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Published online by Cambridge University Press:  10 December 2009

Rowena Jacobs
Affiliation:
University of York
Peter C. Smith
Affiliation:
University of York
Andrew Street
Affiliation:
University of York
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Measuring Efficiency in Health Care
Analytic Techniques and Health Policy
, pp. 219 - 232
Publisher: Cambridge University Press
Print publication year: 2006

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References

Aigner, D., Lovell, C. A. K. and Schmidt, P. 1977. ‘Formulation and estimation of stochastic frontier production function models’. Journal of Econometrics 6: 21–37.CrossRefGoogle Scholar
Ali, A. I. and Seiford, L. M. 1993. ‘The mathematical programming approach to efficiency analysis’, in Fried, H. O, Lovell, C. A. K. and Schmidt, S. S. (eds.), The Measurement of Productive Efficiency. Oxford: Oxford University Press, pp. 120–59.Google Scholar
Allen, R., Athanassopoulos, A., Dyson, R. G. and Thanassoulis, E. 1997. ‘Weight restrictions and value judgements in data envelopment analysis: evolution, development and future directions’. Annals of Operations Research 73: 13–34.CrossRefGoogle Scholar
Anand, S., Ammar, W., Evans, T., Hasegawa, T., Kissimova-Skarbek, K., Langer, A., Lucas, A. O., Makubalo, L., Marandi, A., Meyer, G., Podger, A., Smith, P. C. and Wibulpolprasert, S. 2002. Report of the Scientific Peer Review Group on Health Systems Performance Assessment. Geneva: World Health Organization.Google Scholar
Atkinson, T. 2005. Atkinson Review: Final Report. Measurement of Government Output and Productivity for the National Accounts. Basingstoke: Palgrave Macmillan.Google Scholar
Audit Commission and Department of Health 1999. National Health Service Trust Profiles Handbook – 1997/98. London: Audit Commission.
Baltagi, B. H. 2005. Econometric Analysis of Panel Data, 3rd edn. Chichester: Wiley.Google Scholar
Banker, R. D. and Morey, R. C. 1986. ‘Efficiency analysis for exogenously fixed inputs and outputs’. Operations Research 34: 513–21.CrossRefGoogle Scholar
Banker, R. D., Charnes, A. and Cooper, W. W. 1984. ‘Some models for estimating technical and scale inefficiencies in data envelopment analysis’. Management Science 30: 1078–92.CrossRefGoogle Scholar
Banker, R. D., Conrad, R. F. and Strauss, R. P. 1986. ‘A comparative application of data envelopment analysis and translog methods: an illustrative study of hospital production’. Management Science 32: 30–44.CrossRefGoogle Scholar
Banker, R. D., Gadh, V. M. and Gorr, W. L. 1993. ‘A Monte Carlo comparison of two production frontier estimation methods: corrected ordinary least squares and data envelopment analysis’. European Journal of Operational Research 67: 332–43.CrossRefGoogle Scholar
Banker, R. D., Charnes, A., Cooper, W., Swarts, J. and Thomas, D. 1989. ‘An introduction to data envelopment analysis with some models and their uses’. Research in Governmental and Non-Profit Accounting 5: 125–63.Google Scholar
Barth, W. and Staat, M. 2005. ‘Environmental variables and relative efficiency of bank branches: A data envelopment analysis-bootstrap approach’. International Journal of Business Performance Management 7: 228–40.CrossRefGoogle Scholar
Bates, J. M., Baines, D. and Whynes, D. K. 1996. ‘Measuring the efficiency of prescribing by general practitioners’. Journal of the Operational Research Society 47: 1443–51.CrossRefGoogle Scholar
Bates, J. M., Baines, D. and Whynes, D. K. 1998. ‘Assessing efficiency in general practice: an application of data envelopment analysis’. Health Services Management Research 11: 103–8.CrossRefGoogle ScholarPubMed
Battese, G. E. and Coelli, T. 1988. ‘Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data’. Journal of Econometrics 38: 387–99.CrossRefGoogle Scholar
Battese, G. E. and Coelli, T. 1992. ‘Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India’. Journal of Productivity Analysis 3: 153–69.CrossRefGoogle Scholar
Bauer, P. W., Berger, A. N., Ferrier, G. D. and Humphrey, D. B. 1998. ‘Consistency conditions for regulatory analysis of financial institutions: a comparison of frontier efficiency methods’. Journal of Economics and Business 50: 85–114.CrossRefGoogle Scholar
