Forecasting the Economic Demand for Occupational Education

  • Oleg A. KOSORUKOV Lomonosov Moscow State University, Moscow

Abstract

The article deals with the dynamic model of the regional labor market. This model aims at the development of optimal structure of professional training, with regard to the socio-economic development and the labor market dynamics. The study also considers the problem of optimal placement of students in the system of educational institutions dealing with occupational education. The article provides mathematical description of the basic processes, related to the labor market, and describes the requirements for model functions and methods for finding the model parameters. The study also provides the required source data for the proposed model. The method of mathematical modeling was used as a tool required for taking management decisions. The system-dynamic modeling was used as the main method of mathematical modeling. The article introduced and described endogenous and exogenous model parameters. The required equations determining the dynamics of the processes are provided. The author proposed parametric classes of functions with a view to describe relevant processes and described algorithms to evaluate their parameters as well as the related statistical and expert data. In conclusion, the author introduced and described endogenous and exogenous model parameters as well as equations required for determining the dynamics of relevant processes. The author proposed parametric classes of functions with the view of describing relevant processes and provided algorithms to evaluate their parameters as well as related statistical and expert data.

References

[1] Adams, P.D. et al. 1994. Forecasts for the Australian economy using the MONASH model. International Journal of Forecasting, 10(4): 557–571.
[2] Alho, J.M. 2014. Forecasting demographic forecasts. International Journal of Forecasting, 30(4): 1128–1135.
[3] Bruno, G., Esposito, E., Genovese, A., and Piccolo, C. 2015. Institutions and facility mergers in the Italian education system: Models and case studies. Socio-Economic Planning Sciences, 53 23-32.
[4] Dixon, P.B., and Rimmer, M.T. 2003. A New Specification of Labour Supply in the MONASH Model with an illustrative application. Australian Economic Review, 36(1): 22–40.
[5] Friedberg, R.M. 2001. The impact of mass migration on the Israeli labor market. Quarterly Journal of Economics. 1373–1408.
[6] Hakan, Ş., and Serdar, A. 2016. Pre-Service Math Teachers’ Opinions about Dynamic Geometry Softwares and Their Expectations from Them. International Electronic Journal of Mathematics Education, 11(3): 421-431.
[7] Hasanefendic, S., Heitor, M., and Horta, H. 2016. Training students for new jobs: The role of technical and vocational higher education and implications for science policy in Portugal. Technological Forecasting and Social Change, 11: 76-87
[8] Hendry, D.F. 2009. The methodology of empirical econometric modeling: Applied econometrics through the looking-glass. In Palgrave handbook of econometrics. Springer, 3: 54–67.
[9] Kőmíves, P.M., and Dajnoki, K. 2015. Ranking Systems as the Connection between the Higher Education and the Labour Market in Hungary. Procedia Economics and Finance, 32: 292–297.
[10] Kwiek, M. 2015. The unfading power of collegiality? University governance in Poland in a European comparative and quantitative perspective. International Journal of Educational Development, 43: 77–89.
[11] Lenar, S., Artur, F., Leysan, S., and Aliya, S. 2015. Forecasted Trends and Problems of Education. Procedia-Social and Behavioral Sciences, 191: 1124–1127.
[12] Malakellis, M., and Dixon, P.B. 1994. The economic implications of an improvement in labour productivity: Comparative dynamic results from the MONASH model. A Comparison of Economy-Wide Models of Australia, Economic Planning Advisory Commission. Commission Paper (2): 159–190.
[13] Mondschean, T., and Oppenheimer, M. 2011. Regional Long-term and Short-term Unemployment and Education in Transition: The Case of Poland. The Journal of Economic Asymmetries, 8(2): 23–48.
[14] Müller, W. 2005. Education and youth integration into European labour markets. International Journal of Comparative Sociology, 46(5-6): 461–485.
[15] Oppedisano, V. 2014. Higher education expansion and unskilled labour market outcomes. Economics of Education Review, 40: 205–220.
[16] Saving, J.L. 1999. Migration, labor–leisure choice, and Pareto suboptimal redistribution. Regional Science and Urban Economics, 29(5): 559–573.
[17] Tang, H.V., and Yin, M.-S. 2012. Forecasting performance of grey prediction for education expenditure and school enrollment. Economics of Education Review, 31(4): 452–462.
[18] Topel, R.H. 1986. Local labor markets. The Journal of Political Economy. 111–143.
[19] Wolbers, M.J. 2016. A generation lost? Prolonged effects of labour market entry in times of high unemployment in the Netherlands. Research in Social Stratification and Mobility, 5: 15-26.
[20] Yamauchi, F., and Tiongco, M. 2013. Why women are progressive in education? Gender disparities in human capital, labor markets, and family arrangement in the Philippines. Economics of Education Review, 32: 196–206.
Published
2016-12-12
How to Cite
KOSORUKOV, Oleg A.. Forecasting the Economic Demand for Occupational Education. Journal of Advanced Research in Law and Economics, [S.l.], v. 7, n. 5, p. 1066–1085, dec. 2016. ISSN 2068-696X. Available at: <https://journals.aserspublishing.eu/jarle/article/view/528>. Date accessed: 21 nov. 2024.

Keywords

labor market; regional economy; staff turnover models; economic demand; labor migration