1 / 28

Efficiency Measurement

William Greene Stern School of Business New York University. Efficiency Measurement. Session 8. Applications. Range of Applications. Regulated industries – railroads, electricity, public services Health care delivery – nursing homes, hospitals, health care systems (WHO)

moanna
Download Presentation

Efficiency Measurement

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. William Greene Stern School of Business New York University Efficiency Measurement

  2. Session 8 Applications

  3. Range of Applications • Regulated industries – railroads, electricity, public services • Health care delivery – nursing homes, hospitals, health care systems (WHO) • Banking and Finance • Many, many (many) other industries. See Lovell and Schmidt survey…

  4. Discrete Variables • Count data frontier • Outcomes inside the frontier: Preserve discrete outcome • Patents (Hofler, R. “A Count Data Stochastic Frontier Model,” • Infant Mortality (Fe, E., “On the Production of Economic Bads…”)

  5. Count Frontier P(y*|x)=Poisson Model for optimal outcome • Effects the distribution: P(y|y*,x)=P(y*-u|x)= a different count model for the mixture of two count variables • Effects the mean:E[y*|x]=λ(x) while E[y|x]=u λ(x) with 0 < u < 1. (A mixture model) • Other formulations.

  6. Alvarez, Arias, Greene Fixed Management • Yit = f(xit,mi*) where mi* = “management” • Actual mi = mi* - ui. Actual falls short of “ideal” • Translates to a random coefficients stochastic frontier model • Estimated by simulation • Application to Spanish dairy farms

  7. Fixed Management as an Input Implies Time Variation in Inefficiency

  8. Random Coefficients Frontier Model [Chamberlain/Mundlak: Correlation mi* (not mi-mi*) with xit]

  9. Estimated Model First order production coefficients (standard errors). Quadratic terms not shown.

  10. Inefficiency Distributions Without Fixed Management With Fixed Management

  11. Holloway, Tomberlin, Irz: Coastal Trawl Fisheries • Application of frontier to coastal fisheries • Hierarchical Bayes estimation • Truncated normal model and exponential • Panel data application • Time varying inefficiency • The “good captain” effect vs. inefficiency

  12. Sports • Kahane: Hiring practices in hockey • Output=payroll, Inputs=coaching, franchise measures • Efficiency in payroll related to team performance • Battese/Coelli panel data translog model • Koop: Performance of baseball players • Aggregate output: singles, doubles, etc. • Inputs = year, league, team • Policy relevance? (Just for fun)

  13. Macro Performance Koop et al. • Productivity Growth in a stochastic frontier model • Country, year, Yit = ft(Kit,Lit)Eitwit • Bayesian estimation • OECD Countries, 1979-1988

  14. Mutual Fund Performance • Standard CAPM • Stochastic frontier added • Excess return=a+b*Beta +v – u • Sub-model for determinants of inefficiency • Bayesian framework • Pooled various different distribution estimates

  15. Energy Consumption • Derived input to household and community production • Cost analogy • Panel data, statewide electricity consumption: Filippini, Farsi, et al.

  16. Hospitals • Usually cost studies • Multiple outputs – case mix • “Quality” is a recurrent theme • Complexity – unobserved variable • Endogeneity • Rosko: US Hospitals, multiple outputs, panel data, determinants of inefficiency = HMO penetration, payment policies, also includes indicators of heterogeneity • Australian hospitals: Fit both production and cost frontiers. Finds large cost savings from removing inefficiency.

  17. Law Firms • Stochastic frontier applied to service industry • Output=Revenue • Inputs=Lawyers, associates/partners ratio, paralegals, average legal experience, national firm • Analogy drawn to hospitals literature – quality aspect of output is a difficult problem

  18. Farming • Hundreds of applications • Major proving ground for new techniques • Many high quality, very low level micro data sets • O’Donnell/Griffiths – Philippine rice farms • Latent class – favorable or unfavorable climate • Panel data production model • Bayesian – has a difficult time with latent class models. Classical is a better approach

  19. Railroads and other Regulated Industries • Filippini – Maggi: Swiss railroads, scale effects etc. Also studied effect of different panel data estimators • Coelli – Perelman, European railroads. Distance function. Developed methodology for distance functions • Many authors: Electricity (C&G). Used as the standard test data for Bayesian estimators

  20. Banking • Dozens of studies • Wheelock and Wilson, U.S. commercial banks • Turkish Banking system • Banks in transition countries • U.S. Banks – Fed studies (hundreds of studies) • Typically multiple output cost functions • Development area for new techniques • Many countries have very high quality data available

  21. Sewers • New York State sewage treatment plants • 200+ statewide, several thousand employees • Used fixed coefficients technology • lnE = a + b*lnCapacity + v – u; b < 1 implies economies of scale (almost certain) • Fit as frontier functions, but the effect of market concentration was the main interest

  22. Summary

  23. Inefficiency

  24. Methodologies • Data Envelopment Analysis • HUGE User base • Largely atheoretical • Applications in management, consulting, etc. • Stochastic Frontier Modeling • More theoretically based – “model” based • More active technique development literature • Equally large applications pool

  25. SFA Models • Normal – Half Normal • Truncation • Heteroscedasticity • Heterogeneity in the distribution of ui • Normal-Gamma • Classical vs. Bayesian applications • Flexible functional forms for inefficiency

  26. Modeling Settings • Production and Cost Models • Multiple output models • Cost functions • Distance functions, profits and revenue functions

  27. Modeling Issues • Appropriate model framework • Cost, production, etc. • Functional form • How to handle observable heterogeneity – “where do we put the zs?” • Panel data • Is inefficiency time invariant? • Separating heterogeneity from inefficiency • Dealing with endogeneity • Allocative inefficiency and the Greene problem

  28. Range of Applications • Regulated industries – railroads, electricity, public services • Health care delivery – nursing homes, hospitals, health care systems (WHO, AHRQ) • Banking and Finance • Many other industries. See Lovell and Schmidt “Efficiency and Productivity” • 27 page bibliography. • Table of over 200 applications since 2000

More Related