Pricing strategy and moral hazard: Copay coupons in pharmaceuticals

International Journal of Industrial Organization 70 (2020) 102611

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International Journal of Industrial Organization journal homepage: www.elsevier.com/locate/ijio

Pricing strategy and moral hazard: Copay coupons in pharmaceuticalsR Chung-Ying Lee Department of Economics, National Taiwan University, Taiwan

a r t i c l e

i n f o

Article history: Received 31 March 2018 Revised 5 March 2020 Accepted 6 March 2020 Available online 14 March 2020 JEL classification: I11 L11 L65 Keywords: Pricing Moral hazard Copay coupons Pharmaceuticals

a b s t r a c t Branded drug manufacturers issue copay coupons to compete with generics as their brands are coming off patent. To explore the impact of copay coupons on pricing and welfare, I estimate a model of demand and supply using data on sales, advertising, and copayment for cholesterol-lowering drugs and perform a counterfactual analysis to simulate equilibrium pricing with copay coupons used for price discrimination and moral hazard. Copay coupons issued for price discrimination make the drug with coupons affordable for more consumers and increase consumer welfare even when a small fraction of consumers receive a coupon. Coupons used for moral hazard significantly mitigate price competition and improve consumer welfare only when coupon penetration is sufficiently high. © 2020 Elsevier B.V. All rights reserved.

1. Introduction Coupons have long been prevalent in consumer goods. In 2005, coupons also started to play an important role in the pharmaceutical industry. The coupons distributed by drug manufacturers, called copay coupons or copay cards, reduce consumers’ out-of-pocket costs of prescription drugs. Many top-selling drugs, including cholesterol fighter Lipitor, blood thinner Plavix, and blood pressure drug Diovan, started to offer copay coupons as they were coming off patent. As of 2016, 20% of branded prescriptions in commercial insurance plans were associated with copay coupons.1 Copay coupons have been highly controversial as they become more and more popular. UnitedHealthcare, one of the major pharmacy benefit managers in the US, claims that copay coupons encourage “more use of expensive brand medications while taking the focus away from clinically equivalent and much lower cost generics.”2 On the other hand, PhRMA, a drug industry trade group, argues that “without these copay coupons, many patients would not be able to afford their medicines and would leave the pharmacy empty-handed.”3 R I deeply thank Andrew Sweeting, Peter Arcidiacono, James Roberts, and Wilfred Amaldoss for their invaluable guidance and support. I am grateful to all seminar attendants at Duke University and participants at IIOC (Chicago, 2012) for their insightful discussions and comments. I thank the managing editor Pierre Dubois and two anonymous referees for excellent comments and suggestions. I would particularly like to thank David Ridley, Peter Arcidiacono, and Andrew Sweeting for their help to obtain the data. E-mail address: [email protected] 1 IQVIA, Medicines Use and Spending in the U.S., May 2017. 2 UnitedHealthcare, Manufacturer Coupons, Dec 21, 2017. 3 PhRMA, Copay coupons can help ease patients out-of-pocket costs, Feb 16, 2018.

https://doi.org/10.1016/j.ijindorg.2020.102611 0167-7187/© 2020 Elsevier B.V. All rights reserved.

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C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Despite the fast-growing use of copay coupons and simmering debate over the coupon programs, little empirical work has been done to examine what makes drug manufacturers issue coupons, how pricing changes under different incentives to issue coupons, and how social welfare is affected by copay coupons. In this paper, I provide a counterfactual analysis of drug pricing and welfare with the introduction of copay coupons, using a model estimated with data from the market for cholesterol-lowering drugs. I consider two incentives for drug manufacturers to issue coupons: price discrimination and moral hazard. Coupons are widely used by firms in the consumer products industry to compete for price-sensitive consumers. In a market with consumer heterogeneity in price sensitivity, firms can rely on consumer self-selection (Narasimhan (1984); Sweeney (1984); Levedahl (1984); Varian (1989)) or targeting (Shaffer and Zhang (1995)) to price discriminate consumers using coupons. These strategies can apply to the pharmaceutical industry in the U.S. where drug manufacturers compete in price for patients who share prescription drug costs. In addition, the special market structure in the pharmaceutical industry creates a moral hazard incentive to issue coupons. For most prescription drugs, doctors and patients make purchase decisions, and insurance companies pay for most of the drug costs.4 To reduce spending, insurance companies in the U.S. usually ask for a lower cost share when patients choose less expensive drugs. By issuing copay coupons directly to patients, drug manufacturers can circumvent this insurance benefit design and lower patients’ out-of-pocket cost to induce them to choose the drugs with coupons, which can greatly increase spending by insurance plans. The goal of my counterfactual analysis is to quantify the total effect of copay coupons on the market through strategic interactions. The direction of effects stemming from the moral hazard incentive is clear. Coupon issuers would try to raise prices by as much as they can to take advantage of insurance companies, leading to a higher copay for consumers without coupons and decreasing overall consumer welfare when the coupon penetration is low. On the other hand, the price discrimination motive has a more ambiguous effect since pricing for coupon users and nonusers depends on the distribution of consumer price sensitivity. In either case, the size of the effects hinges on the substitution patterns in the markets. To lay the foundations for the counterfactual analysis, I estimate a model with rich substitution patterns and consumer heterogeneity in price sensitivity, using unique data on sales, advertising, and copayments. The model captures competition among drugs along different dimensions, including class, molecule, form, and version. The model also allows price sensitivities to be drawn from a binary distribution, which helps to explain why branded drug prices usually stay high after patent expiration. The estimates suggest that drugs with more shared characteristics are closer substitutes and that 13% of the consumers are much less price sensitive than the rest. In the counterfactual analysis, I simulate the outcomes of a copay coupon program introduced by the manufacturer of a branded cholesterol-lowering drug right after patent expiration. I consider different assumptions about the take-up of coupons, the ability of the branded manufacturer to direct coupons to the most price-sensitive consumers, and the insurer’s tolerance for a price increase of the drug with coupons. To disentangle the welfare effects driven by different incentives to issue coupons, I calculate the equilibrium results in three cases: 1) price discrimination only, 2) moral hazard only, and 3) price discrimination and moral hazard. The counterfactual results differ a lot across the three cases. Copay coupons, when used only for price discrimination (through coupon targeting), slightly mitigate price competition and make the drug with coupons more affordable for pricesensitive consumers, which increases consumer welfare even when only 1% of the price-sensitive consumers receive a coupon. When coupons are used only for moral hazard, the coupon issuer raises the full price of the drug with coupons by as much as possible. At the same time, the coupon issuer sets a higher price for its other brands to make the drugs with coupons more attractive. These pricing strategies greatly soften price competition, increasing the profits of the coupon issuer and the spending by the insurer. Consumer welfare increases only when coupon penetration is sufficiently high. When the two incentives to issue coupons are jointly considered by the coupon issuer, price competition is further alleviated. Compared to the moral hazard case, the average prices of competitors’ brands are higher, the coupon issuer makes more profits, the insurer spends more, and consumer welfare gets lower. The counterfactuals help to inform the legal and policy debate over copay coupons in several ways. First, I show how pricing differs under the two incentives to issue coupons. We can look at how drugs are priced in practice after coupons are introduced to markets to determine whether the argument about copay coupons from drug manufacturers or insurance companies is well founded. Second, we can better understand how the agents in pharmaceuticals, including drug manufacturers, insurance companies, and patients, are affected by copay coupons. Learning who gains and who loses from copay coupons, we can make policies that take care of certain groups without hurting the others too much. Furthermore, it is shown that the welfare implications of copay coupons vary with coupon penetration. The results can serve as the basis for policies aiming to regulate the scale of copay coupon use. The study contributes to the literature on coupons by considering the moral hazard incentive to issue coupons which can exist in a market with the agency problem. Theoretical work by Holmes (1989) and Corts (1998) show that offering coupons helps price discrimination in an oligopoly setting since coupons can attract price-sensitive consumers. Shaffer and Zhang (1995) considers coupon targeting another mechanism to price discriminate in a competitive market. Empirically,

4 In the U.S., doctors decide on the treatment and prescribe, but patients can choose to substitute generics for their branded counterparts at pharmacies unless the doctors request the branded drugs be dispensed as written.

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Narasimhan (1984) finds that coupon users are more price sensitive than nonusers in several categories of consumer packaged goods. Nevo and Wolfram (2002) uses breakfast cereal data to show that coupons can spur price competition and lower shelf prices when they are widely available. In pharmaceuticals, doctors and patients make drug choice decisions, and insurance companies pay for most drug costs. The separate roles of decision maker and payer create a moral hazard problem, incentivizing drug manufacturers to use coupons to attract consumers by lowering their copays. I investigate the welfare impact of copay coupons driven by both price discrimination and moral hazard. The empirical paper on coupons closest to my work is Dafny et al. (2017), who study substitution between branded drugs and their generic equivalents caused by the introduction of copay coupons. Using difference-in-differences models that utilize cross-state variation in the availability of coupons, they show that copay coupons significantly reduce the percentage of prescriptions filled with generics. My paper differentiates from theirs in three major ways. First, I consider substitution among all drugs in a therapeutic category while they focus on the competition between branded and generic drugs of the same molecule. Studying copay coupons at the category level and incorporating strategic interactions among all drugs for the same medical purpose provides a bigger picture of the welfare impact of copay coupons. Second, branded drugs and their generic equivalents are assumed to have exactly the same quality in Dafny et al. (2017). While branded drugs and generics share active ingredients, Branstetter et al. (2016), Bronnenberg et al. (2015), and Kamenica et al. (2013) provide evidence that suggests difference in quality and consumer perspectives between brands and generics. In my demand model, I relax the assumption that generics are perfect substitutes for branded drugs, which helps capture the effect of physical and psychological drug differences on consumer welfare.5 Third, the counterfactual analysis conducted in my paper simulate equilibrium outcomes under various assumptions on the incentives to issue coupons, insurer’s exclusion threat, and coupon penetration. The results from the flexible settings can be helpful to inform legal and policy debate over copay coupons. In addition, the structural model in the paper incorporates two important features of pharmaceutical demand. First, I apply a generalized extreme value (GEV) model developed by Bresnahan et al. (1997) to capture substitution patterns along multiple dimensions. Arcidiacono et al. (2013) use a similar model to study the welfare impact of me-too and generic drugs. The model has a nesting structure that allows for consumer switching based on different drug characteristics, including class, molecule, form, and version. This strength facilitates simulation of introducing copay coupons since the model well captures how consumers’ choices change when they are given coupons. Second, following Berry et al. (2006) and Ching (2010a), I consider two types of consumers who differ in price sensitivity. The consumer heterogeneity helps to explain branded drugs’ pricing after they lose patent protection. Also, the consumer heterogeneity allows coupon targeting in the counterfactuals. The coupon issuer is assumed to have the information about consumer types and target the coupons at the price-sensitive consumers who are more likely to use coupons. Finally, this paper combines several unique data sets that cover the major aspects in pharmaceutical demand, including prescription drug sales, physician advertising, direct-to-consumer advertising (DTCA), and copayments. Jayawardhana (2013) and Ridley (2015) are two of the few empirical studies that use such rich data in the demand estimation for pharmaceuticals. Most of the other papers in the literature use a single source of advertising to represent marketing efforts and/or include full prices in demand, which can lead to serious problems in estimation especially for the cholesterol-lowering drug markets. Physician advertising and DTCA both play an important role in demand for cholesterollowering drugs, as shown later in the estimation results. Ignoring any of the advertising efforts can make the estimates less precise or even biased. On the other hand, using full prices in the demand for prescription drugs can underestimate the price coefficient since consumers face only a small portion of the costs in making a drug choice. The rest of the paper is organized as follows. Section 2 provides industry background and relevant information about copay coupons. Section 3 describes data. Section 4 develops models for copayments, demand, and supply. Section 5 discusses estimation strategies and results. Section 6 presents counterfactuals for introducing copay coupons under different scenarios. Section 7 concludes. 2. Copay coupons in pharmaceuticals Copay coupons are instantaneous rebates to patients usually offered by branded drug manufacturers.6 They are distributed on drug manufacturers’ websites or provided by sales representatives through doctors’ offices. The coupons reduce patients’ copayments when they fill a prescription at pharmacies. Suppose a one-month supply of a branded drug costs $150 and its generic equivalent costs $30. A patient’s insurance copays for the two drugs are $40 and $10, respectively. If indifferent between the branded drug and generic, the patient would choose the less expensive generic without a copay coupon, and the insurer pays $20 for the prescription. If the branded drug manufacturer gives a copay coupon that reduces the out-of-pocket cost to $5, the patient would choose the branded drug and the insurer must pay $110. 5 Ching (2010a) also allows the quality difference between branded drugs and generics in estimating the demand model using data from 14 pharmaceutical markets in the U.S. 6 There are alternative names for copay coupons, including copay cards, copay assistance programs, and copay savings programs. Copay assistance or savings programs can refer to programs run by drug manufacturers, governments, or nonprofit organizations. To avoid confusion, I use copay coupons throughout the paper to mean the programs specifically run by drug manufacturers.

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In this case, the branded drug manufacturer helps the patient to pay $35 for the copayment and earns $110 from the insurer.7 Many branded drug manufacturers started to offer copay coupons as their drugs were losing patent protection in recent years. In December 2010, Pfizer launched a “Lipitor for You” program which allowed patients to pay as little as $4 for a month’s supply of Lipitor, the best-selling drug in the history of pharmaceuticals. A one-month supply of Lipitor normally had a retail price of $150, and the copay for generic Lipitor was about $10. Thus, the $4 copay program offered by Pfizer was very attractive, helping to keep about one-third of Lipitor’s prescriptions within five months of its patent expiration in late 2011.8 Many top-selling drugs followed the strategy as they were coming off patent, including blood thinner Plavix and blood pressure drug Diovan (see Fig. 3 in the appendix for the sample copay coupons). According to IMS Health, the spending on copay coupons in 2015 was estimated to be $7 billion.9 This amount exceeded the $5.17 billion spending on direct-to-consumer advertising, calculated by Nielsen, and accounted for 2.2% of gross branded drug sales in the U.S.10 Copay coupons can help to combat generic entry by lowering the costs for patients and influencing doctors’ decisions.11 In March 2013, 62% of the 374 copay coupons found from www.internetdrugcoupons.com, a large drug coupon website, were for branded drugs with generic alternatives.12 As many blockbuster drugs went off-patent, and few new drugs were available to replace the revenue lost from patent expiration, copay coupons have become one of the strategies pursued by branded drug companies to retain their revenue. Copay coupons can help to price compete with generics without cutting the full branded prices. Using copay coupons, branded drug manufacturers need to pay part of the out-of-pocket cost for patients. But the benefit from insurance payments exceeds the cost of copay coupons since patients’ share is usually less than one-third of a full drug price. Thus, copay coupons can effectively induce patients to use branded drugs over cheaper alternatives at the cost of higher insurer spending, which creates a moral hazard problem. The “shadow claims system” of copay coupons further contributes to their popularity. In the shadow system, prescription information is first sent to a pharmacy benefit manager (PBM) for adjudication, who processes prescription drug claims for employers. After the PBM adjudicates the prescription and sends the copay information back to the pharmacy, copay coupon programs reduce the copay for the coupon user. Thus, copay coupons are invisible to PBM’s or insurers when they are used. This shadow claims system prevents PBM’s and insurers from rejecting the individual use of copay coupons. However, insurers can remove the drugs with coupons completely from their prescription drug list or formulary. For example, Express Scripts, CVS, and UnitedHealth have excluded many drugs from their formulary since 2016 in response to the proliferation of the coupon programs for those drugs.13 The greater use of coupons has attracted a lot of legal and policy controversy. Copay coupons are banned in federal health programs, including Medicaid and Medicare, because they are considered illegal kickbacks that encourage unnecessary spending.14 They are legal in all commercial markets except those in Massachusetts, where only manufacturers of branded drugs without competing generic equivalents are allowed to offer coupons. In 2017, New Jersey and California are introducing similar legislation to limit coupon use.15 The higher overall drug spending associated with coupons has also made health plans and consumer groups seek to ban copay coupons by filing lawsuits against the drug manufacturers.16 But so far none of these lawsuits has been successful to restrict the use of copay coupons in most commercial markets. 3. Data Data are obtained from five sources: drug sales data from IMS Health, physician advertising data from Encuity Research, direct-to-consumer advertising data from Kantar Media, copayment data from MarketScan, and market size data from the Centers for Disease Control and Prevention (CDC). First, in the drug sales data, I observe national retail dollars and unit sales of each molecule-form-strength-brandmanufacturer combination, e.g. branded atorvastatin 10 mg tablet by Pfizer. I multiply the unit sales of a drug by its

7 Grande (2012) provides a detailed example to illustrate the economic effects of copayments, cost-sharing, and copay coupons on consumers, firms, and health insurances. 8 “New Coupons Aim To Keep People Off Generic Drugs,” Associated Press, August 20, 2012. 9 Bloomberg Businessweek, That Drug Coupon Isn’t Really Clipping Costs, Dec 24, 2015. 10 FiercePharma, Pharma’s DTC ad spending soars past $5B in 2015, Mar 7, 2016. 11 In practice, copay coupons can influence doctors’ decisions through two channels. First, patients with a coupon may communicate with their doctors about the lower costs of the drugs with coupons. Second, some drug manufacturers give doctors coupons to share with their patients. Either way, doctors can learn about the reduced copayments with coupons and take this into consideration when writing a prescription. 12 According to Ross and Kesselheim (2013), 53.5% of the copay coupon programs were from drugs with within-class generic alternatives, 8.3% were from drugs with FDA-approved therapeutic equivalents, and 38.2% were from drugs without lower cost alternatives. 13 Bloomberg Businessweek, That Drug Coupon Isn’t Really Clipping Costs, Dec 24, 2015. 14 The prohibition does not apply to the insurance sold through the online health insurance marketplaces launched on October 1, 2013. The United States Department of Health and Human Services held that the insurance offered through the exchanges is not federal health care program subject to the prohibition. 15 FiercePharma, Pharma’s copay coupons, a key marketing tool, face new limits in California, Feb 6, 2017. 16 Law 360, Pfizer, Others Sued By Health Plans Over Copay Coupons, Mar 7, 2012.

