy and a more comprehensive use of words to define each category (see the last column of Table 1).

The importance the literature attributes to social skills and other socioemotional skills has considerably increased during the last decade and policy debates emphasize the core relevance of these skills across workers’ life-cycles (ILO 2021a). Research shows that employers in the United States and Europe rank some social skills above cognitive ones, particularly in low-skilled labour markets (Bowles, Gintis, and Osborne 2001; Kureková et al. 2016). Moreover, studies point to an increasing complementarity between cognitive skills and social skills (Arntz, Gregory, and Zierahn 2016; Deming and Kahn 2018; Borghans, Weel, and Weinberg, Bruce 2014; Deming and Kahn 2018; Weinberger 2014). As computers substitute for a wider set of non-interactive tasks, interpersonal skills are becoming increasingly central in a wide range of professional jobs (Autor 2014; Lu 2015). Interestingly, according to research in Europe, the increased demand for interactive skills does not substitute formal education, but appears in addition to it (Kureková et al. 2016). The implications for workers in emerging and developing countries are potentially significant, as large shares of employment in these countries are concentrated in sectors and occupations (e.g., services) that require disproportionately interpersonal skills.

Our third category, manual skills takes as point of departure how Autor, Levy, and Murnane (2003) categorize manual tasks. In their analysis, manual tasks are divided between routine and non-routine ones. The former are activities that necessitate finger-dexterity skills and the latter activities requiring “situational adaptability, visual and language recognition, and in-person interactions” (Acemoglu and Autor 2011, 1077). Broadly speaking, routine manual tasks are more prevalent in production and operative occupations and non-routine manual tasks in service occupations, but also in some production and operative positions requiring physical or situational adaptability (ibid).

The model by Autor, Levy, and Murnane (2003) captures the vulnerability of routine manual labour in light of automation and outsourcing. We implement this category in our taxonomy using words such as picking or sorting, repetitive assembly, and mixing or baking ingredients. However, not all manual tasks enter this category. Several tasks require motor processing and visual capabilities that, given the contemporary state of technological progress, cannot easily be programmable (e.g., driving a car through traffic, cleaning and housekeeping). In the United States, lower-skilled workers have thus moved into service occupations that are associated with such tasks, away from occupations that rely strongly on tasks that can be routinized (Autor and Dorn 2013). Outside of high-income countries, excess supply of labour is disproportionately absorbed by low-value-added urban services, which contributes to an underdeveloped manufacturing sector (e.g., Rodrik 2018). Non-routine manual skills relate to tasks such as accommodating, serving, or cleaning, but also attending cattle in farms, pruning and treating plants, driving, and even performing gymnastics and other sports requiring balance.

Finally, we complement the categorization of manual skills with a ‘physical skills’ sub-category that pertains specifically to personal attributes, physical strength and effort, such as resistance, as well as related tasks such as those requiring walking and running, and carrying heavy loads.

Manual skills have been less studied in the literature than cognitive ones. One reason is that these tasks, especially the part that can be routinized, have been declining in the advanced world (Spitz‐Oener 2006). Another reason is that manual skills, and more generally low-skill jobs, are underrepresented in online sources, such as the BurningGlass data, and are therefore not the focus of studies using these data (Hershbein and Kahn 2018). Yet, there are some findings from the advanced world that shed light on the importance of integrating this skills category into a research-oriented taxonomy, given that these tasks are important when considering workers at the lower end of the wage distribution (Autor and Dorn 2013). While research is scant outside the United States, we expect these tasks to be at least as important in the emerging and developing world, if not more important. Recently, Lise and Postel-Vinay (2020) studied the returns of manual skills requirements using O-NET data from the United States. They find that manual skills have in general moderate returns, and that they are easily accumulated on the job but also relatively fast lost when not employed. Roys and Taber (2019) explore the payoff to different skills for low-skilled workers in the United States over the life cycle. They find that while the payoff related to interpersonal skills has increased over time, the returns of manual skills remain the most relevant factor determining the wages of low-skilled workers. From a policy perspective, the finding suggests that investments in manual skills are key for improving the wages of these workers. Importantly, however, while there is a clear understanding that education and training is a path to develop cognitive skills, the literature has not sufficiently explored how to develop manual skills further. Meanwhile, policy practitioners, through their experience implementing these policies, point to the importance of work-based learning to develop technical skills, including manual ones (Kis and Windisch 2018; ILO 2017; European Commission 2013; CEDEFOP 2015). Therefore, understanding these developments better has profound implications for low-skilled workers. This extends to large segments of workers who perform manual work in emerging and developing countries, including in informal labour markets.

While our taxonomy focuses on cognitive, socioemotional and manual skills, irrespective of their routine intensity, the discussion above shows that routine intensity of skills matters for understanding important labour market trends. As a result, routine intensity is central for the literature on skill-biased technological change. Specifically, the substitutability of routine-manual and routine-cognitive tasks by technology underlies the declining demand for middle-skilled labour, and hence the polarization of employment structures in high-income countries (Autor, Levy, and Murnane 2003; Spitz‐Oener 2006; Goos, Manning, and Salomons 2014). Routine intensity also plays a role in understanding labour market trends outside of high-income countries. Recent research in this area relies on survey data available for a range of countries, while imputing routine intensity for countries where such survey data is not available. The available survey data shows that country-specific factors determine routine intensity, including the level of economic development, the degree of technology use, a country’s role in global trade, and the educational level of its workforce (Lewandowski et al. 2019). Differently from high-income countries, low- and middle-income countries have seen no, or only a smaller, decline in routine tasks between 2000 and 2017 (Lewandowski, Park, and Schotte 2020). In addition, middle-skilled jobs are not necessarily intense in routine tasks, such that a study for Argentina finds some evidence of a reallocation of employment towards middle-skilled jobs (Maurizio and Monsalvo 2021).

To capture routine intensity in our categorization of skills, we followed the economic literature (Autor, Levy, and Murnane 2003; Spitz‐Oener 2006; Atalay et al. 2020) to identify those keywords that are associated with routine skills. We deviated, however, from that literature by implementing routine intensity as a cross-cutting category, expecting that routine intensity may affect the different types of skills that our taxonomy is based on. This “ad hoc”-classification, which is available from the authors on request, still requires further validation and we refrain from presenting it here. To nevertheless give an impression of our approach, we added a column to our table, capturing whether a group of keywords falls into the category of routine-intense skills. More specifically, we coded as zero keywords that are typically identified as non-routine tasks and as one keywords that are typically associated with routine tasks. We add a value of two to categories where most tasks can typically be routinized, but there may be some exceptions (e.g., among ‘finger-dexterity skills’, not all sewing can be automated). Finally, a fourth category (coded as three) captured categories where we cannot say with certainty whether these are routine tasks or not (such as the activities falling under ‘financial skills’).

[1]  To illustrate this, one may think of science and engineering associate professionals (such as aircraft pilots), machine operators, sales workers, or cleaners and helpers. These occupations differ in average qualification levels, but each occupation entails a combination of skills from at least two of the broader categories of cognitive, socioemotional, and manual skills. The same is true for most other occupations.

[2]  A noteworthy contribution is the framework for core skills developed in ILO (2021a), which identifies core skills that improve workers’ resilience vis-à-vis transformative changes in contemporary labour markets, with a view to guiding practitioners on the integration of these core skills in national education and training policies.

[3]  See Ministerio de Trabajo y Seguridad Social (2020) and Velardez (2021) for details on the O-NET Project Uruguay, which is in its pilot phase and has implemented a survey following the US O-NET model. It so far characterizes 22 selected occupations in Uruguay. The objective (as in the case of US O-NET) is to provide a complete and very detailed characterization of the requirements and attributes of workers within each occupation. This source is thus not tasked with creating skills categorizations that can be used for research purposes, but with providing a close to exhaustive list of skills observed in each occupation.

[4]  For example, “teamwork” and “collaboration” refer to the same skill but their respective use has changed across time (Deming and Noray 2020). Our taxonomy should encompass all keywords in situations of this kind.

[5]  By grounding our taxonomy in the academic literature, we aim for a taxonomy that is suitable for research. In contrast, two noteworthy initiatives have classified skills in job vacancy data with a more direct policy angle. The CEDEFOP-OVATE analysis categorizes job vacancy data according to the European ESCO scheme, focussing on countries from the European Union and the United Kingdom (Cedefop 2019). Stops et al. (2020) classify German vacancy data based on categories from the German BERUFENET. These approaches thus exploit pre-existing classification schemes for the European countries analyzed, which depending on the context, might require further systematic aggregation before they can be analyzed for research purposes.

[6]  In the task-based model, Autor, Levy, and Murnane (2003) define tasks as units of a discrete work activity that map to workers skills, i.e., their ability to perform a certain task (Acemoglu and Autor 2011).

[7]  For example, “character skills” have been found to be highly correlated with “cognitive skills (narrow sense)”. Still, the words used to characterize each set of skills are specific to each sub-category and there is no word that repeats in both sub-categories.

[8]  See for example: Acemoglu and Autor (2011); Atalay et al. (2020); Frey and Osborne (2017); Hardy et al. (2018); Keister and Lewandowski (2017); Spitz‐Oener (2006); Autor and Dorn (2013).

[9]  See for example: Atalay et al. (2020), Arntz, Gregory, and Zierahn (2016), Beaudry, Green, and Sand (2016); Deming and Kahn (2018); Hardy et al. (2018); Hershbein and Kahn (2018); Spitz‐Oener (2006); Modestino, Shoag, and Ballance (2020).

