ESRI Discussion Paper Series No.396

Impact of Hospital Decision-making on Drug Markets:
The Case of Biosimilars

Kyogo Kanazawa
July 2024

Economic and Social Research InstituteCabinet Office
Tokyo, JapanThe views expressed in “ESRI Discussion Papers” are those of the authors and not those of
the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
(Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Impact of Hospital Decision-making on Drug Markets:
The Case of Biosimilars∗
Kyogo Kanazawa†
July 11, 2024

Abstract
In the healthcare industry, governments regulate prices for several reasons. To appropriately regulate prices for policy objectives, it is necessary to understand the responseof medical demand to price changes. This study analyzed the Japanese pharmaceuticalmarket for Filgrastim, an expensive biologic drug, and its generics (biosimilars) using
a demand model that explicitly incorporates the introduction of generics by hospitals,
using data from 184,954 hospitalized patients. We analyzed the impact of both original and generic drug prices on the introduction of generic drugs in hospitals using thehazard model as a first step and estimated a discrete choice model for each patient’s
drug choice as a second step; however, the choice set for each patient was restricted tothe drugs that were available at their hospital at that time. Then, using the results ofboth steps, we conducted a counterfactual simulation and showed that a 10% reductionin the price of generic drugs would increase its market share by 12.04% in the mostrecent year and that this spreading effect mostly comes from the hospitals’ decision to
introduce generics.
Keywords: Biologic; Biosimilar; Generic Drug; Hospital; Pharmaceutical; Japan.
JEL Codes: I11; K23; L65; O34.
∗

I am particularly grateful to my adviser Hidehiko Ichimura for his invaluable guidance and continuous
support. I also thank Professors Toshiaki Iizuka, Haruko Noguchi, Naoki Wakamori, and Yasutora Watanabe,
who were members of my dissertation review committee, and participants of the seminars at Keio University,
Otaru University of Commerce, JEA, Happy-Hour Seminar, Yokohama City University, AWEHE, Yokohama
National University, and ESRI for their constructive comments. The Diagnosis Procedure Combination(DPC) database used in this paper is provided by Hideo Yasunaga and Kiyohide Fushimi, and financially
supported by Grant-in-aid from the Ministry of Health, Labour, and Welfare, Japan (grant number: H30Policy-Designated-004). I also thank Hiroki Matsui and Yusuke Sasabuchi for their advice, particularly
regarding the DPC database.
†kanazawa-kyogo-kb@ynu.ac.jp, Faculty of International Social Sciences, Yokohama National University

1

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

1

Introduction

In recent years, the increase in medical costs due to the aging population and the sophistication of medical technology has become an issue in many developed countries. Therefore,
controlling medical costs while taking into consideration the welfare of patients, profits ofmedical institutions, and profits and development incentives of pharmaceutical companies
and medical device manufacturers is important. To address this problem, governments inmany countries including Japan, the focus of this study, regulate prices of medical practices
and pharmaceuticals. For governments to appropriately regulate prices for policy objectives,
it is first necessary to properly understand the response of demand to price changes.
In demand analysis of the pharmaceutical market, a common model is that a patient or
doctor chooses a drug from the drugs available in the market at that time. However, regardingprescriptions in hospitals, the board of directors of a hospital, for example, decides in advancethe drugs to purchase, and the prescriptions for patients are selected from among those drugs.
Therefore, using a demand model that explicitly incorporates the introduction of generics
by hospitals, this study analyzes the Japanese market for Filgrastim, an expensive biologic
drug mainly used for neutropenia in patients with cancer, and its generics (biosimilars, BS),
using data from 184,954 hospitalized patients in Japan.
First, we analyzed the impact of original and generic drug prices on the introductionof generic drugs in hospitals using a hazard model with each hospital as the data unit.
The results showed that a higher price for the original drug accelerated the introduction ofgenerics, whereas a higher price for generics decelerated the introduction of generics.
Second, we estimated a discrete choice model for each patient’s drug choice. The estimation method was based on the method of Dunn (2012), including its instrumental variables;
however, the choice set for each patient was restricted to the drugs that were available attheir hospital at that time. The results showed that drug prices did not have a significant
effect on patients’ drug choices.
Finally, using the results of the first and second models of the estimation, we conducted a
counterfactual simulation to see the effect of changes in the price of generics on their market
share. The results showed that a 10% reduction in the price of generic drugs increased
their market share by 12.04% in the most recent year. Additionally, we conducted several
simulations to decompose the factors of this result and showed that this spreading effectmostly comes from hospitals’ decisions to introduce generics.
This study contributes to existing literature in two ways. First, this study is based on
the discrete choice model of Berry (1994) and Berry et al. (1995), a standard method in theempirical industrial organization field, and its applications to pharmaceuticals (Cleanthous2

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"(2002); Iizuka (2007); Dunn (2012); Duso et al. (2014); Kaiser et al. (2014); Dubois andLasio (2018)). This study contributes to the literature by estimating the choice set of eachagent in part. While the hazard model in this field is mainly used to analyze the timing of
new drugs entering the market (Danzon et al. (2005); Cockburn et al. (2016)), we used it toanalyze the timing of the introduction of new generic drugs by hospitals.
Second, this study contributes to the literature on hospital behavior. Existing studies
on hospital-level behavior have analyzed price negotiations with suppliers (Grennan andSwanson (2020)), changes in behavior due to changes in revenue policies (Duggan (2000);
Dafny (2005)), and responses to economic recession (Dranove et al. (2017)). However, tothe best of our knowledge, no existing studies have analyzed drug adoption behavior at thehospital level, and this is the first study to do so.
The reminder of this paper is organized as follows. Section 2 describes the industry
background of the market. Section 3 describes the hospitalization data and summarizes thestatistics. Section 4 explains and conducts hazard model regressions of generics introduction
by hospitals. Section 5 describes and conducts the discrete drug choice model of patients.
Section 6 conducts simulations using the estimation results of the previous two sections andcompares the simulation results with the actual data and other simulation results using thepatients’ drug choice model only. Section 7 presents the conclusion of the study.

2

Industry Backgrounds

2.1

Focused DrugIn this study, we focus on the biologic drug Filgrastim, its generic drugs (biosimilars), andother competitive pharmaceuticals.
Filgrastim is a biologic drug primarily used in patients with neutropenia for chemotherapy. In Japan, Filgrastim was approved in 1991 and three types of Filgrastim biosimilars
were approved between 2013 and 2014. It was the first biologic drug to have more than one
biosimilar in Japan. Other competing drugs for neutropenia include Nartograstim, Lenograstim, Mirimostim, and Pegfilgrastim. In this study, 31 products of different sizes and dosage
forms were analyzed.
Biologic drugs are pharmaceuticals that “are derived from proteins and other substances
produced by living organisms, such as mammalian cells, viruses and bacteria” (International
Federation of Pharmaceutical Manufacturers & Associations (2012)). In contrast to mosttraditional, small-molecule drugs, biologic drugs require more sophisticated technologies for
manufacturing and quality control and therefore tend to be priced higher. In 2016, biologic

3

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

drugs occupied about a quarter of global pharmaceutical market sales, and it seems that this
proportion will continue to increase (Evaluate Pharma (2017)).
Biosimilars are generic drugs for biologic drugs. To show high similarity to an alreadyapproved biologic, a biosimilar product needs to meet more rigorous standards of safety
and efficacy, accompanied by more clinical tests, than a generic product of a small-moleculedrug. In Japan, only 11 biosimilars were approved as of 2017 although over 130 biologic
drugs existed. As patents for biologic drugs will expire in the future, it seems that theentries of biosimilars will increase.