Benton, P. L., Anthony, P., Evans, H., Light, S. M., Mountney, L. M. and Sanderson, H. F. 1998. ‘The development of Healthcare Resource Groups version 3’. Journal of Public Health Medicine 20: 351–8.CrossRefGoogle ScholarPubMed
Bessent, A., Bessent, W., Elam, J. and Clark, T. 1988. ‘Efficiency frontier determination by constrained facet analysis’. Operations Research 36: 785–96.CrossRefGoogle Scholar
Bhattacharya, A., Lovell, C. A. K. and Sahay, P. 1997. ‘The impact of liberalization on the productive efficiency of Indian commercial banks’. European Journal of Operational Research 98: 332–47.CrossRefGoogle Scholar
Blank, J. L. T. and Valdmanis, V. 2005. ‘A modified three-stage data envelopment analysis: The Netherlands’. European Journal of Health Economics 6: 65–71.CrossRefGoogle ScholarPubMed
Bond, S. 2002. ‘Dynamic panel data models: a guide to micro data methods and practice’. Portuguese Economic Journal 1: 141–62.CrossRefGoogle Scholar
Bowlin, W. F., Charnes, A., Cooper, W. and Sherman, H. D. 1985. ‘Data envelopment analysis and regression approaches to efficiency estimation and evaluation’. Annals of Operations Research 2: 113–38.CrossRefGoogle Scholar
Breyer, F. 1987. ‘The specification of a hospital cost function’. Journal of Health Economics 6: 147–57.CrossRefGoogle ScholarPubMed
Burgess, D. F. 1975. ‘Duality theory and pitfalls in the specification of technologies’. Journal of Econometrics 3: 105–21.CrossRefGoogle Scholar
Burgess, J. F. and Wilson, P. W. 1995. ‘Decomposing hospital productivity changes, 1985–1988: a nonparametric Malmquist approach’. Journal of Productivity Analysis 6: 343–63.CrossRefGoogle Scholar
Casu, B., Girardone, C. and Molyneux, P. 2004. ‘Productivity change in European banking: a comparison of parametric and non-parametric approaches’. Journal of Banking and Finance 28: 2521–40.CrossRefGoogle Scholar
Caves, D. W., Christensen, L. R. and Diewert, W. E. 1982. ‘The economic theory of index numbers and the measurement of input, output and productivity’. Econometrica 50: 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: 429–44.CrossRefGoogle Scholar
Charnes, A., Cooper, W. W. and Rhodes, E. 1981. ‘Evaluating program and managerial efficiency: an application of Data Envelopment Analysis to program follow through’. Management Science 27: 668–97.CrossRefGoogle Scholar
Charnes, A., Cooper, W. W., Lewin, A. Y. and Seiford, L. M. (eds.) 1994. Data Envelopment Analysis: Theory, Methodology, and Application. Boston: Kluwer Academic Publishers.CrossRefGoogle Scholar
Charnes, A., Cooper, W. W., Wei, Q. L. and Huang, Z. 1989. ‘Cone ratio data envelopment analysis and multi-objective programming’. International Journal of Systems Science 20: 1099–118.CrossRefGoogle Scholar
Chilingerian, J. A. 1994. ‘Exploring why some physicians' hospital practices are more efficient: taking data envelopment analysis inside the hospital’, in Charnes, A., Cooper, W. W., Lewin, A. Y. and Seiford, L. M. (eds.), Data Envelopment Analysis: Theory, Methodology and Application. Boston: Kluwer Academic Publishers, pp. 167–94.CrossRefGoogle Scholar
Christensen, L. R. and Greene, W. H. 1976. ‘Economies of scale in US electric power generation’. Journal of Political Economy 84: 655–76.CrossRefGoogle Scholar
Christensen, L. R., Jorgenson, D. W. and Lau, L. J. 1973. ‘Transcendental logarithmic production functions’. Review of Economics and Statistics 55: 28–45.CrossRefGoogle Scholar
Coase, R. H. 1937. ‘The nature of the firm’. Economica 4: 386–405.CrossRefGoogle Scholar
Coelli, T. 1996a. ‘A guide to FRONTIER version 4.1: a computer program for stochastic frontier production and cost function estimation’. Working Paper 96/07, Centre for Efficiency and Productivity Analysis, University of New England, Armidale, NSW.
Coelli, T. 1996b. ‘A guide to DEAP version 2.1: a data envelopment analysis (computer) program’. Working Paper 96/08, Centre for Efficiency and Productivity Analysis, University of New England, Armidale, NSW.
Coelli, T. 1998. ‘A multi-stage methodology for the solution of orientated data envelopment analysis models’. Operations Research Letters 23: 143–49.CrossRefGoogle Scholar
Coelli, T. and Perelman, S. 1996. ‘Efficiency measurement, multiple-output technologies and distance functions: with application to European railways’. Discussion Paper 96/05, CREPP, University of Liege.