[10]  ‘Personality traits’ are defined by Roberts (2009) in the psychology literature as “the relatively enduring patterns of thoughts, feelings, and behaviours that reflect the tendency to respond in certain ways under certain circumstances” (in Almlund et al. 2011, 8). As this terminology could convey a sense of immutability ‒ even if that was not the intent of the psychology literature ‒ some strands of the literature prefer to avoid it (see, for example, Kautz et al. 2014; Heckman, Jagelka, and Kautz 2019).

[11]  For example, Almlund et al. (2011); Kautz et al. (2014); Heckman and Kautz (2012); Mischel and Shoda (1995); and Mischel and Shoda (2008). The exception are behavioural economists (see for example Thaler 2008), who believe instead that situations have specific constraints or incentives, which determine behaviour almost entirely (Almlund et al. 2011; Kautz et al. 2014).

[12]  Brunello and Schlotter (2011) summarize the arguments of the social psychology literature, including Cherniss et al. (1998), Boyatzis (2008) and Goleman (2000).

[13]  As illustrated by Heckman and Kautz (2012, 454), “including the measures of personality in a regression with cognitive measures explains additional variance.”

[14]  See Almlund et al. (2011), Borghans et al. (2008) and Heckman and Kautz (2012) for a review of the relevant literature from psychology and economics. Studies in occupational psychology moreover emphasize the relationship between conscientiousness and other moderator variables, like motivation, ability, and work engagement (see Bakker, Demeranti, and ten Brummelhuis (2012) and references therein).

[15]  These categories might need to be assessed critically as far as workers’ self-descriptions are concerned. Some workers may understand the importance of signalling certain character skills rather than these being a part of their personality. In addition, some traits are expected more often of women than men, and undervalued in terms of monetary returns (Grugulis and Vincent 2009).

Data and descriptive statistics

To implement our skills taxonomy empirically, we rely on online data from the Uruguayan online job board BuscoJobs. BuscoJobs is a private job board. It contains detailed information on (i) workers searching for jobs, (ii) vacancies posted by firms, and (iii) applications jobseekers have made to the vacancies posted. Our data cover the entire content uploaded on the job board from January 2010 through December 2020 in Uruguay, where BuscoJobs has its headquarters.[1]

To post online vacancies through BuscoJobs, enterprises need to create an account on the portal and pay a small fee. The portal offers several types of enterprise subscriptions, which cover different time spans and allow firms to publish a distinct number of vacancies (going from three to 600 vacancies, depending on the length and the type of subscription). All subscriptions enable firms to view users’ curriculum vitaes (CVs) for the duration of the chosen subscription. Given the information that firms provide in the vacancies they post, the database contains rich information on firm characteristics, industry, vacancy requirements in terms of education and experience, and vacancy characteristics such as working conditions. In addition, each vacancy has a job title and an open text describing each vacancy, which will be crucial for implementing our skills taxonomy and generating the International Standard Classification of Occupations (ISCO) variable.

As a distinguishable feature of BuscoJobs, the job board does not only offer the possibility to firms to post vacancies, but also allows jobseekers to register and apply for these vacancies directly through the portal. Jobseekers can create an account for free. They include in their profile basic personal information, information about their educational attainment, their entire history of employment, the technical skills they possess, and the languages they master. This allows us to observe rich longitudinal data on individuals’ characteristics and employment biographies. Jobseekers can also express their job preferences, whether they are actively seeking a job, whether they are employed or unemployed and whether they want their profiles to be fully visible by employers.[2] Finally, the applicants include their past work experiences and ongoing employment spells. This is detailed in an open-text format, which is important for us to characterize the skills associated with these spells, according to our skills taxonomy, and to create the ISCO variables.

Another advantage of the BuscoJobs database, in comparison to other job boards and job aggregators in the country, is that BuscoJobs does not include duplicate job postings and presents a much lower volatility over time (Equipos Consultores 2020). Most importantly, as mentioned above, BuscoJobs covers a comparatively long time series from 2010 through 2020, and captures vacancies, applicant employment biographies, and applications. This stands in contrast to other major job boards and aggregators in Uruguay and other data sources that are prominently used in the literature, such as the BurningGlass data, as these only contain vacancy data.

2.1 Coverage and applicability of applicants’ and vacancy data to identify and measure skills variables

When assessing the demand and supply of skills, and pursuing any kind of related analytical work, online data have two general advantages, compared to other traditionally used data sources, such as labour force surveys (see also Hershbein and Kahn 2018).

First, online vacancies and applicants’ data have a high degree of granularity. This allows identifying detailed trends in skills and skills needs by generating variables of the tasks performed on the job, in the case of job seekers, and the tasks requested in vacancies. These statistical indicators can be computed without recurring to proxies, such as occupational categories or industries. They can thus measure an important but largely overlooked phenomenon, namely, the actual skills compositions of jobs and changes in skills within occupations (Hershbein and Kahn 2018). Furthermore, online job vacancy and applicants’ data provide detailed information on geographical location and, depending on the data source, enterprises and applicants’ characteristics, including occupational categories and industries. These variables can often be computed in a robust way even at a highly disaggregated level, thanks to the large sample sizes usually present in online job board and job aggregator data (ILO 2020).

Second, online vacancy and applicants’ data is typically collected at high frequency, making it possible to avoid time gaps associated with more common data collection methods, such as survey data, which are sampled in ‒ sometimes long ‒ intervals. Given the real-time character of online data, some of the delays associated with the processing and cleaning of survey data can be avoided (ILO 2020), in particular if the initial database associated with an online job board or job aggregator is well structured.

One of the major drawbacks associated with the use of data from online job boards and aggregators is their representativeness. As they are not based on random sampling, it is difficult to draw generalised conclusions about the universe of firms or the overall working age population, as the data tend to be biased towards certain segments of the labour market, in particular higher-skilled occupations (Cedefop 2021; Fabo and Kureková 2022; Hershbein and Kahn 2018).

The representativeness depends on several characteristics inherent to the country of interest for the analysis, including the internet penetration rate. In Uruguay, 83.4 per cent of individuals used the internet in 2019, according to ITU data.[3] This is considerably above the global average, estimated at 56.7 per cent, and the regional level, reaching 68.3 per cent for Latin America and the Caribbean (LAC). These figures suggest that exploiting web-based big data in the Uruguayan context could be associated with less bias, compared to other countries. Nonetheless, also among more advanced countries where a high share of individuals uses the internet on a regular basis (e.g., the European Union, where 83.8 per cent of individuals used the internet in 2019, ibid), there are major differences in the share of vacancies posted online. Within the countries of the European Union, for example, the proportion of job vacancies published online in 2017 varied from below 50 per cent in countries such as Romania or Denmark to close to 100 per cent in Finland, Sweden and Estonia (ILO 2020). One factor explaining such differences is the share of the population living in urban or rural areas. In urban areas, the services sector tends to play a larger role and the incidence of online job advertisements is thus higher. In contrast, print media and word-of-mouth communication seem to be more relevant in rural areas (ILO 2020). In the context of Uruguay, it is important to note that a large majority of the working-age population (94.7 per cent) resided in urban areas in 2020 (authors’ calculation based on the household survey introduced in the next sub-section). This is related to the prominent role of the capital Montevideo and the high degree of centralization in Uruguay.

Online vacancies also tend to be advertised by larger firms (e.g., international firms) and less by smaller firms, or firms operating in the construction, agricultural and hospitality sectors (ILO 2020). Furthermore, the incidence of informality matters, since informal jobs often do not appear on public online job boards or aggregators (Cedefop 2021). Interestingly, Uruguay has a significantly lower rate of informality than the LAC region as a whole. In 2019, close to 24 per cent of all employment was informal in Uruguay (authors’ calculations)[4] compared to 56.4 per cent in LAC (ILO 2021b, 54).

Uruguay’s labour market characteristics are thus amenable to the use of online portals by firms and jobseekers. Beyond this, it is important to assess the degree and direction of biases of our data. As such, in the following we assess empirically the representativeness of the BuscoJobs data and investigate whether representativeness has changed over the period analyzed.

2.2 BuscoJobs applicants’ data

The BuscoJobs applicants’ database has 666,797 user profiles, out of which 388,041 include information related to their previous work experience, which is fundamental for our analysis. Since we could construct the detailed employment biographies of these individuals, our overall sample size consists of 1,231,555 job spells. As shown in Figure 1, we have good data coverage for all years between 2010 and 2020 and observe that the number of individuals joining the job board tended to increase between 2010 and 2017, to slightly decrease again until 2020.

The applicants’ database provides rich information about applicants’ personal characteristics (e.g., sex, birth dates, and places of residence), as well as previous work experience (e.g., job spells’ dates, position, location and a detailed open-text description of the position), and their education background. As mentioned above, applicants can also indicate the skills developed in each job spell and can participate in a soft-skills test, the results of which appear on the general profile of each user and are available to us. Furthermore, prior to the process of recognizing and classifying skills through our taxonomy, a classification of occupations was carried out through a machine-learning approach, where we created variables that further characterized employment spells in terms of the associated two-digit ISCO-08 occupation (more details on the machine-learning approach are provided in Chapter 3). This occupation variable, along the skills variables created, will allow us to capture skills compositions within occupations and assess the representativeness of the data in terms of the occupational distribution.

