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The Tax Man Cometh - but is he Efficient?

Published online by Cambridge University Press:  26 March 2020

Finn R Førsund ,
Sverre A.C. Kittelsen ,
Fode Lindseth  and
Dag Fjeld Edvaedsen
Finn R Førsund*
Affiliation:
Department of Economics, University of Oslo and the Frisch Centre
Sverre A.C. Kittelsen
Affiliation:
The Frisch Centre
Fode Lindseth
Affiliation:
The Norwegian Directorate of Taxes
Dag Fjeld Edvaedsen
Affiliation:
The Norwegian Building Research Institute
*
e-mail: [email protected]
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Abstract

The performance of local tax offices of Norway is studied over a three-year period applying Data Envelopment Efficiency analysis and a Malmquist productivity index. The estimates are bias-corrected using a bootstrap approach recently developed for DEA models. The results show that bias correction and the construction of confidence intervals give a quite different picture without bootstrapping. A set of best practice offices is identified for future work on finding explanations for good performance. The productivity development of individual offices is classified into the four categories: productivity improving cost increase, productivity improving cost savings, productivity decreasing cost savings and productivity decreasing cost increase.

Keywords

Tax officeProductivityEfficiencyScale efficiencyDEABootstrapC60D24L89
Type
Articles
Information
National Institute Economic Review , Volume 197 , 01 July 2006 , pp. 106 - 119
Copyright
Copyright © 2006 National Institute of Economic and Social Research

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References

Banker, R. D. (1993), ‘Maximum likelihood, consistency and data envelopment analysis: a statistical foundation,’ Management Science, 39(10), pp. 1265–73.CrossRefGoogle Scholar
Banker, R.D., Charnes, A. and Cooper, W.W. (1984), ‘Some models for estimating technical and scale inefficiencies’, Management Science, 30, pp. 1078–92.CrossRefGoogle Scholar
Bird, S.M., Cox, Sir D., Farewell, V. T., Goldstein, H., Holt, T. and Smith, P.C. (2005), ‘Performance indicators: good, bad, and ugly,’ Journal of the Royal Statistical Society, Series A, 168 (Part 1), pp. 1–27.CrossRefGoogle Scholar
Caves, D.W., Christensen, L.R. and Diewert, E. (1982), ‘The economic theory of index numbers and the measurement of input, output, and productivity,’ Econometrica, 50(6), pp. 1393–414.CrossRefGoogle Scholar
Charnes, A., Cooper, W.W. and Rhodes, E. (1978), ‘Measuring the efficiency of decision making units’, European Journal of Operational Research, 2(6), pp. 429–44.CrossRefGoogle Scholar
Edvardsen, D.F. (2004), ‘Four essays on the measurement of productive efficiency’, Ph.D. thesis, Department of Economics, School of Economics and Commercial Law, Göteborg University.Google Scholar
Edvardsen, D.F., F⊘rsund, F.R. and Kittelsen, S.A.C. (2003), ‘Far out or alone in the crowd: classification of self-evaluators in DEA’, Working paper 2003:7 from the Health Economics research program, University of Oslo.Google Scholar
Efron, B. (1979), ‘Bootstrap methods: another look at the jackknife’, Annals of Statistics, 7, pp. 1–6.CrossRefGoogle Scholar
Farrell, M.J. (1957), ‘The measurement of productive efficiency,’ Journal of the Royal Statistical Society, Series A, 120 (III), pp. 253–81.Google Scholar
F⊘rsund, F.R. and Hjalmarsson, L. (1974), ‘On the measurement of the productive efficiency’, Swedish Journal of Economics, 76, pp. 141–54.Google Scholar
F⊘rsund, F.R. and Hjalmarsson, L. (1979), ‘Generalised Farrell measures of efficiency: an application to milk processing in Swedish dairy plants’, Economic Journal, 89, pp. 294–315.CrossRefGoogle Scholar
F⊘rsund, F.R. and Hjalmarsson, L. (2004), ‘Are all scales optimal in DEA? Theory and empirical evidence’, Journal of Productivity Analysis, 21(1), pp. 25–48.CrossRefGoogle Scholar
F⊘rsund, F.R. and Kalhagen, K.O. (1999), ‘Efficiency and productivity of Norwegian colleges’, in Westermann, G. (ed.), Data Envelopment Analysis in the Service Sector, Wiesbaden, Deutscher Universitäts-Verlag, pp. 269–308.CrossRefGoogle Scholar
F⊘rsund, F.R., Kittelsen, S.A.C. and Lindseth, F. (2005), ‘Efficiency and productivity of Norwegian tax offices’, Memorandum 29/2005 from the Department of Economics, University of Oslo.Google Scholar
Frisch, R. (1965), Theory of Production, Dordrecht, D. Reidel Publishing Company.CrossRefGoogle Scholar
Moesen, W. and Persoons, A. (2002), ‘Measuring and explaining the productive efficiency of tax offices: a non-parametric best-practice frontier approach’, Tijdschrift voor Economie en Management XLVII (3), pp. 399–416.Google Scholar
Silverman, B.W. (1986), Density Estimation for Statistics and Data Analysis, Chapman and Hall.CrossRefGoogle Scholar
Simar, L. and Wilson, P.W. (1998), ‘Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models’, Management Science, 44, pp. 49–61.CrossRefGoogle Scholar
Simar, L. and Wilson, P.W. (1999), ‘Estimating and bootstrapping Malmquist indices’, European Journal of Operations Research, 115(3), pp. 459–71.CrossRefGoogle Scholar
Simar, L. and Wilson, P.W. (2000), ‘Statistical inference in nonparametric frontier models: the state of the art’, Journal of Productivity Analysis, 13, pp. 49–78.CrossRefGoogle Scholar
Simar, L. and Wilson, P.W. (2002), ‘Nonparametric tests of returns to scale’, European Journal of Operations Research, 139, pp. 115–32.CrossRefGoogle Scholar
Simar, L. and Wilson, P.W. (2005), ‘Estimation and inference in two-stage, semi-parametric models of production processes’, Journal of Econometrics (forthcoming).Google Scholar
Torgersen, A.M., F⊘rsund, F.R. and Kittelsen, S.A.C. (1996), ‘Slack-adjusted efficiency measures and ranking of efficient units,’ Journal of Productivity Analysis, 7, pp. 379–39.CrossRefGoogle Scholar