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strength in milligrams to construct the sales in milligrams, which is then aggregated to the level of molecule-form-brandmanufacturer. Following Berndt et al. (1996), I calculate the patient-days of a molecule-form-brand-manufacturer combination by dividing the unit sales in milligrams by the recommended daily dosage.17 I calculate prices per patient-day by dividing the retail dollar sales by the patient days. Second, physician advertising data contain monthly spending on detailing, the visit of pharmaceutical sales representatives to physicians to provide information about a drug.18 Third, direct-to-consumer advertising data provide monthly national advertising spending for each drug, aggregated over different types of media including television, radio, magazine, newspaper, internet and outdoor. The monthly data on sales and advertising begin in January 2003 and continue through August 2011. To calculate the 24-month advertising stock for later estimation, I choose January 2005 as the beginning date of the sample for estimation.19 Fourth, copayment data between 2005 and 2011 are obtained from the MarketScan Research Databases, the largest collection of employer-based patient data in the United States, through National Bureau of Economic Research (NBER). The files contain prescription-level claim data on copayments and full prices from 150 large employers and cover 40 million enrollees, or 23.5% employment-based enrollees, in the United States.20 The copayment data will be used to estimate the relationship between full price and copayment, and the estimated relationship will then be used to predict the copayment faced by consumers in the demand model. Finally, in the calculation of market share, I take the potential market to be 33.5% of US adults aged 20 and over during the sample period. This is based on the data from CDC National Health and Nutrition Examination Survey, United States, 20052008, which estimates 71 million (33.5%) US adults aged 20 and over had high low-density lipoprotein cholesterol (LDL-C) and needed to take cholesterol-lowering medication. I focus on the markets of HMG-CoA reductase inhibitors (statins), the major cholesterol-lowering medicines or lipid regulators.21 There are several reasons to look at this market for research on copay coupons. First, the cholesterol-lowering drug market is large and copay coupons are now available for the major drugs in this market. Cholesterol-lowering drugs were the third largest therapeutic class by spending in 2011, at 20.1 billion US dollars. There were over 260 million prescriptions filled in 2011, and nearly 20 million Americans regularly used a cholesterol medicine.22 According to Rx Pharmacy Coupons, a website that collects copay coupon information, the popular branded cholesterol-lowering drugs whose patents expired in recent years, such as Lipitor, Crestor, and Vytorin, all started to offer copay coupons after their patent expiration. Second, cholesterol-lowering drugs ranked first in spending on direct-to-consumer advertising among all therapeutic classes in years 2009 to 2011. Cholesterol-lowering drug manufacturers together spent on average 500 million dollars each year on DTCA.23 This shows that firms in the cholesterol-lowering drug market invested heavily in direct communication with consumers, which serves as the basis for copay coupon distribution. Finally, I focus on the role of copay coupons in helping branded drug manufacturers to retain sales after the patent expiration of their drugs. The patent of two cholesterollowering drugs (Pravachol and Zocor) expired during the sample period, and generics entered right after their patent expiration. Entry of generic drugs dramatically changes the competitive environment, creating an opportunity to learn how consumers switch from branded drugs to cheaper alternatives. The cholesterol-lowering drugs in the sample and their relevant facts are summarized in Table 1. The variations in class, molecule, form, and version serve as the basis for modeling substitution among drugs. There are two classes: statins and statin combinations. The first statin, Mevacor, entered the market early in 1987, and statin combinations are relatively new as a treatment for high cholesterol. Statins combined with other molecules are treated as a different class since the combinations may have different effects on patients. Drugs in the classes come in three forms: tablet, sustained-action tablet, and capsule. Sustained-action is a mechanism that helps to dissolve a drug over time and release it more slowly and steadily into bloodstream so that a patient can take drugs less frequently. Because of the convenience of this mechanism, some consumers may prefer drugs in sustained-action tablet to drugs in tablet or capsule. Finally, three of the statins (Zocor, Pravachol, and Mevacor) have generic alternatives. They all have a maximum number of generic equivalents greater than

17 The daily recommended dosage data are obtained from Clinical Pharmacology, an online database for drug information widely used by hospitals and retail pharmacies in the US. 18 Kornfield et al. (2013) show that free samples and office-based detailing accounted for the majority of spending on advertising to physicians during 2001 to 2010. The distribution of free samples is usually made by sales representatives during their visits to physicians. Spending on detailing can capture some of the effects of free samples on drug sales. 19 The sales data for the last nine months of my sample (December 2010 – August 2011) can be affected by the Lipitor coupon program launched in December 2010. I conduct a robustness check by excluding the period with the Lipitor coupons. The results presented in Appendix H.1 show that the Lipitor coupon program has a minimal impact on my estimation. 20 The data only cover privately insured individuals and do not include prescription drug claims from Medicare and Medicaid. This limitation can lead to over- (under-) estimated copayments for an average patient if generic utilization is higher (lower) in Medicare Part D plans. 21 According to the American Heart Association (AHA), statins are recommended for most patients because they are the only cholesterol-lowering drug class that has been directly associated with reducing the risk of a heart attack or stroke. Most papers in the economics literature using data from cholesterol drugs focus on statins, such as Calfee et al. (2002), Richard and Van Horn (2004), Jayawardhana (2013), Stremersch et al. (2013), Ching et al. (2015), and Carrera et al. (2018). 22 IQVIA, The Use of Medicines in the United States: Review of 2011. 23 Source: Kantar Media Ad$Spender Database

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C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611 Table 1 Statins and statin combinations. Class

Molecule

Brand Name

Form

Brand Entry Date

Statin

Atorvastatin Fluvastatin Fluvastatin Lovastatin Lovastatin Pravastatin Rosuvastatin Simvastatin Amlodipine/Atorvastatin

Lipitor Lescol Lescol XL Altoprev Mevacor Pravachol Crestor Zocor Caduet

TAB CAP SA TAB SA TAB TAB TAB TAB TAB TAB

Jan 1997 Apr 1994 No. 2000 Jul 2002 Sep 1987 No. 1991 Aug 2003 Jan 1992 Mar 2004

Ezetimibe/Simvastatin Lovastatin/Niacin Niacin/Simvastatin

Vytorin Advicor Simcor

TAB SA TAB SA TAB

Jul 2004 Dec 2001 Feb 2008

Statin combination

1st Generic Entry Date

Max Num Generics

Feb 2002 May 2006

11 14

Jun 2006

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Table 2 Summary Statistics for Sales Data. Variable

Obs

Mean

Std. Dev.

Min

Max

Full sample Price per 30-day supply Market share Spending on detailing in $1000’s Spending on DTCA in $1000’s

3248 3248 3248 3248

29.65 0.0079 993.32 607.14

29.80 0.0164 3088.25 2707.14

1.13 0.0000 0.00 0.00

137.00 0.0948 18,412.89 29,298.84

Brands without generic equivalents Price per 30-day supply Market share Spending on detailing in $1000’s Spending on DTCA in $1000’s

635 635 635 635

68.53 0.0193 5026.81 3105.21

19.61 0.0266 5344.41 5455.65

33.97 0.0000 0.00 0.00

122.70 0.0948 18,412.89 29,298.84

Brands with generic equivalents Price per 30-day supply Market share Spending on detailing in $1000’s Spending on DTCA in $1000’s

287 287 287 287

70.51 0.0010 116.38 0.59

25.70 0.0033 219.78 3.23

25.04 0.0000 0.00 0.00

129.87 0.0499 2289.70 31.74

Generics Price per 30-day supply Market share Spending on detailing in $1000’s Spending on DTCA in $1000’s

2326 2326 2326 2326

13.99 0.0056 0.38 0.00

13.78 0.0116 5.01 0.00

1.13 0.0000 0.00 0.00

137.00 0.0790 67.18 0.00

The observation is at the monthly date-molecule-version-form-manufacturer level.

ten, implying severe within-molecule competition after their patents expired. I treat the generics of the same molecule from different manufacturers as separate products in the model for demand and supply.24 Table 2 presents the summary statistics for the full sample as well as the subsamples for branded drugs with and without generic equivalents, and generics. Each observation is a combination of monthly date, molecule, version (branded/generic), form, and manufacturer. The prices are adjusted to January 2003 dollars but do not account for rebates or discounts.25 Branded drugs without generic equivalents have a similar average price as branded drugs with generic equivalents, but the average market share of the former is almost twenty times the market share of the latter. On average, the price of generics is one fifth of the price of branded drugs. The average market share of generics is about five times as large as the market share of branded drugs that lost patent protection. Table 3 shows that, over the sample period, there are on average 8.8 molecule-forms without generic equivalents each month, compared to 2.2 molecule-forms with generic competition. Since multiple manufacturers can produce a drug after its patent expiration, there are on average 24 generic molecule-forms each month. In each category, a drug manufacturer on average owns fewer than two molecule-forms, suggesting the production of drugs does not concentrate on just a few manufacturers. The summary statistics for the copay data from MarketScan are presented in Table 4.26 Drugs with a higher full price tend to have a higher copay. On average, generics have the lowest price and copay while brands with generic equivalents have the highest price and copay. The average price of brands with generic equivalents is higher than that of brands without generic 24 Some of the generics are “authorized generics,” which are approved by FDA as brand-name drugs but marketed as generic drugs by outside licensees. Pricing decisions by outside licensees are typically independent of the brand (FTC 2011). I thus treat the authorized generics as competitors to their branded counterparts. 25 The problem of unobserved rebates and discounts is standard in the pharmaceutical economics literature. See, for example, Arcidiacono et al. (2013) and Yurukoglu et al. (2017). 26 The full prices here, like the full price data from IMS, do not account for rebates and discounts.

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Table 3 Summary Statistics for Counts of Products. Variable

Obs

Mean

Std. Dev.

Min

Max

Full sample Number of molecule-forms Number of manufacturers Number of molecule-forms per manufacturer

104 104 104

34.98 18.92 1.79

15.66 7.14 0.19

14.00 10.00 1.40

53.00 27.00 2.07

Brands without generic equivalents Number of molecule-forms Number of manufacturers Number of molecule-forms per manufacturer

104 104 104

8.81 6.32 1.40

0.85 0.47 0.12

7.00 6.00 1.14

10.00 7.00 1.50

Brands with generic equivalents Number of molecule-forms Number of manufacturers Number of molecule-forms per manufacturer

104 104 104

2.22 1.62 1.30

0.97 0.49 0.25

1.00 1.00 1.00

3.00 2.00 1.50

Generics Number of molecule-forms Number of manufacturers Number of molecule-forms per manufacturer

104 104 104

23.95 14.96 1.44

14.79 7.09 0.36

6.00 6.00 1.00

41.00 23.00 1.82

The observation is at the monthly level. Table 4 Summary Statistics for Copay Data. Variable

Obs

Mean

Std. Dev.

Min

Max

Full sample Price per 30-day supply Copay per 30-day supply Brand

126,052 126,052 126,052

84.64 19.61 0.79

46.70 15.77 0.40

0.00 0.00 0.00

3,327.80 1105.25 1.00

Brands without generic equivalents Price per 30-day supply Copay per 30-day supply

91,607 91,607

96.70 22.83

31.11 14.97

0.00 0.00

3,203.76 1105.25

Brands with generic equivalents Price per 30-day supply Copay per 30-day supply

8512 8512

120.20 25.01

90.27 23.22

0.00 0.00

3327.80 280.66

Generics Price per 30-day supply Copay per 30-day supply

25,933 25,933

30.33 6.47

25.09 4.62

0.00 0.00

911.70 182.40

The observation is at the plan-drug level.

equivalents, which is consistent with the “generic paradox.” Faced with generic competition, branded drug manufacturers segment the market and focus on the consumers with less elastic demand (Grabowski and Vernon, 1992; Scherer, 1993; Bhattacharya and Vogt, 2003).27 Spending on advertising varies a lot depending on drug status and versions. Branded drugs on average have $5 million dollars spent on detailing and $3 million dollars on DTCA each month before patent expiration, and the spending drops greatly as generic competitors enter the market. Generic drug manufacturers spend very little on detailing and nothing on DTCA. As shown in Fig. 4 in the appendix, spending on the two types of advertising for the major branded drugs in the sample fluctuates from month to month and tends to drop to a very low level as the patent expires. Fig. 1 presents sales measured by patient-days. In general, the market for cholesterol-lowering drugs grew over time with the introduction of new branded drugs as well as the entry of generics. The three major brands are atorvastatin (Lipitor), simvastatin (Zocor), and rosuvastatin (Crestor). Zocor lost its patent protection in mid-2006. The generic equivalents took over the market of Zocor within half a year after the patent expired and expanded the overall statin share. Sales in patientdays of branded atorvastatin (Lipitor) was quite stable during the sample period, but its market share became smaller with the rapid growth in sales of the generic competitors. Crestor, the newest brand in the sample which was rolled out in 2003, contributed to the drops in the other branded drugs’ sales. Fig. 2 shows prices per 30-day supply. Most branded drug prices fell between 50 and 100 dollars. Prices of drugs under patent protection generally experienced a slight increase over time. In contrast, drugs with generic alternatives had more obvious price changes. Prices of branded simvastatin (Zocor), pravastatin (Pravachol) and lovastatin (Mevacor) tended to fall slightly as generics just entered, but they moved back to the original level when generic competition intensified and average generic prices became very low. The pricing pattern is consistent with the “Generic Competition Paradox.” Branded 27 Note that the average prices in Table 4 are higher than their counterparts in Table 2 because the plan level data are noisier and the average prices for the plan level data are more sensitive to large values.

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

.6 .4

simvastatin (G) simvastatin (B)

rosuvastatin (B) pravastatin (G)

lovastatin (G) .2

Patient-Days in Billions

.8

1

8

ezetimibe/simvastatin (B)

pravastatin (B) fluvastatin (B)

atorvastatin (B)

20

03 2 0 m1 03 20 m7 04 20 m1 04 20 m7 05 20 m1 05 20 m7 06 20 m1 06 20 m 7 07 2 0 m1 07 20 m7 08 20 m1 08 20 m7 09 20 m 1 09 2 0 m7 10 20 m1 10 20 m7 11 20 m1 11 m 7

0

amlodipine/atorvastatin (B)

Date

100 50

20

03 20 m 1 03 20 m7 04 2 0 m1 04 20 m7 05 20 m1 05 20 m 7 06 2 0 m1 06 20 m7 07 20 m1 07 20 m7 08 20 m1 08 20 m7 09 20 m1 09 20 m7 10 20 m1 10 20 m7 11 20 m1 11 m 7

0

US Dollar

150

Fig. 1. Sales (Patient-Days).

