• Nem Talált Eredményt

Interpreting the education process as a generator of human capital can raise several objections. One possible objection is that each person is unique, and a child cannot be ‘substituted’ with another. Thus, irrespective of how many pupils are trained, there is no ‘mass production’ in education, but rather a ‘production’ of unique individuals, each possessing their own personal capital. Another conceivable objection is that this type of capital cannot be deconstructed into constituent skills, aptitudes, or knowledge, since it embodies the synergistic outcome of all skills or

knowledge held by an individual. Hence, what matters is not (only) a student’s per-formance in math, literature, or foreign language, but the combination of all that she/he grasped during different education cycles. Likewise, one can argue that in an information-based society, an individual can gather information from various sources and that the knowledge transmitted through formal education represents only a frac-tion of it. Addifrac-tionally, if we consider that ECE and primary educafrac-tion are educafrac-tion cycles separate from other cycles, from the moment of ‘human capital recognition’ in the labour market, this means that there is no clear economic value attached to its outcomes. Hence, how should we evaluate its contribution to the formation of a fu-ture socially recognisable value? Furthermore, on the input side, teachers bring their own human capital into the educational processes. However, this is the outcome of distinctive education cycles and its ‘production’ implies separate efforts. Should these be accounted for in the current cycles? How can such inclusion be done?

These questions (and others not specified here) might cast a shadow of doubt on the adopted conceptual framework.

Extended answers to the questions that can be raised with respect to the theo-retical foundation require a more detailed discussion. However, we reiterate our standpoint that the human capital is a distinctive type of capital, critical to a society following a sustainable development path, and that social interactions among indi-viduals lead to the emergence of such a capital at macro societal levels. We argue that even if in this ‘post-modern’ age there is an abundance of freely available infor-mation at a scale that is unprecedented in history, formal education still remains a key channel for individual evolution and knowledge transmission, and that the out-come of ECE and primary education should not be treated as ‘residual’ with respect to human capital formation. Rather, the quality of such an outcome should be con-sidered as a critical prerequisite for further accumulation and ‘recognition’ of indi-viduals’ overall stock of such a capital.

However, even if the human capital view on education is accepted as concep-tual grounds for the analysis, a facile criticism might be related to the variables’ se-lection: Are these variables the most representative ones for a ‘full’ description of ECE and primary education processes? The reality is that several other variables can be considered for both inputs and outputs, but the analytical objective of this study is limited. It only aims to illustrate the idea that if the human capital approach is con-sidered, then: a) it is possible to select the inputs and outputs based on these grounds;

and b) it is possible to reach some results that provide relevant insights on the topic.

Certainly, the first argument is not the same as claiming that this study advanc-es an operational selection methodology. Instead, it should be understood in the sense that the human capital approach to education might serve as conceptual grounds to derive a systematic methodology for the selection of input/output varia-bles, instead of an ad hoc choice of these. For the second argument, one of the most

important questions related to the empirical part of the proposed analysis can be phrased as: How plausible are these findings? At this point, we first note that based on our results, almost none of the included countries display perfectly robust results across all estimation methods of efficiency, neither in terms of inputs nor outputs.

However, some of the countries show, for most of the methods, relatively higher efficiency in terms of inputs or outputs (without it being necessarily the same in both cases). For instance, Japan appears to be more efficient than several European coun-tries, if the input-based approach is considered, but not when applying the output-based approach. Correlatively, even if the Nordic countries do not appear to be so efficient in input-based assessments (with the notable exception of Iceland), there is a clear gain in their efficiency when the output-based estimates are involved. Indeed, in terms of output-based efficiency, the Nordic countries strongly dominate all the other countries in the dataset (including Japan and the United States), followed by Germany and the Baltic countries. If Japan, Cyprus, or the United Kingdom might obtain results comparable with the current ones by involving less resources, the Nordic countries display a potential to increase the production of the human capi-tal based on their current allocated human and financial resources.

