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The E¤ects of Competition on Prescription Payments
in Retail Pharmacy Markets
y
Jihui Chen
June 2018
Abstract
Using pharmacy claims from New Hampshire between 2009 and 2011, I study the
extent to which pharmacy competition a¤ects prescription payments. I measure phar-
macy competition by the distance to nearby rivals, as well as a
xed-travel-time HHI
(DunnandShapiro,2014).Aftercontrolling for various factors, including insurer, phar-
macy, drug, and area characteristics, I
nd higher average drug prices in more concen-
trated seller (pharmacy) markets, but lower prices in more concentrated buyer (insurer)
markets. The distance e¤ect is more pronounced if a nearby pharmacy belongs to the
same national chain. In addition, I show heterogeneous distance e¤ects across dif-
ferent drug types and areas. My analysis contributes to the empirical literature on
competition measures by adding new evidence from the retail pharmaceutical market.
Keywords: Market Structure; Pharmacy Competition; Insurer Concentration; Re-
tail Prescription Drugs
JEL codes: L11, L65, D4, I13
I am grateful to the editor, Charles Courtemanche, three anonymous reviewers, Michael R. Baye, and
Rati Ram for their very helpful comments and suggestions, which have greatly improved the paper. In
addition, I thank Uktamjan Kamilov, Raina Kirchner, Edna Mensah, Bryan Titzler, and Yi Wang for
their excellent research assistance, as well as Adam Shapiro for generously sharing the Stata codes used
to generate the
xed-travel-time HHI in their papers, and Robert Picard for kindly developing Stata
codes to help generate distance variables. I also thank session participants at the 2016 Southern Economic
Association Annual Meetings, 2017 Midwest Economic Association Annual Meetings, and 2017 Chinese
Economist Society Annual China Conference for their helpful comments. Finally, I acknowledge the New
Hampshire Department of Health and Human Services, the New Hampshire Insurance Department, and
their designated agencies for generously providing the pharmaceutical claim and related data used in this
study. The views expressed in this study are solely mine and are not necessarily those of any agency of the
State of New Hampshire. The usual caveat applies.
yDepartment of Economics, Illinois State University, Campus Box 4200, Normal, IL 61790 U.S.A; Tel:
(309) 438-3616; Fax: (309) 438-5228; Email: jchen4@ilstu.edu.
1
1 Introduction
According to the IMS Institute for Healthcare Informatics, prescription drugs account for
1
about one-
fth of total health care costs in the U.S., and are expected to continue to rise. In
recent years, public concern over high drug prices has been fueled by price scandals involv-
ing life-saving drugsfor example, Turing PharmaceuticalsDaraprim in 2015 and Mylans
2 3
EpiPen in 2016. However, when facing rising prices, unlike the case with other products,
cheaper substitutes may not be available for medications. Both anecdotal evidence and aca-
demic research (Sorensen, 2001; Chen, 2015) highlight considerable price variations across
pharmacies, and suggest that consumers should search for lower prices, even those with in-
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surance coverage. As high-deductible insurance plans become more common, the insured
are required to pay out-of-pocket for prescriptions at insurersnegotiated rates before their
deductible is satis
ed. Moreover, depending on coverage, insured patientscosts may di¤er
between in-network and out-of-network pharmacies, whether one uses a preferred pharmacy,
and, to a lesser extentdue to variations in coinsurancepharmaciescosts to acquire prescrip-
5
tion drugs from manufacturers or wholesalers. This paper examines patient-level payments
to pharmacies for the 200 most-prescribed drugs using data extracted from pharmacy claims
collected by the state of New Hampshire (NH) from 2009 to 2011, taking into consideration
both pharmacy and insurer competition.
Alarge literature on health markets has developed several area-level competition mea-
sures. For example, studies on hospital care construct a Her
ndahl-Hirschman Index (HHI)
1Source: IMS Institute for Healthcare Informatics. (2012, February). Healthcare Spending Among Pri-
vately Insured Individuals Under Age 65.
2Source: This 62-year-old drug just got 5,000% more expensive,by Laura Lorenzetti, September 21,
2015 (http://fortune.com/2015/09/21/turing-pharmaceuticals-drug-prices-daraprim/).
3Source: Mylans EpiPen Pricing Crossed Ethical Boundaries,by Daniel Kozarich, September 27, 2016
(http://fortune.com/2016/09/27/mylan-epipen-heather-bresch/).
