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Predictors of Economic Outcomes among Romanian Youth: The Influence of Education—An Empirical Approach Based on Elastic Net Regression

Author

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  • Ana-Maria Zamfir

    (Department of Education, Training and Labour Market, National Scientific Research Institute for Labour and Social Protection, 010643 Bucharest, Romania)

  • Adriana AnaMaria Davidescu

    (Department of Education, Training and Labour Market, National Scientific Research Institute for Labour and Social Protection, 010643 Bucharest, Romania
    Department of Statistics and Econometrics, Bucharest University of Economic Studies, 010552 Bucharest, Romania)

  • Cristina Mocanu

    (Department of Education, Training and Labour Market, National Scientific Research Institute for Labour and Social Protection, 010643 Bucharest, Romania)

Abstract

Young people have to be provided with opportunities to access prosperous, resilient and fulfilling lives. Investing in education and skills is considered one of the most important ways to support young people’s well-being and to enable them to enjoy good career prospects. Using the framework of human capital theory, we explored the role of education among the factors explaining wage variation among Romanian youth. We built our analysis on micro-data for Romania from the EU Statistics on Income and Living Conditions 2020. In order to identify the most important factors influencing the wage distribution, we employed the elastic net regression approach. Moreover, we considered the phenomenon of expansion of education and ran the analysis by alternately using a traditional measure for education and a relative measure reflecting the theory of education as positional good. We ran the analysis for different cohorts of the population, focusing the discussion on the results for young people. Our findings confirm the importance of education for wage distribution together with other factors of influence, such as gender, degree of urbanization, region, sector of employment and working experience. Our conclusions are relevant for designing more effective educational and social policies to deal with various disadvantages faced by youth in Romania.

Suggested Citation

  • Ana-Maria Zamfir & Adriana AnaMaria Davidescu & Cristina Mocanu, 2022. "Predictors of Economic Outcomes among Romanian Youth: The Influence of Education—An Empirical Approach Based on Elastic Net Regression," IJERPH, MDPI, vol. 19(15), pages 1-15, July.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9394-:d:877090
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    References listed on IDEAS

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    1. Romina Boarini & Hubert Strauss, 2010. "What is the private return to tertiary education?: New evidence from 21 OECD countries," OECD Journal: Economic Studies, OECD Publishing, vol. 2010(1), pages 1-25.
    2. Claudio E. Montenegro & Harry Anthony Patrinos, 2014. "Comparable Estimates of Returns to Schooling Around the World," Working Papers wp390, University of Chile, Department of Economics.
    3. Ferdi Botha, 2014. "Life Satisfaction and Education in South Africa: Investigating the Role of Attainment and the Likelihood of Education as a Positional Good," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(2), pages 555-578, September.
    4. Till von Wachter, 2020. "The Persistent Effects of Initial Labor Market Conditions for Young Adults and Their Sources," Journal of Economic Perspectives, American Economic Association, vol. 34(4), pages 168-194, Fall.
    5. Dickson, Matt & Smith, Sarah, 2011. "What determines the return to education: An extra year or a hurdle cleared?," Economics of Education Review, Elsevier, vol. 30(6), pages 1167-1176.
    6. Heckman, James J. & Lochner, Lance J. & Todd, Petra E., 2006. "Earnings Functions, Rates of Return and Treatment Effects: The Mincer Equation and Beyond," Handbook of the Economics of Education, in: Erik Hanushek & F. Welch (ed.), Handbook of the Economics of Education, edition 1, volume 1, chapter 7, pages 307-458, Elsevier.
    7. Mehmet Pinar, 2019. "Multidimensional Well-Being and Inequality Across the European Regions with Alternative Interactions Between the Well-Being Dimensions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(1), pages 31-72, July.
    8. Theodore W. Schultz, 1962. "Reflections on Investment in Man," NBER Chapters, in: Investment in Human Beings, pages 1-8, National Bureau of Economic Research, Inc.
    9. Polachek, Solomon W., 2008. "Earnings Over the Life Cycle: The Mincer Earnings Function and Its Applications," Foundations and Trends(R) in Microeconomics, now publishers, vol. 4(3), pages 165-272, April.
    10. Stiglitz, Joseph E, 1975. "The Theory of "Screening," Education, and the Distribution of Income," American Economic Review, American Economic Association, vol. 65(3), pages 283-300, June.
    11. Gary S. Becker, 1964. "Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education, First Edition," NBER Books, National Bureau of Economic Research, Inc, number beck-5, June.
    12. Michael Spence, 1981. "Signaling, Screening, and Information," NBER Chapters, in: Studies in Labor Markets, pages 319-358, National Bureau of Economic Research, Inc.
    13. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    14. David N. F. Bell & David G. Blanchflower, 2011. "Young people and the Great Recession," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 27(2), pages 241-267.
    15. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    16. Peet, Evan D. & Fink, Günther & Fawzi, Wafaie, 2015. "Returns to education in developing countries: Evidence from the living standards and measurement study surveys," Economics of Education Review, Elsevier, vol. 49(C), pages 69-90.
    17. Alessa K. Durst, 2021. "Education as a Positional Good? Evidence from the German Socio-Economic Panel," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(2), pages 745-767, June.
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