Performance Planning

  • Peter Bogetoft
Chapter
Part of the Management for Professionals book series (MANAGPROF)

Abstract

Modern benchmarking is model based. It builds on comprehensive multiple-input, multiple-output relations estimated from actual practices. The level of complexity that benchmarking models can capture vastly exceeds those of mental models and textbook examples. A benchmark model allows us to make substantiated evaluations of the past performances of individual firms, as we have explained in the preceding chapters. The framework, and in particular the underlying model of the technology, however, allows us to do much more than that.

Keywords

Sugar OECD Paradi 

References

  1. .
    Afriat SN (1972) Efficiency estimation of production functions. Int Econ Rev 13:568–598Google Scholar
  2. .
    Agrell PJ, Bogetoft P (2000) Ekonomisk nätbesiktning. Final report stem. Technical report, SUMICSID AB (In Swedish)Google Scholar
  3. .
    Agrell PJ, Bogetoft P (2001a) Incentive regulation. Working PaperGoogle Scholar
  4. .
    Agrell PJ, Bogetoft P (2001b) Should health regulators use DEA? In: Fidalgo Eea (ed) Coordinacion e Incentivos en Sanidad, Asociasion de Economia de la Salud, Barcelona, pp.133–154Google Scholar
  5. .
    Agrell PJ, Bogetoft P (2003) Norm models. Consultation report, Norwegian Water Resources and Energy Directorate (NVE)Google Scholar
  6. .
    Agrell PJ, Bogetoft P (2004) Nve network cost efficiency model. Technical report, Norwegian Energy Directorate NVEGoogle Scholar
  7. .
    Agrell P, Bogetoft P (2007) Development of benchmarking models for German electricity and gas distribution. Consultation report, Bundesnetzagentur, Bonn, GermanyGoogle Scholar
  8. .
    Agrell PJ, Bogetoft P (2008) Electricity and gas dso benchmarking whitepaper. Consulation report, BundesnetzagenturGoogle Scholar
  9. .
    Agrell P, Bogetoft P (2009) International benchmarking of electricity transmission system operators - e3grid project. Consultation report, open version, Council of European Energy RegulatorsGoogle Scholar
  10. .
    Agrell PJ, Bogetoft P (2010a) Benchmarking of german gas transmission system operators. Consultation report, Bundesnetzagentur (BNetzA)Google Scholar
  11. .
    Agrell PJ, Bogetoft P (2010b) A primer on regulation and benchmarking with examples from network industries. Technical Report version 05, SUMICSID ABGoogle Scholar
  12. .
    Agrell PJ, Tind J (2001) A dual approach to noconvex frontier models. J Productivity Anal 16:129–147Google Scholar
  13. .
    Agrell PJ, Bogetoft P, Tind J (2002) Incentive plans for productive efficiency, innovation and learning. Int J Prod Econ 78:1–11Google Scholar
  14. .
    Agrell PJ, Bogetoft P, Bjørndalen J, Vanhanen J, Syrjänen M (2005a) Nemesys subproject A: system analysis. Consultation report, NordenergiGoogle Scholar
  15. .
    Agrell PJ, Bogetoft P, Tind J (2005b) Dea and dynamic yardstick competition in scandinavian electricity distribution. J Productivity Anal 23:173–201Google Scholar
  16. .
    Agrell P, Bogetoft P, Halbersma R, Mikkers M (2007) Yardstick competition for multi-product hospitals. NZa Research Paper 2007/1, NZa, NetherlandsGoogle Scholar
  17. .
    Agrell PJ, Bogetoft P, Cullmann A, von Hirschhausen C, Neumann A, Walter M (2008) Ergebnisdokumentation: Bestimmung der effizienzwerte verteilernetzbetreiber strom. Consultation report, BundesnetzagenturGoogle Scholar
  18. .
    Aigner DJ, Chu SF (1968) On estimating the industry production function. Am Econ Rev 58:826–839Google Scholar
  19. .
    Aigner DJ, Lovell CAK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econom 6:21–37Google Scholar
  20. .
    Andersen J, Bogetoft P (2007) Gains from quota trade: theoretical models and an application to the Danish fishery. Eur Rev Agric Econ 34(1):105–127Google Scholar
  21. .
    Andersen P, Petersen NC (1993) A procedure for ranking efficient units in data envelopment analysis. Manag Sci 39(10):1261–1264Google Scholar
  22. .
