Competition and Multilevel Technology Adoption: A

Dynamic Analysis of Electronic Medical Records

Adoption in U.S. Hospitals ∗

Yanfei Wang

Business School

Renmin University of China, Beijing, China

August 13, 2019

Abstract

This paper develops and estimates a dynamic oligopoly model, to study the multilevel adop-

tion of Electronic Medical Records (EMR) technologies in U.S. hospitals. I find evidence that

hospitals compete and engage in vertical product differentiation in the adoption process. My

counterfactual experiments show that hospitals’ adoption of a higher level of EMR technolo-

gies is greatly deterred by competitive effects. I conduct various counterfactual experiments to

evaluate the effectiveness and design of the U.S. government’s subsidy policy for EMR adoption

enacted in 2009. A policy strategy capturing market and hospital heterogeneities would have

been more effective. Adoption would be further accelerated if the policy had been passed ear-

lier. In addition, by overlooking competitive effects during the adoption, the effectiveness of the

policy would be over-predicted.

∗This article is a revised version of a chapter from my dissertation at Boston University. I am deeply indebted toMarc Rysman for his continued guidance and support. I thank Randall Ellis and Albert Ma for their commentsand suggestions. I have benefited from conversations with Mo Xiao, Ginger Jin, Christopher Knittel, FrancescoDecarolis, Leila Agha, Philip Haile, Ying Fan, Haizhen Lin, Joris Pinkse, Gautam Gowrisankaran, Chun-Yu Ho, andWei Lin. I also thank the seminar participants at Boston University and Boston College. I also acknowledge theHealth Information Management Systems Society (HIMSS) for providing access to their data. All errors are my own.Address for correspondence: yanfei330@gmail.com.

i

1 Introduction

By October 2018, more than $30 billion had been spent by the U.S. government to subsidize the

adoption of electronic medical record (EMR) technologies and promote their nationwide imple-

mentation.1 Studies regarding how hospitals make adoption decisions and how they have been

affected by the subsidy policy are crucial for evaluating the policy. However, competitive effects

that may exist during the adoption process have been overlooked by policy-makers. If hospitals

adopt EMR strategically as a result of competition, their behavior could be significantly different

from the scenario in which they make independent decisions. My paper examines and quantifies

the importance of the competitive effects in EMR adoption by developing a dynamic oligopoly

model that takes into account strategic interactions of forward-looking hospitals. Furthermore, the

dynamic structural model that describes hospitals’ adoption behavior allows me to evaluate the

effectiveness and design of the subsidy policy through counterfactual experiments.

EMR technologies refer to a number of applications with various functionalities. Basic appli-

cations such as clinical data repository (CDR) create a real-time database to collect and store

patients’ information. Advanced EMR technologies such as computerized practitioner order en-

try (CPOE) are built upon the basic EMR technologies and help physicians to interact with EMR

data and further improve their healthcare quality. For example, the implementation of CPOE could

alert a physician to potential errors in medication orders and patients’ adverse drug reactions. The

basic applications are the foundation of EMR technologies and must first be installed to ensure the

successful and smooth implementation of advanced EMR technologies. To capture the multilevel

feature of EMR technologies, I categorize the basic EMR technologies, including enterprise EMR,

order entry (OE), CDR, and clinical decision support (CDS), as Level 1 (L1) applications, and the

advanced EMR technologies, including CPOE and physician documentation (PD) as Level 2 (L2)

applications.2, 3

Previous studies show that markets do reward hospitals for their quality investments (e.g.,

Chandra et al., 2016). Through investing on EMR technologies, hospitals could gain a competitive

1https://www.cms.gov/Regulations-and-guidance/legislation/EHRIncentivePrograms/DataAndReports.html,accessed June 20, 2019

2The way to define the basic/advanced applications is consistent with previous literature (see Miller and Tucker,2009; Lee et al., 2013; Dranove et al., 2014; McCullough et al., 2016).

3The functionalities of each application are further explained in Section 2.

1

advantage by achieving higher healthcare quality and attracting more patients. EMR adoption helps

hospitals to improve both health outcomes and patients’ satisfaction during the treatment process,

and a higher-level of EMR adoption is associated with a higher-level of quality. Hospitals with

basic EMR technologies can better consolidate information through digitalization of data, which

helps healthcare providers store, retrieve and examine these data more efficiently. The advanced

EMR technologies help hospitals to further improve healthcare quality by providing physicians

with effective decision support and easy care coordination. For example, some studies find that

CPOE significantly reduces mortality for high-risk patients (McCullough et al., 2016), and reduces

the likelihood of adverse patient safety events, particularly for less complex patients (Freedman

et al., 2018b). In addition, EMR adoption may help patients’ interactions with physicians and

improve patients’ satisfaction during the treatment process. Physicians could have greater and

faster information availability with the help of EMR, and this allows them more time to talk to

patients and address their concerns.4

Although EMR technologies potentially increase hospitals’ demand, the marginal benefits of

adoption may decline over the number of adopters in the market, due to more intense competition.5

Moreover, this multilevel feature of EMR adoption provides hospitals with a source for vertical

product differentiation, as competition would be stronger for hospitals of the same adoption level.6

In other words, hospitals may be less likely to adopt a certain level of EMR technologies if there

are more adopters of this level in the market.

As a result, the first objective of my paper is to examine whether hospitals compete and dif-

ferentiate in the adoption process. Furthermore, I aim to quantify the importance of competitive

effects, particularly for the adoption of L2 EMR, as the national implementation of the advanced

applications, such as CPOE, is one of the primary goals of the government.7 The adoption decisions

4Hsu et al. (2005), studying outpatient visits in a medical office building, find that overall patient satisfaction withvisits increases 7 months after the introduction of health IT, and satisfaction also increases in communication aboutmedical issues and comprehension of decisions made during the visit. DesRoches et al. (2008) find that physicianswho use EMR report positive effects on quality of care and high levels of satisfaction.

5Schmidt-Dengler (2006) studies the adoption of nuclear magnetic resonance imaging by U.S. hospitals in astrategic environment and finds evidence of competitive effects. Tay (2003) also points out that predicted increasesin demand from adopting a health technology fall when neighboring hospitals have the technology.

6An important early model of vertical differentiation is developed by Shaked and Sutton (1982). Empiricalstudies about product differentiation include (Mazzeo, 2002; Seim, 2006; Augereau et al., 2006; Rysman et al.,2019). Particularly in the health industry, Tay (2003) shows that quality differentiation is particularly important inthe hospital market, where the choices of Medicare patients are unaffected by prices; Jin (2005) finds that healthmaintenance organizations use voluntary disclosure of product quality to differentiate from competitors.

7For example, to apply for the Medicare/Medicaid incentive programs, hospitals must use CPOE for medication

2

for forward-looking hospitals in a competitive environment should be different from the scenario

in which they make independent choices on adoption: a hospital’s adoption of L2 EMR could be

deterred either because there are enough L2 adopters in the market and the benefits of adoption

are small, or because the hospital realizes that its future benefits from adoption would decrease

as more hospitals adopt the technology.8 If the competitive effects are found to deter adoption

significantly, this could create an important source of market failure. Moreover, the hospital may

have the preemptive motive to adopt early: its adoption would inhibit the others from adopting by

decreasing their potential benefits, and this would allow the hospital to gain monopolist’s rent for a

longer period.9 If the competitive effects are substantial, the hospital would gain from preemption

and adopt earlier than in the scenario in which its rivals do not respond to its adoption. It is

interesting to examine whether the preemption exists in the adoption process.10

Competitive effects are also crucial for evaluating the effectiveness of the subsidy policy. In

2009, the American Recovery and Reinvestment Act established the Medicare/Medicaid incentive

program to subsidize the adoption of EMR.11 The subsidy lowers every potential adopter’s adoption

costs. If there is a strong competitive effect, a potential adopter has to balance the lower adoption

costs and the lower adoption benefits due to enhanced competition, and consequently would be less

likely to adopt EMR compared to the case of no competition. Thus, by overlooking competitive

effects, policy-makers may over-estimate the effectiveness of the policy.

The second objective of my paper is to evaluate the effectiveness and design of this subsidy

policy. Previous studies evaluating this policy are based mainly on statistical results or reduced-

form regressions (see Adler-Milstein et al., 2013; DesRoches et al., 2013; Dranove et al., 2015; Adler-

Milstein and Jha, 2017). The primary challenge in assessing the policy is to accurately predict the

counterfactual situation. It is difficult to tell from the data whether hospitals adopted EMR in

orders for more than 30% of patients taking medications.8An early theoretical model of technology adoption is from Reinganum (1981), who predicts that the effect of

increasing the number of firms upon the equilibrium adoption schedule is ambiguous: on the one hand, firms mayadopt early to gain a competitive advantage; on the other, each firm will capture less of the post-adoption marketand may have less incentive to adopt.

9Fudenberg and Tirole (1985) define the “preemptive adoption” as “firms will adopt sooner than they wouldchoose to were their rivals’ adoption dates fixed”, and “firms adopt preemptively to prevent or delay adoption bytheir opponents”.

10For example, Schmidt-Dengler (2006) quantifies the preemption effect in hospitals’ adoption of nuclear magneticresonance imaging, and finds the effect is small.

11As former U.S. president Barack Obama stated in a speech in 2009, “We will update and computerize ourhealthcare system to cut red tape, prevent medical mistakes, and help reduce healthcare costs by billions of dollarseach year.”

3

response to the policy, or would have done so even with no subsidy.12 The dynamic structural

model developed and estimated in my paper allows me to perform counterfactual experiments to

evaluate the effectiveness of this subsidy policy. Furthermore, I explore alternative design options

of the policy to examine if the adoption could be further accelerated. Doing so sheds light on

future design of the subsidy policy that is often used by governments as a way to encourage entry,

particularly in health industries.13

For these purposes, I conduct my estimation in two steps by using U.S. hospitals’ adoption

decisions from 1999 to 2008. The first step is to examine the drivers of EMR adoption through

reduced-from regressions, and to find evidence of competitive effects. The second step is to develop

and estimate a dynamic structural model that takes hospitals’ strategic interactions into account.

In the first step, I perform a set of flexible reduced-form regressions to study how hospitals make

decisions about adoption, and which level to adopt. More importantly, I examine whether hospi-

tals’ adoption probability is negatively affected by neighboring hospitals’ adoption. Empirically,

the primary challenge regarding the estimation is to control for unobserved market heterogeneity.14

Hospitals’ choices could be correlated as a result of unobserved market factors, such as local pref-

erence for EMR and local cost advantages.15 I follow a method similar to Lin (2015) to construct

market-level group dummies that indicate different levels of profitability from EMR adoption, and

then use them to control for unobserved market heterogeneity to some extent.16 However, other

omitted market-level factors that are persistent over time may still make hospitals’ choices corre-

lated and cause bias in the estimation. To address this concern, I use an instrument method similar

to Jin (2005). A valid instrument here needs to be correlated with hospitals’ adoption decisions,

but uncorrelated with omitted local demand (or cost) factors. Consider hospitals that have the

same owners or belong to the same system but serve different geographical markets. Their choices

12As Adler-Milstein et al. (2013) put it, “ We were unable to definitively differentiate which hospitals adoptedEHRs or achieved meaningful use specifically in response to the incentives, and which hospitals had already done soor would have done so without the incentives.”

13For example, the US Health Resource and Services Administration has subsidized the entry costs of primarycare physicians, dentists, and mental health professionals that locate in Health Professional Shortage Areas (Dunneet al., 2013).

14The method to include market fixed effects is infeasible for probit and logit models, as a result of the incidentalparameters problem in a nonlinear panel data model.

15Rysman et al. (2019) find that in the case of Leadership in Energy & Environmental Design Certification,unobserved market characteristics make buildings’ choices of certification level correlated, and their differentiationstrategies difficult to identify.

16This concept to construct market (or firm) group dummies, is often used in the previous literature; see Collard-Wexler (2013) and Fan and Xiao (2015).

4

are correlated because of similar organizational features or similar adoption costs, while they also

respond to local unobservables that are independent across markets. I hence use hospital A’s sister

(or system) hospital B’s adoption choices (where hospital B is located in another market) as the

instrumental variable (IV) for hospital A’s choices.17 With these empirical strategies, I find con-

sistent evidence for the presence of competitive effects and hospitals’ incentive to differentiate: the

probability of adopting L1 (L2) would decrease with more L1 (L2) adopters in the market.

I then develop a dynamic structural model of this multilevel technology adoption, based on the

framework of Ericson and Pakes (1995).18 The model takes the strategic interaction of forward-

looking hospitals into account. Hospitals are assumed to possess private information and make

adoption decisions by maximizing their discounted present value of payoffs. I estimate this model

and recover the profit and cost parameters by applying a two-stage method similar to Bajari et al.

(2007) (BBL).19 In the first stage, I estimate the policy functions and the evolution of states, which

are characterized by flexible reduced-form regressions. In the second stage, I forward-simulate the

conditional value functions and recover the profit and cost parameters by maximizing the predicted

probabilities of the observed actions, which is similar to the pseudo-maximum-likelihood estimators

introduced by Aguirregabiria and Mira (2002). Again, the presence of competitive effects is found in

the multilevel adoption process: the profits from L1 adoption decline substantially over the number

of hospitals that installed L1 only, and the profits from L2 adoption decrease greatly as more

hospitals installed L2. Hospitals of the same adoption level compete more strongly than hospitals

with different levels, and they use EMR adoption as a source of vertical product differentiation.

Based on the estimated structural parameters and the Pakes and McGuire (1994) algorithm, I

solve the finite-state equilibrium outcomes and simulate hospitals’ adoption behavior, to perform the

counterfactual experiments. My first set of experiments examines the importance of competitive

effects in the adoption process. By comparing the scenario in which hospitals make adoption

decisions with competitive effects in a duopoly market to the scenario in which hospitals make

independent choices as if they were in a monopoly market, I find that the competitive effects

on EMR adoption are substantial: it would take a longer time for hospitals in a competitive

17The empirical strategies are discussed in detail in Section 4.1.18The Ericson and Pakes (1995) framework has been used widely to study the dynamics in healthcare industry

(see Gowrisankaran and Town, 1997; Dunne et al., 2013; Lin, 2015).19Several papers apply the BBL method to study regulation in a model where firms make entry and exit decisions,

including Ryan (2012) and Suzuki (2013).

5

environment to adopt L2 EMR, especially for the later adopter in a duopoly market. Previous

studies show that EMR adoption could be deterred by adoption costs (DesRoches et al., 2008)

or concerns about patients’ privacy (Miller and Tucker, 2009), but typically overlook competitive

effects, which also play a very important role in hindering adoption. Then I examine whether the

preemption exists in the duopoly market, by performing a counterfactual experiment that removes

the preemptive motive. Similar to the method of Igami (2017), I assume hospital A in the duopoly

market does not respond to the other hospital B’s adoption, so that hospital B does not have the

preemptive motive. I focus on how hospital B would behave differently from the case with strategic

interactions. I find no evidence of preemption, and it may imply that the competitive effects are

not large enough for hospitals to gain from preemption.

