IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/16823.html
   My bibliography  Save this paper

The Diversity of Concentrated Prescribing Behavior: An Application to Antipsychotics

Author

Listed:
  • Anna A. Levine Taub
  • Anton Kolotilin
  • Robert S. Gibbons
  • Ernst R. Berndt

Abstract

Physicians prescribing drugs for patients with schizophrenia and related conditions are remarkably concentrated in their choice among antipsychotic drugs. In 2007 the single antipsychotic drug prescribed by a physician accounted for 66% of all antipsychotic prescriptions written by that physician. Which particular branded antipsychotic was the prescriber's "favorite" varied widely across physicians, i.e. physician prescribing concentration patterns are diverse. Building on Frank and Zeckhauser's [2007] characterization of physician treatments varying from "custom made" to "ready-to-wear", we construct a model of physician learning that generates a number of hypotheses. Using 2007 annual antipsychotic prescribing behavior on 17,652 physicians from IMS Health, we evaluate these predictions empirically. While physician prescribing behavior is generally quite concentrated, prescribers having greater volumes, those with training in psychiatry, male prescribers, and those not approaching retirement age tend to have less concentrated prescribing patterns.

Suggested Citation

  • Anna A. Levine Taub & Anton Kolotilin & Robert S. Gibbons & Ernst R. Berndt, 2011. "The Diversity of Concentrated Prescribing Behavior: An Application to Antipsychotics," NBER Working Papers 16823, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:16823
    Note: EH PR
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w16823.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Duggan, Mark, 2005. "Do new prescription drugs pay for themselves?: The case of second-generation antipsychotics," Journal of Health Economics, Elsevier, vol. 24(1), pages 1-31, January.
    2. Tomas J. Philipson & Seth A. Seabury & Lee M. Lockwood & Dana P. Goldman & Darius N. Lakdawalla, 2010. "Geographic Variation in Health Care: The Role of Private Markets," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(1 (Spring), pages 325-361.
    3. Jovanovic, Boyan & Nyarko, Yaw, 1996. "Learning by Doing and the Choice of Technology," Econometrica, Econometric Society, vol. 64(6), pages 1299-1310, November.
    4. Joseph J. Doyle, Jr. & Steven M. Ewer & Todd H. Wagner, 2008. "Returns to Physician Human Capital: Analyzing Patients Randomized to Physician Teams," NBER Working Papers 14174, National Bureau of Economic Research, Inc.
    5. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    6. Frank, Richard G. & Zeckhauser, Richard J., 2007. "Custom-made versus ready-to-wear treatments: Behavioral propensities in physicians' choices," Journal of Health Economics, Elsevier, vol. 26(6), pages 1101-1127, December.
    7. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    8. Coscelli, Andrea & Shum, Matthew, 2004. "An empirical model of learning and patient spillovers in new drug entry," Journal of Econometrics, Elsevier, vol. 122(2), pages 213-246, October.
    9. Amitabh Chandra & Douglas O. Staiger, 2007. "Productivity Spillovers in Health Care: Evidence from the Treatment of Heart Attacks," Journal of Political Economy, University of Chicago Press, vol. 115(1), pages 103-140.
    10. Stern, S. & Trajtenberg, M., 1998. "Empirical Implications of Physician Authority in Pharmaceutical Decisionmaking," Papers 24-98, Tel Aviv.
    11. Phelps, Charles E., 2000. "Information diffusion and best practice adoption," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 5, pages 223-264, Elsevier.
    12. Marianne Bertrand & Antoinette Schoar, 2003. "Managing with Style: The Effect of Managers on Firm Policies," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1169-1208.
    13. Skinner, Jonathan & Fisher, Elliott, 1997. "Regional Disparities in Medicare Expenditures: An Opportunity for Reform," National Tax Journal, National Tax Association;National Tax Journal, vol. 50(3), pages 413-425, September.
    14. Coscelli, Andrea, 2000. "The Importance of Doctors' and Patients' Preferences in the Prescription Decision," Journal of Industrial Economics, Wiley Blackwell, vol. 48(3), pages 349-369, September.
    15. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    16. Skinner, Jonathan & Fisher, Elliott, 1997. "Regional Disparities in Medicare Expenditures: An Opportunity for Reform," National Tax Journal, National Tax Association, vol. 50(3), pages 413-25, September.
    17. Charles E. Phelps, 1992. "Diffusion of Information in Medical Care," Journal of Economic Perspectives, American Economic Association, vol. 6(3), pages 23-42, Summer.
    18. Steven N. Kaplan & Mark M. Klebanov & Morten Sorensen, 2008. "Which CEO Characteristics and Abilities Matter?," NBER Working Papers 14195, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mariana Carrera & Dana Goldman & Geoffrey Joyce, 2013. "Heterogeneity in Cost-Sharing and Cost-Sensitivity, and the Role of the Prescribing Physician," NBER Working Papers 19186, National Bureau of Economic Research, Inc.
    2. Mayank Aggarwal & Anindya S. Chakrabarti & Chirantan Chatterjee, 2023. "Movies, stigma and choice: Evidence from the pharmaceutical industry," Health Economics, John Wiley & Sons, Ltd., vol. 32(5), pages 1019-1039, May.
    3. Vincenzo Atella & Federico Belotti & Domenico Depalo, 2017. "Drug therapy adherence and health outcomes in the presence of physician and patient unobserved heterogeneity," Health Economics, John Wiley & Sons, Ltd., vol. 26(S2), pages 106-126, September.
    4. Sara Parker‐Lue, 2020. "The impact of reducing pharmaceutical industry payments on physician prescribing," Health Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 382-390, March.
    5. Berndt, Ernst R. & Gibbons, Robert S. & Kolotilin, Anton & Taub, Anna Levine, 2015. "The heterogeneity of concentrated prescribing behavior: Theory and evidence from antipsychotics," Journal of Health Economics, Elsevier, vol. 40(C), pages 26-39.
    6. David Cutler & Jonathan Skinner & Ariel Dora Stern & David Wennberg, 2013. "Physician Beliefs and Patient Preferences: A New Look at Regional Variation in Health Care Spending," NBER Working Papers 19320, National Bureau of Economic Research, Inc.

