Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Role of Pharmacists in Generic Pharmaceutical Adoption
筧悠夫、中嶋亮

2024 年 6 月 17 日
DP2024-015
https://ies.keio.ac.jp/publications/23948/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
17 June, 2024

Role of Pharmacists in Generic Pharmaceutical Adoption
筧悠夫、中嶋亮
IES Keio DP2024-015
2024 年 6 月 17 日
JEL Classification: D12, I11, I18, L65
キーワード: generic pharmaceuticals, authorized generic, brand premiums,
pharmacist behavior, information provision【要旨】
This study investigates how pharmacists dispense generic drugs by considering
patients' brand preferences. While the literature shows that pharmacists, as
experts, underestimate brand premiums, our data show that they frequently
dispense brand-identical generics, known as authorized generics. We model
patients' generic drug choices and pharmacies' dispensing decisions to explain
how patients' brand preferences vary across pharmacies and to determine forprofit pharmacists' heterogeneous dispensing behavior. Using Japanesepharmacists' dispensing data, our empirical results show significant variations
in patients' brand preferences and perceived differences in the quality ofantibiotics. Furthermore, our findings show that one of the factors behind thesedifferences is the provision of information by pharmacists.
筧悠夫
慶應義塾大学経済学研究科
東京都港区三田２−１５−４５
haruokakehi@keio.jp
中嶋亮
慶應義塾大学経済学部
東京都港区三田２−１５−４５
nakajima@econ.keio.ac.jp
謝辞：We thank Yoko Ibuka, Koji Ishibashi, Hiroki Kawai, and Tatsuo Tanaka, andthe participants of a lecture on empirical industrial organization, SIHS SEMINAR
series, 15th IHEA World Congress, T-JEMIOW, Japanese Economics Association
Spring Meeting for their helpful comments. We also thank Taisuke Ichihara andYuya Yamada at Japan System Techniques Co., Ltd. (JAST), for providing medical
claim data. This work was supported by JSPS Fellows Grant Number 23KJ1904.

Role of Pharmacistsin Generic Pharmaceutical Adoption*
Haruo Kakehi†
Ryo Nakajima ‡June 17, 2024

Abstract
This study investigates how pharmacists dispense generic drugs by considering patients’brand preferences. While the literature shows that pharmacists, as experts, underestimate brandpremiums, our data show that they frequently dispense brand-identical generics, known as authorized generics. We model patients’ generic drug choices and pharmacies’ dispensing decisions to explain how patients’ brand preferences vary across pharmacies and to determine
for-profit pharmacists’ heterogeneous dispensing behavior. Using Japanese pharmacists’ dispensing data, our empirical results show significant variations in patients’ brand preferences
and perceived differences in the quality of antibiotics. Furthermore, our findings show that oneof the factors behind these differences is the provision of information by pharmacists.

Keywords: Authorized generic; Brand premiums; Generic pharmaceuticals; Pharmacist behavior;
Information provision
JEL classification : D12, I11, I18, L65* We thank Yoko Ibuka, Koji Ishibashi, Hiroki Kawai, and Tatsuo Tanaka, and the participants of a lecture on em-

pirical industrial organization, SIHS SEMINAR series, 15th IHEA World Congress, T-JEMIOW, Japanese Economics
Association Spring Meeting for their helpful comments. We also thank Taisuke Ichihara and Yuya Yamada at Japan
System Techniques Co., Ltd. (JAST), for providing medical claim data. This work was supported by JSPS Fellows
Grant Number 23KJ1904.
†
Graduate School of Economics, Keio University. 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan. Email:haruokakehi@keio.jp‡
Faculty of Economics, Keio University. 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan. Email: nakajima@econ.keio.ac.jp1

1

Introduction

Understanding the source of preferences for health products is crucial for promoting cost-effective
policies. The literature shows that in the healthcare market, consumers have a greater willingness
to pay for brand products, attributed to their brand premiums, and preferences for these premiums
depend largely on consumer characteristics such as education level and occupation (Bronnenberg
et al., 2015; Janssen, 2023). For example, Bronnenberg et al. (2015) shows that consumers withmedical expertise are less likely to purchase national-brand pharmaceuticals than are those without such expertise. Conversely, recent experimental studies suggest that consumers change theirpurchasing behavior depending on external information such as nutrition labels and other customers’ purchases (Fichera and von Hinke, 2020; Carrera and Villas-Boas, 2023). Carrera andVillas-Boas (2023) shows that information on other consumers’ purchases can significantly increase consumers’ generic purchase probability. While these studies inform us of the importanceof external information as the source of brand preferences, the relationship between changes in
consumer brand preferences and external information outside experimental settings remains unclear.
Our study empirically demonstrates how consumers’ brand preferences relate to the behavior of
professional experts by investigating the dispensing of generic drugs among Japanese pharmacists.
Within the generic pharmaceutical market, brand companies market authorized generics (AGs),
which are completely identical to their brand-name counterparts and distinguished from ordinary
generics (OGs), which are merely equivalent, according to their brand premium. The empirical
puzzle is that despite extensive literature indicating a strong consumer preference for brand premiums (i.e., AGs), some consumers still opt for nonbrand OGs. To address this issue, we analyze
the dispensing behavior of pharmacists, focusing specifically on their inventory choices between
brand-identical AGs and brand-equivalent OGs.
One significant advantage of focusing on the Japanese market is that the government uniformly
sets retail prices of generic pharmaceuticals, regardless of whether they are AGs or OGs, andsimultaneously sells them1 . Therefore, considering the bioequivalence of generic drugs, the distinction between AGs and OGs is due mainly to the perceived brand premium attached to AGs
1

In Japan, the government prohibits physicians, pharmacists, and pharmaceutical companies from advertising prescription drugs to patients.

2

rather than to price differences. Furthermore, the Japanese government has recently enhanced therole of pharmacies alongside physicians. Each pharmacist exclusively chooses either an AG or anOG, considering the patient’s preference for generic pharmaceuticals, independent of physicians’
preferences. By utilizing these practices, our study investigates how consumers’ brand preferences
are formed by considering the interactions between pharmacists and patients.
We model both the consumer’s choice of generics and the pharmacy’s decision to adopt an
AG to elucidate the roles of the demand side (patients) and supply side (pharmacists) of antibiotics. Initially, we define consumer demand for brand-name drugs, AGs, and OGs. Our model
indicates that consumers’ drug preferences differ across pharmacies, considering the prominentrole of pharmacists in Japan. Given these patients’ demands, each pharmacy determines whether
to adopt an AG or an OG to maximize its profits, which is linked with its generic dispensing share.
Expanding on the correlated random coefficient (CRC) model (Suri, 2011; Michler et al., 2019),
our three-period CRC model shows that the adoption of an AG by pharmacies hinges on patients’preferences for AGs.
Our empirical findings reveal that patients generally prefer AGs over OGs. This preference implies that pharmacies substituting AGs for OGs can enhance the likelihood of generic substitution,with a potential increase in the generic share ranging from 1.00% to 1.56%—a notable increase
considering the already substantial degree of adoption of generics across pharmacies. Additionally,
our analysis demonstrates significant variations in patients’ brand preferences across pharmacies.
We find that these variations account for approximately one-third of the average preference forAGs. Moreover, our results suggest that a patient’s brand preference varies even for identicalproducts—brand-name products and AGs. These findings substantiate our assertion that patients
have a heterogeneous preference for the perceived quality of brand-name drugs and their genericcounterparts.
Finally, to understand the source of patients’ heterogeneous preferences, we focus on the roleof pharmacists in providing information, a key aspect of recent Japanese health care system reforms. Our findings indicate that although patients generally prefer AGs to OGs, patients are more
likely to prefer OGs to AGs if pharmacies offer detailed drug information, leading to heterogeneous distribution practices among pharmacies. When pharmacies provide a minimal amount ofdrug information, patients favor AGs; however, when comprehensive pharmaceutical details are3

available, patients lean toward OGs. Consequently, our findings suggest that the variation in pharmacists’ information provision is a primary factor influencing brand preference.
This paper joins the growing empirical literature that examines the source of brand premiums.
The literature examines the drivers of brand premiums from the consumer side, including consumers’ information and expertise (Bronnenberg et al., 2015, 2020; Janssen, 2023), inertia andloyalty (Keane, 1997; Dubé et al., 2010), and search and learning (Crawford and Shum, 2005).
Recent studies in the health product market show that information, such as nutrition labels forgroceries (Fichera and von Hinke, 2020) and sales rankings for generic drugs (Carrera and VillasBoas, 2023), influences consumer behavior and decision-making. Our study demonstrates that thesource of brand premiums depends on the information-providing behavior of professional experts
and that the direct pharmaceutical information provision of pharmacists, mandated by the Japanese
government, is related to the change in consumers’ brand premiums.
This paper is also a part of the literature exploring healthcare provider behavior. One strandof literature has focused mainly on physicians’ behavior as professional experts (Chalkley and
Tilley, 2005; Iizuka, 2007, 2012; Clemens and Gottlieb, 2014; Epstein and Ketcham, 2014; Chanet al., 2022). However, given the recent interest in generic drugs (Appelt, 2015; Ito et al., 2020;
Janssen and Granlund, 2023) and the behavior of pharmacies (Bennett and Yin, 2019; Starc andSwanson, 2021; Atal et al., 2022; Janssen and Zhang, 2023), there is still a gap in the understanding of the role of pharmacists. Iizuka (2012), who has examined the financial incentives behind
physicians’ prescriptions of generic drugs in Japan, states that in fact, the role of the pharmacist
as another key agent for the patient is seriously understudied. The most closely related study toours is Brekke et al. (2013), which conducts a theoretical and empirical analysis of the financial
incentives for pharmacists and the market share of generic drugs. Our paper differs from the literature in two main respects. First, we consider the role of patients’ brand preferences, whichvary across pharmacies. By utilizing the unique characteristics of AGs and Japanese regulations,
we explicitly acknowledge the heterogeneity of patients’ brand preferences to explain the different
AG and OG dispensing patterns across pharmacies. Second, our study examines the informationalrole of pharmacists. The literature has not fully analyzed the influence of pharmacists on patient
brand preferences. Drawing on the recent policy reforms enacted by the Japanese government
since 2016, our research analyzes the relationship between information provided by pharmacists
4and patients’ pharmaceutical preferences.
The remainder of this paper is organized as follows. Section 2 provides background information on the Japanese healthcare market. Section 3 details the data used. In Section 4, we introduce atheoretical model that accounts for both patient demand and pharmacy adoption decisions. Section
5 outlines our empirical approach and estimation methodologies, emphasizing the identification
assumptions. Section 6 presents the estimation results, focusing on patients’ AG preferences andtheir heterogeneity. Section 7 elaborates on the pharmacist’s factors that affect heterogeneous patient brand preferences. Finally, Section 8 concludes the paper.

2

Institutional Background

This section explains the supply side of the Japanese pharmaceutical system, elucidating the processes of prescribing and dispensing, especially focusing on the role of pharmacists. This study
provides a brief summary of the Japanese government’s efforts to promote the use of generic drugs.
Furthermore, we explore a particular type of generic drug—an AG—which is identical to its brandname equivalent and produced by the original manufacturer.

2.1

Japan’s Pharmaceutical Supply System

Historically, in Japan, medical care was characterized primarily by prescribing and dispensing byphysicians within hospitals and was heavily influenced by traditional Eastern medicine (Iizuka,2012). However, since the 1940s, the Japanese government has promoted the separation of physicians’ medical services and pharmacists’ dispensing roles to deliver high-quality medical care.
Therefore, when examining Japan’s pharmaceutical system, it is critical to understand the roles ofpharmacists2 .
Following these policy transitions, the role of pharmacists has significantly expanded, particularly in terms of how they affect patients’ pharmaceutical choices. First, the introduction of2This situation differs significantly from that of other countries, particularly the U.S. The U.S. pharmaceuticalsystem involves multiple intermediaries, including insurers, pharmacy benefit managers (PBMs), and pharmacies.
Within this system, insurers offer health plans that cover prescription drugs, dictate which drugs are included intheir formularies, and set patients’ out-of-pocket costs. PBMs, in contrast, act as negotiators, securing discounts andrebates from drug manufacturers, managing formularies, and processing prescription drug claims. Their negotiations
can influence drug tiering, thus affecting patients’ out-of-pocket costs.