Coelli, T. and Perelman, S. 2000. ‘Technical efficiency of European railways: a distance function approach’. Applied Economics 32: 1967–76.CrossRefGoogle Scholar
Coelli, T., Rao, D. and Battese, G. 1998. An Introduction to Efficiency and Productivity Analysis. Boston: Kluwer Academic Publishers.CrossRefGoogle Scholar
Cook, R. D. and Weisberg, S. 1982. Residuals and Influence in Regression. London: Chapman and Hall.Google Scholar
Cooper, W. W., Seiford, L. M. and Tone, K. 2000. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and data envelopment analysis-solver Software. Boston: Kluwer Academic Publishers.Google Scholar
Cornwell, C., Schmidt, P. and Sickles, R. C. 1990. ‘Production frontiers with cross-sectional and time-series variation in efficiency levels’. Journal of Econometrics 46: 185–200.CrossRefGoogle Scholar
Coulter, A., and Magee, H. 2003. The European Patient of the Future. Maidenhead, Berkshire: Open University Press.Google Scholar
Davidson, R. and MacKinnon, J. G. 1985. ‘Testing linear and loglinear regressions against Box–Cox alternatives’. Canadian Journal of Economics 18: 499–517.CrossRefGoogle Scholar
Dismuke, C. and Sena, V. 1999. ‘Has diagnosis-related group payment influenced the technical efficiency and productivity of diagnostic technologies in Portuguese public hospitals?’ Health Care Management Science 2: 107–16.CrossRefGoogle ScholarPubMed
Doyle, J. and Green, R. 1994. ‘Efficiency and cross-efficiency in data envelopment analysis: derivations, meanings and uses’. Journal of the Operational Research Society 45: 567–78.CrossRefGoogle Scholar
EuroQol Group 1990. ‘EuroQol – a new facility for the measurement of health-related quality of life’. Health Policy 16: 199–208.
Everitt, B., Landau, S. and Leese, M. 2001. Cluster Analysis. London: Arnold.Google Scholar
Färe, R. and Grosskopf, S. 1996. Intertemporal Production Frontiers: With Dynamic data envelopment analysis. Boston: Kluwer Academic Publishers.CrossRefGoogle Scholar
Färe, R., Grosskopf, S., Lindgren, B. and Poullier, J. P. 1997. ‘Productivity growth in health care delivery’. Medical Care 35: 354–66.CrossRefGoogle ScholarPubMed
Färe, R., Grosskopf, S., Lindgren, B. and Roos, P. 1992. ‘Productivity changes in Swedish pharmacies 1980–1989: a non-parametric Malmquist approach’. Journal of Productivity Analysis 3: 85–101.CrossRefGoogle Scholar
Färe, R., Grosskopf, S., Norris, M. and Zhang, Z. 1994. ‘Productivity growth, technical progress and efficiency changes in industrialised countries’. American Economic Review 84: 66–83.Google Scholar
Farley, D. E. 1989. ‘Measuring case mix specialization and the concentration of diagnoses in hospitals using information theory’. Journal of Health Economics 8: 185–207.CrossRefGoogle ScholarPubMed
Farley, D. E. and Hogan, C. 1990. ‘Case-mix specialization in the market for hospital services’. Health Services Research 25: 757–83.Google ScholarPubMed
Farrell, M. J. 1957. ‘The measurement of productive efficiency’. Journal of the Royal Statistical Society, Series A, 120: 253–90.CrossRefGoogle Scholar
Farsi, M., Filippini, M. and Kuenzle, M. 2003. ‘Unobserved heterogeneity in stochastic frontier models: a comparative analysis’. Working Paper 03–11, Department of Economics, University of Lugano.
Feldstein, M. S. 1967. Economic Analysis for Health Service Efficiency: Econometric Studies of the British National Health Service. Amsterdam: North-Holland.Google Scholar
Fernández, C., Koop, G. and Steel, M. 2000. ‘A Bayesian analysis of multiple-output production frontiers’. Journal of Econometrics 98: 47–79.CrossRefGoogle Scholar
Ferrier, G. D. and Lovell, C. A. K. 1990. ‘Measuring cost efficiency in banking: econometric and linear programming evidence’. Journal of Econometrics 46: 229–45.CrossRefGoogle Scholar
Ferrier, G. D. and Valdmanis, V. 1996. ‘Rural hospital performance and its correlates’. Journal of Productivity Analysis 7: 63–80.CrossRefGoogle Scholar
Fisher, I. 1922. The Making of Index Numbers. Boston: Houghton Mifflin.Google Scholar
Folland, S. T. and Hofler, R. A. 2001. ‘How reliable are hospital efficiency estimates? Exploiting the dual to homothetic production’. Health Economics 10: 683–98.CrossRefGoogle ScholarPubMed
Fried, H. O., Lovell, C. A. K. and Schmidt, S. S. (eds.) 1993. The Measurement of Productive Efficiency. Oxford: Oxford University Press.Google Scholar
Fried, H. O., Lovell, C. A. K. and Eeckaut, vanden P. 1993. ‘Evaluating the performance of U.S. credit unions’. Journal of Banking and Finance 17: 251–65.CrossRefGoogle Scholar
