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The Cost of Influence:How Gifts to Physicians Shape Prescriptions and Drug Costs

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  • Melissa Newham
  • Marica Valente

Abstract

This paper studies how gifts – monetary or in-kind payments – from drug firms to physicians in the US affect prescriptions and drug costs. We estimate heterogeneous treatment effects by combining physician-level data on antidiabetic prescriptions and payments with causal inference and machine learning methods.We find that payments cause physicians to prescribe more brand drugs, resulting in a cost increase of $ 30 per dollar received. Responses differ widely across physicians, and are primarily explained by variation in patients’ out-of-pocket costs. A gift ban is estimated to decrease drug costs by 3-4 %. Taken together, these novel findings reveal how payments shape prescription choices and drive up costs.

Suggested Citation

  • Melissa Newham & Marica Valente, 2023. "The Cost of Influence:How Gifts to Physicians Shape Prescriptions and Drug Costs," Working Papers 2023-03, Faculty of Economics and Statistics, Universität Innsbruck.
  • Handle: RePEc:inn:wpaper:2023-03
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    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. Tatyana Deryugina & Garth Heutel & Nolan H. Miller & David Molitor & Julian Reif, 2019. "The Mortality and Medical Costs of Air Pollution: Evidence from Changes in Wind Direction," American Economic Review, American Economic Association, vol. 109(12), pages 4178-4219, December.
    3. Hugh Gravelle & Anthony Scott & Peter Sivey & Jongsay Yong, 2016. "Competition, prices and quality in the market for physician consultations," Journal of Industrial Economics, Wiley Blackwell, vol. 64(1), pages 135-169, March.
    4. Bart J. Bronnenberg & Jean-Pierre Dubé & Matthew Gentzkow & Jesse M. Shapiro, 2015. "Do Pharmacists Buy Bayer? Informed Shoppers and the Brand Premium," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1669-1726.
    5. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    6. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    7. Diogo G. C. Britto & Paolo Pinotti & Breno Sampaio, 2022. "The Effect of Job Loss and Unemployment Insurance on Crime in Brazil," Econometrica, Econometric Society, vol. 90(4), pages 1393-1423, July.
    8. Mark Duggan & Fiona Scott Morton, 2010. "The Effect of Medicare Part D on Pharmaceutical Prices and Utilization," American Economic Review, American Economic Association, vol. 100(1), pages 590-607, March.
    9. Emily Oster, 2019. "Unobservable Selection and Coefficient Stability: Theory and Evidence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 187-204, April.
    10. Gravelle, Hugh & Liu, Dan & Propper, Carol & Santos, Rita, 2019. "Spatial competition and quality: Evidence from the English family doctor market," Journal of Health Economics, Elsevier, vol. 68(C).
    11. Mariana Carrera & Dana P. Goldman & Geoffrey Joyce & Neeraj Sood, 2018. "Do Physicians Respond to the Costs and Cost-Sensitivity of Their Patients?," American Economic Journal: Economic Policy, American Economic Association, vol. 10(1), pages 113-152, February.
    12. King, Marissa & Bearman, Peter S., 2017. "Gifts and influence: Conflict of interest policies and prescribing of psychotropic medications in the United States," Social Science & Medicine, Elsevier, vol. 172(C), pages 153-162.
    13. 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.
    14. X Nie & S Wager, 2021. "Quasi-oracle estimation of heterogeneous treatment effects [TensorFlow: A system for large-scale machine learning]," Biometrika, Biometrika Trust, vol. 108(2), pages 299-319.
    15. Leemore Dafny & Christopher Ody & Matt Schmitt, 2017. "When Discounts Raise Costs: The Effect of Copay Coupons on Generic Utilization," American Economic Journal: Economic Policy, American Economic Association, vol. 9(2), pages 91-123, May.
    16. Michael Allan Ribers & Hannes Ullrich, 2020. "Machine Predictions and Human Decisions with Variation in Payoffs and Skill," CESifo Working Paper Series 8702, CESifo.
    17. Anusua Datta & Dhaval Dave, 2017. "Effects of Physician‐directed Pharmaceutical Promotion on Prescription Behaviors: Longitudinal Evidence," Health Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 450-468, April.
    18. Patricia M. Danzon & Mark V. Pauly, 2002. "Health Insurance and the Growth in Pharmaceutical Expenditures," Journal of Law and Economics, University of Chicago Press, vol. 45(S2), pages 587-613.
    19. John A. List & Azeem M. Shaikh & Yang Xu, 2019. "Multiple hypothesis testing in experimental economics," Experimental Economics, Springer;Economic Science Association, vol. 22(4), pages 773-793, December.