5

nonproprietary name prescribing has facilitated the choices of patients and pharmacists between
brand-name or generic drugs3 . This flexibility was limited before 2012 when only proprietaryname prescriptions were available, where in most cases, the brand-name drugs written on theseprescriptions were dispensed as they were and provided to patients at pharmacies4 . However, theimplementation of the nonproprietary name prescription enabled generic and brand choices fromphysicians to pharmacists. A recent survey showed that nonproprietary name prescriptions accountfor the majority of all prescriptions (Ministry of Health, Labour and Welfare, 2023b)5 .
Second, most patients receive pharmaceutical dispensing outside hospitals. Under the Uniform Drug Pricing Policy, the Ministry of Health, Labour, and Welfare (MHLW) routinely sets
and revises uniform drug prices based on wholesale market prices (Ito et al., 2020)6 . In the past,physicians could profit from in-hospital dispensing due to significant price-cost margins7 . However, to curb rising social security spending, the MHLW has reduced this margin to less than 10%,
thereby suppressing such financial incentives8 . Given the cost of dispensing-related equipment,
the once-prevalent practice of in-hospital dispensing has declined, as it has become economicallyunsustainable for clinics or hospitals to maintain in-hospital dispensing. Consequently, the shareof out-of-hospital dispensing services continues to increase. According to recent MHLW statistics(Ministry of Health, Labour and Welfare, 2023b), the out-of-hospital prescription rate has reached3

Physicians are responsible for prescribing drugs using two main methods. The first, called proprietary nameprescription, involves prescribing a drug by its trade name. The second, known as nonproprietary name prescription,
was introduced in 2012 and involves the pharmacist prescribing a drug by its active ingredient name. The difference
between these two methods lies in the physician’s approach when writing the prescription. In the former, the physician
specifies the individual brand label, whether original or generic. Physicians prescribe the latter without focusing on
those specific labels.
4
Before 2006, pharmacies were strictly prohibited from selling nonprescription generic drugs. Until 2012, only
proprietary name prescriptions were available, but it was a period when a specified brand-name drug in a prescription
could be replaced by a generic equivalent. Iizuka (2012) studied the incentives affecting Japanese physicians’ decisionmaking between generic and brand-name drugs during this period.
5
The exact figures are 52.8% for nonproprietary name prescriptions and 29.3% and 13.6% for proprietary nameprescriptions specifying the original brand-name drug and generic drug, respectively, in 2022. Pharmacists can interchange brand-name and generic drugs regardless of a physician’s specific prescription.
6
Until 2018, drug price revisions occurred every two years. Afterward, such revisions have been carried out
annually.
7
Once physicians issue a prescription, pharmacists dispense the medication. This process can occur in one oftwo settings. The first is in-hospital dispensing, in which patients receive their medications within the same medical
institution where they consulted. Alternatively, out-of-hospital dispensing occurs when patients go to independent
pharmacies that are not affiliated with the hospital. In in-hospital dispensing, both pharmacists and physicians candispense pharmaceuticals, whereas only pharmacists conduct out-of-hospital dispensing.
8
The MHLW conducts a drug survey and publishes the average disparity rate, which indicates the deviation between the official price (i.e., the uniform drug price) and the actual market price at which transactions occur. For
example, the average overall gap rate for pharmaceuticals in 2020 was 8%, which has decreased in recent years.

6almost 80%. Since only pharmacists can dispense drugs in out-of-hospital dispensing, this transition further amplifies the impact of pharmacists on patients.
Third, pharmacists have multiple financial incentives to provide information to patients. In
Japan, since April 1986, pharmacists have received additional payments for providing patientswith pharmaceutical information. The family pharmacist program, launched by the MHLW in
April 2016, has especially intensified the role of pharmacists. Family pharmacists can secure additional payments by satisfying rigorous criteria for exclusive and continuous treatment for a patient.
Once pharmacists are certified as family pharmacists, they can access all patient prescriptions andvisiting histories of healthcare providers. The literature shows that family pharmacists provide
high-quality information to change patients’ drug usage9 .
These institutional and environmental changes in Japan’s healthcare system indicate that pharmacists play a more prominent role in dispensing pharmaceuticals than they used to. In addition,
Japanese pharmacists are obliged and financially incentivized to provide pharmaceutical counseling to ensure that medications are used safely and appropriately. Therefore, patients’ choice
between generic or brand-name drugs is largely influenced by the guidance they receive frompharmacists.

2.2

Generic Drug Promotion Policies in Japan

Promoting the use of generic drugs is an effective way in which to control healthcare costs. Since
the 2000s, the Japanese government has actively promoted the use of generic drugs by setting
numerical targets. Consequently, the substitution rate of generic drugs, which was relatively low
among developed countries and did not even reach 40% in 2010, nearly reached 80% by 2024.
Most generic promotion policies are based primarily on financial incentives. Subsidy-based incentives on the drug supply side have been implemented to encourage the prescription and dispensing of generic drugs. For example, pharmacies that achieve a certain rate of generic prescriptions
can receive a special subsidy (referred to as a generic dispensing add-on)10 . Since drug price9

For example, Nishikawa et al. (2023) shows that family pharmacists can prevent polypharmacy by providing
high-quality medication assessments.
10
Physicians also have financial incentives, and when a physician writes a nonproprietary name prescription andprescribes a drug under its generic name, the government provides an increased medical payment to the clinic or
hospital with which the physician is affiliated (referred to as a nonproprietary name prescription add-on).

7cost margins have been set at low levels in recent years, pharmacies seek to increase their genericprescription rate and receive greater compensation by dispensing these generics. Other financial
incentives are also provided to patients on the demand side. Specifically, the uniform drug prices
set by the MHLW are set to 40% to 50% for generic drugs compared to original drugs, providing
patients with an economic incentive to purchase less expensive but bioequivalent generic drugs.
In addition to these monetary incentives, healthcare policy changes, such as the introductionof nonproprietary name prescriptions in 2012, have played an important role. The new systemallows patients to choose between brand and generic drugs at the pharmacy, encouraging genericsubstitution. Notably, proprietary prescriptions that prohibit substitution still exist today as an
exception due to physician requests, representing less than 5% of all prescriptions, but substitution
from brand to generic is generally encouraged11 .

2.3

AGs

OGs are designed to be equivalent to their brand-name counterparts, encompassing identical active
ingredients, efficacy, and dosage. They become available when the patent of the original drug
expires and the drug can legally be manufactured and sold by other companies12 . However, their
manufacturing processes and additives often differ, due primarily to different production methods
or cost-saving measures13 . In contrast, AGs are essentially brand-name drugs repackaged and soldunder generic names by the original manufacturer.14 . Therefore, AGs are identical to their brandname counterparts in terms of quality, strength, additives, manufacturing process, and dosage form.
There are specific reasons why Japanese pharmaceutical companies choose to sell AGs. First,
the perceived identical qualities of AGs compared to brand-name drugs can foster patient loyalty
11

The exact percentage of proprietary name prescriptions for which substitution for generic drugs is prohibited was
4% in 2022 (Ministry of Health, Labour and Welfare, 2023b), as is the above percentage of nonproprietary nameprescriptions.
12
The World Health Organization (WHO) defines a generic drug as “a pharmaceutical product, usually intended to
be interchangeable with an innovator product, that is manufactured without a license from the innovator company andmarketed after the expiry date of the patent or other exclusive rights.”
13
Two main factors contribute to the differences between generic and brand-name drugs. First, even after the
primary patent expires, other patents, generally related to manufacturing, often remain, thus prompting alternative
production methods by generic manufacturers to avoid patent violation. Second, profitability demands may leadgeneric manufacturers to utilize less costly excipients.
14
The Food and Drug Administration (FDA) in the U.S. defines an AG drug as “an approved brand-name drug thatis marketed without the brand-name on its label.”

8and potentially higher demand than those for other generics. Second, the infrastructure used to
produce the original branded drug can be used to produce an AG, allowing companies to maintain economies of scale15 . Nevertheless, reduced profitability due to a potential lack of first-moveradvantage, which originates from the Japanese patent examination system, can dampen manufacturers’ motivation to enter the generic market through prescribing AGs1617 . Consequently, only
some new generics are sold as AGs in Japan.
In Table B1, we present the names of the main oral AG drugs available on the Japanese market from 2014 to 202118 . During this period, 775 drugs were marketed as generics, but only 74(9.54%) were marketed as AGs by December 2020. In addition, to keep inventory costs as low as
possible, pharmacies commonly refrain from carrying both an OG and an AG with the same active
ingredient. For example, our data show that only 4.3% of pharmacies maintain an inventory of
both AG and OG antibiotics.

3

Data

3.1

Claim Data

The main analysis draws on medical claim data from 2014 to 2021 provided by Japan System
Techniques (JAST). This dataset comprises claim details from the Health Insurance Society (foremployees of large corporations) and the Mutual Aid Association (for public servants and educators). Moreover, this dataset offers detailed information on medical procedures performed at
15

Refer to Hirosaki (2019) for a comprehensive discussion on the AG market in Japan and the pricing strategies oforiginal drug manufacturing companies.
16
For example, manufacturing and branding AGs in the U.S. can offer a significant strategic advantage comparedto OGs, as noted by the Federal Trade Commission in 2011. Specifically, pharmaceutical companies with patents onbranded drugs have the exclusive right to market AGs six months before their patents expire. Introducing their generics
during this window allows them to generate large revenues and increase their market share. This early entry into themarket gives them a distinct advantage, positioning them firmly in the generic market before other potential genericmanufacturers can begin selling the versions of their drugs.
17
In Japan, unlike in other countries, the timeline for introducing AGs is impacted by a re-evaluation requirement
for the safety and efficacy of authorized drugs. Due to this re-evaluation requirement, neither AGs nor OGs can be soldduring the re-evaluation period. Consequently, the earliest that a company that manufactures a brand-name drug canlaunch an AG drug is either six months before the patent expires or after the re-evaluation period, whichever comes
later. If a basic patent expires before the end of the re-evaluation period, then the introduction of the AG coincides
with that of the OG.
18
In proprietary name prescriptions, drugs are prescribed under a name that combines generic name ingredients,
dosage form, and contents.

9healthcare facilities, the names of ailments and injuries, and prescription drugs dispensed by pharmacies19 . During the study period, 114,121,902 claims were observed, representing 7,839,803
patients. The gender ratio was 90.47, with average ages of 38.5 and 39.08 years for males andfemales, respectively.

3.2

Antibiotics

We explore the dispensing decision of pharmacies regarding the antibiotic levofloxacin, marketed
under the brand name Cravit20 . This drug is prescribed for various infections, including pharyngitis, tonsillitis, pneumonia, otitis media, chlamydia, and gonorrhea. We focus on 250mg and 500mg
levofloxacin for the following reasons: (1) the introduction of its generics within our data period,(2) the availability of an AG, and (3) the consistent demand for the drug, given that no new alternative is introduced during the period. Notably, the patent for the original brand-name drug lapsed
in 2010. Both the OG and AG versions of Cravit began being offered for sale simultaneously in
December 2014. Figure 1 displays the prescription shares for brand-name levofloxacin and the OG
and AG versions, broken down by quarter. Importantly, the data used in the analysis are limited toout-of-hospital prescriptions.
JAST collects an extensive range of claim data for corporate and public employees but omits
data for self-employed and elderly people. Compared with the MHLW’s National Database (NDB),
which offers publicly accessible but aggregated data for the entire Japanese population, the average age in the JAST dataset is approximately 10 years younger for both genders21 . Nonetheless,we confirm the consistency between the NDB and JAST datasets in terms of the brand-to-genericratio and the AG-to-OG ratio of levofloxacin across gender and age groups, which suggests that
the JAST data are reasonably representative of the broader population regarding levofloxacin prescriptions.
19

All information that can identify individuals is anonymized, and unique IDs are assigned.
According to the Anatomical Therapeutic Chemical (ATC) Classification System, levofloxacin is denoted as ATC4
J01MA12.
21
The NDB managed by the MHLW compiles comprehensive health claim data, including a wide array of healthservices provided to individuals across all age groups. This database is a critical resource for public health researchand policy analysis, although data are aggregated at the yearly prefecture level and are used primarily for macrolevelstudies.
20

10Figure 1: Prescription share of antibiotics

Notes: This figure shows the transition of share regarding brand-name, OG, and AG drugs for the antibiotic levofloxacin (its brand name is Cravit). The data period is from January 2015 to December 2021 and aggregated at themonth level.