My second set of experiments evaluates the subsidy-based Medicare/Medicaid incentive pro-

grams announced in 2009. Generally, eligible hospitals could begin receiving annual payments from

these programs in any year from 2011 to 2015, for a consecutive four-year period. I first analyze to

what extent the policy spurs the adoption, given that hospitals and markets are heterogeneous. I

find that large hospitals would be affected less than small hospitals given the same level of subsidy,

and would be more likely to have adopted before 2015 even with no subsidy. The current policy

also overlooks market heterogeneity. My study shows that hospitals in a small market have less

incentive to adopt, and would still lag behind even with the subsidies. As a result, a policy strat-

egy capturing hospital and market heterogeneities is a more efficient method to ensure nationwide

adoption.

Second, I explore alternative design of the policy to assess whether EMR adoption could be

further accelerated. For example, it is interesting to examine the case if the subsidy plans had been

announced earlier than 2009, as policies promoting nationwide adoption had undergone years of

bipartisan support and broad consensus since 2004.20 In a dynamic environment, hospitals’ adop-

tion probability would rise immediately after the announcement of the policy, as their option value

of adopting would increase by receiving subsidies at some time in the future. My counterfactual

results confirm the expectation. Particularly, hospitals would generally have adopted L2 EMR half

a year earlier, had the policy been passed in 2007. Thus, by speeding up the policy-making process

20The discussion of EMR adoption became important since 2004, when former U.S. president George Bush estab-lished the Office of the National Coordinator, which is tasked with the development and implementation of a strategicplan to guide the nationwide implementation of health IT (Lee et al., 2013).

6

or announcing the policy earlier, adoption would be further accelerated.21

Finally, I study the role that competition plays in the effectiveness of the incentive plans.

I find that by ignoring competitive effects during the adoption process, the effectiveness of the

subsidy policy may be over-predicted. When competitive effects are taken into account, the policy

would still spur adoption significantly, but hospitals would lag behind relative to the case without

competition.

The presence of competition and differentiation in EMR adoption could challenge the intuition

that hospitals’ adoption of EMR stimulates rivals’ adoption, as a result of network externalities.

Network externalities have been found to affect technology adoption in previous studies, including

Gowrisankaran and Stavins (2004) and Tucker (2008). EMR technology exhibits network external-

ities through its feature of information exchange. However, whether network externalities actually

affect hospitals’ adoption remains unresolved in the literature. Miller and Tucker (2009) study the

effects of state privacy regulation on the adoption of enterprise EMR and find that the suppression

of network externalities would inhibit adoption. Miller and Tucker (2014) empirically find that

larger hospital systems are more likely to exchange electronic patient information internally but

are less likely to exchange patient information externally with other hospitals. Lee et al. (2013) find

no evidence of meaningful network externalities in hospitals’ EMR investments, and point out that

“only 14% of California hospitals electronically exchange medical record information with compet-

ing hospitals” based on a 2007 AHA annual survey. Freedman et al. (2018a) and Lin (2019a) point

out that EMR from different vendors typically are not interoperable. My findings of competitive

effects in the adoption suggest that either the network externalities are dominated by competitive

effects, or that hospitals do not take network externalities into account as no communication ac-

tually occurred in the early period of adoption.22 As pointed by Daniel Chavez, executive director

of the San Diego Regional Healthcare Information Exchange, “Providers all have vested interests

in health IT to improve care and optimize workflows, and all need to participate in efforts like

HIEs (Health Information Exchange) to achieve their business goals. But using the same tools,

even shared tools, is a way for hospitals as organizations to remain highly competitive rather than

21It is also related to the “ossification” problem in the law literature, which refers to the procedural constraintsimposed on federal agencies and makes the federal regulatory process burdensome and inefficient (McGarity, 1992;Mashaw, 1994).

22This paper does not separately measure the competitive effect and the network effect if any. It finds that thecompetitive effect plays a greater role and quantifies only the net effect.

7

become more collaborative.”23

Recent studies also examine hospitals’ choices for EMR vendors conditional on adoption and find

evidence of network externalities. On one hand, by choosing the same vendor, hospitals enjoy a local

cost advantage (for example, through knowledge spillovers) and the possibility of communicating

better with neighboring hospitals. On the other hand, competitive effects give hospitals incentives

to differentiate over the vendor choices, and “lock-in” their patients by not sharing the information.

Freedman et al. (2018a) find that hospitals tend to agglomerate on the choices of EMR vendors

using the cross-sectional data on adoption from 2005 and 2012. Lin (2019a) reaches similar results

by studying stand-alone hospitals’ adoption from 2006 to 2009, and finds that hospitals’ profit

increases with the prevalence of a vendor in the market.24 Relative to their papers, my study

includes the early adoption stage by studying hospitals’ adoption decisions from 1999 to 2008.

More importantly, their works focus on examining whether there is horizontal differentiation or

coordination/agglomeration in the choices of EMR vendors, whereas I emphasize vertical product

differentiation along EMR adoption levels. Their results do not conflict with mine. In practice,

hospitals could be differentiated along EMR adoption levels, and at the same time, they may tend

to coordinate with each other (based on results of Freedman et al., 2018a; Lin, 2019a) by choosing

the same vendor conditional on adoption.

The remainder of the paper is structured as follows. Section 2 explains various EMR technolo-

gies. Section 3 describes the data and Section 4 presents reduced-form evidence on the adoption

process. Section 5 specifies the dynamic structural model, and Section 6 explains the estimation

method. Section 7 presents and interprets the empirical results. Section 8 describes the counter-

factual experiments, and Section 9 concludes.

2 EMR Technologies

EMR technologies refer to a wide range of information technologies used by hospitals to collect,

store and display patient information, help physicians make decisions, and coordinate healthcare.

23This is from the article “Health IT’s Effect on Inter-Hospital Competition” written by Helen Greggin 2014. https://www.beckershospitalreview.com/healthcare-information-technology/health-it-s-effect-on-inter-hospital-competition.html, accessed June 20, 2019.

24Another related paper Lin (2019b) finds that affiliated hospitals behave differently from stand-alone hospitals.Affiliated hospitals deviate from the local market-leading vendor (which has the highest local market share) andwould be more likely to choose their system-dominant vendor (which most member hospitals adopt).

8

The key applications include enterprise EMR, order entry (OE), clinical data repository (CDR),

clinical decision support (CDS), computerized practitioner order entry (CPOE), and physician

documentation (PD).

Enterprise EMR is a basic EMR system that underlies other potential add-ins such as clinical

decision support, clinical data repository, and order entry (Miller and Tucker, 2009); OE pro-

vides electronic forms to streamline hospital operations, for instance, by replacing paper and faxes

(Dranove et al., 2014); CDR is a hospital’s real-time database of patients’ information, such as

test results, patient demographics, and discharge summaries; CDS supports clinicians’ decisions

by providing treatment plans; CPOE is a more advanced and sophisticated application—other

than providing patient information and clinical guidelines, it also alerts the physician to potential

errors in medication orders and potential adverse drug reactions; PD is also an advanced applica-

tion that helps physicians coordinate care by providing structured templates for entering patients’

information that is meaningful for other practitioners.

Among these applications, enterprise EMR, OE, CDR and CDS are the basic ones; they are

relatively easy to install and implement. They mainly collect, store, and display patients’ infor-

mation and are the foundation for the advanced EMR functionalities. However, the basic EMR

technologies lack advanced functionalities such as effective decision support (e.g., alerting about

potential adverse drug reactions), and easy care coordination (e.g., providing structured templates

for clinical documentation).

CPOE and PD are the advanced EMR technologies, and could help physicians make effective

use of the electronic patient data stored in basic EMR technologies. They improve healthcare qual-

ity by providing more effective physician support and smoother coordination between practitioners.

McCullough et al. (2016) find the adoption of health IT, such as CPOE, could significantly reduce

mortality for high-risk patients. Freedman et al. (2018b) find evidence that CPOE reduces the like-

lihood of adverse patient safety events, particularly for less complex patients. They attribute the

mechanism driving the findings to the decision support functionality of the advanced EMR tech-

nologies. Relative to CPOE, PD improves healthcare quality through a different channel: it enables

physicians to document and communicate patient information clearly, and thus, its functionality

mainly focuses on communication and care coordination between multiple providers (Freedman

et al., 2018b). McCullough et al. (2016) find that health IT improves quality by facilitating coor-

9

dination and communication across providers, which is more important for high-risk patients than

for low-risk ones.

The installation and implementation of advanced EMR technologies are much more costly than

that of basic EMR technologies. Hospitals need to spend an average of $80, 000 to $100, 000 per

bed for the required project planning, software, hardware, implementation, and training (Laflamme

et al., 2010). Dranove et al. (2015) point out that these costs could reach $10 million or much higher,

for an average-sized hospital.

Hence, I define the basic EMR technologies (enterprise EMR, OE, CDR and CDS) as Level 1

(L1) applications, and the advanced EMR technologies (CPOE and PD) as Level 2 (L2) applications

in my paper. This categorization is consistent with the previous literature (see Dranove et al., 2014;

Miller and Tucker, 2009; Lee et al., 2013; McCullough et al., 2016).25, 26

3 Data

I combine data on EMR adoption and data on markets to create a panel dataset of hospitals’

adoption decisions and hospital and market characteristics.

3.1 EMR Data

The data on EMR adoption and hospital characteristics are from Healthcare Information and

Management Systems Society (HIMSS) Analytics Database. The data have been widely used to

study EMR diffusion, including Hillestad, et al. (2005), Miller and Tucker (2009), Dranove et al.

(2014), Dranove et al. (2015), McCullough et al. (2016), and Freedman et al. (2018b).

HIMSS data contain information about hospitals’ address, number of beds, and the contract

time for various EMR applications if installed.27 These EMR technologies include the L1 applica-

tions of enterprise EMR, OE, CDR and CDS, and L2 applications of CPOE and PD. Furthermore,

I denote the adoption of any L1 applications as L1 adoption, and the adoption of any of the L2

25Dranove et al. (2014) define a hospital as having basic EMR applications if it has adopted OE, CDR, or CDS.The hospital has advanced EMR if it has adopted either CPOE or PD; Miller and Tucker (2009) describe enterpriseEMR as a basic EMR system that underlies other potential add-ins, such as CDS, CDR, and OE. Lee et al. (2013)and McCullough et al. (2016) define enterprise EMR as the basic application, as in Miller and Tucker (2009), andCPOE as the sophisticated EMR application.

26An alternative definition of the levels is examined as a robustness check in Section 4.3.27The contract time in the dataset is the year when a hospital first entered a contract with the EMR vendor.

10

EMR as L2 adoption. A hospital is defined as a potential adopter (also Level 0 or L0) if it has

not adopted any EMR applications, as a Level 1 adopter if it has adopted only Level 1 (not yet

Level 2), and as a Level 2 adopter if it has adopted both levels. The adoption time for each level

is defined as the contract year when the hospital first enters a contract of any application of this

level.28

I use HIMSS’s annual data from 2006 to 2008 to collect each hospital’s adoption time for each

level if adopted, and create a panel dataset about hospitals’ adoption status and decisions from

1999 to 2008.29 For example, if a hospital decides to adopt L1 (and enters a contract for any L1

application) in 2002, and upgrades to L2 by signing a contract for any L2 application in 2005, it

would be a potential adopter from 1999 to 2002, an L1 adopter from 2003 to 2005, and an L2

adopter from 2006 to 2008. The installation process usually takes time: I assume in my model that

it takes 1 year for a typical hospital from entering the contract to the EMR actually being installed.

For a robustness check, as discussed in Section 4.3, I relax this assumption by assuming that the

adoption process takes 2 years, and the results do not change . I focus on adoption decisions after

1999, since the adoption rate was quite low before that.30 My sample period covers the crucially

early stage of the adoption process, prior to the announcement of the government’s EMR incentive

plan in 2009. Hence, I create the variables “number of L1 adopters” and “number of L2 adopters”

in each market each year. These are the key variables of interest, as they capture the competition

for each adoption level.

3.2 Market Definition, Market Characteristics and Sample Selection

The market is defined according to the Health Service Area (HSA) measure, developed by Makuc

et al. (1991). The HSA is a self-contained geographic region with respect to healthcare service. It

usually consists of 3 – 4 counties.31 I apply this market measure to the HIMSS adoption data, and

obtain 793 HSA markets. The markets in my data cover over 95% of the HSA markets in the U.S.

Annual market characteristics from 1999 to 2008 are mainly from Area Resource Files (ARF).

28Previous studies about EMR adoption also identify the date of adoption based on the contract date (see, e.g.,Dranove et al., 2014).

29I use data from 2006 to 2008 instead of 2008 only, in case hospitals report the information about the contractyear in 2006 or 2007, but not 2008.

30The adoption rate of L2 was less than 5% before 1999.31Other studies have used this method to define markets, including Miller and Tucker (2009).

11

The ARF provide county-level information on demographic, environmental and health resource

variables. Some sources for ARF include the American Hospital Association and the U.S. Census

Bureau. I use the variable “total hospital admissions” to approximate healthcare demand in each

market. Although each hospital’s admissions in the market may be affected by the EMR adoption

pattern in the market, as patients may flow to hospitals with EMR, total market-level admissions

are not affected and are considered exogenous.

The EMR incentive program announced in 2009 subsidizes EMR adoption as an increasing

function of the number of inpatients covered by Medicare/Medicaid. Moreover, health IT is likely

to be more effective among relatively low-income populations (Miller and Tucker, 2011), which

could be a potential motivation for adoption by hospitals in a market with more people in poverty.

To examine and control these factors, I draw the variables “number of persons eligible for Medicare”

and “percent of persons in poverty” from the ARF. All the ARF variables are aggregated at the

HSA level.

Furthermore, to control for the effect of the market’s competition level on EMR adoption, I use

the data on each hospital’s total discharges from the Healthcare Cost Report Information System

(HCRIS) to construct the market share and then the HHI (Herfindahl-Hirschman index) for each

HSA market from 1999 to 2008.32,33 Relative to the variables of number of L1/L2 adopters, which

capture the competition level for EMR adoption, the HHI variable represents the competition (or

concentration) level in the hospital market. They may have different impacts on EMR adoption.34

I restrict my sample to markets with fewer than six hospitals to focus on competition in

oligopolistic markets. There are 530 such markets, which account for 67% of the markets in the

whole dataset. Previous empirical studies show that there is little competitive effect as the sixth

entrant enters healthcare markets. For example, Abraham et al. (2007) study entry and competi-

tion in local hospital markets and find that most of the entry effects come from having a second and

32Medicare-certified institutional providers are required to submit an annual cost report to a Medicare Admin-istrative Contractor. The cost report contains provider information such as facility characteristics, utilization data,cost, and charges by cost center. The Centers for Medicare & Medicaid Services maintain the cost report data inHCRIS.

33One concern is that the HHI variable might be endogenous, as hospitals use the adoption of EMR as a competitivestrategy to attract patients. However, the demand shift affected by EMR adoption is not large enough to affect themarket concentration captured by HHI. Thus, the HHI variable is considered exogenous here. Alternatively, I use“the number of hospitals” in each market to control for its competition level, and the results are similar.