Fried, H. O., Schmidt, S. S. and Yaisawarng, S. 1999. ‘Incorporating the operating environment into a nonparametric measure of technical efficiency’. Journal of Productivity Analysis 12: 249–67.CrossRefGoogle Scholar
Fried, H. O., Lovell, C. A. K., Schmidt, S. S. and Yaisawarng, S. 2002. ‘Accounting for environmental effects and statistical noise in data envelopment analysis’. Journal of Productivity Analysis 17: 157–74.CrossRefGoogle Scholar
Gerdtham, U. G., Rehnberg, C. and Tambour, M. 1999. ‘The impact of internal markets on health care efficiency: evidence from health care reforms in Sweden’. Applied Economics 31: 935–45.CrossRefGoogle Scholar
Gilthorpe, M. S. and Cunningham, S. J. 2000. ‘The application of multilevel, multivariate modelling to orthodontic research data’. Community Dental Health 17: 236–42.Google ScholarPubMed
Giuffrida, A. 1999. ‘Productivity and efficiency changes in primary care: a Malmquist index approach’. Health Care Management Science 2: 11–26.CrossRefGoogle ScholarPubMed
Giuffrida, A. and Gravelle, H. 2001. ‘Measuring performance in primary care: econometric analysis and data envelopment analysis’. Applied Economics 33: 163–75.CrossRefGoogle Scholar
Giuffrida, A., Gravelle, H. and Sutton, M. 2000. ‘Efficiency and administrative costs in primary care’. Journal of Health Economics 19: 983–1006.CrossRefGoogle ScholarPubMed
Goldstein, H. and Spiegelhalter, D. J. 1996. ‘League tables and their limitations: statistical issues in comparisons of institutional performance’. Journal of the Royal Statistical Society, series A, 159: 385–443.CrossRefGoogle Scholar
González, E. and Gascón, F. 2004. ‘Sources of productivity growth in the Spanish pharmaceutical industry 1994–2000’. Research Policy 33: 735–45.CrossRefGoogle Scholar
Greene, W. H. 1990. ‘A gamma-distributed stochastic frontier model’. Journal of Econometrics 46: 141–63.CrossRefGoogle Scholar
Greene, W. H. 1993. ‘The econometric approach to efficiency analysis’, in Fried, H. O., Lovell, C. A. K. and Schmidt, S. S. (eds.), The Measurement of Productive Efficiency. Oxford: Oxford University Press, pp. 68–119.Google Scholar
Greene, W. H. 1995. Limdep Version 7.0 User's Manual. Castle Hill, NSW: Econometric Software, Inc.Google Scholar
Greene, W. H. 2000. Econometric Analysis. Upper Saddle River, N. J.: Prentice-Hall.Google Scholar
Greene, W. H. 2002. Limdep Version 8.0 Econometric Modelling Guide. Plainview, N.Y.: Econometric Software, Inc.Google Scholar
Greene, W. H. 2004. ‘Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization's panel data on national health care systems’. Health Economics 13: 959–80.CrossRefGoogle ScholarPubMed
Greene, W. H. 2005. ‘Reconsidering heterogeneity in panel data estimators of the stochastic frontier model’. Journal of Econometrics 126: 269–303.CrossRefGoogle Scholar
Grifell-Tatjé, E. and Lovell, C. A. K. 1995. ‘A note on the Malmquist productivity index’. Economics Letters 47: 169–75.CrossRefGoogle Scholar
Grosskopf, S. and Valdmanis, V. 1987. ‘Measuring hospital performance: a non-parametric approach’. Journal of Health Economics 6: 89–107.CrossRefGoogle ScholarPubMed
Hadley, J. and Zuckerman, S. 1994. ‘The role of efficiency measurement in hospital rate setting’. Journal of Health Economics 13: 335–40.CrossRefGoogle Scholar
Harris, J. E. 1977. ‘The internal organisation of hospitals: some economic implications’. Bell Journal of Economics 8: 467–82.CrossRefGoogle Scholar
Hauck, K. and Street, A. 2005. ‘Performance assessment in the context of multiple objectives: a multivariate multilevel approach’, Centre for Health Economics, University of York, Mimeo.
Hauck, K., Rice, N. and Smith, P. 2003. ‘The influence of health care organisations on indicators of health system performance’. Journal of Health Services Research and Policy 8: 68–74.CrossRefGoogle ScholarPubMed
Hausman, J. 1978. ‘Specification tests in econometrics’. Econometrica 46: 1251–71.CrossRefGoogle Scholar
Hill, P. W. and Goldstein, H. 1998. ‘Multilevel modelling of educational data with cross classification and missing identification for units’. Journal of Educational and Behavioural Statistics 23: 117–28.CrossRefGoogle Scholar
Hirschberg, J. G. and Lloyd, P. J. 2000. ‘An application of post-data envelopment analysis bootstrap regression analysis to the spillover of the technology of foreign-invested enterprises in China’. Paper 732, Department of Economics, University of Melbourne.