To assess the representativeness of the database, we compare selected characteristics of the job board’s users with information from the Uruguayan household survey, the Encuesta Continua de Hogares.[5] We mostly rely on data for 2020 for our analysis, unless a variable of interest is not available, in which case we refer to 2019 data.[6]

Within the applicants’ data, 54.6 per cent are women, which is in line with estimates for the working-age population in Uruguay, of which 52.2 per cent were female in 2020. 66.5 per cent of the applicants were located in the Montevideo area in 2020, compared to 41.3 per cent of the overall labour force. The overrepresentation of the Montevideo area in the BuscoJobs applicants’ database is likely related to the different job-search methods used in large metropolitan areas, such as Montevideo, compared with smaller urban areas or rural areas. This is consistent with the previous finding of the higher relevance of print media and word-of-mouth communication in rural areas (ILO 2020). Indeed, with 53.6 per cent a comparatively large share of those reporting to have used the internet to find a job were living in the Montevideo area in 2020.[7]

Figure 1. Absolute number of individuals joining BuscoJobs in a given year, 2010-2020

Notes: We exclude a small share of individuals joining the job board in earlier years, since our data cover all profiles that were active between January 2010 and December 2020.

In terms of the age distribution, the applicants’ data contains a disproportionately high share of younger applicants (Figure 2). This discrepancy is the most pronounced among individuals aged 25-29, who account for 26.2 per cent of BuscoJobs users in 2020, compared to nine per cent of the Uruguayan working-age population in the same year. More similar representations are observed for the age category 35-39, that accounts for 12.5 per cent of BuscoJobs users and 11.3 per cent of the working-age population. The tendency is inverted as older age categories are taken into account.

The larger share of young portal users is intuitive given their stronger familiarity and use of IT tools, compared with older workers. It can also be rationalized by younger workers entering the labour market for the first time, and thus searching more actively for employment. However, it is somewhat difficult to put our findings into perspective, since previous studies using online applicants’ data do not provide similar age information.[8] Marinescu and Skandalis (2021) are an exception, reporting an average age of 31.8 for individuals using the online job-search platform of the French public employment services. This is even slightly below the average age we find (33.6 years). Other studies directly focus on young individuals, perhaps because these are frequent users of online job-search engines when applying for jobs (Barbarasa, Barrett, and Goldin 2017; Kureková and Žilinčíková 2018).

Figure 2. Age distribution, BuscoJobs applicants' database compared to household survey data, 2020

Notes: Author’s calculations based on BuscoJobs applicants’ database (orange line) and the Uruguayan household survey data (blue line). Household survey data calculations consider the labour force as the comparison group.

When looking at educational attainment, we find that applicants using the BuscoJobs job board have a higher level of education compared to the Uruguayan labour force. In 2019, 27.3 per cent of BuscoJobs applicants possessed an undergraduate degree and 32.3 per cent a graduate or technical degree. Meanwhile, according to data from the Uruguayan household survey, 12.3 per cent of the labour force had achieved the first degree of tertiary education, and 2.3 per cent had completed a degree of the secondary stage of tertiary education.[9]

In terms of the occupational distribution, Figure 3 shows a certain divergence when comparing BuscoJobs applicants’ employment spells in 2020 with the national estimates for the same year. Notably, a disproportionately large share of the BuscoJobs users was employed in a clerical support profession (34.8 per cent), while none of the users indicated any work experience in an agricultural profession, meaning that this category is completely absent from the database. This suggests that when analyzing the applicants’ data, it may be appropriate to focus on certain groups of workers that are well represented without making inferences to the whole population and/or to use weighting techniques to improve representativeness. As a matter of comparison, while many studies exploiting online applicants’ data do not provide information on individuals’ occupations, Marinescu and Rathelot (2018) show that there is significant overlap ‒ but also some divergence ‒ between the online data from the job board CareerBuilder and national survey data for the United States.[10]

Figure 3. Comparison of the occupational distribution across applicants' employment spells and the national occupational employment distribution in 2020 (%)

Notes: Author’s calculations based on BuscoJobs applicants’ database (orange bars) and Uruguayan household survey data (blue bars). The calculations based on the household survey take into account all people in employment. Occupations are defined according to the one-digit ISCO 08 classification.

While the applicants’ dataset is not fully representative of the overall employed population, it has several comparative advantages. The dataset includes reasonably large samples for occupations requiring intermediate or lower levels of formal qualification, such as services and sales workers, clerical support workers, and even workers from elementary occupations. This suggests that the data can be used for analyses that extend beyond highly qualified workers, which one might not have thought at first glance.[11] Moreover, as mentioned above, the database contains rich information and its granularity – in particular the precise information on tasks performed on the job by individuals – is an asset to obtain a better understanding of skills dynamics.

Figure 4. Representativeness of BuscoJobs occupations in applicants’ data, relative to occupational distribution in Uruguayan household survey data (2010-2020)

Notes: Author’s calculations based on BuscoJobs applicants’ database and Uruguayan household survey data. The Household Survey calculations are based on all people in employment. Based on Hershbein and Kahn (2018b), the x-axis illustrates the BuscoJobs share in occupations in 2010 minus the share in the same occupation and year in the Uruguayan Household Survey. Meanwhile, the y-axis illustrates these differences between the two samples for each year from 2011 to 2020. The 45-degree line shows occupations where the shares between BuscoJobs and the Household Survey, did not change from 2010. Occupations are defined according to the one-digit ISCO 08 classification.

Although BuscoJobs’ applicants are disproportionally concentrated among younger and more educated jobseekers in the Montevideo area, looking for jobs in clerical and technical occupations, the distributions are stable across time as illustrated in Figure 4. A primary concern when using this data is whether the representatives of the sample changes over time as this would be a threat to internal validity of any analysis exploiting a temporal dimension (Hershbein and Kahn 2018; Deming and Noray 2020). Figure 4 shows that no major changes are observed over time. Based on Hershbein and Kahn (2018b), the x-axis illustrates the BuscoJobs share in occupations in 2010 minus the share in the same occupation and year in the Uruguayan Household Survey. Meanwhile, the y-axis illustrates the same per centage point difference for each year from 2011 to 2020. The 45-degree line shows occupations where the shares between BuscoJobs and the Household Survey, did not change from 2010. The only group clearly deviating from this line are services and sales workers, where the year of 2010 seems to represent an outlier.

2.3 BuscoJobs vacancy data

BuscoJobs is one of the leading job boards in Uruguay. According to the provider’s calculations, it captures around 50 per cent of online vacancies in Uruguay (BuscoJobs market statistics shared with the authors), providing information that is detailed and reliable. Vacancies are recent and have high turnover and most of the fields available for each vacancy are complete (Di Capua, Queijo, and Rucci 2020). The job adds posted are unique and are clean from repetitions, which is not a given among job portals. As other job portals and job aggregators in Uruguay post the same vacancies several times, it is estimated that the effective coverage of BuscoJobs is in fact closer to 60 per cent of online vacancies.

We restrict our attention to vacancies that refer to jobs located in Uruguay and the period from 2010 to 2020. This results in a total of 86,966 vacancies, posted by more than 6,000 firms. It is important to note that not every observation corresponds necessarily to one open position. In fact, 70.3 per cent of the job advertisements posted on the job board are associated to one vacancy only, while 12.1 per cent are associated to two vacancies (i.e., the firm aims to hire two people with the same job posting), and 8.1 per cent are linked to three to five open vacancies.

As shown in Figure 5, the number of vacancies posted in the job board has increased constantly to reach a peak in 2014. The observed numbers have remained at a high level also over the following years, with a drop in the job postings in 2020. This might be explained by the economic slowdown induced by the COVID-19 pandemic.

Figure 5. Absolute number of job postings per year

Notes: Authors’ compilation based on BuscoJobs online vacancies.

The job board allows capturing very detailed information associated with each job advertisement. This includes characteristics of the firm posting the vacancy (e.g., name and location), characteristics of the vacancy (e.g., desired age of the candidate and sex[12], work experience expected, and the wage range offered for the advertised position) and a detailed description of the job advertised, along with the educational and the skills requirements. Furthermore, BuscoJobs generated upon request a variable indicating the economic sector to which an enterprise is associated, following the International Standard Industrial Classification (ISIC), Revision 4, at the four-digit level. For this, the BuscoJobs data were matched to the administrative database of enterprises produced by the country’s statistical institute (Instituto Nacional de Estadísticas, INE) with information until 2017[13], using unique firm identifiers. From 2018 forward, the matching to the ISIC Revision 4 was done manually using the economic activity reported by firms when registering in BuscoJobs. Finally, as for the applicants’ data, we created a variable capturing occupational categories, following the two-digit ISCO-08 classification and using a machine-learning approach (see Chapter 3 for details).