Date amlodipine/atorvastatin (B)

atorvastatin (B)

ezetimibe/simvastatin (B)

fluvastatin (B)

lovastatin (B)

lovastatin (G)

lovastatin/niacin (B)

niacin/simvastatin (B)

pravastatin (B)

pravastatin (G)

rosuvastatin (B)

simvastatin (B)

simvastatin (G) Fig. 2. Price per 30 Patient-Days.

drug manufacturers would price compete with the first few generic equivalents for the general consumers. As many generics enter the markets, they choose to concentrate on the consumers with a strong preference for branded products instead of further cutting prices. 4. Model In this section, I discuss the models of copayments, demand, and supply. The copayment model assumes a linear relationship between full drug price and copayment. The predicted copayments will be used in the demand estimation as the price faced by consumers. For the consumer demand, I consider a random-coefficient discrete-choice model. The er-

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

9

ror structure is based on the model used in Bresnahan et al. (1997), which allows unobserved preferences to be correlated across multiple nests. By differentiating products along multiple dimensions, the model can capture rich substitution patterns in the markets. I also consider consumer heterogeneity in price sensitivity using a simple two-type version of the random-coefficient model following Berry et al. (2006) and Ching (2010a). Finally, I construct a static supply-side model to estimate marginal costs, identify the parameters for consumer heterogeneity, and more precisely estimate the parameters in the demand model. 4.1. Copayments The price faced by insured consumers is a small share of the full price. In the United States, insurance plan enrollees may pay a fixed amount for each prescription regardless of the drug cost (copayment), or a percent of the prescription drug cost (coinsurance). The tier pricing system designed by insurance companies usually puts less expensive drugs in lower tiers and requires a smaller copayment or coinsurance from enrollees. For example, a typical three-tier system has generics in tier one, branded drugs without generic substitutes in tier two, and branded drugs with generic substitutes in tier three. According to the 2011 Annual Survey by Kaiser Family Foundation, 72–85% of the covered workers with three tiers of cost sharing have copays for drugs, and 7–11% of them have coinsurance. To have a model that nests copayment and coinsurance for an average consumer, I assume

pck jt = γ0 + γ1 Brand j + γ2 pk jt + ek jt ,

(1)

pck jt

where is the cost shared by the consumer for product j in period t under insurance plan k, Brandj is equal to one if product j is branded and zero otherwise, and pkjt is the full price for product j in period t under insurance plan k. The constant (γ 0 ) is the base payment for generic drugs, γ 1 captures the difference in the base payment between branded and generic drugs, and γ 2 is the fraction of the full price shared by consumers. The model is simple but flexible enough to approximate different cost-sharing systems employed by insurance companies.28 4.2. Demand In the demand model, a consumer makes a discrete choice given a set of product characteristics. The consumer here refers to the combination of patients and doctors. I assume that they make joint decisions to maximize utility and ignore the possible principal-agent problem.29 A product here is a combination of molecule, form, version, and manufacturer. There are four dimensions along which products are differentiated: class, version, molecule, and form. As shown in Table 1, drugs of different molecules can be classified into statins and statin combinations. Three molecules have generic versions and two molecules have multiple forms. An individual in period t chooses from Jt products, indexed j = 1, 2, . . . , Jt . The indirect utility consumer i obtains from j in period t is

ui jt = αi pcjt + xjt β + μ j + ξ jt + i jt ,

(2)

where is the copayment for product j in time t, and xjt is a set of time-varying observed product characteristics. μj is a product fixed effect and ξ jt is a time-varying component that captures unobserved demand shocks. Idiosyncratic taste parameter,  ijt , is assumed to be independent across consumers but correlated among products. The mean utility for product j in time t is δ jt = xjt β + μ j + ξ jt . Consumers have an outside option, which includes non-drug treatments and no treatment. I normalize the utility of the consumer from this outside option to zero because I cannot identify relative utility levels. The vector xjt has several time-varying components that may affect consumer utility. I include the logarithm of detailing stock and the logarithm of DTCA stock. Berndt et al. (1996), Iizuka and Jin (2005), Ching and Ishihara (2012), and Ching et al. (2015) find high carryover rates of advertising to physicians and consumers in the prescription drug markets. I thus use the stock rather than flow of advertising spending in the demand for cholesterol drugs.30 Similarly to Mizik and Jacobson (2004), Dubé et al. (2005), Narayanan et al. (2005), Shapiro (2018a), and Shapiro (2018b), I allow for depreciation of detailing and DTCA effects by assuming pcjt

L ADET = jt

L 

τ =0

λτDET L log(1 + DET L j,t−τ )

(3)

28 The copayment model here focuses on the relationship between full price and cost share and does not consider the deductible, which is the amount of money an individual pays for drug expenses before her insurance plan starts to pay. Modeling deductibles requires a more complicated setup with premium setting. I leave this as an interesting future research opportunity. 29 The assumption of physicians and pharmacists being a perfect agent for a patient is also made in Coscelli and Shum (2004), Crawford and Shum (2005), and Ching (2010a) to keep the model tractability. 30 By allowing advertising spending to enter directly consumer utility, I assume the two types of advertising have persuasive effects. Allowing the informative role of advertising can greatly increase the computational burden. The advertising variables here can be better viewed as a control for time-varying product characteristics.

10

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

and CA ADT = jt

L 

τ =0

λτDT CA log(1 + DT CA j,t−τ ),

(4)

L and ADT CA are the advertising stocks, DET L where ADET j,t−τ and DT CA j,t−τ the lagged advertising spending, and λDETL and jt jt λDTCA the depreciation rates. In addition, I include time dummies for each period and time-since-entry dummies for each of the first twenty four months after drug entry. Time dummies capture the change in the quality of outside goods and time-since-entry dummies handle increasing consumer awareness of the existence of a new drug.31 32 To allow for rich substitution patterns, I incorporate two features into the demand model. First, I follow Berry et al. (2006) and model two types of potential consumers who differ in their “taste” for price. Second, following McFadden et al. (1978), I assume the unobserved idiosyncratic parameter ( ijt ) to have a generalized extreme value (GEV) distribution. I separately discuss the model setup with the two features in the following paragraphs. In the appendix, I perform robustness checks to examine the two distributional assumptions by (i) estimating a demand model with three types of consumers (Appendix H.2), and (ii) estimating two nested logit models with different hierarchical nesting structures (Appendix H.3). I model two types of consumers who differ in their price coefficient. Specifically,

αi =

 αH αL

if i is high type . if i is low type

(5)

There are several reasons for using the binary random price coefficient to capture consumer heterogeneity in price sensitivity. First, patients who are under their insurance deductible can be more price sensitive since they are responsible for the full costs of drugs (Lieber, 2017; Brown, 2019). The high type represents the patients over their deductible and the low type represents the patients under their deductible. Second, the binary random price coefficient helps explain price differentials between branded and generic drugs.33 In the absence of consumer heterogeneity in price sensitivity, the high price of branded drugs after patent expiration would be attributed to a jump in marginal costs, which is not quite consistent with intuition. Third, the consumer heterogeneity in price sensitivity provides the price discrimination incentive to issue coupons. The distribution of the two types of consumers and type-specific price coefficients determine how prices are set for coupon users and nonusers, which will be discussed in more detail in Section 6. The other assumptions for the counterfactual analysis with coupon targeting can also be reasonably made under two types of consumers. Fourth, the binary random price coefficient is widely used in the literature of demand estimation (Berry et al., 2006; Berry and Jia, 2010; Ching, 2010a; 2010b; Berry et al., 2016) as a concise way to model discrete random price coefficient. Compared to a continuous random price coefficient (such as BLP), the binary random price coefficient makes model estimation much less computationally demanding. To overcome the hierarchical structure of the multilevel nested logit model and allow  ijt to be correlated among products, I follow McFadden et al. (1978) and assume the unobserved idiosyncratic parameter to have a generalized extreme value (GEV) distribution with multivariate cumulative distribution function

F (i0t , i1t , . . . , iJt ) = exp [−G(ei0t , ei1t , . . . , eiJt )], which implies that the market share of product j in time t from the high type is given by

eδ jt +α sHjt =

H



pcjt



GHj eδi0t , eδi1t +α

GH eδi0t , eδi1t +α

H pc 1t

H

pc1t

, . . . , eδiJt +α

, . . . , eδiJt +α

H pc Jt

H

pcJt





,

(6)

where GHj is the partial derivative of GH with respect to the jth argument. Similarly, the market share of product j in time t from the low type is given by

eδ jt +α sLjt =

L c p jt





GLj eδi0t , eδi1t +α

GL eδi0t , eδi1t +α

L pc 1t

L c p1t

, . . . , eδiJt +α

, . . . , eδiJt +α

L pc Jt



L c pJt



.

(7)

31 In addition to the physician and direct-to-consumer advertising, Ching et al. (2015) documented the impact of publicity on the demand for statins. I ignore this effect due to lack of data on media coverage. 32 The mix of log advertising spending and other linear variables is used in the advertising literature, such as Dubé et al. (2005) and Shapiro (2016), to allow for diminishing marginal effects of advertising. 33 Frank and Salkever (1997) first provide empirical evidence on the consumer heterogeneity in price sensitivity to explain the pricing puzzle that branded drug manufacturers keep raising prices after generic entry. Ching (2010a,b) consider consumer heterogeneity in modeling competition between branded drugs and generics.

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Following Bresnahan et al. (1997), I specify GH and GL as



GH eδi0t , eδi1t +α

H

pc1t

, . . . , eδiJt +α

H

pcJt



= eδ0t +





al

l

G

L



L c L c eδi0t , eδi1t +α p1t , . . . , eδiJt +α pJt



= eδ0t +

 l



  k

 al

j



  k

I ( j, k, l )e

δ jt +α H pc jt ρl

I ( j, k, l )e

δ jt +α L pc jt ρl

11

ρl  (8)

ρl  ,

(9)

j

where I(j, k, l) is an indicator variable taking on the value of one if product j has the k-th value of the lth characteristic, and ρ l is the nesting parameter along the lth dimension. The scaling parameter al is defined as al = L 1−ρl . m=1 (1−ρm )

The market share of product j in time t is the weighted average of type-specific market shares. Let φ t be the fraction of market in time t accounted for by the high type consumers. The market share for product j in time t can be expressed as

s jt = φt sHjt + (1 − φt )sLjt .

(10)

4.3. Firm behavior Assume there are f = 1, 2, . . . , Ft firms in period t competing in a Bertrand–Nash game with a market size Mt . Firm f produces a subset of Jt products, Jft , in period t. The profit of firm f, omitting the time subscript, is

f =



( p j − mc j )Ms j ( p, ADET L , ADT CA , ξ ; θ ) − DET L j − DT CA j ,

(11)

j∈J f

where mcj is the marginal cost of product j and M the market size. The marginal cost is assumed to be

log(mc jt ) = mol j + ηGeneric j + ζt + h(τ jt ) + ω jt ,

(12)

where molj is the molecule fixed effects, Genericj the generic indicator, ζ t the time fixed effects, h(τ jt ) the function of time since entry, and ωjt is the (time-varying) unobserved cost shocks. Given the prices, product attributes, advertising spending, and marginal costs, firms simultaneously choose prices to maximize profits.34 The first order condition with respect to price is given by

sj +

 k∈J f

( pk − mck )

∂ sk = 0. ∂ pj

(13)

It is worth being clear about a couple of limitations of the models before we proceed to the next section. First, I assume that doctors and patients jointly make decisions to maximize utility. A similar approach is taken in Coscelli and Shum (2004), Crawford and Shum (2005), and Ching (2010a) due to lack of micro-level data to distinguish the roles in prescription decision making. Thompson (1993) discusses that conflicts of interest between physicians and patients can arise from gifts given by drug companies to physicians, physicians’ risk sharing in health maintenance organizations and hospitals, research on patients, etc. Government intervention and self-regulation by the pharmaceutical industry have aimed to alleviate the problem, and the conflicts of interest can be less serious during the sample periods.35 Second, I do not try to deal with how pricing influences entry of drugs. There is usually a long and uncertain lag between price setting and the decision to enter, especially for the entry of branded drugs. This can be addressed by adding an endogenous entry model to the current framework, which will greatly increase the computational burden and is beyond the scope of this paper. Moreover, Fig. 5 in Appendix Appendix F shows that the brunt of variation in my sample stems from generic entry right after the exclusivity period for both simvastatin and pravastatin, the only two drugs losing patent protection in the middle of my sample period. The figure suggests that the assumption of exogenous generic entry roughly holds in the sample. Third, I rule out consumer learning about the quality of generic drugs. Ching (2010a) estimates an empirical demand model with aggregate learning using prescription drug data during the80s when the diffusion of generic drugs is fairly slow. The diffusion of generics is much faster in my data, as discussed in Section 3, so the role of learning is relatively minor in the demand here. Finally, my supply model abstracts away from the negotiations between insurers and drug manufacturers due to the lack of data on insurance benefit designs, premiums, rebates, and discounts.36 Since the price measures in my data do 34 To simplify the estimation and counterfactual analysis, I do not consider the decision on advertising in firms’ problem and assume there is no strategic interaction in advertising. I perform a robustness check in Appendix H.4 to address this issue. 35 For example, The Physician Payments Sunshine Act, effective on August 1, 2013, requires manufacturers of drugs that participate in U.S. federal health care programs to report certain payments and items of value given to physicians. In 2008, Pharmaceutical Research and Manufacturers of America (PhRMA) strengthened the Code on Interactions with Healthcare Professionals to ensure that biopharmaceutical marketing practices comply with the highest ethical and professional standards. 36 Recently, Feng (2019) constructs price measures net of rebates and discounts using financial filing data. Olssen and Demirer (2019) estimates rebates using insurers’ formulary choices.

12

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

not account for rebates or discounts, the marginal costs estimated using the supply model would absorb the rebates and discounts. In the later counterfactuals where one branded drug manufacturer is able to issue copay coupons, I assume those rebates and discounts are fixed. It is possible that the ability to issue coupons gives the drug manufacturer a stronger bargaining position and reduces its rebates and thus marginal costs. This caveat should be kept in mind when we look at the counterfactual results.

5. Estimation The estimation of demand parameters closely follows Berry et al. (1995, 2004) and Nevo (20 0 0). I assume that the are mean independent of a set of instruments at the true parameters. That is,

demand and pricing unobservables  E ξ j (0 ) | Z = E ω j (0 ) | Z = 0. Using the contraction mapping suggested by BLP, I am able to compute ξ j given a set of parameter values and observed market shares:37

ξ jt = δ jt (s, θ ) − xjt β − μ j .

(14)

The marginal cost is computed from the first order condition:

mc = p − (θ , δ )−1 s(θ , δ )

(15)

where  j,k = −∂ sk /∂ p j I jk with Ijk equal to one if j and k are produced by the same firm. Then we can derive

ω = log( p − (θ , δ )−1 s(θ , δ )) − η − g − h(τ ).

(16)

Estimation of the parameters is undertaken by the generalized method of moments (GMM). I minimize the objective function of  ZWZ , where W is the weighting matrix. Let Zξ be the instruments for ξ , and Zω be the instruments for ω. The sample moments are (the time subscript are suppressed)

 Z  =

1 J

1 J

j

j

Zξ , j ξ j (α , β AC , β AD )



Zω, j ω j (α , β AC , β AD , η j )

.

(17)

The choice of instruments for the price and advertising relies on the identifying assumption used in Bresnahan (1987) and Berry et al. (1995). I assume that the location of each drug in product space is exogenous and that a drug’s markup, which is a function of prices and advertising spending, is correlated with its relative isolation in the product space. Since I do not include product characteristics in the indirect utility, rather than summing up the characteristics of own products and the other firms’ products as in BLP, I follow Arcidiacono et al. (2013) and count the number of products in a category defined in various ways. Specifically, I use the number of molecules in the same form, the number of molecules in the same form and class, whether generics are present in the same form, whether generics are present in the same molecule, the number of generics present in the same molecule, the number of generics present in the same form, and the number of generics present in the same form and class.38 Table 11 in the appendix shows that the F-statistics from the first-stage regressions of endogenous variables on the instruments range between 156 and 444, suggesting that the instruments are highly relevant. I discuss identification in an intuitive way. Nesting parameters (ρ l ’s) are identified from changes in the aggregate market share for each nest when the number of products in a nest varies. Take as an example the special case of the GEV model with only molecule nests. If the nesting parameter is one, the model reduces to a simple logit model. The market share would be roughly the same for each drug if they share similar product characteristics. If the nesting parameter is zero, drugs of the same molecule are perfect substitutes. Adding one drug to a molecule nest or changing the price of a drug in a nest does not affect the market shares of drugs in the other nests. Identification of the fraction of high type consumers (φ ) relies on the first order conditions with respect to prices on the supply side. If φ is zero or one, the optimal pricing suggests that competition from generics will always drive prices of branded drugs down. Thus, the pricing changes with generic entry would identify φ . The type-specific price coefficients (α H and α L ) can be identified from variation in the product mix over time. Identification of the linear parameters is more straightforward. With the product fixed effects (μj ), intertemporal variation in advertising spending within product helps to identify the advertising effects and advertising depreciation rates in the demand model.

5.1. Results I first discuss the results for copay estimation using the MarketScan data. The observed copayments in the prescriptions may be subject to a selection bias because they can depend on which drug a patient decides to use. To avoid the selection bias, I average copays and full prices over prescriptions for a combination of drug, plan, and month.

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

13

Table 5 Copay Estimates. Variable

Est.

S.E.

Full price for 30-day supply (γ 2 ) Brand dummy (γ 1 ) Constant (γ 0 ) R-squared Number of obs

0.0723 0.0159 11.6010 1.1084 4.2776 0.4892 0.2096 126,052

The level of observation is drug-plan-month. Standard errors, clustered by insurance plan, are reported. Table 6 Selected Demand and Cost Parameters. Demand Nesting parameters: Class Brand Molecule Form Proportion of high type (φ H ) Price coeff for high (α H ) Price coeff for low (α L ) Persistence of detailing (λDETL ) Persistence of DTCA (λDTCA ) log(Detailing stock) log(DTCA stock) Time since entry dummy: 1 month 2 months 3 months 4 months 5 months 6 months 12 months 18 months 24 months Objective function value Number of observations

Est.

S.E.

