Rethinking the skills gap

Workers and jobs: Skills mismatch and productivity

The most immediate problem associated with mismatch concerns its effect on productivity. The literature studying this effect looks at existing matches of workers and jobs and tries to determine the extent to which workers have adequate skills to perform their jobs. This issue has two sides. Over- or under-qualification (also called vertical mismatch) occurs when workers have the right type of skills, but are too skilledor not skilled enough. Think of, in the over-skilled category, a linguist teaching a Spanish class, or, in the under-skilled category, a mechanic working as an engineer. Horizontal mismatch (also called field-of-study mismatch) occurs when workers do not have the type of skills required by the job, but they have other skills at a similar level—such as a biology teacher taking over physics classes in a school that fails to recruit a physics teacher.

The early literature on mismatch used self-reported data generated from workers’ responses to questions about whether they felt under- or over-qualified for their job. A limitation of this approach is that self-reported questions capture workers’ under- or over-confidence at the same time as potential mismatch. Beginning in 2011, the OECD’s Programme for the International Assessment of Adult Competencies (PIAAC) began its Survey of Adult Skills, an assessment designed to provide representative data on workers’ skills. The survey delivered an innovation by producing data involving skill proficiencies that are assessed, rather than self-declared. Thus far, the data include skill measurements of 250,000 individuals in 33 countries.

The official OECD measure was introduced by a group of researchers in 2013 [2]. The approach combines declarative measures of job qualification with objective measures of skill proficiency. For each occupation and country included in the PIAAC data, the authors determine a range of skill proficiencies based on the minimum and maximum proficiencies of workers who have defined themselves as being well-matched to their positions. A worker is defined as over-skilled if their skill proficiency is higher than this maximum, under-skilled when their skill proficiency is lower than theminimum. The authors find that 86% of workers are well-matched, 4% are under-skilled, and 10% are over-skilled. Another study using PIAAC data shows that there is a negative correlation between this measure of mismatch and labor productivity at the industry level: industries where there are more under-qualified or over-skilled workers exhibit lower levels of labor productivity [3].

Two studies offer evidence beyond developed countries. The first provides a review of worldwide mismatch research [4]. It finds that over-education affects 30% of workers, and under-education affects 26% of workers, with some variation surfacing across continents. The second study applies the OECD method to measure mismatch in developing countries, using the World Bank’s STEP Household Skills Survey. The authors find that over-education is the most prevalent form of mismatch.

A framework developed in 2015 analyzes worker–occupation matches [5]. If a worker does not possess the abilities that are necessary to learn the skills required by an occupation, then theyare “mismatched.” Estimating a structural model on US data, the study finds that being mismatched early in one’s career harms a worker’s wages in a large and persistent manner.

Quantifying the effect of the overall level of labor market mismatch is much more difficult than measuring the effect of being mismatched on the productivity of individual workers. For example, given the skills workers have and the skills jobs require at some point in time, how much would production increase if it were possible to reallocate mismatched workers to different jobs where their skills are better matched to the requirements, so that production could be maximized? Answering this question convincingly is difficult because it requires assumptions about the functions that link production inputs to outputs. Considering both labor and physical capital, very large effects are shown from misallocation across firms [6]. The analysis claims that mismatch is responsible for a productivity gap of 40–60% between India and the US, and 30–50% between China and the US. These results show that the mismatch of production inputs (labor and capital) is a substantial source of inefficiencies and a large contributor to the differences between developing and developed countries.

Job seekers and vacancies: Skills mismatch and unemployment

If the skills that firms require and the skills that workers possess are sufficiently far apart, then at least some workers will not be hired. Therefore, skills mismatch generates not only a productivity loss, but unemployment as well. Unemployment carries with it huge economic and personal costs. Hence, understanding the effect of skills mismatch on unemployment is important for crafting effective policy.

Labor market mismatch generates unemployment if the unemployed workers looking for jobs and firms with vacant positions looking for workers cannot form a match because the worker and vacancies are “not right” for each other. This idea can be formalized by modeling the labor market as being divided into segments, with workers (and vacancies) unable to move from one labor market segment to another. If there are deviations between the distributions of workers and jobs among the various segments of the labor market, then some workers will remain unemployed, while, at the same time, some firms will not be able to fill all positions.

If unemployment is caused by mismatch, then there is a tight link between the dispersion in labor market conditions across labor market segments and the aggregate unemployment rate. The idea is that, if there are jobs in occupations with certain skill sets while unemployed workers are available with different skill sets, then one should see large differences in the ratio of vacancies over unemployment across occupations with different skill requirements. This prediction allows empirical researchers to quantify the aggregate effect of mismatch on unemployment. The challenge is to measure how much lower the unemployment rate would be if—hypothetically, of course—it was possible to reallocate unemployed workers to those occupations where they are most likely to find jobs.