2.2

Drug Price in Japan

In Japan, the retail prices of pharmaceuticals are open to everyone and deterministicallydetermined by the government based on their ex-retail price and ex-average wholesale price.
Therefore, it is possible to recover the average wholesale price from that formula. During
the sample period, price revisions were made every two years (2012, 2014, 2016, and 2018)
in April. As in Iizuka (2007) and Iizuka (2012), we can restore the unobserved averagewholesale prices from observed retail prices.
Specifically, if the retail price is revised in year t + 2, the next retail price of the drug j
is determined as follows
R
Pj,t+2= PjtW × (1 + rtax,t+2 ) + 0.02 × PjtR

where P R indicates the retail price (including tax), P W indicates the average wholesale price(excluding tax), and rtax indicates the consumption tax rate (5% until March 2014 and 8%
since April 2014). Therefore, the average wholesale price, including the tax on drug j in year
t, is given byR− 0.02 × PjtR ) × (1 + rtax,t )/(1 + rtax,t+2 ).
PjtW × (1 + rtax,t ) = (Pj,t+2(1)

Table 1 shows the retail prices of the focused drugs in the sample period. For example,
the retail price of Filgrastim (original) syringe 75µg was 10,055 JPY in FY2012-13 and 9,481
JPY in FY2014-15, thus, the average wholesale price including tax for FY2012-13 is obtainedby (9481 − 0.02 ∗ 10055) ∗ 1.05/1.08 = 9022.125 JPY. Figure 1 shows the graphs of pricetransitions of Filgrastim (original) syringe 75µg. The transition of the wholesale price issimilar to that of the retail price, and the retailers’ markups (retail price - wholesale price)
did not move significantly.1
1

Since a different system is applied, the average wholesale price of Pegfilgrastim cannot be calculated

4

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Table 1: Drug Prices
Drug \FY
Filgrastim (original) Syringe 75µg
Filgrastim (original) Syringe 150µg
Filgrastim (original) Syringe 300µg
Filgrastim (original) Injection 75µg
Filgrastim (original) Injection 150µg
Filgrastim (original) Injection 300µg
Filgrastim (F) 75µg
Filgrastim (F) 150µg
Filgrastim (F) 300µg
Filgrastim (Mochida) 75µg
Filgrastim (Mochida) 150µg
Filgrastim (Mochida) 300µg
Filgrastim (NK) 75µg
Filgrastim (NK) 150µg
Filgrastim (NK) 300µg
Filgrastim (Teva) 75µg
Filgrastim (Teva) 150µg
Filgrastim (Teva) 300µg
Filgrastim (Sandoz) 75µg
Filgrastim (Sandoz) 150µg
Filgrastim (Sandoz) 300µg
Lenograstim 50µg
Lenograstim 100µg
Lenograstim 250µg
Mirimostim 400
Mirimostim 800
Nartograstim 25µg
Nartograstim 50µg
Nartograstim 100µg
Nartograstim 250µg
Pegfilgrastim 3600µg

2010-11 2012-13 2014-15 2016-17 2018-19
10,981
10,055
9,481
8,477
7,536
21,811
20,04818,936
16,961
15,091
27,32524,92623,542
20,96918,58010,90810,055
9,481
8,635
7,845
22,27020,04818,90017,624
15,91027,027
24,781
23,475
21,117
19,168
6,882
6,143
5,071
4,069
10,871
9,987
8,238
6,497
17,17915,093
12,525
10,163
6,882
6,143
5,071
4,069
10,871
9,987
8,238
6,497
17,17915,093
12,525
10,163
6,882
6,143
5,071
4,069
10,871
9,987
8,238
6,497
17,17915,093
12,525
10,163
6,882
6,143
5,071
4,069
10,871
9,987
8,238
6,497
17,17915,093
12,525
10,163
6,143
3,971
2,657
9,987
8,238
6,497
15,093
9,757
5,526
6,155
5,685
5,430
4,749
4,079
11,33910,445
9,907
8,542
7,388
28,14425,881
24,56621,09818,197
13,37013,28726,09523,89723,293
20,434
18,154
4,732
4,659
4,421
3,937
3,531
9,564
8,712
8,225
7,252
6,432
19,064
17,362
16,256
14,396
13,04429,284
28,125
26,34823,80021,754- 106,660 106,660 106,660

Notes: The data source is the Ministry of Health, Labour, and Welfare, Japan. These prices are in JPY(100 JPY was roughly 1 USD at that time). “Filgrastim (original)” refers to the original filgrastim productproduced by Kyowa Hakko Kirin, and other “Filgrastim (...)” refers to the biosimilar products offilgrastim, with the names of the producing companies in parentheses.

5

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Figure 1: Drug Price and Wholesale Price: Filgrastim (original) Syringe 75µg

Notes: The data source is the Ministry of Health, Labour, and Welfare, Japan. These prices are in JPY(100 JPY was roughly 1 USD at that time). “Filgrastim (original)” refers to the original filgrastim productproduced by Kyowa Hakko Kirin. Wholesale prices were calculated using Equation (1).

However, we could not restore the wholesaler price for each biosimilar product due tothe different pricing systems for biosimilars. The first biosimilar to be approved is priced
at 70% of the original price, and subsequent biosimilars are priced at the same level as
the lowest-priced biosimilar at the time of approval. In addition, when the drug price isrevised, the new price calculated based on the weighted average of all biosimilars is applied
to all biosimilars. Therefore, the wholesale price for each biosimilar company could not becalculated, and only the average wholesale price of all biosimilars was calculated.
Next, we clarify the reasons for using wholesale prices in our analysis.

2.3

DPC/PDPS

We focused on inpatients at Japanese hospitals participating in the Diagnosis Procedure
Combination/Per-Diem Payment System (DPC/PDPS), a comprehensive evaluation systemof medical fees for acute inpatient care, in this study. Approximately 1,000 hospitals in Japan
participate in the DPC/PDPS, and all of them cover approximately 50% of all inpatients inusing this method. Therefore, the average wholesale price of Pegfilgrastim was calculated by extrapolating
the markup rate of Filgrastim (original) syringe 75µg, the most popular product of original Filgrastim andproduced by the same company Kyowa Hakko Kirin.

6

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

acute care beds in Japan.
Notably, the payment of medical fees for acute inpatient care in the DPC/PDPS is thesum of the comprehensive evaluation portion and piece-rate evaluation portions in participating hospitals. The comprehensive evaluation portion is calculated based on the numberof points per day set for each patient’s DPC and the number of days hospitalized. Hence, theactual treatment given to the patient is not directly related to the medical fee. As prescription drugs are included in the comprehensive evaluation portion, the retail price of drugs in
hospitals participating in this system does not affect either the hospital’s revenue or the patient’s cost; only the wholesale price affects the hospital’s cost. Therefore, in the subsequent
analyses, the model was formulated by assuming that only wholesale prices matter.