    20. Sofia Amaral-Garcia, 2020. "Medical Device Companies and Doctors: Do their Interactions Affect Medical Treatments ?," Working Papers ECARES 2020-18, ULB -- Universite Libre de Bruxelles.
    21. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    22. Sridhar Narayanan & Puneet Manchanda, 2009. "Heterogeneous Learning and the Targeting of Marketing Communication for New Products," Marketing Science, INFORMS, vol. 28(3), pages 424-441, 05-06.
    23. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    24. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
    25. Papanicolas, Irene & McGuire, Alistair, 2015. "Do financial incentives trump clinical guidance? Hip Replacement in England and Scotland," Journal of Health Economics, Elsevier, vol. 44(C), pages 25-36.
    26. Leila Agha & Dan Zeltzer, 2022. "Drug Diffusion through Peer Networks: The Influence of Industry Payments," American Economic Journal: Economic Policy, American Economic Association, vol. 14(2), pages 1-33, May.
    27. Méndez, Susan J. & Scott, Anthony & Zhang, Yuting, 2021. "Gender differences in physician decisions to adopt new prescription drugs," Social Science & Medicine, Elsevier, vol. 277(C).
    28. Lu, Fangwen, 2014. "Insurance coverage and agency problems in doctor prescriptions: Evidence from a field experiment in China," Journal of Development Economics, Elsevier, vol. 106(C), pages 156-167.
    29. Sexton, Joseph & Laake, Petter, 2009. "Standard errors for bagged and random forest estimators," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 801-811, January.
    30. Ramkumar Janakiraman & Shantanu Dutta & Catarina Sismeiro & Philip Stern, 2008. "Physicians' Persistence and Its Implications for Their Response to Promotion of Prescription Drugs," Management Science, INFORMS, vol. 54(6), pages 1080-1093, June.
    31. Liu, Ya-Ming & Yang, Yea-Huei Kao & Hsieh, Chee-Ruey, 2009. "Financial incentives and physicians' prescription decisions on the choice between brand-name and generic drugs: Evidence from Taiwan," Journal of Health Economics, Elsevier, vol. 28(2), pages 341-349, March.
    32. Abby Alpert & William N Evans & Ethan M J Lieber & David Powell, 2023. "Origins of the Opioid Crisis and its Enduring Impacts," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(2), pages 1139-1179.
    33. Mark Duggan & Fiona M. Scott Morton, 2006. "The Distortionary Effects of Government Procurement: Evidence from Medicaid Prescription Drug Purchasing," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(1), pages 1-30.
    34. Susan Athey & Guido Imbens & Thai Pham & Stefan Wager, 2017. "Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges," American Economic Review, American Economic Association, vol. 107(5), pages 278-281, May.
    35. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
    36. Alon Bergman & Matthew Grennan & Ashley Swanson, 2021. "Lobbying Physicians: Payments from Industry and Hospital Procurement of Medical Devices," NBER Working Papers 29583, National Bureau of Economic Research, Inc.
    37. Sendhil Mullainathan & Ziad Obermeyer, 2019. "Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care," NBER Working Papers 26168, National Bureau of Economic Research, Inc.
    38. Lundin, Douglas, 2000. "Moral hazard in physician prescription behavior," Journal of Health Economics, Elsevier, vol. 19(5), pages 639-662, September.
    39. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    40. Ghosh, Ausmita & Simon, Kosali & Sommers, Benjamin D., 2019. "The Effect of Health Insurance on Prescription Drug Use Among Low-Income Adults:Evidence from Recent Medicaid Expansions," Journal of Health Economics, Elsevier, vol. 63(C), pages 64-80.
    41. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2022. "Automatic Debiased Machine Learning of Causal and Structural Effects," Econometrica, Econometric Society, vol. 90(3), pages 967-1027, May.
    42. Noemi Kreif & Andrew Mirelman & Rodrigo Moreno-Serra & Taufik Hidayat, & Karla DiazOrdaz & Marc Suhrcke, 2020. "Who benefits from health insurance? Uncovering heterogeneous policy impacts using causal machine learning," Working Papers 173cherp, Centre for Health Economics, University of York.
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    Cited by:

    1. Amaral-Garcia, S.;, 2022. "Medical Device Companies and Doctors: Do their interactions affect medical treatments?," Health, Econometrics and Data Group (HEDG) Working Papers 22/10, HEDG, c/o Department of Economics, University of York.

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    More about this item

    Keywords

    public health; payments to physicians; gift ban; heterogeneous treatment effects; causal machine learning;
    All these keywords.

    JEL classification:

    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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