34For example, Karaca-Mandic et al. (2017) find that the medical technology of drug-eluting stents diffused fasterin markets where cardiology practices faced more competition, but the structure of the hospital market did notmatter.

12

a third hospital enter the market. The entry of a fourth hospital has little additional effect.35 Lin

(2015) also finds that for the nursing home market, the fifth and the sixth competitors have minimal

competitive impacts. Although I consider the restricted sample for most of the results, it would

nonetheless be interesting to examine the whole sample, and I include this as a robustness check

in Section 4.3. As expected, the magnitude of estimated competitive effects for EMR adoption is

much smaller for the whole sample.

As a result, I create a panel data set covering 1,535 hospitals’ adoption status and decisions

from 1999 to 2008 in 530 HSA markets. Table 1 reports the summary statistics for 2008.

Table 1: Summary Statistics

Variable Obs. Mean Std. Dev.

Number of beds in the hospital 1,535 99.016 113.52HHI (Herfindahl-Hirschman Index) 530 0.59 0.24Total hospital admissions in the market(1000) 530 13.86 17.36Number of persons eligible for Medicare in the market(1000) 530 21.12 21.87Percent of persons in poverty in the market 530 15.92 5.46

Time 2008

Figure 1 shows the adoption rates of Level 1 and Level 2 EMR applications over the sample

period. The adoption rate of L1 EMR started from 38.6%, meaning that 592 of the 1,535 hospitals

had adopted L1 by 1999. This rate increased steadily to 69.8% (approximately 1,071 hospitals) in

2008, showing that L1 EMR were widely adopted by the end of the sample period. The adoption

rate of L2 EMR is much smaller. It increased from a very low value of 5.4% (83 hospitals) in 1999

to 28.9% (443 hospitals) in 2008. Hazard rates of each adoption level are presented in Figure 2,

showing the adoption decisions for hospitals at different levels over time. The hazard rate from L0

to L1 is the ratio of the number of potential adopters that decide to adopt only L1 applications,

to the total number of potential adopters each year. The hazard rate from L0 to L2 is the ratio of

the number of potential adopters that decide to adopt Level 2 applications, to the total number of

potential adopters each year. Similarly, the hazard rate from L1 to L2 is defined as the rate of L1

adopters that decide to upgrade to L2. From the figure, the hazard rate from L0 to L1 is slightly

higher than the other two, indicating a relatively stronger incentive to adopt L1 applications. These

35Their method is based on the classic paper by Bresnahan and Reiss (1991), which finds that once the markethas between three and five firms, the next entrant has little effect on competitive conduct.

13

three hazard rates generally increased from the beginning of the sample period, and fell after 2005.

Notably, the hazard rate from L1 to L2 dropped substantially from 2006. This may suggest that,

as hospitals with higher incentives had already adopted EMR in the early stage, adoption began

to slow down because the remaining hospitals had various concerns, such as substantial costs,

patients’ privacy, and uncertainty about return on investment. These concerns may be particularly

important for L1 adopters.

Figure 1: Adoption Rate of L1&L2 Applications

010

2030

4050

6070

80Ad

optio

n R

ate

(%)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008year

L1 application L2 application

Figure 2: Hazard Rate

02

46

810

12H

azar

d R

ate

(%)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008year

From potential adopter to L1 adopterFrom potential adopter to L2 adopterfrom L1 adopter to L2 adopter

4 Reduced-form Results: Hospitals’ Adoption Decisions

As an initial piece of evidence on the drivers of adoption decisions, I examine how hospitals make

decisions through reduced-form regressions. The results are be used as the policy functions during

the estimation of the dynamic structural model in the next section.

4.1 Level 1 Adopters

Active hospitals include Level 1 adopters and potential adopters. I first examine the behavior of

Level 1 adopters. Level 1 adopters make decisions about whether to upgrade to Level 2. I estimate

the following model:

Pr(Yit = 1|wit) = F (w′itλw) (4.1)

14

where variable Yit = 0 if Level 1 adopter i does not upgrade at time t, and Yit = 1 if it upgrades

to Level 2. F (·) is a known function. The vector wit consists of the constant term, and a set

of variables that might affect the adoption decision, including the number of Level 1 adopters

and Level 2 adopters in the market, other observed market characteristics (HHI, total hospital

admissions, number of persons eligible for Medicare, and percent of persons in poverty), observed

hospital characteristics (number of beds), and market and year fixed effects. The specifications

considered here include a linear probability model, a probit model, a logit Model, and an IV probit

model.

I first estimate a linear probability model by OLS with market fixed effects. In this case,

F (w′itλw) = w′itλw. Table 2 Specification I shows the results from this OLS regression. The

coefficient on “number of Level 2 adopters in each market” is negative and significant, indicating

the presence of differentiation: Level 1 adopters are more likely to stay at Level 1 and not upgrade,

if there are more Level 2 hospitals. The coefficients on year dummies increase over time, showing

a time trend for EMR adoption. The possible explanations are that adoption costs are falling,

revenues from EMR use are climbing, or people are favoring this technology more.

I then estimate a probit model and a logit model without market fixed effects. In the case of

a probit model, F (·) is a cumulative distribution function (cdf) of standard normal distribution,

and F (·) is a cdf of logistic distribution in the case of a logit model. The market fixed effects are

infeasible for probit and logit models, owing to the incidental parameters problem in the nonlinear

panel data model. Specification II shows the results. The results from Specifications I and II

are quite different: for example, the significantly negative coefficient on “Number of L2 adopters”

becomes insignificant. This could be the result of different model specifications36, or the lack of

market fixed effects in Specification II. In any case, it is necessary to include unobserved market

heterogeneity in the model: unobserved market heterogeneity in profitability for Level 1 and Level

2 adoption would greatly affect adoption behavior, as profitable markets tend to support more

adopters.

To control unobserved market heterogeneity, I follow a method similar to Lin (2015). The idea

is to construct market-level group dummies that indicate different levels of profitability from L1

36The linear probability model has a number of shortcomings. For instance, it often produces nonsense predictedprobabilities.

15

and L2 adoption, based on the results of regressions with market fixed effects. This allows hospitals

to have more incentives to adopt if they are in a market with a high level of profitability. This

concept to construct market (or firm) group dummies that capture the unobserved heterogeneity

persistent over time, is often used in the previous literature. For example, Collard-Wexler (2013)

constructs so-called market-category effects based on the average number of plants in a market

across time. Fan and Xiao (2015) use a similar method to categorize firms into two groups, one

with low entry cost, and the other with high entry cost, based on a logit model that explains firms’

entry decisions.

Specifically, I first run two regressions with the market fixed effects. The regressions regress

the number of Level 1 and Level 2 adopters in each market respectively, on observed market

characteristics (i.e., HHI, total hospital admissions, and number of persons eligible for Medicare

in each market) and market and year fixed effects. The first regression provides the market fixed

effects for profitability from Level 1 adoption, while the second one gives the market fixed effects

for Level 2 adoption. I then compute the 10th, 20th, ..., 90th percentiles for the estimated market

fixed effects of each level. Accordingly, each market is assigned by 18 group dummies: The first

nine dummies indicate which percentile range the market is in for L1 adoption, and the second nine

dummies indicate which percentile range it is in for L2 adoption. If a market is above the 90th

percentile for both L1 and L2 adoption, it is categorized as one of the most profitable markets to

adopt EMR. Following Collard-Wexler (2013), I refer to these dummies as market-category effects.

I perform the probit and logit regressions for Level 1 adopters again, but this time controlling

for the market-category effects. The results are reported in Table 2 Specification III. Relative to

Specification II, both pseudo R-squared and log pseudo likelihood improve noticeably in Specifica-

tion III, which allows the model to better fit the data. Similar to Specification I, the coefficient on

the number of L2 adopters is significantly negative. Moreover, the coefficient on the number of L1

adopters is positive and precisely estimated. The results indicate the presence of differentiation in

the adoption process. Level 1 adopters are more likely to upgrade if there are more Level 1 adopters

or fewer Level 2 adopters. By computing the average marginal effects for the logit regression, one

more L1 adopter in the market would increase the probability of upgrading by 1.5%, while one

more L2 adopter would decrease this probability by 4.1%. The probit model gives the exact same

marginal effect of the number of L1 adopters (1.5%), and a very similar effect of the number of L2

16

adopters (3.9%).

Even if unobserved market heterogeneity in profitability from L1 and L2 adoption is controlled

to some extent, other omitted market-level factors that are persistent over time might still cause bias

in the estimation. As a result, adoption decisions may be correlated among hospitals. To address

this concern, I use the instrument method similar to Jin (2005). A valid instrument here needs to

be correlated with neighboring hospitals’ adoption decisions, but uncorrelated with omitted local

demand (or cost) factors.

Consider hospitals that have the same owners or belong to the same system, but serve different

geographical markets (I refer to these hospitals as “sisters”). On the one hand, sister hospitals have

correlated adoption decisions, because they always share similar organizational features, require

similar service quality and have similar technology adoption costs.37 On the other hand, as they

serve different markets, they also respond to omitted local factors when making adoption decisions,

and these omitted local factors are assumed independent across markets.38 Thus, for hospital i’s

adoption in market A, its sister j’s adoption behavior in another market B is a valid IV: They

have correlated adoption behavior as sisters, and hospital j’s adoption behavior does not respond

to unobserved local factors in market A.

Thus, I construct the instruments for the endogenous variables “number of Level 1 adopters”

and “number of Level 2 adopters.” The exact calculation proceeds as follows. I first create two

dummies, L1 and L2, for each hospital, indicating whether it is an L1 adopter and whether it is

an L2 adopter. I then identify the hospital’s sisters in other markets and construct the variable

IV L1, as the mean of dummies L1 of its sisters in other markets, and similarly construct IV L2

as the mean of dummies L2. The instruments IV n1 for “number of Level 1 adopters” and IV n2

for “number of Level 2 adopters” are obtained from summing IV L1 and IV L2 for the hospitals

in each market respectively. Stand-alone hospitals do not have sisters and cannot contribute to the

construction of the instruments. Therefore, I create the variable “proportion of sister hospitals,”

defined as the ratio of the number of hospitals with sisters in other markets to the number of all

hospitals in the market. This variable has a mean of 0.33 and a standard deviation of 0.32. Because

37Regarding EMR adoption, sister hospitals could share a licensing fee, human-capital training material, and costsfor external consultants, etc (Lin, 2019b).

38Jin (2005) makes similar assumptions in the context of health maintenance organizations’ voluntary disclosureof product quality.

17

Table 2: Adoption for Level 1 Adopters

I II III IV

OLS Probit Logit Probit Logit IV Probit

Number of L1 adopters 0.004 0.068* 0.146* 0.23*** 0.481*** 0.461(0.008) (0.041) (0.088) (0.065) (0.140) (0.344)

Number of L2 adopters -0.051*** -0.053 -0.125 -0.593*** -1.314*** -0.52*(0.011) (0.056) (0.124) (0.104) (0.228) (0.305)

Log of number of beds 0.006 0.069** 0.155** 0.120*** 0.262*** 0.08(0.004) (0.034) (0.077) (0.041) (0.087) (0.055)

HHI 0.032 0.16 0.339 0.070 0.136 0.229(0.038) (0.187) (0.420) (0.193) (0.415) (0.315)

Log of total admissions 0.173** -0.021 0 0.709*** 1.568*** 0.701**(0.068) (0.154) (0.348) (0.200) (0.443) (0.282)

Log of number of persons 0.098 0.099 0.196 1.427*** 3.027*** 1.274***eligible for Medicare (0.091) (0.100) (0.226) (0.159) (0.323) (0.308)Percent of persons in -0.156 -2.419*** -5.498*** 0.654 1.325 0.160poverty (0.299) (0.770) (1.786) (0.793) (1.811) (1.287)Year 2000 dummy 0.004 0.305 0.836 0.252 0.670 0.152

(0.005) (0.261) (0.764) (0.327) (0.808) (0.496)Year 2001 dummy 0.012 0.599** 1.597* 0.587 1.509 0.490

(0.009) (0.292) (0.823) (0.391) (0.922) (0.456)Year 2002 dummy 0.027*** 0.767*** 1.989*** 1.030*** 2.308*** 0.941**

(0.009) (0.269) (0.763) (0.369) (0.887) (0.444)Year 2003 dummy 0.042*** 0.958*** 2.428*** 1.262*** 2.956*** 1.298***

(0.013) (0.269) (0.758) (0.371) (0.881) (0.445)Year 2004 dummy 0.065*** 1.102*** 2.740*** 1.565*** 3.727*** 1.587***

(0.013) (0.261) (0.741) (0.373) (0.886) (0.456)Year 2005 dummy 0.087*** 1.203*** 2.989*** 1.789*** 4.149*** 1.628***

(0.017) (0.262) (0.742) (0.373) (0.881) (0.463)Year 2006 dummy 0.098*** 1.247*** 3.053*** 1.871*** 4.313*** 1.830***

(0.017) (0.260) (0.739) (0.377) (0.893) (0.481)Year 2007 dummy 0.08*** 1.04*** 2.61*** 1.679*** 3.883*** 1.662***

(0.016) (0.264) (0.748) (0.379) (0.896) (0.499)Year 2008 dummy 0.074*** 0.900*** 2.274*** 1.513*** 3.483*** 1.576***

(0.016) (0.269) (0.760) (0.385) (0.911) (0.512)Proportion of sister 0.335hospitals (0.204)Market-category effects No No No Yes Yes YesMarket fixed effects Yes No No No No NoObs. used in the estimation 6162 6162 6162 6162 6162 3287(Pseudo) R-squared 0.151 0.060 0.060 0.208 0.210Log pseudo-likelihood -905 -904 -763 -760

Robust standard errors are clustered at the market level and are in parentheses. The unit of observation ishospital-year. The year dummy for 1999 is omitted in all the specifications. The results of market-categoryeffects are not reported. ∗∗∗p < .01,∗∗ p < .05,∗ p < .10.

18

“proportion of sister hospitals” is observable to local customers, I include it as a control variable

directly in the regression. Moreover, markets without any sister hospitals are not considered. As a

result, the subsample for this IV method includes 272 markets and 3,287 hospital-year observations

(compared to 6,162 observations in the full sample). To provide more support for the validity of

the instruments, I calculate that the instrument IV n1 and “number of Level 1 adopters” have a

correlation coefficient of 0.30, and the instrument IV n2 and “number of Level 2 adopters” have a

correlation coefficient as high as 0.65 in the subsample.

I then perform the two-step IV probit model developed by Newey (1987),39 taking the number

of Level 1 and Level 2 adopters as endogenous. Table 2 Specification IV shows the results. The

coefficient on “number of Level 2 adopters” is still negative and significant, indicating that there

are incentives for differentiation.

4.2 Potential Adopters

Potential adopters make decisions about whether to adopt and which level to adopt. I estimate the

following multinomial logit model:

Pr(Yit = j|wit) =exp(w′itλj)∑j exp(w′itλj)

(4.2)

where j = 0 if the potential adopter chooses not to adopt any EMR, j = 1 if the potential adopter

chooses to adopt Level 1 EMR, and j = 2 if the potential adopter adopts Level 2 EMR. The variable

wit including the constant term, the number of Level 1 adopters and Level 2 adopters in the market,

other observed market characteristics (HHI, total hospital admissions, number of persons eligible

for Medicare, and percent of persons in poverty) and hospital characteristics (number of beds),

market-category effects and year fixed effects.