Hollingsworth, B. 2003. ‘Non-parametric and parametric applications measuring efficiency in health care’. Health Care Management Science 6: 203–18.CrossRefGoogle ScholarPubMed
Hollingsworth, B. and Parkin, D. 2001. ‘The efficiency of the delivery of neonatal care in the United Kingdom’. Journal of Public Health Medicine 23: 47–50.CrossRefGoogle Scholar
Hollingsworth, B. and Smith, P. C. 2003. ‘The use of ratios in data envelopment analysis’. Applied Economics Letters 10: 733–5.CrossRefGoogle Scholar
Horrace, W. C. and Schmidt, P. 1996. ‘Confidence statements for efficiency estimates from stochastic frontier models’. Journal of Productivity Analysis 7: 257–82.CrossRefGoogle Scholar
Hough, J. R. 1985. ‘A note on economies of scale in schools’. Applied Economics 17: 143–4.CrossRefGoogle Scholar
Iezzoni, L. I. 2003. Risk Adjustment for Measuring Healthcare Outcomes, 3rd edn. Baltimore: Health Administration Press.Google Scholar
Intriligator, M. D. 1978. Econometric Models, Techniques and Applications. Englewood Cliffs, N. J.: Prentice-Hall.Google Scholar
Jensen, U. 2000. ‘Is it efficient to analyse efficiency rankings?’ Empirical Economics 25: 189–208.CrossRefGoogle Scholar
Jondrow, J., Lovell, C. A. K., Materov, I. S. and Schmidt, P. 1982. ‘On the estimation of technical inefficiency in the stochastic frontier production function model.’ Journal of Econometrics 19: 233–8.CrossRefGoogle Scholar
Koopmans, T. C. 1951. ‘An analysis of production as an efficient combination of activities’, in Koopmans, T. C. (ed.), Activity Analysis of Production and Allocation. Monograph No. 13, New York: Wiley.Google Scholar
Kooreman, P. 1994. ‘Nursing home care in The Netherlands: a nonparametric efficiency analysis’. Journal of Health Economics 13: 301–16.CrossRefGoogle ScholarPubMed
Kuh, E. and Meyer, J. R. 1955. ‘Correlation and regression estimates when the data are ratios’. Econometrica 23: 400–16.CrossRefGoogle Scholar
Kumbhakar, S. C. 1990. ‘Production frontiers, panel data, and time-varying technical efficiency’. Journal of Econometrics 46: 201–11.CrossRefGoogle Scholar
Kumbhakar, S. C. and Lovell, C. A. K. 2000. Stochastic Frontier Analysis. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Laspeyres, E. 1871. ‘Die Berechnug einer mittleren Waaren-preissteigerung’. Jahrbücherfür Nationalökonomie und Statistik 16: 296–314.Google Scholar
Lee, Y. H. and Schmidt, P. 1993. ‘A production function model with flexible temporal variation in technical efficiency’, in Fried, H. O., Lovell, C. A. K. and Schmidt, S. S. (eds.), The Measurement of Productive Efficiency. New York: Oxford University Press, pp. 237–55.Google Scholar
Lewis, H. F. and Sexton, T. R. 2004. ‘Data envelopment analysis with reverse inputs and outputs’. Journal of Productivity Analysis 21: 113–32.CrossRefGoogle Scholar
Linna, M. 1998. ‘Measuring hospital cost efficiency with panel data models’. Health Economics 7: 415–27.3.0.CO;2-9>CrossRefGoogle ScholarPubMed
Linna, M. and Häkkinen, U. 1998. ‘Determinants of cost efficiency of Finnish hospitals: a comparison of data envelopment analysis and stochastic frontier analysis’. Systems Analysis Laboratory Research Report A78, Helsinki University of Technology.
Löthgren, M. 1998. ‘How to bootstrap data envelopment analysis estimators: a Monte Carlo comparison’. Working Paper Series in Economics and Finance 223, Stockholm School of Economics.
Löthgren, M. 2000. ‘Specification and estimation of stochastic multiple-output production and technical inefficiency’. Applied Economics 32: 1533–40.CrossRefGoogle Scholar
Lovell, C. A. K. 2000. ‘Measuring efficiency in the public sector’, in Blank, J. L. T. (ed.), Public Provision and Performance: Contributions from Efficiency and Productivity Measurement. The Hague: North-Holland.Google Scholar
Lozano-Vivas, A., Pastor, J. T. and Pastor, J. M. 2002. ‘An efficiency comparison of European banking systems operating under different environmental conditions’. Journal of Productivity Analysis 18: 59–77.CrossRefGoogle Scholar
Maddala, G. S. 1988. Introduction to Econometrics. London: Collier Macmillan.Google Scholar
Malmquist, S. 1953. ‘Index numbers and indifference surfaces’. Trabajos de Estatistica 4: 209–42.CrossRefGoogle Scholar
Maniadakis, N. and Thanassoulis, E. 2000. ‘Assessing productivity changes in United Kingdom hospitals reflecting technology and input prices’. Applied Economics 32: 1575–89.CrossRefGoogle Scholar
Maniadakis, N., Hollingsworth, B. and Thanassoulis, E. 1999. ‘The impact of the internal market on hospital efficiency, productivity and service quality’. Health Care Management Science 2: 75–85.CrossRefGoogle ScholarPubMed
Martin, S. and Smith, P. C. 2003. ‘Using panel methods to model waiting times for National Health Service surgery’. Journal of the Royal Statistical Society, Series A, 166: 369–87.CrossRefGoogle Scholar
Martin, S. and Smith, P. C. 2005. ‘Multiple public service performance indicators: towards an integrated statistical approach’. Journal of Public Administration, Research and Theory 15: 599–613.CrossRefGoogle Scholar
McKinnish, T. G. 2000. ‘Model sensitivity in panel data analysis: some caveats about the interpretation of fixed effects and differences estimators’. Unpublished manuscript, Department of Economics, University of Colorado, Boulder. Available at http://spot.colorado.edu/~mckinnis/fe053100.pdf (accessed February 2006).
