Journal of Productivity Analysis

, Volume 43, Issue 3, pp 269–279

Productivity of tax offices in Norway

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

Abstract

The performance of local tax offices is studied over time using data envelopment analysis to calculate Malmquist productivity indices. The index has the proper homogeneity properties of a total factor productivity index. One input, cost, and six output categories of the main service activities carried out by tax offices, are specified. A bootstrap approach is applied to establish confidence intervals for the individual indices enabling an identification of units that have significant productivity decline, growth, or no change. A novel visual test groups units into these three possible categories. This way of showing consequences of uncertainty should facilitate more tailor-made policies to promote efficiency and productivity improvements. Productivity changes are distributed from a 26 % decline to a 35 % increase over the three-year period with an average growth of 4 %. Inspecting individual unit results, the confidence intervals tend to be wider the larger the units, thus providing more accurate insights than point estimates for actions to improve productivity. Looking at positive and negative changes in cost and productivity together the development of offices is classified into four categories of interest to policymakers; efficient cost increase, efficient cost saving, inefficient cost saving, and inefficient cost increase.

Keywords

Tax office Malmquist productivity index DEA Bootstrap Confidence intervals 

JEL classification

C60 D24 H21 L89 

References

  1. Banker RD (1993) Maximum likelihood, consistency and data envelopment analysis: a statistical foundation. Manag Sci 39(10):1265–1273CrossRefGoogle Scholar
  2. Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies. Manag Sci 30:1078–1092CrossRefGoogle Scholar
  3. Barros CP (2005) Performance measurement in tax offices with a stochastic frontier model. J Econ Stud 32(6):497–510CrossRefGoogle Scholar
  4. Barros CP (2006) Measuring total productivity in Lisbon tax offices with a Malmquist index. Tijdschrift voor Econo en Manag 51(1):25–46Google Scholar
  5. Barros CP (2007) Technical and allocative efficiency of tax offices: a case study. Int J Public Sect Perform Manag 1(1):41–61CrossRefGoogle Scholar
  6. Berg SA, Førsund FR, and Jansen ES (1992) Malmquist indices of productivity growth during the deregulation of Norwegian banking, 1980-89. The Scand J Econ 94, Supplement. Proceedings of a symposium on productivity concepts and measurement problems: welfare, quality and productivity in the service industries, S211–S228Google Scholar
  7. Bird SM, Cox Sir D, Farewell VT, Goldstein H, Holt T, Smith PC (2005) Performance indicators: good, bad, and ugly. J R Stat Soc Ser A 168(Part 1):1–27CrossRefGoogle Scholar
  8. Caves DW, Christensen LR, Diewert E (1982) The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica 50(6):1393–1414CrossRefGoogle Scholar
  9. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444CrossRefGoogle Scholar
  10. Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7:1–6CrossRefGoogle Scholar
  11. Färe R, Grosskopf S, Lindgren B, Roos P (1992) Productivity changes in Swedish pharmacies 1980–1989: a non-parametric Malmquist approach. J Prod Anal 3:85–101CrossRefGoogle Scholar
  12. Färe R, Grosskopf S, Margaritis D (2008) Efficiency and productivity: Malmquist and more. In: Fried HO, Lovell CAK, Schmidt SS (eds) The measurement of productive efficiency and productivity growth, vol 5. Oxford University Press, New York, pp 522–622CrossRefGoogle Scholar
  13. Farrell MJ (1957) The measurement of productive efficiency. J R Stat Soc Ser A 120(3):253–281CrossRefGoogle Scholar
  14. Førsund FR, Hjalmarsson L (1979) Generalised Farrell measures of efficiency: an application to milk processing in Swedish dairy plants. Econ J 89:294–315CrossRefGoogle Scholar
  15. Førsund FR, Hjalmarsson L (2004a) Are all scales optimal in DEA? Theory and empirical evidence. J Prod Anal 21(1):25–48CrossRefGoogle Scholar
  16. Førsund FR, Hjalmarsson L (2004b) Calculating scale elasticity in DEA models. J Oper Res Soc 55:1023–1038CrossRefGoogle Scholar
  17. Førsund FR, 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 269–308CrossRefGoogle Scholar
  18. Førsund FR, Kittelsen SAC, Lindseth F, Edvardsen DF (2006) The tax man cometh-but is he efficient? Natl Inst Econ Rev 197(July):106–119CrossRefGoogle Scholar
  19. Frisch R (1965) Theory of production. D. Reidel Publishing Company, DordrechtCrossRefGoogle Scholar
  20. Gini C (1931) On the circular test of index numbers. Metron 9(2):3–24Google Scholar
  21. González MX, Miles D (2000) Eficiencia en la inspeccion de hacienda. Revista de Economia Aplicada 8(24):203–219Google Scholar
  22. Grifell-Tatjé E, Lovell CAK (1995) A note on the Malmquist productivity index. Econ Lett 47:169–175CrossRefGoogle Scholar
  23. Katharaki M, Tsakas M (2010) Assessing the efficiency and managing the performance of Greek tax offices. J Adv Manag Res 7(1):58–75CrossRefGoogle Scholar
  24. Li S-K, Cheng Y-S (2007) Solving the puzzles of structural efficiency. Eur J Oper Res 180:713–722CrossRefGoogle Scholar
  25. Malmquist S (1953) Index numbers and indifference surfaces. Trabajos de Estadistica 4:209–224CrossRefGoogle Scholar
  26. Moesen W, Persoons A (2002) Measuring and explaining the productive efficiency of tax offices: a non-parametric best-practice frontier approach. Tijdschrift voor Econo en Manag XLVII(3):399–416Google Scholar
  27. Pastor JT, Lovell CAK (2005) A global Malmquist productivity index. Econ Lett 88:266–271CrossRefGoogle Scholar
  28. Silverman BW (1986) Density estimation for statistics and data analysis. Chapman and Hall, LondonCrossRefGoogle Scholar
  29. Simar L, Wilson PW (1998) Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Manage Sci 44:49–61CrossRefGoogle Scholar
  30. Simar L, Wilson PW (1999) Estimating and bootstrapping Malmquist indices. Eur J Oper Res 115(3):459–471CrossRefGoogle Scholar
  31. Simar L, Wilson PW (2000) Statistical inference in nonparametric frontier models: the state of the art. J Prod Anal 13:49–78CrossRefGoogle Scholar
  32. Simpson H (2009) Productivity in public services. J Econ Surv 23(2):250–276CrossRefGoogle Scholar
  33. Thirtle C, Shankar B, Chitkara P, Chatterjee S, Mohanty MS (2000) Size does matter: technical and scale efficiency in Indian state tax jurisdictions. Rev Dev Econ 4(3):340–352CrossRefGoogle Scholar
  34. Tulkens H, van den Eeckaut P (1995) Non-parametric efficiency, progress, and regress measures for panel data: methodological aspects. Eur J Oper Res 80:474–499CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

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

Personalised recommendations