    APQC (2011) American productivity and quality center. URL http://www.apqc.org/
  23. .
    Asmild M, Bogetoft P, Hougaard JL (2013) Rationalising inefficiency: a study of Canadian bank branches. Omega 41:80–87Google Scholar
  24. .
    Banker RD (1980) A game theoretic approach to measuring efficiency. Eur J Oper Res 5:262–268Google Scholar
  25. .
    Banker RD (1984) Estimating most productive scale size using data envelopment analysis. Eur J Oper Res 17(1):35–54Google Scholar
  26. .
    Banker RD, Morey RC (1986) Efficiency analysis for exogenously fixed inputs and outputs. Oper Res 34(4):513–521Google Scholar
  27. .
    Banker RD, Thrall R (1992) Estimation of returns to scale using data envelopment analysis. Eur J Oper Res 62:74–84Google Scholar
  28. .
    Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30:1078–1092Google Scholar
  29. .
    Banker RD, Charnes A, Cooper WW, Clarke R (1989) Constrained game formulations and interpretations for data envelopment analysis. Eur J Oper Res 40:299–308Google Scholar
  30. .
    Battese G, Coelli T (1992) Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. J Productivity Anal 3:153–169Google Scholar
  31. .
    Bogetoft P (1986) An efficiency evaluation of Danish police stations (In Danish). Technical reportGoogle Scholar
  32. .
    Bogetoft P (1990) Strategic responses to dea-control - a game theoretical analysis. Technical report, Copenhagen Business SchoolGoogle Scholar
  33. .
    Bogetoft P (1994a) Incentive efficient production frontiers: an agency perspective on DEA. Manag Sci 40:959–968Google Scholar
  34. .
    Bogetoft P (1994b) Non-cooperative planning theory. Springer, BerlinGoogle Scholar
  35. .
    Bogetoft P (1995) Incentives and productivity measurements. Int J Prod Econ 39:67–81Google Scholar
  36. .
    Bogetoft P (1996) DEA on relaxed convexity assumptions. Manag Sci 42:457–465Google Scholar
  37. .
    Bogetoft P (1997) DEA-based yardstick competition: the optimality of best practice regulation. Ann Oper Res 73:277–298Google Scholar
  38. .
    Bogetoft P (2000) DEA and activity planning under asymmetric information. J Productivity Anal 13:7–48Google Scholar
  39. .
    Bogetoft P, Gammeltvedt TE (2006) Mergers in norwegian electricity distribution: a cost saving exercise? Working paper, NVE, NorwayGoogle Scholar
  40. .
    Bogetoft P, Hougaard JL (2003) Rational inefficiencies. J Productivity Anal 20:243–271Google Scholar
  41. .
    Bogetoft P, Katona K (2008) Efficiency gains from mergers in the healthcare sector. Technical report, Nederlandse Zorgautoriteit NZAGoogle Scholar
  42. .
    Bogetoft P, Nielsen K (2004) Monitoring farm, herd and cow performance - efficiency analyses. Technical report, Royal Agricultural University and www.kv{\ae}gforskning.dkGoogle Scholar
  43. .
    Bogetoft P, Nielsen K (2005) Internet based benchmarking. J Group Decis Negotiation 14(3):195–215Google Scholar
  44. .
    Bogetoft P, Nielsen K (2008) DEA based auctions. Eur J Oper Res 184:685–700Google Scholar
  45. .
    Bogetoft P, Nielsen K (2012) Efficient and confidential reallocation of contracts: how the Danish sugar industry adapted to the new sugar regime. J Business Econ ZfB 81(2):165–180Google Scholar
  46. .
    Bogetoft P, Otto L (2011) Benchmarking with DEA, SFA, and R. Springer, New YorkGoogle Scholar
  47. .
    Bogetoft P, Pruzan P (1991) Planning with multiple criteria, 1st edn. North-Holland, AmsterdamGoogle Scholar
  48. .
    Bogetoft P, Wang D (2005) Estimating the potential gains from mergers. J Productivity Anal 23:145–171Google Scholar
  49. .
    Bogetoft P, Wittrup J (2011) Productivity and education: benchmarking of elementary school in denmark. Nordic Econ Policy Rev 2:257–294Google Scholar
  50. .
    Bogetoft P, Tama J, Tind J (2000) Convex input and output projections of nonconvex production possibility sets. Manag Sci 46:858–869Google Scholar
  51. .