4Studies show that insured patients may sometimes incur a lower cost without using in-
surance for prescription drugs. Source: Prescription Drugs May Cost More with Insurance
Than without It, by Charles Ornstein and Katie Thomas, Dec. 9, 2017, New York Time
(https://www.nytimes.com/2017/12/09/health/drug-prices-generics-insurance.html).
5For an example of Mayo Medical Plans 2018 prescription drug coverage, visit:
http://www.mayo.edu/pmts/mc6200-mc6299/mc6213-11.pdf.
2
basedonactualmarketshare(KesslerandMcClellan,2000; GowrisankaranandTown,2003),
while others use physician density, such as the number of physicians per capita (Bradford and
Martin, 2000; Richardson et al., 2003). More recent work has adopted a novel and objective
measure: travel distance to competing providers (Dunn and Shapiro, 2014; Gravelle et al.,
2016), which alleviates the endogeneity concern that arises from the existing alternatives,
by which unobserved factors may simultaneously determine market structure and provider
pricing. However, similar studies of retail pharmacy markets are scarce, largely due to data
unavailability.
This paper aims to
ll the gap. Unlike in other product markets, physicians, rather than
consumers, determine the use of prescription drugs. Meanwhile, individual patients play no
role in negotiating payments between pharmacies and insurers or pharmacy bene
t managers
(PBMs). Furthermore, prescription drug prices have been rising at a faster rate than general
ination for decades, triggering heated debate and high-pro
le congressional scrutiny of the
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pharmaceutical industry. These unique features of the pharmaceutical market make it
particularly interesting to study.
Myidenti
cation strategy is to use both the between-area and cross-time variations that
arise from payments received by pharmacies and competition among pharmacies, as well as
insurers, over a three-year period. To gauge the extent to which pharmacy concentration
a¤ects prices in the retail prescription drug market, I construct three competition measures.
First, I examine the distance e¤ects of up to the
fth nearest pharmacy on drug pricing
(Gravelle et al., 2016). Second, I construct two sets of distance variables, one to rival
pharmacies and the other to ones same-chain pharmacies, and explore possible business-
stealing and cannibalization e¤ects, respectively (Davis, 2006). Third, I compute the so-
called Fixed-Travel-Time Her
ndahl-Hirschman index(FTHHI), which incorporates both
the distance and travel time to competing pharmacies in the same area (Dunn and Shapiro,
6Source: How Do We Deal with Rising Drug Costs?by Jonathan D. Rocko¤, Wall Street Journal, April
10, 2016 (http://www.wsj.com/articles/how-do-we-deal-with-rising-drug-costs-1460340357, accessed August
25, 2016).
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2014, 2018).
After controlling for drug and pharmacy characteristics, locality, year, month, drug class,
andmanufacturer
xede¤ects,I
ndthatpharmaciesreceivelowerpaymentsinmoreconcen-
trated buyer (insurer) markets, but higher payments in more concentrated seller (pharmacy)
markets. The latter e¤ect is more pronounced if nearby competitors belong to the same na-
tional pharmacy chain. Moreover, I
nd considerable business-stealing e¤ects from nearby
rivals, but little evidence of cannibalization e¤ects from ones own establishments. These
ndings are robust to various controls and model speci
cations. Next, I perform additional
analyses, and conclude that the distance e¤ects among competing pharmacies di¤er across
di¤erent drug types and area types. Speci
cally, the distance e¤ect is more noticeable among
prescription drugs that consumers are less likely to price search, or among pharmacies located
in relatively a uent areas or counties that do not border another state.
1.1 Related Literature
This paper is related to previous studies on health providers (e.g., physicians and hospitals),
which conclude that higher market concentration leads to higher negotiated service prices
(Bakeretal., 2014; DunnandShapiro, 2014; Gravelleetal., 2016; Kleiner, White, andLyons,
2015). My analysis adds to this literature by examining the issue in the retail prescription
drug market using comprehensive patient-level data.
A large literature on health markets has developed various measures of competition.
For example, studies on hospital care construct HHI based on market share (Kessler and
McClellan, 2000; Gowrisankaran and Town, 2003; Gaynor et al., 2011), while those on
physician services have used physician density (Bradford and Martin, 2000; Richardson et
al., 2003), but more recent work relies on a novel and objective measure, travel distance to
competitors, which alleviates the endogeneity concern that arises from alternative measures
(Dunn and Shapiro, 2014, 2018; Gravelle et al., 2016).
Focusing on Medicare bene
ciaries, Kessler and McClellan (2000) examine hospital com-
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