0.4903 0.4016 0.4265 0.4499 0.1298 −0.2032 −1.4014 0.6601 0.6187 0.1121 0.1049

0.0004 0.0034 0.0009 0.0006 0.0008 0.0013 0.0002 0.0598 0.2851 0.0378 0.0277

3.3744 3.0668 3.5883 3.7043 4.0525 3.1262 −0.7714 −0.3880 −0.4079

1.3264 1.2731 1.0810 1.0765 1.0505 1.0336 0.3893 0.3030 0.3203

Cost Molecules: Atorvastatin Fluvastatin Lovastatin Pravastatin Rosuvastatin Simvastatin Amlodipine/Atorvastatin Ezetimibe/Simvastatin Lovastatin/Niacin Niacin/Simvastatin Generic indicator Time since entry dummy: 1 month 2 months 3 months 4 months 5 months 6 months 12 months 18 months 24 months

Est.

S.E.

12.3124 39.1167 38.0916 50.4502 41.6583 35.5525 20.8303 35.4562 41.8399 25.1586 −18.6122

3.0873 4.8314 4.5162 5.3491 3.0460 4.5964 2.9770 2.9395 3.0873 3.9563 3.4953

48.4321 45.9278 48.8883 49.1473 52.1699 50.7918 6.6921 5.5948 5.7420

12.2800 10.4839 11.2833 11.2786 11.5131 9.2667 2.7087 2.0592 1.6392

73,059.4 3,248

Product-clustered standard errors are reported, where product is a combination of molecule, form, version, and manufacturer. Dummies for one to twenty four months since entry are included in the estimation, and only results for the selected time-since-entry dummies are reported. Results for the monthly date fixed effects are omitted.

All estimated coefficients in Table 5 are statistically significant at a 5% level.39 The predicted copayment for the branded drugs with an average full price of $70 (shown in Table 2) is $20.9. The predicted copayment for the generics with an average full price of $14 is $5.3. The predicted copayment numbers are roughly consistent with the average copayments and coinsurance for the covered workers with three or four-tier prescription drug cost sharing reported in the Employer Health Benefits Survey by the Kaiser Family Foundation.40 41 The first two columns in Table 6 present the selected demand parameters and their standard errors, respectively. Most estimates are statistically significant at a 5% level. All estimated nesting parameters are less than 0.5, which suggests the

37

Because of the model specifications, the contraction mapping here is slightly modified. The proof of invertibility is put in Appendix Appendix B. Fig. 5 in the appendix shows a surge in the number of generics right after the exclusivity period for simvastatin and pravastatin, suggesting most generic entries are exogenous. The Figure also suggests the counts of exclusivity periods that have expired can be more credibly exogenous than the counts of generics. However, since there are only two branded drugs in my sample that experienced expiration of an exclusivity period, replacing the counts of generics in different categories with the counts of expired exclusivity periods will greatly reduce the variation in the instruments. 39 The histogram in Appendix Appendix G shows a unimodal and quite symmetric distribution of the residuals. The residual plot in Appendix Appendix G shows no apparent patterns in the data after fitting the model. There is a point with low leverage found in the residual plot. Dropping the point does not significantly change the estimation results. 40 The Employer Health Benefits Survey by the Kaiser Family Foundation shows the average copayment for the first-tier drugs between 2003 and 2009 is $9 to $11, and the average coinsurance for the first-tier drugs is 18–21%. The average copayment for the second-tier drugs between 2003 and 2009 is $20 to $27, and the average coinsurance for the first-tier drugs is 23–27%. Note that the reported average copayment and coinsurance are for all drug classes. My estimates are based on the cholesterol-lowering drugs only. 41 Entry of generics does not significantly affect the copayment schedule. Table 12 in Appendix Appendix D shows that the coefficient on the dummy for branded drugs with generic equivalents is not statistically significant (Model 3). Adding the dummy does not increase the adjusted R2 . Thus, I use the results from the parsimonious model presented in Table 5 for the demand estimation. 38

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C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

drugs with more shared characteristics are closer substitutes. The estimate for the fraction of high type consumers is about 13%, and the high type consumers’ estimated price coefficient is a lot smaller than that of the low type in absolute value. The estimated coefficients on the control variables make intuitive sense. I select a 23-months lag length for detailing and DTCA. The estimated effects of the two types of advertising are similar, but the detailing effect is more persistent. The estimated depreciation rate of detailing is 0.66, which implies that 92% of the detailing effect dissipates within six months. The estimated depreciation rate of DTCA is 0.61, which implies that 95% of the DTCA effect dissipates within six months.42 The coefficients on the month-since-entry variables show that, on average, drugs that have been on the market for less than six months enjoy a higher market share than older drugs. There are marketing efforts early in a drug lifecycle not captured in the advertising variables included in my demand model, such as free samples, journal advertising, and conferences. Those unobserved marketing efforts can help explain the pattern shown in the estimates for months since entry. Columns 3 and 4 in Table 6 report the selected cost parameters and their standard errors, respectively. Atorvastatin has lower marginal costs than the other molecules, reflecting the long history of drug manufacturing of its manufacturer (Pfizer) and the economies of scope resulting from Pfizer’s rich product portfolio. The marginal cost of generic drugs for a 30-day supply is $19 lower than that of their branded counterpart. The cost difference between branded and generic drugs is close to the estimate of $23.7 in Arcidiacono et al. (2013), in which data on antiulcer drugs from 1991 to 2010 are used for estimation.43 Furthermore, the marginal cost declines quickly since a drug’s entry, which implies that drug production gets much more efficient as a drug becomes mature on the market. Table 7 contains price elasticities for September 2006, three months after generic simvastatins’ entry. The price elasticities help to understand the results of counterfactuals in which Merck is assumed to issue copay coupons for the branded simvastatin. There are two findings from the table worth noting. First, almost all own price elasticities are negative and larger than one in absolute value. The result is consistent with the prediction of the economic theory that profit-maximizing firms price on the elastic portion of the demand curve. Second, the cross-price elasticities of drugs with more shared characteristics are generally larger. For example, one percent price increase of branded lovastatin in sustained-action tablet has the largest impact on branded lovastatin in tablet, leading to a 0.6% increase in share. In contrast, the drugs with a different molecule in a different form, such as branded fluvastatin in capsule, are minimally affected by the price change of lovastatin in sustained-action tablet. The asymmetric cross price elasticities demonstrate the strength of the demand model in capturing the rich substitution patterns in the markets.

6. Counterfactual analysis Using the estimates of the demand and supply parameters, I construct counterfactuals to explore the effects of copay coupons on equilibrium pricing and welfare at the initiation of a coupon program. I assume that Merck, the manufacturer of branded simvastatin (Zocor), decides to issue copay coupons to consumers when its patent expires. I choose Zocor as the one and only drug for copay coupon issuance for three reasons. First, I observe the entry of generic simvastatins in the data. I can learn from the estimation results how consumers switched to those generics from the other drugs. Second, Zocor was the best-selling branded drug before Lipitor and Crestor were launched. Understanding the outcomes of Zocor’s copay coupon program can shed light on the welfare impact of the coupon programs of Lipitor and Crestor. Third, focusing on a single coupon program can help learn about the changes in equilibrium pricing and welfare that are directly caused by the introduction of a coupon program. It is thus useful to maintain one coupon program in the counterfactuals as a first step to show how copay coupons affect the markets.44 I make some simplifying assumptions to facilitate the counterfactuals. First, the fraction of consumers receiving copay coupons is assumed to be exogenous since Merck cannot fully control how many consumers actually receive the coupons. Second, I assume the copay formula (Eq. 1) to be fixed but allow the representative insurer to put a cap on Zocor prices as a way to strike back at the copay coupons. The price cap acts as a take-it-or-leave-it threat to the coupon issuer. Any Zocor price above the cap will trigger a formulary exclusion.45 Third, I consider the effects of issuing coupons only during the first five months (July 2006 to No. 2006) after Zocor’s patent expires. During this period, there were only three manufacturers for generic simvastatins: Teva, Ranbaxy, and Dr.

42 The results of persistence in advertising effects are quite consistent with the results in Mizik and Jacobson (2004) and Shapiro (2018b) though they look at a different therapeutic class. 43 There are two possible explanations for the cost difference between branded and generic drug. First, the recalls of generic products in recent years suggest that the manufacturing quality of generics can be substantially lower and less costly (Fox, 2017). Branstetter et al. (2016) also discuss evidence that suggests physical and psychological difference in the quality between the two versions. Second, the cost difference reflects the additional rebates paid by branded manufacturers to insurers or PBMs to obtain a more favorable position for their products in the formulary. 44 While considering copay coupons a strategic tool for competition used by multiple firms is an interesting extension, the number of brands in the cholesterol-lowering drug markets will make the game with copay coupons very complicated. I view the strategic role of copay coupons as a promising avenue for future research. 45 Exclusion threat is by far one of the few effective actions taken by insurance companies in response to copay coupons. For example, Express Scripts, CVS, and UnitedHealth, have excluded many drugs from their formulary since 2016 because of the proliferation of the coupon programs for those drugs. Other measures taken by insurance companies are quite limited in scale, at least in the short run. Adjusting plan design to increase enrollees’ cost share of drugs with coupons happened recently but only in very few cases. Filing a legal challenge to ban the use of copay coupons has met with little success.

Branded

Branded

Generic

Generic

Molecule (form)

Share

Amlodipine/ Atorvastatin (TAB)

Ezetimibe/ Simvastatin (TAB)

Lovastatin/ Niacin (SA TAB)

Atorvastatin Fluvastatin (TAB) (CAP)

Fluvastatin (SA TAB)

Lovastatin (TAB)

Lovastatin (SA TAB)

Pravastatin (TAB)

Rosuvastatin (TAB)

Simvastatin (TAB)

Lovastatin (TAB)

Pravastatin (TAB)

Amlodipine/Atorvastatin (TAB) Ezetimibe/Simvastatin (TAB) Lovastatin/Niacin (SA TAB) Atorvastatin (TAB) Fluvastatin (CAP) Fluvastatin (SA TAB) Lovastatin (TAB) Lovastatin (SA TAB) Pravastatin (TAB) Rosuvastatin (TAB) Simvastatin (TAB) Lovastatin (TAB) Pravastatin (TAB) Simvastatin (TAB)

0.30% 3.49% 0.17% 8.85% 0.09% 0.47% 0.01% 0.13% 0.31% 1.90% 0.93% 0.81% 0.40% 2.79%

−1.88 0.19 0.02 0.76 0.01 0.00 0.00 0.00 0.03 0.11 0.08 0.00 0.00 0.00

0.03 −3.87 0.01 0.47 0.00 0.04 0.00 0.01 0.01 0.20 0.03 0.02 0.05 0.27

0.04 0.11 −1.83 0.65 0.01 0.07 0.00 0.04 0.03 0.10 0.07 0.00 0.00 0.02

0.03 0.12 0.01 −0.98 0.01 0.00 0.00 0.00 0.03 0.18 0.12 0.00 0.00 0.02

0.01 0.42 0.05 0.19 0.06 −4.00 0.00 0.43 0.00 0.08 0.01 0.02 0.05 0.23

0.03 0.08 0.01 0.73 0.01 0.00 −1.94 0.60 0.03 0.11 0.08 0.00 0.00 0.02

0.01 0.45 0.09 0.37 0.00 1.48 0.05 −4.84 0.01 0.11 0.03 0.05 0.04 0.21

0.03 0.06 0.01 0.87 0.01 0.00 0.00 0.00 −1.36 0.12 0.09 0.00 0.01 0.00

0.03 0.27 0.01 0.98 0.01 0.01 0.00 0.00 0.02 −2.79 0.08 0.01 0.02 0.10

0.03 0.07 0.01 1.08 0.01 0.00 0.00 0.00 0.03 0.14 −1.56 0.00 0.00 0.01

0.00 0.24 0.00 0.08 0.00 0.02 0.00 0.02 0.00 0.07 0.00 −3.85 0.21 1.76

0.00 0.19 0.00 0.06 0.00 0.02 0.00 0.00 0.01 0.05 0.00 0.08 −18.23 1.03

0.03 0.07 0.01 0.65 −1.93 0.15 0.00 0.00 0.02 0.10 0.07 0.00 0.00 0.02

Simvastatin (TAB) 0.00 0.25 0.00 0.08 0.00 0.02 0.00 0.01 0.00 0.07 0.00 0.15 0.24 −11.11

Note: Cell entries (i, j), where i indexes row and j indexes column, give the percent change in market share of brand j with respect to a one-percent change in price of i. All branded drugs are included in the table. Only the generic drugs with the largest market share in their molecules are reported.

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Table 7 Estimated Price Elasticities for September 2006.

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C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Reddy’s Laboratories.46 No other firms were allowed to enter the market of simvastatins during these five months.47 With limited generic competition, branded drug manufacturers have a greater incentive to use copay coupons than when generic competition is severe. Once there are many generics in the market, generic prices are usually very low, and the coupons would not be as attractive to consumers.48 Suppose there are Jt drugs in period t, and that the fraction of consumers receiving a copay coupon for Zocor is q ∈ (0, 1). Firms compete in a Bertrand game and maximize their own profits by simultaneously choosing their full prices ( p1 , p2 , . . . , pJt ) and, for Merck, the copay for Zocor with a coupon ( p˜cZ ). The insurer decides the insurance copay for each drug ( pc1 , pc2 , . . . , pcJ ) using the copay formula. Consumers with a copay coupon face copays [ pc1 , pc2 , . . . , min{ pcZ , p˜cZ }, . . . , pcJ ], t t and consumers without a copay coupon face copays [ pc1 , pc2 , . . . , pcZ , . . . , pcJ ]. The consumers with a coupon compare the t coupon copay with the insurance copay. If the coupon copay is higher than the insurance copay for Zocor, the consumers will not use the coupon since the coupon provides no savings in the out-of-pocket costs.49 The profit from Zocor in period t with copay coupons is

   c ˜cZt , pcZt } − mcZt sCZt − DET LZt − DT CAZt , πZt = Mt (1 − q )( pZt − mcZt )sNC Zt + q pZt − pZt + min{ p sNC Zt

(18)

sCZt

where is Zocor’s share in the market without a coupon and the share in the market with a coupon. The revenue from a coupon user is insurer’s net payment ( pZt − pcZt ) plus the minimum of coupon copay and insurance copay (min{ p˜cZt , pcZt }). The profit function for the other drugs is the same as before. With equilibrium prices calculated using the first order conditions, I can calculate the change in firm profits, consumer welfare, and insurer spending. Under the GEV error structure, the expected consumer surplus in period t, measured in dollars, before the copay coupon program is introduced can be expressed as

E (CS0t ) = Mt

  1   1   φ log(GtH ) + (1 − φ ) log(GtL ) + CE , H L −α −α

(19)

where CE is the Euler’s constant. The expected consumer surplus in period t, measured in dollars, after the copay coupons are issued is

  1   φ q log(GtH,C ) + (1 − q ) log(GtH,NC ) −α H  1    L,C L,NC + (1 − φ ) q log ( G ) + ( 1 − q ) log ( G ) + C , t E t −α L

E (CS1t ) = Mt

(20)

where GtH,C and GtH,NC are the G function values for the high type consumers with and without a copay coupon, respectively. GtL,C and GtL,NC are the G function values for the low type consumers with and without a copay coupon, respectively. The change in the expected consumer surplus in period t is

E (CSt ) = E (CS1t ) − E (CS0t ).

(21)

The Euler’s constants in E(CS1t ) and E(CS0t ) simply cancel out. In the exercise so far, Merck can set pZ = ∞ and p˜cZ ≈ 0 for any q > 0 to maximize the payment from the insurer. This cannot happen in practice because insurance companies can always remove a drug with coupons from their prescription drug list and refuse to pay for the drug. To take care of this insurer threat, I assume the insurer imposes a price cap for Zocor which equals 0%, 10%, and 20% above its current price. Any Zocor price beyond these levels will trigger a removal from the formulary.50 Different motives for issuing copay coupons can generate different equilibrium outcomes and policy implications. I construct three counterfactuals to disentangle the welfare effects driven by different incentives to issue coupons. In the first case (PD case), Merck issues copay coupons solely for price discrimination by targeting coupons at the price-sensitive consumers and ignoring the additional profits they can make from the insurer by raising the full price for the coupon users.51 46 Teva and Ranbaxy were the first challengers of Zocor’s primary U.S. patent and were granted 180-day exclusivity by FDA to sell generic simvastatins. Dr. Reddy’s Laboratories received a license from Zocor to sell authorized generic simvastatins. 47 In late December 2006, other generics started to enter the market and thus I exclude this month in the policy simulation. 48 Lack of an entry model also makes it difficult to tackle the generic entrants after the exclusivity period. Morton (1999), Reiffen and Ward (2005), and Ching (2010b) have modeled generic firms’ entry decisions. 49 This assumption will ensure that Merck sets a coupon copay less than the average insurance copay if they want the coupons to work. In practice, consumers’ insurance copay varies according to their insurance plan. Drug manufacturers may put a cap on a patient’s savings from coupons. For example, in 2011 Pfizer paid up to $50 of the difference between a patient’s $4 copay using the Lipitor coupon and their normal insurance copay for branded drugs, up to an annual limit of $600 (Purvis and Schondelmeyer, 2013). 50 The choice of the price caps is motivated by the fact that Lipitor’s real price increased by about 23.8% before UnitedHealth stopped covering Lipitor for its plan enrollees in January 2013. Source: “Rx Price Watch Case Study: Efforts to Reduce the Impact of Generic Competition for Lipitor,” American Association of Retired Persons, June 2013. 51 With insurance and prescription data, many consulting firms now help drug manufacturers to target consumers with the strongest incentive to use coupons. For example, IMS analyzed insurance coverage and prescription history and designed the coupon distribution strategy at the physician level. Alpha 1C in Connecticut provides coupon targeting recommendations based on patient demographics and the key performance indicators of physicians. ConnectiveRx in New Jersey identifies potential coupon users and messages patients to inform them about coupon availability.