Mooney, C. Z. and Duval, R. D. 1993. Bootstrapping: A Nonparametric Approach to Statistical Inference. London: Sage Publications.CrossRefGoogle Scholar
Newhouse, J. P. 1994. ‘Frontier analysis: how useful a tool for health economics?’ Journal of Health Economics 13: 317–22.CrossRefGoogle Scholar
Nishimizu, M. and Page, J. M. 1982. ‘Total factor productivity growth, technical progress and technical efficiency change: dimensions of productivity change in Yugoslavia, 1965–78’. Economic Journal 92: 920–36.CrossRefGoogle Scholar
Nunamaker, T. R. 1985. ‘Using data envelopment analysis to measure the efficiency of non-profit organizations: a critical evaluation’. Management and Decision Economics 6: 50–8.CrossRefGoogle Scholar
Office of Water Services 1999. Future Water and Sewerage Charges 2000–05: Draft Determination. London: Office of Water Services.
Ozcan, Y. A. and Cotter, J. J. 1994. ‘An assessment of efficiency of area agencies on aging in Virginia through data envelopment analysis’. The Gerontologist 34: 363–70.CrossRefGoogle ScholarPubMed
Paasche, H. 1874. ‘Ueber die Presentwicklung der letzen Jahre nach den Hamburger Börsennotirungen’. Jahrbücher für Nationalökonomie und Statistik 23: 168–78.Google Scholar
Parkin, D. and Hollingsworth, B. 1997. ‘Measuring production efficiency of acute hospitals in Scotland, 1991–94: validity issues in data envelopment analysis’. Applied Economics 29: 1425–33.CrossRefGoogle Scholar
Paul, C. J. M., Johnston, W. E. and Frengley, G. A. G. 2000. ‘Efficiency in New Zealand sheep and beef farming: the impacts of regulatory reform’. Review of Economics and Statistics 82: 325–37.CrossRefGoogle Scholar
Pedraja-Chaparro, F., Salinas-Jiménez, J. and Smith, P. C. 1997. ‘On the role of weight restrictions in data envelopment analysis’. Journal of Productivity Analysis 8: 215–30.CrossRefGoogle Scholar
Pedraja-Chaparro, F., Salinas-Jiménez, J. and Smith, P. C. 1999. ‘On the quality of the data envelopment analysis model’. Journal of the Operational Research Society 50: 636–44.CrossRefGoogle Scholar
Pindyck, R. S. and Rubinfeld, D. L. 1991. Econometric Models and Economic Forecasts. New York: McGraw-Hill.Google Scholar
Pitt, M. M. and Lee, L. F. 1981. ‘The measurement and sources of technical efficiency in the Indonesian weaving industry’. Journal of Development Economics 9: 43–64.CrossRefGoogle Scholar
Polachek, S. and Yoon, B. 1996. ‘Panel estimates of a two-tiered earnings frontier’. Journal of Applied Econometrics 11: 169–78.3.0.CO;2-#>CrossRefGoogle Scholar
Puig-Junoy, J. 1998a. ‘Measuring health production performance in the Organisation for Economic Co-operation and Development’. Applied Economics Letters 5: 255–9.CrossRefGoogle Scholar
Puig-Junoy, J. 1998b. ‘Technical efficiency in the clinical management of critically ill patients’. Health Economics 7: 263–77.3.0.CO;2-I>CrossRefGoogle ScholarPubMed
Resti, A. 1997. ‘Evaluating the cost-efficiency of the Italian banking system: what can be learned from the joint application of parametric and non-parametric techniques’. Journal of Banking and Finance 21: 221–50.CrossRefGoogle Scholar
Rice, N. and Jones, A. 1997. ‘Multilevel models and health economics’. Health Economics 6: 561–75.3.0.CO;2-X>CrossRefGoogle ScholarPubMed