3

Data

In this study, we used the Diagnosis Procedure Combination (DPC) database, which consistsof inpatients at Japanese hospitals participating in the DPC/PDPS. The data consistedof hospitalization episode units and data such as hospital ID, patient ID, admission date,discharge date, patient’s sex, birthday, height, weight, disease codes (ICD-10), prescription
drugs, and prescription date. One disadvantage is that the patient ID is unique only tothe hospital in which the patient is admitted. If the same patient was admitted to thesame hospital multiple times during the data period, it was identified as the same person.
However, if the patient was admitted to a different hospital, they could not be identified as
the same person.
The main target of the drugs to be analyzed is neutropenia, but there exists a large
number of patients who use one or more of these drugs without a corresponding diseasecode (D70 in ICD-10). In addition, as this drug is used by patients with a wide variety of
diseases, and the demand for this drug can vary greatly depending on the disease, we limited
the sample of patients with representative diseases in terms of consumption as much as
possible. Therefore, we used the following procedure to extract patient and hospitalization
data for the analysis:
First, we obtained all hospitalization records in which the ICD-10 chapter of the maindisease was C00-D48 (neoplasms) or D50-D89 (diseases of the blood and blood-forming
organs and certain disorders involving the immune mechanism). Subsequently, we summed
the total amount of focused drugs prescribed for each ICD-10 code in the middle category of
the main disease in these hospitalization records. Consequently, the hospitalization recordsof patients with a main disease of C81-C96 (malignant neoplasms, stated or presumed to
be primary, of lymphoid, haematopoietic and related tissue) accounted for the majority of7

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"prescriptions (55.01%); therefore we chose these records as our analyzed sample.
Second, from the hospitalization data with the codes C81-C96 as the main disease, we
determined the sample hospitalization and reference date for each patient using the following
procedure. For patients who used the focused drug at least once, the first hospitalization inwhich the drug was used was selected as the sample hospitalization, and the date on which
the drug was first prescribed during hospitalization was used as the reference date. For
patients who had never used the focused drugs, the first hospitalization within the sample
period was considered as the sample hospitalization. The median number of days until thefirst prescription for each number of days of hospitalization was calculated from the data of
the patients for whom the focused drug was prescribed, and the median days corresponding
to the number of days of hospitalization for that hospitalization after the date of admission
became the reference date for that patient.
Based on the data of sample hospitalizations and reference dates, we excluded patients
whose hospitalization started before June 2010, missing or abnormal values for height, weight,
or birthday, and those who used multiple focused drugs at their reference date. To furtheralign with the biosimilar introduction analysis for each hospital, we limited the analysis
to patients whose reference date was between 4/1/2013 and 3/31/2018, after the year ofbiosimilar implementation, hospitals that had at least one sample patient in each year in thefiscal years 2013-2017, hospitals that had data in the Hospital Yearbook (Byoin Nenkan)
2014 (which we used for hospital characteristics), and hospitals that used at least one of the
focused drugs. The final sample included 184,954 patients and 762 hospitals.
Table 2 Panel (a) shows summary statistics for the patients in the sample. They are, on
average, relatively old, but they also include the young, the infants, and the babies. Males
are slightly more prevalent (56%). Approximately 68% of the sample patients received
chemotherapy. Only about 15% of patients have the ICD-10 code for neutropenia. Panel(b) compares the summary statistics of the patients who use at least one of the focused
drugs (“Any Drug”) and those who do not use any focused drugs (“Outside”). “Any Drug”
patients are slightly younger, and 93.9% of them undergo chemotherapy.
Table 3 shows the number of patients for each product for each year.2 Since the launchof biosimilars, the market share of original drugs of Filgrastim has been gradually decreasing and that of biosimilars has been increasing. Moreover, among biosimilars, there is aconsiderable difference in market share among companies.
Table 4 shows summary statistics for the hospitals in the sample. The biosimilar introduction rate varies slightly with their area but largely with their owner. The number of2

Patients who used more than one product in the data (34.3% of patients who used any of the focused
drugs) were counted as the first product used.

8

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Table 2: Summary statistics of the sample patients(a) AllAgeSex (0:male 1:female)
Body Surface Area (m2 )Chemotherapy
D70

Mean Min Max Median Std. Dev.
66.21
0 105
70
17.20
0.44
0
1
1.56 0.14 3.18
1.56
0.23
0.68
0
1
0.15
0
1
-

(b) Any drug or OutsideAge
Sex (0:male 1:female)
Body Surface Area (m2 )Chemotherapy
D70N

Any Drug
Mean Median
64.65
68
0.446
1.562
1.565
0.939
0.290
82,436

OutsideMean Median
67.47
71
0.440
1.556
1.561
0.469
0.043
102,518

Notes: Panel (a) shows the summary statistics of all 184,954 sample patients and Panel (b) shows that ofthe patients who used at least one of the focused drugs (“Any Drug”) and those who do not use any
focused drugs (“Outside”). Body surface area was calculated from the patients’ weights and heights using
0.725
0.425
the Du Bois Method (body surface area (m2 ) = height(cm)× weight(kg)× 0.007184). “D70” is anindicator that takes a value of 1 if at least one of the disease codes (occasion of admission, comorbidity on
admission, post-hospitalization onset disease, etc.) included D70, and 0 otherwise.

9

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Table 3: Number of Patients: Drug and Fiscal Year
Drug \FY
Filgrastim (original) S75µg
Filgrastim (original) S150µg
Filgrastim (original) S300µg
Filgrastim (original) I75µg
Filgrastim (original) I150µg
Filgrastim (original) I300µg
Filgrastim (F) 75µg
Filgrastim (F) 150µg
Filgrastim (F) 300µg
Filgrastim (Mochida) 75µg
Filgrastim (Mochida) 150µg
Filgrastim (Mochida) 300µg
Filgrastim (NK) 75µg
Filgrastim (NK) 150µg
Filgrastim (NK) 300µg
Filgrastim (Sandoz) 75µg
Filgrastim (Sandoz) 150µg
Filgrastim (Sandoz) 300µg
Filgrastim (Teva) 75µg
Filgrastim (Teva) 150µg
Filgrastim (Teva) 300µg
Lenograstim 50µg
Lenograstim 100µg
Lenograstim 200µg
Mirimostim 400
Mirimostim 800
Nartograstim 25µg
Nartograstim 50µg
Nartograstim 100µg
Nartograstim 250µg
Pegfilgrastim 3600µg
Outside
Total

2013
2014
2015
2016
2017
Total
6,697 5,819 3,691 2,449 1,508 20,164
851
710
475
325
226
2,587
816
884
658
591
561
3,510
208
214
113
74
40
649
51
34
24
37
23
169
123
111
82
73
48
437
46
546
651
929 1,021
3,193
1
46
70
108
87
312
13
54
86
72
86
311
351 1,510 2,616 4,086 4,279 12,842
22
132
226
376
320
1,076
27
79
111
144
176
537
115
617 1,131 1,325 1,529
4,717
8
57
116
160
158
499
17
43
51
40
56
207
0
0
121
220
218
559
0
1
12
12
5
30
0
0
15
21
4
40
16
20
22
71
72
201
1
0
1
7
2
11
0
0
0
0
0
0
293
224
188
211
149
1,065
4,556 4,685 3,769 3,383 2,393 18,786
941
947
873
820
732
4,313
0
115
100
80
89
39
423
4
8
7
8
4
31
357
283
250
218
187
1,295
21
10
9
7
8
55
3
0
4
1
0
8
0
261 1,829 1,174 1,145
4,409
18,620 20,221 20,215 21,255 22,207 102,518
34,273 37,616 37,496 38,286 37,283 184,954

Notes: Each patient is counted as their first prescription drug. “Filgrastim (original)” refers to the original
filgrastim product produced by Kyowa Hakko Kirin, and other “Filgrastim (...)” refers to the biosimilar
products of filgrastim, with the names of the producing companies in parentheses.

