IDEAS home Printed from https://ideas.repec.org/p/abo/neswpt/w0206.html
   My bibliography  Save this paper

The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables

Author

Listed:
  • Galina Besstremyannaya

    (Stanford University, Department of Economics; New Economic School, CEFIR)

Abstract

The paper evaluates the effect of residency matching and prospective payment on technical and cost efficiency of local public hospitals. Efficiency is estimated using panel data quantile regression models with two endogenous treatment variables. We exploit nationwide longitudinal databases on Japanese hospital participation in the two reforms and on financial performance of local public hospitals in 2005-2012. The results demonstrate that more efficient hospitals opt for each of the reforms, and participation further improves efficiency. The introduction of regional caps in residency matching resulted in efficiency losses, particularly in large prefectures, while a step towards best-practice rate setting in inpatient prospective payment system had no effect on efficiency dynamics.

Suggested Citation

  • Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, New Economic School (NES).
  • Handle: RePEc:abo:neswpt:w0206
    as

    Download full text from publisher

    File URL: https://www.nes.ru/files/Preprints-resh/WP206.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    2. Behr, Andreas, 2010. "Quantile regression for robust bank efficiency score estimation," European Journal of Operational Research, Elsevier, vol. 200(2), pages 568-581, January.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Chunping Liu & Audrey Laporte & Brian S. Ferguson, 2008. "The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations," Health Economics, John Wiley & Sons, Ltd., vol. 17(9), pages 1073-1087, September.
    5. Aragon, Y. & Daouia, A. & Thomas-Agnan, C., 2005. "Nonparametric Frontier Estimation: A Conditional Quantile-Based Approach," Econometric Theory, Cambridge University Press, vol. 21(2), pages 358-389, April.
    6. George E. Battese & Greg S. Corra, 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 21(3), pages 169-179, December.
    7. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    8. MOTOHASHI Kazuyuki, 2009. "Productivity of Medical Services: TFP (Total Factor Productivity) and DEA (Data Envelopment Analysis) of Japanese Hospitals(in Japanese)," ESRI Discussion paper series 210, Economic and Social Research Institute (ESRI).
    9. Harding, Matthew & Lamarche, Carlos, 2009. "A quantile regression approach for estimating panel data models using instrumental variables," Economics Letters, Elsevier, vol. 104(3), pages 133-135, September.
    10. Wheelock, David C. & Wilson, Paul W., 2009. "Robust Nonparametric Quantile Estimation of Efficiency and Productivity Change in U.S. Commercial Banking, 1985–2004," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 354-368.
    11. Galina Besstremyannaya, 2011. "Managerial performance and cost efficiency of Japanese local public hospitals: A latent class stochastic frontier model," Health Economics, John Wiley & Sons, Ltd., vol. 20(S1), pages 19-34, September.
    12. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    13. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    14. Holmstrom, Bengt & Milgrom, Paul, 1991. "Multitask Principal-Agent Analyses: Incentive Contracts, Asset Ownership, and Job Design," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 7(0), pages 24-52, Special I.
    15. Bruce Hollingsworth & P.J. Dawson & N. Maniadakis, 1999. "Efficiency measurement of health care: a review of non‐parametric methods and applications," Health Care Management Science, Springer, vol. 2(3), pages 161-172, July.
    16. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    17. Hodgkin, Dominic & McGuire, Thomas G., 1994. "Payment levels and hospital response to prospective payment," Journal of Health Economics, Elsevier, vol. 13(1), pages 1-29, March.
    18. Hadley, Jack & Zuckerman, Stephen, 1994. "The role of efficiency measurement in hospital rate setting," Journal of Health Economics, Elsevier, vol. 13(3), pages 335-340, October.
    19. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    20. Nawata, K. & Kawabuchi, K., 2013. "Evaluation of the DPC-based inclusive payment system in Japan for cataract operations by a new model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 76-85.
    21. Tim Coelli & Sergio Perelman, 2000. "Technical efficiency of European railways: a distance function approach," Applied Economics, Taylor & Francis Journals, vol. 32(15), pages 1967-1976.
    22. Jayanta Bhattacharya & William B. Vogt & Aki Yoshikawa & Toshitaka Nakahara, 1996. "The Utilization of Outpatient Medical Services in Japan," Journal of Human Resources, University of Wisconsin Press, vol. 31(2), pages 450-476.
    23. Galina Besstremyannaya, 2013. "The impact of Japanese hospital financing reform on hospital efficiency: A difference-in-difference approach," The Japanese Economic Review, Japanese Economic Association, vol. 64(3), pages 337-362, September.
    24. Battese, George E. & Corra, Greg S., 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 21(3), pages 1-11, December.
    25. Bruce Hollingsworth & Andrew Street, 2006. "The market for efficiency analysis of health care organisations," Health Economics, John Wiley & Sons, Ltd., vol. 15(10), pages 1055-1059, October.
    26. Kris Knox & Eric Blankmeyer & J. Stutzman, 2007. "Technical efficiency in texas nursing facilities: A stochastic production frontier approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 31(1), pages 75-86, March.
    27. Yuichiro Kamada & Fuhito Kojima, 2010. "Efficiency in Matching Markets with Regional Caps: The Case of the Japan Residency Matching Program," Discussion Papers 10-011, Stanford Institute for Economic Policy Research.
    28. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    29. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 70, Elsevier.
    30. Koutsomanoli-Filippaki, Anastasia I. & Mamatzakis, Emmanuel C., 2011. "Efficiency under quantile regression: What is the relationship with risk in the EU banking industry?," Review of Financial Economics, Elsevier, vol. 20(2), pages 84-95, May.