Roll, Y., Cook, W. and Golany, B. 1991. ‘Controlling factor weights in data envelopment analysis’. IEEE Transactions 23: 2–9.CrossRefGoogle Scholar
Rosko, M. D. 2001. ‘Cost efficiency of US hospitals: a stochastic frontier approach’. Health Economics 10: 539–51.CrossRefGoogle ScholarPubMed
Ryan, M., Scott, D. A., Reeves, C., Bate, A., Teijlingen, E., Russell, E. M., Napper, M. and Robb, C. M. 2001. ‘Eliciting public preferences for healthcare: a systematic review of techniques’. Health Technology Assessment 5(5): 1–4.CrossRefGoogle ScholarPubMed
Salinas-Jiménez, J., Pedraja-Chaparro, F. and Smith, P. C. 2003. ‘Evaluating the introduction of a quasi-market in community care: assessment of a Malmquist index approach’. Socio-Economic Planning Sciences 37: 1–13.CrossRefGoogle Scholar
Scheel, H. 2001. ‘Undesirable outputs in efficiency valuations’. European Journal of Operational Research 132: 400–10.CrossRefGoogle Scholar
Schleifer, A. 1985. ‘A theory of yardstick competition’. Rand Journal of Economics 16: 319–27.CrossRefGoogle Scholar
Schmidt, P. 1985. ‘Frontier production functions’. Econometric Reviews 4: 289–328.CrossRefGoogle Scholar
Schmidt, P. and Lin, T. 1984. ‘Simple tests of alternative specifications in stochastic frontier models’. Journal of Econometrics 24: 349–61.CrossRefGoogle Scholar
Schmidt, P. and Lovell, C. A. K. 1980. ‘Estimating stochastic production and cost frontiers when technical and allocative inefficiency are correlated’. Journal of Econometrics 13: 83–100.CrossRefGoogle Scholar
Schmidt, P. and Sickles, R. C. 1984. ‘Production frontiers and panel data’. Journal of Business and Economic Studies 2: 299–326.Google Scholar
Shephard, R. W. 1970. Theory of Cost and Production Functions. Princeton: Princeton University Press.Google Scholar
Simar, L. and Wilson, P. W. 2004. ‘Estimation and inference in two-stage, semi-parametric models of production processes’. Discussion Paper. 0307, Institut de Statistique, Université Catholique de Louvain.
Skinner, J. 1994. ‘What do stochastic frontier cost functions tell us about inefficiency?’ Journal of Health Economics 13: 323–8.CrossRefGoogle ScholarPubMed
Smith, P. C. 1997. ‘Model misspecification in data envelopment analysis’. Annals of Operations Research 73: 233–52.CrossRefGoogle Scholar
Smith, P. C. 2002. Measuring Up: Improving Health System Performance in Organisation for Economic Co-operation and Development Countries. Paris: Organisation for Economic Co-operation and Development.Google Scholar
Smith, P. C. 2003. ‘Formula funding of public services: an economic analysis’. Oxford Review of Economic Policy 19: 301–22.CrossRefGoogle Scholar
Smith, P. C. and Street, A. 2005. ‘Measuring the efficiency of public services: the limits of analysis’. Journal of the Royal Statistical Society, Series A, 168: 401–17.CrossRefGoogle Scholar
Smith, P. C., Rice, N. and Carr-Hill, R. 2001. ‘Capitation funding in the public sector’. Journal of the Royal Statistical Society, Series A, 164: 217–41.CrossRefGoogle Scholar
Smith, V. K. 1981. ‘Elasticities of substitution for a regulated cost function’. Economic Letters 7: 215–19.CrossRefGoogle Scholar
Söderlund, N. and van der Merwe, R. 1999. ‘ Hospital benchmarking analysis and the derivation of cost indices’. Discussion Paper 178, Centre for Health Economics, University of York.