3.3

Survey on Pharmacists

Given these claim data, we carry out a survey to clarify the dispensing practices of Japanese pharmacists in partnership with MCI Co., Ltd. This company maintains a pharmacist survey panel with
7,481 registered pharmacists. Our survey targets a randomly selected sample of 100 supervising
pharmacists who are in charge of drug procurement. We specifically design questionnaires to explore the dispensing patterns of generic antibiotics. Appendix A shows the detailed results of oursurvey.
For the dispensing practices of antibiotic generic drugs, including AGs and OGs, our survey
first suggests that pharmacists generally prioritize a stable drug supply and reliability of pharmaceutical companies over procurement costs in selecting generic drug manufacturers. However, thistrend differs when dispensing AGs. When pharmacists adopt AGs, patient preferences for andtrust in AGs become more crucial factors than does the stability of the AG supply. These results
indicate that AGs retain brand premiums due to their bioequivalence with brand-name drugs, andpharmacists sell AGs considering patients’ brand preferences toward AGs. Finally, the majority of11pharmacists provide information about the efficacy and safety of generic drugs or dispense AGs
when patients refuse generic dispensing. These patterns indicate that the brand premiums of AGs
and the information that pharmacists provide may affect patients’ decision-making.
Based on these findings, we assume that pharmacists weigh patients’ demands when dispensing
AGs. Consequently, we propose a behavioral model in which pharmacists dispense AGs considering patients’ preferences.

4

Model

In this section, we develop a model delineating both patient choice in generic drug consumption
and pharmacy decisions concerning their generic drug inventory. Upon visiting a pharmacy witha prescription, we assume a scenario wherein patients receive a brand-name drug, an OG, or anAG, contingent on his or her preferences and subject to the limitations imposed by the pharmacy’s
available stock. Initially, we define patients’ preferences for brand-name and generic drugs, which
are crucial in shaping patients’ demand-side decisions. Subsequently, we model the pharmacy’s
decision to hold an inventory of generic drugs, representing the supply-side perspective. The distinctive features of our model are that (1) there is variation in patient preferences for pharmaceuticals across pharmacies and that (2) pharmacies, knowing these heterogeneous preferences, choose
their generic drug inventory.

4.1

Patient Demand

Consider a scenario where patient i receives a prescription for a generic antibiotic from a physician
and goes to pharmacy j. At this point, the patient is presented with the following option: a brandname drug or its generic counterpart. If he or she chooses the generic option, then he or she is given
either an OG or an AG. Importantly, the choice between an OG and an AG is not at the patient’s
discretion because the pharmacy’s inventory includes only one of the two alternatives.
A patient is assumed to make a decision based on immediate utility maximization rather than
dynamic utility maximization when selecting an antibiotic type. As such, our analysis does notconsider the learning effect and inertia concerning the choice of medications22 . The data corrobo22

Iizuka (2012) and Ito et al. (2020) focus on chronic diseases such as hypertension and dyslipidemia, demonstrating12

rate that patients do not use antibiotics frequently in a short period23 .
Let the utility functions of patient i visiting pharmacy j in period t for a brand-name drug, an
AG and an OG, respectively, be specified as follows:B
B+ εB= αPtB + βtB + βjt
Uijt
ijt
A
A
Uijt
= αPtG + βtA + βjt
+ εA
ijt

(1)

O
Uijt
= αPtG + εO
ijt ,where PtB and PtG are the regulated uniform prices of the brand-name and generic pharmaceuticals,
B
AO
respectively, and Uijt
, Uijt, and Uijt
denote the utilities of patient i at pharmacy j during period t

for the brand name drug, the AG, and the OG, respectively. In the utility specifications, the utility
for the OG serves as a baseline option.
The parameter βtB encapsulates the average preference for the higher quality of the brand-namedrug relative to that of the OG drug. The underlying presumption is that despite the biosimilarity of brand names and generic drugs, patients are generally more willing to opt for brand names
than for their generic counterparts, which is considered a brand premium. Conversely, as an AG
originates from the same manufacturers as does a brand-name drug, patients often perceive theAG more favorably than they do the OG. Consequently, βtA can signify the drug-specific (contextindependent) patient’s preference, which is also a part of the brand premium. Furthermore, given
the significant role of pharmacists, as discussed in Section 2.1, patient preferences may vary acrossAB
represent the pharmacy-specific (context-dependent) paand βjt
pharmacies. We assume that βjt

tient brand-name preference and AG preference, respectively. These parameters capture the varied
preferences of patients purchasing drugs at different pharmacies. We posit that these patients’ preferences regarding brand names or AG drugs can vary, even for the same drug, influenced by factors
such as the pharmacist’s explanation, drug information provided by the pharmacist, and how the
drug is prescribed.
B
A
Although the coefficients βtB and βtA , alongside βjt
and βjt, capture the brand premium thatthat brand-name drug inertia plays a significant role in consumer choices of generic pharmaceuticals. In contrast, ourstudy concentrates on antibiotics, analyzing the short-term choices between brand and generic drugs.
23
The claim data described below show that 77.3% of all patients receive a prescription for levofloxacin only once.
Additionally, the average duration of each levofloxacin prescription is 264 days, while the majority of prescription
days reported in Ito et al. (2020), which examines the inertia of brand-name drugs in dyslipidemia, is 28 days.

13

arises from the commonality of the manufacturers of brand names and AGs, they are subject to a
distinct interpretation. The brand premium that consumers attribute to a brand-name drug and an
AG is derived from context-independent factors, including scientific differences in minor efficacy,color, and additives in comparison to an OG, and context-dependent factors, such as a patient’s
perceived quality and misperception of brand benefits due to pharmaceutical information provided
by pharmacists. The parameters βtB and βtA encapsulate context-independent factors, which areAB
represent contextand βjt
uniform among patients and pharmacies. Conversely, the parameters βjt
BA
dependent factors that vary by pharmacy. Therefore, βjt
and βjtreflect patients’ perceived quality

differences across pharmacies. These parameters can influence patients’ perceptions of brandname drug and AG quality.
Figure 2: Brand Preferences
B
Uijt
A
Uijt
B
βjt
A
βjt

βtB
βtA
O
Uijt

Notes: This figure shows that the patients receive utility from two different sources, which are context-independent
BAfactors βtB and βtA and context-dependent factors βjt
and βjt. Note that we abbreviate the utility from price andpreference shock. While context-independent factors remain uniform across pharmacies, context-dependent factors
vary, leading to different perceptions across patients of identical products, namely, AGs and brand-name products.

Figure 2 shows how these brand premiums are different. In each pharmacy, patients receive context-independent utility (blue line) for AGs βtA and brand-name drugs βtB and contextAB
dependent utility for AGs βjt
and brand-name drugs βjt(red line). Note that we assume that theutility from price and preference shocks is negligible, simplifying the illustration. This diagramsuggests that patients derive utility from two different sources and that context-dependent utility
results in differing preferences for entirely identical AGs and brand-name drugs. In the provided
14

AB
equations, the terms εO
ijt , εijt , and εijt denote the idiosyncratic preference shocks for individual pa-

tients, which emphasizes our assumption that the unique attributes of patient i do not systematicallyaffect the utility of consuming any drug type.
Let Yjtℓ be the share of type-ℓ generic drugs at pharmacy j in period t. Since a pharmacy
dispenses either an OG or an AG, but not both, the share of a brand-name drugs is given by YjtB =ℓ= ln(Yjtℓ )−ln(YjtB )1−Yjtℓ for ℓ ∈ {O, A}, where O and A are the OG and the AG, respectively. yjt

is defined as the patient’s log odds of purchasing ℓ generic drugs at pharmacy j during period tABrelative to those of brand-name drugs. Assuming that each of the tuples (εOijt , εijt , andεijt ) adheres

to an i.i.d. type-I extreme value distribution, we have the following equations for the log odds
associated with brand-name drug and AG premiums:
A
A
B
yjt
= α∆Pt + (βtA − βtB ) + (βjt
− βjt
)

(2)

O
B
yjt
= α∆Pt − (βtB + βjt).

where ∆Pt = PtG − PtB .

4.2

Pharmacy’s Supply

Let us now turn to a pharmacy’s decisions regarding generic antibiotics, assuming that each pharmacy stocks either an OG or an AG and dispenses one of the two types. Specifically, let pharmacy
j choose ℓ ∈ {O, A} generic drug type at time t. We assume that since the price–cost margins ofbrand-name and generic antibiotics are negligibly small, the primary determinant of a pharmacy’sℓrevenue from the sale of antibiotics is the generic dispensing subsidy, denoted by subsidyjt. Oncethe subsidy is granted, it is added to the total sales volume, encompassing the sales of generic andℓbrand-name drugs. Therefore, the profit function πjt
is given byℓℓπjt= subsidyjt· njt − (fjB + fjℓ ),(3)where njt is the number of patients who visit pharmacy j at time t to purchase antibiotics and fjB
and fjℓ represent the fixed costs incurred by the pharmacy for holding inventories of the brand-nameand type ℓ generic antibiotic drugs, respectively. We assume, for simplicity, that the number of15

patients njt is exogenous in that patients do not discriminate between pharmacies when purchasing
antibiotics and invariably purchase either a brand name antibiotic or a generic antibiotic at thepharmacy they enter24 .
As noted in the previous section, the amount or level of subsidy that a pharmacy can receive
depends on its overall prescription rate for generic drugs. Let us denote the ratio of the number ofantibiotics to the total number of prescriptions at pharmacy j during period t as rjt . We calculate
the total generic share as Yjtℓ rjt + gjt , with gjt being the overall generic share of drugs excluding
antibiotics25 . Consequently, the generic dispensing subsidy is given as follows:ℓ= st 1 Yjtℓ rjt + gjt ≥ ct ,subsidyjt(4)

where st represents the amount of the subsidy and ct represents the threshold of the generic prescription share that determines whether the subsidy is granted. While both values fluctuate overtime, they remain constant across all pharmacies.
We define hA
jt as a binary indicator that signifies whether pharmacy j opts for an AG in periodt. Guided by profit maximization, a pharmacy is led to select the AG (represented as hA
jt = 1) ifOA. This≥ πjt
the profit obtained from doing so exceeds that from choosing an OG, that is, if πjt

condition holds if the following inequality is met:

1

YjtA ≥ cjt

−1

YjtO ≥ cjt

(fjA − fjO )≥,st njt(5)where to simplify the notation, the original threshold of generic prescription share ct is referred toas the pharmacy-specific threshold cjt , defined as (ct − gjt )/rjt .
If we consider the indicator function 1(Y ≥ c) to be a nondecreasing function of Y and theAO
threshold c to be common on the left-hand side of Equation (5), then the profit difference πjt− πjt

also becomes a nondecreasing function of the log share difference ln(YjtA ) − ln(YjtO ). In summary,the share of generic antibiotics Yjtℓ is the key factor in a pharmacy’s decision to stock either an AG
24
In Japan, pharmacies are prohibited from advertising and promoting their own pharmaceutical products. Therefore, patients do not know ex ante which pharmacies have AGs and which have OGs.
25
To elaborate, suppose that there are K drugs other than antibiotics, with rjkt being the share of drug k at pharmacyPKj during period t; thus, rjt + k=1 rjkt = 1 must be satisfied. Let Gjkt present the genericPshare of drug k at pharmacy
j during period t. With these conditions, we compute the total generic share as Yjtℓ rjt + k Gjkt rjkt = Yjtℓ rjt + gjt ,PKwhere we define gjt = k=1 Gjkt rjkt .

16

Figure 3: Pharmacists’ Comparative Advantage
A
Uijt

A
βjt

UijA′ tβtA

βtA

βjA′ tO
Uijt= UijO′ t

Notes: This figure shows how pharmacies’ comparative advantage drives their AG adoption. Note that we abbreviate
the utility from price and preference shock. While context-independent factors remain uniform across pharmacies,
context-dependent factors vary as the degree of comparative advantage varies, leading to different AG adoption levels
across pharmacies .

or an OG, even when considering other pharmacy-specific variables (fjA , fjO , njt , cjt , st ).

4.3

Pharmacies’ Comparative Advantage

Assume that each pharmacy, fully aware of patient preferences for antibiotics, optimizes its genericdrug inventory to maximize its generic dispensing share. In period t, pharmacy j faces two potential choices, to select an AG or to select an OG, associated with the potential generic shares YjtAand YjtO , respectively. If the pharmacy chooses to stock and dispense an AG, then YjtA is realized;
otherwise, YjtO is realized. According to Equation (2) from the demand-side model, the differencein the potential shares in logs is connected to the AG premiums perceived by patients.
O
A
A
.
− yjt
= βtA + βjt
ln(YjtA ) − ln(YjtO ) = yjt(6)Equation (6) demonstrates that the difference in the potential shares in the logarithm correlates
positively with the difference in patient preferences. Therefore, pharmacy j in period t maximizesA
its generic share by stocking an AG if patients have higher AG premiums βtA + βjt.