Despite severe measurement issues, there is remarkable consensus in the literature on some basic facts about unemployment due to labor market mismatch. This consensus can be summarized around three main findings: first, that geographic mismatch is negligibly small; second, that skills mismatch, as measured by mismatch across occupations or industries, is an important contributor to unemployment; and third, that skills mismatch is larger during recessions. These three findings are discussed in the following sections.

The causes of skills mismatch and how to address them

Unexpected events or phenomena may affect occupations in different ways. For instance, a 2003 study illustrates that the emergence of computers and information technologies (IT) reduced the demand for routine jobs, which were to some degree made obsolete by computers, and increased the demand for non-routine jobs, which proved to be relatively complementary to the computer [11]. For example, bank tellers and bank managers have likely been affected in opposite ways by the IT revolution in general, and by automated teller machines (ATMs) in particular. Like technological changes, recessions also change the relative demand of different goods. Sectoral shocks may translate into asymmetric occupational shocks; for example, bakers may be less sensitive to recessions due to the relative inelasticity of baked goods, while the jobs of restaurant waiters may be more sensitive because people eat out less during hard times. Because of these shocks, the fact that mismatch exists is not in itself surprising. The relevant question is: why does it seem to be so persistent?

Workers who work (or look for a job) in an occupation where the number of workers exceeds the number of positions have ways to adjust. They can apply to other higher-demand occupations that require similar skills, or they can acquire new skills through training. Alternatively, employers could adjust to workforce shortages by changing the skill content of occupations, or by training up workers from similar occupations to fit new skill requirements. Some recent empirical literature documents that employers adjust the task and skill content of jobs (for the same occupation) with the business cycle, “upskilling” when workers are more abundant in a recession [12].

Adjustment, whether by workers or employers, may be difficult and costly to achieve in the short term, especially when confronting large skill differences between origin and target occupations. Most policy interventions are based on the implicit assumption that this is the reason for the skills gap. The European Commission, for instance, believes that “Europe needs a radical rethink on how education and training systems can deliver the skills needed by the labor market.”As a result, it set up the Rethinking Education initiative “to reform education systems across the EU so as to meet growing demand for higher skills levels and reduce unemployment.”

A recent analysis uses data on wages and profits across industries in addition to data on job-finding rates to show that it is possible to quantify how much of mismatched unemployment stems from a lack of adjustment by workers or from a lack of adjustment by firms. On the workers’ side, the following scenario is identified: There are industries where workers have a hard time finding jobs, but where they earn high wages if they do; and there are other industries where jobs are plentiful, but wages are low. This is what one would expect to see if workers operate along a “no arbitrage” condition. That is, if they can move between industries, but will only do so if they are given the right incentives. If, on the other hand, there are many industries where both job-finding rates and wages are high, and others where both are low, the logical conclusion would be that mismatch persists because workers lack the skills required to move into better jobs. Using data for the US over the 1979–2010 period, the study finds that mismatch cannot be fully explained by barriers faced by workers and firms in adjusting to changing skills demand and supply, respectively.

This then raises the question: If workers adjust to changes in skills demand, and employers adjust to changes in supply, how can mismatch persist? The answer lies in the way wages are set. If wages reflect the relative abundance or the relative shortage of skilled workers, then workers’ and employers’ capacities to adjust would lead to the elimination of mismatch. However, jobs in industries that generate high profits (such as the retail trade, educational services, mining, and forestry) tend to pay low wages, and are therefore unattractive to workers, while jobs in industries that pay higher wages (like finance, computer and electronics manufacturing, and paper and printing) are not profitable to firms.

Other researchers, based on very different approaches, have also emphasized the role of wage setting. Among the forces suggested to be at work are: automated screening systems that rule out potential candidates who might have surfaced in subjective, human resources screening processes; and a preference for hiring experienced candidates over investing in training for inexperienced but promising candidates. If workers do not move into low-unemployment occupations, the problem may not be that they cannot train or adjust, but that wages are too low to attract them. In the UK, for instance, less than half of STEM graduates work in scientific occupations, and there is no wage premium in other occupations for having a STEM degree. Firms, on the other hand, are more interested in hiring workers with STEM skills because these workers are not only very productive but also relatively cheap—despite widespread public perception that STEM graduates earn high salaries. Thus, companies open lots of vacancies for STEM positions, but then find it very difficult to fill them.