    Bogetoft P, Strange N, Thorsen BJ (2003) Efficiency and merger gains in the Danish forestry extension service. Forest Sci 49(4):585–595Google Scholar
  52. .
    Bogetoft P, Fried H, Eeckaut PV (2004) Power benchmarking: what’s wrong with traditional benchmarking and how to do it right. Technical report, Credit Union Research and Advice, Credit Union National Association, http://thepoint.cuna.org/
  53. .
    Bogetoft P, Bramsen JM, Nielsen K (2006a) Balanced benchmarking. Int J Bus Perform Manag 8(4):274–289Google Scholar
  54. .
    Bogetoft P, Färe R, Obel B (2006b) Allocative efficiency of technically inefficient production units. Eur J Oper Res 168(2):450–462Google Scholar
  55. .
    Bogetoft P, Boye K, Neergaard-Petersen H, Nielsen K (2007a) Reallocating sugar beet contracts: can sugar production survive in Denmark. Eur Rev Agric Econ 34(1):1–20Google Scholar
  56. .
    Bogetoft P, Fried H, Eeckaut PV (2007b) The university benchmarker: an interactive computer approach. In: Bonaccorsi A, Daraio C (eds) Universities And Strategic Knowledge Creation, Chap 14. Edward Elgar Publishing, Cheltenham, NorthamptonGoogle Scholar
  57. .
    Bogetoft P, Christensen D, Damgård I, Geisler M, Jakobsen T, Krøigaard M, Nielsen J, Nielsen J, Nielsen K, Pagter J, et al. (2009) Secure multiparty computation goes live. Financial cryptography and data security. Springer, Berlin, pp. 325–343Google Scholar
  58. .
    Bogetoft P, Kristensen T, Pedersen KM (2010) Potential gains from hospital mergers in Denmark. Health Care Manag Sci Energy Policy, 30(8):637-647Google Scholar
  59. .
    Bowlin W (1997) A proposal for designing employment contracts for government managers. Socioecon Plann Sci 31:205–216Google Scholar
  60. .
    Brännlund R, Färe R, Grosskopf S (1995) Environmental regulation and profitability: an application to Swedish pulp and paper mills. Environ Resour Econ 6(1):23–36Google Scholar
  61. .
    Brännlund R, Chung Y, Färe R, Grosskopf S (1998) Emissions trading and profitability: the Swedish pulp and paper industry. Environ Resour Econ 12:345–356Google Scholar
  62. .
    Bundesnetzagentur (2007) Bericht der bundesnetzagentur nach § 112a enwg zur einführung der anreizregulierung nach § 21a enwg. Report, BundesnetzagenturGoogle Scholar
  63. .
    Caves DW, Christensen LR, Diewert WE (1982) The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica 50(6):1393–1414Google Scholar
  64. .
    Chambers RG (1988) Applied production analysis: a dual approach. Cambridge University Press, CambridgeGoogle Scholar
  65. .
    Chambers RG, Chung Y, Färe R (1998) Profit, directional distance functions, and nerlovian efficiency. J Optim Theory Appl 2:351–364Google Scholar
  66. .
    Chang KP (1999) Measuring efficiency with quasiconcave production frontiers. Eur J Oper Res 115:497–506Google Scholar
  67. .
    Chang K, Guh Y (1991) Linear production functions and the data envelopment analysis. Eur J Oper Res 52:215–233Google Scholar
  68. .
    Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444Google Scholar
  69. .
    Charnes A, Cooper WW, Rhodes E (1979) Short communication: measuring the efficiency of decision making units. Eur J Oper Res 3:339Google Scholar
  70. .
    Charnes A, Cooper WW, Lewin AY, Seiford LM (1995) Data envelopment analysis: theory, methodology and applications. Kluwer, BostonGoogle Scholar
  71. .
    Charnes A, Cooper WW, Wei QL, Huang ZM (1989) Cone ratio data envelopment analysis and multi-objective programming. Int J Syst Sci 20:1099–1118Google Scholar
  72. .
    Che YK (1993) Design competition through multidimensional auctions. RAND J Econ 24(4):668–680Google Scholar
  73. .
    Christensen LR, Jorgenson DW, Lau LJ (1973) Transcendental logarithmic production frontiers. Rev Econ Stat 55:28–45Google Scholar
  74. .