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

17

In the second case (MH case), I assume coupons are randomly distributed to show the effects of coupons driven only by moral hazard. In the last case (PD-MH case), Merck issues coupons for both price discrimination and moral hazard. In the first counterfactual, I calculate the equilibrium outcomes under different coupon penetration rates. In the last two counterfactuals, I calculate the equilibrium outcomes under different combinations of coupon penetration rates and the insurer’s tolerance for a Zocor price increase. 6.1. Issuing copay coupons for price discrimination only To examine the effects of copay coupons when they are used only for price discrimination, I assume the copay coupons are targeted at the low type consumers. I decompose equation (18) to show the profit function for Zocor with coupon targeting under q < 1 − φ (time subscript suppressed) and assume the coupon copay is lower than the insurance copay so that the coupons can effectively lower the out-of-pocket costs. That is,

 πZ = M φ sNC,H + (1 − φ − q )sNC,L ( pZ − mcZ ) + MqsC,L ( pZ − pcZ + p˜cZ − mcZ ) − DET LZ − DT CAZ , Z Z Z

(22)

where sNC,H is Zocor’s conditional market share of high type consumers without a coupon, sNC,L Zocor’s conditional market Z Z

share of low type consumers without a coupon, and sC,L Zocor’s conditional market share of low type consumers with a Z coupon. Note that when coupons are targeted at the low type consumers, all coupon users are low type who account for q of the market. Coupon nonusers include φ of the consumers who are high type and (1 − φ − q ) of the consumers who are low type. The first order condition with respect to Zocor’s full price, pZ , is

 ∂ sNC,H ∂ sNC,L Z Z M φ + (1 − φ − q ) +M φ + (1 − φ − q ) ( pZ − mcZ ) ∂ pZ ∂ pZ   ∂ sC,L ∂ pcZ + Mq Z ( pZ − pcZ + p˜cZ − mcZ ) + MqsC,L 1− = 0. Z ∂ pZ ∂ pZ

sNC,H Z

sNC,L Z





(23)

With the market segmented by coupons, drug manufacturers can price discriminate consumers by setting a price for the coupon users and a possibly higher price for the coupon nonusers who are on average less price sensitive. The first two terms in Eq. 23 represent the “price discrimination” effect of traditional coupons since they are the change in profits from the market without coupons in response to a change in the full Zocor price. The third term is equal to zero as the Zocor share of coupon users is not affected by the full price as long as the coupon copay is less than the insurance copay, i.e. ∂ sC,L /∂ pZ = 0. The last term, which is always positive, represents the “moral hazard” effect specific to copay coupons. Merck Z makes additional profits of (1 − ∂ pcZ /∂ pZ ) from each coupon user by raising the full price of Zocor and making the insurer pay the bill.52 To investigate the effects of copay coupons when the coupons are issued solely for price discrimination, I shut down the moral hazard effect in Eq. 23 and assume Merck ignores the additional profits they can make by charging the coupon users a higher full price. Imposing this assumption and rearranging Eq. 23 gives

  ∂ sNC,H ∂ sNC,L NC,L Z Z φ sNC,H + ( 1 − φ − q ) s + + ( 1 − φ − q ) ( pZ − mcZ ) = 0. φ Z Z ∂ pZ ∂ pZ

(24)

Solving the system of first order conditions with respect to the full price of each product (pj ) and the copay for Zocor with a coupon ( p˜cZ ) simultaneously under different levels of coupon penetration (q), I derive the equilibrium prices and coupon copay and use these values to calculate the average insurance copays, market shares, consumer welfare, firm profits, and insurer spending. The results summarized in Table 8 show the coupons help to screen consumers. As a larger fraction of low type consumers receive a coupon, the average price sensitivity of the coupon nonusers gets smaller, and the equilibrium full price of Zocor goes up. The copay with Zocor coupons stays at $14.5 under different coupon penetration rates because the coupons are always targeted at the low type consumers and their price sensitivity does not change with coupon penetration. The equilibrium full price of the other branded drugs goes up with coupon penetration. Knowing that the consumers who do not receive a coupon have a lower average price sensitivity than those who do, Merck and its competitors raise full prices. The pricing for copay coupons increases Zocor’s market share and consumer welfare. Table 8 shows that Zocor’s market share grows proportionately with coupon penetration while the market share of the other products slightly goes down. This suggests copay coupons expand overall market more than steal business from competitors. The fact that the market expansion effect outweighs the business stealing effect helps to explain the higher consumer welfare after coupons are introduced to the market. Those who receive a coupon find Zocor more affordable and become more likely to use it while 52 A higher full price increases the insurance copay responsible for by Merck by ∂ pcZ /∂ pZ . Since the insurance copay is a fraction of the full price, a one-dollar increase in the full price results in an increase in the insurance copay by less than one dollar, making the last term in Eq. 23 positive.

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C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611 Table 8 Counterfactual Analysis: Price Discrimination Only. Coupon penetration:

0%

1%

5%

10%

Avg price (30-day supply) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

76.8 46.7 51.9 65.7 45.3

76.8 46.7 53.0 65.9 45.3

76.8 46.7 56.0 66.6 45.3

76.9 46.7 58.5 67.3 45.3

Avg copay (30-day supply) Copay using Zocor coupon Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

− 21.4 7.7 19.6 20.6 7.6

14.5 21.4 7.7 19.7 20.6 7.6

14.5 21.4 7.7 19.9 20.7 7.6

14.5 21.4 7.7 20.1 20.7 7.6

Market share (%) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

1.03 5.19 3.51 12.32 3.74

2.01 5.14 3.39 12.32 3.70

5.95 4.93 2.96 12.31 3.55

10.86 4.66 2.46 12.31 3.35

Base 1031.6

1.9

9.6

19.3

37.1 21.0 30.6 558.2 14.3 813.8

30.7 −0.2 −0.3 0.1 −0.1 36.0

153.4 −1.0 −1.4 0.8 −0.7 180.8

307.3 −2.1 −3.1 1.9 −1.4 362.6

Welfare change ($million’s) Consumer welfare Profits: Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics Insurer spending

those without coupons pay only a slightly higher price for products in the category. The net effect of copay coupons on consumer welfare is thus positive. The copay coupons increase Merck’s profit at the cost of a higher insurer spending. The profit from Zocor increases by 83% when only 1% of consumers receive a coupon. As coupon penetration grows to 10%, the profit from Zocor exceeds more than ten times its current level. The insurer spending increases by 45% when 10% of consumers receive a coupon. The change in consumer welfare can turn negative if the insurer passes on higher costs to consumers by raising premiums. 6.2. Issuing copay coupons for moral hazard only To examine the moral hazard effect of copay coupons, I assume the Zocor coupons are distributed so that coupon users and nonusers are equally price sensitive. Merck is not able to price discriminate using coupons in this case, so its pricing is purely driven by moral hazard. Under this random coupon distribution, equation (18) can be written as



 πZ = M (1 − q ) φ sNC,H + (1 − φ )sNC,L ( pZ − mcZ ) + Mq φ sC,H + (1 − φ )sC,L ( pZ − pcZ + p˜cZ − mcZ ) − DET LZ − DT CAZ . Z Z Z Z (25) The first order condition with respect to Zocor’s full price becomes

   

 ∂ sNC,H ∂ sNC,L NC,H NC,L Z Z ( 1 − q ) φ sZ + ( 1 − φ )sZ + φ + (1 − φ ) ( pZ − mcZ ) + q φ sC,H + (1 − φ )sC,L (1 − γ2 ) = 0. Z Z ∂ pZ ∂ pZ (26) Recall γ 2 < 1 is the coefficient on full price in the copayment formula. The last term in Eq. (26), which is always positive, captures the moral hazard effect. In pricing Zocor’s full price, Merck considers the profits they can make by raising the Zocor price for coupon users and letting the insurer fully pay for the price increase. The last term in Eq. (26) would make the equilibrium full Zocor price infinite, so a cap on Zocor prices is needed to limit the insurer spending. Table 9 shows that Merck prices more aggressively than in the PD case. I first discuss the results under a price cap equal to the current Zocor price. When coupons are used for moral hazard, Merck always matches the Zocor price to the price cap and sets a very low copay value for coupon users. At the same time, Merck raises the price for its other brands to make Zocor more attractive to coupon users. Under a 1% coupon penetration rate, Merck’s pricing alleviates price competition and makes the equilibrium prices of the brands by the other firms higher than the counterparts in Table 8.

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

19

Table 9 Counterfactual Analysis: Moral Hazard Only. Price cap of Zocor:

Current price ($76.8)

1.10 × Current price ($84.5)

1.20 × Current price ($92.2)

Coupon penetration:

0%

1%

5%

10%

1%

5%

10%

1%

5%

10%

Avg price (30-day supply) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

76.8 46.7 51.9 65.7 45.3

76.8 46.5 81.3 69.6 45.1

76.8 46.5 91.1 67.5 45.1

76.8 46.5 105.4 65.1 45.1

84.5 46.5 81.1 70.2 45.1

84.5 46.5 91.8 68.5 45.1

84.5 46.5 107.7 66.5 45.1

92.2 46.5 80.9 70.7 45.1

92.2 46.5 92.3 69.3 45.1

92.2 46.5 109.4 67.7 45.1

Avg copay (30-day supply) Copay using Zocor coupon Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

− 21.4 7.7 19.6 20.6 7.6

9.7 21.4 7.6 21.8 20.9 7.5

9.5 21.4 7.6 22.5 20.8 7.5

9.3 21.4 7.6 23.5 20.6 7.5

9.1 22.0 7.6 21.7 21.0 7.5

9.0 22.0 7.6 22.5 20.8 7.5

8.8 22.0 7.6 23.7 20.7 7.5

8.6 22.5 7.6 21.7 21.0 7.5

8.5 22.5 7.6 22.6 20.9 7.5

8.3 22.5 7.6 23.8 20.8 7.5

Market share (%) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

1.03 5.19 3.51 12.32 3.74

2.61 4.10 0.62 20.66 2.67

6.41 3.93 0.45 20.14 2.56

11.17 3.72 0.29 19.46 2.42

2.42 4.11 0.64 20.78 2.67

6.26 3.94 0.46 20.22 2.56

11.07 3.73 0.29 19.49 2.42

2.25 4.11 0.67 20.88 2.68

6.12 3.94 0.48 20.29 2.57

10.96 3.73 0.29 19.53 2.43

Base 1031.6

−6.2

17.2

48.0

−7.2

17.5

49.7

−8.0

17.9

51.4

37.1 21.0 30.6 558.2 14.3 813.8

48.7 −5.1 −2.7 147.3 −4.8 136.5

152.1 −5.7 −5.9 122.3 −5.2 255.4

279.9 −6.6 −10.9 87.3 −5.7 400.1

54.6 −5.0 −1.7 163.3 −4.8 158.4

178.0 −5.7 −4.9 139.2 −5.2 300.3

331.0 −6.5 −10.2 105.6 −5.7 473.7

59.3 −5.0 −0.7 177.2 −4.8 177.2

202.8 −5.7 −4.1 154.0 −5.2 341.7

381.0 −6.5 −9.7 121.4 −5.7 543.1

Welfare change ($million’s) Consumer welfare Profits: Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics Insurer spending

Merck keeps raising prices of its other brands when coupons become more available, but the other branded competitors do not follow suit. As coupon penetration rate increases from 1% to 10%, the average price of Merck’s other brands rises from $81 to $105, but the average price of brands by the other firms drops from $70 to $65. This implies that when the coupon copay is very low and coupon penetration is sufficiently high, the coupon program starts to make the other branded firms cut prices to compete for coupon users. The market share of generic simvastatin is much more affected by Zocor’s coupons than in the PD case. Here, the equilibrium copay for coupon users is closer to the copay for generic simvastatin, which greatly lowers the market share of generic simvastatin when the coupon penetration rate is 10%. As a result of lower prices of non-Merck brands and higher prices of Merck brands, the combined market share of branded drugs by non-Merck firms stays roughly at the same level as coupons proliferate. Some of the welfare changes are different from those of the PD case. Consumer welfare decreases when coupons are received by only 1% of the consumers. This is because most of the consumers suffer from the higher equilibrium prices (copays), and only a tiny fraction of the consumers benefit from the low copay with coupons. At a coupon penetration rate of 10%, the benefits to consumers brought by coupons exceed the costs, increasing overall consumer welfare by 5%. As in the PD case, the profit from Zocor improves a lot with coupon introduction. However, the results of profit changes for the other firms are mixed. The other brands benefit from the mitigated price competition, but profits of generic drugs decrease due to the low coupon copay for Zocor. Moreover, Merck’s aggressive pricing and the mitigated price competition drive up the insurer spending more than in the PD case. When the insurer sets a higher cap on Zocor prices, the equilibrium Zocor price jumps to the new cap level. The return of coupons gets larger since Merck can earn more from the insurer for each use of the coupon. Therefore, Merck sets a lower coupon copay and a higher price for its other brands to attract more coupon users. Price competition is further mitigated, resulting in a larger increase in profits from most brands and a higher rise in insurer spending. Changes in consumer welfare are roughly the same under different price caps because the lower copay for coupon users cancels out the effect of higher prices on coupon nonusers. 6.3. Issuing copay coupons for both price discrimination and moral hazard When issuing copay coupons for both price discrimination and moral hazard, Merck targets the coupons at the low type consumers and take advantage of the insurer by lowering consumers’ out-of-pocket costs and further raising the full price of Zocor. With coupon targeting, the profit function can be written as Eq. (22). The first order condition with respect to

20

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Table 10 Counterfactual Analysis: Price Discrimination And Moral Hazard. Price cap of Zocor:

Current price ($76.8)

1.10 × Current price ($84.5)

1.20 × Current price ($92.2)

Coupon penetration:

0%

1%

5%

10%

1%

5%

10%

1%

5%

10%

Avg price (30-day supply) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

76.8 46.7 51.9 65.7 45.3

76.8 46.5 81.6 70.3 45.1

76.8 46.5 88.7 70.8 45.1

76.8 46.5 95.5 75.0 45.1

84.5 46.5 81.3 70.8 45.1

84.5 46.5 89.1 72.2 45.1

84.5 46.5 95.5 75.7 45.1

92.2 46.5 80.9 71.2 45.1

92.2 46.5 88.9 73.3 45.1

92.2 46.5 95.0 75.9 45.1

Avg copay (30-day supply) Copay using Zocor coupon Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

− 21.4 7.7 19.6 20.6 7.6

14.5 21.4 7.6 21.8 21.0 7.5

14.5 21.4 7.6 22.3 21.0 7.5

14.5 21.4 7.6 22.8 21.3 7.5

14.4 22.0 7.6 21.8 21.0 7.5

14.4 22.0 7.6 22.3 21.1 7.5

14.4 22.0 7.6 22.8 21.4 7.5

14.3 22.5 7.6 21.7 21.0 7.5

14.3 22.5 7.6 22.3 21.2 7.5

14.3 22.5 7.6 22.7 21.4 7.5

Market share (%) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

1.03 5.19 3.51 12.32 3.74

2.65 4.10 0.63 20.68 2.67

6.61 3.92 0.53 20.21 2.55

11.56 3.70 0.47 19.33 2.41

2.45 4.10 0.65 20.80 2.67

6.42 3.92 0.55 20.33 2.56

11.38 3.71 0.49 19.47 2.42

2.27 4.11 0.68 20.91 2.68

6.25 3.93 0.57 20.38 2.56

11.22 3.72 0.50 19.55 2.42

Base 1031.6

−10.0

−2.4

6.7

−11.3

−3.4

6.1

−12.4

−4.4

5.5

37.1 21.0 30.6 558.2 14.3 813.8

53.6 −5.1 −2.2 154.7 −4.8 145.4

177.1 −5.8 −2.8 157.5 −5.2 298.4

331.7 −6.6 −3.1 164.5 −5.7 491.5

59.8 −5.0 −1.1 170.6 −4.8 167.0

204.1 −5.7 −1.8 174.3 −5.2 342.0

384.7 −6.6 −2.1 182.1 −5.7 562.3

64.7 −5.0 −0.2 184.6 −4.8 185.6

230.0 −5.7 −0.7 190.6 −5.2 383.4

436.6 −6.5 −1.1 198.5 −5.7 630.3

Welfare change ($million’s) Consumer welfare Profits: Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics Insurer spending

Zocor’s full price is

φ

sNC,H Z

+ (1 − φ − q )



sNC,L Z

 ∂ sNC,H ∂ sNC,L Z Z + φ + (1 − φ − q ) ( pZ − mcZ ) + qsC,L (1 − γ2 ) = 0. Z ∂ pZ ∂ pZ

(27)

The terms before the last one in Eq. (27) represent the trade-off between full price and the demand of consumers without a coupon and capture the price discrimination incentive. The last term in the equation, qsC,L (1 − γ2 ), is always positive and Z captures the moral hazard incentive since it measures the additional profits brought by a higher full price paid by the insurer for coupon users. Again, a cap on Zocor prices is needed to prevent the price from going to infinity. Table 10 shows that, as in the MH case, Merck always sets the price of Zocor at the price cap to exploit the moral hazard benefits. Compared to the MH case, however, the average price sensitivity of coupon users is higher and the average price sensitivity of coupon nonusers is lower, thanks to coupon targeting. Therefore, Merck sets a higher copay for coupon users and a lower price for coupon nonusers than in the MH case to attain a similar market share for Zocor. The consumer screening by coupons also causes the average price of the other brands to increase more in equilibrium. The more softened price competition makes the profit from Zocor and the profit from brands by non-Merck firms increase more than in the MH case. As a result, consumer welfare gets lower and insurer spending rises more than their counterparts in Table 9. For comparison, the difference-in-differences estimates in Dafny et al. (2017) indicate that, during the five years following generic entry, copay coupons cause a 60+ percent increase in the utilization of branded drugs and that drugs with coupons experience price growth of 12–13 percent per year.53 While the price cap assumed in my counterfactuals is quite close to the yearly price growth documented in Dafny et al. (2017), my counterfactual results show a larger increase in the branded utilization. The reason for the difference in the change in branded utilization can be my ignoring costs of switching from generic to branded simvastatin and consumer learning about drug efficacy (Coscelli, 20 0 0; Crawford and Shum, 20 05; Lee, 2016). High switching costs, psychological and physical, and the time it takes to eliminate uncertainty about a drug’s effectiveness or side effects can both prevent patients from immediately adopting drugs with coupons even if the coupons can substantially save their out-of-pocket costs. Thus, my results can be viewed as an upper bound for the effect of coupons on market share. A limitation of the counterfactuals above is that the DTCA spending and detailing spending stay fixed throughout the analysis. Solving for the optimal advertising spending would turn the problem into a dynamic game because the advertis53 During their sample period from June 2007 to December 2010, the share of branded retail spending accounted for by drugs with coupons increased from 26% to 54%.

