Skill mismatch comparing educational requirements vs attainments by occupation

Abstract

Purpose The purpose of this paper is to overcome the problems that skill mismatch cannot be measured directly and that demand side data are lacking. It relates demand and supply side characteristics by aggregating data from jobs ads and jobholders into occupations. For these occupations skill mismatch is investigated by focussing on demand and supply ratios, attained vis-à-vis required skills and vacancies’ skill requirements in relation to the demand-supply ratios. Design/methodology/approach Vacancy data from the EURES job portal and jobholder data from WageIndicator web-survey were aggregated by ISCO 4-digit occupations and merged in a database with 279 occupations for Czech Republic, being the only European country with disaggregated occupational data, coded educational data, and sufficient numbers of observations. Findings One fourth of occupations are in excessive demand and one third in excessive supply. The workforce is overeducated compared to the vacancies’ requirements. A high demand correlates with lower educational requirements. At lower occupational skill levels requirements are more condensed, but attainments less so. At higher skill levels, requirements are less condensed, but attainments more so. Educational requirements are lower for high demand occupations. Research limitations/implications Using educational levels is a limited proxy for multidimensional skills. Higher educated jobholders are overrepresented. Practical implications In Europe labour market mismatches worry policy makers and Public Employment Services alike. Originality/value The authors study is the first for Europe to explore such a granulated approach of skill mismatch.

Unraveling the MNE wage premium

“…In addition to the online survey filled out by web visitors, an offline version is available in countries with low literacy rates, which are completed with the help of an interviewer. Previous research has found the WageIndicator dataset to be representative in terms of the distribution across industries (Fortanier, 2008) and employees' demographics, even though employees with a low level of literacy (i.e., with a low level of education, corresponding to a value of 0 for the Education variable in our sample) as well as employees over the age of 50 were found to be relatively underrepresented in the dataset (De Vries, Tijdens, & Steinmetz, 2016;Tijdens, Beblavý, & Thum-Thysen, 2018). While this is a limitation, it does not undermine the value of our study given that (a) key groupings in terms of both education and age levels are well represented in the dataset and thus in our sample; (b) we account for different levels of education and age/experience in our estimations; and (c) we are interested in the comparison of MNEs versus domestic firms' employees having similar education levels and age/experience.…”

“…The ''WageIndicator'' project was initiated in the Netherlands in 1999 and is managed by the WageIndicator Foundation, a coalition of AIAS, the University of Amsterdam Institute for Labour Studies, and local trade unions in 92 countries, which aims to improve labor market access and transparency. Previous studies have compared wages reported in WageIndicator samples and the distribution of samples across industries to the data of national statistics offices and found no inconsistencies (Fortanier, 2008;Tijdens, Beblavý, & Thum-Thysen, 2018). A further, extensive explanation of the dataset, which is very suitable for our purposes, and its possibilities and limitations, can be found in the Appendix.…”

Bridging the Skill Gap in Robotics: Global and National Environment

“…The current demand for skills in robotics was analyzed using open-access job vacancy databases Indeed.com and hh.ru. Online job ads reflect the employer's views about an ideal candidate, provide actual and objective information, and are a prospective tool for measuring labor demand, investigating skill requirements in particular areas, and identifying skills mismatch (Beblavý et al, 2016;Štefánik, 2012;Tijdens et al, 2018). A search for resources containing representative selection of vacancies suitable for automated collection and processing of textual data was conducted in 2017.…”

Assessing employers’ satisfaction with Indian engineering graduates using expectancy-disconfirmation theory

“…Employers who hire graduates attach considerable significance to skills possessed by the latter (Mawson and Haworth, 2018). Interestingly, skill mismatch refers to incongruity between an employee’s “educational and vocational attainments and the requirements of the job” (Tijdens et al , 2018). Since the 1970s, social scientists have been focussing their research on the potential gap between academic attainments of workers and the skills they actually use at work (Llorente et al , 2018).…”