Journal of the Operational Research Society

, Volume 68, Issue 4, pp 399–415 | Cite as

Productivity development of Norwegian institutions of higher education 2004–2013

  • Dag Fjeld Edvardsen
  • Finn R. Førsund
  • Sverre A. C. Kittelsen
Article

Abstract

Productivity growth of institutions of higher education is of interest for two main reasons: education is an important factor for productivity growth of the economy, and in countries where higher education is funded by the public sector, accountability of resource use is of key interest. Educational services consist of teaching, research and the “third mission” of dissemination of knowledge to the society at large. A bootstrapped Malmquist productivity change index is used to calculate productivity development for Norwegian institutions of higher education over the 10-year period 2004–2013. The confidence intervals from bootstrapping allow part of the uncertainty of point estimates stemming from sample variation to be revealed. The main result is that the majority of institutions have had a positive productivity growth over the total period. However, when comparing with growth in labour input, the impact on productivity varies a lot.

Keywords

institutions of higher education Farrell efficiency measures Malmquist productivity index bootstrapping 

JEL Classification

C18 C43 C61 D24 H52 I21 

References

  1. Andersen P and Petersen NC (1993). A procedure for ranking efficient units in Data Envelopment Analysis. Management Science 39(10):1261–1264.CrossRefGoogle Scholar
  2. Banker RD and Chang H (2006). The super-efficiency procedure for outlier identification, not for ranking efficient units. European Journal of Operational Research 175(2):1311–1320.CrossRefGoogle Scholar
  3. Berg SA, Førsund FR and Jansen ES (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking, 1980–89. The Scandinavian Journal of Economics 94(Supplement):S211-S228.CrossRefGoogle Scholar
  4. Carrington R, Coelli T and Rao DSP (2005). The performance of Australian universities: Conceptual issues and preliminary results. Economic Papers 24(2):145–163.CrossRefGoogle Scholar
  5. Caves DW, Christensen LR and Diewert E (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica 50(6):1393–1414.CrossRefGoogle Scholar
  6. Coelli TJ, Rao DS, O’Donnell CJ and Battese GE (2005). An Introduction to Efficiency and Productivity Analysis (second editon). Springer: New York.Google Scholar
  7. Daraio C, Bonaccorsi A and Simar L (2015). Efficiency and economies of scale and specialization in European universities: A directional distance approach. Journal of Informetrics 9(3):430–448.CrossRefGoogle Scholar
  8. De Witte K and López-Torres L (2015). Efficiency in education. A review of literature and a way forward. Journal of the Operational Research Society. Pre-published 16 December 2015, doi:10.1057/jors.2015.92.
  9. Edvardsen DF, Førsund FR og Kittelsen SAC (2010). Effektivitets- og produktivitetsanalyser på StatRes-data [Efficiency- and productivity analysis based on StatRes data]. Rapport 2/2010, Kapittel 4, 31–47. Ragnar Frisch Centre for Economic Research: Oslo.Google Scholar
  10. Edvardsen DF, Førsund FR og Kittelsen SAC (2014). Produktivitetsanalyse av universitets- og høgskolesektoren [Productivity analysis of the university and college sector]. Rapport 3/2014. Ragnar Frisch Centre for Economic Research: Oslo.Google Scholar
  11. Edvardsen DF, Førsund FR, Hansen W, Kittelsen SAC and Neurauter T (2006). Productivity and regulatory reform of Norwegian electricity distribution utilities. In: Coelli T and Lawrence D (eds). Performance Measurement and Regulation of Network Utilities. Edward Elgar Publishing: Cheltenham, pp. 97-131.Google Scholar
  12. Efron B (1979). Bootstrap methods: another look at the jackknife. Annals of Statistics 7(1):1–6.CrossRefGoogle Scholar
  13. Färe R, Grosskopf S and Lovell CAK (1994a). Production Frontiers. Cambridge University Press: Cambridge.Google Scholar
  14. Färe R, Grosskopf S and Margaritis D (2008). Efficiency and productivity: Malmquist and more. In: Fried HO, Lovell CAK and Schmidt SS (eds). The Measurement of Productive Efficiency and Productivity Growth. Oxford University Press: New York, pp. 522–622.CrossRefGoogle Scholar
  15. Färe R, Grosskopf S, Lindgren B and Roos P (1992). Productivity changes in Swedish pharmacies 1980–1989: A non-parametric approach. Journal of Productivity Analysis 3(1–2):85–101.CrossRefGoogle Scholar
  16. Färe R, Grosskopf S, Norris M and Zhang Z (1994b). Productivity growth, technical progress and efficiency change in industrialized countries. American Economic Review 84(1):66–83.Google Scholar
  17. Farrell MJ (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society Series A 120(3):253–281.CrossRefGoogle Scholar
  18. Fernández-Santos Y and Martínez-Campillo A (2015). Has the teaching and research productivity of Spanish public universities improved since the introduction of the LOU? Evidence from the bootstrap technique. Revista de Educación 367(1–3):90–114. doi:10.4438/1988-592X-RE-2015-367-284 Google Scholar
  19. Flegg AT, Allen DO, Field K and Thurlow TW (2004). Measuring the efficiency of British universities: A multi-period data envelopment analysis. Education Economics 12(3):231–249.CrossRefGoogle Scholar
  20. Frisch R (1965). Theory of production. D. Reidel Publishing Company: Dordrecht.CrossRefGoogle Scholar