For the assessment of the representativeness of the BuscoJobs online vacancy dataset, we focus on the year of 2020 and again compare selected summary statistics to those derived for the same year from the Uruguayan household survey. This comparison is indicative only, given that the online job vacancy data and household survey data are collected in an inherently different way. Vacancy data depend on turnover rates in the labour market, which may differ across economic sectors, while the household survey data represent a snapshot of workers’ characteristics in a given moment. Nevertheless, this comparison yields meaningful insights to assess the representativeness ‒ or lack thereof ‒ of the data.[14]

To begin with, 61.3 per cent of the vacancies are located in the capital of Montevideo in 2020, compared to 41.3 per cent of the overall labour force. Moreover, the data from BuscoJobs online vacancies include a disproportionally high share of high- and medium-skilled professional categories, and underrepresents low-skilled occupations, when compared to national labour market estimates (see Figure 6). With 86.0 per cent of all online vacancies, the broad ISCO-08 categories 2-5 (i.e., professionals, technicians and associate professionals, clerical support workers, and service and sales workers) dominate in the BuscoJobs data. This suggests that the data allow for meaningful analysis especially of these groups of workers, which cover a significant share of the employed overall (54.0 per cent according to the household survey). Low-skilled occupations, captured by the broad category ‘elementary occupations’, are underrepresented in the BuscoJobs data, although they still account for 5.6 per cent of the observations (compared with 17.2 per cent in the household survey). Furthermore, skilled agricultural occupations are completely absent from the BuscoJobs online vacancy database, while they account for 4.6 per cent of the occupations observed in Uruguay.

Overall, these patterns are not surprising as national employment distributions across occupations deviate in similar ways from other online vacancy sources studied in the literature. This includes the BurningGlass data, which capture vacancies for professional jobs in a more comprehensive way than other types of vacancies (Deming and Kahn 2018; Hershbein and Kahn 2018; ILO 2020). While the BuscoJobs vacancy data are not fully representative of the Uruguayan labour market and tend to overrepresent high-skilled workers, they nevertheless cover a meaningful number of medium-skilled, and even some low-skilled jobs. Similar conclusions arise when looking instead at the distribution across industrial sectors (see Appendix Table A1).

Figure 6: Comparison of the occupational distribution across BuscoJobs job vacancies and the national occupational employment distribution in 2020 (%)

Note: Authors’ calculations based on BuscoJobs online vacancies database and Uruguayan household survey data. Occupations are defined according to the one-digit ISCO 08 classification. The share in the household survey was calculated considering all people in employment.

As discussed above, we again look at changes in the representativeness of the BuscoJobs vacancy data over time to ensure the internal validity of temporal analyses. Reassuringly, Figure 7 illustrates no major changes in the occupational distribution over time, when comparing it with the same distributions from the Uruguayan household survey. The only exception are again services and sales workers in 2010.

Figure 7. Representativeness of BuscoJobs occupations in vacancy data, relative to occupational distribution in Uruguayan household survey data (2010-2020)

Notes: Author’s calculations based on BuscoJobs vacancy database and Uruguayan household survey data. The calculations based on the household survey consider all people in employment. Based on Hershbein and Kahn (2018), the x-axis illustrates the BuscoJobs share in occupations in 2010 minus the share in the same occupation and year in the Uruguayan Household Survey. Meanwhile, the y-axis illustrates these differences between the two samples for each year from 2011 to 2020. The 45-degree line shows occupations where the shares between BuscoJobs and the Household Survey, did not change from 2010. Occupations are defined according to the one-digit ISCO 08 classification.

[1]  The job board was first launched in 2007 in Uruguay, then expanded to 20 Latin American countries and Spain (BuscoJobs 2021), and is currently present in 33 countries globally (BuscoJobs Internacional 2021). In addition to its wide coverage in Latin America, BuscoJobs exists in four African countries (Ghana, Kenya, Nigeria, South Africa), six countries in Asia and the Pacific (Australia, India, Indonesia, Malaysia, New Zealand, the Philippines), three European countries (Spain, Portugal, Italy) and the United States. It uses the of names “BuscoJobs” (for Spanish speaking countries), “Findojobs” (for English speaking countries) and “Cercojobs” (in Italy).

[2]  The job board allows users also to take a soft-skills test, which is provided by the enterprise d’Anchiano (D’Anchiano 2021) and allows to generate a list of soft skills possessed by each user, as selected by the users from the skills that are part of this test. Furthermore, technical skills can be reported on their general profile and in association with each single work experience. These variables are different from the ones we generate and are not considered in our methodology to implement the skills taxonomy given the large number of missing values.

[3]  The ITU data were accessed through the World Bank (2021).

[4]  The Uruguayan informality figure is based on the household survey introduced in the following sub-section, following the ILO harmonized approach.

[5]  This representative cross-sectional survey has been run by the National Statistical Office in Uruguay on a regular basis since 1968 (INE Uruguay 2020). The sampling is based on census data from 2011.

[6]  Note that labour market indicators follow ILO definitions, and thereby numbers may deviate from nationally published figures. All indicators from the household survey data are calculated based on the working-age population, aged 15 and older, unless specified otherwise.

[7]  Authors’ calculations using the Household Survey data.

[8]  This is the case for studies on the United States, which analyze search behaviour on the job board CareerBuilder (Marinescu and Rathelot 2018; Marinescu and Wolthoff 2020) and job aggregator Glassdoor (Marinescu, Skandalis, and Zhao 2021). Presumably, the authors did not observe applicants’ age.

[9]  The data from Uruguayan household survey has been aligned to the ISCED-97 educational classification, considering the levels of “5 - First stage of tertiary education” and “6 - Second stage of tertiary education”.

[10]  The authors report a correlation coefficient of 0.71 between the two distributions. Note, however, that their reference group are jobseekers, and not the national employment distribution, as in our case.

[11]  For example, Deming and Noray (2020) focus on college graduates, while Deming and Kahn (2018) focus on professional jobs, noting that these are particularly well represented in online data. These authors match job vacancies from BurningGlass with survey data sources.

[12]  More than 25 per cent of the vacancies indicate a preferred sex of applicants. Roughly two thirds of the job postings indicating a preference for female candidates are associated with occupational categories “4 - Clerical support workers” and “5 - Service and sales workers”. Job postings indicating a preference for male candidates are more evenly spread across occupational categories, with a slightly higher incidence within the category “9 – Elementary occupations”.

[13]  Also called “Directory of Enterprises and Establishments”, available at: http://www.ine.gub.uy:82/Anda5/index.php/catalog/709 (accessed 1 February 2022).

[14]  This comparison should ideally be complemented by other sources that capture vacancies directly, such as the Job Openings and Labor Turnover Survey (JOLTS) from the United States (Hershbein and Kahn 2018). Unfortunately, a similar survey is not conducted by the Uruguayan National Statistical Office (INE Uruguay 2021).

Empirical implementation of the skills taxonomy

3.1 Text-mining model

To create the skills variables in the BuscoJobs data, we employ a text-mining approach and build on the taxonomy and the keywords and expressions[1] that characterize each of the fourteen skill sub-categories that we pre-defined in Chapter 1. These keywords and expressions are the unique skills of which the sub-categories consist of. Our model consists of around 800 unique skills.[2] We consider a skill sub-category as present in the vacancy or job spell of applicants whenever we identify at least one of its associated keywords or expressions (i.e., unique skill) or a pertinent synonym of one of its keywords. By using various keywords and expressions to define each subcategory, we are considering that each skill can be expressed in multiple ways. We carry out this process through Natural Language Processing (NLP) methods using Python. We also coded related variables capturing how many times a relevant unique skill (keyword/expression) appears in the data, as a proxy for skill intensity. We now describe the details of this method and then evaluate its performance in terms of the share of vacancies and applicants’ employment spells that the model could classify.

To begin with, we decided to rely on the open text-variables from the BuscoJobs data. Specifically, for the vacancy data we use the job title and vacancy description, while for the applicants’ data we focus on the applicants’ description of each job spell. Compared to other variables in the database, the open-text variables contain the most detailed information on the skills demanded and supplied. These variables are available for almost all vacancies (99.9 per cent) and most of the applicants’ job spells (68.5 per cent), in a functional manner as they contain a comparatively low share of missing values or meaningless observations (e.g., those with single letters and characters instead of meaningful text description). In addition, these variables are typically present in similar data sources and thus will allow for replicating our methodology using different databases.

Since we rely on free-text descriptions, we then organized the text variables in a way that allows computers to read, understand and process them. This NLP method works through machine learning (ML) techniques, storing words and the ways in which these words are combined in logical sequences. To do this, we processed our data, with the aim of distilling cleaned and useful pieces of information that facilitate the mapping between the skills taxonomy and BuscoJobs data in an efficient way:

- Translation of keywords and expressions: As explained in Chapter 1, our skills taxonomy initially defines fourteen sub-categories through keywords/expressions that we identified from the existing literature on skills dynamics. Since our online data are in Spanish, we first translated the keywords/expressions from English. Appendix Table A2 provides this translation.

- Text normalization: As is typical for data analyses of this kind (see, e.g., Gentzkow, Shapiro, and Taddy 2019), we normalized the text through text-mining techniques, mainly using the Natural Language Toolkit (NLTK)[3] library in Python. This step was similarly performed for the relevant variables in the BuscoJobs data and for the list of keywords/expressions in the skills taxonomy. It included: (1) lowercasing of capital letters; (2) “unidecoding” to simplify characters and delete accents that are used in Spanish (for example, changing “á” to “a”); (3) eliminating all characters that are not actual text (for example, ”NaN” or “Null” values, which are the common representation of missing data in Python); (4) eliminating stop words, which are commonly used prepositions and conjunctions that do not provide useful information by themselves (for example, the Spanish equivalents of “the”, “a”, “of”, and “in”)[4]; and (5) eliminating other concepts that do not add value, such as names of months, days, cities and countries and single letters.

