17-21 June 2010, Lugano, Switzerland

Transcription

1 International Doctoral Courses in Health Economics and Policy Course: Health Econometrics Laboratory I Empirical Methods in Health Economics: Analysis of Efficiency and Consumer Choice June 2010, Lugano, Switzerland Instructors: Prof. Massimo Filippini, University of Lugano and ETH Zurich (mfilippini@ethz.ch) Prof. William Greene, Stern Business School, New York (wgreene@stern.nyu.edu) Dr. Mehdi Farsi, University of Lugano and ETH Zurich (mfarsi@ethz.ch) Day-Time Instructor Topics Additional Information Thursday Filippini Introduction to efficiency analysis Technical and allocative efficiency; Scale economies Data, definition of variables, Functional form Total cost versus variable cost function Parametric and Non-parametric approaches Deterministic and stochastic frontier models Estimation of several frontier models The focus of this session is on the motivation and underlying theory of efficiency analysis as well as its applications in health economics and the related policy issues. Some individual or group work is also included. Friday Greene Introduction to discrete choice models Random utility models; revealed vs stated preferences Analysis of binary choices: estimation and inference Panel data models for binary choice Multivariate probit Applications in health economics data This session will include examples and some individual computer work, in which discrete choice models will be applied to real data from the health sector. The instructions on how to use the software will be provided. Saturday Greene Ordered response models, count models Multinomial response: multinomial and nested logit Recent developments in discrete choice models Unobserved heterogeneity in discrete choice models: random coefficient and latent class models Applications in health economics data This session will include individual computer work with real heath care data. The focus will be on the analysis of consumers decisions with heterogeneity and recent developments in panel data discrete choice models. Sunday Farsi Applications of frontier models in cross-section and panel data, econometric specification of efficiency Advances in stochastic frontier models Unobserved heterogeneity in frontier models: panel data with random coefficient and latent class models Presentation and critical discussion of a selection of published papers (Part 1). List of papers below. The morning session follows the lecture on stochastic frontier models. The focus of the session is on the application of various models and the interpretation of the results. The afternoon session includes the first part of student presentations. Monday Farsi Cost frontier analysis using real data and discussing the modeling and estimation issues referring to the models discussed during the course. The approach and results will be compared with other studies. Presentation and critical discussion of a selection of published papers (Part 2). List of papers below. Final overview and discussions. In this session students will have the opportunity to build up and estimate their own models and see the practical issues first-hand. The session continues with presentations and discussion of papers. A brief discussion of what we learned will follow at the end. 1

2 Important notes: Personal computer: Each student should bring her(his) own laptop computer, which is required during most of the sessions. Those who cannot bring their own laptops should contact Mehdi Farsi as soon as possible in order to make other arrangements ahead of time. Presentations: Students are required to present and discuss a paper at one of the sessions on Sunday afternoon or Monday. These presentations are individual but may also be arranged in groups of two or three students. Each student (group) presents one paper from the provided list (see Papers for Presentation below). Unlisted relevant papers can also be accepted with the instructors prior agreement. Each one of the students should a list of 2 or 3 papers to Mehdi Farsi (mfarsi@ethz.ch) not later than May 27. The assigned articles for presentation will be posted by May 28. All students should read all the selected articles and participate in the discussions. The presentations should include a clear discussion of the paper s objectives, methodology and results. The allocated time for each presentation including questions/discussions is about 30 minutes. Evaluation: At the end of the course, each student is required to submit her(his) own review of the paper they present. The review is supposed to be short, about 2 or maximum 3 pages. The last deadline for submitting these reviews is Friday, July 9 th. The review should include a concise discussion of the goals of the paper, the adopted specification, a short presentation and interpretation of the main results and a critical review of the paper s points of strength and weakness. The evaluation of the students will be based on the presentation, the submitted reviews and participation in the class discussions. Reading materials: A list is provided below. All the lecture notes and most of the reading materials are downloadable at: The required password will be provided to enrolled students. The students are strongly recommended to study the materials before the workshop, giving priority to the readings labeled by an asterisk (*). References: Thursday Session * Lecture notes Hadley, J. and Zuckerman, S. (1994), The Role of Efficiency Measurement in Hospital rate setting, Journal of Health Economics, 13, Newhouse, J. P. (1994), Frontier Estimation: How useful a Tool for Health Economics?, Journal of Health Economics, 13, * Chambers, Robert G. (1988) Applied Production Analysis: A Dual Approach, Cambridge University Press. (mainly chapters 1 and 2) * Breyer, F. (1987), The Specification of a Hospital Cost Function, Journal of Health Economics, 6, Crémieux, P. Y. and Ouellette, P. (2001), Omitted Variable Bias and Hospital Costs, Journal of Health Economics, 20, Griffin, R. C., Montgomery, J. M. and Rister, M. E. (1987), Selecting Functional Form in Production Function Analysis, Western Journal of Agricultural Economics, December, Salvanes, K. and Tjotta, S. (1998), A Note on the Importance of Testing for Regularities for Estimated Flexible Functional Forms, Journal of Productivity Analysis, 9, * Murillo-Zamorano, L. R. (2004). Economic Efficiency and Frontier Techniques, Journal of Economic Surveys, 18 (1):