    Coelli T, Prasada Rao DS, Battese G (1998) An introduction to efficiency and productivity analysis. Kluwer, BostonGoogle Scholar
  75. .
    Coelli T, Estache A, Perelman S, Trujillo L (2003) A primer on efficiency measurement for utilities and transport regulators. Technical Report 129, World Bank PublicationsGoogle Scholar
  76. .
    Cooper WW, Seiford LM, Tone K (2000) Data envelopment analysis. Kluwer, BostonGoogle Scholar
  77. .
    Cooper WW, Seiford LM, Tone K (2007) Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software, 2nd edn. Springer, SecaucusGoogle Scholar
  78. .
    Cox D, Hinkley D (1974) Theoretical statistics. Chapman and Hall, LondonGoogle Scholar
  79. .
  80. .
    Dalen DM (1996) Strategic responses to relative evaluation of bureaus: implication for bureaucratic slack. J Productivity Anal 7:29–39Google Scholar
  81. .
    Dalen DM, Gomez-Lobo A (1997) Estimating cost functions in regulated industries under asymmetric information. Eur Econ Rev 31:935–942Google Scholar
  82. .
    Dalen DM, Gomez-Lobo A (2001) Yardstick on the road: regulatory contracts and cost efficiency in the Norwegian bus industry. Working Paper, Norwegian School of ManagementGoogle Scholar
  83. .
    Debreu G (1951) The coefficient of resource utilization. Econometrica 19(3):273–292Google Scholar
  84. .
    Demsetz H (1968) Why regulate utilities? J Law Econ 11(1):55-65Google Scholar
  85. .
    Denrell J (2005) Selection bias and the perils of benchmarking. Harvard Bus Rev 83(4):114–119Google Scholar
  86. .
    Deprins D, Simar L, Tulkens H (1984) Measuring labor efficiency in post offices. Technical report. In: Marchand M, Pestieau P, Tulkens H (eds) The performance of public enterprises: concepts and measurements. North Holland, Amsterdam, pp. 243–267Google Scholar
  87. .
    Dorfman R, Samuelson P, Solow R (1958) Linear programming and economic analysis. McGraw-Hill, New YorkGoogle Scholar
  88. .
    Eldenburg LG, Wolcott SK (2005) Cost management - measuring, monitoring, and motivating performance. Wiley, New YorkGoogle Scholar
  89. .
    Farrell MJ (1957) The measurement of productive efficiency. J Royal Stat Soc 120:253–281Google Scholar
  90. .
    Färe R, Grosskopf S (2000) Network DEA. Socioecon Plann Sci 34:35–49Google Scholar
  91. .
    Färe R, Primont D (1995) Multi-output production and duality: theory and applications. Kluwer, BostonGoogle Scholar
  92. .
    Färe R, Grosskopf S, Lovell CAK, Yaisawatng S (1993) Derivation of shadow prices for undesirable outputs: a distance function approach. Rev Econ Stat 75:374–380Google Scholar
  93. .
    Färe R, Grosskopf S, Lindgren B, Ross P (1994) Productivity development in swedish hopsitals: a malmquist output index approach. In: Data envelopment analysis: theory, methodology, and application, Chap 13. Kluwer, Boston, pp 253–272Google Scholar
  94. .
    Färe R, Grosskopf S, Lundström M, Roos P (2007) Evaluating health care efficiency. Scientific Report 1: 2007, R. R., Institute of Applied EconomicsGoogle Scholar
  95. .
    Fethi M, Jackson PM, Weyman-Jones TG (2001) European airlines: a stochastic dea study of efficiency with market liberalisation. Technical report, University of Leicester Efficiency and Productivity Research UnitGoogle Scholar
  96. .
    Försund F, Hjalmarsson L (1979) Generalized farrell measures of efficiency: an application to milk processing in Swedish dairy plants. Econ J 89:294–315Google Scholar
  97. .
    Førsund F, Kittelsen S (1998) Productivity development of Norwegian electricity distribution utilities. Resour Energy Econ 20:207–224Google Scholar
  98. .
    Fox KJ (1999) Efficiency at different levels of aggregation: public vs. private sector firms. Econ Lett 65:173176Google Scholar
  99. .
    Gale D (1960) The theory of linear economic models. McGraw-Hill, New YorkGoogle Scholar
  100. .