10

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Table 4: Summary Statistics of the sample hospitals(a) Area(b) Owner

Area

N

Introduce Biosimilar

Hokkaido
Tohoku
Kanto
Chubu
Kinki
Chugoku
Shikoku
Kyushu

43
61
165
131
141
56
31
134

32 (74%)46 (75%)117 (71%)
95 (73%)100 (71%)
39 (70%)22 (71%)95 (71%)

Total

762

546 (72%)

Owner

N

Introduce Biosimilar

National
87
Public Medical
297
Social Insurance 17
Medical Corp.
167
Other
82
National Univ.
48
Private Univ.
64

77 (89%)225 (76%)
10 (59%)90 (54%)50 (62%)43 (90%)51 (80%)Total

546 (72%)762(c) Number of Beds

Number of Beds

Mean

Max

MedianIntroduce Biosimilar 480.94
No Biosimilar
340.9780 1,505
60 1,275435
279

233.17 546
233.82 216

Total

60

400

238.91441.27

Min

1,505

Std. Dev.

N

762

Notes: All panels show summary statistics of the 762 sample hospitals.“Introduce Biosimilar” is defined as
hospitals that prescribed biosimilars (in our focused drugs) at least once during the sample period.

11

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

beds in the biosimilar introduction hospitals is larger than other hospitals on average.

4

Hospital Model of Biosimilar Introduction

For pharmaceutical products used in hospitals, such as the focused drugs examined in thisstudy, each hospital decides in advance which pharmaceuticals to use and manage. Figure
2 Panel (a) shows the share of the focused drug combinations used in FY2017 by hospitals
in our dataset. There are clear differences in the types of drugs used by different hospitals.
Panel (b) focuses on Filgrastim and shows that while 44.09% of hospitals prescribed onlybiosimilars in FY2017, 14.17% of them prescribed only brand name drugs, and 25.46% ofthem prescribed both, indicating that hospitals use different strategies.
Although the launch date of biosimilars is the same throughout Japan, the timing of
their introduction varies significantly between hospitals. Figure 3 shows the proportion of
hospitals that introduce biosimilars monthly. Although the adoption rate defined by the firstbiosimilar prescription (red line with circles) at the end of the sample period (March 2018)
was 72%, the timing of adoption differed for each hospital and biosimilars were gradually
spreading. If we define adoption by the first biosimilar prescription and final prescription of
the original drug in the sample period (blue line with triangles), this tendency remains.3
Therefore, before considering a model for patients’ choice of drugs, we first consider themodel of biosimilar adoption by hospitals. We construct a simple theoretical model that
describes biosimilar adoption in Section 4.1 and then the empirical model in Section 4.2.
Section 4.3 presents the estimation results.

4.1

Theoretical Model

When a biosimilar enters the market, each hospital decides whether to use it, consideringfactors such as its efficacy, drug price, wholesale price, and administrative costs. Here weconstruct a simple theoretical hospital model that describes the biosimilar adoption in our
sample. Hospitals choose whether to prepare a original or biosimilar drug based on theirprofits and patient benefits. For simplicity, we assume that the number of patients in eachhospital is fixed and normalized to one. For each patient, she and/or her doctor choose
whether to use the pharmaceutical prepared by the hospital (original or biosimilar) or nothing(cost 0). For the institutional reasons explained in Section 3, the sale for each patient is3
Of the 546 hospitals that prescribed biosimilars at least once, 397 (72.71%) hospitals also prescribedbiosimilars in the last month of the data period (March 2018), and 487 (89.19%) hospitals had their last prescription in the data period after December 2017, suggesting that the majority continued to adopt biosimilars
during the data period.

12

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Figure 2: Share of drug combinations used in FY2017 by hospital(a) All Drugs(b) Filgrastim
Notes: These graphs show the share of hospitals categorized by drugs prescribed in FY2017. The use of eachdrug in each hospital was judged based on whether the drug was prescribed at least once in that hospital in
FY2017.

13

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Figure 3: Proportion of hospitals introducing biosimilars in each month

Notes: The red line (circles) shows the proportion of hospitals introducing biosimilars where theintroduction of biosimilars was defined as the first biosimilar prescription in that hospital. The blue line
(triangles) shows the proportion where the introduction was defined by the first biosimilar prescription andfinal prescription of the original drug (in the sample period) in that hospital. We omitted the blue line in2018 because it rapidly became similar to the red line by definition.

14

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

fixed (total sales are fixed), and only the wholesale prices of the drugs are considered as theircosts. Then the revenue function of the hospital that chooses drug j (∈ {o, b}, corresponding
to “original” and “biosimilar,” respectively) is
πj = R − sj (pj )pj + EUj (pj ) − Cj(j = o, b)(2)where R is the total sales (fixed), pj is the wholesale price of drug j, sj (pj ) is the share ofpatients who use drug j (and 1 − sj (pj ) patients use nothing), EUj (pj ) is ex-ante averageutility of patients if the hospital chose drug j the hospital takes into account, and Cj is theadministrative costs when the hospital prepares drug j.
The hospital chooses drug b if the revenue from that is larger than that from a counterpart, that is, if πb ≥ πo ⇔ πb − πo ≥ 0. To consider the relationship between prices andbiosimilar introduction, we differentiate πb − πo with respect to po and pb and have
∂EUo (po )∂so (po )∂(πb − πo )=−+ so (po ) +
po
∂po
∂po
∂po
∂(πb − πo )∂EUb (pb )∂sb (pb )=− sb (pb ) −
pb .
∂pb
∂pb
∂pb

(3)
(4)

The first term in Equation (3) indicates the decrease in the patients’ average utility, the second term indicates the increased cost due to the price change, and the third term represents
the decreasing cost due to the decreasing share of patients who use drug a. If the third term
effect is dominated by the first and second term effects, ∂(πb − πo )/∂po > 0 holds and thenthe increase of po increases the biosimilar introduction and vice versa. The interpretationof Equation (4) is the same and the only difference is their sign. If the third term effectis dominated by the first and second term effects, ∂(πb − πo )/∂pb < 0 holds and then theincrease of pb decreases the biosimilar introduction and vice versa.4
In the simple setting, we consider each hospital decides which drug will they use ineach period given the wholesale prices. Adding hospital-specific switching costs to thissimple model and considering the hospital-specific time needed to switch can explain thegradual spread of biosimilars (Figure 3). Other explanations include the gradual reductionof uncertainty about the efficacy and risk of biosimilars. However, the important point isthat, in all cases, wholesale prices are factors that can affect hospital decision-making, andthe decisions are made each period. To incorporate these features, we estimate the hazard
4

We can easily extend this model to allow hospitals to prepare for both original drugs and biosimilars.
In that case, Equation (4) remains very similar and almost the same proposition holds. Contrastingly,
Equation (3) becomes slightly more complicated because the change in po also affects the share of each drug
and patients’ average utility in the revenue function, and these are not erased when we differentiate therevenue by po .