Söderlund, N., Milne, R., Gray, A. and Raftery, J. 1995. ‘Differences in hospital case mix, and the relationship between case mix and hospital costs’. Journal of Public Health and Medicine 17: 25–32.Google ScholarPubMed
Sommersguter-Reichmann, M. 2000. ‘The impact of the Austrian hospital financing reform on hospital productivity: empirical evidence on efficiency and technology changes using a non-parametric input-based Malmquist approach’. Health Care Management Science 3: 309–21.CrossRefGoogle ScholarPubMed
Stevenson, R. F. 1980. ‘Likelihood functions for generalized stochastic frontier estimation’. Journal of Econometrics 13: 57–66.CrossRefGoogle Scholar
Stone, M. 2002. ‘How not to measure the efficiency of public services (and how one might)’. Journal of the Royal Statistical Society, Series A, 165: 405–34.Google Scholar
Street, A. 2003. ‘How much confidence should we place in efficiency estimates?’ Health Economics 12: 895–907.CrossRefGoogle ScholarPubMed
Street, A. and Dawson, D. 2002. ‘Costing hospital activity: the experience with healthcare resource groups in England’. European Journal of Health Economics 3: 3–9.CrossRefGoogle ScholarPubMed
Tambour, M. 1997. ‘The impact of health care policy initiatives on productivity’. Health Economics 6: 57–70.3.0.CO;2-#>CrossRefGoogle ScholarPubMed
Thanassoulis, E. 1993. ‘A comparison of regression analysis and data envelopment analysis as alternative methods for performance assessments’. Journal of the Operational Research Society 44: 1129–44.CrossRefGoogle Scholar
Thanassoulis, E. 2001. Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software. Dordrecht: Kluwer Academic Publishers.CrossRefGoogle Scholar
Thompson, R. G., Langemeier, L. N., Lee, C. T. and Thrall, R. M. 1990. ‘The role of multiplier bounds in efficiency analysis with application to Kansas farming’. Journal of Econometrics 46: 93–108.CrossRefGoogle Scholar
Timmer, C. P. 1971. ‘Using a probabilistic frontier production function to measure technical efficiency’. Journal of Political Economy 79: 776–94.CrossRefGoogle Scholar
Tofallis, C. 2001. ‘Combining two approaches to efficiency assessment’. Journal of the Operational Research Society 52: 1225–31.CrossRefGoogle Scholar
Torgerson, A. M., Forsund, F. R. and Kittelesen, S. A. C. 1996. ‘Slack-adjusted efficiency measures and ranking of efficient units’. Journal of Productivity Analysis 7: 379–98.CrossRefGoogle Scholar
Törnqvist, L. 1936. ‘The Bank of Finland's consumption price index’. Bank of Finland Monthly Bulletin 10: 1–8.Google Scholar
Üstün, T. B., Chatterji, S., Mechbal, A. Murray, C. J. L. and WHS Collaborating Groups. 2003. ‘The World Health Surveys’, in Murray, C. J. L. and Evans, D. B. (eds.), Health Systems Performance Assessment: Debates, Methods and Empiricism. Geneva: World Health Organization.Google Scholar
Varian, H. R. 1978. Microeconomic Analysis. New York: W. W. Norton.Google Scholar
Vitaliano, D. F. 1987. ‘On the estimation of hospital cost functions’. Journal of Health Economics 6: 305–18.CrossRefGoogle ScholarPubMed
Wagstaff, A. 1989. ‘Estimating efficiency in the hospital sector: a comparison of three statistical cost frontier models’. Applied Economics 21: 659–72.CrossRefGoogle Scholar
Ware, J. E. and Sherbourne, C. D. 1992. ‘The MOS 36-item Short Form Health Status Survey (stochastic frontier-36)’. Medical Care 30: 473–83.CrossRefGoogle Scholar
White, H. A. 1980. ‘A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity’. Econometrica 84: 817–30.CrossRefGoogle Scholar
Williams, A. 2001. ‘Science or marketing at World Health Organization? A commentary on “World Health 2000”’. Health Economics 10: 93–100.CrossRefGoogle Scholar
Williamson, O. E. 1973. ‘Markets and hierarchies: some elementary considerations’. American Economic Association 63: 316–34.Google Scholar
Wong, Y. H. B. and Beasley, J. E. 1990. ‘Restricting weight flexibility in data envelopment analysis’. Journal of the Operational Research Society 41: 829–35.CrossRefGoogle Scholar
World Health Organization 2000. World Health Report 2000. Geneva: World Health Organization.
World Health Organization 2001. Report of the Scientific Peer Review Group on Health Systems Performance Assessment. Geneva: World Health Organization.
Yang, M., Goldstein, H., Browne, W. and Woodhouse, G. 2002. ‘Multivariate multilevel analyses of examination results’. Journal of the Royal Statistical Society, Series A, 165: 137–46.CrossRefGoogle Scholar
Zellner, A. 1962. ‘An efficient method of estimating seemingly unrelated regressions and tests of aggregation bias’. Journal of the American Statistical Association 57: 500–79.CrossRefGoogle Scholar
Zhao, Y., Guthridge, S., Magnus, A. and Vos, T. 2004. ‘The burden of disease and injury in Aboriginal and non-Aboriginal populations in the Northern Territory’. Medical Journal of Australia 180: 498–502.Google ScholarPubMed
Zuckerman, S., Hadley, J. and Lezzoni, L. I. 1994. ‘Measuring hospital efficiency with frontier cost functions’. Journal of Health Economics 13: 255–80.CrossRefGoogle ScholarPubMed

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  • References
  • Rowena Jacobs, University of York, Peter C. Smith, University of York, Andrew Street, University of York
  • Book: Measuring Efficiency in Health Care
  • Online publication: 10 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511617492.013
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  • References
  • Rowena Jacobs, University of York, Peter C. Smith, University of York, Andrew Street, University of York
  • Book: Measuring Efficiency in Health Care
  • Online publication: 10 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511617492.013
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  • References
  • Rowena Jacobs, University of York, Peter C. Smith, University of York, Andrew Street, University of York
  • Book: Measuring Efficiency in Health Care
  • Online publication: 10 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511617492.013
Available formats
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