3 Nelson, R. A. (1985). Returns to Scale from Variable and Total Cost Functions Evidence from the Electric Power Industry, Economics Letters, 18, Filippini M., Farsi M., Fetz A. (2005): Benchmarking Analysis in Electricity Distribution. CEPE Report Nr. 4. Farsi, Mehdi and Massimo Filippini (2004): An Empirical Analysis of Cost Efficiency in Non-profit and Public Nursing Homes, Annals of Public and Cooperative Economics, 75 (3): Farsi, Mehdi and Massimo Filippini (2006): An analysis of efficiency and productivity in Swiss hospitals, Swiss Journal of Economics and Statistics, 142 (1): * Greene, W. H. (2008). The Econometric Approach to Efficiency Analysis, In The Measurement of Productive Efficiency and Productivity Growth, H. O. Fried, C. A. Knox Lovell and S. S. Schmidt (editors), Oxford University Press, Oxford, Chapter 2, Kumbhakar, Subal C. and C. A. Knox Lovell (2000) Stochastic Frontier Analysis, Cambridge University Press, Cambridge. Coelli, Tim, D. S. Prasada Rao, and George E. Battese (1998) An Introduction to Efficiency and Productivity Analysis, Kluwer Academic Publishers. Friday and Saturday Sessions Lecture notes (Introduction to NLOGIT-LIMDEP) * Lecture notes for Discrete Choice models Materials related to Lab assignments (files available online) * Greene, William H. (2007): Econometric Analysis, 6 th edition, Prentice Hall, Upper Saddle River, New Jersey. (Chapters 14, 16, 17 and 23) * McFadden, Daniel (2001): Economic Choices, American Economic Review, June 2001, 91 (3): McFadden, Daniel and K. E. Train (2000): Mixed MNL models for discrete response, Journal of Applied Econometrics, 15: Greene, W. H and David A. Heshner (2002): The Mixed Logit Model, State of Practice, Working paper, Stern School of Business, New York University (September 2002). Greene, W. H (2002): Convenient Estimators for the Panel Probit Model: Further Results, Working paper, Stern School of Business, New York University (March 2002). Greene, W. H (2007): Functional Form and Heterogeneity in Models for Count Data, Working paper, Stern School of Business, New York University (April 2007). Hensher, David A., John M. Rose and William H. Greene (2005): Applied Choice Analysis: A Primer, Cambridge University Press. Train, Kenneth E. (2003): Discrete Choice Methods with Simulation, Cambridge University Press. (Chapters 2, 3 and 5) NLOGIT 4.0, Reference Guide, Econometric Software, Inc., 2007, Plainview, NY, USA. LIMDEP 9.0, Econometric Modeling Guide, Volumes 1 and 2, Econometric Software, Inc., 2007, Plainview, NY, USA. Sunday and Monday Sessions * Lecture notes for Stochastic Frontier models Additional readings on Stochastic Frontier models (posted online) * Greene, William (1997) Frontier Production Functions in Handbook of Applied Econometrics, Vol. II: Microeconomics, Editors: M. H. Pesaran and P. Schmidt, chapter 3, pp Battese, George E. and Tim J. Coelli (1992) Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India, Journal of Productivity Analysis, 3 (1): * Greene, William (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 (10), p * Greene, William (2005): Reconsidering Heterogeneity in Panel Data Estimators of the Stochastic Frontier Model, Journal of Econometrics, 126 (2):