The results are reported in Table 3. I find negative and significant coefficients on “number of

Level 1 adopters” and “number of Level 2 adopters” in each market for both L1 and L2 adoption.

This shows the strong competitive effects of EMR adoption. By computing the average marginal

effects, I find that one more L1 adopter would decrease the probability of adopting L1 by 7.8%,

39Newey (1987) develops a consistent two-step estimator for the probit model with endogenous regressors. Thefirst step is to regress endogenous variables on valid instruments and the other exogenous explanatory variables, andthe second step is to include the residuals from the first step in the probit model.

19

and decrease the probability of adopting L2 by 1.5%. One more L2 adopter would decrease the

probability of adopting L1 by 3.5%, and that of adopting L2 by 4.0%. This result also shows the

incentive to differentiate, as competition is more intense among hospitals of the same adoption

level. The competitive effect from L1 adopters is stronger on L1 adoption, than on L2 adoption.

Similarly, L2 adopters deter the adoption of L2 more than that of L1.

Other results are as follows. Hospitals with more beds are more likely to adopt, especially Level

2. Larger hospitals can make better use of EMR applications by facilitating internal information

exchange and coordination. Another notable result is that the increasing year effects indicate a

rising trend of EMR adoption.

4.3 Robustness Checks

In my model, one assumption is that a typical hospital takes 1 year from making the decision of

adoption to actually having EMR technologies installed. A concern is that the installation process

is complicated and may take more than 1 year in some cases. Therefore, I relax this by assuming

that it takes 2 years to install and implement EMR technologies.40 The variables “number of L1

adopters” and “number of L2 adopters” variables in the market are recalculated accordingly. I

then perform a logit regression for L1 adopters, as in Specification III in Table 2, and multinomial

logit regression for potential adopters as in Table 3. Table 4 displays the coefficients, standard

errors, and average marginal effects for the variables of interest, which are the number of L1 and

L2 adopters in the market. The results do not change much. The competitive effects and incentives

to differentiate are still very strong with this modified assumption.

I conduct a robustness check to see, whether the results of competition and differentiation hold,

with an alternative definition of levels. As described before, I define L1 adoption as the adoption

of any of the basic applications, including enterprise EMR, OE, CDR, and CDS. L2 adoption is

defined as the adoption of either CPOE or PD. Some papers focus on the adoption of CDR, and

find that hospitals tend to agglomerate on the choices of EMR vendors (e.g., Freedman et al.,

2018a; Lin, 2019a). Therefore, I change the definition of L1 adoption to CDR adoption, and retain

the definition of L2 adoption. With this new definition, I again perform the logit regression for L1

40Data from 144 hospitals suggest that implementation occurs 2.03 years on average after the contract date,according to 2007 HIMSS Analytics Database survey.

20

Table 3: Adoption for Potential Adopters

Adopt L1 Adopt L2

Number of L1 adopters -1.896*** -0.921***(0.174) (0.238)

Number of L2 adopters -0.922*** -2.079***(0.192) (0.245)

Log of number of beds 0.455*** 0.732***(0.060) (0.102)

HHI 0.597* 0.33(0.319) (0.435)

Log of total admissions 0.172 2.215***(0.394) (0.593)

Log of number of persons eligible for Medicare -0.395 2.584***(0.241) (0.380)

Percent of persons in poverty 1.645 3.978**(1.215) (1.943)

Year 2000 dummy 0.122 0.816(0.261) (0.592)

Year 2001 dummy 0.513** 2.062***(0.250) (0.596)

Year 2002 dummy 1.051*** 2.307***(0.260) (0.586)

Year 2003 dummy 1.654*** 3.446***(0.243) (0.590)

Year 2004 dummy 1.458*** 4.187***(0.284) (0.634)

Year 2005 dummy 1.848*** 4.259***(0.288) (0.677)

Year 2006 dummy 1.498*** 4.038***(0.321) (0.680)

Year 2007 dummy 1.512*** 4.263***(0.334) (0.665)

Year 2008 dummy 0.73* 3.123***(0.429) (0.807)

Market-category effects YesObs. used in estimation 6893(Pseudo) R-squared 0.186Log pseudo-likelihood -1694

Robust standard errors are clustered at the market level and are inparentheses. The unit of observation is hospital-year. The results ofmarket-category effects are not reported. ∗∗∗p < .01,∗∗ p < .05,∗ p <.10.

21

Table 4: Robustness Check: Change of Installation Time

L1 Adopters (Logit) Potential Adopters (Multinomial Logit)Upgrade to L2 Adopt L1 Adopt L2

Coef. M. E. Coef. M. E. Coef. M. E.

Number of L1 adopters 0.708*** 0.022 -1.779*** -0.074 -0.891*** -0.015(0.136) (0.160) (0.234)

Number of L2 adopters -1.379*** -0.042 -0.828*** -0.032 -1.897*** -0.037(0.220) (0.179) (0.240)

Obs. used in estimation 6162 6893

Robust standard errors are clustered at the market level and are in parentheses. Theunit of observation is hospital-year. Observed hospital and other market characteristics,market-category effects, and time dummies are not reported. ∗∗∗p < .01,∗∗ p < .05,∗ p <.10.

adopters, as in Specification III in Table 2 and multinomial logit regression for potential adopters

as in Table 3. Table 5 shows the results, which again indicate the existence of competitive effects

and the incentive to differentiate. As discussed in the Introduction, I focus on the study of adoption

levels, while Freedman et al. (2018a) and Lin (2019a) emphasize the choices of vendors conditional

on adoption. Thus, their results do not conflict with mine.

Table 5: Robustness Check: Define L1 as CDR

L1 Adopters (Logit) Potential Adopters (Multinomial Logit)Upgrade to L2 Adopt L1 Adopt L2

Coef. M. E. Coef. M. E. Coef. M. E.

Number of L1 adopters 0.957*** 0.030 -2.023*** -0.052 -0.531*** -0.009(0.134) (0.259) (0.249)

Number of L2 adopters -1.216*** -0.038 -0.443** -0.010 -1.610*** -0.032(0.246) (0.214) (0.183)

Obs. used in estimation 4122 9398

Robust standard errors are clustered at the market level and are in parentheses. Theunit of observation is hospital-year. Observed hospital and other market characteristics,market-category effects, and time dummies are not reported. ∗∗∗p < .01,∗∗ p < .05,∗ p <.10.

To focus on oligopolistic market competition, I restrict my sample to markets with fewer than

six hospitals. Although previous studies show that the competitive effects from another entry would

be dampened in markets with more firms (e.g., Bresnahan and Reiss, 1991; Abraham et al., 2007;

Lin, 2015), it is still interesting to see whether this is the case in the context of EMR adoption.

22

Therefore, I examine the whole sample with all 793 markets as a robustness check. I perform the

logit regression for L1 adopters, as in Specification III in Table 2, and multinomial logit regression

for potential adopters as in Table 3. The results are presented in Table 6. As expected, the

competitive effects are still present, but smaller, than in Specification III in Table 2, and Table 3.

Table 6: Robustness Check: All Markets

L1 Adopters (Logit) Potential Adopters (Multinomial Logit)Upgrade to L2 Adopt L1 Adopt L2

Coef. M. E. Coef. M. E. Coef. M. E.

Number of L1 adopters 0.002 0.000 -0.049** -0.002 -0.099*** -0.003(0.020) (0.025) (0.023)

Number of L2 adopters -0.179*** -0.007 0.006 0.001 -0.108*** -0.003(0.025) (0.016) (0.028)

Obs. used in estimation 20091 18991

Robust standard errors are clustered at the market level and are in parentheses. Theunit of observation is hospital-year. Observed hospital and other market characteristics,market-category effects, and time dummies are not reported. ∗∗∗p < .01,∗∗ p < .05,∗ p <.10.

5 Structural Model

To conduct the counterfactual experiments so as to further evaluate the importance of competition

and the effects of the incentive policy, I require a structural model. Hence, I develop a dynamic

structural model, capturing the competitive effects of each adoption level and the strategic inter-

action between hospitals. My model builds on the theoretical framework developed by Ericson and

Pakes (1995), which is designed to capture the evolution of an industry with heterogeneous firms.

There are three types of hospitals in my model: potential adopters, L1 adopters and L2 adopters.

The active hospitals that make adoption decisions include potential adopters and Level 1 adopters.

Time is discrete and infinite, and each decision period is 1 year. Specifically, the adoption game

is played as follows. At the beginning of each period, hospitals observe the current state realiza-

tions and their private draws from the cost distribution. Active hospitals then make simultaneous

decisions. Potential adopters decide whether, and which level, to adopt. Level 1 adopters choose

to stay at Level 1 or upgrade to Level 2. The adoption is irreversible. After all these decisions are

made, hospitals incur costs if they decide to adopt or upgrade. They receive profits based on the

23

level at the beginning of each period. Adoption affects profits from the period following adoption.

Finally, markets evolve to the next period.

The equilibrium notion used here is that of Markov perfect Nash equilibrium as defined by

Maskin and Tirole (1988), where players’ strategies are functions of current, pay-relevant state

variables and private shocks. Forward-looking hospitals make adoption decisions to maximize the

expected present discounted value of flow profits, given the strategies of the others.41

5.1 States and Actions

The state sit for hospital i in market j at time t, is captured by the following variables: 42

sit = {xit, εit}

xit = {Lit, n1jt, n2jt, hi, zjt, τ t}

where xit is the observed state, and εit is the unobserved state (hospitals’ private information). For

the observed state variables, Lit, n1jt, and n2jt are endogenously determined by the model, while

hi, zjt, and τ t are exogenous variables. The variable Lit denotes hospital i’s adoption levels: a

potential adopter is at Level 0 (L = 0), a Level 1 hospital is at Level 1 (L = 1) , and a Level 2

hospital is at Level 2 (L = 2). The variables n1jt and n2jt represent the number of Level 1 adopters

and Level 2 adopters, respectively, in market j at time t. They capture the competitive effects and

are the key variables in the model. Variable hi denotes the observed hospital characteristics, such

as the number of beds in the hospital. The number of beds is assumed not to be affected by the

adoption decisions (EMR adoption is considered a relatively small decision, and does not affect the

number of beds). Let zjt denote the observed exogenous market characteristics, including HHI,

total hospital admissions, number of persons eligible for Medicare, percent of persons in poverty,

and market-category effects. Let τ t denote the year effects of adoption, which are the coefficients

on year dummies from reduced-form regressions in Table 2 and Table 3. These year effects capture

41The introduction of private shocks associated with actions guarantees that at least one pure strategy equilibriumexists in this setting. Please see Doraszelski and Satterthwaite (2010) and Gowrisankaran (1995) for more discussionabout the existence of an equilibrium. There may be multiple equilibria. In the paper, I use the method of Bajariet al. (2007) that assumes the data are generated by a single Markov perfect Nash equilibrium profile and recoversthe equilibrium beliefs directly from the data.

42For simplicity, I use sit to denote the state for hospital i in market j at time t, instead of sijt.

24

the time trend for EMR adoption and are assumed to follow a first order autoregressive process.43

The unobserved vector εit is the idiosyncratic private cost shock associated with each action that

hospital i may take at time t, and is assumed to follow an i.i.d. Type 1 extreme value distribution.

Potential adopters and Level 1 adopters are active hospitals in the market. The action for an

active hospital i at time t is denoted by ait. Potential adopters make decisions about whether and

which level to adopt. Let ait = 0 denote the action of not adopting, ait = 1 denote the action of

adopting the first level and becoming Level 1 adopters, and ait = 2 denote the action of adopting

both levels and becoming Level 2 adopters. Level 1 adopters make decisions about whether to

upgrade to Level 2. Let ait = 1 denote the action of not upgrading, and ait = 2 denote the action

of upgrading to Level 2 and becoming Level 2 adopters.

5.2 Profit and Cost Function

Hospitals receive profits every period, conditional on the adoption of EMR.44 Let φ(s|γ) represent

the conditional per-period profit function for a hospital in state s. The functions are linear in s,

and the profit of not adopting is normalized to 0. Specifically:

φ(sit|γ) =1(Lit = 0)× 0

+ 1(Lit = 1)(γ11n1jt + γ12n2jt + γ1hhi + γ1zzjt)

+ 1(Lit = 2)(γ21n1jt + γ22n2jt + γ2hhi + γ2zzjt)

(5.1)

The functions depend on hospital i’s own characteristics and market-level characteristics.45

The externalities of adoption are captured by the key variables of n1jt and n2jt. The revenue from

adoption is expected to increase over the size of the hospital, measured by the number of beds

(hi). The market concentration, measured by HHI, potentially affects the profit functions, and it

would be interesting to quantify this effect. Total hospital admissions in the market are to proxy

the market demand for healthcare. The variables of number of persons eligible for Medicare and

43The specification of year effects is further discussed in Section 6.1.2.44In my model, I cannot separately identify the returns and maintenance costs of EMR per period after the

adoption, since they are both functions of the state variables. Thus, I assume a sunk cost at the time of adoption,and the ongoing profits per period after the adoption.

45Adopting EMR can change profits through different channels, for example, by raising revenue per patient orprocedure, raising the number of patients or procedures, or lowering operating costs. I do not distinguish betweenthese channels, and all of them are captured by to the extent that they vary with the exogenous market and hospitalcharacteristics in the profit function.

25

percent of persons in poverty capture not only the demand for healthcare but also demand for

EMR technologies, as studies show that health IT is likely to be more effective among relatively

low-income populations (Miller and Tucker, 2011). Moreover, these variables could indicate the

possible returns from the Medicare/Medicaid EMR incentive plan enacted in 2009, as subsidies are

functions of inpatients covered by Medicare/Medicaid. The market-category effects, again, capture

the unobserved market heterogeneity in profitability from L1 and L2 adoption.

The upfront adoption costs include costs for software and hardware installation, and staff train-

ing. The cost functions consist of fixed costs, variable costs associated with time, and hospitals’

private idiosyncratic shocks, which are associated with actions. Other than the private shocks,

costs are considered homogeneous for hospitals at the same time.46

Potential adopters incur costs when adopting Level 1 or Level 2, and Level 1 adopters pay the

costs to upgrade to Level 2. No cost is incurred when potential adopters choose not to adopt, or

when Level 1 adopters stay at Level 1. Specifically, the costs are as follows:

C(sit, ait|c) + εit(ait) =1(Lit = 0, ait = 0)0

+ 1(Lit = 1, ait = 1)0

+ 1(Lit = 0, ait = 1))(c10 + c1ττ1t )

+ 1(Lit = 0, ait = 2))(c20 + c2ττ2t )

+ 1(Lit = 1, ait = 2))(c21 + c12τ τ12t )

+ εit(ait),

(5.2)

where τ1t , τ2t , τ

12t capture the time effects. From the results in Tables 2 and 3, we notice that the year

effects significantly affect adoption, and there is a roughly rising trend over time. The reasons could

be that the price and functionality of EMR systems change, that governments’ policies regarding

EMR adoption develop, or simply that people’s taste for EMR changes as time goes by. To include

the time effects in the model, I allow the adoption costs to depend on year effects obtained from

the multinomial logit regression for potential adopters, and allow the upgrade costs to depend on

46Ideally, the costs should be modeled heterogeneous on observed hospital and market characteristics. However, asI have already assumed profits to be heterogeneous on observables, I cannot model heterogeneity for both post-entryprofits and entry costs. This is common for dynamic models with only entry data (see Fan and Xiao, 2015), becausewe cannot tell whether the variation of entry times is caused by heterogeneity from post-entry profits, or heterogeneityfrom entry costs.

