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Economics of the Pharmaceutical Industry: Current Issues

Paper Session

Saturday, Jan. 5, 2019 10:15 AM - 12:15 PM

Hilton Atlanta, 301
Hosted By: Health Economics Research Organization
  • Chair: Robert Kaestner, University of Chicago

Drug Diffusion through Peer Networks: The Influence of Industry Payments

Dan Zeltzer
,
Tel Aviv University
Leila Agha
,
Dartmouth College

Abstract

Peer effects may amplify the decisions of early technology adopters as information spreads through local networks. Drug detailing efforts of pharmaceutical companies may leverage peer influence within existing provider networks to broaden their reach beyond the directly targeted physicians. Using matched physician data from Medicare Part D and Open Payments, we investigate the influence of pharmaceutical payments on the prescription of new anticoagulant drugs. First, we show that pharmaceutical payments target physicians who share patients with many different providers and thus may influence a broader network of peers. Within a difference in differences framework, we find a physician's own prescription of new anticoagulant drugs increases following a pharmaceutical payment, relative to the physician-specific baseline prescribing rate for that drug. The effect scales with the size of the payment, with large payments such as speaking and consulting fees spurring larger increases in prescribing than small payments for food. Peers of targeted physicians also increase their prescribing of the new drug after the targeted physician receives a large payment, in some cases introducing entirely new patients to the drug class. To estimate the scale of peer effects in prescription decisions, we use peer payments as an instrumental variable for peer prescription volume. We find that when doctor's peers increase their use of a new drug by 1 beneficiary per quarter, the doctor's own use rises by 0.16 beneficiaries per quarter. Results suggest that spillover effects on peers are an important channel through which payments influence prescriptions. More broadly, results suggest that peer influence could be used by policies aimed at expediting practice change in medicine.

A Dose of Managed Care: Controlling Drug Spending in Medicaid

Amanda Starc
,
Northwestern University
David Dranove
,
Northwestern University
Christopher Ody
,
Northwestern University

Abstract

We study the effect of privatizing Medicaid benefits on drug prices and utilization. Drug spending would fall by 22.4 percent if the drug benefit was fully administered by private insurers. One-third of the decrease is driven by private insurers' ability to negotiate lower point-of-sale prices with pharmacies. The remaining two-thirds are driven by the greater use of lower cost drugs, such as generics, and are only realized in states that give private insurers the flexibility to design prescription drug benefits. Privatization does not decrease prescriptions per enrollee and spending cuts are smaller for drugs that lower medical spending.

Delegating Decision-Making to the Machine: Experimental Evidence from Health Insurance

Maria Polyakova
,
Stanford University
Kate Bundorf
,
Stanford University
Ming Tai-Seale
,
University of California-San Diego

Abstract

With the proliferation of on-line shopping tools and the advancement of predictive algorithms, personalized decision-making support software for consumers - especially in markets for household finances - is becoming commonplace. Does delegating consumer decisions to algorithms affect consumer choices and market efficiency? We present the results of a randomized controlled trial in which we offered (elderly) consumers a decision-making support software for choosing among pharmaceutical insurance plans. We find that algorithmic “expert” recommendation significantly alters consumer choices (plan switching rate increases by 8pp relative to 28% baseline rate). At the same time, personalized, but passive, informational treatment has little effect on switching. We find that selection into who uses the support tool is quantitatively large. Consumers that are more likely to switch their insurance plans are much more likely to take up our intervention. We find similar patterns for other measures of choice behavior. We use a discrete choice model of consumer decision-making to analyze the mechanisms that underlie the aggregate treatment effects and to estimate how the algorithmic decision-making support affects consumer welfare.
Discussant(s)
David Molitor
,
University of Illinois Urbana-Champaign
Mark Duggan
,
Stanford University
Geoffrey Joyce
,
University of Southern California
JEL Classifications
  • I1 - Health
  • I1 - Health