While the context-independent preference for AGs is assumed to be positive, denoted byβtA > 0, individual pharmacies may face positive or negative context-dependent patient preferAA
ences, denoted by βjt. For example, let us consider pharmacies j and j ′ with βjt> 0 > βjA′ t . If

17

all other factors are equal, then pharmacy j is more inclined to stock an AG in period t, while
pharmacy j ′ favors an OG. Although the actual inventory choice may depend on various factors,
this illustrates a relative difference in their tendencies to dispense either AG or OG. In essence,a pharmacy’s decision to carry a particular generic depends crucially on the context-dependent
preferences of its patients. Given the knowledge pharmacies have about these patient preferences,A
signals a comparative advantage for pharmacy j in terms of selling AGs, while aa higher βjt

lower βjA′ t suggests that pharmacy j ′ has a sales advantage in terms of selling OGs. Figure 3 illustrates how comparative advantage drives the AG adoption choices of pharmacies. Note thatthe context-independent preferences βtA (blue lines) are the same across pharmacies and that theAand βjA′ t (red lines) differ across pharmacies. While pharmacy jcontext-dependent preferences βjt′

has a comparative advantage in terms of selling AGs, pharmacy j has a disadvantage in terms ofselling AGs.
A
vary across pharTherefore, we are interested in how context-dependent AG preferences βjt

macies since this parameter captures how pharmacists respond to patients’ brand preferences. The
below section describes how we empirically estimate this heterogeneous preference.

5

Empirical Specification

This section outlines the procedure for estimating a patient’s AG premium, which may vary acrosspharmacies. According to the above model, the patient’s choice of generic drug type depends not
only on his or her preferences (demand side) but also on the availability of the generic drug at
his or her pharmacy (supply side). Since pharmacies make dispensing decisions based on theirknowledge of patients’ generic drug preferences, the type of generic that a patient can access is
influenced by his or her own preferences. In this situation, unobserved heterogeneous patient
preferences introduce endogeneity into the estimation of AG premiums.
Furthermore, we introduce an additional framework on patient preferences, as outlined in
Lemieux (1998) and Carneiro et al. (2001). Let us decompose the patient’s heterogeneous prefer-18

ences as follows:B
Bβjt= θjB + ξjt

(7)

A
A
βjt
= θjA + ξjt,

where θjB and θjA represent a patient’s permanent preferences for brand-name drugs and AGs drugs
BA
at pharmacy j, respectively. Similarly, ξjt
and ξjt
denote the transitory preference shocks for thesedrugs in the same pharmacy26 . We assume that transitory preferences are uncorrelated with eachother and with other preference parameters.
To address the challenge of identifying heterogeneous preferences, θjB and θjA , we adopt a projection method based on the approach taken by Lemieux (1998) and Suri (2011). This method
involves considering the linear projections of θjB onto θjA , leading to subsequent orthogonal decomposition as follows:θjB = ϕθjA + τj(8)where ϕ is the projection coefficient given by
Cov(θjB , θjA )=ϕ=Var(θjA )s

Var(θjB )Corr(θjB , θjA ).
Var(θjA )(9)

The sign of ϕ corresponds to the correlation between brand-name preference θjB and AG preferenceθjA . Additionally, the parameter τj represents a residual component of θjB that is orthogonal to θjA . If
the patient’s brand name and AG preferences are exactly the same, then the parameter ϕ is 1. If ϕ is
not 1, then patients perceive quality differences between brand-name products and AGs, indicating
the over- or underestimation of brand benefits. While we do not assume a perfect correlation, it
is reasonable to anticipate a positive sign for ϕ given the perceived similarity of the two types of
products by patients.
We obtain the following results by reformulating these decomposition outcomes into log odds
26This transformation implies that context-dependent factors are decomposed into permanent factors, such as theexistence of family pharmacists, and transitory factors, including the method of providing information.

19

equations, as specified by Equation (2):
A
A
B
yjt
= α∆Pt + (βtA − βtB ) + (1 − ϕ)θjA − τj + (ξjt
− ξjt
)

(10)

B
O
.
= α∆Pt − βtB − ϕθjA − τj − ξjt
yjt

Then, the difference in the log odds, given by Equation (6), can be expressed as
A
O
A
yjt
− yjt
= βtA + θjA + ξjt.

(11)

The decision for pharmacy j to offer AGs, denoted by hA
jt , is correlated with the patient’s permanent preference for AGs, which is expressed as θjA . Furthermore, θjA empirically represents thecomparative advantage for each pharmacy in adopting an AG.
Consider yjt as the observed log odds of generic antibiotics at pharmacy j in period t. Given
that a pharmacy carries either an AG or an OG exclusively, the log odds yjt can be depicted as alinear combination of two potential log odds.
A
A O
yjt = hA
jt yjt + (1 − hjt )yjt

(12)

Substituting Equation (10) into Equation (12) yields
A A
A
A A
B
yjt = α∆Pt − βtB + βtA hA
jt + θj hjt − ϕθj − τj + ξjt hjt − ξjt(13)

Rearranging the above equation, we then obtain the following empirical specification:A
yjt = α∆Pt − βtB + (βtA + θjA )hA
jt − ϕθj − τj + νjt ,

(14)

A A
B
where νjt = ξjt
hjt − ξjt
is a composite error term.

Equation (14) is a CRC model, as discussed in (Heckman and Vytlacil, 1998; Suri, 2011).
In this model, the coefficient βtA + θjA for a pharmacy’s inventory choice hA
jt is correlated withthe choice itself. If incorrectly specified as a fixed effects model, then the equation becomes thefollowing:′′yjt = α∆Pt − βtB + βtA hA
jt − τj + νjt ,

20

(15)

where τj′ denotes individual fixed effects. The relationships between the estimated and original
A A′= νjt + θjA hA
parameters are τj′ = τj + ϕθjA and νjt
jt . The term θj hjt is part of the composite error,

as it is unobservable and varies across pharmacies and time. In the special case where θjA = 0,
the CRC model reduces to the fixed effects model. Otherwise, the endogeneity issue remains due′to the inherent correlation between hA
jt and the composite error νjt . Therefore, using the fixed

effects model generally yields a biased estimate of overall AG premiums βtA and fails to identify
the projection coefficient ϕ related to brand and AG premiums.

5.1

Estimation Method

To estimate the structural parameters (βtA , θjA , ϕ) specified in Equation (14), we apply the projection method developed by Suri (2011) in the framework of the CRC model. The core idea is thatpharmacies use their knowledge of patients’ preferences for generics as a comparative advantage
in deciding whether to stock an AG or an OG. This decision is correlated with patients’ heterogeneous preferences θjA , as captured by linearly projecting θA j, representing the AG preference
associated with pharmacy j, onto the history of its inventory decisions hA jt. By embedding the
projection equation in the estimation strategy, we aim to mitigate the endogeneity problem causedby the correlation between θjA and hA
jt .
For clarity, we outline the two-period estimation method; a three-period approach is detailedin Appendix F. Aligned with the approach of Chamberlain (1984), the linear projection in the
two-period case is given as follows27 :A
A A
θjA = λ0 + λ1 hA
j1 + λ2 hj2 + λ3 hj1 hj2 + υj .

(16)

27
The key difference between Chamberlain’s original projection method and Suri’s generalized approach concernsA
the interaction term from the historical endogenous choice variables. When the interaction term hA
j1 hj2 is omitted fromthe projection equation, Equation (16), the orthogonal residual obtained from the projection υj may be correlated with
the interaction term in the reduced form of the two-period CRC model. This correlation can generate endogeneity
issues in the reduced-form estimation based on Equations (18).

21Substituting Equation (16) into Equation (14) for each period yields
AA A
yj1 = (α∆P1 − β1B − ϕλ0 ) + (β1A + λ0 + λ1 − ϕλ1 )hA
j1 − ϕλ2 hj2 + (λ2 − ϕλ3 + λ3 )hj1 hj2+ (υj hA
j1 − ϕυj + τj + νjt )AAA A
yj2 = (α∆P2 − β2B − ϕλ0 ) − ϕλ1 hA
j1 + (β2 + λ0 + λ2 − ϕλ2 )hj2 + (λ1 − ϕλ3 + λ3 )hj1 hj2+ (υj hA
j2 − ϕυj + τj + νjt )(17)
We derive the following two reduced-form equations from the above equations :AA A
yj1 = δ1 + κ1 hA
j1 + κ2 hj2 + κ3 hj1 hj2 + ζj1

(18)

A
A A
yj2 = δ2 + κ4 hA
j1 + κ5 hj2 + κ6 hj1 hj2 + ζj2 ,where ζj1 and ζj2 are composite error terms in the estimation. The association between the reduced
form and structural parameters is illustrated as follows:κ1 = (1 − ϕ)λ1 + β1A + λ0 ,
κ2 = −ϕλ2 ,κ3 = (1 − ϕ)λ3 + λ2 ,(19)

κ4 = −ϕλ1 ,κ5 = (1 − ϕ)λ2 + β2A + λ0 ,
κ6 = (1 − ϕ)λ3 + λ1

There are six reduced-form parameters (κ1 , κ2 , κ3 , κ4 , κ5 , κ6 ) and six structural parameters (λ1 , λ2 , λ3 , β1A , β2A , ϕ)28
P
Considering the normalization j θjA = 0, we can express λ0 in terms of λ1 , λ2 , λ3 . Specifically,
λ0 can be represented as λ0 = −λ1 h̄1 − λ2 h̄1 − λ3 h̄1 h̄2 , where h̄1 and h̄2 represent the average
AG adoption rates across pharmacies in each period. The estimation procedure consists of two
steps. First, we perform seemingly unrelated regressions on Equation (18). We obtain the reducedform parameters and the variance-covariance matrix from this estimation. Second, we estimate the28

In the three-period model, there are 21 reduced-form parameters and 11 structural parameters, which indicatesthat the structural parameters are overidentified.

22

structural parameters using optimal minimum distance (OMD) estimates based on the first-stage
estimates under an appropriate restriction matrix that embodies the parameter restrictions given by
Equation (19)29 .

5.2

Identification

This section provides the necessary identification assumptions for estimating the structural parameters, as stated in Equation (14). Following the approach in Suri (2011), we require the assumptionof the mean zero of the composite error term τj + νjt conditional on the patient’s heterogeneous
A A
A
preference θjA and the pharmacy’s historical adoption patterns (hA
j1 , hj2 , hj1 hj2 ) for the two-period

CRC model; that is,
A
A A
E(τj + νjt |θjA , hA
j1 , hj2 , hj1 hj2 ) = 0.

(20)

The conditional mean zero assumption for the composite error can be discussed in two parts,
one for τj and the other for νjt . First, we can immediately show that the condition for τj issatisfied: the orthogonality of τj on θjA implies that E(τj |θjA ) = 0. It should be obvious thatA
A A
E(τj |θjA , hA
j1 , hj2 , hj1 hj2 ) = 0 holds from the law of iterated expectation. Second, the conditionalmean zero assumption for νjt is not immediately obvious and requires some preconditions for its
A A
A
A
A
B
A
validation. Considering that νjt = hAjt ξjt − ξjt , the condition E(νj |θj , hj1 , hj2 , hj1 hj2 ) = 0 is
BA
do not affect the decision of pharmacy j toand ξjt
satisfied if the transitory preference shocks ξjt
30
introduce AGs, denoted by hA
jt .