15

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"model and include wholesale prices as explanatory variables to analyze hospitals’ biosimilar
introduction behavior.

4.2

Estimation Model

We estimated the effect of the drug prices of original products and biosimilars on the timingof biosimilar introduction in each hospital using the time-varying Weibull hazard model. We
represent the probability that the number of days until the introduction of a biosimilar isgreater than t as a survival function S(t) = Pr(T > t). We defineS(th ) = exp(−λ(th ) · tph )(5)λ(th ) = exp(α1 poriginal,th + α2 pbiosim,th + βxh )(6)where h indicates hospital, th indicates the number of days from biosimilar launch (May31, 2013) to biosimilar adoption by hospital h, poriginal,th indicates the wholesale price of theoriginal drug, pbiosim,th indicates that of biosimilars, xh is a characteristic vector of hospitalh, and α, β, and p are parameters which will be estimated (p is a “shape parameter” of thishazard model). The estimated values are parameters that maximize the likelihood calculated
from S(th ). This model can be transformed into an easily interpretable form as follows:log(th ) = α1 poriginal,t + α2 pbiosim,t + βxh + h(7)where h is an independent and identically distributed shock following an extreme distribution. This equation indicates that positive (negative) coefficients of price indicate thatthe higher price delays (accelerates) the biosimilar introduction, and the expected time tointroduction becomes longer (shorter) on average. Specifically, we use the wholesale price ofFilgrastim (original) Syringe 75µg (the most prevalent original drug product) as a poriginal,thand that of Filgrastim (Mochida) 75µg (the most prevalent biosimilar product) as a pbiosim,t .
The characteristics of the hospital we used are xh = (areah , ownerh , bedh ) where areah
is a region dummy (which divides Japan into eight regions), ownerh is a owner dummy(seven categories basically defined by the Ministry of Health, Labour, and Welfare, Japan),and bedh is the number of beds. The sample included 762 hospitals for which sample patients were present every year during the sample period. To determine the average effect per
patient, each hospital was weighted by the total number of sample patients in the sample
period.
One concern is that the wholesale prices (poriginal,th , pbiosim,th ) used in this analysis are
the national average calculated from the Equation (1) but the actual wholesale prices may
16

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Table 5: Estimation Results: Weibull Hazard Model of Biosimilar Introduction
(1)
All

(2)
Larger Hospitals

poriginal,t-0.0042644∗(0.0021912)-0.0043306∗(0.0022993)

pbiosim,t0.0035618∗(0.0019861)

0.0036505∗(0.0020872)# of Beds-0.0001282(0.0002488)

0.0003747(0.0002818)

ln(p) (Shape Parameter)

0.1894726∗∗(0.0847713)

0.2038324∗∗(0.0919238)

Region Dummies
Owner Dummies
Nlog-pseudo likelihood

XX
762-271565.26

X
X
381-245139.97

Notes: The unit of observation is the hospital. poriginal,t and pbiosim,t are the wholesale prices of Filgrastim(original) Syringe 75µg and Filgrastim (Mochida) 75µg calculated from Equation (1). Each observation was
weighted by the total number of sample patients at that hospital in the sample period. The robust standard
errors are reported in parentheses. *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

differ from hospital to hospital. For example, a large hospital may purchase a larger volumeof pharmaceuticals and thus receive a discount. However, because the model controls for
hospital characteristics (xh = (areah , ownerh , bedh )), this does not cause a serious estimation problem as long as the actual wholesale prices for each hospital can be sufficiently
explained by these variables.

4.3

Results

Column (1) of Table 5 presents the Weibull hazard regression results. These results are
consistent with the implication that the higher the wholesale price of the original productis, the faster the introduction of the biosimilar is, and the higher the wholesale price of thebiosimilar is, the slower its introduction is.
One concern is that the timing of biosimilar introduction may not have been correctly
measured for hospitals with few patients because the timing of introduction was definedas the first time some biosimilar products were prescribed to a patient in that hospital.

17

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Therefore, we focused on larger hospitals with a median number of sample patients or moreand conducted the same analysis. These hospitals accounted for more than 90% of all sample
patients. Column (2) of Table 5 shows the results using only larger hospitals; these results
are very similar to those obtained using all hospitals.
Based on these results, we can simulate the timing of biosimilar introduction for eachhospital in the counterfactual scenarios when the prices of the original and biosimilar products are changed. By combining this with the patient-level drug choice model to be estimatednext, we can also calculate biosimilar usage rates and market shares for the counterfactual
cases.

5

Patient Model of Drug Choice

5.1

Model

In this section, we consider a pharmaceutical choice model for sample patients. As this choice
is affected (or may be determined in many cases) by doctors in the hospital, this model
includes not only patient’ characteristics but also variables related to doctors and hospitals,
such as the wholesale prices of drugs (Section 3). The majority (65.70%) of patients who
used focused drugs used only one product, therefore, we considered a cross-sectional static
discrete choice model, where we defined the first drug used by a patient as their choice.
Patient i on the reference date s in FY t in hospital h selects drug (or outside option)
j from their choice set Jh(i),s that maximizes their utility uijt
max uijt = (α0 + α1 zi )pjt + ξj + β1 zi xj + ω0 ln(monthjs + 1) + ijtj∈Jh(i),s(8)where pjt indicates the wholesale price, xj = (drugj , genericj , moleculej ) indicates pharmaceutical characteristics where drugj is a drug (not outside option) dummy, genericj is a
generic dummy, and moleculej is a molecule dummy vector, ξj is an unobservable drug
characteristic (scalar), zi = (z1i , z2i ) are observable patient characteristics where z1i includes
age, age2 , sex, body surface area, a chemotherapy dummy, and z2i includes a neutropenia
code (D70) dummy, monthjs indicates months after launch, ijt is an independent and identically distributed shock following extreme distribution, and (α0 , α1 , β1 , γ0 , γ1 , ω0 ) ≡ θD areparameters which will be estimated.
The choice set Jh(i),s is the set of drugs available at hospital h(i) for patient i on reference
date s (including the outside option) and is constructed as follows: For each pair of hospital
h-drug j, we used all available data (not limited to the sample patients) to determine whether

18

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

the drug was prescribed, and the first and last prescription dates if it was prescribed at least
once. If the patient i’s hospital h(i) had prescribed drug j at least once and reference dates was within the range of its first and last prescription dates, then j ∈ Jh(i),s , otherwise,j∈/ Jh(i),s . The 689 patients for whom Jh(i),s included only an outside option were excluded
from the estimation.
Due to computational difficulties, we imposed several assumptions on the parametersand estimated the model
max uijt =δjt + α1 z1i pjt + β11 zi drugj + β12 z1i genericj + β13 z2i moleculej + ω0 ln(monthjs + 1) + ijtj∈Jh(i),s(9)where δjt is a mean utility of drug j in FY t:
δjt ≡ α0 pjt + ξj .