26

the year effects from the logit regression for L1 adopters. In other words, variable τ1t consists of the

coefficients on year dummies from the first column in Table 3, variable τ2t of the coefficients on year

dummies from the second column, and variable τ12t of the coefficients on year dummies from the

logit regression in Table 2.47 As these reduced-form regressions examine the drivers of potential

adopters’ and L1 adopters’ decisions, respectively, the year effects obtained should capture the

corresponding time trend for different levels of EMR adoption.

In summary, the parameters θ to be estimated include γ in the profit functions and c in the

cost functions. Parameters γ11, γ12, γ21 and γ22 measure the competitive effects and are of great

interest in the paper.

5.3 Equilibrium

In each period, forward-looking hospitals make adoption decisions to maximize the expected present

discounted value of flow profits, given the strategies of the others. Based on the current state

variables, hospital i’s strategy at t: σit, is a mapping from state vectors to actions:

σit : sit 7→ ait

The value function for a hospital at adoption level L (L = 0, 1, 2) and in state s, is denoted by

VL(s). The Bellman equations are as follows:

V0(s) = maxa∈{0,1,2}

{βE[Va(s

′)|s]

+ φ(s)− C(s, a)− ε(a)

}(5.3)

V1(s) = maxa∈{1,2}

{βE[Va(s

′)|s]

+ φ(s)− C(s, a)− ε(a)

}(5.4)

V2(s) = φ(s) + βE[V2(s

′)|s]

(5.5)

Given the private shocks, potential adopters maximize their profits by choosing whether to adopt,

and which level to adopt, while Level 1 adopters choose whether to upgrade. Level 2 adopters

are assumed inactive, as I do not model a higher level in this paper and the adoption process is

considered irreversible.

The equilibrium is a Markov perfect Nash equilibrium (MPNE), which is defined as the strategy

47The year effects follow an AR(1) process and are discussed in Section 6.1.2.

27

σ∗ that satisfies:

V (s|σ∗i , σ∗−i) ≥ V (s|σ′i, σ∗−i)

for any σ′i, and any s.

6 Estimation

For the estimation, I apply the method of Bajari et al. (2007) (BBL), which provides a two-stage

algorithm for estimating dynamic games under the assumption that the behavior is consistent with

Markov perfect equilibrium.48 In the first stage, I estimate the policy functions and the evolution

of states, which are characterized by flexible reduced-form regressions. In the second stage, the

structural parameters are estimated by imposing the optimality conditions. Forward-looking agents

choose their actions by maximizing the expected discounted present value of profits, given their

beliefs in equilibrium. I apply the method of forward simulation to obtain the value functions

associated with different actions, then the probabilities of each action. The structural parameters

are recovered by maximizing the predicted probabilities of the observed actions, similar to a pseudo-

maximum-likelihood estimator introduced by Aguirregabiria and Mira (2002).

The BBL method alleviates computational burden, as the two-step approach does not require

equilibrium computations. Also, this method can be used to estimate models with multiple equi-

libria under the assumption that only one equilibrium is observed in the data (i.e., the same policy

function predicts behavior across markets). Because the first-step estimation recovers the agents’

correct beliefs for the equilibrium actually played in the data, there is no need for the researcher

to make assumptions about which of many potential equilibria is being played.

6.1 The First Step: Transitions between States

The first step examines the policy functions and the evolution of states. In the equilibrium actually

played in the data, agents are assumed to have correct beliefs about the environment as well as other

players’ behavior, and they make adoption decisions based on the current state vectors they face.

48It is difficult to apply the Ericson and Pakes (1995) framework to the data, because of the complexity ofcomputing an equilibrium and multiple equilibria. To ease the computational burden, a series of papers propose atwo-stage method to estimate dynamic games, including Bajari et al. (2007), Aguirregabiria and Mira (2007), Pakeset al. (2007), and Pesendorfer and Schmidt-Dengler (2008). These papers have built on the insights of Rust (1987),Hotz and Miller (1993), and Hotz et al. (1994).

28

The state variables in the next period are functions of the current state and actions. The decision-

making process and state transitions are characterized by different specifications of reduced-form

regressions.

6.1.1 Adoption Decisions of Hospitals

Active hospitals include Level 1 adopters and potential adopters. Level 1 adopters make decisions

about whether to upgrade to Level 2. I use the logit regression shown in Table 2 Specification III

as the policy function. Potential adopters make decisions about whether and which level to adopt.

I use the multinomial logit regression shown in Table 3 to capture their decisions. I choose these

regressions because they fit the data well, and the coefficients are estimated precisely. The adoption

decisions update hospitals’ status in the next period indicated by Lit, as well as the number of L1

and L2 adopters in the market, denoted by n1jt and n2jt, respectively.

6.1.2 Evolution of Exogenous Variables

The exogenous variables hi, zjt and τt include hospital characteristics (the number of beds for each

hospital), market characteristics (HHI, total hospital admissions, number of persons eligible for

Medicare, percent of persons in poverty, and market-category effects), and year effects for EMR

adoption. Hospital and market characteristics are assumed to stay fixed at the values when the

hospitals make the decisions.49 Year effects τ1t , τ2t and τ12t which affect the installation costs of

adoption, are assumed to follow an AR(1) process as follows:

τat+1 = µa0 + µaττat + ωat+1 (a = 1, 2, or 12) (6.1)

where ωat is a shock following a normal distribution, that is, ωat ∼ N(0, σa).

One concern about this AR(1) specification is that it may not capture the possible breaks of

policy, technology, or preference during the sample period. EMR adoption has become a serious

and particularly important topic since 2004, when former U.S. president Bush proposed that EMR

technologies should be widespread in the U.S. healthcare system by 2014. Moreover, he announced

several new initiatives, such as doubling funding for demonstration projects in health IT, and using

49In other words, for the forward simulation starting from period t, hospitals assume the exogenous hospital andmarket characteristics stay constant at the current values.

29

the federal government to foster the adoption of health IT. This proposition received bipartisan

support. In 2009, former U.S. president Obama signed the American Recovery and Reinvestment

Act into law, to promote the national adoption of EMR. Furthermore, regarding EMR technology,

the integrated EMR system became popular around the mid-2000s. For the preference for EMR,

people started to have concerns about patients’ privacy, and uncertainty surrounded adopters’

cost savings from EMR. To address these concerns, I consider alternative specifications to include

a policy/technology/preference break around the mid-2000s. Specifically, I create time dummies

D2003. D2004, ..., D2007 to indicate periods in or after 2003, 2004, ..., 2007 respectively. I add

these time dummies to the above AR(1) regressions one by one and find that only the coefficient

on D2006 in the AR(1) regression for τ12t is precisely estimated, and is negative in this case. This

result shows that year effects τ12t experienced a drop in 2006, suggesting that the upgrading trend

slowed down. Therefore, for the transition of τ12t , the following specification is used:

τ12t+1 = µ120 + µdD2006 + µ12τ τ12t + ω12

t+1 (6.2)

For the transition of τ1t and τ2t , I still use the specification as in (6.1).50 Table A-1, A-2 and A-3 in

the appendix show the results.

6.2 The Second Step: Recovering Structural Parameters

The first step recovers policy functions and the law of motion for states. The second step finds

parameters that rationalize these observed policy functions as the optimal actions.

One benefit of the BBL method is that it significantly reduces the computational burden.

This is because the profit (conditional on adoption) and adoption cost functions are linear in all

parameters, and thus, are the value functions. This method avoids computing the value functions

many times for different sets of parameters. I apply forward simulation to obtain the choice-specific

value functions to estimate the parameters.

For each state sit, I simulate the paths of play for every action that hospital i might take.

In each path, I draw choice-specific shocks for each hospital in each period, combined with the

observed state to determine the choices. States evolve as outlined in Section 6.1. The future path

50I also examine the specification of AR(2), but the coefficients on the second lagged terms are not significant.

30

of play is simulated for a long period (100 periods), until the discounted present value of profits is

sufficiently small. The discount factor β needs to be fixed for econometric purposes, and I fix it

at 0.9 in the paper.51 For each simulated path of play, I obtain the discounted present value of all

the payoffs. I then simulate for S = 1000 times and obtain the choice-specific value function as the

mean of these discounted present values.52 The probability of each action is computed by using

the aggregation properties of the extreme value distribution of private shocks. The estimates are

recovered by maximizing the probabilities of the observed action.

There are two types of active hospitals: potential adopters and Level 1 adopters. I first illustrate

the case for potential adopters in detail.

In every period, potential adopter i chooses from “Not Adopt”(a = 0), “Adopt Level 1”(a = 1),

and “Adopt Level 2”(a = 2). I simulate the path of play for each action that hospital i might take,

as specified in Section 6.1. The unconditional per-period payoff function for hospital i at time t is

π

(sit, σit(s)|θ

)= φ(sit|θ)− C

(sit, σit(s)|θ

)− ε(σit(s)

)(6.3)

For each simulated path s conditional on action ait, I simulate for T = 100 periods, and compute

the discounted present value of all the future payoffs:

V s(sit|ait, θ) =T∑τ=0

βτπit+τ (6.4)

Since all the profit functions and adoption cost functions are linear in the parameters, equation

(6.4) can be written as the inner product of the discounted value of the state vector and the vector

of parameters:

V s(sit|ait, θ) = Ds(sit|ait)θ′ (6.5)

where D is the discounted present value of state vectors.53

The choice-specific value function is the expected discounted present value of all future payoffs

for hospital i:

E

( ∞∑τ=0

βτπit+τ

∣∣∣∣∣ sit, σi, σ−i), (6.6)

51Other studies use 0.9 as the discount factor; see, for example, Ryan (2012).52I also simulate for S = 1500 and S = 2000 times, and the results are almost the same.53Note that D includes the expected discounted present value of the private shock ε as well.

31

and can be approximated by:

V (sit|ait, θ) =1

S

∑s

V s(sit|ait, θ) (6.7)

Three choice-specific value functions are generated through this procedure: V (sit|ait = 0, θ),

V (sit|ait = 1, θ), and V (sit|ait = 2, θ), which, respectively, denote the value functions associated

with Not Adopt (ait = 0), Adopt Level 1 (ait = 1), and Adopt Level 2 (ait = 2). I then recover the

conditional choice probabilities predicted by the model: P̂L=0(ait = 0|sit, θ), P̂L=0(ait = 1|sit, θ) as

well as P̂L=0(ait = 2|sit, θ).

P̂ (ait = k|sit, θ) =exp [V (sit|ait = k, θ)]

exp [V (sit|ait = 0, θ)] + exp [V (sit|ait = 1, θ)] + exp [V (sit|ait = 2, θ)]

(k = 0, 1, 2, andLit = 0)

(6.8)

Similarly, I simulate the choice-specific value functions for each level 1 adopter in each period.

Level 1 adopters make decisions about whether to upgrade to Level 2. In this case, Level 1 adopters

stay at Level 1 when a = 1, and upgrade to Level 2 when a = 2. The conditional choice probabilities

predicted by the model are:

P̂ (ait = k|sit, θ) =exp [V (sit|ait = k, θ)]

exp [V (sit|ait = 1, θ)] + exp [V (sit|ait = 2, θ)](k = 1, 2, andLit = 1) (6.9)

After the probabilities of each decision predicted by the model are generated, I use a pseudo-

likelihood estimator by maximizing the model’s probability P̂ of the actions actually taken by the

hospital in the data. This probability is given by

l =∑t

∑i

ln P̂ (ait = kit|sit, θ) (6.10)

where kit is the observed action by hospital i at time t in the real data.

The standard errors of the parameters are estimated using the technique of bootstrapping.

There are 530 markets in my sample. Therefore, I draw ns random samples of 530 markets with

replacement. (Each time, some markets from the original sample may be drawn more than once,

and others may not be drawn.) For each constructed random sample, I estimate the parameters of

32

the model.54 The estimator of the asymptotic covariance matrix is the sample variance of the ns

sets of estimated parameters. I report standard errors using ns = 400 bootstraps.55

7 Structural Parameters

The estimated structural parameters are presented in Table 7. The results show the presence of

competitive effects in the adoption: the parameter for “number of L1 adopters” is significantly neg-

ative for the L1 profit function, and the parameter for “number of L2 adopters” is also significantly

negative for the L2 profit function. Thus, the payoffs from each level decline substantially with the

number of adopters of this level. Moreover, the L1 profit function is also decreasing in the number

of L2 adopters in the market (−0.033), but this effect is quite small and only marginally significant.

That is, the presence of L2 adopters will deter the L2 adoption a lot more than the L1 adoption.

Meanwhile, the L2 profit function does not respond much to the number of L1 adopters. These

varying degrees of competition show the hospitals’ incentives to differentiate from each other along

the adoption levels.

The effect of market concentration on technology adoption is often of great interest.56 In the

case of EMR adoption, my results show the parameters for HHI are insignificant for both the L1

and L2 profit functions. This suggests that the concentration level of the hospital market does not

play a role in the EMR adoption, once the competition level of EMR adoption is controlled for.

Karaca-Mandic et al. (2017) study the adoption of medical technology of drug-eluting stents, and

find the similar results.

The other results are as expected. Hospitals with more beds benefit more from EMR adoption.

Markets with more admissions have greater demand for healthcare, which results in higher profits

from L2 adoption. The results also show that markets with more persons eligible for Medicare have

greater benefits from L2 adoption, but lower benefits from L1. This indicates that such markets

have a strong need for the advanced EMR applications. The parameters for market-category effects

in the profit functions are all positive and significant. Moreover, they are increasing as the markets

54For each bootstrapped sample, I re-estimate all of the stages of estimation procedure, so the resulting standarderrors reflect the multistage nature of the procedure.

55I have used different numbers of bootstraps, including 200, 300, and 400, and the results are quite similar.56Gowrisankaran and Stavins (2004) find that firms in concentrated markets are more likely to adopt the automated

clearing house electronic payments system. Freedman et al. (2018b)show that hospitals have more incentives to choosethe same EMR vendors (agglomeration) in more competitive markets.

