English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Vera Maltseva - Candidate of Sciences in Economics, Associate Professor, Department of World Economy, Ural State University of Economics. Address: 62 Vosmogo Marta St, 620144 Yekaterinburg, Russian Federation. E-mail: maltsevava@gmail.comSkill mismatch implies discrepancy between the skills of job candidates or employed workers and job requirements. Types of mismatch are identified based on three criteria: quality of mismatch (surplus vs shortage), reporting party (employer vs worker/candidate), and type of skills (cognitive vs technical). Differences in types of skill mismatch account for considerable variation in qualitative interpretation and quantitative measurement. The problem of skill mismatch has been widely debated across the OECD countries, yet it remains understudied in Russian research literature. The issue raises concerns among education and labor market researchers as well as practitioners, so this article analyzes the available findings from the prospective of their potential use by educational institutions being the key consumers of data on skill mismatch and the ones that should tackle the problem.Five types of skill mismatch are identified, along with the specific challenges of measurement and interpretation. The article describes three methods of skill mismatch measurement to be selected as a function of which type of skill supply and demand data is used: indirect, objective direct, and subjective direct measurement. It also classifies methods of measuring the cognitive skills gap in the major cross-national studies: PIAAC, STEP, and OECD Skills for Jobs Database. It transpires that cross-national comparisons of cognitive skills mismatch mostly have to use a mixed approach due to limitations typical of cross-country research, such as the lack of objective data on skills demand and relying on subjective or indirect data alone. For this reason, the results of most cross-national skills mismatch assessments cannot be implemented by educational institutions.

The Concept of Skills Mismatch and the Problem of Measuring Cognitive Skills Mismatch in Cross-National Studies