5.2

(10)

Estimation Method

Our estimation method is based on Dunn (2012)’s method. At the first stage, we estimatedthe following equation using the maximum likelihood method, where α1 , β1 , ω0 , and δjt wereidentified.
exp(δjt + α1 z1i pjt + β11 zi drugj + β12 z1i genericj + β13 z2i moleculej )k∈Jhs exp(δkt + α1 z1i pkt + β11 zi drugk + β12 z1i generick + β13 z2i moleculek ))Pr(j|δ, α, β) = P
it(11)
In the second stage, δˆjt estimated at the first stage was regressed on the wholesale price andeach product dummy to obtain the remaining parameter α0 .
δˆjt = α0 pjt +JX

ξj 0 1{j = j 0 } + ejt(12)

j 0 =1In this regression, the wholesale price pjt may have been correlated with the error term
ejt and the ordinary least squares estimates may be biased. This endogeneity problem
could arise if, for example, wholesale prices are determined through negotiations between
hospitals and pharmaceutical companies and the unobserved time-varying quality of the
product affects their bargaining power. To address this problem, we used Dunn (2012)’s
instrumental variable approach based on Gaynor and Vogt (2003).
The instruments we used are demand and markup predicted from the parameters es-

19

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

timated in the first stage but calculated with α and e set to zero. First, we defined thedemand as
Djt ≡

I
X
i=1

Pr(j|δ, α, β)

(13)

it

and responsiveness to prices asI∂Djt X ∂ Prit (j|δ, α, β)
≡
.
∂pjt
∂p
jt
i=1

(14)

Based on these definitions, we constructed the demand instrument
I
Djt(j|δ(α = 0, e = 0), α = 0, β)(15)and markup instrument
I(j|δ(α = 0, e = 0), α = 0, β)
Djt
I (j|δ(α=0,e=0),α=0,β)
∂Djt
∂pjt

.

(16)

As the generic market may have different features compared to the original drug market, weconstructed additional instruments by interacting the two instruments above with a genericdummy. Finally, we used four instrumental variables.
Why are these instrumental variables valid? In the standard firm profit-maximizing
model, the demand and markup of a product affect its pricing and satisfy the condition for
instrumental variables correlated with price. However, demand and markups do not satisfy
the exclusion restriction because they depend on the error term ejt directly and through pricepjt . Therefore, by substituting α = e = 0 and removing the part of the demand and markup
that depends on the price pjt or the error term ejt , they become valid instrumental variables
that satisfy the exclusion restriction. Intuitively, these instruments exploit the structure thatthe individual demographics zi of the patient are correlated with price pjt through demand
and markup but are not correlated with the error term ejt . Computationally, δ(α = 0, e = 0)
is calculated as the average of δˆjt estimated in the first stage for product j. This is because
if we impose α = 0 and e = 0 in the second stage Equation (12); only product dummies thatare independent of year t remain.
Another endogenous concern is the bias that comes from patients’ choice of hospital.
For example, if patients who prefer original drugs choose to be admitted to hospitals thatprescribe original drugs, the δjt of original drugs are overestimated because the share of20

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

original drugs in these hospitals is higher than average. However, since the drugs understudy are usually used to reduce the side effects of cancer treatment, it is unlikely thatpatients choose hospitals with these drugs in mind; thus, the bias is considered to be small.

5.3

Results

Table 6 presents the results of the first stage of the maximum likelihood estimation. Many
coefficients have statistically significant estimates, some of which are reasonable. For example, the positive coefficient for Drug×Pchemo indicates that patients using chemotherapy
are more likely to use one of the drugs, and a positive coefficient for Drug×D70 indicatesthat patients diagnosed with neutropenia are also more likely to use one of the drugs.
Table 7 presents the first-step regression results of the second stage, which regresses
the wholesale price on the instruments. The demand instrument and its interaction termwith the generic dummy variable were strictly significant. Table 8 presents the results ofthe second-stage regression. In all the cases, the price effect was not significant. This may
reflect the fact that given hospital-prepared drugs by the choice set Jh(i),s , wholesale prices
are no longer considered when the patient or doctor makes a choice.

6

Simulation

To what extent does a drug price change in original products and biosimilars promote thediffusion of biosimilars? This is a key question to consider in pharmaceutical price-setting
policies aimed at promoting the use of biosimilars and reducing medical costs. In thissection, we conduct simulations to answer this question quantitatively by combining the
hospital model of biosimilar introduction in Section 4 and the patient model of drug choicein Section 5 (“the hospital-set model”). In addition, we estimate and simulate a model that
does not use the hospital model but only the patient model and does not have a productchoice set for each hospital (common to all hospitals)(“the all-set model”), and compare theresults with those of the hospital-set model. First, we compared the simulated biosimilar
shares at the actual price to the actual shares and then calculated the simulated share when
the price was changed hypothetically as a counterfactual scenario.

6.1

Simulation Procedure

The simulation procedures for the hospital-set model are as follows: First, based on thehazard model estimation results described in Section 4, we calculated the predicted survival
curve for biosimilar introduction for each hospital at the product prices specified in the21

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Table 6: First Stage Multinomial Logistic Regression Results
Price×Age

0.000000168∗∗(6.70e-08)

Price×Age2-4.29e-10(5.93e-10)

Price×Sex

0.00000140∗∗∗(0.000000480)

Price×Bsa

0.00000962∗∗∗(0.00000126)

Price×Pchemo

0.00000666∗∗∗(0.000000947)

Drug×Age

0.0430∗∗∗(0.00219)

Drug×Age2-0.000487∗∗∗(0.0000203)

Drug×Sex-0.0752∗∗∗(0.0176)

Drug×Bsa-0.805∗∗∗(0.0452)

Drug×Pchemo

2.540∗∗∗(0.0236)

Drug×D70

1.827∗∗∗(0.0385)

Filgrastim×D70

0.209∗∗∗(0.0354)

Nartograstim×D70

0.987∗∗∗(0.0661)

Lenograstim×D70

0.275∗∗∗(0.0370)

Generic×Age

0.0191∗∗∗(0.00306)

Generic×Age2-0.0000517∗(0.0000275)

Generic×Sex

0.0211(0.0230)

Generic×Bsa

0.224∗∗∗(0.0608)

Generic×Pchemo

0.213∗∗∗(0.0352)ln(month + 1)

0.0577(0.0499)
X
184,265

product×year dummies
N

Notes: “Price” indicates the wholesale price calculated using Equation (1). Body surface area was calculated from the patients’
weight and height using the Du Bois Method (body surface area (m2 ) = height(cm)0.725 × weight(kg)0.425 × 0.007184). “D70”is an indicator that takes the value of 1 if at least one of the disease codes (occasion of admission, comorbidity on admission,or post hospitalization onset disease, etc.) included D70, and 0 otherwise. The robust standard errors are reported inparentheses. *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

22

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Table 7: Second Stage Instrumental Variable Estimation Results: first step
(2)
price
0.683∗∗∗(0.178)(3)
price0.498∗∗(0.250)-1.514∗∗∗(0.268)-1.118∗∗∗(0.295)Markup IV--0.463(0.449)Markup IV × Generic-0.813∗(0.460)X
134
0.993
F(4, 101) = 18.18
0.0000Demand IV

Demand IV× Genericproduct dummiesNadj. R-squared
F testProb > F

X
134
0.993
F(2, 103) = 15.91
0.0000Notes: We use only those product j–year t pairs for which δjt was precise estimated where the z-values are
greater than 2 in the first stage in Table 6. “Price” indicates the wholesale price calculated from Equation(1). “Demand IV” is defined by Equation (15) and “Markup IV” is defined by Equation (16). The standard
errors are reported in parentheses. *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

Table 8: Second Stage Instrumental Variable Estimation Results
(1)

price

product dummiesNadj. R-squared(2)(3)
product×year dummies
OLS
IV(Demand) IV(Demand, Markup)
0.0000269
0.00000456-0.0000228(0.0000364) (0.0000581)(0.0000694)
X
134
0.888

X
134
0.887

X
134
0.885

Notes: We used only those product j–year t pairs for which δjt was precisely estimated where the z-values
were greater than 2 in the first stage in Table 6. “Price” indicates the wholesale price calculated using
Equation (1). Column (1) shows the results of ordinary least squares regression. Column (2) shows theresults of the instrumental variable regression, which uses “Demand IV” defined by Equation (15) and theinteraction of “Demand IV” and a generic dummy as instruments. Column (3) shows the results of the
instrumental variable regression, which also uses “Markup IV” defined by Equation (16) and theinteraction of “Markup IV” and a generic dummy as instruments, in addition to the instruments in column(2). The standard errors are reported in parentheses. *, **, and *** denote 10%, 5%, and 1% significancelevels, respectively.