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

21

ing effects are estimated to be persistent. To investigate how innocuous this assumption is, I perform a robustness check by making Merck double and triple the DTCA spending for Zocor during the coupon period. The equilibrium outcomes in Appendix H.4 show little qualitative change. This exercise implies that the counterfactual results in general are not deeply affected by advertising changes. To sum up, the two motives for issuing copay coupons have very different consumer welfare implications. Copay coupons used for price discrimination slightly mitigate price competition and make the drug with coupons more affordable for pricesensitive consumers, which improves consumer welfare even when only 1% of the consumers receive a coupon. On the other hand, when copay coupons are used for moral hazard, the coupon issuer raises the full price of the drug with coupons by as much as possible and sets a higher price for its other brands to make the drug with coupons more attractive to coupon users. The pricing significantly softens price competition, improving consumer welfare only when coupon penetration is sufficiently high.54 7. Conclusion To understand the impact of copay coupons on pricing and welfare in pharmaceuticals, I estimate a model with consumer heterogeneity and rich substitution patterns and calculate changes in consumer welfare, firm profits, and insurer spending caused by copay coupons in the counterfactuals. I consider the price discrimination incentive and moral hazard incentive to issue coupons and find that copay coupons have different welfare implications under the two incentives. Coupons used only for price discrimination make the drug with coupons affordable for more consumers and increase consumer welfare even when a small fraction of consumers receive a coupon. When used for moral hazard, copay coupons significantly mitigate price competition, which benefits the coupon issuer and the other branded manufacturers. The softened price competition greatly increases insurer spending, but consumer welfare improves only when coupon penetration is sufficiently 0 high. The findings from the counterfactual analysis suggest that, instead of completely banning copay coupons, we should consider policies that remove or reduce the moral hazard component of coupons. For example, insurance companies can increase copays one-to-one when full prices exceed a certain level in markets where generics are available.55 This would make a price increase more costly for coupon issuers since they need to pay more to reduce patients’ copay. Taxing large price increases that result from a coupon launch or subjecting price hikes to regulatory or political scrutiny can also alleviate the moral hazard problem.56 There are some potential challenges to a definite policy recommendation that should be addressed. First, copay coupons can be used as a strategic tool. In the paper, I consider a single coupon issuer after patent expiration. Using coupons for competition can expand the overall market and have different welfare implications. Second, fully solving for advertising, free samples, and other marketing efforts with the introduction of coupons can be difficult because of the complex interaction among them.57 Branded drugs usually cut spending on advertising and free samples after generics enter because of generics’ free-riding problem. Copay coupons can give branded drug manufacturers an incentive to invest in those efforts in the face of generic competition. Third, we can better understand the welfare implications of copay coupons for the long run if we consider how insurers adjust their insurance plan design to pass through the higher costs associated with copay coupons. Finally, in addition to price discrimination and moral hazard, dynamics can motivate branded drug manufacturers to use copay coupons. For example, they may use coupons to build brand loyalty and enhance patient adherence, which can increase profits in the long run if patients have high switching costs. Copay coupons can also deter generic entry since the low copay for coupon users makes it very costly for generics to compete with branded drugs. Incorporating these dynamic incentives would complement the static analysis in the paper and provide more insights on the welfare impact of copay coupons. Appendix A. Sample Copay Coupons

54 While I use only the data from the cholesterol-lowering drug markets for the analysis, the directions of welfare changes resulting from copay coupons for cholesterol-lowering drugs can apply to other markets with similar settings and substitution patterns. For example, antidepressants fall into multiple therapeutic classes with several molecules in each. A few of the branded antidepressants experienced patent expiration in the20 0 0s and faced generic competition. Drug manufacturers for branded antidepressants persistently invest in detailing and DTCA to communicate drug efficacy with both doctors and patients. If the own- and cross-price elasticities for antidepressants are close to the estimates in the paper, we may use the counterfactual results to think about the welfare effects of the coupons for antidepressants such as Zoloft and Pristiq. 55 Such insurance plan design has been popular in many European countries, where a patient can choose the more expensive branded drugs over their generic equivalents but needs to fully cover the price difference between the brands and generics. See World Health Organization. (2018). Medicines Reimbursement Policies in Europe. Retrieved from http://www.euro.who.int/__data/assets/pdf_file/0011/376625/pharmaceutical- reimbursement- eng.pdf 56 An example of political scrutiny is calling the CEO of a drug company to testify before Congress, as Congress did for the CEOs of Mylan, Valeant, and Turing following their drug price hikes in recent years. 57 For example, Dubois et al. (2017) show that banning advertising leads to a reduction in the quantity of potato chips sold, but the effect is partially offset by firms lowering prices due to the higher consumer sensitivity in the absence of advertising.

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C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Fig. 3. Sample Copay Coupons.

Appendix B. Invertibility of Market Share Function in GEV Model In this appendix, I show the invertibility of market share function discussed in BLP (1995) can be extended to the case of GEV model with a slight modification. They define a function f: RK → RK . If the following conditions are satisfied, then there is a unique fixed point x0 to f in RK . 1. ∀x ∈ RK , f(x) is continuously differentiable with ∀j and k, ∂ fj (x)/∂ xk ≥ 0 and Kk=1 ∂ f j (x )/∂ xk < 1. 2. min j infx f (x ) ≡ x > −∞. 3. There is a value, x¯, with the property that if for any j, x j ≥ x¯, then for some k, fk (x) < xk . Conditions (2) and (3) are trivial in my case, so I only show the proof of condition (1). Consider f (δ ) = δ + minl {ρl }[log(s ) − log(s(δ ))]. In my demand model,

G(eδ ) = eδ0 +





al



 

l

I ( j, k, l )e

δj ρl

ρl 

j

k

and δj 1  sj = a l e ρl δ G (e )

l





I ( j , k jl , l )e

δ j ρl

ρl −1

=

δj

l

G

j

ρ −1

al e ρl T jl l

We can show

.

⎞ ⎛ δj δj

  ρ −1 al e ρl T jl l  min{ρl } ∂ fj e ρl ⎝ ⎠ 1 − ( 1 − ρl ) = 1 + min{ρl }s j − > 0. δj ∂δ j ρl T jl ρl −1 ρ l l

al  e

l

T jl 

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

For m = j, we can show

⎡

∂ fj = min{ρl }⎣sm − ∂δm and

 m

  ρl − 1 

ρl

l

⎛ ⎝

δj

ρ −1

al e ρl T jl l

δj

l

ρ −1

23

⎞

⎤ δm ρl ⎠ I (m, k jl , l )e ⎦ > 0, T jl

al  e ρl T jl l

⎛ ⎡ ⎞ δj

⎤ δm ρ −1 al e ρl T jl l   1  I (m, k jl , l )e ρl ∂ fj m ⎝ ⎠ 1 − ( 1 − ρl ) ⎦ = 1 + min{ρl }⎣ sm − δj ∂δm ρl T jl ρl −1 ρ m l 

= 1 + min{ρl }



l



sm − 1

al  e

l

T jl 

< 1.

m

Appendix C. IV Relevance Table 11 Results from the first-stage regressions. Variable

First-stage F-statistic

p-value

Variable

pc log(1 + DET L j,t ) log(1 + DET L j,t−1 ) log(1 + DET L j,t−2 ) log(1 + DET L j,t−3 ) log(1 + DET L j,t−4 ) log(1 + DET L j,t−5 ) log(1 + DET L j,t−6 ) log(1 + DET L j,t−7 ) log(1 + DET L j,t−8 ) log(1 + DET L j,t−9 ) log(1 + DET L j,t−10 ) log(1 + DET L j,t−11 ) log(1 + DET L j,t−12 ) log(1 + DET L j,t−13 ) log(1 + DET L j,t−14 ) log(1 + DET L j,t−15 ) log(1 + DET L j,t−16 ) log(1 + DET L j,t−17 ) log(1 + DET L j,t−18 ) log(1 + DET L j,t−19 ) log(1 + DET L j,t−20 ) log(1 + DET L j,t−21 ) log(1 + DET L j,t−22 ) log(1 + DET L j,t−23 )

444.2973 164.2610 167.6845 168.5753 171.5078 174.2392 176.2974 182.2131 183.4302 180.8645 177.7022 178.6720 178.5411 174.8620 172.5887 170.5825 167.5369 164.4483 163.2023 161.4678 161.1398 158.4680 156.6150 158.5313 162.7069

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

log(1 + DT CA j,t ) log(1 + DT CA j,t−1 ) log(1 + DT CA j,t−2 ) log(1 + DT CA j,t−3 ) log(1 + DT CA j,t−4 ) log(1 + DT CA j,t−5 ) log(1 + DT CA j,t−6 ) log(1 + DT CA j,t−7 ) log(1 + DT CA j,t−8 ) log(1 + DT CA j,t−9 ) log(1 + DT CA j,t−10 ) log(1 + DT CA j,t−11 ) log(1 + DT CA j,t−12 ) log(1 + DT CA j,t−13 ) log(1 + DT CA j,t−14 ) log(1 + DT CA j,t−15 ) log(1 + DT CA j,t−16 ) log(1 + DT CA j,t−17 ) log(1 + DT CA j,t−18 ) log(1 + DT CA j,t−19 ) log(1 + DT CA j,t−20 ) log(1 + DT CA j,t−21 ) log(1 + DT CA j,t−22 ) log(1 + DT CA j,t−23 )

First-stage F-statistic

p-value

298.4066 282.8092 278.1687 284.4516 296.1008 307.9684 289.9094 277.7821 265.4812 248.3451 236.7368 230.8109 222.3941 214.7854 207.7515 203.7940 192.3494 176.1511 163.6520 157.3614 152.5761 148.3254 139.3733 132.1807

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

24

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Appendix D. Copay estimation

Table 12 Copay Estimation Results. (1)

(2)

(3)

0.1317∗∗∗ (0.0179)

0.0723∗∗∗ (0.0159) 11.6010∗∗∗ (1.1084)

Constant

8.4629∗∗∗ (1.4979)

4.2776∗∗∗ (0.4892)

0.0719∗∗∗ (0.0162) 11.5948∗∗∗ (1.0999) 0.4140 (0.7296) 4.2913∗∗∗ (0.4976)

N R2 adj. R2

126,052 0.15 0.15

126,052 0.21 0.21

126,052 0.21 0.21

Price for a 30-day supply Brand Brand with generic equivalents

The level of observation is year-plan-drug. The dependent variable is copay for a 30-day supply. Standard errors, clustered by insurance plan, are in parentheses. ∗ p < .1, ∗ ∗ p < .05, ∗ ∗ ∗ p < .01.

Appendix E. Advertising Spending

03 20 m1 03 20 m7 04 20 m1 04 20 m7 05 20 m1 05 20 m7 06 20 m1 06 20 m7 07 20 m1 07 20 m7 08 20 m1 08 20 m7 09 20 m1 09 20 m7 10 20 m1 10 20 m7 11 20 m1 11 m 7

20

0

10

20

Millions of US Dollar 30

03 20 m1 03 20 m7 04 2 0 m1 04 20 m 7 05 20 m1 05 2 0 m7 06 20 m1 06 20 m7 07 20 m 1 07 20 m7 08 20 m1 08 20 m7 09 20 m1 09 20 m7 10 20 m1 10 20 m7 11 20 m1 11 m 7

20

0

5

10

15

Millions of US Dollar 20

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Atorvastatin (B)

Atorvastatin (B)

Appendix F. Timing of Generic Entry Pravastatin (B) Rosuvastatin (B)

Pravastatin (B)

Rosuvastatin (B)

(b) DTCA

Fig. 4. Advertising Spending.

25

Date Simvastatin (B)

(a) Detailing

Date

Simvastatin (B)

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

10 5

lovastatin simvastatin pravastatin

20

03 2 0 m1 03 20 m 7 04 2 0 m1 04 20 m 7 05 20 m1 05 20 m7 06 20 m1 06 2 0 m7 07 20 m 1 07 20 m7 08 20 m1 08 20 m7 09 20 m1 09 20 m7 10 20 m1 10 20 m7 11 20 m1 11 m 7

0

Number of generics

15

26

Date Fig. 5. The number of generics following patent expiration of the branded drugs in the sample. The vertical dashed lines represent the end of the exclusivity period.

Appendix G. Residuals from the Copay Estimation

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

27

Fig. 6. Residuals from the copay estimation.

Appendix H. Robustness Checks I consider four robustness checks to examine how sensitive the results of estimation and counterfactuals are to different sample periods and model assumptions. H.1. Estimation using the sample without Llipitor coupons (Jjan 2005 – Nov 2010) The sales data from IMS for the last nine months of my sample for estimation (December 2010 – August 2011) can be affected by the Lipitor coupon program launched in December 2010. Since I cannot observe the coupon activity in the data,

28

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611 Table 13 Selected Demand and Cost Parameters. Demand Nesting parameters: Class Branded Molecule Form Proportion of high type (φ H ) Price coeff for high (α H ) Price coeff for low (α L ) Persistence of detailing (λDETL ) Persistence of DTCA (λDTCA ) log(Detailing stock) log(DTCA stock) Time since entry dummy: 1 month 2 months 3 months 4 months 5 months 6 months 12 months 18 months 24 months Objective function value Number of observations

Est.

S.E.

0.4908 0.4064 0.4214 0.4549 0.1298 −0.2013 −1.4117 0.6601 0.6186 0.1119 0.1183

0.0005 0.0036 0.0008 0.0007 0.0007 0.0012 0.0002 0.0767 0.2599 0.0355 0.0262

3.4368 3.1966 3.7203 3.8363 4.1905 3.2443 −0.6636 −0.2896 −0.3538

1.3276 1.2408 1.0543 1.0503 1.0250 1.0282 0.3880 0.3044 0.3072

Cost Molecules: Atorvastatin Fluvastatin Lovastatin Pravastatin Rosuvastatin Simvastatin Amlodipine/Atorvastatin Ezetimibe/Simvastatin Lovastatin/Niacin Niacin/Simvastatin Generic indicator Time since entry dummy: 1 month 2 months 3 months 4 months 5 months 6 months 12 months 18 months 24 months

Est.

S.E.

12.0696 38.8594 36.9451 50.2452 42.6706 34.6302 18.9110 36.2227 41.7900 23.2429 −16.9164

3.0881 4.5506 4.3807 5.0699 3.0405 4.3830 2.9651 2.9286 3.0881 4.3233 3.1635

47.4496 45.4682 48.5065 48.7941 51.8801 50.2120 6.2816 5.0697 5.2447

12.2668 10.2798 11.0577 11.0233 11.2229 9.1086 2.8703 2.2136 1.7415

65,383.2 2778

Product-clustered standard errors are reported, where product is a combination of molecule, form, version, and manufacturer. Dummies for one to twenty four months since entry are included in the estimation, and only results for the selected time-since-entry dummies are reported. Results for the monthly date fixed effects are omitted.

