Abstract

The aim of this article is to establish a comparison in the degree of efficiency of European universities in the management of the labour insertion of their graduates. The methodology used is the data envelopment analysis (DEA). This type of analysis enables the measurement of the relative efficiency of different organizational units in situations where there is information about multiple inputs and outputs of resources. We define one hundred and twenty-six Decision Making Units (DMU) corresponding to each of the European universities analysed in our study. Developed analysis has allowed to determine the position that each of them occupies in relation to an efficiency frontier. Obtained results have allowed identifying 13 universities that show a score 100. In the interval 99-90 are 5. Between 89-80, we have 7. Between 79-70, 7. For the interval 69-60, 13. Between 59-50 are 19. Between 49-40, 20. Between 39-30, 13. And finally between 29-20 there are 19. The universities with a score of 100 belongs to France (Ecole polytechnique and Ecole des Ponts ParisTech), Italy (Politecnico di Torino), Portugal (Universidade Nova de Lisboa), Spain (University of Navarra and University Carlos III of Madrid), Sweden (Chalmers University of Technology), Switzerland (University of St. Gallen) and United Kingdom (University of Cambridge and University of Oxford). These universities represent the optimum of efficiency if they are compared with the others analysed. The universities that have to improve the employability of its graduates by more than 74% to reach the optimum of efficiency are mostly in the United Kingdom and Sweden, but there are in other regions as Austria, Belgium, Denmark, Germany and Netherlands. Among the main conclusions of this study, we would like to highlight how European university students present employment levels above those workers with lower levels of education. This data points to the high level of general efficiency achieved by university education in improving the degree of employability of its students.

Keywords: data envelopment analysis, efficiency, employment, higher education, productivity

JEL Classification: 032.

Cite as: Blanco, M., Bares, L., Hrynevych, O. (2019). University brand as a key factor of graduates employment. Marketing and Management of Innovations, 3, 193-208. https://doi.org/10.21272/mmi.2019.3-15

This work is licensed under a Creative Commons Attribution 4.0 International License

References

1. Avkiran, N. K. (2001). Investigating technical and scale efficiencies of Australian universities through data envelopment analysis. Socio-Economic Planning Sciences, 35(1), 57-80.
2. Bartual, A. M. and Garrido, R. S. (2011). Analisis de la eficiencia y liderazgo de los puertos espanoles por areas geograficas. Revista de estudios regionales, (91), 161-184.
3. Becker, G. S. (1994). Human capital revisited. In Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education (3rd Edition) (pp. 15-28). The university of Chicago press.
4. Belmonte, L.J. and Plaza, J.A. (2008). Analisis de la eficiencia en las cooperativas de credito en Espana. Una propuesta metodológica basada en el analisis envolvente de datos (DEA). Revista CIRIEC, 63, 113-133.
5. Bessent, A. M. and Bessent, E. W. (1980). Determining the comparative efficiency of schools through data envelopment analysis. Educational Administration Quarterly, 16(2), 57-75.
6. Camilli, G., Vargas, S., Ryan, S. and Barnett, W. S. (2010). Meta-analysis of the effects of early education interventions on cognitive and social development. Teachers College Record, 112(3), 579‑620.
7. Charnes, A., Cooper, W. W. and Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
8. Correas, A. G. and Jorge, M. L. (2010). Analisis de diferentes medidas de la eficiencia investigadora y factores explicativos en las universidades públicas espanolas. Investigaciones de Economía de la Educación, 5, 703-732.
9. Chu Ng, Y. and Li, S. K. (2000). Measuring the research performance of Chinese higher education institutions: an application of data envelopment analysis. Education Economics, 8(2), 139-156.
10. Emrouznejad, A. and Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8.
11. Esping-Andersen, G., & Regini, M. (Eds.). (2000). Why deregulate labour markets?. OUP Oxford.
12. Heath, A. F., Rothon, C. and Kilpi, E. (2008). The second generation in Western Europe: Education, unemployment, and occupational attainment. Annu. Rev. Sociol, 34, 211-235.
13. Johnes, J. (2006). Data envelopment analysis and its application to the measurement of efficiency in higher education. Economics of Education Review, 25(3), 273-288.
14. Koksal, G. and Nalçaci, B. (2006). The relative efficiency of departments at a Turkish engineering college: A data envelopment analysis. Higher Education, 51(2), 173-189.
15. Lee, B. L. and Worthington, A. C. (2016). A network DEA quantity and quality-orientated production model: An application to Australian university research services. Omega, 60, 26-33.
16. Leitner, K. H., Prikoszovits, J., Schaffhauser-Linzatti, M., Stowasser, R. and Wagner, K. (2007). The impact of size and specialisation on universities’ department performance: A DEA analysis applied to Austrian universities. Higher Education, 53(4), 517-538.
17. Marti, M. L., Puertas, R., & Calafat, C. (2014). Calidad y eficiencia de las Universidades Públicas Espanolas. Revista de estudios regionales, 99, 135-154.
18. Martín R. (2007). La eficiencia productiva en el ambito universitario: aspectos claves para su evaluación. Estudios de Economía aplicada, 25(3), 791-811.
19. Nickell, S. (1997). Unemployment and labor market rigidities: Europe versus North America. Journal of Economic perspectives, 11(3), 55-74.
20. Nunez, I. and Livanos, I. (2010). Higher education and unemployment in Europe: an analysis of the academic subject and national effects. Higher Education, 59(4), 475-487.
21. Pastor, J. M. (1995). Eficiencia, cambio productivo y cambio tecnico en los bancos y cajas de ahorro espanolas: Un analisis de frontera no parametrico. Valencia, Institut Valencià d’Investigacions Economiques.
22. Ruggiero, J. and Vitaliano, D. F. (1999). Assessing the efficiency of public schools using data envelopment analysis and frontier regression. Contemporary Economic Policy, 17(3), 321-331.
23. Sagarra, M., Mar-Molinero, C. and Agasisti, T. (2017). Exploring the efficiency of Mexican universities: Integrating data envelopment analysis and multidimensional scaling. Omega, 67, 123-133.
24. Sarrico, C. S., Hogan, S. M., Dyson, R. G. and Athanassopoulos, A. D. (1997). Data envelopment analysis and university selection. Journal of the operational research society, 48(12), 1163-1177.
25. Taylor, B. and Harris, G. (2004). Relative efficiency among South African universities: A data envelopment analysis. Higher Education, 47(1), 73-89.
26. Warning, S. (2004). Performance differences in German higher education: Empirical analysis of strategic groups. Review of Industrial Organization, 24(4), 393-408.