    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. Berndt, Ernst R. & Gibbons, Robert S. & Kolotilin, Anton & Taub, Anna Levine, 2015. "The heterogeneity of concentrated prescribing behavior: Theory and evidence from antipsychotics," Journal of Health Economics, Elsevier, vol. 40(C), pages 26-39.
    2. David Molitor, 2018. "The Evolution of Physician Practice Styles: Evidence from Cardiologist Migration," American Economic Journal: Economic Policy, American Economic Association, vol. 10(1), pages 326-356, February.
    3. Janet M. Currie & W. Bentley MacLeod, 2018. "Understanding Doctor Decision Making: The Case of Depression," NBER Working Papers 24955, National Bureau of Economic Research, Inc.
    4. Andrew J. Rettenmaier & Zijun Wang, 2012. "Regional variations in medical spending and utilization: a longitudinal analysis of US Medicare population," Health Economics, John Wiley & Sons, Ltd., vol. 21(2), pages 67-82, February.
    5. Andrew J. Epstein & Jonathan D. Ketcham, 2014. "Information technology and agency in physicians' prescribing decisions," RAND Journal of Economics, RAND Corporation, vol. 45(2), pages 422-448, June.
    6. Maynou, L. & McGuire, A. & Serra-Sastre, V., 2019. "Exploring the Impact of New Medical Technology on Workforce Planning," Working Papers 19/07, Department of Economics, City University London.
    7. Balat, Jorge & Papageorge, Nicholas W. & Qayyum, Shaiza, 2017. "Positively Aware? Conflicting Expert Reviews and Demand for Medical Treatment," IZA Discussion Papers 10919, Institute of Labor Economics (IZA).
    8. Frank, Richard G. & Zeckhauser, Richard J., 2007. "Custom-made versus ready-to-wear treatments: Behavioral propensities in physicians' choices," Journal of Health Economics, Elsevier, vol. 26(6), pages 1101-1127, December.
    9. Jonathan Skinner & Douglas Staiger, 2007. "Technology Adoption from Hybrid Corn to Beta-Blockers," NBER Chapters, in: Hard-to-Measure Goods and Services: Essays in Honor of Zvi Griliches, pages 545-570, National Bureau of Economic Research, Inc.
    10. Cesare Fracassi, 2017. "Corporate Finance Policies and Social Networks," Management Science, INFORMS, vol. 63(8), pages 2420-2438, August.
    11. Umberto Garfagnini & Bruno Strulovici, 2012. "Social Learning and Innovation Cycles (revision of DP#1516, The Dynamics of Innovation)," Discussion Papers 1546, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    12. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    13. Epstein, Andrew J. & Nicholson, Sean, 2009. "The formation and evolution of physician treatment styles: An application to cesarean sections," Journal of Health Economics, Elsevier, vol. 28(6), pages 1126-1140, December.
    14. Nuno Camacho & Bas Donkers & Stefan Stremersch, 2011. "Predictably Non-Bayesian: Quantifying Salience Effects in Physician Learning About Drug Quality," Marketing Science, INFORMS, vol. 30(2), pages 305-320, 03-04.
    15. Hosken, Daniel & Wendling, Brett, 2013. "Informing the uninformed: How drug advertising affects check-up visits," International Journal of Industrial Organization, Elsevier, vol. 31(2), pages 181-194.
    16. James B. Rebitzer & Mari Rege & Christopher Shepard, 2008. "Influence, information overload, and information technology in health care," Advances in Health Economics and Health Services Research, in: Beyond Health Insurance: Public Policy to Improve Health, pages 43-69, Emerald Group Publishing Limited.
    17. Zhu, Z.;, 2023. "The Value of Patients: Heterogenous Physician Learning and Generic Drug Diffusion," Health, Econometrics and Data Group (HEDG) Working Papers 23/12, HEDG, c/o Department of Economics, University of York.
    18. Maynou, Laia & Pearson, Georgia & McGuire, Alistair & Serra-Sastre, Victoria, 2022. "The diffusion of robotic surgery: Examining technology use in the English NHS," Health Policy, Elsevier, vol. 126(4), pages 325-336.
    19. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    20. Dubois, Pierre & Tunçel, Tuba, 2021. "Identifying the effects of scientific information and recommendations on physicians’ prescribing behavior," Journal of Health Economics, Elsevier, vol. 78(C).

    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • I10 - Health, Education, and Welfare - - Health - - - General
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    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:nbr:nberwo:16823. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.