I check the robustness of the estimation results by re-estimating the model using data before December 2010. The following tables show that excluding the period with Lipitor coupons gives estimation results consistent with those in the main text.

Branded

Generic

Molecule (form)

Share

Amlodipine/ Atorvastatin (TAB)

Ezetimibe/ Simvastatin (TAB)

Lovastatin/ Niacin (SA TAB)

Atorvastatin Fluvastatin (TAB) (CAP)

Fluvastatin (SA TAB)

Lovastatin (TAB)

Lovastatin (SA TAB)

Pravastatin (TAB)

Rosuvastatin (TAB)

Simvastatin (TAB)

Lovastatin (TAB)

Pravastatin (TAB)

Simvastatin (TAB)

Branded

Amlodipine/Atorvastatin (TAB) Ezetimibe/Simvastatin (TAB) Lovastatin/Niacin (SA TAB) Atorvastatin (TAB) Fluvastatin (CAP) Fluvastatin (SA TAB) Lovastatin (TAB) Lovastatin (SA TAB) Pravastatin (TAB) Rosuvastatin (TAB) Simvastatin (TAB)

0.30% 3.49% 0.17% 8.85% 0.09% 0.47% 0.01% 0.13% 0.31% 1.90% 0.93%

−1.86 0.19 0.02 0.76 0.01 0.00 0.00 0.00 0.03 0.11 0.08

0.03 −3.89 0.01 0.47 0.00 0.04 0.00 0.01 0.01 0.19 0.03

0.04 0.11 −1.82 0.65 0.01 0.07 0.00 0.04 0.02 0.10 0.07

0.03 0.11 0.01 −0.97 0.01 0.00 0.00 0.00 0.03 0.17 0.12

0.03 0.07 0.01 0.64 −1.92 0.15 0.00 0.00 0.02 0.10 0.07

0.01 0.43 0.05 0.18 0.06 −4.03 0.00 0.43 0.00 0.08 0.01

0.03 0.08 0.01 0.72 0.01 0.00 −1.94 0.61 0.03 0.11 0.08

0.01 0.46 0.09 0.36 0.00 1.46 0.05 −4.86 0.01 0.11 0.03

0.03 0.06 0.01 0.86 0.01 0.00 0.00 0.00 −1.35 0.12 0.09

0.03 0.26 0.01 0.96 0.01 0.01 0.00 0.00 0.02 −2.78 0.08

0.03 0.07 0.01 1.06 0.01 0.00 0.00 0.00 0.03 0.14 −1.54

0.00 0.24 0.00 0.08 0.00 0.02 0.00 0.02 0.00 0.07 0.00

0.00 0.19 0.00 0.06 0.00 0.02 0.00 0.00 0.01 0.05 0.00

0.00 0.25 0.00 0.08 0.00 0.02 0.00 0.01 0.00 0.07 0.00

Generic

Lovastatin (TAB) Pravastatin (TAB) Simvastatin (TAB)

0.81% 0.40% 2.79%

0.00 0.00 0.00

0.02 0.05 0.27

0.00 0.00 0.02

0.00 0.00 0.02

0.00 0.00 0.02

0.02 0.05 0.24

0.00 0.00 0.02

0.05 0.04 0.21

0.00 0.01 0.00

0.01 0.02 0.10

0.00 0.00 0.01

−3.87 0.21 1.74

0.08 −18.40 1.02

0.15 0.24 −11.16

Note: Cell entries (i, j), where i indexes row and j indexes column, give the percent change in market share of brand j with respect to a one-percent change in price of i. All branded drugs are included in the table. Only the generic drugs with the largest market share in their molecules are reported.

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Table 14 Estimated Price Elasticities for September 2006.

29

30

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

H.2. Demand estimation with three types of consumers To investigate how limiting the assumption of two types of consumers is, I re-estimate the demand model using three types of consumers to capture consumer heterogeneity in price sensitivity. Modifying Eq. 5 and adding a medium type gives:

 H α αi = α M αL

if i is high type if i is medium type . if i is low type

(28)

The proportion of high type is denoted by φ H , and the proportion of medium type is denoted by φ M . Results presented in the following tables show that the estimated price coefficients for the three types of consumers match those of the two-type model. The proportion of low type is estimated to be 87%, similar to the proportion of low type in the two-type model. The counterfactual results based on the three-type model are also very consistent, both quantitatively and qualitatively, with the counterfactual results based on the two-type model.

Table 15 Selected Demand and Cost Parameters (3 Types of Consumers). Demand Nesting parameters: Class Brand Molecule Form Proportion of high type (φ H ) Proportion of medium type (φ M ) Price coeff for high (α H ) Price coeff for medium (α M ) Price coeff for low (α L ) Persistence of detailing (λDETL ) Persistence of DTCA (λDTCA ) log(Detailing stock) log(DTCA stock) Time since entry dummy: 1 month 2 months 3 months 4 months 5 months 6 months 12 months 18 months 24 months Objective function value Number of observations

Est.

S.E.

0.4507 0.3781 0.4017 0.4504 0.0294 0.0991 −0.0738 −0.2378 −1.5030 0.6568 0.6128 0.1033 0.0997

0.0001 0.0004 0.0001 0.0001 0.0006 0.0021 0.0143 0.0012 0.0002 0.0585 0.3910 0.0411 0.0308

3.9440 3.4944 4.0265 4.1456 4.5104 3.6487 −0.6183 −0.2593 −0.3064

1.3925 1.4059 1.2045 1.1960 1.1746 1.1001 0.3551 0.2775 0.3220

Cost

Est.

S.E.

Molecule dummy: Atorvastatin Fluvastatin Lovastatin Pravastatin Rosuvastatin Simvastatin Amlodipine/Atorvastatin Ezetimibe/Simvastatin Lovastatin/Niacin Niacin/Simvastatin Generic indicator

12.3044 39.6723 36.2642 47.4607 43.0705 33.1735 15.4624 38.3760 40.6964 25.7607 −15.7756

3.4079 4.1931 3.5822 4.0466 3.3681 3.7135 3.3046 3.2734 3.4079 4.4061 2.2944

Time since entry dummy: 1 month 2 months 3 months 4 months 5 months 6 months 12 months 18 months 24 months

48.5603 46.3372 49.4366 49.6995 52.7064 51.4241 7.2688 6.1392 6.0566

12.4529 10.4833 11.3108 11.3333 11.5920 9.3166 2.9025 2.2763 1.7727

60,835.1 3248

Product-clustered standard errors are reported, where product is a combination of molecule, form, version, and manufacturer. Dummies for one to twenty four months since entry are included in the estimation, and only results for the selected time-since-entry dummies are reported. Results for the monthly date fixed effects are omitted.

Branded Molecule (form)

Share

Generic

Amlodipine/ Atorvastatin (TAB)

Ezetimibe/ Simvastatin (TAB)

Lovastatin/ Niacin (SA TAB)

Atorvastatin Fluvastatin (TAB) (CAP)

Fluvastatin (SA TAB)

Lovastatin (TAB)

Lovastatin (SA TAB)

Pravastatin (TAB)

Rosuvastatin (TAB)

Simvastatin (TAB)

Lovastatin (TAB)

Pravastatin (TAB)

Simvastatin (TAB)

Branded

Amlodipine/Atorvastatin (TAB) Ezetimibe/Simvastatin (TAB) Lovastatin/Niacin (SA TAB) Atorvastatin (TAB) Fluvastatin (CAP) Fluvastatin (SA TAB) Lovastatin (TAB) Lovastatin (SA TAB) Pravastatin (TAB) Rosuvastatin (TAB) Simvastatin (TAB)

0.30% 3.49% 0.17% 8.85% 0.09% 0.47% 0.01% 0.13% 0.31% 1.90% 0.93%

−1.72 0.18 0.02 0.69 0.01 0.00 0.00 0.00 0.03 0.10 0.08

0.03 −4.29 0.01 0.42 0.00 0.05 0.00 0.02 0.01 0.22 0.03

0.04 0.10 −1.81 0.64 0.01 0.06 0.00 0.03 0.02 0.10 0.07

0.03 0.10 0.01 −0.94 0.01 0.00 0.00 0.00 0.03 0.18 0.12

0.03 0.06 0.01 0.64 −1.92 0.15 0.00 0.00 0.02 0.10 0.07

0.00 0.48 0.05 0.16 0.06 −4.46 0.00 0.51 0.00 0.08 0.01

0.03 0.07 0.01 0.71 0.01 0.00 −1.94 0.65 0.03 0.11 0.08

0.01 0.54 0.09 0.33 0.00 1.74 0.05 −5.56 0.01 0.11 0.03

0.03 0.06 0.01 0.84 0.01 0.00 0.00 0.00 −1.29 0.12 0.09

0.02 0.30 0.01 1.00 0.01 0.01 0.00 0.00 0.02 −3.01 0.08

0.03 0.06 0.01 1.06 0.01 0.00 0.00 0.00 0.03 0.14 −1.50

0.00 0.27 0.00 0.07 0.00 0.02 0.00 0.02 0.00 0.07 0.00

0.00 0.21 0.00 0.06 0.00 0.02 0.00 0.01 0.00 0.06 0.00

0.00 0.27 0.00 0.08 0.00 0.03 0.00 0.01 0.00 0.08 0.00

Generic

Lovastatin (TAB) Pravastatin (TAB) Simvastatin (TAB)

0.81% 0.40% 2.79%

0.00 0.00 0.00

0.02 0.05 0.30

0.00 0.00 0.02

0.00 0.00 0.02

0.00 0.00 0.02

0.02 0.05 0.26

0.00 0.00 0.01

0.06 0.04 0.24

0.00 0.01 0.00

0.01 0.02 0.11

0.00 0.00 0.01

−4.33 0.23 2.03

0.08 −20.25 1.12

0.17 0.26 −12.38

Note: Cell entries (i, j), where i indexes row and j indexes column, give the percent change in market share of brand j with respect to a one-percent change in price of i. All branded drugs are included in the table. Only the generic drugs with the largest market share in their molecules are reported.

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Table 16 Estimated Price Elasticities for September 2006 (3 Types of Consumers).

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C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Table 17 Counterfactual Analysis: Price Discrimination Only (3 Types of Consumers). Coupon penetration:

0%

1%

5%

10%

Avg price (30-day supply) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

76.8 46.7 51.9 65.7 45.3

74.4 46.5 80.6 67.7 45.1

74.1 46.5 88.5 69.1 45.1

73.9 46.5 95.1 69.6 45.1

Avg copay (30-day supply) Copay using Zocor coupon Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

− 21.4 7.7 19.6 20.6 7.6

14.9 21.3 7.6 21.7 20.8 7.5

14.9 21.2 7.6 22.3 20.9 7.5

14.9 21.2 7.6 22.8 20.9 7.5

Market share (%) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

1.03 5.19 3.51 12.32 3.74

2.65 3.28 0.50 27.54 2.10

6.62 3.13 0.42 26.75 2.01

11.56 2.95 0.36 25.74 1.89

Base 1500.0

−5.1

2.1

11.2

38.3 18.8 27.6 575.9 12.7 813.8

51.7 −7.5 −5.3 199.5 −6.2 121.1

173.2 −8.0 −5.8 197.9 −6.5 264.9

323.7 −8.6 −6.5 193.5 −6.8 441.6

Welfare change ($million’s) Consumer welfare Profits: Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics Insurer spending

Table 18 Counterfactual Analysis: Moral Hazard Only (3 Types of Consumers). Price cap of Zocor:

Current price ($76.8)

1.10 × Current price ($84.5)

1.20 × Current price ($92.2)

Coupon penetration:

0%

1%

5%

10%

1%

5%

10%

1%

5%

10%

Avg price (30-day supply) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

76.8 46.7 51.9 65.7 45.3

76.8 46.5 80.7 68.5 45.1

76.8 46.5 90.2 69.8 45.1

76.8 46.5 100.6 70.5 45.1

84.5 46.5 80.1 68.8 45.1

84.5 46.6 96.2 72.1 45.5

84.5 46.6 108.6 72.1 45.6

92.2 46.5 80.2 69.8 45.1

92.2 46.5 89.8 70.4 45.1

92.2 46.6 108.5 72.8 45.5

Avg copay (30-day supply) Copay using Zocor coupon Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

− 21.4 7.7 19.6 20.6 7.6

14.6 21.4 7.6 21.7 20.8 7.5

14.6 21.4 7.6 22.4 20.9 7.5

14.6 21.4 7.6 23.2 21.0 7.5

14.4 22.0 7.6 21.7 20.9 7.5

14.1 22.0 7.6 22.8 21.1 7.6

14.1 22.0 7.6 23.7 21.1 7.6

14.3 22.5 7.6 21.7 20.9 7.5

14.3 22.5 7.6 22.4 21.0 7.5

13.9 22.5 7.6 23.7 21.1 7.6

Market share (%) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

1.03 5.19 3.51 12.32 3.74

2.53 3.29 0.51 27.53 2.10

6.24 3.16 0.41 26.66 2.02

10.86 2.99 0.31 25.55 1.92

2.33 3.29 0.53 27.67 2.10

5.50 1.82 0.23 51.42 1.24

9.81 1.37 0.14 56.05 0.95

2.16 3.29 0.55 27.78 2.11

5.90 3.16 0.43 26.90 2.02

9.99 1.73 0.18 49.12 1.18

Base 1,500.0

−5.4

2.5

12.6

−6.5

46.2

69.7

−7.5

1.1

57.0

38.3 18.8 27.6 575.9 12.7 813.8

51.3 −7.5 −4.9 199.8 −6.2 123.4

171.1 −7.9 −6.0 186.5 −6.4 255.1

320.6 −8.5 −7.7 167.6 −6.7 417.3

56.6 −7.4 −4.2 214.3 −6.2 142.9

171.0 −12.5 −14.0 755.7 −8.7 143.3

330.1 −14.0 −17.1 823.9 −9.5 296.9

60.8 −7.4 −3.6 226.9 −6.1 159.5

220.6 −7.9 −4.8 213.1 −6.4 331.4

389.6 −12.8 −14.8 707.0 −8.9 382.4

Welfare change ($million’s) Consumer welfare Profits: Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics Insurer spending

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

33

Table 19 Counterfactual Analysis: Price Discrimination And Moral Hazard (3 Types of Consumers). Price cap of Zocor:

Current price ($76.8)

1.10 × Current price ($84.5)

1.20 × Current price ($92.2)

Coupon penetration:

0%

1%

5%

10%

1%

5%

10%

1%

5%

10%

Avg price (30-day supply) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

76.8 46.7 51.9 65.7 45.3

76.8 46.5 80.4 67.8 45.1

76.8 46.5 88.4 69.2 45.1

76.8 46.5 95.0 69.7 45.1

84.5 46.5 80.4 68.8 45.1

84.5 46.5 88.0 69.4 45.1

84.5 46.5 94.6 70.0 45.1

92.2 46.5 79.9 69.1 45.1

92.2 46.5 87.6 69.7 45.1

92.2 46.5 94.2 70.2 45.1

Avg copay (30-day supply) Copay using Zocor coupon Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

− 21.4 7.7 19.6 20.6 7.6

14.8 21.4 7.6 21.7 20.8 7.5

14.8 21.4 7.6 22.3 20.9 7.5

14.8 21.4 7.6 22.7 20.9 7.5

14.7 22.0 7.6 21.7 20.9 7.5

14.7 22.0 7.6 22.2 20.9 7.5

14.7 22.0 7.6 22.7 20.9 7.5

14.6 22.5 7.6 21.7 20.9 7.5

14.6 22.5 7.6 22.2 20.9 7.5

14.6 22.5 7.6 22.7 21.0 7.5

Market share (%) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

1.03 5.19 3.51 12.32 3.74

2.59 3.28 0.51 27.58 2.10

6.55 3.13 0.43 26.80 2.01

11.49 2.95 0.37 25.80 1.89

2.39 3.28 0.53 27.71 2.10

6.36 3.14 0.45 26.94 2.01

11.31 2.95 0.38 25.94 1.89

2.21 3.29 0.56 27.83 2.10

6.18 3.14 0.46 27.06 2.01

11.14 2.96 0.40 26.07 1.89

Base 1500.0

−5.4

1.8

11.0

−6.6

0.9

10.5

−7.6

0.1

10.1

38.3 18.8 27.6 575.9 12.7 813.8

53.5 −7.5 −5.1 204.2 −6.2 127.5

182.8 −8.0 −5.5 203.3 −6.5 279.9

344.0 −8.6 −6.2 199.6 −6.8 468.1

59.1 −7.5 −4.3 219.0 −6.2 147.5

209.1 −8.0 −4.8 218.5 −6.5 321.2

396.2 −8.6 −5.5 215.1 −6.8 536.0

63.5 −7.4 −3.7 231.6 −6.2 164.3

234.3 −7.9 −4.2 231.5 −6.4 359.4

447.5 −8.6 −4.9 228.3 −6.8 600.7

Welfare change ($million’s) Consumer welfare Profits: Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics Insurer spending

H.3. Nested Lłogit model I estimate two nested logit models with different hierarchical nesting structures and compare the estimation results with those of my demand model with the GEV error structure. Table 20 shows that simply reversing the order of the nests for form and brand can dramatically change the point estimates in demand and cost. Price coefficients, advertising effects, and time and molecule dummies are quite sensitive to the nesting order. The objective function values of the two nested logit models are much larger than that of the GEV model, which suggests the model fit of the GEV model is much better. In addition, the cross-price elasticities of the nested logit models are more restrictive than those of the GEV model. For example, the cross-price elasticities of the three generics with respect to a change in one of their prices presented in the bottom right corner of Tables 21 and 22 are very close even though they have different molecules.