23

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

scenario. Second, for each patient, we obtained the predicted probability of biosimilar introduction to their hospital on their reference date. Third, using this probability as a weight,
each patient was split into pre- and post-biosimilar-introduction observations. For example,
if the predicted biosimilar-introduction probability was 0.3, the weight for a pre-biosimilar
observation was 0.7 and that for a post-biosimilar observation was 0.3. Fourth, a counterfactual pharmaceutical choice set was constructed for each observation. For pre-biosimilar
observations, we excluded all biosimilars from the (original) choice set of the hospital on
the reference date. For post-biosimilar observations, we added to the (original) choice seton the reference date all biosimilars that the hospital had prescribed at least once in theoriginal data and that had already been launched at the reference date. For hospitals that
had not prescribed any biosimilars in the original data, we added the biosimilar productwith the largest market share, Filgrastim (Mochida) 75µg, to the choice set. Fifth, basedon the patient model in Section 5, we calculated the choice probability for each product foreach observation using the product price specified in the scenario, the choice set modified in
the fourth step, and the estimated parameters of the patient model. Finally, the calculated
choice probabilities were weighted and summed to obtain the number of patients using eachproduct in each year.
To simulate the all-set model, we calculated the choice probability of each productfor each patient based on the product price specified in the scenario and the estimatedparameters of the patient model with the choice set that included all products available
at the reference date and was common to all hospitals. The choice probability was thensummed to obtain the number of patients using each product in each year.

6.2

Actual Scenario

Here, we simulate the actual scenario, that is, the product prices are set to the actual prices,and the simulated results are compared with the actual number of patients for each year.
Figure 4 shows the simulation results of the hospital-set model, simulation results of
the all-set model, and actual share of the sample data. In almost all years, the hospital-set
model predicted values closer to the actual share than the all-set model. Particularly, in
FY2016 and FY2017, the hospital-set model was closer to the actual share than the single
model, which overpredicted the original drug share and underpredicted the biosimilar share.

6.3

Hypothetical Scenario

We simulated a counterfactual scenario if the wholesale price of biosimilars was 10% lower(the prices of other drugs would remain the same) to consider the extent to which lowering
24

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Figure 4: Simulated Share at Actual Prices(a) FY2013(b) FY2014(c) FY2015(d) FY2016(e) FY2017
Note: These graphs show the actual and simulated numbers of patients who used original drugs and biosimilars and those who did not use any drugs in each fiscal year. The bars “actual” are the actual number ofpatients. The bars “hospital-set” indicate the simulation results using the estimation results of Table 5 (1),6, and 8 (3) with actual prices. The bars “all-set” indicate the simulation results using the estimation resultsof Table A1 and A2 (3) with actual prices. The detailed simulation procedure is described in Section 6.1.

25

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

the price of biosimilars could promote their widespread use.
Figure 5 shows the simulation results of the hospital-set model, simulation results of
the all-set model, and actual shares of the sample data. The hospital-set model showed astronger penetration effect by lowering the price of biosimilars compared to the all-set model,
with the number of patients using biosimilars increasing by 12.04% in FY2017 compared tothe actual value. Particularly, in FY2016 and FY2017, the hospital-set model yielded morereasonable results than the all-set model, which yielded the non-intuitive result that the shareof biosimilars was smaller than the actual value when wholesale prices decreased. Therefore,a hospital model for biosimilar introduction is important for considering and simulating
pharmaceutical price changes in this market.
To confirm whether hospitals or patients drive these results, we ran the following four
hypothetical scenarios and compared the results: (1) actual prices, (2) wholesale prices ofbiosimilars (BS) in the discrete choice model were 10% lower (the biosimilar prices in thehospital model would remain the same), and (3) wholesale prices of biosimilars in the hospitalmodel were 10% lower (the biosimilar prices in the discrete choice model would remain thesame); and (4) wholesale prices of biosimilars in both models were 10% lower. Figure 6
presents the simulation results for the four scenarios. Panel (e) shows that the share ofgeneric users increased slightly when only the price in the discrete choice model was changed(“BS 10% lower for patients”), but when only the price in the hospital model was changed(“BS 10% lower for hospitals”), the increase in generic users and decrease in original users
were quite similar to that when the biosimilar price in both models was changed (“BS 10%
lower”). The other panels exhibit similar results. Therefore, the main factor responsible forthe increase in the number of generics users when the generic price decreases is the hospital’s
decision to introduce generics. This suggests that if the government wants to promote theuse of generics and reduce medical costs, a policy targeting hospitals works better than one
targeting patients or doctors.

7

Conclusions

In the analysis of the pharmaceutical market, it is generally assumed that a patient or
physician chooses a drug from all drugs available in the market at that time. However, in
hospitals, the board of directors, for example, decides in advance which drugs to purchase,and the patient is prescribed those drugs. In this study, we proposed a model for drug
demand that explicitly incorporates the introduction of generic drugs by hospitals. Using
this model, we analyzed the market for the biologic drug Filgrastim and its generic in Japan.
The simulation results showed that if the price of a generic drug were to decrease by 10%,26

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Figure 5: Simulated Share at Hypothetical Prices, 10% lower biosimilars prices(a) FY2013(b) FY2014(c) FY2015(d) FY2016(e) FY2017
Note: These graphs show the actual and simulated numbers of patients who used original drugs and biosimilars and those who did not use any drugs in each fiscal year. The bars “actual” are the actual numberof patients. The bars “hospital-set” indicate the simulation results using the estimation results of Table 5(1), 6, and 8 (3) with 10% lower biosimilar prices. The bars “all-set” indicate the simulation results using
the estimation results of Table A1 and A2 (3) with 10% lower biosimilar prices. The detailed simulation
procedure is described in Section 6.1.

27

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Figure 6: Simulated Share at Hypothetical Prices, Factor Decomposing(a) FY2013(b) FY2014(c) FY2015(d) FY2016(e) FY2017
Note: These graphs show the simulated numbers of patients who used original drugs and biosimilars andthose who did not use any drugs in each fiscal year. All simulations used the estimation results of Table 5(1), 6, and 8 (3). The bars “actual price” are the simulation results with actual prices. The bars “BS 10%
lower for patients” are the simulation results with 10% lower biosimilar prices in the discrete choice model.
The bars “BS 10% lower for hospitals” show the simulation results with 10% lower biosimilar prices in thediscrete choice model. The bars “BS 10% lower” represent the simulation results with 10% lower biosimilar
prices for both models. The detailed simulation procedure is explained in Section 6.1.

