A Web-Based Approach to Measure Skill Mismatches and Skills Profiles for a Developing Country:. Jeisson Arley Cárdenas Rubio
accurately anticipating employer requirements to fill their vacancies. Those workers whose skills are not in demand might choose between remaining outside of the labour market (being inactive), being unemployed or getting employed in the informal sector. Based on the Colombian evidence (discussed above), a high proportion of people select the last two options: the informal sector or unemployment.
At the same time, a relatively high proportion of companies in Colombia complain about the scarcity of workforce according to their needs, which leads to a situation where there are vacancies to be filled. However, due to skill mismatches, the Colombian labour supply does not have the necessary characteristics to fill these vacancies (see Chapter 2). As a consequence, to reduce unemployment and informality problems, information asymmetries between supply (individuals) and demand (employers) for labour must be addressed. Tackling these problems might have a large positive impact on regions like Colombia where unemployment and informality rates are relatively high, and there is a large gap between the labour demand and supply of skills.
As the OECD (2017b) has pointed out, to tackle informality and improve economic stability Latin American countries like Colombia should invest in human capital. The same organisation argues that more education in terms of quantity and quality increases a person’s likelihood of finding a job and reduces the probabilities of being unemployed or working in the informal sector. Moreover, to guarantee the effectiveness of human capital investments and to avoid any labour market mismatches as described in Chapter 2 (e.g. overeducation), governments and other institutions need to promote skills that meet company requirements (Gambin, Green, and Hogarth 2009; OECD 2012).
Given the importance of skill mismatches, institutions such as the World Bank (2010), the OECD (2016a), and the ILO (2017b) agree that fostering education and suitable skills (to strengthen human capital) might have a large positive impact on the main employment problems of Latin America (e.g. Colombia). Thus, it is essential for Colombia to achieve at least the minimum skill levels in its population, and to improve the relevance of education and training systems in order to reduce unemployment and promote well-being (OECD 2015a; González Espitia and Mora Rodríguez 2011).
As González-Velosa and Rosas-Shady (2016) mentioned, advanced education and training systems achieve the above by encompassing tools to identify current and future skill requirements for the productive sector. With these tools, curriculum contents can be updated, and the relevance of education and training increased. Consequently, approaches that identify possible skill mismatches, when combined with a functional system of active labour market policies, can ensure better matches between employers and workers (ILO 2016a).
3.4. An international example of skill mismatch measures
Examples of the above can be found in different regions. As Mavromaras et al. (2013) highlight, the most developed approaches to measure skill mismatches (skill shortages) can be found in the UK. For instance, the Migration Advisory Committee (MAC) built 12 indicators34 of shortage using data for labour demand and supply. With this set of indicators, the MAC advises to the UK Government on where skill shortages can be filled by immigration from outside the European Economic Area (EEA). In addition, the UK Commission for Employment and Skills (UKCES) and (subsequently) the Department for Education (DfE) carried out a biennial Employer Skills Survey (ESS), which provides insights about the skill problems employers are facing to fill their vacancies and the actions they are taking to solve them. The survey contributes to public policy decisions when addressing the skills challenge and prompting people to adopt relevant skills for the workplace (Vivian 2016).
Another example in the UK is the Local Economy Forecasting Model (LEFM), developed by Cambridge Econometrics (CE) in collaboration with the Institute for Employment Research at the University of Warwick (Cambridge Econometrics 2013). Based on the 2011-based Sub-National Population Projections (SNPP) developed by the Office for National Statistics (ONS), and assuming that the historical relationship between growth in the local area and the region or the UK economy will hold in the future, this model allows researchers to project/anticipate different economic scenarios (skill forecast), as well as to evaluate possible skill mismatches at occupation or qualification levels, among other outcomes (Cambridge Econometrics 2013).
Moreover, reports such as “The Future of Work: Jobs and Skills in 2030” interview experts (senior business leaders, trade union representatives, education and training providers, policymakers, academics, etc.) from different sectors and conduct a comprehensive literature review, workshops, among other researches, to analyse sector trends and examine future economic scenarios (possible skill mismatches) and their implications for the labour demand for skills in the UK (skill foresight). These kinds of prospective labour studies are valuable because they estimate future employer requirements and address the education and VET system according to possible future needs using different and robust sources of information (UKCES 2014).
Other valuable efforts include the O*NET system launched in 1998, which is updated by the US Department of Labour, and the European Skills, Competences, Qualifications and Occupations (ESCO) in Europe, which is updated under the jurisdiction of the European Union. Based on the US Standard Occupational Classification (SOC) system, the O*NET system periodically consults a variety of resources—such as a national sample of establishments and their workers, occupational experts and analysts, among others—to collect information on hundreds of standardised and occupation-specific descriptors, e.g. knowledge, skills, tasks, work activities, and other descriptors (National Research Council 2010). Consequently, the O*NET provides an updated and detailed description of requirements for each occupation (skills, tasks, knowledge, etc.). With this valuable information, government officials can understand ongoing changes in the nature of work and their implications on the US workforce. Moreover, the O*NET identifies specific groups of occupations, such as “Bright Outlook occupations” or, in other words, occupations that are expected to grow swiftly in the coming years (potential skill mismatches) or will have considerable numbers of job vacancies. Consequently, this system helps the government to develop and train the workforce depending on their skill needs.
In addition, the Cedefop has made important advances towards quantifying skill needs in Europe. For example, the Occupational Skills Profiles (OSP) approach aims to integrate and complement several European sources of skill requirement information in order to provide updated occupational profiles for the region (Cedefop 2012b). Importantly, as mentioned above, the European Commission has built the ESCO, a multilingual classification system, which attempts to cover all European skills, competencies, qualifications, and occupations. It is important to note that occupations in the ESCO follow the structure of the International Standard Classification of Occupations (ISCO-08) at the four-digit level, and that the ESCO provides lower levels of disaggregation of skills for each occupation, such as an exhaustive list of 13,485 relevant skills (skills pillar) (European Commission 2017). This system was created to be compatible with other European platforms and supports an automated matching of jobseeker skills and vacancies. Consequently, in principle, the ESCO can be used to identify mismatches between CVs and vacancies in Europe.
3.5. Lack of accurate information to develop well-orientated public policies
In contrast with the above-mentioned classification systems in the US and Europe, Colombia does not have these kinds of advanced tools to base its education and training policies on them (González-Velosa and Rosas-Shady 2016). There exist some approaches to analyse the labour market in terms of skills, but there is not an integrated information system for skill mismatch analysis (Saavedra and Medina 2012). Institutions that have tried to measure, directly or indirectly, human capital characteristics have used different statistical approaches and skill concepts.
Since 2006, the Colombian statistics office (DANE) carries out a monthly cross-sectional household survey, the GEIH, to measure the characteristics of the Colombian workforce. The GEIH is nationally representative and constitutes the main source for official labour market information in Colombia. For instance, based on the GEIH, each month the national government