- Extended keywords and expressions in the skills taxonomy: So far, our pre-defined taxonomy does not consider that words can be expressed in multiple ways, while referring to the same concepts (e.g., when referring to “collaboration”, this could be done through the noun or through the verb “collaborate”). To account for this, we relied on the process of “stemming”, which reduces the words to their roots. This is equivalent to extending the set of words so they capture their various forms of expression (i.e., as a noun or verb, feminine or masculine forms, singular and plural forms, etc.). To give one example, the Spanish word “estadística” (i.e., “statistics”) has the root “estadistic”, which pertains to the various words capturing this concept, such as “estadística”, “estadístico”, “estadísticas”, “estadísticos.” The words stemmed can be either keywords or words that are part of an expression.

- Tokenization of the skills taxonomy: We tokenized the text information, moving from free-text format to a vector model. Tokenization yields a segmentation of texts into single words (so-called tokens) or phrases (i.e., a combination of several words, called n-grams, where “n>1” denotes the number of words included). The tokens and n-grams are then arranged as separated elements in a long list. To give an example, we had initially included in our skills taxonomy the expression “predictive analytics” as one of the terms characterizing “cognitive skills (narrow sense)”. We manually identify and consider other (Spanish) versions of the same concept, such as "Análisis Predictivo de Datos" or – after applying the text-normalization described before – "analisis predictivo datos". Once we carry out the tokenization, a total of seven elements are added to our list: the tokens "analisis", "predictivo" and "datos"; the 2-grams "analisis predictivo", "analisis datos" and "predictivo datos"; and the original 3-gram "analisis predictivo datos". In this way, we give our taxonomy greater power to capture relevant concepts within the free-text descriptions of vacancies and applicants’ job spells. The tokenization is carried out using the NLTK library but requires a manual revision of the results. For example, for “programming language” (included in the skills sub-category of “software (specific) skills and technical support”) we kept the token “programming” but deleted the token “language”, as the latter does not pertain to software skills and would have induced an error in the classification. The number of keywords/expressions in our taxonomy was still small enough to allow for such manual processing within a reasonable amount of time.

- Tokenization of the vacancy and applicants’ data: We similarly tokenized the relevant BuscoJobs variables, using an automated process based on the NLTK library, to distil all possible text combinations from the data. We imposed two restrictions on this automated process: (1) We identified all possible combinations of tokens, 2-grams, and 3-grams, but neglected combinations of four and more words. We imposed this restriction as our taxonomy includes normalized expressions with a maximum length of three words (such as the above example of "analisis predictivo datos"). (2) We kept the order of words as they appear in the original text description, as otherwise the task would not be manageable for a regular server to process.

With the text organized, we proceeded to create the skills variables:

- Initial variable creation: For the creation of the skills variables, we coded indicator variables for each of the fourteen sub-categories of skills, which take the value of one whenever a relevant token or n-gram was identified in the BuscoJobs data. We also coded related variables capturing how many times a relevant keyword/expression appeared in the data, as a proxy for skill intensity. Here, we did not count repetitions of the same keyword/expression, but considered each keyword/expression only once per observation.

- Refined variable creation using synonyms: We further expanded the initial list of keywords by also accounting for their synonyms. For this, we used an automated webscrapping method that targeted the website www.wordreference.com and recorded, for each initial keyword from our taxonomy, direct, or first-order, synonyms.[5] Once we had identified these additional keywords, we again performed the steps above with the extended keyword list (mainly the “stemming” process) and re-coded the skills variables. As we show in the section below, this method significantly increases the probability of identifying skills in the data.

- Manual correction: We scrutinized all synonyms manually and excluded a few, whose meaning would have caused misleading classifications. This was especially true for some synonyms in the manual skills category, which might have mistakenly captured managerial activities. For example, “solucionar” is most relevant in the context of finding solutions but was identified as a synonym of “reparar” (“to repair”). For a similar reason, we manually changed “controlar” (“to control”) to “controlar máquinas”, “controlar aparatos”, and “controlar artefactos” (“control machines” etc.). We also added relevant synonyms that we identified based on our work with the BuscoJobs data and previous knowledge, including accountant software programmes that frequently appear in vacancy texts and are relevant for capturing financial skills. To facilitate possible replications of our methodology, we provide the full set of initial keywords and additional synonyms in Table A3 in the Appendix.

3.2 Evaluation of the variable coding

Based on the number of observations (vacancies and applicants’ job spells) we could classify and the corresponding number of skills per observation (Table 2), we are satisfied with the performance of our text-mining approach.[6] Using only the initial keywords and expressions and neglecting for the time being the number of times keywords/expressions appear in the data (i.e., the proxy for capturing the intensity of skills), we assigned on average 0.88 of our 14 skills sub-categories to each applicant-job spell observation (column (1) in Table 2), while the same average was 2.53 in the case of the vacancy data (column (3)). This is associated with the fact that 47.0 per cent of the applicants’ observations cannot be assigned any skill sub-category, whereas the same is true for 13.8 per cent of vacancies (columns (1) and (3)). Once we additionally consider synonyms, we assign an average of 1.47 skills sub-categories to the applicants’ job spells and 3.90 skills sub-categories to the vacancy data (columns (2) and (4)). The use of synonyms significantly reduces the number of applicants’ observations that cannot be assigned any skill to 35.9 per cent, and in the case of vacancies to only 5.7 per cent. This means that we can classify a meaningful number of skills, especially considering the substantial heterogeneity in the quality of information available in the free-text descriptions we use to code the skills variables (in particular, the self-reported text from applicants does not follow any standardized format). This gives hope to the possibility of replicating our methodology in similar data sources.

In addition, Table 2 allows drawing two central conclusions. First, the text-mining model performs significantly better when using a combination of keywords and synonyms to capture skills. This implies that the use of synonyms is a decisive step for implementing a skills taxonomy in online labour intermediation data. Second, the vacancy data tends to be richer than the applicants’ job spells in terms of the information provided that can be used to capture skills. For example, 6.3 per cent of vacancies are assigned eight or more skills sub-categories when using keywords and synonyms, compared with zero per cent in the applicants’ data. We note, however, that the statistics on applicants refer to a given employment spell. Once we aggregate skills over workers’ employment biographies, the average number of skills sub-categories increases to 2.45 (standard deviation of 2.51) per person (for the method that is based on keywords and their synonyms). Also, the share of persons without any assigned skill sub-category is only 24.6 per cent, compared to 35.9 per cent of all employment spells that cannot be classified.

Table 2. Evaluation of the success of the empirical implementation, all years

Applicants' data

Vacancy data

(1)

(2)

(3)

(4)

Average number of assigned skills sub-categories

0.88

1.47

2.53

3.90

(standard deviation)

(1.12)

(1.66)

(1.81)

(2.51)

Share with 0 skills

0.470

0.359

0.138

0.057

Share with 1 skill

0.322

0.268

0.191

0.105

Share with 2 skills

0.124

0.156

0.200

0.131

Share with 3 skills

0.049

0.100

0.181

0.157

Share with 4 skills

0.021

0.056

0.143

0.159

Share with 5 skills

0.009

0.030

0.082

0.142

Share with 6 skills

0.004

0.017

0.043

0.113

Share with 7 skills

0.001

0.009

0.016

0.073

Share with 8+ skills

0.000

0.006

0.007

0.063

N

843,761

843,761

87,019

87,019

Notes: Columns (1) and (3) refer to the skills sub-categories coded using the initial keywords and expressions, whereas columns (2) and (4) pertain to those coded also considering synonyms; see Chapter 1 for the definition of the 14 skills sub-categories (i.e., each skills sub-category is equal to one, whenever at least one unique skill/descriptive word is present in the data, and equal to zero otherwise). For the applicants’ data, the level of observation is at the applicant-job spell level. The vacancy data refer to individual postings.

Why can some observations not be classified? Despite the overall success of our text-mining model, we find that a notable share of observations, particularly in the applicants’ data, does not have any associated skill sub-category. A lack of sufficient text description drives this pattern. The unclassified employment spells have only 5.5 words on average (i.e., with many not containing any meaningful description) whereas the number of words significantly increases for employment spells with one or several mapped skills sub-categories (see Table 3). This may in part be related to the fact that some applicants include descriptions that are largely self-explanatory. For example, there are observations that only include the description “biology teacher”, presumably because the tasks and skills of a biology teacher are considered as being common knowledge.

Table 3. Correlation between the number of identified skills sub-categories and the number of words available in the text descriptions, all years

Applicants' data

Vacancy data

Number of

words

(mean)

Words per skill sub-category

Number of

words

(mean)

Words per skill sub-category

Number of assigned skills sub-categories

(1)

(2)

(3)

(4)

0

5.5

21.0

1

9.9

9.9

30.1

30.1

2

18.5

9.3

41.0

20.5

3

31.0

10.3

54.5

18.2

4

46.5

11.6

66.6

16.7

5

66.3

13.3

79.5

15.9

6

91.0

15.2

95.0

15.8

7

121.5

17.4

111.9

16.0

8

161.8

20.2

145.2

18.2

9

223.0

24.8

184.7

20.5

10

456.0

45.6

275.8

27.6

11

333.0

30.3

-

-

12

-

-

524.0

43.7

Notes: Columns (1) and (3) refer to the mean number of words needed to identify the given number of skills sub-categories for the applicants’ job spells and vacancies, respectively. Columns (2) and (4) display the mean number of words per skill sub-category (i.e., with indicator variables equal to one). The results are based on the initial keywords and expressions, while neglecting the synonyms. The conclusions do not change when including also the synonyms.