34

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611 Table 20 Selected Demand and Cost Parameters of Two Nested Logit Models. Demand Nested logit model 1 Nesting parameters: Class (Level 1) Molecule (Level 2) Form (Level 3) Brand (Level 4) Proportion of high type (φ H ) Price coeff for high (α H ) Price coeff for low (α L ) Persistence of detailing (λDETL ) Persistence of DTCA (λDTCA ) log(Detailing stock) log(DTCA stock) Time since entry dummy: 1 month 2 months 3 months 4 months 5 months 6 months 12 months 18 months 24 months Objective function value Number of observations Nested logit model 2 Nesting parameters: Class (Level 1) Molecule (Level 2) Brand (Level 3) Form (Level 4) Proportion of high type (φ H ) Price coeff for high (α H ) Price coeff for low (α L ) Persistence of detailing (λDETL ) Persistence of DTCA (λDTCA ) log(Detailing stock) log(DTCA stock) Time since entry dummy: 1 month 2 months 3 months 4 months 5 months 6 months 12 months 18 months 24 months Objective function value Number of observations

Est.

S.E.

0.5542 0.7141 0.8079 0.6473 0.0817 −1.1354 −1.9737 0.6688 0.6232 0.1465 0.2584

0.0014 0.0000 0.0003 0.0032 0.0251 0.3487 0.0527 0.1322 0.4024 0.0895 0.0512

5.4594 5.5648 6.3796 6.6215 7.1623 5.4699 −0.4059 0.0345 0.2544

2.0867 2.0402 1.6341 1.6028 1.6035 1.4987 0.5926 0.4703 0.3584

Cost Molecules: Atorvastatin Fluvastatin Lovastatin Pravastatin Rosuvastatin Simvastatin Amlodipine/Atorvastatin Ezetimibe/Simvastatin Lovastatin/Niacin Niacin/Simvastatin Generic indicator Time since entry dummy: 1 month 2 months 3 months 4 months 5 months 6 months 12 months 18 months 24 months

Est.

S.E.

46.6902 47.2405 51.0161 61.2837 48.7473 46.7753 66.6988 6.2869 56.4510 33.0743 −41.9045

3.7448 12.8749 7.6306 8.1184 3.7073 7.2504 3.5073 3.5587 3.7448 4.9916 5.3589

63.6149 57.5959 57.9101 57.2789 59.8794 60.6970 −2.7800 10.6798 4.5801

11.6561 12.2384 12.7009 12.5351 12.9889 11.4630 14.5963 4.3565 2.2986

29.0744 45.4517 51.3936 64.4496 44.1742 48.3160 45.7251 3.6697 51.8127 30.9519 −39.9779

3.0666 11.8784 8.8668 10.7348 3.0203 8.7754 3.0186 3.0271 3.0666 4.4192 7.8711

60.4310 54.6319 54.4661 54.2421 56.5687 55.6496 10.8299 8.8002 5.2017

11.4137 11.8152 11.9620 11.8670 12.3824 9.7914 2.4996 2.4961 2.9942

206,449.1 3,248

0.8151 0.7893 0.6485 0.6220 0.0981 −1.0069 −2.4090 0.6363 0.6057 0.2126 0.2748

0.0028 0.0142 0.0146 0.0125 0.0189 0.1941 0.0735 0.1458 0.5055 0.1084 0.0730

6.0690 6.4637 7.3705 7.6591 8.2689 6.7842 −0.6431 0.0742 0.4259

2.5563 2.4649 2.0319 2.0092 2.0570 1.8254 0.6168 0.5243 0.4194

Molecules: Atorvastatin Fluvastatin Lovastatin Pravastatin Rosuvastatin Simvastatin Amlodipine/Atorvastatin Ezetimibe/Simvastatin Lovastatin/Niacin Niacin/Simvastatin Generic indicator Time since entry dummy: 1 month 2 months 3 months 4 months 5 months 6 months 12 months 18 months 24 months

231,192.8 3248

Product-clustered standard errors are reported, where product is a combination of molecule, form, version, and manufacturer. Dummies for one to twenty four months since entry are included in the estimation, and only results for the selected time-since-entry dummies are reported. Results for the monthly date fixed effects are omitted.

Branded

Generic

Molecule (form)

Share

Amlodipine/ Atorvastatin (TAB)

Ezetimibe/ Simvastatin (TAB)

Lovastatin/ Niacin (SA TAB)

Atorvastatin Fluvastatin (TAB) (CAP)

Fluvastatin (SA TAB)

Lovastatin (TAB)

Lovastatin (SA TAB)

Pravastatin (TAB)

Rosuvastatin (TAB)

Simvastatin (TAB)

Lovastatin (TAB)

Pravastatin (TAB)

Simvastatin (TAB)

Branded

Amlodipine/Atorvastatin (TAB) Ezetimibe/Simvastatin (TAB) Lovastatin/Niacin (SA TAB) Atorvastatin (TAB) Fluvastatin (CAP) Fluvastatin (SA TAB) Lovastatin (TAB) Lovastatin (SA TAB) Pravastatin (TAB) Rosuvastatin (TAB) Simvastatin (TAB)

0.30% 3.49% 0.17% 8.85% 0.09% 0.47% 0.01% 0.13% 0.31% 1.90% 0.93%

−4.42 1.31 0.11 1.39 0.02 0.02 0.00 0.01 0.07 0.22 0.22

0.24 −1.01 0.10 1.28 0.01 0.02 0.00 0.01 0.07 0.20 0.20

0.25 1.32 −3.35 1.35 0.02 0.02 0.00 0.01 0.07 0.21 0.22

0.06 0.31 0.03 −2.49 0.02 0.03 0.00 0.02 0.09 0.30 0.28

0.07 0.36 0.03 2.06 −5.03 0.66 0.00 0.02 0.11 0.33 0.32

0.04 0.19 0.02 1.21 0.25 −2.55 0.00 0.01 0.06 0.21 0.17

0.09 0.46 0.04 2.57 0.03 0.04 −4.26 0.55 0.14 0.40 0.42

0.08 0.42 0.03 2.40 0.03 0.04 0.21 −2.12 0.13 0.38 0.39

0.09 0.45 0.04 2.52 0.03 0.04 0.00 0.03 −2.78 0.40 0.41

0.05 0.27 0.02 1.64 0.02 0.03 0.00 0.02 0.08 −3.61 0.24

0.09 0.45 0.04 2.56 0.03 0.04 0.00 0.03 0.14 0.40 −1.75

0.00 0.01 0.00 0.33 0.00 0.02 0.00 0.00 0.00 0.08 0.00

0.00 0.01 0.00 0.35 0.00 0.02 0.00 0.00 0.07 0.08 0.01

0.00 0.01 0.00 0.33 0.00 0.02 0.00 0.00 0.00 0.08 0.02

Generic

Lovastatin (TAB) Pravastatin (TAB) Simvastatin (TAB)

0.81% 0.40% 2.79%

0.00 0.00 0.00

0.00 0.00 0.01

0.00 0.00 0.01

0.02 0.03 0.14

0.01 0.02 0.10

0.03 0.06 0.26

0.08 0.00 0.00

0.03 0.01 0.04

0.00 0.16 0.01

0.02 0.04 0.18

0.00 0.00 0.07

−3.37 0.09 0.43

0.05 −14.81 0.42

0.05 0.09 −9.10

Note: Cell entries (i, j), where i indexes row and j indexes column, give the percent change in market share of brand j with respect to a one-percent change in price of i. All branded drugs are included in the table. Only the generic drugs with the largest market share in their molecules are reported.

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Table 21 Estimated Price Elasticities for September 2006 (Nested Logit 1).

35

36

Branded

Branded

Generic

Generic

Molecule (form)

Share

Amlodipine/ Atorvastatin (TAB)

Ezetimibe/ Simvastatin (TAB)

Lovastatin/ Niacin (SA TAB)

Atorvastatin Fluvastatin (TAB) (CAP)

Fluvastatin (SA TAB)

Lovastatin (TAB)

Lovastatin (SA TAB)

Pravastatin (TAB)

Rosuvastatin (TAB)

Simvastatin (TAB)

Lovastatin (TAB)

Pravastatin (TAB)

Simvastatin (TAB)

Amlodipine/Atorvastatin (TAB) Ezetimibe/Simvastatin (TAB) Lovastatin/Niacin (SA TAB) Atorvastatin (TAB) Fluvastatin (CAP) Fluvastatin (SA TAB) Lovastatin (TAB) Lovastatin (SA TAB) Pravastatin (TAB) Rosuvastatin (TAB) Simvastatin (TAB)

0.30% 3.49% 0.17% 8.85% 0.09% 0.47% 0.01% 0.13% 0.31% 1.90% 0.93%

−3.48 0.77 0.06 1.70 0.02 0.02 0.00 0.01 0.07 0.27 0.22

0.14 −0.94 0.06 1.70 0.02 0.02 0.00 0.01 0.07 0.27 0.22

0.14 0.77 −2.57 1.70 0.02 0.02 0.00 0.01 0.07 0.27 0.22

0.07 0.42 0.03 −1.61 0.02 0.03 0.00 0.01 0.08 0.29 0.23

0.09 0.48 0.04 2.01 −4.59 1.01 0.00 0.02 0.09 0.32 0.26

0.04 0.23 0.02 1.04 0.39 −2.50 0.00 0.01 0.04 0.18 0.13

0.09 0.49 0.04 2.06 0.02 0.03 −4.10 1.28 0.09 0.33 0.27

0.08 0.46 0.04 1.94 0.02 0.03 0.11 −1.65 0.08 0.31 0.25

0.09 0.48 0.04 2.04 0.02 0.03 0.00 0.02 −2.95 0.33 0.26

0.07 0.37 0.03 1.58 0.02 0.03 0.00 0.01 0.07 −2.91 0.20

0.09 0.49 0.04 2.05 0.02 0.03 0.00 0.02 0.09 0.33 −2.70

0.00 0.00 0.00 0.13 0.00 0.01 0.00 0.00 0.00 0.04 0.00

0.00 0.00 0.00 0.14 0.00 0.01 0.00 0.00 0.01 0.04 0.00

0.00 0.00 0.00 0.13 0.00 0.01 0.00 0.00 0.00 0.04 0.00

Lovastatin (TAB) Pravastatin (TAB) Simvastatin (TAB)

0.81% 0.40% 2.79%

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.01 0.01 0.05

0.00 0.00 0.01

0.02 0.04 0.17

0.00 0.00 0.00

0.01 0.01 0.02

0.00 0.05 0.01

0.01 0.02 0.08

0.00 0.00 0.02

−4.16 0.07 0.32

0.04 −17.37 0.32

0.04 0.07 −10.85

Note: Cell entries (i, j), where i indexes row and j indexes column, give the percent change in market share of brand j with respect to a one-percent change in price of i. All branded drugs are included in the table. Only the generic drugs with the largest market share in their molecules are reported.

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

Table 22 Estimated Price Elasticities for September 2006 (Nested Logit 2).

C.-Y. Lee / International Journal of Industrial Organization 70 (2020) 102611

37

Table 23 Counterfactual Analysis with An Increase in DTCA: Price Discrimination And Moral Hazard. 1.10 × Current price ($84.5)

Price cap of Zocor: Change in monthly DTCA:

0%

100%

200%

Coupon penetration:

0%

1%

5%

10%

1%

5%

10%

1%

5%

10%

Avg price for 30-day supply Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

76.8 46.7 51.9 65.7 45.3

84.5 46.5 81.3 70.8 45.1

84.5 46.5 89.1 72.2 45.1

84.5 46.5 95.5 75.7 45.1

84.5 46.5 81.8 70.2 45.1

84.5 46.5 89.4 70.8 45.1

84.5 46.5 96.8 75.0 45.1

84.5 46.5 82.2 69.8 45.1

84.5 46.5 89.8 70.4 45.1

84.5 46.5 97.2 71.9 45.1

Avg copay for 30-day supply Copay using Zocor coupon Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

21.4 7.7 19.6 20.6 7.6

14.4 22.0 7.6 21.8 21.0 7.5

14.4 22.0 7.6 22.3 21.1 7.5

14.4 22.0 7.6 22.8 21.4 7.5

14.5 22.0 7.6 21.8 21.0 7.5

14.5 22.0 7.6 22.3 21.0 7.5

14.5 22.0 7.6 22.9 21.3 7.5

14.5 22.0 7.6 21.8 20.9 7.5

14.5 22.0 7.6 22.4 21.0 7.5

14.5 22.0 7.6 22.9 21.1 7.5

Market share (%) Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics

1.03 5.19 3.51 12.32 3.74

2.45 4.10 0.65 20.80 2.67

6.42 3.92 0.55 20.33 2.56

11.38 3.71 0.49 19.47 2.42

2.69 4.10 0.62 20.66 2.67

6.65 3.92 0.52 20.19 2.55

11.62 3.70 0.45 19.32 2.41

2.84 4.09 0.60 20.57 2.66

6.81 3.91 0.50 20.10 2.55

11.76 3.68 0.43 19.47 2.40

Base 1031.6

−11.3

−3.4

6.1

−10.5

−2.9

6.3

−9.9

−2.5

7.1

37.1 21.0 30.6 558.2 14.3 813.8

59.8 −5.0 −1.1 170.6 −4.8 167.0

204.1 −5.7 −1.8 174.3 −5.2 342.0

384.7 −6.6 −2.1 182.1 −5.7 562.3

69.7 −5.1 −2.4 152.1 −4.8 158.1

214.3 −5.8 −3.1 155.1 −5.2 332.6

395.6 −6.6 −3.6 162.2 −5.7 552.3

76.0 −5.1 −3.2 139.9 −4.8 152.1

220.9 −5.8 −4.0 142.6 −5.3 326.4

401.5 −6.7 −5.0 143.5 −5.8 541.9

Welfare change ($million’s) Consumer welfare Profits: Branded simvastatin (Zocor) Generic simvastatin Other brands by Merck Other brands by non-Merck Other generics Insurer spending

H.4. Counterfactual analysis with an increase in DTCA In the counterfactual analysis, detailing and DTCA stay fixed when coupons are introduced. Solving for the optimal advertising spending would turn the problem into a dynamic game because the advertising effects are estimated to be persistent. To investigate how innocuous this assumption is, I simulate the equilibrium outcomes under price discrimination and moral hazard by doubling and tripling the monthly DTCA spending for Zocor during the coupon period. Because copay coupons are targeted at consumers, it is more relevant to assume an increase in DTCA spending though I believe the results can carry over to the case of an increase in detailing. Table 23 shows little qualitative change in the equilibrium outcomes. The quantitative changes are pretty consistent with the findings in Dubois et al. (2017). An increase in DTCA lowers consumer sensitivity to price, leading to a slight increase in the coupon copay. Since DTCA directly enters consumer utility, a higher level of DTCA spending expands the market share of Zocor while shrinking the market share of the other brands. In terms of welfare, consumers benefit from more DTCA, profits from Zocor get larger, and the insurer spending slightly decreases due to the smaller market share of the other brands. This exercise shows that the counterfactual results in general are robust to DTCA increases. CRediT authorship contribution statement Chung-Ying Lee: Conceptualization, Methodology, Software, Data curation, Visualization, Investigation, Writing - original draft, Writing - review & editing. References Arcidiacono, P., Ellickson, P.B., Landry, P., Ridley, D.B., 2013. Pharmaceutical followers. Int. J. Ind Org. 31 (5), 538–553. Berndt, E.R., Bui, L.T., Lucking-Reiley, D.H., Urban, G.L., 1996. The roles of marketing, product quality, and price competition in the growth and composition of the Us antiulcer drug industry. In: The Economics of New Goods. University of Chicago Press, pp. 277–328. Berry, S., Carnall, M., Spiller, P.T., 2006. Airline hubs: costs, markups and the implications of customer heterogeneity. Competition policy and antitrust. Berry, S., Eizenberg, A., Waldfogel, J., 2016. Fixed costs and the product market treatment of preference minorities. J. Ind. Econ. 64 (3), 466–493. Berry, S., Jia, P., 2010. Tracing the woes: an empirical analysis of the airline industry. Am. Econ. J.: Microecon. 2 (3), 1–43. Berry, S., Levinsohn, J., Pakes, A., 1995. Automobile prices in market equilibrium. Econom.: J. Econom. Soc. 841–890. Berry, S., Levinsohn, J., Pakes, A., 2004. Differentiated products demand systems from a combination of micro and macro data: the new car market. J. Polit. Econ. 112 (1), 68–105.

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