Halkos, George and Tzeremes, Nickolaos (2011): Adjusting for cultural effects on countries’ education policy efficiency:an application of conditional full frontiers measures.
Download (153kB) | Preview
In this paper using Data Envelopment Analysis (DEA) we evaluate the influence of national culture on education policy efficiency for 20 OECD countries. For that reason bootstrap techniques have been employed in order to produce biased corrected efficiency scores and confidence intervals are been calculated. By using probabilistic approaches it conditions the effect of national cultural values on the obtained countries’ educational efficiencies. The empirical results indicate that the efficiency of education policy is mainly influenced from differences of individualistic and masculinity values among the countries. However the results clearly indicate that education policy reforms must be based outside those national cultural bounds in order to support national economies on their foreseen challenges.
|Item Type:||MPRA Paper|
|Original Title:||Adjusting for cultural effects on countries’ education policy efficiency:an application of conditional full frontiers measures|
|Keywords:||Data Envelopment Analysis; Education; Linear programming; Statistics|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General
I - Health, Education, and Welfare > I2 - Education and Research Insititutions > I21 - Analysis of Education
C - Mathematical and Quantitative Methods > C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling > C60 - General
|Depositing User:||Nickolaos Tzeremes|
|Date Deposited:||07. Apr 2011 18:48|
|Last Modified:||15. Feb 2013 21:19|
Abbott, M. and Doucouliagos, C. (2003). ‘The efficiency of Australian universities: a data envelopment analysis’, Economics of Education Review, 22, pp. 89–97.
Banker, R.D., Charnes, A. and Cooper, W.W. (1984). ‘Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis’, Management Science, 30, pp.1078 – 1092.
Bee, M., Dolton, P.J. (1985), ‘Educational production in independent secondary schools’, Bulleting of Economic Research, 37(1), pp. 27-40.
Bradley, S., Johnes, J. and Little, A. (2010), ‘Measurement and determinants of efficiency and productivity in the further education sector in England’, Bulleting of Economic Research, 62(1), pp. 1-30.
Charnes, A., Cooper, W.W. and Rhodes, L.E. (1978). ‘Measuring the efficiency of decision making units’ European Journal of Operational Research, 2, pp.429-444.
Cherchye, L., De Witte, K. and Ooghe, E. (2007), ‘Equity and efficiency in private and public education: a nonparametric comparison’, Discussions Paper Series (DPS) 07.25, University of Leuven (KU Leuven), Belgium: Centre for Economic Studies.
Daraio, C. and Simar, L. (2005a). ‘Conditional Nonparametric frontier models for convex and non convex technologies: a unifying approach’, Working Paper 2005/12, LEM Pisa, Italy: Laboratory of Economics and Management.
Daraio, C. and Simar, L. (2005b). ‘Introducing environmental variables in nonparametric frontier models: A probabilistic approach’, Journal of Productivity Analysis, 24, pp. 93–121.
Daraio, C. and Simar, L. (2007). ‘Advanced robust and nonparametric methods in efficiency analysis. Methodology and Applications’, in Färe, R., Grosskopf, S. and Russell, R. (eds), Studies in Productivity and Efficiency, New York: Springer.
Dyson, R.G. and Shale, E.A. (2010). ‘Data envelopment analysis, operational research and Uncertainty’, Journal of the Operational Research Society, 61, pp. 25—34.
Efron, B. (1979). ‘Bootstrap methods: another look at the jackknife’, Annals of Statistics 7:1-16.
Fanti, L. and Gori, L. (2010), ‘Public education, fertility incentives, neoclassical economic growth and welfare’, Bulleting of Economic Research, 62(1), pp. 59-77.
Farrell, M. (1957). ‘The measurement of productive efficiency’, Journal of the Royal Statistical Society Series A, 120, pp. 253–281.
Flegg, A.T., Allen, D.O. and Thurlow, W.T. (2004). ‘Measuring the efficiency of British Universities: A Multi-Period Data Envelopment Analysis’, Education Economics, 12, pp. 231-249.