4 Greene, William (2002a) Fixed and random effects in stochastic frontier models Working paper, Stern School of Business, New York University (October 2002). Greene, William (2002b) Alternative panel data estimators for stochastic frontier models Working paper, Stern School of Business, New York University (September 2002). Aigner, D., C. A. K. Lovell and P. Schmidt (1977). Formulation and Estimation of Stochastic Frontier Production Function Models, Journal of Econometrics, 6, Jondrow, J., I. Materov, K. Lovell, and P. Schmidt (1982) On the estimation of technical inefficiency in the stochastic frontier production function model Journal of Econometrics, 19, 2/3 (August), Battese, G. E. and T. J. Coelli (1988): Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data. Journal of Econometrics, 38, Alvarez, A., C. Arias and W. Greene. (2004). Accounting for unobservables in production models: management and inefficiency. Working Paper No. E2004/72, Centro de Estudios Andaluces, Spain. Fried, H. O., C. A. Knox Lovell and S. S. Schmidt (eds) (2008). The Measurement of Productive Efficiency and Productivity Growth, Oxford University Press, Oxford. Fried, H. O., C. A. Knox Lovell and S. S. Schmidt (2008). Efficiency and Productivity, In The Measurement of Productive Efficiency and Productivity Growth, H. O. Fried, C. A. Knox Lovell and S. S. Schmidt (editors), Oxford University Press, Oxford, Chapter 1, Rosko, M. D. and R.L. Mutter (2008): Stochastic Frontier Analysis of Hospital Inefficiency: A Review of Empirical Issues and an Assessment of Robustness, Medical Care Research and Review, 65 (2): Jacobs, Rowenta, Peter C. Smith and Andrew Street (2006): Measuring Efficiency in Health Care: Analytic Techniques and Health Policy, Cambridge University Press. Bloor, Karen and Alan Maynard (2006): The productivity of health care (editorial), Health Economics, 15 (12): Hollingsworth, Bruce and Andrew Street (2006): The market for efficiency analysis of health care organisations (editorial), Health Economics, 15 (11): Street, Andrew (2003): How Much Confidence Should We Place in Efficiency Estimates? Health Economics, 12 (11): Papers for Presentation Please choose one paper from the following list for your presentation. i) Efficiency and cost analysis 1. Arocena, Pablo and Ariadna García-Prado (2007): Accounting for quality in the measurement of hospital performance: evidence from Costa Rica, Health Economics, 16 (7): Blank, J.L.T. & Hulst, B.L.V., Productive innovations in hospitals: an empirical research on the relation between technology and productivity in the Dutch hospital industry. Health Economics, 18(6), Deily, Mary E. and Niccie L. McKay (2006): Cost inefficiency and mortality rates in Florida hospitals, Health Economics, 15 (4): Folland, Sherman T. and R. A. Hofler (2001). How reliable are hospital efficiency estimates? Exploiting the dual to homothetic production, Health Economics, 10: Hauck, K. and A. Street (2006): Performance assessment in the context of multiple objectives: A multivariate multilevel analysis, Journal of Health Economics; 25 (6): Herr, A. (2008): Cost and technical efficiency of German hospitals: Does ownership matter?, Health Economics, 17 (9): Heshmati, A. (2002): Productivity measurement in Swedish departments of gynecology and obstetrics, Structural Change and Economic Dynamics, 13 (2002): Liu, C., A. Laporte and B. Ferguson (2008): The Quantile Regression Approach to Efficiency Measurement: Insights from Monte Carlo Simulations, Health Economics, 17 (9):

5 9. Preyra, C. and G. Pink (2006): Scale and Scope Efficiencies through Hospital Consolidations, Journal of Health Economics, 25 (6): Rodríguez-Álvarez, Ana and C. A. Knox Lovell (2004): Excess capacity and expense preference behaviour in National Health Systems: an application to the Spanish public hospitals, Health Economics, 13 (2): Rosko, M. D. (2001), Cost Efficiency of US Hospitals: a Stochastic Frontier Approach, Health Economics, 10, Suraratdecha, C. & Okunade, A.A., Measuring operational efficiency in a health care system: A case study from Thailand. Health Policy, 77(1), Worthington, Andrew C. (2004). Frontier Efficiency Measurement in Health Care: A Review of Empirical Techniques and Selected Applications, Medical Care Research and Review, Vol. 61, No. 2, Yaisawarng, Suthathip and James F. Burgess Jr (2006): Performance-based budgeting in the public sector: an illustration from the VA health care system, Health Economics, 15 (3): ii) Consumer choice models 15. Borah, Bijan J. (2006): A mixed logit model of health care provider choice: analysis of NSS data for rural India, Health Economics, 15 (9): Bridges, S. & Disney, R., Debt and depression. Journal of Health Economics, 29(3), Cameron, T.A., DeShazo, J. & Johnson, E.H., The effect of children on adult demands for health-risk reductions. Journal of Health Economics, 29(3), Frijters, Paul, Michael A. Shields, and Stephen Wheatley Price (2007): Investigating the quitting decision of nurses: panel data evidence from the british national health service, Health Economics, 16 (1): Green, C. & Gerard, K., Exploring the social value of health-care interventions: a stated preference discrete choice experiment. Health Economics, 18(8), Helvoort-Postulart, D.V. et al., Discrete choice experiments for complex health-care decisions: does hierarchical information integration offer a solution? Health Economics, 18(8), Kjær, Trine, Mickael Bech, Dorte Gyrd-Hansen, and Kristian Hart-Hansen (2006): Ordering effect and price sensitivity in discrete choice experiments: need we worry?, Health Economics, 15 (11): Latif, E. (2009): The impact of diabetes on employment in Canada, Health Economics, 18 (5): Paterson, R. W., K. Boyle, C. Parmeter, J. Neumann and P. de Civita (2008): Heterogeneity in preferences for smoking cessation, Health Economics, 17 (12): Regier, D.A. et al., Bayesian and classical estimation of mixed logit: An application to genetic testing. Journal of Health Economics, 28(3), Sfekas, A., Learning, forgetting, and hospital quality: an empirical analysis of cardiac procedures in Maryland and Arizona. Health Economics, 18(6), Tenn, S., Herman, D.A. & Wendling, B., The role of education in the production of health: An empirical analysis of smoking behavior. Journal of Health Economics, 29(3), Winkelmann, R., Reforming health care: Evidence from quantile regressions for counts. Journal of Health Economics, 25(1), Witt, J., Scott, A. & Osborne, R.H., Designing choice experiments with many attributes. An application to setting priorities for orthopaedic waiting lists. Health Economics, 18(6),