Second, there is a clear sensitivity of the results with respect to the thought-out assumptions on education technologies. However, it appears, perhaps not surprising-ly, that the ‘variable returns to scale/free disposability hull’ assumptions about these technologies are more plausible than the ‘constant returns to scale’ one. Broadly, our findings suggest that European countries should place more emphasis on improv-ing education technologies by adoptimprov-ing more uniform education standards and mech-anisms.

Overall, these arguments can be synthesised by the idea that the DEA approach is able to provide a useful tool for assessing the efficiency of ECE and primary edu-cation processes.

References

ABBOTT, M. DOUCOULIAGOS, C. [2003]: The efficiency of Australian universities: a data envelopment analysis. Economics of Education Review. Vol. 22. No. 1. pp. 89–97.

http://doi.org/10.1016/S0272-7757(01)000 68-1

ACEMOGLU,D.AUTOR,D. [2012]: What does human capital do? A review of Goldin and Katz’s The Race between Education and Technology. Journal of Economic Literature. Vol. 50.

No. 2. pp. 426–463. http://doi.org/10.1257/jel.50.2.426

BANKER,R. D.NATARAJAN, R. [2008]: Evaluating contextual variables affecting productivity using data envelopment analysis. Operations Research. Vol. 56. No. 1. pp. 48–58.

http://doi.org/10.1287/opre.1070.0460

BESSTREMYANNAYA,G.SIMM,J.[2015]: Robust Non-Parametric Estimation of Cost Efficiency with an Application to Banking Industry. CEFIR/NES Working Paper series. Working Paper No. 217. August. https://www.nes.ru/files/Preprints-resh/WP217.pdf

BESSTREMYANNAYA, G. [2011]: Managerial performance and cost efficiency of Japanese local public hospitals: a latent class stochastic frontier model. Health Economics. Vol. 20.

Issue S1. pp. 19–34. http://doi.org/10.1002/hec.1769

BESSTREMYANNAYA, G. [2013]: The impact of Japanese hospital financing reform on hospital efficiency: a difference-in-difference approach. The Japanese Economic Review. Vol. 64.

No. 3. pp. 337–362. https://doi.org/10.1111/j.1468-5876.2012.00585.x

BOGETOFT,P.OTTO,L. [2011]: Benchmarking with DEA, SFA, and R, in International Series in Operations Research and Management Science. Springer. New York.

http://doi.org/10.1007/978-1-4419-7961-2

BOGETOFT,P.OTTO,L. [2015]: ‘R’ package ‘Benchmark and Frontier Analysis Using DEA and SFA’, July 8. Vignette available at: https://cran.r-project.org/package=Benchmarking CHARNES,A.COOPER,W.W.RHODES,E. [1978]: Measuring the efficiency of decision making

units. European Journal of Operational Research. Vol. 2. Issue 6. pp. 429–444.

http://doi.org/10.1016/0377-2217(78)90138-8

DARAIO, C. SIMAR, L. WILSON, P. [2016]: Nonparametric Estimation of Efficiency in the Presence of Environmental Variables. Technical Report No. 2. Dipartimento di Ingegneria Informatica Automatica e Gestionale Antonio Ruberti. Sapienza Università di Roma.

http://www.dis.uniroma1.it/~bibdis/RePEc/aeg/report/2016-02.pdf

DARAIO, C. SIMAR, L. [2007]: Advanced Robust and Nonparametric Methods in Efficiency Analysis. Springer. New York. http://doi.org/10.1007/978-0-387-35231-2

FARRELL,M.J. [1957]: The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General). Vol. 120. No. 3. pp. 253–290. http://doi.org/10.2307/2343100 GIBBONS, S. SILVA, O. [2011]: School quality, child wellbeing and parents’ satisfaction.