Based on the behavioral model of patients and pharmacies presented in the previous section,
pharmacies dispense the generic drug type to maximize their generic share by using their knowledge of their patients’ heterogeneous preferences. If the pharmacy focuses only on the permanent(long-term) part of the patient’s preference, θjA and θjB , and ignores the transitory (short-term) part,ABξjtand ξjt, then the conditional zero mean assumption in Equation (20) holds. This assumption

may be justified because the pharmacy’s inventory decisions are based on long-term contracts and29

We refer to a Stata package provided by Cabanillas et al. (2018) to perform the OMD estimation.
This line of reasoning, linking the conditional mean zero assumption for the composite error term in the empiricalmodel with the relationship between an agent’s temporary shocks and his or her decision-making in the theoreticalmodel, draws parallels with arguments presented in Lemieux (1998).
30

23are therefore less sensitive to patients’ short-term preferences for generics, which change fromperiod to period. Thus, patients’ permanent (long-term) preferences are predetermined, and conditional on this assumption, it seems plausible to argue that patients’ transitory (short-term) preferences and decisions about generic inventories are independent.
While the zero conditional mean assumption given in Equation (20) is reasonable to someextent, we also estimate it under an alternative assumption as follows:A
A A
E(τj + νjt |θjA , hA
j1 , hj2 , hj1 hj2 , Mjt ) = 0,(21)

where Mjt represents a vector of local market characteristics at time t for pharmacy j. In thesubsequent empirical analysis, this term encompasses an interaction term between time and theprefecture dummy for the pharmacy’s location.
Given that we control for relevant time-variant market characteristics, which can confound boththe patient’s transitory preference shock and the pharmacy’s generic introduction, we find it moreA
B
convincing that changes in patient preference shocks ξjt
and ξjt
do not influence the pharmacy’s

introduction of whether to carry AG drugs, as indicated hA
jt . Under the weaker assumption of a conditional zero mean, as described by Equation (21), we conduct empirical analysis by incorporating
the market characteristics into a reduced-form equation, Equation (18):AA A
yj1 = δ1 + κ1 hA
j1 + κ2 hj2 + κ3 hj1 hj2 + ι1 Mj1 + ζj1

(22)

A
A A
yj2 = δ2 + κ4 hA
j1 + κ5 hj2 + κ6 hj1 hj2 + ι2 Mj2 + ζj2 ,5.3

Validating the Model Assumptions

Given patients’ different preferences, the behavioral model posits that pharmacies maximize theirprofits when dispensing generics. We examine the consistency of our model assumptions with
empirical data in the below three ways.
First, a positive estimate for the parameter ϕ should be observed. Given that ϕ denotes thecorrelation between brand-name drug preferences and those for AG drugs, we anticipate a positive
value. This expectation arises because both types of drugs produced by the same company are
identical in terms of their appearance and content. If there is a departure from the perfect cor-24

relation case, ϕ = 1, then patients perceive quality differences between the essentially identicalbrand-name and AG drugs.
Second, we explore the relationship between heterogeneous patient preferences θjA and pharmacy inventory choices hA
jt within the framework of the supply-side model. This model posits
that such preferences directly influence which drug types pharmacies choose to stock, as presented
by the linear projection shown in Equation (16). To confirm the validity of this relationship, thesignificance of parameters (λ1 , λ2 , λ3 ) should be confirmed through a joint test31 . Additionally,
if θjA = 0, then we cannot observe a correlation between θjA and hA
jt . Therefore, the significant
relationship between θjA and hA
jt suggests that the specification of our CRC model is valid over the
fixed effects model.
Finally, we examine the distribution of the context-dependent patient AG preferences θjA . The
significant variation indicates that differences in generic drug inventories across pharmacies are
driven by patient preferences, consistent with our model assumptions. We calculate this heterogeneous preference using Equation (16) with the structural parameters (λ̂0 , λ̂1 , λ̂2 , λ̂3 ) and set ν tozero. To gauge the relative importance of context-dependent patient preference θjA , we compare its
standard deviation to the magnitude of the time-averaged context-independent preferences βtA .

6Estimation Results

In examining the adoption of AG drugs by pharmacies, we focus on pharmacies prescribing levofloxacin. For consistency in the empirical analysis, we exclude pharmacies, as identified in the
JAST claim data, that prescribe both OG and AG forms of levofloxacin (constituting 4.32% of
the sample). Furthermore, small pharmacies with extremely low numbers of prescriptions are excluded from our main analysis. Therefore, pharmacies in the bottom 5% of the total number ofprescriptions are excluded from the analysis. Moreover, as shown in Figure 1, in 2015, when thefirst-year generic levofloxacin was fully introduced, both OG and AG adoption rates by pharmacies increased but were not stable. In addition, the dispensing behavior of pharmacies may havechanged due to the introduction of the family pharmacist program in 2016. Therefore, the empiri31

The same joint test for parameter significance can also be performed in a three-period example based on thecorresponding linear projection equation.

25

cal analysis was conducted from April 2016 to December 2021, with two consecutive years as one
period of the empirical model presented in Section 4.

6.1

Sample Statistics

Table 1, Panel A, provides statistics on levofloxacin prescriptions across various pharmacies concerning the ratio of the number of generic levofloxacin prescriptions (generic prescription ratio)
and the number of AG levofloxacin prescriptions (AG prescription ratio) for each period. Importantly, the value of the brand-name share of levofloxacin is 0 or 1. In the theoretical model, YjtB and
Yjtℓ represent the probabilities of pharmacy j adopting a brand drug or ℓ type generic drug in year t,
respectively, and they are never equal to 0 or 1. However, if these probabilities are sufficiently close
to 0 or 1, then the observed brand or generic share, the empirical analog of those probabilities, mayℓ, the log odds of the left-hand side of the empirical modeltake a value of 0 or 1. In such cases, yjt

shown in Equation (14), cannot be defined. Therefore, for pharmacies with a brand-name share of0, namely, a generic share of 1, we replace the share with a small positive constant ϵ > 0 to performthe empirical analysis. In the baseline analysis, estimation is performed as ϵ = 10−2 considering
the size of prescription numbers in Table 1, but estimation is also performed for several alternativevalues of ϵ to check the robustness of the estimation results.
For pharmacies that have a brand share of 1, which means a generic share of 0, it is not possible to determine whether the pharmacy holds an AG, and thus, the value hjt representing whether
pharmacy j holds an inventory in period t cannot be determined32 . Consequently, we omit pharmacies from the sample if they have a generic share of zero at any point during period t in ourbaseline analysis. However, we assume that if a pharmacy has no observed generic prescriptions
during a particular period, then it still holds the type of generic drug it most recently had in stock.
Specifically, if pharmacy j has a zero generic share in period t but had prescribed a generic drug
before period t, then we derive hjt from the observed generic types. We then estimate the genericshare for pharmacy j in t to check the robustness of the results.
Table 1, Panel B, shows the distribution of pharmacy store attributes (number of prescriptions,
whether the pharmacy is a chain or individual store, and the concentration index of prescriptions
32
Even if a pharmacy’s observed generic drug share is zero, it may still stock generic drugs. However, this absenceof prescriptions prevents the determination of whether the inventory includes AG or OG types.

26

Table 1: Sample Statistics
2016-20172018-20192020-2021

0.00
1.00
0.90
0.72
0.35
0.00
1.00
1.00
0.56
0.48

0.00
1.00
1.00
0.82
0.29
0.00
1.00
1.00
0.59
0.47

0.00
1.00
1.00
0.87
0.27
0.00
1.00
1.00
0.62
0.47

113.05476.31
0.83
0.24
0.07
0.26
0.02
0.33
12164

119.86527.58
0.81
0.25
0.14
0.35
0.03
0.37
12164

61.55
236.12
0.83
0.24
0.14
0.35
0.2
0.27
12164

Panel (A)Generic Share

AG shareminmax
median
mean
s.d.
min
max
medianmeans.d.

Panel (B)
Number of Prescriptions

mean
s.d.
Concentration Index
mean
s.d.
Chain Store
mean
s.d.
Family Pharmacists Prescriptions means.d.
Observations

Notes: This table shows the descriptive statistics of our three-period data on the antibiotic levofloxacin (brand name is
Cravit). In each period, data start from April and end in March, except for the last period. We aggregate the data in eachperiod. In Panel A, the generic share is defined as the ratio of the number of generic drug prescriptions to the numberof total prescriptions for antibiotics. The AG share is defined as the ratio of the number of AG prescriptions to thenumber of generic drugs, including both AG and OG prescriptions. In Panel B, we report pharmacy characteristics. In
the first row, we define the number of prescriptions as the total number of prescriptions in all prescribed drugs groups.
The concentration index is the Herfindahl–Hirschman index using the share of antibiotic prescriptions from hospitals.
Chain store is the dummy concerning whether or not each pharmacy is chain. Family pharmacist prescriptions denotes
the share of prescriptions by family pharmacists.

27

Table 2: Share and Characteristics
Categories

Pharmacy Size

Pharmacy Type

Concentration Index

Family Pharmacist

AG Share

AG Share

AG Share

AG Share

Large

Small

0.580

0.618

Chain

Individual

0.531

0.602

High

Low

0.624

0.438

Presence

Absence

0.359

0.597

Test Statistics(p-value)-2.993(0.002)-7.267(<0.001)
16.945(<0.001)-9.104(<0.001)

Notes: This table presents the average AG share based on patients’ attributes. For each pharmacy, we categorize
two groups and test the significance of the share difference between them. In the first row, we define pharmacy sizeas the number of prescriptions and distinguish between the top 10% (large) and bottom 10% (small) sizes. Similar
categorizations are applied to the concentration index. We assume that if family pharmacists dispense the antibiotics
even once, then it indicates the presence of family pharmacists at that pharmacy. For the concentration index, we use
the Herfindahl–Hirschman index.

from hospitals to pharmacies) for each period. The number of prescriptions refers to the number ofall prescriptions, including antibiotics and other pharmaceuticals, dispensed by each pharmacy33 .
The prescription concentration index is the Herfindahl–Hirschman index, which is given by thesum of the squares of the shares of prescriptions from each pharmacy in a given pharmacy. The
index, ranging from 0 to 1, reflects prescription sourcing—a higher value indicates that a larger
proportion of prescriptions are received from a single hospital. Chain store is a dummy variableindicating whether a pharmacy is part of a chain store. Family pharmacist prescriptions are theshare of prescriptions by family pharmacists. These descriptive statistics indicate that our datatarget primarily small-scale, privately owned pharmacies that receive many prescriptions from a
specific hospital.
Table 2 compares the mean AG prescription share of levofloxacin to identify various phar33

Note that our claim data constitute only a subset of the total prescriptions dispensed by pharmacies.

28

Table 3: Adoption Transition
Adoption Pattern

AAA

OOO

AAO

AOO

OOA

OAA

AOA

OAO

Fraction of Sample (%)
Number of Switch

52.98
0

34.52
0

1.75
1

2.36
1

3.74
1

4.26
1

0.15
2

0.20
2

Notes: The table illustrates the adoption patterns of AGs and OGs in pharmacies across three periods. Pattern AAA
signifies that the pharmacy consistently adopts AGs throughout all periods, whereas OOO denotes the exclusive dispensing of OGs.

macy characteristics. For pharmacy size, we categorize pharmacies based on their prescription
volumes—those in the top 10% quantile are classified as large pharmacies, and those in the bottom 10% quantile are classified as small pharmacies. The results show that small pharmacies are
more likely to dispense AGs than are large pharmacies. For pharmacy type, we classify pharmacies according to whether or not they are chain stores. We also categorize the top and bottom
deciles of the pharmacy’s concentration index into high and low groups, respectively. Finally, wecategorize pharmacies based on the presence of family pharmacists. We assume that if family pharmacists dispense antibiotics even once, then this indicates the presence of family pharmacists atthat pharmacy. These results reveal significant disparities in AG dispensing rates across pharmacy
attributes.
According to our CRC model, a pharmacy’s adoption history plays a significant role. We
define the adoption status of a pharmacy in each period hjt based on the predominant generictype dispensed during that period. Importantly, pharmacies do not frequently change their genericinventory types and typically alter them annually. However, some pharmacies switch between
stocking AGs and stocking OGs during the analysis period spanning two years for three-period
estimates. Therefore, we consider a pharmacy to have adopted the generic type most frequently
dispensed during the period. Specifically, a pharmacy is considered to have adopted AGs if it
dispensed more AGs than it did OGs during a given period.
Table 3 shows the pattern of transitions of the generic type introduced by pharmacies. The
pattern AAA signifies that the pharmacy consistently adopts AGs throughout all periods, whereas
OOO denotes the exclusive dispensing of OGs. While the majority of pharmacies continue to use
AGs or OGs in these three periods, 12.46% of the pharmacies change the generic type at least once
during the three periods.
29

6.2

Average Brand Preference

Table 4 presents the fixed effects estimates derived from Equation (15). We report the estimatedaverage preference βtA in both the two- and three-period models. The estimated coefficient βtA is
positive in both the two- and three-period models, regardless of the fixed effects specifications.
However, the fixed effects model does not completely rectify adoption endogeneity and considers
A. Nonetheless, our descriptive results suggest a positive preference forthe effect of pharmacists βjt

AG among patients.
Table 4: Fixed Effects Estimates
Two Period
β1A
β2A

(1)
0.198
(0.066)0.305(0.067)

β3A
Pharmacy FE
Year FE
Year × Local Market FE
Observations

×
×
19636

Three Period

(2)
(3)
0.202
0.167
(0.066) (0.057)
0.336
0.283
(0.067) (0.055)
0.284(0.057)×××××
19636 29454

(4)
0.167
(0.057)0.293(0.056)0.308(0.057)
×
×
×
29454

Notes: Cluster-robust standard errors are in parentheses. In the two-period analysis, we utilize data from the firstand last periods of the three-period dataset. Our model incorporates both pharmacy-level fixed effects and year-leveldummies. In Columns (2) and (4), we include a year × prefecture-level dummy.