33

Table 7: Structural Parameters

L1 L2

Number of L1 adopters -0.207*** 0.019(0.029) (0.014)

Number of L2 adopters -0.033* -0.268***Profit (0.018) ( 0.044)

Log of number of beds 0.071*** 0.111***(0.014) (0.015)

HHI -0.033 0.013(0.052) (0.059)

Log of total admissions -0.047 0.388***(0.046) (0.076)

Log of number of persons eligible -0.194*** 0.464***for Medicare (0.032) (0.069)Percent of persons in poverty -0.263 0.122

(0.166) (0.220)Market-category effects:

between the 10th and 20th percentiles 0.467*** 0.711***(0.106) (0.103)

between the 20th and 30th percentiles 0.479*** 0.883***(0.108) (0.119)

between the 30th and 40th percentiles 0.606*** 1.119***(0.114) (0.145)

between the 40th and 50th percentiles 0.756*** 1.308***(0.118) (0.158)

between the 50th and 60th percentiles 0.806*** 1.415***(0.122) (0.182)

between the 60th and 70th percentiles 0.972*** 1.496***(0.130) (0.198)

between the 70th and 80th percentiles 1.047*** 1.758***(0.136) (0.219)

between the 80th and 90th percentiles 1.184*** 1.913***(0.148) (0.236)

above the 90th percentiles 1.471*** 2.240***(0.167) (0.264)

L0 to L1 L0 to L2 L1 to L2

Constant 8.017*** 27.214*** 22.120***Cost (1.116) (1.824) (2.114)

Year effects -0.470*** -0.822*** -0.930***(0.119) (0.105) (0.108)

∗∗∗p < .01,∗∗ p < .05,∗ p < .10.

34

move from the lowest percentile range to the highest. This is consistent with the way in which

I define the market-category effects. In the cost functions, we observe negative and significant

parameters for year effects. As year effects follow AR(1) and increasingly converge to the steady

state, the negative parameters indicate that the costs of EMR are decreasingly converging over

time. In reality year effects for EMR adoption could also capture the change in people’s preference

for EMR and pressure from the government, etc. Thus, my estimates of cost here capture not only

the pure cost for this technology per se but also the adoption trend.

To adopt EMR technology with L2 applications such as CPOE, U.S. hospitals need to spend

an average of $80, 000 to $100, 000 per bed, for the required project planning, software, hardware,

implementation, and training (Laflamme et al., 2010). Dranove et al. (2015) point out that the

cost of installing and operating an EMR system by an average-sized hospital is at least $10 million

and could be much higher. The average number of beds for hospitals in my sample is 99, and $10

million would translate to around $100, 000 per bed. The costs provided by these two papers are

roughly consistent. I apply these estimates here, and assume that the costs of adopting L2 EMR

are $100, 000 per bed in 2008. This would translate to upfront costs to implement Level 2 in 2008 of

$9, 900, 000 for an average hospital in my dataset that has 99 beds. From Table 7, the initial costs

for an average potential adopter to adopt Level 2 are 27.214 units in 1999 and 24.647 in 2008.57

By equating 24.647 units to $9, 900, 000, 1 unit would stand for $401, 672. To put these results into

context, an average potential hospital with 99 beds in my dataset would pay costs of $3, 082, 431

(7.674 units) to adopt Level 1 and $9, 900, 000 to adopt Level 2 in 2008. An average L1 adopter

would pay costs of $7, 583, 969 (18.881 units) to upgrade to L2.

Regarding the profits, the average hospital in an average market would receive profits of $273,940

(0.682 units) per year after adopting Level 1, and would receive profits of $813,787 (2.026 units) per

year after adopting Level 2. The competitive effects are large in terms of profits. For an average

hospital, one more L1 adopter in the market would decrease its annual profits from adopting Level

1 by $83,146 and its annual profits from adopting Level 2 by $13,255, keeping other variables

constant. If there were one more L2 adopter in the market, an average hospital’s annual profits

from L2 would decrease by $107,648. I further discuss the competitive effects and measure their

5724.647 is computed as 27.214 + (−0.822) × (3.123), where 3.123 is the year effect of 2008 for L2 adoption fromTable 3.

35

magnitude in the counterfactual experiments in the following section.

8 Experiments

The estimated structural parameters, along with the underlying theoretical model, allow me to

perform the counterfactual experiments. Through solving for the equilibrium outcomes, and sim-

ulating hospitals’ adoption behavior, my first class of experiments analyzes the competitive effects

during the adoption process and the strategic interaction between hospitals. To what extent would

competitive effects affect the adoption times? Do hospitals have preemptive adoption? These are

the primary questions I aim to address.

The second class of experiments evaluates the government’s incentive plans for EMR. First, I

evaluate the effectiveness of the policy. To what extent would the policy spur adoption? Would

the effectiveness vary by hospital or market characteristics? Is the policy cost-effective? Second,

I examine alternative design of the subsidy policy, to assess whether adoption could be further

accelerated. Finally, I study the role that competition plays in the effectiveness of the incentive

plans. Competitive effects for EMR adoption are typically overlooked by researchers and policy-

makers. To what extent would the effectiveness of the subsidy policy be affected by the competitive

effects?

The remainder of this section is structured as follows. Section 8.1 describes the method I use

to solve the equilibrium outcomes. Section 8.2 examines the role that competitive effects play in

the adoption process. Section 8.3 evaluates the subsidy policy.

8.1 Method

Based on the estimated structural primitives and the Pakes and McGuire (1994) algorithm, I solve

the finite-state equilibrium outcomes for hypothetical monopoly markets and symmetric duopoly

markets, in order to perform the counterfactual experiments. Ideally, the MPNE for every market in

the sample could be solved. The computational burdens prevent me from doing this. Nevertheless,

it would be very informative and important to study monopoly and duopoly markets, considering

that my study focuses on concentrated markets.

The state vector for a hospital at adoption level Lit, besides the private shocks εit, is denoted

36

by

sit = {n1jt, n2jt, hi, zjt, τt},

where n1jt and n2jt are the discrete-valued endogenous variables, hi, zjt, τt are the exogenous vari-

ables, and εit is the vector of private shocks. For simplicity, the exogenous hospital and market

variables hi, zjt are assumed fixed. These variables include the number of beds for each hospi-

tal, HHI, total hospital admissions, number of persons eligible for Medicare, percent of persons

in poverty, and market-category effects. Previous results show that it is important to take the

time trend into consideration (e.g., Dranove et al., 2015) .58 It is particularly important for dy-

namic models, since forward-looking agents rely on the trend to make better decisions regarding

the adoption timing. Year effects τ1t , τ2t and τ12t are assumed to follow an AR(1) process in my

estimation, as discussed in Section 6.1.2. I discretize each of them to 10 levels, and compute the

transition matrix, based on the method of Tauchen (1986). This method allows me to choose values

for the year effects and the transition probabilities so that the resulting finite-state Markov chain

mimics closely the underlying continuous-valued autoregression. See the Appendix for more details.

Note that there is a permanent drop in the constant term for the AR(1) process of τ12t from 2006.

Therefore, I compute the MPNE from 2006, and simulate hospitals’ behavior since then.

For a hospital at level L, the variation of its state is characterized by (n1jt, n2jt, τt), as variables

hi, zjt are assumed constant. In my model, there are 3, 000 possible states for the hospital in

a monopoly market, and the number of states would be 6, 000 for each hospital in a symmetric

duopoly market.59 Let sk denote the k-th state, and let V0(s), V1(s), and V2(s) denote the value

functions for potential adopters, Level 1 adopters, and Level 2 adopters, respectively, at state s.

Using the aggregation properties of the extreme value distribution of the private shocks ε, the

58Dranove et al. (2015) point out that, prior to the policy, there was a steadily rising trend for EMR adoption,and thus, at least part of the growth after the policy is simply the continuation of this pre-existing trend.

59For the monopolist, the values of (n1jt, n2jt) could be (0, 0), (0, 1), and (1, 0). For the hospital in a symmetricduopoly market, (n1jt, n2jt) could take six different values, as (0, 0), (1, 0), (0, 1), (2, 0), (1, 1), and (0, 2). Each ofthe year effects is discretized to 10 levels, and thus, τt could take 1, 000 different values.

37

Bellman equations for these value functions can be written as follows:

V0(s) = ln {exp [βMV0(s)]

+ exp [−C(s, L = 0, a = 1) + βMV1(s)]

+ exp [−C(s, L = 0, a = 2) + βMV2(s)]} , (8.1)

V1(s) = ln {exp [φ(s, L = 1) + βMV1(s)]

+ exp [−C(s, L = 1, a = 2) + φ(s, L = 1) + βMV2(s)]} (8.2)

V2(s) = φ(s, L = 2) + βMV2(s), (8.3)

where M is the transition probability matrix, with the kj-element being the probability from the

k-th state sk to the j-th state sj .

Let P0(a|s) denote the probabilities of not adopting (if a = 0), adopting L1 (if a = 1), and

adopting L2 (if a = 2), for a potential adopter (L = 0) in state s. Similarly, let P1(a|s) denote the

probabilities of staying at L1 (if a = 1) and upgrading to L2 (if a = 2), for an L1 adopter in state

s. Based on the value functions of V0(s), V1(s), and V2(s), the probabilities of each action a are:

P0(a|s) =

exp

(−C(s, L = 0, a) + βMVa(s)

)exp(V0(s)

) (a = 0, 1, 2) (8.4)

P1(a|s) =

exp

(−C(s, L = 1, a) + φ(s, L = 1) + βMVa(s)

)exp(V1(s)

) (a = 1, 2). (8.5)

Note that C(s, L = 0, a = 0) = 0 and C(s, L = 1, a = 1) = 0.

If these choice probabilities are known, along with the transition probability matrix for year

effects Mτ , the transition probability matrix M can be computed.60 For example, for a monopolist

at L = 0 in state sk with n1 = 0, n2 = 0 and τ = τk, keeping other variables constant, the

probability of getting to state sj with n1 = 0, n2 = 1 and τ = τ j would be:

M(k, j) = P0(a = 2|sk)Mτ (k, j), (8.6)

60The transition probability matrix for year effects Mτ is discussed in the Appendix, which in my case is a1000-by-1000 matrix, and the kj-th element indicates the probability from the k-th state τk, to the j-th state τ j .

38

where P0(a = 2|sk) is the probability of a potential adopter adopting L2 in state sk and Mτ (k, j)

is the probability from τk to τ j .

I then apply an iterative algorithm to compute the value functions as well as the probabilities

of each choice. The procedure is as follows.

1. Guess a set of initial values for the probabilities of each choice P 0L(a|s) for active hospitals.

2. Use the probabilities of each choice and the transition matrix for year effects Mτ to compute

the state transition matrix M . Then plug M into the Bellman equations (8.1), (8.2) and (8.3) to

compute the value functions for hospitals at different adoption levels.

3. Using the computed value functions from step 2, equation (8.4) and (8.5), calculate a new

set of probabilities of each choice for active hospitals P 1L(a|s).

4 Repeat steps 2 and 3 to generate P 2L(a|s), P 3

L(a|s).,... PnL (a|s), until these probabilities

converge.

After solving the equilibrium values of PL(a|s), I simulate the hospitals’ paths of play in a

monopoly market and a symmetric duopoly market from 2006. My primary interest is the impor-

tance of the competitive effects and the evaluation of the government’s incentive plan.

8.2 Competitive Effects

Industrial Organization researchers have devoted substantial energy to studying relationship of

market structure and the intensity of competition by entry models (Bresnahan and Reiss, 1991;

Berry, 1992; Mazzeo, 2002; Seim, 2006; Augereau et al., 2006). Generally, firms’ entry decisions

rely on entry cost and post-entry revenue, which are functions of the firm’s characteristics, market

characteristics and the intensity of competition. My model of technology adoption is consistent with

the entry model, in which hospitals choose whether and when to enter (or adopt). In this section,

I first quantify the effect of competition on EMR adoption, especially for the adoption of L2 EMR,

as the national implementation of these advanced applications is the goal of the government.61 To

do that, I compare hospitals’ adoption behavior in a symmetric duopoly market, to the scenario

in which they face no competition and act independently. Then I examine whether preemptive

adoption exists. Similar to the method of Igami (2017), I conduct an experiment to remove the

preemption motive in the symmetric duopoly market, by assuming that one hospital A does not

61The use of CPOE is necessary for the Medicare/Medicaid incentive program enacted in 2009.

39

respond to the other hospital B’s adoption behavior. In this case, hospital B does not have the

preemption motive. The preemption is present if hospital B adopts significantly earlier in the

duopoly market with strategic interactions than in the case with no preemptive motive.62

8.2.1 The Effect of Competition on EMR adoption

To quantify to what extent competitive effects affect adoption times, I simulate hospitals’ adoption

behavior in a symmetric duopoly market under two scenarios. The first is based on my estimation

results, in which hospitals compete in the adoption process. I refer to the model in the first case as

the baseline model. In the second scenario, I assume that hospitals make decisions independently,

as if they were in a monopoly market with no other hospitals to compete with in the adoption

process.63 My experiment identifies the competitive effects because that first, the hospitals are

assumed to have the same characteristics and hospital heterogeneity is controlled for.64 Second,

market characteristics are also assumed to be the same under the two scenarios. Therefore, the

competitive effects could be quantified by comparing hospitals’ behavior in the two cases.

Specifically, suppose that the hospitals in my experiment have 100 beds, and are initially poten-

tial adopters. The hypothetical markets are all assumed to have 15, 000 hospital admissions each

year, 30, 000 persons eligible for Medicare, and 20% of people in poverty. Regarding the market

category effects for EMR profitability, the markets are between the 40th and 50th percentiles. The

hospitals’ paths of play starting from 2006 are simulated, based on the equilibrium probabilities

computed from Section 8.1. The simulation number is 10, 000, and I obtain the timing of adoption

by averaging over the 10, 000 simulations.

The simulation results are reported in Table 8.65 The average time needed to adopt L2 of

EMR is substantially shorter for the potential adopters that act independently as if they were in

a monopoly market, and the difference is as large as 11.47 years. To delve deeper into the reasons

for this large discrepancy, I compute the average time when the first of the two hospitals adopts L2

EMR over all the simulations, and also the average time when the second adopts. The difference

62A and B are identical hospitals except for the private shocks in each period.63Note that hospitals in both scenarios are forward-looking. Thus, hospitals that act independently will also take

the adoption trend captured by year effects into account.64Note that the two hospitals in the duopoly market have i.i.d. private shocks, and their draws can be different

in the simulations. Nevertheless, I assume that each hospital’s draws are the same in each period in each simulationunder the two scenarios, to ensure the differences are not caused by the private draws.

65All hospitals adopt Level 2 eventually in my simulations.

40

is small between the times when the first hospital adopts L2 EMR in the case with competition

and in the case without competition (7.40 years vs. 6.79 years). The first adoption is slightly later

in the case with competitive effects, because forward-looking hospitals realize the benefits from

adoption will decrease as the other hospital adopts EMR in the future. We observe a very large

difference in the times when the second hospital adopts L2 EMR in the two cases. The time needed

is 41.51 years in the former case and 19.05 years in the latter. In the baseline model, once the first

adoption occurs, the second hospital’s potential profits from adoption would be greatly reduced,

and its adoption would be significantly deterred. As a result, we find that the large discrepancy

in the first row of Table 8, which shows the average time for L2 adoption (24.28 years vs. 12.81

years), is mainly driven by the delay to the second adoption in a competitive environment.

