A logit model for the estimation of the educational level influence on unemployment in Romania

Oancea, Bogdan and Pospisil, Richard and Dragoescu, Raluca (2016): A logit model for the estimation of the educational level influence on unemployment in Romania. Published in: Proceedings of the Knowledge for Market Use Conference (1 November 2017): pp. 334-344.

Abstract

Education is one of the main determinants of the unemployment level in all EU countries. In this paper we used a logit model to estimate the effect of the educational level on the unemployment in Romania using data recorded at the Population and Housing Census 2011. Besides the educational level we also used other socio-demographic variables recorded at the Census like gender, marital status, residential area. Data processing was achieved using R software system and since the data set used for model estimation was very large we used special techniques suited for big data processing. The results showed that the lowest odds ratio to be unemployed was recorded for population with tertiary education which is consistent with other studies at international level and with the official statistics data, but our study indicates that tertiary education has a greater impact on unemployment in Romania than in other EU countries.

• Item Type: MPRA Paper
• Language: English
• Keywords: educational level, unemployment, logit, higher education
• Subjects: I - Health, Education, and Welfare > I2 - Education and Research Institutions > I20 - General; J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J24 - Human Capital ; Skills ; Occupational Choice ; Labor Productivity

References

Altbeker, A., & Storme, E. (2013). Graduate unemployment in South Africa: A much exaggerated problem, The Centre for Development and Enterprise, Johannesburg.

Arrow, K. (1973). Higher education as a filter. Journal of Public Economics, 2, 193-216.

Daly, M. C., Osborne, J., & Valletta, R. G. (2007). Educational Attainment, Unemployment, and Wage Inflation, Economic Review, Federal Reserve Bank of San Francisco, 49-61.

Dănănică, D. E., & Babucea, A. G. (2008). Risk of employment – a logistic regression approach, Analele Universităţii “Constantin Brâncuşi” from Târgu Jiu, Seria Economie, 1, 123-136.

Drăgoescu, R.M., (2015). Analiza prin metode cantitative a evoluției și tendințelor în învățământul superior din România, Ph.D. Thesis, The Bucharest University of Economic Studies.

Garrouste, C., Kozovska, K., & Perez, E.A. (2010). Education and long term unemployment. JRC Scientific and Technical Reports, European Commission.

Hosmer, D. & Lemeshow, S. (2000). Applied Logistic Regression, Second Edition. New York: John Wiley & Sons, Inc.

Mincer, J. (1994). Human Capital: A Review. In Clark Kerr et al. (eds) Labour economic and Industrial Relations: Markets and Institutions. Cambridge: Harvard University Press.

Nickell, S. (1973). Education and lifetime patterns of unemployment. Journal of Political Economy, 87(5), 117-131.

Nunez, I., & Livanos, I. (2010). Higher education and unemployment in Europe: an analysis of the academic subject and national effects. Higher Education, 59(4), 475-487.

Plümper, T., & Schneider, C. J. (2007). Too much to die, too little to live: unemployment, higher education policies and university budgets in Germany. Journal of European Public Policy, 14(4), 631- 653.

OECD. (2015). Education at a Glance 2015: OECD Indicators. OECD Publishing, Paris.

Spence, M. (1981). Signaling, screening, and information. In S. Rose (Ed.) Studies in Labour Markets. Chicago: University of Chicago Press.

Winkelmann, R. (1996). Employment prospects and skills acquisition of apprenticeship trained workers in Germany. Industrial and Labour Relations Review, 49, 658-672.

Wooldridge, J. M. (2008). Introductory Econometrics. South Western College.