Table 5 displays our model estimation results from Equation (16) for both the two- and threeperiod models. Note that the estimated parameters do not provide a behavioral interpretation,
serving primarily for model assumption validation. As discussed in Section 5.3, the joint test ofparameters (λ1 , . . . , λ7 ) allows us to evaluate the model assumption represented in Equation (16).
Therefore, we implement a Wald test on the null hypothesis that all parameters equal zero. The
Wald statistics indicate that while the assumption does not apply to the two-period model, as shownin Columns (1)–(2), it is valid for the three-period model, as shown in Columns (3)–(4). Given thatthe two-period model exhibits less variation in adoption hjt than does the three-period model, it
is plausible that the two-period model is insufficient for extracting heterogeneous preference θjA
30

Table 5: Projection Estimates

Two Period

Three Period

(1)

(3)

(2)

-0.168 0.052
0.068(2.852) (0.314) (0.054)-0.154 0.075
0.248
λ2(2.692) (0.474) (0.211)
0.209 -0.082 0.063
λ3(3.237) (0.564) (0.052)-0.110λ4(0.230)-0.957λ5(0.210)-0.083λ6(0.220)0.742
λ7(0.275)0.748
0.242 27.212
Wald Statistics[0.861] [0.970] [0.000]
Local Market Controls
×
Observations
19636 19636 29454
λ1

(4)
0.017
(0.049)0.243(0.191)0.100(0.050)-0.046(0.208)-0.958(0.200)-0.097(0.199)0.824(0.267)25.434[0.000]×
29454

Notes: Standard errors are in parentheses. P values are in brackets. The table presents the parameter estimates from
Equation (16). In the two-period model, data from the first and last periods of the three-period dataset are utilized.
The Wald test assesses the joint significance of these estimated parameters.

from adoption history hjt . In the following analysis, our primary empirical approach employs thethree-period model.
Table 6 provides our empirical findings on the context-independent preference of βtA , interpreted as the average patient preference for AGs during period t. Column (2) shows our baseline
results. In Columns (1) and (2), our results show a consistently positive average AG preferenceβtA over all three periods, suggesting that patients prefer AGs over OGs because of the greater
premiums for AGs compared to those for OGs in terms of scientific efficacy, color, and additives.
While βtA varies across periods, the magnitude of the preference stays relatively steady, and patients consistently prefer AGs. Furthermore, from a pharmacy perspective, pharmacies dispensing
AGs stand to gain a greater generic share by taking the average positive AG preference among
patients. Consequently, pharmacies that dispense AGs likely capture a larger share of the genericmarket, benefiting from the patients’ overall positive preference for AGs.

31

Table 6: Average AG Preference
Three Period
β1A
β2A
β3A
χ2
Local Market Controls
Observations

(1)
0.080
(0.112)0.206(0.112)0.201(0.112)

(2)
(3)
(4)
0.124
0.157
0.130
(0.094) (0.159) (0.190)
0.255
0.349
0.335
(0.094) (0.160) (0.191)
0.266
0.348
0.354
(0.093) (0.157) (0.185)

(5)
(6)
0.159
0.233
(0.156) (0.088)
0.353
0.369
(0.157) (0.090)
0.362
0.435
(0.155) (0.106)

17.04

18.25
×
29454

16.42
×
29454

29454

17.12
×
30516

16.31
×
28098

19.65
×
30483

Notes: Standard errors are in parentheses. Columns (1) and (2) are our main results. Variations in data and model
specifications are seen in Columns (3)–(6). In Column (3), we include all pharmacies without excluding the bottom
5% according to size. Column (4) omits those pharmacies that fall within the bottom 10% in terms of size. The valuesof ε are adjusted in Column (5) to 0.001. Moreover, in Column (6), if pharmacies dispense only brand-name drugs,
then the adoption measure hjt is sourced from the previous year for each period.

Columns (3)–(6) investigate the robustness of our primary findings. In Column (3), we consider
all pharmacies, omitting the exclusion of the bottom 5% based on prescription size, whereas Column (4) excludes the bottom 10% of pharmacies. Column (5) presents the outcome when ε is set
to 0.001. Finally, in our primary analysis, we exclude pharmacies that dispense only brand-namedrugs. Column (6) addresses this sample selection by imputing the adoption hjt for pharmacies
that do not dispense generic drugs. We posit that if a pharmacy does not dispense generics in a
specific period but had prescribed either AGs or OGs in the previous period, then it would continuethat adoption in the current period. These analyses confirm that patients consistently and positively
prefer AGs to OGs across all three periods.
We can also interpret these results in terms of generic substitution. For each pharmacy adoptingOGs, we can calculate the counterfactual generic share if AGs are adopted instead of OGs, using
the average AG preference βtA 34 . Table 7 shows how much the generic shares would change if
pharmacies counterfactually adopted AGs instead of OGs in each adoption group, except for thosethat adopt AGs throughout all periods. This finding indicates a 1.00–1.56% increase in the generic34

For each adoption pattern group g, we calculate the change in the generic share when pharmacies adopt AGsd
A dO
cg = 1 PTg PJg Yjt −Yjt , where Tg and Jg are the number of periods in whichinstead of OGs as follows: ∆Y
t=1
j=1
d
Tg Jg
O
Yjt

pharmacies adopt OGs and the number of pharmacies in each adoption pattern group, respectively. See Appendix C
for the detailed calculation.

32

Table 7: Generic Substitution via Average AG Preference
Adoption Pattern
Generic Share Change (%)
Generic Share (%)
Fraction of Sample (%)
Number of Switch

AAA

OOO

AAO

AOO

OOA

OAA

AOA

OAO

52.98
0

1.16
89.02
34.52
0

1.56
89.67
1.75
1

1.34
91.16
2.36
1

1.04
85.83
3.74
1

1.00
81.47
4.26
1

1.21
98.65
0.15
2

1.10
86.36
0.20
2

Notes: This table reports the magnitude of generic substitution via estimated average AG preference βtA in Table 6,
Column (2). For each pharmacy adopting OGs, we can calculate the counterfactual generic share if it adopts AGs
instead of OGs and take the average in each adoption group. Since pharmacies categorized in the adoption pattern
AAA have already adopted AGs, we cannot calculate the degree of generic substitution. We report the actual average
generic share in each adoption group during the three periods. The fraction of samples is the same as that in Table 3.

share. As the table illustrates, the prevalence of generic drug adoption is already high in every
pharmacy, and substituting OGs with AGs could yield an appreciable increase in the generic share,
which should not be overlooked.

6.3

Recovered Context-Dependent Preference
Table 8: Recovered Context-Dependent Preference

Adoption Pattern

AAA

OOO

AAO

AOO

OOA

OAA

AOA

OAO

Context-dependent Preference: θ̂jA
Fraction of Sample (%)
Number of Switch

0.021 -0.062
52.98 34.52
0
0

0.152
1.75
1

-0.044 0.038
2.36
3.74
1
1

0.184
4.26
1

-0.902
0.15
2

0.181
0.20
2

Notes: This table reports the recovered context-dependent preference θ̂A . Estimates correspond to Column (2) in Table
6. The fraction of samples is the same as that in Table 3.

Table 8 reports the recovered context-dependent AG preference. Since we estimate the contextdependent preference θjA by the linear projection of the adoption history hjt , the recovered preference θ̂jA depends on the adoption patterns, as shown in Table 3. The results suggest that a substantial difference in AG preference exists across pharmacies. The one-standard-deviation change
in the recovered context-dependent AG preference θ̂jA is 0.073. Given that the estimated contextindependent patient preference βtA over three periods averages 0.215, as reported in Column (2) ofTable 6, the context-dependent preference θ̂jA represents 36.52% of the average preference βtA .

33

In Table 8, the recovered preference for pharmacies that adopt AGs in all three periods (AAA)is positive, whereas it is negative for pharmacies that adopt OGs throughout the same periods(OOO). The sign of θ̂jA indicates a pharmacy’s comparative advantage for dispensing generic drugs,
as explained in Section 4.3. While the majority of pharmacies have a comparative advantage forAGs, more than one-third of pharmacies have a comparative advantage for OGs. These estimation
results imply that although patients generally prefer AGs over OGs, the degree of this relative
preference varies among pharmacies, leading them to stock either AGs or OGs based on theirrespective comparative advantages.
However, the recovered context-dependent AG preference may reflect cost factors affecting thepharmacy’s inventory decisions beyond the patient’s context-dependent preference. In the previous
section, we assume that pharmacists adopt AGs to maximize profits, and Equation (5) shows thatA
) and (2) the inventory
the adoption decision hjt hinges on (1) patients’ brand preferences (i.e., βjt

cost difference between AGs and OGs (i.e., fjA − fjO ). Consequently, the recovered θ̂jA from thelinear projection in Equation (16) may conflate demand factors such as patient preferences with
supply factors such as adoption costs. To isolate these conflating demand and supply factors, we
regress θ̂jA on a nonlinear function of pharmacy-specific, time-invariant characteristics Fj , related
mainly to the cost of dispensing generics.
The residual from the regression is considered the “true demand-driven” patient preference,
having partialled out the cost factors related to pharmacy inventory decisions. We use this residualized or “partialled-out” context-dependent AG preference θ̃jA to validate the model’s assumptions.
In the empirical specification, we include the pharmacy’s (1) management type (small chain, large
chain, near hospital, or individual), (2) size, (3) concentration index, and (4) size of prescriptionissuing hospitals. Our main analysis employs a random forest for nonlinear functions. Even whenutilizing other methodologies, including lasso, Xgboost, and polynomial functions, as alternative
specifications, the magnitude of the residual remains consistent.
In Appendix E, we detail the estimation procedures and confirm the robustness of the results
across various specifications. In addition, we investigate which cost factors at pharmacies are associated with recovered context-dependent preferences θ̃jA . Figure E1 uses the feature importancemeasures from our baseline random forest to show which cost factors are related to θ̂jA 35 . The
35

We evaluate feature importance in the random forest model based on Gini importance, which measures the im-34

results highlight the substantial contributions of the pharmacy’s size and the characteristics of thehospital issuing prescriptions to the recovered context-dependent preferences.
Figure 4: Recovered Preference θ̃jA

Notes: This figure reports the estimated probability density of empirically estimated AG preference θ̃jA . To estimate
the AG preference θ̃jA , we regress the estimated θ̂jA on the nonlinear function of the pharmacy’s cost-related factors.

Figure 4 displays the distribution of θ̃jA , highlighting variations in patient AG preferences acrosspharmacies. Given that one standard deviation of θ̃jA is computed at 0.070, it accounts for approximately 34% of the time-averaged context-independent AG preference βtA . Remarkably, even after
accounting for factors potentially related to inventory costs, the relative importance of contextdependent AG preference to context-independent AG preference remains largely unchanged, dropping only slightly from 36.52% to 34.88%. This minor decrease underscores the robustness of thesubstantial role played by context-dependent AG preferences, despite the potential influence of
inventory-related factors. Furthermore, the shape of the distribution shown in the figure indicatesthat pharmacies with a comparative advantage for AGs and those with a comparative advantage
for OGs are evenly distributed, showing no significant skew toward one side of the comparativeadvantage. These estimation results suggest that despite accounting for cost factors related to inventory decisions, the variation in patients’ context-dependent preferences remains considerable
portance of each feature by the total decrease in Gini impurity that it brings about across all the trees in the forest.

35

across pharmacies.

6.4

Correlation of Brand Preferences

While we find significant heterogeneity in patients’ AG preferences, Table 9 also suggests that such
heterogeneity exists in patients’ perceived preferences for brand-name drugs. This table reports
positive values of ϕ, reflecting a positive correlation between preferences for brand-name drugs
and AGs. However, since the estimated ϕ significantly differs from ϕ = 1, patients’ preferences
for brand-name drugs and AGs are not perfectly identical, even though they are identical products
differing only in terms of their packaging. An inspection of Equation (9) identifies the determinants
behind this phenomenon. Given that ϕ > 1, (1) Corr(θjB , θjA ) is close to one, and/or (2) Var(θjB )is larger than Var(θjA ). Given that brand-name drugs and AGs are identical products, we would
expect Corr(θjB , θjA ) ≃ 1, implying that Var(θjB ) is larger than Var(θjA ), or that patients’ preference
for brand-name drugs is more widely distributed across pharmacies compared to their preference
for AGs.
Table 9: Relationship between Brand-Name and AG Preference
Three PeriodϕP-values for H0 : ϕ = 1
Local Market Controls
Observations

(1)
(2)
1.415
1.497
(0.241) (0.249)

(3)
(4)
(5)
1.467
1.385
1.505
(0.253) (0.234) (0.264)

(6)
1.674
(0.294)

0.000

0.000
×
30516

0.000
×
30483

29454

0.000
×
29454

0.000
×
28098

0.000
×
29454

Notes: Standard errors are in parentheses. All columns correspond to the specifications in Table 6. Columns (1) and(2) display the primary results. Variations in data and model specifications are seen in Columns (3)–(6). In Column(3), we include all pharmacies without excluding the bottom 5% by size. Column (4) omits those pharmacies that fallwithin the bottom 10% in terms of size. The value of ε is adjusted in Columns (5) to 0.001. Moreover, in Column (6),if pharmacies dispense only brand-name drugs, then the adoption measure hjt is sourced from the previous year foreach period.

