Search

Skills and skill mismatches affect workers’ productivity. However, current approaches to measuring this problem fail to specify the underlying mechanism. In this paper, we develop a new perspective by integrating skill proficiency and skill use into a new concept called ‘effective skill’. Effective skill is defined as a multiplicative function of skill proficiency and skill use. The intuitive understanding of this concept is that a skill can have no effect on productivity if it is not used and, vice versa, the effect of using skills is moderated by the skill proficiency level. We develop a skill matching model using data from the OECD PIAAC Survey. We show that there is no effect of numeracy on wages, other than through the use of numeracy skills. Moreover, we show that a skill mismatch model based on this concept is superior to alternative skill mismatch models in explaining wage differences.

Workers with a college degree earn substantially more than workers with no such degree. Using recent data from 22 OECD (Organization for Economic Cooperation and Development) countries, we estimate this college wage premium at 28 per cent for male full-time working employees, on average, ranging from 18 per cent in Sweden to 50 per cent in the Slovak Republic. This premium is largely explained by the higher skill levels of graduates from higher education combined with their use of these skills at work, as well as the match with job requirements for this skill proficiency and skill use. We find no effect of labour market institutions (e.g. the employment protection legislation or the coverage rate) on cross-country differences in the college wage premium. However, we find that cross-country variation in this premium is related to the relative supply of higher educated workers. Moreover, we find evidence that cross-country differences in the college wage premium are related to the degree to which educational credentials signal skills.

Policy measures to combat low literacy are often targeted at the level of municipalities or regions with an above-average population with low literacy levels. However, current surveys on literacy do not contain enough respondents at this level to allow for reliable estimates, at least when using only direct estimation techniques. To provide more reliable results at a detailed regional level, alternative methods must be used. The aim of this paper is to obtain literacy estimates at the municipality level using model-based small area estimation techniques in a hierarchical Bayesian framework. To do so, we link Dutch Labour Force Survey data to the most recent literacy survey available, that of the Programme for the International Assessment of Adult Competencies (PIAAC). We estimate the average score, as well as the percentage of people with a low literacy level. Additional complications arise, as the PIAAC framework assumes that test scores reflect an underlying latent construct. Moreover, as an adaptive design has been used with rotating modules, not all respondents are assigned the same test items. This is why an item response model is used with multiple imputation resulting in 10 so-called plausible values for the literacy proficiency level per respondent. Variance estimators for our small area predictions explicitly account for this imputation uncertainty. The average literacy score is estimated with a unit-level model, while the percentage of low literates is estimated using an area-level model utilizing pooled variance. Optimal models are selected using a conditional Akaike information criterion score. Municipalities with less than 40,000 inhabitants were clustered with neighboring municipalities to ensure sufficiently large sample sizes. The PIAAC survey is currently carried out in 36 countries. Most of these countries also have labor force surveys that contain similar information as the one used in this analysis. This opens up the possibility of applying the same method in other countries.