    31. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    32. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    33. Miika Linna, 1998. "Measuring hospital cost efficiency with panel data models," Health Economics, John Wiley & Sons, Ltd., vol. 7(5), pages 415-427, August.
    34. Cristina Bernini & Marzia Freo & Attilio Gardini, 2004. "Quantile estimation of frontier production function," Empirical Economics, Springer, vol. 29(2), pages 373-381, May.
    35. Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
    36. Yuichiro Kamada & Fuhito Kojima, 2012. "Stability and Strategy-Proofness for Matching with Constraints: A Problem in the Japanese Medical Match and Its Solution," American Economic Review, American Economic Association, vol. 102(3), pages 366-370, May.
    37. Nawata, Kazumitsu & Nitta, Ayako & Watanabe, Sonoko & Kawabuchi, Koichi, 2006. "An analysis of the length of stay and effectiveness of treatment for hip fracture patients in Japan: Evaluation of the 2002 revision of the medical service fee schedule," Journal of Health Economics, Elsevier, vol. 25(4), pages 722-739, July.
    38. Abe, Yukiko, 2007. "The effectiveness of financial incentives in controlling the health care expenditures of seniors," Japan and the World Economy, Elsevier, vol. 19(4), pages 461-482, December.
    39. Atsushi Fuj & Makoto Ohta, 1999. "Stochastic cost frontier and cost inefficiency of Japanese hospitals: a panel data analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 6(8), pages 527-532.
    40. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    41. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    42. Chalkley, Martin & Malcomson, James M., 2000. "Government purchasing of health services," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 15, pages 847-890, Elsevier.
    43. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
    44. Mamatzakis, E & Koutsomanoli-Filippaki, Anastasia & Pasiouras, Fotios, 2012. "A quantile regression approach to bank efficiency measurement," MPRA Paper 51879, University Library of Munich, Germany.
    45. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, Center for Economic and Financial Research (CEFIR).
    2. Galina Besstremyannaya, 2015. "Heterogeneous effect of residency matching and prospective payment on labor returns and hospital scale economies," Discussion Papers 15-001, Stanford Institute for Economic Policy Research.
    3. Besstremyannaya, Galina, 2017. "Heterogeneous effect of the global financial crisis and the Great East Japan Earthquake on costs of Japanese banks," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 66-89.
    4. Galina Besstremyannaya, 2013. "The impact of Japanese hospital financing reform on hospital efficiency: A difference-in-difference approach," The Japanese Economic Review, Japanese Economic Association, vol. 64(3), pages 337-362, September.
    5. Galina Besstremyannaya, 2015. "The adverse effects of incentives regulation in health care: a comparative analysis with the U.S. and Japanese hospital data," Working Papers w0218, Center for Economic and Financial Research (CEFIR).
    6. Galina Besstremyannaya, 2015. "The adverse effects of incentives regulation in health care: a comparative analysis with the U.S. and Japanese hospital data," Working Papers w0218, New Economic School (NES).
    7. Galina Besstremyannaya & Sergei Golovan, 2023. "Measuring heterogeneity in hospital productivity: a quantile regression approach," Journal of Productivity Analysis, Springer, vol. 59(1), pages 15-43, February.
    8. Galina Besstremyannaya, 2014. "The adverse effects of value-based purchasing in health care: dynamic quantile regression with endogeneity," Discussion Papers 14-006, Stanford Institute for Economic Policy Research.
    9. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    10. Galina Besstremyannaya, 2014. "Heterogeneous effect of coinsurance rate on healthcare costs: generalized finite mixtures and matching estimators," Discussion Papers 14-014, Stanford Institute for Economic Policy Research.
    11. Chunping Liu & Audrey Laporte & Brian S. Ferguson, 2008. "The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations," Health Economics, John Wiley & Sons, Ltd., vol. 17(9), pages 1073-1087, September.
    12. Panagiotidis, Theodore & Printzis, Panagiotis, 2021. "Investment and uncertainty: Are large firms different from small ones?," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 302-317.
    13. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    14. Roberto Colombi & Gianmaria Martini & Giorgio Vittadini, 2017. "Determinants of transient and persistent hospital efficiency: The case of Italy," Health Economics, John Wiley & Sons, Ltd., vol. 26(S2), pages 5-22, September.
    15. Mahmut Yaşar & Catherine J. Morrison Paul, 2009. "Size and Foreign Ownership Effects on Productivity and Efficiency: An Analysis of Turkish Motor Vehicle and Parts Plants," Review of Development Economics, Wiley Blackwell, vol. 13(4), pages 576-591, November.
    16. Andini, Corrado & Andini, Monica, 2015. "A Note on Unemployment Persistence and Quantile Parameter Heterogeneity," IZA Discussion Papers 8819, Institute of Labor Economics (IZA).
    17. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    18. Vaneet Bhatia & Sankarshan Basu & Subrata Kumar Mitra & Pradyumna Dash, 2018. "A review of bank efficiency and productivity," OPSEARCH, Springer;Operational Research Society of India, vol. 55(3), pages 557-600, November.
    19. Wang, Yongqiao & Wang, Shouyang & Dang, Chuangyin & Ge, Wenxiu, 2014. "Nonparametric quantile frontier estimation under shape restriction," European Journal of Operational Research, Elsevier, vol. 232(3), pages 671-678.
    20. Woon Leong Lin & Chin Lee & Siong Hook Law, 2021. "Asymmetric effects of corporate sustainability strategy on value creation among global automotive firms: A dynamic panel quantile regression approach," Business Strategy and the Environment, Wiley Blackwell, vol. 30(2), pages 931-954, February.

    More about this item

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:abo:neswpt:w0206. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Vladimir Ivanyukhin (email available below). General contact details of provider: https://edirc.repec.org/data/nerasru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.