Figure 8 further highlights the importance of including synonyms in the variable-coding process. By definition, the number of classified observations increases for each of the fourteen sub-categories. Interestingly, this effect is not uniform across categories. In the case of the applicants’ data, synonyms make a substantial difference for capturing cognitive skills (narrow sense), social skills, people management skills, and finger-dexterity skills. For the vacancy data, synonyms are additionally decisive for significantly increasing the number of identified character skills. Moreover, cognitive skills (narrow sense), customer service skills, people management skills, social skills, and finger-dexterity skills are the five categories that appear most often in the applicants’ data (i.e., looking at the variable that accounts for synonyms). These sub-categories, likewise, play the greatest role for the vacancy data, although there are some differences in the ordering. In addition, character skills, computer skills and software skills feature prominently in the vacancy data, but this trend is less obvious in the applicants’ data.

Figure 8. Skills distribution for applicants’ and vacancy data, comparing the initial keyword approach and the extended approach relying on keywords and synonyms, all years

1. Applicants’ data

2. Vacancy data

Notes: The figure displays the frequency with which the fourteen skills sub-categories appear in the applicants’ data (panel (a)) and the vacancy data (panel (b)), comparing the approach that relies on initial keywords and expressions (blue bars) with the approach that also exploits synonyms (orange bars).

Despite the success of NLP methods when extracting information about skills from the vacancies and applicants’ job spells and creating our skills variables, complementing this effort with a prediction model could have potential. However, a prediction model requires that a proportion of the data are classified using an external source of categorization (i.e., one that is not linked to our taxonomy or its implementation). In this way, the already classified part of the data can serve as a benchmark to train the prediction model against which the results obtained from our coding of the variables can be compared to. For example, this benchmark can be a classification carried out by a group of experts, based on clearly defined criteria and a review process of the classification results. Additionally, this benchmark must be long and varied enough to “teach” the computer and train the model properly, thus making it a demanding exercise. The lack of such a benchmark for skills variables currently prevents us from employing a prediction model. In the future, we will explore whether it might be possible to use a benchmark classification from O-NET Uruguay, which is currently in its pilot phase.

We already followed a similar approach for coding two-digit ISCO 08-occupations for both the vacancy and job applicants’ data. The same text variables used to classify skills were exploited and the pre-processing of the variables followed the same stages described above, namely, translation, text normalization, stemming, and tokenization. However, we took advantage of the existence of an external classification of occupations (carried out by BuscoJobs) to execute two classification processes using machine-learning techniques: (i) a classification based on text-mining techniques and NPL, similar to that carried out in our classification of skills; and (ii) a classification based on a predictive model, using as benchmark the existing classification carried out by BuscoJobs.

As part of process (i), we created a dictionary of keywords and expressions with high frequency using the free texts of both the vacancies and applicants’ job-spells variables, to which the corresponding ISCO-08 code was assigned. In specific situations, this required manual classification. Then, this dictionary was paired with the pre-processed texts achieving the classification based on the text matches. Regarding process (ii), we observed approximately 5,000 observations already classified at the four-digit level by BuscoJobs in each of the databases. Based on this and process (i), we trained a predictive model. After several tests, we concluded that a two-stage model would be the best approach. In the first stage, the model was able to predict the ISCO classification at the one-digit level, and the second stage was used for the classification model at the two-digit ISCO level. The strategy for training the model also included various steps: Reducing the databases to those observations when the dictionary-based model differed from the BuscoJobs’ classification; separating this subset into a training sample (two thirds of the data) and a test sample (one third); and searching for the best combination of model and parameters according to the results. Ultimately, we tested three models to process the text columns: Random Forest, Support Vector Machine, and Gradient Boosting. Through a comprehensive testing strategy of different combinations of hyperparameters and various cross-validations for each combination, we chose Gradient Boosting to code the one- and two-digit ISCO 08-occupations in the vacancy data and Random Forest for the applicants’ data.

3.3 Relevance of source types and comparison to O-NET-based results

We now investigate the usefulness of having combined a comprehensive set of studies and sources to devise our initial skills taxonomy (see Chapter 1). Alternatively, researchers could merely rely on one or several of the prominent studies using online vacancy data. Table 4 indicates that most identified keywords and expressions indeed stem from such studies (72.3 per cent for the vacancy data and 60.6 per cent for the applicants’ data).[7] Yet, supplementary keywords and expressions from non-online data sources play a meaningful role, accounting for 17.7 per cent of identified unique skills in vacancies and 30.6 per cent in applicants’ job spells. The final source of O-NET Uruguay adds an additional 10.0 per cent for vacancies and 8.8 per cent for applicants. These latter two sources as thus important for capturing a broader set of skills. In particular, the two latter sources allow us to capture manual skills, which continue to be comparatively more important outside of Europe and the United States. Overall, this confirms the usefulness of our approach of combining a comprehensive set of different, seminal sources.

Table 4. Number of identified keywords/expressions in the vacancy and applicants’ data, attributable to different types of sources (absolute and % for all years)

Unique skills (keywords/expressions) captured

(1)

Source type 1:

Online data

(2)

Source type 2:

Non-online data

(3)

Source type 3:

O-NET Uruguay

(3)

Vacancies

372,879

269,603

65,853

37,411

(72.31%)

(17.66%)

(10.03%)

Applicants

1,065,305

645,809

325,416

94,080

(60.62%)

(30.55%)

(8.83%)

Notes: The table displays the number of keywords/expressions identified across vacancies and applicants’ job spells and how these are attributable to different types of sources. For each vacancy or applicants’ job spell, we only consider unique skills (i.e., a keyword/expression could appear multiple times but is considered only once per observation). Moreover, we only consider initial keywords/expressions, and neglect synonyms, as these are less straight-forward to attribute to source types. Source types are the following: Type 1 refers to studies based on online-data, namely DK (2018), DN (2020), HK (2012), KBHT (2016). Type 2 refers to non-online based data, namely ALM (2003), S-O (2006), APST (2020). Type 3 refers to O-NET Uruguay, which we have used as a supplementary source. See Chapter 1 and Table 1 for more details.

We also assess how our approach compares to one that would have relied on imputing US O-NET data at the occupational level. For this purpose, we map O-NET skills categories to our taxonomy. For both the O-NET and the BuscoJobs applicants’ data, we then compute scores that capture the relevance of cognitive, socioemotional and manual skills at the one-digit occupational level, normalized to sum up to 100. This comparison yields clear differences between the country-specific BuscoJobs results and the US-based O-NET results. Across occupations, manual skills matter comparatively little according to the data from the United States. In contrast, manual skills play a larger role in the Uruguayan data, consistent with the expectation that manual skills matter more outside of high-income economies (Figure 9).[8] This is especially the case for plant and machine operators and assemblers, elementary occupations, and crafts and related trades workers. Accordingly, these occupations have lower scores in the socioemotional skills category and, especially, the cognitive skills category.

A potential concern with this comparison is that the O-NET data are representative for occupations in the United States, whereas we documented in Chapter 2 that the BuscoJobs data are not representative for the Uruguayan labour market. Possibly, the comparison in Figure 9 is partly shaped by differences in the population covered. Yet even when we consider the example of clerical support workers, for which the BuscoJobs data have a particularly high coverage, there are discrepancies between the O-NET and BuscoJobs results. The Uruguayan data suggest a less important role for cognitive skills, while emphasizing manual skills more. Consistent with our general motivation for this study and previous findings in the literature (Lewandowski et al. 2019; Lewandowski, Park, and Schotte 2020), these results confirm the importance of employing country-specific data when assessing skills dynamics outside of Europe and the United States.

Figure 9. Relative importance of cognitive, socioemotional and manual skills at the one-digit occupational level, comparing O-NET data and BuscoJobs applicant data (2019)

1. Cognitive skills

2. Socioemotional skills

100. Manual skills

Notes: We focus on 2019 as this represents the most recent year prior to any distortions induced by the COVID-19 pandemic. For the BuscoJobs analysis, we focus on the applicants’ data, transformed to an annual panel. Across applicants’ job spells, we sum up the number of relevant keywords and expressions (including synonyms) identified in the data, per broad skill category and one-digit ISCO-08 occupation; expressed relative to the total number of unique keywords and expressions, including synonyms, that define each broad skill category. We then normalize the resulting scores for the three broad skill levels, such that their sum equals 100. The O-NET results were obtained by first mapping O-NET skills to the 14 skill sub-categories used in this paper. We rely on the O-NET database 24.1, where SOC 2010 codes were mapped to ISCO-08 four-digits using the crosswalk of the Bureau of Labour Statistics. The O-NET importance and level scores were standardized to a scale ranging from zero to 100. The data was then aggregated to ISCO-08 occupations for each skill by taking a simple average of standardized importance and level scores. Finally, a composite score was computed by taking the product between the average standardized importance and level scores.

[1]  “Keywords” refers to one-word concepts whereas “expressions” refers to concepts with more than one word.

[2]  These unique keywords and expressions include synonyms. Unlike other studies using web scrapping platforms such as BurningGlass (e.g., Deming and Noray 2020; Deming and Kahn 2018; Hershbein and Kahn 2018), the unique skills in our case originate from the conceptual taxonomy developed in Chapter 1, rather than from the data itself. In contrast, BurningGlass, for example, uses machine-learning techniques to search for skills requirements posted in vacancies, then collects all requirements into a dictionary of "unique skill requirements", which authors can use for their own categorizations (Deming and Kahn 2018, n. 6).