Table 8: Simulation Results of Hospitals in a Duopoly Market (Starting From L = 0)

With WithoutDifference

Competition Competition

Average time for L2 adoption 24.28 12.8111.47

(0.329)

Average time when the 1st adoption7.40 6.79

0.61of L2 EMR occurs (0.082)

Average time when the 2nd adoption41.51 19.05

22.46of L2 EMR occurs (0.385)

The time span (year) is 200, and the number of simulations is 10000. Standard errorsare in parentheses.

From these experiments, I find that competitive effects in a duopoly market would greatly

deter adoption, particularly for the second adoption. Previous studies typically attribute the low

adoption of EMR to concerns about adoption costs or patients’ privacy, but ignore the competitive

effects. Based on my findings, the competitive effects also play a very important role in hindering

adoption. These results shed light on the subsidy policy designed to promote national adoption of

EMR technology: A policy strategy capturing market heterogeneity in competitive effects would

be more effective for achieving this goal. This policy analysis is further discussed in Section 8.3.3.

41

8.2.2 Preemption

In a dynamic adoption game with competitive effects, a potential adopter may have the preemptive

motive by adopting sooner than in the scenario in which its rivals do not respond to its adoption.

By preemption, the hospital would enjoy the monopolist’s profits for a longer period, because its

adoption would decrease the rivals’ adoption profits as a result of competitive effects, and hence

deter the rivals from adoption. Thus, if the competitive effects are substantial, hospital would

engage in preemption in the adoption process. To examine whether the preemption exists in

the symmetric duopoly market, I conduct a counterfactual experiment to remove the preemptive

motive. I assume that one hospital (named as hospital A) does not respond to the other hospital

B’s adoption behavior, so that B does not have the preemptive incentive. My focus is on how

hospital B differently best responds to such a hypothetical hospital A, compared to the baseline

model that captures hospitals’ strategic interactions.

Specifically, hospital A ignores hospital B’s adoption and makes adoption decisions as if in the

baseline model with modified states. For example, if hospital A is in the state where hospital A is

at L0 and hospital B is at L2 (n1 = 0 and n2 = 1), hospital A will follow the baseline strategy as

if in the state where both hospitals are at L0 (n1 = 0 and n2 = 0).66 Then I solve for hospital B’s

strategy in this case.

Through simulations, I find that it takes 24.43 years on average for hospital B to adopt L2

EMR in this no-preemption experiment. Hospital B adopts 0.15 years earlier in the baseline model

(24.43 years vs. 24.28 years), and this difference is not statistically significant. Therefore, I find no

evidence of preemption in the adoption process. The reason could be that the competitive effects

are not large enough for hospitals to engage in preemption.67

66My method is similar to Igami (2017). He examines to what extent incumbents preempt potential entrants byinnovating more aggressively in the hard disk drive industry. In his no-preemption counterfactual experiment, heforces the potential entrants to ignore incumbents’ innovation behavior, and follow the strategy in the baseline modelthat captures strategic interactions, but with a modified state.

67In another counterfactual experiment, I increase the competitive effect by decreasing the parameter for numberof L2 adopters in the L2 profit function by 30% (from −0.268 to −0.348), and find that hospital B would adoptapproximately 1 year earlier in the baseline model than in the no-preemption experiment. This preemption effect isstatistically significant at the 5% significance level.

42

8.3 Evaluation of Stimulative Plans

I perform policy experiments to evaluate the stimulative plans for EMR adoption. In 2009, the

American Recovery and Reinvestment Act devoted an estimated $27 billion and established a

Medicare/Medicaid incentive program to stimulate adoption of EMR technology, motivated by the

low adoption rate up to that point.68 As reported by the Centers for Medicare & Medicaid Services,

by October 2018, more than $24.8 billion in payments had been made in the Medicare incentive

program and more than $6 billion in the Medicaid incentive program, and more than 642,600

eligible professionals, eligible hospitals, and critical access hospitals had been actively registered in

the Medicare/Medicaid incentive programs.69

Although adoption rates have risen substantially since the announcement of the incentive pro-

gram, the extent to which the recent increase can be attributed to the program is unclear. As

adoption rates were already increasing on their own, critics have argued that hospitals would have

adopted EMR even if the incentives had not been put into place, and the financial incentives sim-

ply substituted public money for private funds (Adler-Milstein and Jha, 2017). Previous studies

evaluating this program are based on statistical results or reduced-form regressions (Adler-Milstein

et al., 2013; Adler-Milstein and Jha, 2017; Dranove et al., 2015; DesRoches et al., 2013), which

would offer limited counterfactual analysis. Based on the underlying structural model, which de-

scribes hospitals’ decision-making and captures the year effects of adoption, my experiments seek

to address this concern and offer a more accurate assessment of this policy.

Previous results in Tables 2, 3 and 7 show heterogeneity in the adoption likelihood across both

hospitals and markets. For example, the likelihood is small for hospitals with fewer beds or markets

with fewer hospital admissions. The results are consistent with previous studies (DesRoches et al.,

2013; Jha et al., 2009; Adler-Milstein et al., 2013). However, studies of how hospitals in different

scenarios would respond to the incentive program are very rare. Would the policy’s effects vary

across hospitals and markets? In which case would it be mostly effective? My experiments answer

these questions by providing quantitative results.

This kind of subsidy policy is often used by the government as a way to encourage entry, which

68This incentive program was later renamed Promoting Interoperability Programs in 2018, with an increased focuson interoperability and improving patient access to health information.

69https://www.cms.gov/Regulations-and-guidance/legislation/EHRIncentivePrograms/DataAndReports.html,accessed June 20, 2019.

43

raises the question of how to design the subsidy policy in the case of EMR adoption to make better

use of public money. This question is especially interesting in a dynamic environment, as the

announcement of such a policy would immediately change a hospital’ belief about its current and

future environment. My experiments explore some alternative design options of this subsidy policy,

in order to examine whether hospitals would have adopted EMR earlier in these cases compared to

the real case. This result may shed light on the optimal future design of such subsidy policies.

Finally, I study the impact of competitive effects in the adoption process on the incentive pro-

gram. As discussed in Section 8.2, competitive effects play a significant role in hospitals’ adoption

behavior. Would they also significantly affect the policy’s effectiveness?

In Section 8.3.1, the EMR incentive program is introduced and explained. In Sections 8.3.2

and 8.3.3, hospitals’ adoption behavior is simulated in monopoly and duopoly markets, in order to

examine the above mentioned questions.

8.3.1 The Incentive Program

The program is essentially a subsidy-based incentive plan that gives payments to eligible hospi-

tals and professionals who demonstrate “meaningful use” of EMR. The meaningful use definition

requires that hospitals adopt and meaningfully use EMR to meet a set of core objectives, such

as using CPOE for medications, recording demographics, maintaining up-to-date problem lists,

and providing patients with an electronic copy of their health information upon request.70 Again,

my experiments focus on the effects of this incentive program on the adoption of L2 applications,

instead of hospitals’ status regarding “meaningful use”. Similar to previous studies that evaluate

this stimulative plan (see, e.g., Dranove et al., 2015), the available data prevent researchers from

measuring whether a hospital has met these standards of meaningful use. More importantly, the

adoption of L2 applications such as CPOE is necessary for a meaningful user, and most hospitals

with such applications plan to apply for the rewards from this incentive program by meeting the

meaningful use requirements (Botta and Cutler, 2014). Therefore, substantial policy insights can

be gained by studying the program’s impact on the adoption of L2 applications.

Under the Medicare/Medicaid incentive programs, eligible hospitals that demonstrate meaning-

70More details about this incentive plan and “meaningful use” can be found athttps://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/index.html, accessed June 20,2019.

44

ful use can begin receiving annual payments in any year from 2011 to 2015, for a consecutive 4-year

period.71 Payments consist of a base amount, a discharge related amount and a Medicare/Medicaid-

share related amount.72 Furthermore, the annual payments decrease over this 4-year period, mea-

sured by a “transition factor” (i.e., the initial amount in the first year diminishes by 25% for each

following year). Hospitals receive a total of approximately 250% of the first-year payment. For

instance, the average potential adopter as of 2008 could have expected to receive $2.1 million for

the first year and $5.3 million in total (Dranove et al., 2015). As the adoption costs of the advanced

EMR applications are at least $10 million, these incentives represent approximately 50% (or less)

of the adoption costs for a typical hospital.

In the experiment, I assume hospitals would receive the one-shot payments totaling 50% of the

adoption costs of L2 applications in any year from 2011 to 2015, if they had adopted L2 EMR

by then.73 For example, if a hospital were to adopt L2 before 2011, say, in 2009, it would receive

50% of the adoption costs in 2011; if it were to adopt in 2012, it would receive these payments

in 2013. For hospitals facing higher adoption costs, or receiving lower incentives, the subsidies

might represent a smaller percentage of adoption costs. Thus, my experiments also examine the

cases when the percentages are 30% and 10%. Because of data limitations, I cannot capture the

heterogeneity of hospitals’ rewards precisely in my experiments. By studying the cases in which

large/small hospitals receive different amounts of payments (50%, 30%, and 10% of adoption costs),

the paper attempts to capture some extent of the heterogeneity.74

8.3.2 Monopoly Markets

My first class of experiments focuses on monopoly markets, in which there is lack of the competitive

effects for EMR adoption. The effects of subsidy policy on duopoly markets with competitive effects

are studied in the next section. Specifically, my experiments simulate the hospital’s adoption

71The Medicaid program provides payments based on a “theoretical” 4 years. The overall amount is calculatedonce, and paid over a minimum of 3 years and a maximum of 6 years.

72The amount decreases for hospitals that start getting paid in 2014 and 2015 under the Medicare program. Forexample, if a hospital could demonstrate meaningful use by 2011, it could receive annual payments from 2011 to2014; if a hospital were to begin to receive payments from 2015, it would receive payments for 2015 and 2016.

73The adoption costs of L2 are based on the estimated values in 2008 in my model.74Hospitals that are not meaningful users are subject to a Medicare downward payment adjustment beginning in

2015, unless they can show that compliance with the requirement for being a meaningful EHR user would result insignificant hardship. This penalty-based scheme is not captured in my experiments, as I aim to focus on the subsidy-based program. Moreover, the penalty is only for the Medicare program, and the amount is difficult to measure basedon the available data and studies.

45

behavior for four scenarios in a monopoly market: 1. a medium hospital with 100 beds in a market

with 15,000 hospital admissions; 2. a smaller hospital with 50 beds in a market with 15,000 hospital

admissions; 3. a larger hospital with 200 beds in a market with 15,000 hospital admissions; and 4.

a medium hospital with 100 beds in a larger market with 20,000 hospital admissions.75

First, I simulate the times taken to adopt L2 EMR with no subsidies as the status quo. The

results are shown in Column I in Table 9. In Case 1 without the policy, it would take approximately

12 years for a medium-sized hospital with 100 beds in a market with 15,000 admissions to adopt

L2 from 2006 (i.e., the hospital would adopt L2 EMR in 2017). Relative to Case 1, a smaller

hospital with 50 beds in Case 2 would adopt 2 years later. A larger hospital with 200 beds in

Case 3 would adopt one and half years earlier compared to Case 1, and the hospital with 100 beds

in a larger market with 20,000 admissions would adopt the earliest (Case 4). We clearly observe

the heterogeneity in hospitals’ adoption behavior: large hospitals adopt earlier, and hospitals in a

large market also adopt earlier. In particular, a hospital in a large market with 20,000 admissions

in Case 4 would have adopted by 2015 in the absence of the policy. Therefore, the current policy

might not be very cost-effective, by not taking market heterogeneity into consideration.

With this incentive policy announced in 2009, hospitals will receive the subsidies in any year

from 2011 to 2015, if they adopt L2 EMR by then. Hospitals’ behavior under this circumstance is

simulated and reported in Column II. Three cases are examined, in which the payments are 50%,

30% and 10% of the adoption costs. Note that the numbers in parentheses are the differences from

the status quo without the policy. The numbers represent the years by which the policy shortened

the time to adoption and thus, are indicators for the policy’s effectiveness. From the results, we

observe the following patterns. First, the policy is generally effective, as hospitals would have

adopted much earlier, and would have adopted L2 EMR by 2015 in all cases. Second, the policy

is more effective for the cases in which hospitals would need more time to adopt in the absence

of the policy. For example, it takes the longest time for the hospital in Case 2 (50 beds, 15,000

admissions) to adopt with no subsidy, and the policy is mostly effective for this case (years needed

are shortened by 8.71, 8.37, and 5.40 if the percentages are 50%, 30% and 10%, respectively). The

policy has smaller effects on hospitals that have large adoption likelihood in any case. Thus, the

75Other market characteristics are kept constant. The market is assumed to have 30,000 persons eligible forMedicare and 50% of people in poverty and is between the 40th and 50th percentiles for market-category effects. Asdiscussed in Section 8.1, the simulations start from 2006.

46

Table 9: Simulation Results: Years Needed to Adopt L2 (from 2006) in Monopoly Markets

Without Policy With Policy

SubsidyAmount

The Real Announce Announce Only forDesign in 2008 in 2007 2011

I II III IV V

1 100 beds, 11.95

50% 5.10 4.77 4.48 4.79

(Effect) (6.85) (7.18) (7.47) (7.16)

30% 5.38 4.99 4.67 4.93

15K admissions (Effect) (6.57) (6.96) (7.28) (7.02)

10% 7.55 7.22 6.95 8.54

(Effect) (4.40) (4.73) (5.00) (3.41)

2 50 beds, 13.99

50% 5.28 4.93 4.62 4.93

(Effect) (8.71) (9.06) (9.37) (9.06)

30% 5.62 5.34 5.09 5.17

15K admissions (Effect) (8.37) (8.65) (8.90) (8.82)

10% 8.59 8.20 7.91 10.04

(Effect) (5.40) (5.79) (6.08) (3.95)

3 200 beds, 10.41

50% 4.91 4.67 4.43 4.65

(Effect) (5.50) (5.74) (5.98) (5.76)

30% 5.15 4.91 4.69 4.75

15K admissions (Effect) (5.26) (5.50) (5.72) (5.66)

10% 6.82 6.51 6.27 7.45

(Effect) (3.59) (3.90) (4.14) (2.96)

4 100 beds, 9.27

50% 4.85 4.62 4.40 4.60

(Effect) (4.42) (4.65) (4.87) (4.67)

30% 5.07 4.85 4.63 4.67

20K admissions (Effect) (4.20) (4.42) (4.64) (4.60)

10% 6.34 6.09 5.88 6.73

(Effect) (2.93) (3.18) (3.39) (2.54)

The number of years needed to adopt L2 EMR is calculated as the mean values of 10,000simulations. The time span is 200 years in each simulation. All hospitals start from L = 0.The differences from the status quo in Column I are shown in parentheses.

47

effectiveness varies by the situation. For example, large hospitals would be affected less than small

hospitals given the same level of subsidy, and would be more likely to have adopted before 2015

even with no subsidy. More attention should be paid to smaller hospitals to ensure nationwide

adoption. Furthermore, the current policy completely ignores market heterogeneity. By comparing

Case 1 with Case 4, we observe that a smaller market could be greatly motivated to adopt with the

subsidies but would still lag behind a larger market. Therefore, an effective policy strategy needs

to take market heterogeneity into consideration.