Further analysis is needed to understand why the distribution of patient preferences differs
between brand-name drugs and AGs. However, one possible explanation may be the provisionof information by pharmacists. As discussed in Section 2, pharmacists in Japan are mandated36

to provide information about generic drugs when dispensing medications based on prescriptions.
Therefore, compared to brand-name drugs, patients may be more informed about the quality ofAGs, resulting in a relatively smaller variance in the perceived quality of AGs across pharmacies.
In the next section, to further corroborate the significant role of information in shaping patients’ perceived preferences, we investigate the impact of information provided by pharmacists onpatients’ preferences for generic drugs.

7

Role of Information

In the preceding section, we illustrate that patient preferences for AGs exhibit significant variation
across pharmacies, and this variation arises from differences in patients’ perceptions of quality
between AGs and OGs. However, the specific factors that account for patients’ heterogeneous AG
preferences remain undetermined. Given pharmacists’ primary responsibility for drug dispensationand information provision, we investigate how their role in conveying information affects patients’context-dependent generic preferences.
Table 10: Role of Information for Pharmacists
Three Period
(1)
(2)
(3)
(4)
-0.0228 -0.0191 -0.0169 -0.0139
Family Pharmacist: Presence(0.0035) (0.0045) (0.0033) (0.0042)-0.0040-0.0033
Family Pharmacist: Prescription Share(0.0023)(0.0023)
Observations

9818

9818

9818

9818

Notes: Standard errors are in parentheses. The table shows the relationship between a patient’s AG brand preference
and information provided by pharmacists. An estimate of intercept is omitted. In Columns (1)–(2), the dependent variables represent recovered AG preferences as shown in Table 8, whereas in Columns (3)–(4), the dependent variables
are depicted in Figure 4. We use three-period data and the patient AG preference derived from Column (2) in Table 6.
Family pharmacist presence is defined as a dummy variable representing the presence of family pharmacists at pharmacy j. Family pharmacist prescription share is defined as the dispensing share by family pharmacists at pharmacyj.

Table 10 illustrates the relationship between the recovered AG preference and the information provision by family pharmacists in pharmacy j. We utilize two indicators as proxies for the37pharmacist’s information-providing behavior—(1) a dummy variable representing the presence offamily pharmacists in the pharmacy and (2) the share of prescriptions processed by family pharmacists. In Columns (1)–(2), the dependent variables are recovered AG preference in Table 8,
while the dependent variables in Columns (3)–(4) are in Figure 4. Column (1) shows that patientsare less likely to present brand preference when they receive drugs in pharmacies where family
pharmacists exist. Given the advanced drug instructions that family pharmacists are equipped toprovide, these findings suggest that providing expert information changes patients’ perceived quality differences between AGs and OGs. Column (2) investigates to what extent family pharmacists’information provision is related to patient brand preference. The results show that the more family pharmacists dispense drugs, the less patients present brand preference, but the magnitude isrelatively small. From Columns (3)–(4), we observe a similar trend between AG preferences andinformation provision36 .
This result reveals a strong positive relationship between the provision of information by pharmacies and their comparative advantage over OGs. The decision to stock either an AG or an OG isaffected by both demand factors, such as patients’ generic preferences, and supply factors, such as
inventory costs, which may differ based on the pharmacy’s location and organizational structure.
However, pharmacies facing higher costs for introducing AGs may employ family pharmacists todeliver comprehensive information on generic drugs, thus offsetting the cost-based disadvantagesof providing AGs to patients. This strategic provision of information can facilitate a shift frombrand-name drugs to generics by reducing the patient’s context-dependent preference for AGs
compared to OGs, even when stocking OGs is more cost-effective for pharmacies.

8

Conclusions

This paper focuses on pharmacists who dispense AGs that are identical to the original brand-namedrugs and analyzes how consumers’ brand preferences change with professional experts. Ourmodel and empirical results indicate that patients have a heterogeneous brand preference for AGs,
36

In Appendix E, we examine the relationship between information provision by family pharmacists and contextdependent preferences θˆjA , similar to our investigation of cost factors in Section 6.3. Figure E2 reveals that therelative importance of information provision to context-dependent preferences is considerable, ranking as the third
most influential factor after pharmacy size and the characteristics of hospitals issuing prescriptions.

38

which varies among pharmacists. Moreover, we show that one of the factors behind this difference
is related to the provision of information by pharmacists, which implies that when pharmacists havefewer opportunities to provide detailed information about drugs to patients, patients are more likely
to respond to brand premiums. In contrast, patients are less likely to respond to brand premiums
and use OGs when pharmacists can provide detailed information. Consequently, our results suggestthat a consumer’s brand premium assessment is tethered to the granularity of information sharedwith experts.
There are several limitations to our analysis. First, our study is limited to antibiotics. While
antibiotics are typically prescribed for short-term ailments, the decision-making horizon for pharmacists may differ in medications addressing chronic conditions such as hypertension and allergies. Second, our dataset lacks detailed pharmacy cost information, preventing us from performing
counterfactual analyses of pharmacy adoption behavior. Finally, unlike Starc and Swanson (2021),
our model does not explicitly consider the negotiations between pharmacies and drug wholesalers.
Notably, pharmacy costs are intrinsically linked to bargaining power, implying that they may sway
pharmacists’ dispensing behavior.

39

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Ministry of Health, Labour and Welfare. 2023a. “Explanatory Materials for the Medical Fee Revision in the Fourth Year of Reiwa.” https://www.mhlw.go.jp/stf/
seisakunitsuite/bunya/0000196352_00008.html.
Ministry of Health, Labour and Welfare. 2023b. “Survey on the Use of Generic Drugs.”
https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/
iryou/kouhatu-iyaku/04.html.
Nishikawa, Takakiyo, Nobuo Sakata, Takehiro Sugiyama, and Nanako Tamiya. 2023. “Dispensing by Family Pharmacists as a Potential Factor in Pharmacist-initiated Prescription
Change: A Retrospective Observational Study.” Journal of Epidemiology 33 (12): 618–623.
Starc, Amanda, and Ashley Swanson. 2021. “Preferred Pharmacy Networks and Drug Costs.”
American Economic Journal: Economic Policy 13 (3): 406–446.
Suri, Tavneet. 2011. “Selection and Comparative Advantage in Technology Adoption.” Econometrica 79 (1): 159–209.
Tibshirani, Robert. 1996. “Regression shrinkage and selection via the lasso.” Journal of the Royal
Statistical Society. Series B 58 (1): 267–288.

42Appendix
A

Survey Results

We surveyed pharmacists working in Japanese dispensing pharmacies. This survey targeted 100
pharmacy managers responsible for drug procurement, who were randomly selected from 7,481
pharmacists registered in the healthcare consulting company MCI Co., Ltd., survey panel, all working in dispensing pharmacies across Japan. In this study, the inquiries posed exclusively pertain to
generic drugs within the realm of antibiotics. Furthermore, the identities of the pharmacists who
provided responses were anonymized to maintain confidentiality.
Table A1 presents the characteristics of the responding pharmacies and pharmacists. Pharmacies are categorized by pharmacy type according to a mutually exclusive classification by the
MHLW, influencing the basic technical fees for dispensing (Ministry of Health, Labour and Welfare, 2023a). A pharmacy is classified as near a hospital if 70% or more of its prescriptions are
hospital specific, regardless of whether it is a chain or independent pharmacy. The main revenue
source section indicates the primary income for dispensing generic antibiotics; notably, 76% citedsubsidies for generic drugs. Position details the job titles of respondents, all in charge of drug
procurement. Finally, qualification shows that a significant 84% of respondents hold a family
pharmacist qualification.
Table A2 details the factors influencing the selection of generic pharmaceutical manufacturers by pharmacies. Note that the pharmacists provided up to three responses to this query. In
Japan, numerous pharmaceutical companies produce and market identical generic drugs at different wholesale prices, and pharmacies purchase from one of them in practice. According to Table
A2, a stable supply and the trustworthiness of manufacturers are pivotal in selecting these manufacturers. Interestingly, only 13.85% of the pharmacists deem the wholesale price a significant
factor. These findings suggest that when introducing generic drugs encompassing both AGs andOGs, pharmacies tend to prioritize the stability of generic drug sales over wholesale pricing.
Table A3 shows the factors influencing the selection of AGs among generic pharmaceuticals.
In this query, 91 pharmacists with experience selling AG antibiotics responded to up to three
options. Table A3 indicates that while 20.32% of the pharmacists consider a stable supply to
43Table A1: Pharmacy and Pharmacist Characteristics
Categories

Number% of Total

58
36
35
1

58.00
36.00
35.00
1.00

Main Revenue Source Generic subsidy
Prescription margin

76
24

76.00
24.00

Position

Supervising pharmacist
Others

100
0

76.00
100.00Qualification

Family pharmacist
Non-Family pharmacist

84
16

84.00
16.00

100

100

Pharmacy Type

Near hospitals
Chain
Individual
Inside hospitals

Observations

Notes: The table shows the characteristics of the pharmacies and pharmacists in our survey. We target 100 supervising
pharmacists who are responsible for drug procurement. We classify pharmacy type based on the basic technical fees fordispensing (Ministry of Health, Labour and Welfare, 2023a). This table reports all the options we asked pharmacists
about in our survey.

be important, factors such as AGs’ identical nature to the original brand (28.38%) and patient
preference for AGs (26.62%) were deemed more significant. These results suggest that althoughpharmacists generally value a stable supply of generic drugs, they prioritize the identical natureand patient brand preference when introducing AGs. Furthermore, only 3.53% of the pharmacists
reported adopting AGs based on doctors’ directives, indicating the minimal influence of physician
intervention in this context.
Finally, Table A4 illustrates pharmacists’ actions when patients refuse to accept generic pharmaceuticals. Approximately 25% of pharmacists provide information about the efficacy and safetyof generic drugs. Note that while 46% of pharmacists prescribe the brand-name drug, this includes
those cases where patients continue to refuse generics even after receiving information. This highlights that pharmacists tend to alter patients’ preferences toward generic drugs by providing themwith relevant information.
The overall findings of these surveys indicate that (1) pharmacists dispense brand-name drugs,
OGs, and AGs, operating independently from physicians; (2) pharmacists derive financial benefits
from subsidies allocated for generic drugs; (3) pharmacists dispense AGs taking into account pa-

44

Table A2: Selection Factors for Generic Drugs
Options

Number% of Total

Why pharmacies choose
Stable supply
the generic manufacturer Reliability of the manufacturer
Chain store policy
Low procurement cost
Sales from wholesalers
Sales from the manufacturer
Physician’s requests
Others

81
42
23
32
22
20
6
5

31.51
23.86
17.16
13.85
11.05
7.22
5.17
2.59

Observations

231

100

Notes: This table shows the reasons why pharmacies select a specific generic manufacturer. Pharmacists can select up
to three options for this question. This table reports all the options we asked pharmacists about in our survey.

tient preferences; and (4) pharmacists influence patients’ preferences for generic drugs through theprovision of information.

45

Table A3: Selection Factors for AGs
Options

Number% of Total

AG Adoption Factors Equivalence to brandPatients prefer AG
Stable supply
Patients prefer brandPharmacy adopt AG in other drugs
Physician’s direction
Others

67
45
37
12
9
5
7

28.38
26.62
20.32
9.67
8.03
3.93
3.53

Observations

182

100

Notes: This table reports the reasons why pharmacists adopt AGs. Pharmacists can select up to three options for thisquestion. This table reports all the options we asked pharmacists about in our survey.