[3]  See https://www.nltk.org/ (last accessed 1 December 2021).

[4]  The choice of these stop words also stems from the NLTK library.

[5]  This automated search for keywords only pertains to one-word keywords. In contrast, the method cannot capture more complex semantic concepts (website last accessed on 1 December 2021).

[6]  Note that these statistics refer to those observations for which it was feasible to code skills variables, since they contained non-missing text information. For the vacancies, this was almost always the case, while for the applicants’ job spells, we had to drop around 30 per cent of employment spells that lacked actual text.

[7]  By construction, we expect source type 1 to be the most relevant. This source type was our starting point for devising the taxonomy in Chapter 1, given the similarity in underlying data. The other two source types were instead used as complementary sources, where we included concepts that source type 1 had not yet captured.

[8]  As explained in Chapter 2.1, we made sure that this is not an artefact of the synonyms included, by manually excluding those synonyms that would have mistakenly assigned manual skills to managerial and related activities.

Conclusions

Many countries outside of Europe and the United States currently lack longitudinal data on skills, despite the importance of the topic for policy makers and academic debates. We assess whether data from online job vacancies and applicants’ profiles, which are increasingly becoming available, can be a suitable source for studying skills dynamics. Based on the literature from the social sciences, in particular from labour economics and psychology, we derive a skills taxonomy that is comprehensive but succinct as well as applicable to individual country-contexts and to online data. The taxonomy consists of the three broad categories of cognitive, socioemotional, and manual skills as well as fourteen more detailed sub-categories, which are defined in terms of keywords and expressions. Based on natural language processing techniques, we then implement the taxonomy, exploiting data from the Uruguayan job board BuscoJobs. We are able to classify skills requirements and skills that applicants possess for a large number of job vacancies and applicants’ employment spells (94 and 64 per cent, respectively). We consider this a success going beyond our initial expectations, especially when taking into account that the implementation is based on free-text descriptions that do not necessarily follow a standardized format.

We conclude that data from online job vacancies and applicants’ profiles are a promising source for analyzing skills dynamics, including in countries where job boards and job aggregators do not have a long tradition. This is a relevant finding, given that these data capture country-specific developments, are available in many countries, and entail granular and longitudinal information, often for both labour demand and supply. We also analyze and discuss that such data are not fully representative of countries’ labour forces, which might require weighting techniques and/or an analytical focus on selected labour market segments. The BuscoJobs data are no exception to this trend. Yet, contrarily to what one might have expected ex ante, the data capture in meaningful ways intermediate and even lower educational levels of jobs and jobseekers in addition to highly qualified labour. Moreover, representativeness biases appear not to fluctuate substantially across time. Most importantly, BuscoJobs and similar sources of data allow studying skills dynamics in countries where this would otherwise not be possible given the current state of alternative data sources available.

This conceptual and methodological effort, the first carried out outside Europe and the United States, opens new doors for future research on skills dynamics. Such future research may address empirical questions related to the role of skills in fostering transitions to better jobs and in increasing the resilience of firms and individuals when facing global transformations that affect labour markets. Moreover, one could now study the skills composition of occupations and the within-occupational skills change at the national level, to either understand trends, evaluate the impact of shocks and regulations, or to test for the widespread use of other classifications of skills inspired from high-income countries.

Annex

Table A1. Comparison of the industrial distribution in 2020, BuscoJobs vacancies versus household survey data

Share

BJ vacancies

(1)

Share

HS data

(2)

A - Agriculture, forestry and fishing

NA

8.01

B - Mining and quarrying

0.05

0.15

C – Manufacturing

10.19

10.28

D - Electricity, gas, steam and air conditioning supply

0.02

0.46

E - Water supply; sewerage, waste management and remediation activities

0.09

0.65

F – Construction

4.04

6.69

G - Wholesale and retail trade; repair of motor vehicles and motorcycles

28.70

17.07

H - Transportation and storage

1.60

5.17

I - Accommodation and food service activities

1.74

3.44

J - Information and communication

7.13

2.45

K - Financial and insurance activities

2.13

1.74

L - Real estate activities

1.61

0.57

M - Professional, scientific and technical activities

11.97

4.07

N - Administrative and support service activities

23.71

4.98

O - Public administration and defence; compulsory social security

NA

7.60

P – Education

1.48

7.12

Q - Human health and social work activities

3.84

8.96

R - Arts, entertainment and recreation

0.78

1.62

S - Other service activities

0.93

3.18

T - Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use

NA

5.69

U - Activities of extraterritorial organizations and bodies

NA

0.09

X - Not elsewhere classified

NA

0.01

Notes: The table shows the industry distribution for the BuscoJobs vacancy data (column (1)) in comparison to overall employment, as captured in the household survey (column (2)), in 2020. Industries are classified according to the one-digit level of ISIC Revision 4.

Table A2. Dictionary of initial keywords and expressions, per skill sub-category (in Spanish)

Skill sub-category

Initial keywords/expressions

HABILIDADES COGNITIVAS (SENTIDO ESTRICTO)

resolver problemas, investigacion, analisis, pensamiento critico, matematica, estadistica, matematica, adaptabilidad, direccion, control, planificacion, analisis datos, ingenieria datos, modelamiento datos, visualizacion datos, mineria datos, ciencia datos, analisis predictivo, modelos predictivos, analizar, disenar, reglas diseno, evaluacion, interpretacion, calculo, contabilidad, corregir, medicion, procesamiento informacion, toma decisiones, generacion ideas, memoria

HABILIDADES COMPUTACIONALES (GENERALES)

computadora, hojas calculo, programa, software, excel, powerpoint, internet, word, outlook, office, windows

HABILIDADES COMPUTACIONALES (ESPECÍFICAS)

lenguaje programacion, programacion, java, sql, python, instalacion de computadoras, reparacion de computadoras, mantenimiento computadoras, desarrollo web, diseno web

HABILIDADES DE APRENDIZAJE MAQUINAL E INTELIGENCIA ARTIFICIAL

inteligencia artificial, artificial intelligence, aprendizaje maquinal, machine learning, arboles de decision, apache hadoop, redes bayesianas, automatizacion, redes neuronales, support vector machines, svm, tensorflow, mapreduce, splunk, convolutional neural network, analisis cluster

HABILIDADES FINANCIERAS

presupuesto, contabilidad, finanzas, costos

HABILIDADES DE ESCRITURA

escribir, editar, reportes, propuestas

HABILIDADES DE ADMINISTRACIÓN DE PROYECTOS

administracion proyectos

HABILIDADES DE CARÁCTER

organizado, detallista, multitarea, puntual, energico, iniciativa propia, motivado, competente, diligente, esforzado, confiable, puntual, resistente estres, creativo, independiente

HABILIDADES SOCIALES

comunicacion, trabajo equipo, colaboracion, negociacion, presentacion, equipo, persuasion, escucha, flexibilidad, empatia, asertividad, consejo, entretener, lobby, ensenar, interaccion, habilidades verbales

HABILIDADES DE GESTION DE PERSONAL

supervision, liderazgo, gestion, mentoria, staff, supervision equipo, desarrollo equipo, gestion desempeno, gestion personas

HABILIDADES DE SERVICIO AL CLIENTE

cliente, venta, paciente, persuadir, vender, publicitar, vender, comprar, pagar, servicio cliente

HABILIDADES DE DESTREZA CON LOS DEDOS

recoleccion, clasificacion, ensamblaje, mezclar ingredientes, hornear, costura, corte, maquina tabulacion, empaque productos agicola, controlar maquinas, controlar aparatos, controlar artefactos, equipar, operar, movimientos repetitivos

HABILIDADES DE COORDINACION OJO-MANO-PIE

atender ganado, atender animales, conducir transporte pasajeros, conducir transporte carga, pilotar aviones, podar arboles, gimnasia, deporte equilibrio, acomodar, reparar, renovar, restaurar, servir, limpiar, reaccionar tiempo, manipulacion fina

HABILIDADES FÍSICAS

resistencia, caminar, correr, cargar peso

Notes: The keywords and expressions correspond to those introduced in Chapter 1.