Giménez, V., Prior, D. and Thieme, C. (2007). ‘Technical efficiency, managerial efficiency and objective-setting in the educational system: an international comparison’. Journal of the Operational Research Society, 58, pp. 996 –1007.
Halkos, G.E. and Tzeremes, N.G. (2010), ‘The effect of foreign ownership on SMEs performance: An efficiency analysis perspective’, Journal of Productivity Analysis, DOI: 10.1007/s11123-010-0174-2.
Hofstede, G. (1980). Culture's Consequences, International Differences in Work-Related Values, Beverly Hills, CA: Sage Publications.
Johnes, J. (2006), ‘Measuring efficiency: a comparison of multilevel modeling and data envelopment analysis in the context of higher education’, Bulleting of Economic Research, 58(2), pp. 75-104.
Johnes, J. (2006). ‘Data envelopment analysis and its application to the measurement of efficiency in higher education’, Economics of Education Review, 25, pp. 273-288.
Joumady, O. and Ris, C. (2005). ‘Performance in European Higher Education: A Non-Parametric Production Frontier Approach’. Education Economics, 13, pp.189-205.
Lucas, R.E. (1988). ‘On the mechanics of economic development’, Journal of Monetary Economics, 22, pp. 3–42.
Mancebón, M.J. and Muñiz, M.A. (2008). ‘Private versus public high schools in Spain: disentangling managerial and programme efficiencies’, Journal of the Operational Research Society, 59, pp. 892 –901.
Mardia, K.V., Kent, J.T. and Bibby, J.M. (1979), ‘Multivariate analysis’, New York: Academic Press.
Mimoun, M.B. and Raies, A. (2010), ‘Public education expenditures, human capital investment and intergenerational mobility: a two-stage education model’, Bulleting of Economic Research, 62(1), pp. 31- 56.
Nadaraya, E.A. (1964). ‘On estimating regression’, Theory of Probability Applications, 9, pp.141-142.
OECD Education Database, (2004). ‘Education at a Glance 2004’, http://www1.oecd.org/scripts/cde/members/EDU_UOEAuthenticate.asp, accessed 13 September 2009.
Shackleton, V.J. and Ali, A.H. (1990). ‘Work-related values of managers: A test of the Hofstede model’, Journal of Cross Cultural Psychology, 21, pp. 109-118.
Silverman, B.W. (1986). ‘Density Estimation for Statistics and Data Analysis’, London :Chapman and Hall.
Simar, L. and Wilson, P.W. (1998). ‘Sensitivity analysis of efficiency scores: how to bootstrap in non parametric frontier models’, Management Science, 44, pp. 49-61.
Simar, L. and Wilson, P.W. (2000). ‘A general methodology for bootstrapping in non-parametric frontier models’. Journal of Applied Statistics, 27, pp. 779 -802.
Simar, L. and Wilson, P.W. (2002). ‘Non parametric tests of return to scale’, European Journal of Operational Research, 139, pp.115-132.
Simar, L. and Wilson, P.W. (2008). ‘Statistical inference in non-parametric frontier models: Recent development and Perspectives’, in: Fried, H.O., Lovell, C.A.K. and Schmidt, S.S. (eds), The measurement of productive efficiency and productivity growth, New York: Oxford University Press.
Sondergaard, M. (1994). ‘Hofstede’s consequences: A study of reviews, citations and replications’, Organization Studies, 15, pp.447-456.
Swierczek, F.W. (1994). ‘Culture and conflict in joint ventures in Asia’, International Journal of Project Management, 12, pp. 39-47.
Thanassoulis, E. and Dunstan, P. (1994). ‘Guiding schools to improved performance using Data Envelopment Analysis: an illustration with data from a local education authority’, Journal of the Operational Research Society, 45, pp. 1247–1262.
Triandis, H.C. (1982), ‘Culture’s consequences’, Human Organization, 41, pp. 86-90.
Watson , G.S. (1964), ‘Smoothed regression analysis’, Sankhya Series A, 26, pp. 359-372.