    Government TF (2007) Verordnung zum erlass und zur änderung von rechtsvorschriften auf dem gebiet der energieregulierung. Germany Teil I Nr. 55, BundesgesetzblattGoogle Scholar
  101. .
    Greene W (2008) Econometric analysis, 6th edn. Pearson Prentice Hall, Upper Saddle RiverGoogle Scholar
  102. .
    Greene WH (1990) A gamma-distributed stochastic frontier model. J Econom 46:141–164Google Scholar
  103. .
    Hadley G (1962) Linear programming. Addison Wesley, ReadingGoogle Scholar
  104. .
    Hillier FS, Lieberman GJ (2010) Introduction to operations research, 9th edn. McGraw-Hill, New YorkGoogle Scholar
  105. .
    Ibensoft (2010a) User guide to administration module of interactive benchmarking ib. Technical report, Ibensoft ApSGoogle Scholar
  106. .
    Ibensoft (2010b) User guide to interactive benchmarking ib. Technical report, Ibensoft ApSGoogle Scholar
  107. .
    Jacobs R, Smith PC, Street A (2006) Measuring efficiency in health care. Cambrigde University Press, CambridgeGoogle Scholar
  108. .
    Koopmans T (1951) Activity analysis of production and allocation. Wiley, New YorkGoogle Scholar
  109. .
    Kumbhakar SC, Lovel CAK (2000) Stochastic frontier analysis. Cambridge University Press, CambridgeGoogle Scholar
  110. .
    Kuosmanen T (2001) Dea with efficiency classification preserving conditional convexity. Eur J Oper Res 132:83–99Google Scholar
  111. .
    Kuosmanen T (2003) Duality theory of non-convex technologies. J Productivity Anal 20:273–304Google Scholar
  112. .
    Laffont JJ, Tirole J (1993) A theory of incentives in procurement and regulation. MIT Press, CambridgeGoogle Scholar
  113. .
    Langset T (2009) Rundskriv eø 4/2009 om beregning av inntektsrammer og kostnadsnorm for 2010. (In Norwegian) NVE 2009 04925-4, The Norwegian Water Resources and Energy Directorate (NVE)Google Scholar
  114. .
    Land KC, Lovel CAK, Thore S (1993) Chance-constrained data envelopment analysis. Managerial Decis Econ 14:541–554Google Scholar
  115. .
    Lazear E, Rosen S (1981) Rank-order tournaments as optimum labor contracts. J Political Econ 89:841–864Google Scholar
  116. .
    Lehmann EL (1983) Theory of point estimation. Wiley, New YorkGoogle Scholar
  117. .
    Lewin A, Morey RC (1981) Measuring the relative efficiency and output potential of public sector organizations: an application of data envelopment analysis. J Policy Anal Inf Syst 5:267–285Google Scholar
  118. .
    Littlechild S (1983) Regulation of british telecommunications’ profitability: report to the secretary of state. Technical report, Department of Industry, LondonGoogle Scholar
  119. .
    Lovell CAK (1993) Production frontiers and productive efficiency. In: Fried H, Lovell CAK, Schmidt S (eds) The measurement of productive efficiency: techniques and applications. Oxford University Press, New YorkGoogle Scholar
  120. .
    Luenberger DG (1984) Linear and nonlinear programming, 2nd edn. Addison-Wesley, ReadingGoogle Scholar
  121. .
    Luenberger D (1992) Benefit functions and duality. J Math Econ 21:461–481Google Scholar
  122. .
    Malmquist S (1953) Index numbers and indifference curves. Trabajos de Estatistica 4:209–242Google Scholar
  123. .
    Nalebuff BJ, Stiglitz JE (1983) Prizes and incentives: towards a general theory of compensation and competition. Bell J Econ 14:21–43Google Scholar
  124. .
    OECD (2006) Health care quality indicators project conceptual framework paper. Technical report, OECD Health Working PapersGoogle Scholar
  125. .
    Olesen O, Petersen NC (1995) Chance constrained efficiency evaluation. Manag Sci 41(3):442–457Google Scholar
  126. .
    Olesen O, Petersen NC (2002) The use of data envelopment analysis with probabilistic assurance regions for measuring hospital efficiency. J Productivity Anal 17:83–109Google Scholar
  127. .
    Olesen OB, Petersen NC (2007) Target and technical efficiency in dea – controlling for environmental characteristics. Working Paper, the University of Southern DenmarkGoogle Scholar
  128. .