Table A3. Dictionary of synonyms for keywords (in Spanish)

resolver

solucionar, aclarar, averiguar, descifrar, solventar

investigacion

exploracion, indagacion, averiguacion, busqueda, encuesta, pesquisa, sondeo

analisis

estudio, examen, observacion, comparacion, particion, separacion, distincion

matematico

exacto, cabal, preciso, justo, riguroso, automatico

estadistico

catastral, censual, demografico, descriptivo

adaptabilidad

ductilidad, elasticidad

direccion

gobierno, mando, jefatura, administracion, directivo, gerencia

control

inspeccion, observacion, examen, comprobacion, registro

planificacion

proyecto

ingenio

genio, inteligencia, listeza, talento, perspicacia, capacidad, seso, lucidez, razon

ciencia

sabiduria, sapiencia, conocimiento, erudicion

analizar

examinar, estudiar, observar, averiguar, comparar, considerar, descomponer, detallar, distinguir, individualizar, separar

disenar

proyectar, trazar, esbozar, esquematizar, abocetar, delinear, plantear

evaluacion

valoracion, tasacion, peritaje, estimacion, apreciacion

interpretacion

comentario, explicacion, analisis, apreciacion, lectura, glosa, definicion, conclusion, deduccion, entendimiento, exegesis

calculo

computo

contabilidad

administracion, tesoreria, caja

corregir

enmendar, subsanar, reformar, rehacer, modificar, retocar, perfeccionar

medicion

medida, evaluacion, calculo, sondeo

procesamiento

proceso

decision

determinacion, resolucion

idea

representacion, sensacion, percepcion, imaginacion, ilusion, pensamiento, juicio, comprension, conocimiento, concepto, nocion, reflexion, designio, arquetipo, modelo

memoria

recuerdo, evocacion, retentivo, rememoracion, mencion, conmemoracion

programa

exposicion, plan, planteamiento, proyecto, sistema, linea, conducto, programacion, esquema, borrador, boceto, bosquejo, anuncio, aviso

programacion

programa

computadora

ordenador, calculadora, procesador, electronico

presupuesto

calculo, computo, estimacion, evaluacion, partida, fondo, coste, determinacion

finanzas

negocio, economia, dinero, inversion, hacienda, capital

costos

coste, precio, importe, gasto, tarifa

*

softland, erp, sap, xubio, wave, cloudbooks, nubox, bloomberg, anfix

escribir

transcribir, manuscribir, copiar, anotar, firmar, rubricar, autografiar, trazar, caligrafiar, mecanografiar, taquigrafiar

editar

publicar, imprimir, difundir, reproducir, reimprimir

reportar

contener, refrenar, frenar, aplacar, apaciguar, calmar, sosegar

propuesta

proposicion

organizado

organico, estructurado, sistematizado, planeado, ideado

puntual

regular, exacto, preciso, formal, metodico, escrupuloso, diligente, rapido

energico

activo, decidido, resuelto, firme, eficaz, eficiente, emprendedor, dinamico, intenso, poderoso, tenaz, vigoroso, fuerte, concluyente, autoritario

iniciativa

decision, dinamismo, imaginacion, idea, adelanto, advenimiento, delantera, iniciacion, proyecto

motivado

originar, causar, promover, producir

competente

capacitado, cualificado, apto, idoneo, entendido, experto, diestro, capaz, especialista, eficiente, eficaz, habil, preparado

diligente

rapido, activo, agil, presto, resuelto, solicito, vivo, inquieto, expeditivo, listo

esforzado

animoso, atrevido, bizarro, valiente, luchador, ardoroso, brioso, afanoso

independiente

individualista, autosuficiente, liberado, emancipado, libre, autogobernado, autonomo, autonomico, alejado, aislado, neutral, autarquico, imparcial

comunicacion

comunicado, mensaje, oficio, nota, misiva, escrito, telegrama, circular, aviso, saludo, notificacion

equipo

conjunto, agrupacion, grupo, personal, cuadrilla, brigado, pandilla, camarillo

colaboracion

cooperacion, asistencia, auxilio, ayuda, contribucion

negociacion

convenio, pacto, tratar, concierto, tratado

presentacion

mostrar, manifestacion, exhibicion, exposicion, aparicion

persuasion

argumentacion, convencimiento, atraccion, seduccion, incitacion, sugestion

escuchar

atender, percibir, enterar

flexibilidad

ductilidad, elasticidad, maleabilidad, cimbreo, plasticidad

consejo

recomendacion, sugerenciar, advertencia, aviso, exhortacion, asesoramiento, indicacion, invitacion, observacion, opinion, parecer

entretener

distraer, divertir, agradar, amenizar, animar, recrear, alegrar, deleitar, aliviar

ensenar

instruir, adiestrar, educar, criar, adoctrinar, ilustrar, alfabetizar, catequizar, iniciar, explicar, aleccionar, preparar

supervision

inspeccion, control, revision, verificacion, vigilancia

gestion

tramite, diligencia, papeleo, mandato, encargo, mision, cometido

desempeno

desembargo, rescate, recuperacion, descargo

persona

individuo, sujeto, semejante

cliente

parroquiano, asiduo, comprador, consumidor, usuario

venta

enajenacion, transaccion, cesion, oferta, reventar, negocio, adjudicacion, saldo, comercio, despacho, exportacion

paciente

tolerante, sosegado, calmoso, tranquilo, estoico, resignado, sufrido, enfermo, flematico, manso

persuadir

convencer, inducir, mover, seducir, fascinar, impresionar, atraer, inclinar, incitar, arrastrar, impulsar

vender

traspasar, enajenar, expender, despachar, subastar, saldar, liquidar, exportar

comprar

adquirir, obtener, mercar, comerciar, traficar, negociar, chalanear, comerciar

pagar

abonar, remunerar, sufragar, apoquinar, retribuir, reembolsar, cotizar, desembolsar, compensar, recompensar, gratificar, costear, reintegrar, cancelar, liquidar

servicio

encargo, prestacion, asistencia, actuacion, destino, funcion, mision, oficio, ocupacion, favor, ayuda, auxilio

recoleccion

cosecha, siega, vendimia, acopio, acumulacion

clasificacion

ordenacion, separacion, distribucion

ensamblaje

ensambladura

mezclar

revolver, agitar, aunar, diluir, barajar, enredar

ingrediente

componente, remedio

hornear

gratinar, tostar, dorar, asar, brasear, calentar, cocer, preparar

costura

cosido, zurcido, calado, embaste, encaje, hilar, pespunte, vainica, bordado, dobladillo, cadeneto, sutura

corte

tajo, cortadura, incision, hendidura, herido, amputacion, tajadurar, cisura, tijeretado

empaque

producto

articulo, fruto, manufactura, genero, elaboracion, resultado, obra

equipar

abastecer, proveer, dotar, aprovisionar, surtir, suministrar, vestir

operar

actuar, ejecutar, obrar, elaborar, ejercitar, manipular, efectuar

ganado

ganaderia, reses, animal, rebano, manada, hato, vacado, yeguada

transportar, acarrear, trasladar, canalizar, encauzar

transporte

acarreo, traslado, porte, traslacion, carga, mudanzar, pasaje, transito, transbordo

carga

fardo, bulto, embalaje, lastre

pilotar

navegar

podar

cortar, talar, limpiar, desmochar, cercenar, escamondar, mondar

gimnasia

ejercicio, atletismo, deporte, entrenamiento, acrobacia, ejercitacion

deporte

ejercicio, gimnasia

equilibrio

acomodar

reparar

recomponer, restaurar, arreglar, remendar

renovar

restaurar, reconstruir, sustituir

restaurar

reparar, recomponer, renovar

servir

limpiar

asear, adecentar, acicalar, higienizar, desinfectar, lavar, fregar, barrer, banar, duchar, enjuagar, humedecer, mojar, rociar, quitar, deshollinar, lustrar, abrillantar, pulir, frotar

manipulacion

fabricacion

resistencia

aguante, vigor, vitalidad, fuerza, energia, fortaleza, entereza, potencia

caminar

andar, pasear, trotar, vagar, trasladar, deambular, transitar

correr

trotar, galopar

cargar

embarcar, abarrotar, lastrar, colmar, estibar, transportar, acarrear

Notes: See Chapter 3.1 for details on how the synonyms were obtained. The field marked by a * was added manually to capture software programs typically used by accountants, which are categorized under financial skills. Moreover, we manually went through all synonyms and took out those that capture other meanings than the initial keyword and underlying concept. For example, a synonym of “to transport” (“transporte”) is “conducir”, which can capture a supervisory activity where someone is leading a team. This explains why some cells are empty and others have fewer synonyms than would be identified when no manual correction is done.

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Acknowledgments

This article was written as part of a collaboration between the “Labour Market Trends and Policy Evaluation Unit” of the ILO’s Research Department and the Work Area “Skills Strategies for Future Labour Markets” in the ILO’s Employment Department. We are grateful to Johannes Brehm, Angela Doku, Joana Duran-Franch and Henry Stemmler for excellent assistance in cleaning and processing the original BuscoJobs data and to Marcos Aguiar, Diego Alanis and Jorge Eguren for providing the data and answering detailed questions about them. Sergio Herrera and Javiera Lobos contributed to creating occupational variables, while Lucas Ng kindly shared O-NET-based statistics with us. Thanks, are also due to Janine Berg, David Deming, Cornelius Gregg, Piotr Lewandowski, Clemente Pignatti, Olga Strietska-Ilina, Michael Stops, and Bolormaa Tumurchudur Klok for valuable comments and suggestions; and to Gonzalo Graña and Fernando Vargas (ILO CINTERFOR) for important country-specific feedback. The responsibility for opinions expressed in this article rests solely with its authors, and publication does not constitute an endorsement by the International Labour Office of the opinions expressed in it

1. Online Vacancy

1. Abstract*

1. Abstract

2. About the Authors*

1. About the authors

3. Introduction*

1. Introduction

4. Chapter 01*-modified

1. Assessing skills outside of Europe and the United States: A skills taxonomy for research purposes

5. Chapter 02*

1. Data and descriptive statistics

1. 2.1 Coverage and applicability of applicants’ and vacancy data to identify and measure skills variables

2. 2.2 BuscoJobs applicants’ data

3. 2.3 BuscoJobs vacancy data

6. Chapter 03*

1. Empirical implementation of the skills taxonomy

1. 3.1 Text-mining model

2. 3.2 Evaluation of the variable coding

3. 3.3 Relevance of source types and comparison to O-NET-based results

7. Conclusion

1. Conclusions

8. Annex

1. Annex

9. References*

1. References

10. Acknowledgments

1. Acknowledgments