  21. Førsund FR (2016). Productivity interpretations of the Farrell efficiency measures and the Malmquist index and its decomposition. In: Aparicio J, Lovell CAK and Pastor J (eds.). Advances in efficiency and productivity. Chapter 6. Springer: New York, pp. 121–146.CrossRefGoogle Scholar
  22. Førsund FR and Hjalmarsson L (1979). Generalised Farrell measures of efficiency: An application to milk processing in Swedish dairy plants. Economic Journal 89(354):294–315.Google Scholar
  23. Førsund FR and Hjalmarsson L (2004a). Are all scales optimal in DEA? Theory and empirical evidence. Journal of Productivity Analysis 21(1):25–48.CrossRefGoogle Scholar
  24. Førsund FR and Hjalmarsson L (2004b). Calculating scale elasticity in DEA models. Journal of the Operational Research Society 55(10):1023–1038.CrossRefGoogle Scholar
  25. Førsund FR and Kalhagen KO (1999). Efficiency and productivity of Norwegian colleges. In: Westermann G (ed.). Data Envelopment Analysis in the Service Sector. Deutscher Universitäts-Verlag: Wiesbaden, pp. 269–308. (Also issued as Memorandum11/1999 from Department of Economics, University of Oslo).Google Scholar
  26. Førsund FR, Kittelsen SAC and Edvardsen DF (2015). Productivity of tax offices in Norway. Journal of Productivity Analysis 43(3):269–279.CrossRefGoogle Scholar
  27. Førsund FR, Kittelsen SAC, Lindseth F and Edvardsen DF (2006). The tax man cometh—But is he efficient? National Institute Economic Review 197(1):106–119.CrossRefGoogle Scholar
  28. Gini C (1931). On the circular test of index numbers. Metron 9(2):3–24.Google Scholar
  29. Grifell-Tatjé E and Lovell CAK (1995). A note on the Malmquist productivity index. Economics Letters 47(2):169–175.CrossRefGoogle Scholar
  30. Johnes, J (2004). Efficiency measurement. In: Johnes G and Johnes J (eds.). The International Handbook on the Economics of Education. Edward Elgar: Cheltenham.CrossRefGoogle Scholar
  31. Johnes J (2008). Efficiency and productivity change in the English higher education sector from 1996/97 to 2004/5. The Manchester School 76(6):653–674.CrossRefGoogle Scholar
  32. Johnes J (2014). Efficiency and mergers in English higher education 1996/97 to 2008/09: Parametric and non-parametric estimation of the multi-input multi-output distance function. The Manchester School 82(4):465–487.CrossRefGoogle Scholar
  33. Kempkes G and Pohl C (2010). Efficiency of German universities–some evidence from nonparametric and parametric methods. Applied Economics 42(16):2063–2079.CrossRefGoogle Scholar
  34. Kuosmanen T and Sipiläinen T (2009). Exact decomposition of the Fisher ideal total factor productivity index. Journal of Productivity Analysis 31(1):137–150.CrossRefGoogle Scholar
  35. Margaritis D and Smart W (2011). Productivity change in Australasian universities 1997–2005: A Malmquist analysis. Paper presented at the 52 Annual Conference of the New Zealand Association of Economics 29 June–1 July 2011, Wellington, New Zealand. http://nzae.org.nz/wp-content/uploads/2011/Session5/54_Smart.pdf
  36. Nishimizu M and Page JM (1982). Total factor productivity growth, technological progress and technical efficiency change: Dimensions of productivity change in Yugoslavia 1965–78. Economic Journal 92(368):920–936.Google Scholar
  37. Olesen OB and Petersen NC (2016) Stochastic data envelopment analysis—a review. European Journal of Operational Research 251(1):2–21.CrossRefGoogle Scholar
  38. Parteka A and Wolszczak-Derlacz J (2013). Dynamics of productivity in higher education: Cross-European evidence based on bootstrapped Malmquist indices. Journal of Productivity Analysis 40(1):67–82.CrossRefGoogle Scholar
  39. Silverman BW (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall: London.CrossRefGoogle Scholar
  40. Simar L and Wilson PW (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science 44(1):49–61.CrossRefGoogle Scholar
  41. Simar L and Wilson PW (1999). Estimating and bootstrapping Malmquist indices. European Journal of Operations Research 115(3):459–471.CrossRefGoogle Scholar
  42. Simar L and Wilson PW (2000). Statistical inference in nonparametric frontier models: The state of the art. Journal of Productivity Analysis 13(1):49–78.CrossRefGoogle Scholar
  43. Simar L and Wilson PW (2002). Non-parametric tests of returns to scale. European Journal of Operational Research 139(1):115–132.CrossRefGoogle Scholar
  44. Timmer CP (1971). Using a probabilistic frontier production function to measure technical efficiency. Journal of Political Economy 79(4):776–794.CrossRefGoogle Scholar
  45. Torgersen AM, Førsund FR and Kittelsen SAC (1996). Slack adjusted efficiency measures and ranking of efficient units. Journal of Productivity Analysis 7(4):379–398.CrossRefGoogle Scholar
  46. Tulkens H and van den Eeckaut P (1995). Non-parametric efficiency, progress, and regress measures for panel data: Methodological aspects. European Journal of Operational Research 80(3):474–499.CrossRefGoogle Scholar
  47. Worthington AC (2001). An empirical survey of frontier efficiency measurement techniques in education. Education Economics 9(3):245–268.CrossRefGoogle Scholar
  48. Worthington AC and Lee BL (2008). Efficiency, technology and productivity change in Australian universities, 1998–2003. Economics of Education Review 27(3):285–298.CrossRefGoogle Scholar

Copyright information

© The Operational Research Society 2017

Authors and Affiliations

  • Dag Fjeld Edvardsen
    • 1
  • Finn R. Førsund
    • 2
    • 3
  • Sverre A. C. Kittelsen
    • 3
  1. 1.Catenda ASOsloNorway
  2. 2.Department of EconomicsUniversity of OsloOsloNorway
  3. 3.Frisch CentreOsloNorway

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