28

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

its market share would increase by 12.04% in the most recent year, and that this spreading
effect mostly comes from the hospitals’ decision to introduce generics.
Our study has several limitations. First, this study analyzed a specific subject, pharmaceuticals for neutropenic patients in Japan. Therefore, it is necessary to confirm from otherdata for different systems and different drugs whether the model presented in this study ismore reasonable than existing models.
Second, we only analyzed a limited part of the set of drugs prepared by the hospitals,
namely whether biosimilars were introduced, owing to the limited price variation within thesample period. If we could estimate the entire set of drugs owned by hospitals using data fora longer period or from regions with larger price fluctuations, we could capture the impactof the policy in more detail.
Finally, although this study focused only on the analysis of drug demand, an analysisof the profit and development incentives of pharmaceutical companies is also essential toconsider the impact of the drug price regulation policy. A comprehensive analysis of the profit
and development incentives of pharmaceutical companies, including the data and methods
to be used., is important for the future,

References
Berry, Steven, “Estimating discrete-choice models of product differentiation,” The RAND
Journal of Economics, 1994, 25 (2), 242–262.
, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,”
Econometrica, 1995, 63 (4), 841–890.
Cleanthous, Paris, “Patient Welfare Implications of Innovation in the U.S. AntidepressantMarket,” 2002.
Cockburn, Iain M., Jean O. Lanjouw, and Mark Schankerman, “Patents and theglobal diffusion of new drugs,” American Economic Review, 2016, 106 (1), 136–164.
Dafny, Leemore S., “How do hospitals respond to price changes in emergency departments?,” American Economic Review, 2005, 95 (5), 1525–1547.
Danzon, Patricia M., Y. Richard Wang, and Liang Wang, “The impact of priceregulation on the launch delay of new drugs - Evidence from twenty-five major markets in
the 1990s,” Health Economics, 2005, 14 (3), 269–292.

29

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Dranove, D, C Garthwaite, and C Ody, “How do nonprofits respond to negative wealthshocks? The impact of the 2008 stock market collapse on hospitals,” The RAND Journalof Economics, 2017, 48 (2), 485–525.
Dubois, Pierre and Laura Lasio, “Identifying industry margins with price constraints:
Structural estimation on pharmaceuticals,” American Economic Review, 2018, 108 (12),3685–3724.
Duggan, Mark G., “Hospital ownership and public medical spending,” Quarterly Journalof Economics, 2000, 115 (4), 1343–1373.
Dunn, Abe, “Drug innovations and welfare measures computed from market demand: Thecase of anti-cholesterol drugs,” American Economic Journal: Applied Economics, 2012, 4(3), 167–189.
Duso, Tomaso, Annika Herr, and Moritz Suppliet, “The welfare impact of parallel
imports: A structural approach applied to the German market for oral anti-diabetics,”
Health Economics, 2014, 23, 1036–1057.
Evaluate Pharma, “World Preview 2017, Outlook to 2022,” Technical Report 2017.
Gaynor, Martin and William B. Vogt, “Competition among hospitals,” The RAND
Journal of Economics, 2003, 34 (4), 764–785.
Grennan, Matthew and Ashley Swanson, “Transparency and negotiated prices: The
value of information in hospital-supplier bargaining,” Journal of Political Economy, 2020,
128 (4), 1234–1268.
Iizuka, Toshiaki, “Experts’ agency problems: Evidence from the prescription drug market
in Japan,” The RAND Journal of Economics, 2007, 38 (3), 844–862.
, “Physician Agency and Adoption of Generic Pharmaceutical,” American Economic Review, 2012, 102 (6), 2826–2858.
International Federation of Pharmaceutical Manufacturers & Associations, “Biotheraputics Medicines,” Technical Report 2012.
Kaiser, Ulrich, Susan J. Mendez, Thomas Rønde, and Hannes Ullrich, “Regulationof pharmaceutical prices: Evidence from a reference price reform in Denmark,” Journalof Health Economics, 2014, 36 (1), 174–187.

30

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Appendix

31

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Table A1: First Stage Estimation Results (The All-set Model)
Price×Age

0.000000219∗∗∗(6.60e-08)

Price×Age2-7.67e-10(5.86e-10)

Price×Sex

0.00000120∗∗(0.000000475)

Price×Bsa

0.00000925∗∗∗(0.00000125)

Price×Pchemo

0.00000753∗∗∗(0.000000961)

Drug×Age0.0393∗∗∗(0.00214)

Drug×Age2-0.000485∗∗∗(0.0000198)

Drug×Sex-0.0640∗∗∗(0.0171)

Drug×Bsa-0.783∗∗∗(0.0442)

Drug×Pchemo

2.588∗∗∗(0.0235)

Drug×D70

1.842∗∗∗(0.0383)

Filgrastim×D70

0.154∗∗∗(0.0350)

Nartograstim×D70

0.868∗∗∗(0.0637)

Lenograstim×D70

0.221∗∗∗(0.0367)

Generic×Age

0.0240∗∗∗(0.00282)

Generic×Age2-0.0000739∗∗∗(0.0000253)

Generic×Sex-0.000405(0.0209)

Generic×Bsa

0.176∗∗∗(0.0555)

Generic×Pchemo

0.203∗∗∗(0.0344)ln(month + 1)

0.936∗∗∗(0.0439)
X
184,954

product×year dummies
N

Notes: We did not use a product choice set for each hospital, but used a common product choice set for all hospitals that
included all products available at that time. “Price” indicates the wholesale price, calculated using Equation (1). Body
surface area was calculated from the patients’ weights and heights using the Du Bois Method (body surface area (m2 ) =height(cm)0.725 × weight(kg)0.425 × 0.007184). “D70” is an indicator that takes the value of 1 if at least one of the disease
codes (occasion of admission, comorbidity on admission, post-hospitalization onset disease, etc.) includes D70, and 0
otherwise. The robust standard errors are reported in parentheses. *, **, and *** denote 10%, 5%, and 1% significance levels,
respectively.

32

ESRI Discussion Paper Series No.396"Impact of Hospital Decision-making on Drug Markets: The Case of Biosimilars"

Table A2: Second Stage Estimation Results (The All-set Model)
(1)

price
product dummiesNadj. R-squared(2)(3)
product×year dummies
OLS
IV(Demand) IV(Demand, Markup)
0.000155∗∗∗
0.000236∗∗
0.0000664(0.0000440) (0.000120)(0.0000748)
X
X
X
136
136
136
0.928
0.925
0.925

Notes: We did not use a product choice set for each hospital but used a common product choice set for all
hospitals that included all products available at that time. We used only those product j–year t pairs forwhich δjt was precisely estimated where the z-values were greater than 2 in the first stage in Table A1.
“Price” indicates the wholesale price, calculated using Equation (1). Column (1) shows the results ofordinary least squares regression. Column (2) shows the results of the instrumental variable regression,
which uses “Demand IV” defined by Equation (15) and the interaction of “Demand IV” and a genericdummy as instruments. Column (3) shows the result of the instrumental variable regression, which alsouses “Markup IV” defined by Equation (16) and the interaction of “Markup IV” and a generic dummy as
instruments, in addition to the instruments in column (2). Standard errors are reported in parentheses. *,**, and *** denote 10%, 5%, and 1% significance levels, respectively.

33