ABSTRACT
The study is devoted to the evaluation of the determinants of job-education mismatches and their impact on salaries of university graduates. We use a comprehensive and nationally representative survey of Russian university graduates. The study employs a self-evaluated measure of mismatch and a statistical variant for robustness and interpretation purposes. We find that one-third of the graduates in Russia are horizontally mismatched, and that the share of mismatched graduates in Russia is higher than the average in OECD countries. Graduates from fields that generate more general human capital, or where low pay is common, are more likely to be mismatched. On the contrary, graduates in Medicine, which is a more specialised field, are more likely to be matched. We find that mismatches negatively affect the earnings of university graduates and, the higher is the degree of mismatch, the higher is the penalty for the mismatch. The study depicts that mismatch is penalised in the majority of fields except for low-paid ones.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1. The Flexible Professional in the Knowledge Society (REFLEX). Accessed 15 November 2021. https://cordis.europa.eu/project/id/506352/reporting
2. Higher Education as a Generator of Strategic Competences’ (HEGESCO). Accessed 15 November 2021. http://www.hegesco.org/
3. International Standard Classification of Education (ISCED). Accessed 15 November 2021. http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf
4. International Standard Classification of Occupations (ISCO). Accessed 15 November 2021. https://www.ilo.org/public/english/bureau/stat/isco/
5. As a robustness check, we also used detailed ISCO 3-digit jobs and 3-digit fields of study, but achieved very similar results relative to the basic specification.
6. Russian Federal Statistics Service definition of youth.
7. We used robust standard errors with clusterization of errors by field of study.
8. Missing values might be a potential limitation of our analysis. Nevertheless, given that the majority of observations were paired, this is unlikely to affect our results.
9. VIF-statistics values for model estimated in with industry controls are between 2.79 and 2.85, which shows that multicollinearity is not a significant problem. After removal of industry controls, VIF-statistics values decreases to 1.68–1.72, showing even lower value of possible multicollinearity.
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Notes on contributors
Victor Rudakov
Victor Rudakov is Deputy Head and a Senior research fellow at Center for Institutional Studies of HSE University, Moscow (National Research University Higher School of Economics), Assistant professor at Faculty of Economic Sciences. His main area of research interests are labor economics and economics of education. He is a consultant of World Bank on projects related to education and labour market in CIS countries. Victor Rudakov is the corresponding author and can be contacted at: [email protected]; [email protected]
Hugo Figueiredo
Hugo Figueiredo is a researcher at Centre for Research in Higher Education Policies and Assistant Professor at DEGEIT – Department of Economics, Management, Industrial Engineering and Tourism, University of Aveiro, Aveiro, Portugal. His main research interests are economics of education and labor economics.
Pedro Teixeira
Pedro Nuno Teixeira is a director of CIPES – Centre for Research in Higher Education Policies, Associate professor of School of Economics of University of Porto and General secretary of CHER Association. His main research interests focus on the economics of higher education and the history of economic ideas.
Sergey Roshchin
Sergey Roshchin is a vice-rector at HSE University, Moscow (National Research University Higher School of Economics) and Head of the laboratory for labour market studies at HSE University, Moscow (National Research University Higher School of Economics). His main research interests are labour economics and economics of education.