Table A4: Handling of Patients Refusing Generic Drugs

Handling Patients
Refusing Generics

Options

Number% of Total

Dispense brand drugs
Dispense AG
Provide information of generic drugs
Others

46
29
24
1

46.00
29.00
24.00
1.00

100

100

Observations

Notes: This table shows how pharmacists handle patients who refuse to be dispensed generic drugs. This table reports
all the options we asked pharmacists about in our survey.

46

B

Major Authorized Generics

Table B1 presents the launch dates and market shares of major AG in Japan, calculated using datafrom the JAST database. The fourth and fifth columns detail the market shares of AG one year
and three years post-launch, respectively. These data highlight that AG consistently achieves highmarket shares across various therapeutic categories. Furthermore, the sixth and seventh columns
illustrate the percentages of pharmacies that dispense both AG and OGs at one and three years following the introduction of AG. This reveals a trend where most pharmacies do not simultaneously
dispense both types of generics.
Table B1: Major Authorized Generics in Japan
Name
Valsartan
Levofloxacin
Clopidogrel
Dienogest
Olmesartan

Release Date

Therapeutic Class

2014/06
2014/122015/06
2017/06
2017/09

Hypertension
Antibiotic
Antiplatelet
Endometriosis
Hypertension

AG Share (%)
AG Share (%)
Pharmacy with AG and OG (%) Pharmacy with AG and OG (%)
1 year from release 3 years from release
1 year from release
3 year from release
23.08
37.57
50.63
73.21
65.74

23.46
38.08
45.48
71.62
60.43

0.88
2.96
4.76
4.96
10.08

0.05
0.36
0.38
1.02
0.69

Notes: This table represents the release dates, therapeutic classification, and market share after release for major AG
in Japan. AG share indicates the proportion of an AG to the prescribed generic pharmaceuticals, including OG. The
last two columns show the proportion of pharmacies that hold both AG and OG.

47

C

Generic Substitution via Average AG Preference

Given the average AG preference estimates in our three-period model in Table 6 column (2), we
conduct a counterfactual analysis to interpret the average AG preference in terms of generic substitution. To calculate the generic share under counterfactual AG adoption, we only consider periods t in which pharmacies originally adopted OG. To be precise, let Jg ∈ {JOOO , . . . , JOAO }be the number of pharmacies in the adoption group g (e.g., OOO, OAO, AAO, etc.), and Tg ∈{TOOO , . . . , TOAO } be the number of period in which pharmacies originally adopted OG. For instance, if a pharmacy’s adoption pattern is OAO, JOAO = 59 and TOAO = 2. Then, for each
pharmacy j in the group g during period t, we predict the actual and counterfactual generic sharesas follows:

B
A
A
cO
O
ln(Yc
jt ) − ln(1 − Yjt ) = α̂∆Pt − β̂t + β̂t × 0 − ϕ̂θ̂j − τˆj + ι̂Mjt + νjt ,(Actual)

B
A
A
cA
A
ln(Yc
jt ) − ln(1 − Yjt ) = α̂∆Pt − β̂t + β̂t × 1 − ϕ̂θ̂j − τˆj + ι̂Mjt + νjt .

(Counterfactual)

Note that the difference between the two equations is solely the average AG preference β̂tA . Then,
we report the average change in generic share in each adoption group g and period t given as,
Jg
cOA
1 X Ycjt − Yjt
c.
∆Ygt =
Jg j=1
YcO

(23)

jt

In Table 7, we report the average change in generic share in each adoption group g:∆Ybg =Tg Jg
cO
A
1 X X Yc
jt − Yjt.
Tg Jg t=1 j=1
YcO

(24)

jt

Table C1 shows the generic substitution across all adoption-type pharmacies in each period.
Note that we cannot calculate the counterfactual share for the periods where AG has already been
adopted, resulting in a blank. These results indicate that the magnitude of average AG preference
corresponds to 0.70% – 1.56% increase in generic share.

48

Table C1: Generic Substitution via Average AG Preference in Three Periods
Adoption Pattern

AAA

OOO

Generic Share Change via β1A (%)
0.70
Generic Share Change via β2A (%)
1.37
A
1.42
Generic Share Change via β3 (%)
Generic Share (%)
89.02
Fraction of Sample (%)
52.98 34.52
Number of Switch
0
0

AAO

AOO

OOA

OAA
1.00

1.56
89.67
1.75
1

0.70
1.32
1.37
1.37
91.16 85.83
2.36 3.74
1
1

AOA

OAO
0.72

1.21
81.47
4.26
1

98.65
0.15
2

1.47
86.36
0.20
2

Notes: This table reports the magnitude of generic substitution via estimated average AG preference βtA in Table 5
column (2). For each pharmacy adopting OG, we can calculate the counterfactual generic share if it adopts AG insteadof OG and take the average in each adoption group. We cannot calculate the generic substitution for pharmacies thathave already adopted AG. The fraction of samples is the same in Table 3.

49

D

Recovered Heterogeneous Preference

As discussed in section 5.3, we regress θ̂jA on a nonlinear function of pharmacy characteristicsf (Fj ) to estimate the patient AG preferences denoted as θ̃jA . Formally, we estimate the following
Equation,
θ̂jA = f (Fj ) + εj(25)

where εj is the error term. The primary analysis in figure 4 employs a fifth-degree polynomial of
the cost-associated variable Fj . Figure D1 shows the AG preference estimated by various nonlinear
functions. Panels (a) and (b) illustrate the AG preference estimations using fourth and sixth-degree
polynomials, respectively. Panel (c) presents the outcomes using Lasso (Tibshirani, 1996), panel(d) details the results from Xgboost (Chen and Guestrin, 2016), and panel (e) displays the findings
from the Random Forest (Breiman, 2001). For the Lasso, Xgboost, and Random Forest models, we
use up to fifth-order cross terms in cost variables and employ the hyperparameters in their default
configurations.

50

(a) 4th Polynomial(b) 5th Polynomial(c) 6th Polynomial(d) Xgboost(e) Lasso

Figure D1: The Recovered Patient’s Heterogeneous Preferences

51

E

Pharmacy Characteristics Importance

Notes: This table reports the importance estimated in our random forest. The dependent variables represent recovered
AG preferences as shown in Table 8. Pharmacy size is the number of prescriptions in each pharmacy. Hospital sizeis the number of beds in hospitals where prescription is issued. The Concentration Index is the Herfindahl-Hirschmanindex of prescriptions in each pharmacy. Large chain, Near Hospital, Small Chain, and Individual Chain are dummy
variables.

Figure E1: The Importance of Cost Factors in Random Forest

52

Notes: This table reports the importance estimated in our random forest. The dependent variables represent recovered
AG preferences as shown in Table 8. Pharmacy size is the number of prescriptions in each pharmacy. Hospital sizeis the number of beds in hospitals where prescription is issued. The Concentration Index is the Herfindahl-Hirschmanindex of prescriptions in each pharmacy. Information Provision is the prescription share by family pharmacists in each
pharmacy. Large chain, Near Hospital, Small Chain, and Individual Chain are dummy variables.

Figure E2: The Importance of Cost and Information Factors in Random Forest

53

F

Estimation in Three-Period Model

Following Suri (2011), we rearrange the Equation (14),yjt = α∆Pt − βtB + (βtA + φθj )hA
jt + θj − τj + νjt ,(26)

where θj = −ϕθjA and φ = − ϕ1 .
We utilize the linear projection of θj based on {hj1 , hj2 , hj1 hj2 , hj3 , hj1 hj3 , hj2 hj3 , hj1 hj2 hj3 }, amethod that aligns with the approach of Chamberlain (1984). The following equation provides thegeneralized linear projection:
θj = λ0 + λ1 hj1 + λ2 hj2 + λ3 hj1 hj2 + λ4 hj3 + λ5 hj1 hj3 + λ6 hj2 hj3 + λ7 hj1 hj2 hj3 + υj . (27)
To interpret β A as the mean authorized premium, we adopt the normalization such thatPj θj = 0in the subsequent analysis.
Substituting the Equation (16) into the Equation (14) for each time period yields

yj1 = (α∆P1 − β1B + λ0 ) + β1A + φλ0 + λ1 (1 + φ) hj1 + λ2 hj2 + λ4 hj3+ (φλ2 + λ3 (1 + φ)) hj1 hj2 + (φλ4 + λ5 (1 + φ)) hj1 hj3 + λ6 hj2 hj3+ (φλ6 + λ7 (1 + φ)) hj1 hj2 hj3 + (φυj + υj hj1 + τj + νjt )
yj2 = (α∆P2 − β2B + λ0 ) + λ1 hj1 + β2A + φλ0 + λ2 (1 + φ) hj2 + λ4 hj3+ (φλ1 + λ3 (1 + φ)) hj1 hj2 + λ5 hj1 hj3 + (φλ4 + λ6 (1 + φ)) hj2 hj3+ (φλ5 + λ7 (1 + φ)) hj1 hj2 hj3 + (φυj + υj hj1 + τj + νjt )
yj3 = (α∆P3 − β3B + λ0 ) + λ1 hj1 + λ2 hj2 + β3A + φλ0 + λ4 (1 + φ) hj3+ λ3 hj1 hj2 + (φλ1 + λ5 (1 + φ)) hj1 hj3 + (φλ2 + λ6 (1 + φ)) hj2 hj3+ (φλ3 + λ7 (1 + φ)) hj1 hj2 hj3 + (φυj + υj hj1 + τj + νjt )

54

(28)

We derive the following two reduced-form equations from these equations that we can estimate.
yj1 = δ1 + κ1 hj1 + κ2 hj2 + κ3 hj3 + κ4 hj1 hj2 + κ5 hj1 hj3 + κ6 hj2 hj3 + κ7 hj1 hj2 hj3 + ζj1
yj2 = δ2 + κ8 hj1 + κ9 hj2 + κ10 hj3 + κ11 hj1 hj2 + κ12 hj1 hj3 + κ13 hj2 hj3 + κ14 hj1 hj2 hj3 + ζj2
yj3 = δ3 + κ15 hj1 + κ16 hj2 + κ17 hj3 + κ18 hj1 hj2 + κ19 hj1 hj3 + κ20 hj2 hj3 + κ21 hj1 hj2 hj3 + ζj3 ,(29)
where δ1 = α∆P1 − β1B + λ0 , δ2 = α∆P2 − β2B + λ0 , δ3 = α∆P3 − β3B + λ0 and ζj1 , ζj2 , and ζj3
are composite error term in the estimation. The association between the reduced form parameters55

and the structural parameters is illustrated as follows:κ1 = β1A + φλ0 + λ1 (1 + φ)
κ2 = λ2κ3 = λ4κ4 = φλ2 + λ3 (1 + φ)
κ5 = φλ4 + λ5 (1 + φ)
κ6 = λ6κ7 = φλ6 + λ7 (1 + φ)
κ8 = λ1κ9 = β2A + φλ0 + λ2 (1 + φ)
κ10 = λ4κ11 = φλ1 + λ3 (1 + φ)(30)

κ12 = λ5κ13 = φλ4 + λ6 (1 + φ)
κ14 = φλ5 + λ7 (1 + φ)
κ15 = λ1κ16 = λ2κ17 = β3A + φλ0 + λ4 (1 + φ)
κ18 = λ3κ19 = φλ1 + λ5 (1 + φ)
κ20 = φλ2 + λ6 (1 + φ)
κ21 = φλ3 + λ7 (1 + φ)There are 21 reduced form parameters (κ1 , . . . , κ21 ) and 11 structural parameters (λ1 , . . . , λ7 , β1A , β2A , β3A , φ).
P
Considering the normalization
θj = 0, we can express λ0 in terms of λ1 , . . . , λ7 . Specifically,
λ0 can be represented as λ0 = −λ1 h̄1 − λ2 h̄2 − λ3 h̄1 h̄2 − λ4 h̄3 − λ5 h̄1 h̄3 − λ6 h̄2 h̄3 − λ7 h̄1 h̄2 h̄3 ,where h̄1 , h̄2 , and h̄3 represent the average AG adoption rate across pharmacies in each period.
56θOnce we estimate θj and φ, we can calculate θjA and ϕ as −ϕj and − φ1 , respectively. Therefore, we
obtain θ̂jA as
θ̂jA = φ̂(λ̂0 + λ̂1 hj1 + λ̂2 hj2 + λ̂3 hj1 hj2 + λ̂4 hj3 + λ̂5 hj1 hj3 + λ̂6 hj2 hj3 + λ̂7 hj1 hj2 hj3 )(31)

Now, we have the variance-covariance matrix for (φ̂, λ̂0 , . . . , λ̂7 ). Then, we calculate the variancecovariance matrix for (φ̂λ̂0 , . . . , φ̂λ̂7 ) by the delta method.

57