    Paradi JC, Vela S, Yang Z (2004) Assessing bank and bank branch performance: modeling considerations and approaches. In: Cooper WW, Seiford LM, Zhu J (eds) Handbook on data envelopment analysis. Kluwer, BostonGoogle Scholar
  129. .
    Petersen N (1990) Data envelopment analysis on a relaxed set of assumptions. Manag Sci 36(3):305–314Google Scholar
  130. .
    Post GT (2001) Estimating non-convex production sets using transconcave dea. Eur J Oper Res 131:132–142Google Scholar
  131. .
    Rao CR (1973) Linear statistical inference and its applications, 2nd edn. Wiley, New YorkGoogle Scholar
  132. .
    Resende M (2001) Relative efficiency measurement and prospects for yardstick competition in brazilian electricity distribution. Energy Policy (In Press)Google Scholar
  133. .
    Richmond J (1974) Estimating the efficiency of production. Int Econ Rev 15:515–521Google Scholar
  134. .
    Rigby DK (2011a) Management tools 2011 - an executive’s guide. Technical report, Bain & Company IncGoogle Scholar
  135. .
    Rigby DK (2011b) Management tools and trends 2011. Technical report, Bain & Company IncGoogle Scholar
  136. .
    Rigsrevisionen (2000) Report to the state auditors on court productivity etc. (In: Danish:beretning til statsrevisorerne om retternes produktivitet mv.). Technical report, Danish Auditor General‘s OfficeGoogle Scholar
  137. .
    Ruggiero J (1996) On the measurement of technical efficiency in the public sector. Eur J Oper Res 90:553–565Google Scholar
  138. .
    Seiford LM (1994) A dea bibliography (1978–1992). In: Charnes A, Cooper W, Lewin A (eds) Data envelopment analysis: theory, methodology, and application, Kluwer, Boston, pp. 437–469Google Scholar
  139. .
    Shephard RW (1953) Cost and production functions. Princeton University Press, Princeton, reprinted as Lecture Notes in Economics and Mathematical Systems, 1st edn, vol. 194 (Springer, Berlin, 1981)Google Scholar
  140. .
    Shephard RW (1970) Theory of cost and production functions. Princeton University Press, PrincetonGoogle Scholar
  141. .
    Sheriff G (2001) Using data envelopment analysis to design contracts under asymmetric information. Technical report, University of MarylandGoogle Scholar
  142. .
    Shleifer A (1985) A theory of yardstick competition. Rand J Econ 16:319–327Google Scholar
  143. .
    Silvey SD (1970) Statistical inference. Chapmann and Hall, London (reprinted with corrections 1975)Google Scholar
  144. .
    Smith P (1976) On the statistical estimation of parametric frontier production functions. Rev Econ Stat 58:238–239Google Scholar
  145. .
    Tavaras G (2002) A bibliography of data envelopment analysis (1978–2001). Technical report, Rutgers Centre of Operations ResearchGoogle Scholar
  146. .
    Thanassoulis E (2000) DEA and its use in the regulation of water companies. Eur J Oper Res 127:1–13Google Scholar
  147. .
    Thanassoulis E, Portela M, Allen R (2004) Handbook on data envelopment analysis, Kluwer, Dodrecht, Ch 4 Incorporating Value Judgements in DEA, pp. 99–138Google Scholar
  148. .
    Tirole J (1988) The theory of industrial organization. MIT Press, CambridgeGoogle Scholar
  149. .
    Tulkens H (1993) On fdh efficiency analysis: some methodological issues and applications to retail banking, courts and urban transit. J Productivity Anal 4:183–210Google Scholar
  150. .
    Varian HR (1992) Microeconomic analysis, 3rd edn. Norton, New YorkGoogle Scholar
  151. .
    Walter M, Cullmann A (2008) Potential gains from mergers in local public transport – an efficiency analysis applied to germany. Technical report, Technische Universitat DresdenGoogle Scholar
  152. .
    Wunsch P (1995) Peer comparison and regulation: an application to urban mass transit firms in europe. PhD thesis, Department of Economics, UniversitÈ Catholique de Louvain, p 182Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Peter Bogetoft
    • 1
    • 2
  1. 1.Department of EconomicsCopenhagen Business School CBSFrederiksbergDenmark
  2. 2.Yale School of ManagementYale UniveristyNew HavenUSA

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