Economics of Education Review. Vol. 30. Issue 2. pp. 312–331.

http://doi.org/10.1016/j.econedurev.2010.11.001

GLASS,J.C.MCCALLION,G.MCKILLOP,D.G.RASARATNAM,S.STRINGER,K.S. [2006]:

Implications of variant efficiency measures for policy evaluations in UK higher education.

Socio-Economic Planning Sciences. Vol. 40. No. 2. pp. 119–142.

http://doi.org/10.1016/j.seps.2004.10.004

GOLDIN, C. [2016]: Human capital. In: Diebolt, C. – Haupert, M. (eds.): Handbook of Cliometrics. Springer-Verlag. Berlin, Heidelberg. pp. 55–86. https://doi.org/10.1007/978-3-642-40406-1_23

GRIN, F. [2001]: On effectiveness and efficiency in education: operationalizing the concepts.

Zeitschriftfür Pädagogik. Vol. 43. January. pp. 87–97.

JOHNES,J. [2006]: Data envelopment analysis and its application to the measurement of efficiency in higher education. Economics of Education Review. Vol. 25. No. 3. pp. 273–288.

http://doi.org/10.1016/ j.econedurev.2005.02.005

KOLB, D. A. [1984]: Experiential Learning: Experience as the Source of Learning and Development. Prentice Hall, Inc. Upper Saddle River. https://doi.org/10.1016/B978-0-7506-7223-8.50017-4

KWON,D.-B. [2009]: Human Capital and Its Measurement. 3rd OECD World Forum. ‘Statistics, Knowledge and Policy’ Charting Progress, Building Visions, Improving Life.

27–30 October. Busan.

MANYEKI,J.KOTOSZ,B. [2019]: Estimation of stochastic production functions: the state of the art. Hungarian Statistical Review. Vol. 2. No. 1. pp. 57–89. https://www.oecd.org/site/

progresskorea/44111355.pdf

MAYSTON, D.J. [1996]: Educational attainment and resource use: mystery or econometric mis-specification? Education Economics. Vol. 4. Issue 2. pp. 127–142. https://doi.org/10.1080/

09645299600000013

MININGOU,É.W.VIERSTRAETE,V. [2013]: Households’ living situation and the efficient provi-sion of primary education in Burkina Faso. Economic Modelling. Vol. 35. September.

pp. 910–917. https://doi.org/10.1016/j.econmod.2013.03.002

NAZARKO,J.ŠAPARAUSKAS,J. [2014]: Application of DEA method in efficiency evaluation of public higher education institutions. Technological and Economic Development of Economy.

Vol. 20. No. 1. pp. 25–44. http://doi.org/10.3846/20294913.2014.837116

OECD(ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT) [2001]: The Well-Being of Nations – The Role of Human and Social Capital. Centre for Educational Research and Innovation. http://www.oecd.org/site/worldforum/33703702.pdf

OFFICIAL JOURNAL OF THE EUROPEAN UNION [2009]: Council notices from European Union institu-tions and bodies. Council conclusions of 12 May 2009 on a strategic framework for European cooperation in education and training (ET 2020). C 119. Vol. 52. 28 May. pp. 2–9. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ:C:2009:119:FULL&from=FR

PHILLIPS,D.A.LOWENSTEIN,A.E. [2011]: Early care, education, and child development. Annual Review of Psychology. Vol. 62. January. pp. 483–500. http://doi.org/10.1146/

annurev.psych.031809.130707

SAHLBERG,P. [2006]: Education reform for raising economic competitiveness. Journal of Educational Change. Vol. 7. 5 September. pp. 259–287. http://doi.org/10.1007/s10833-005-4884-6 SIMAR,L.WILSON,P.W. [2000]: A general methodology for bootstrapping in non-parametric

frontier models. Journal of Applied Statistics. Vol. 27. Issue 6. pp. 779–802.

http://doi.org/10.1080/02664760050081951

SIMAR,L.WILSON,P.W. [2002]: Non-parametric tests of returns to scale. European Journal of Operational Research. Vol. 139. Issue 1. pp. 115–132. https://doi.org/10.1016/S0377-2217(01)00167-9

SIMAR,L.WILSON,P.W. [2007]: Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics. Vol. 136. No. 1. pp. 31–64.

http://doi.org/10.1016/j.jeconom.2005.07.009

SIMAR,L. WILSON, P.W. [2008]: Statistical inference in nonparametric frontier models: recent developments and perspectives. In: Fried, H. O. – Knox Lovell, C. A. – Schmidt, Sh. S. (eds.):

The Measurement of Productive Efficiency and Productivity Change. Oxford University Press.

Oxford. pp. 1–124. http://doi.org/10.1093/acprof:oso/9780195183528.003.0004

SIMAR, L.WILSON, P. W. [2011a]: Two-stage DEA: caveat emptor. Journal of Productivity Analysis. Vol. 36. 9 July. pp. 205–218. http://doi.org/10.1007/s11123-011-0230-6

SIMAR,L.WILSON,P.W. [2011b]: Inference by the m out of n bootstrap in nonparametric frontier models. Journal of Productivity Analysis. Vol. 36. 27 November. pp. 33–53.

http://doi.org/10.1007/s11123-010-0200-4

SIMKOVIC,M. [2013]: Risk-based student loans. Washington and Lee Law Review. Vol. 70. No. 1.

pp. 527–648. http://doi.org/10.2139/ssrn.1941070

SIMM,J.BESSTREMYANNAYA,G. [2016]: ‘R’ package ‘Robust Data Envelopment Analysis (DEA) for R’, November 25. Vignette available at: https://cran.r-project.org/web/packages/rDEA/rDEA.pdf

SOTERIOU,A.C.KARAHANNA,E.PAPANASTASIOU,C.DIAKOURAKIS,M.S. [1998]: Using DEA to evaluate the efficiency of secondary schools: the case of Cyprus. International Journal of Educational Management. Vol. 12. No. 2. pp. 65–73. http://doi.org/10.1108/

09513549810204441

SUTHERLAND, D.PRICE,R.JOUMARD, I.CHANTAL,N. [2007]: Performance Indicators for Public Spending Efficiency in Primary and Secondary Education. OECD Economics Department Working Paper No. 546. Organisation for Economic Co-operation and Development. http://doi.org/10.1787/285006168603

THANASSOULIS,E.KORTELAINEN,M.JOHNES,G.JOHNES,J. [2010]: Costs and efficiency of higher education institutions in England: a DEA analysis. Journal of the Operational Research Society. Vol. 62. No. 7. pp. 1282–1297. http://doi.org/10.1057/jors.2010.68 TOBIN, J. [2005]: Quality in early childhood education: an anthropologist’s perspective. Early

Education & Development. Vol. 16. Issue 4. pp. 421–434. https://doi.org/10.1207/

s15566935eed1604_3

VIGNOLES,A.LEVACIC,R.WALKER,J.MACHIN,S.REYNOLDS,D. [2000]: The Relationship between Resource Allocation and Pupil Attainment: A Review. London School of Economics, Centre of Economic Education. London.

WANG,F.KINZIE,M.B.MCGUIRE,P.PAN,E. [2010]: Applying technology to inquiry-based learning in early childhood education. Early Childhood Education Journal. Vol. 37.

6 December. pp. 381–389. https://doi.org/10.1007/s10643-009-0364-6

WORTHINGTON,A.C. [2001]: An empirical survey of frontier efficiency measurement techniques in education. Education Economics. Vol. 9. Issue 3. pp. 245–268. https://doi.org/

10.1080/09645290110086126

ZALAI,E. [2000]: Matematikai közgazdaságtan. A korszerű mikroökonómiai elemzés klasszikus és neoklasszikus szemléletű modelljei. KJK-Kerszöv. Budapest.

KAPCSOLÓDÓ DOKUMENTUMOK