Before the announcement of this incentive program in 2009, such methods to speed up the

adoption of EMR technology went through years of bipartisan support and broad consensus. It

would be interesting to examine whether the policy would be more effective had this incentive

program been passed earlier. This is particularly interesting in a dynamic environment, in which

hospitals are forward-looking. Right after the announcement of the policy, hospitals would have

adjusted their adoption behavior accordingly, as their option value of adopting would have increased

by getting the subsidies sometime in the future. Thus, I simulate hospitals’ adoption times if the

incentive program had been announced in 2008 or 2007. The results are reported in Columns III

and IV, respectively. We observe clearly that the policy would be more effective in all cases, as

the years needed to adopt are shortened compared to the real case of the policy being announced

in 2009.76 In particular, if the policy had been announced in 2007, hospitals would generally have

adopted half a year earlier. This result shows that by simply speeding up the policy-making process

or announcing the policy earlier, adoption would have been accelerated.

Another alternative design of this subsidy policy is subsidizing adoption in only early periods.

For example, Fan and Xiao (2015) find that, in the U.S. local telephone industry, subsidies in only

early periods would reduce the option value of waiting and accelerate entry. If adopters could

receive a considerable and one-shot payment only in 2011, hospitals would have further sped up

their adoption with the hope of obtaining the subsidy in 2011. Thus, it is possible that hospitals

would have adopted earlier than in the case with the real policy design. However, this modified

policy may fail, in that the amount of subsidies is not enough for hospitals to have covered the costs

and adopted earlier before 2011; then, the adoption probabilities would have returned to low levels

as there would have been no subsidy after 2011. In this circumstance, we may observe hospitals

76All the differences are statistically significant at 5% significance level.

48

adopting later than in the case with the real policy design. Hence, I simulate hospitals’ adoption

times under this alternative policy design, and the results in Column V confirm the expectation.

Relative to the case with the real policy design (Column II), if the subsidy had been 50% or 30% of

adoption costs, the adoption process would have accelerated further. However, if the subsidy had

been only 10% of the adoption costs, hospitals would have adopted later.

In summary, my experiments show that the incentive program is generally effective for encour-

aging adoption, and is more effective in cases in which hospitals have otherwise small adoption

probabilities. To make the policies more effective, hospital and market heterogeneities need to be

taken into consideration. For example, large hospitals would be affected less than small hospitals

given the same level of subsidy, and would be more likely to have adopted before 2015 even with no

subsidy. Thus, more attention needs to be paid to small hospitals to ensure nationwide adoption

and to make the incentive program more cost-effective. Additionally, the current policy completely

overlooks market heterogeneity. Hospitals in a large market tend to have adopted EMR before 2015

even with no subsidy, while hospitals in a small market are less likely to have adopted EMR with

no subsidy, and would still lag behind even with the subsidy.

Several alternative design options of this incentive program are examined. If the policy had

been announced earlier than 2009, hospitals would have further accelerated adoption. Particularly,

hospitals would generally adopt half a year earlier had the policy been passed in 2007. Moreover,

if the policy had subsidized adoption only in 2011, its effectiveness would depend on the amount

of subsidies. Compared to behavior in the case with the real policy design, hospitals would have

adopted earlier if the amount of subsidies were considerably large, but would have delayed adoption

if the amount were small.

8.3.3 Duopoly Markets

As shown in Table 8, competitive effects in a duopoly market would substantially deter EMR

adoption, particularly for the second adoption. This opens up the question of whether competitive

effects affect the effectiveness of the subsidy policy. In other words, given the same incentive plan,

how would hospitals behave differently with and without the competitive effects?

Theoretically, the subsidy lowers every potential adopter’s adoption costs. If there is a strong

competitive effect, a potential adopter has to balance the lower adoption costs and the lower adop-

49

tion benefits due to enhanced competition, which is a (partial) equilibrium effect. To examine this

effect, hospitals’ adoption times are simulated in duopoly markets. Similar to Section 8.2, two sce-

narios are considered. The first is based on the estimation results, and captures competitive effects

in the adoption process. The second scenario assumes that hospitals make decisions independently,

as if they were in a monopoly market with no others to compete with in the adoption process. The

configuration is the same as in Section 8.2: hospitals have 100 beds, markets have 15, 000 hospital

admissions, 30, 000 persons eligible for Medicare, 20% of people in poverty and are between the

40th and 50th percentiles.77

Again, to evaluate the effectiveness of the subsidy policy on duopoly markets, I examine three

cases in which the subsidies equal 50%, 30%, and 10% of the adoption costs, respectively. Moreover,

as in the real design in Section 8.3.2, hospitals receive the subsidies in any year from 2011 to 2015 if

they adopt L2 by then. The results are reported in Table 10. In the scenario with no competition

during the adoption process, the policy is very effective: it takes at most 11.09 years for hospitals

to adopt L2 in all cases (i.e., hospitals would have adopted L2 by 2016). However, in the scenario

with competitive effects, although the policy would substantially spur adoption, hospitals still adopt

later than in the scenario without competition. This difference in adoption times is more significant

if the subsidy is lower. Specifically, if the subsidies are 10% of the adoption costs, the first adopter

would adopt by 2011 (5.55 years in Column II) in a competitive environment, while the second

adopter would adopt approximately 2033 (28.19 years in Column II).

These findings suggest that by ignoring competitive effects during the adoption process, one may

over-predict the effectiveness of the subsidy policy. The policy would be very effective if there were

no competitive effects in the adoption process, in the sense that hospitals in all cases would have

adopted by 2016. If the competitive effects are taken into account, the policy would still greatly

spur adoption, but hospitals lag behind relative to the case without competition (Column II vs.

Column IV), especially for the second adopter, because the first adopter decreases the potential

benefits of adoption for the later adopter. As a result, a policy strategy accounting for competitive

effects is essential to spur adoption.

77This market configuration is the same as the monopoly market in Section 8.3.2, except that the duopoly markethas an HHI of 0.5, while the monopoly market has an HHI of 1.

50

Table 10: Simulation Results: Years Needed to Adopt L2 (from 2006) in Duopoly Markets

With Competition Without Competition

Without Policy With Policy Without Policy With PolicyI II III IV

Average time for L2adoption

50% 5.41 50% 5.1324.28 30% 6.24 12.81 30% 5.43

10% 16.66 10% 7.97

Average time whenthe first adoptionoccurs

50% 4.47 50% 4.257.4 30% 4.63 6.79 30% 4.35

10% 5.55 10% 4.94

Average time whenthe second adoptionoccurs

50% 6.4 50% 6.0441.51 30% 7.91 19.05 30% 6.55

10% 28.19 10% 11.09

The number of years needed to adopt L2 EMR is calculated as the mean values of10,000 simulations. The time span is 200 years in each simulation. All hospitals startfrom L = 0.

9 Conclusion

This paper studies the multilevel adoption of EMR technologies by U.S. hospitals in concentrated

markets. A dynamic oligopoly model is developed and estimated to capture strategic interactions

among hospitals. I find evidence of competitive effects: the benefits from adopting L1 EMR (L2

EMR) decline as more hospitals adopt L1 (L2) in the market. This result is consistent with vertical

product differentiation along the EMR levels in the adoption process. Previous researchers and

policy-makers have typically overlooked the importance of competitive effects on EMR adoption.

My study shows that hospitals’ adoption of advanced EMR technologies such as CPOE, is greatly

deterred by competitive effects.

The 2009 Medicare/Medicaid incentive programs enacted to promote national adoption of EMR

are evaluated by the counterfactual experiments. First, I find that hospital and market hetero-

geneities need to be taken into account to make the policy more effective. For example, large

hospitals would be affected less than small hospitals given the same level of subsidy, and would

be more likely to have adopted before 2015 even with no subsidy. Similarly, hospitals in a large

market with more hospital admissions are also more likely to have adopted EMR by 2015. In these

cases, government subsidies mostly crowd out private investments. To make better use of the public

51

funds, more attention needs to be paid to small hospitals, and hospitals in small markets, which

would lag behind even with the subsidy. Second, several alternative forms of the subsidy policy are

explored, to shed light on future policy design. Because hospitals are forward-looking, their beliefs

would vary with the change of policy design, and they would adjust their behavior accordingly. I

find that if the policy had been announced earlier, hospitals’ adoption could be further accelerated.

Particularly, hospitals would have adopted half a year earlier had the policy been passed in 2007.

Finally, competitive effects also play an important role in the effectiveness of this policy. Ignoring

competitive effects during the adoption process leads to overestimates of the effectiveness of the

policy.

52

Appendix A: Supplemental Results

Table A-1: Transition of Year Effects for L1 Adoption(τ1t )

Variable Coefficient Standard Error

Constant 0.471 0.263Lag of τ1t 0.637 0.210

Adjusted R-squared 0.506

Table A-2: Transition of Year Effects for L2 Adoption(τ2t )

Variable Coefficient Standard Error

Constant 1.258 0.401Lag of τ2t 0.676 0.125

Adjusted R-squared 0.778

Table A-3: Transition of Year Effects for L2 Upgrade (from L1)(τ12t )

Variable Coefficient Standard Error

Constant 0.802 0.161Dummy if in or after 2006 -0.779 0.225Lag of τ12t 0.940 0.071

Adjusted R-squared 0.976

53

Appendix B: Finite State Markov-chain Approximations for Year

Effects

I present the details of finite state Markov-chain approximations for year effects τ1t , τ2t and τ12t ,

which are assumed to follow an AR(1) process in my estimation. The method is based on Tauchen

(1986).

I illustrate the method by the example of τ1t , which follows the AR(1) process as follows.

τ1t+1 = µ10 + µ11τ1t + ω1

t+1 (B-1)

Let τ̃1t denote the discrete-valued process that mimics this AR(1) process, and τ̄11 < τ̄12 < · · · < τ̄110

denote the 10 values that τ̃1t may take on. Specifically in my counterfactual experiments, I set τ̄11 ,

τ̄12 , . . . , τ̄110 equal to 0, 0.4, . . . , 3.6, and d = τ̄1k − τ̄1k−1 = 0.4.

I then calculate the transition matrix Mτ1 , where Mτ1(j, k) = Pr(τ̃1t = τ̄1k |τ̃1t−1 = τ̄1j ). For each

j, if 2 ≤ k ≤ 9, set

Mτ1(j, k) = Pr(τ̄1k − d/2 ≤ µ10 + µ11τ̄

1j + ω1

t ≤ τ̄1k + d/2)

= F

(τ̄1k + d/2− µ10 − µ11τ̄1j

σω

)− F

(τ̄1k − d/2− µ10 − µ11τ̄1j

σω

),

otherwise, for k = 1 and 10,

Mτ1(j, 1) = F

(τ̄11 + d/2− µ10 − µ11τ̄1j

σω

), Mτ1(j, 10) = 1− F

(τ̄110 − d/2− µ10 − µ11τ̄1j

σω

),

where F (∗) is the cumulative distribution function of the standard normal distribution.

The transition matrix Mτ2 for year effects τ2, and the transition matrix Mτ12 for year effects

τ12 can be calculated in the same way. The transition matrix Mτ for all the year effects, which is

a 1000-by-1000 matrix, can be obtained as the Kronecker product of Mτ1 , Mτ2 , and Mτ12 :

Mτ = Mτ1 ⊗Mτ2 ⊗Mτ12 .

The kj-th element of Mτ indicates the probability from the k-th state τk, to the j-th state τ j .

54

References

Abraham, J. M., M. Gaynor, and W. B. Vogt (2007). Entry and competition in local hospital markets. The

Journal of Industrial Economics 55 (2), 265–288.

Adler-Milstein, J., M. F. Furukawa, J. King, and A. K. Jha (2013). Early results from the hospital electronic

health record incentive programs. The American Journal of Managed Care 19 (7), 273–84.

Adler-Milstein, J. and A. K. Jha (2017). Hitech act drove large gains in hospital electronic health record

adoption. Health Affairs 36 (8), 1416–1422.

Aguirregabiria, V. and P. Mira (2002). Swapping the nested fixed point algorithm: a class of estimators for

discrete Markov decision models. Econometrica 70 (4), 1519–1543.

Aguirregabiria, V. and P. Mira (2007). Sequential estimation of dynamic discrete games. Econometrica 75 (1),

1–53.

Augereau, A., S. Greenstein, and M. Rysman (2006). Coordination versus differentiation in a standards war:

56k modems. The RAND Journal of Economics 37 (4), 887–909.

Bajari, P., C. L. Benkard, and J. Levin (2007). Estimating dynamic models of imperfect competition.

Econometrica 75 (5), 1331–1370.

Berry, S. (1992). Estimation of a model of entry in the airline industry. Econometrica 60 (4), 889–917.

Botta, M. D. and D. M. Cutler (2014). Meaningful use: Floor or ceiling? Healthcare 2 (1), 48 – 52.

Bresnahan, T. F. and P. C. Reiss (1991). Entry and competition in concentrated markets. Journal of Political

Economy 99 (5), 977–1009.

Chandra, A., A. Finkelstein, A. Sacarny, and C. Syverson (2016). Health care exceptionalism? performance

and allocation in the us health care sector. American Economic Review 106 (8), 2110–44.

Collard-Wexler, A. (2013). Demand fluctuations in the ready-mix concrete industry. Econometrica 81 (3),

1003–1037.

DesRoches, C. M., E. G. Campbell, S. R. Rao, K. Donelan, T. G. Ferris, A. Jha, R. Kaushal, D. E. Levy,

S. Rosenbaum, A. E. Shields, and D. Blumenthal (2008). Electronic health records in ambulatory care —

a national survey of physicians. New England Journal of Medicine 359 (1), 50–60.

DesRoches, C. M., D. Charles, M. F. Furukawa, M. S. Joshi, P. Kralovec, F. Mostashari, C. Worzala, and

A. K. Jha (2013). Adoption of electronic health records grows rapidly, but fewer than half of us hospitals

had at least a basic system in 2012. Health Affairs 32 (8), 1478–1485.

Doraszelski, U. and M. Satterthwaite (2010). Computable Markov-perfect industry dynamics. The RAND

Journal of Economics 41 (2), 215–243.

Dranove, D., C. Forman, A. Goldfarb, and S. Greenstein (2014). The trillion dollar conundrum: com-

55

plementarities and health information technology. American Economic Journal: Economic Policy 6 (4),

239–270.

Dranove, D., C. Garthwaite, B. Li, and C. Ody (2015). Investment subsidies and the adoption of electronic

medical records in hospitals. Journal of Health Economics 44, 309–319.

Dunne, T., S. D. Klimek, M. J. Roberts, and D. Y. Xu (2013). Entry, exit, and the determinants of market

structure. The RAND Journal of Economics 44 (3), 462–487.

Ericson, R. E. and A. Pakes (1995). Markov perfect industry dynamics: A framework for empirical work.

The Review of Economic Studies 62 (1), 53–82.

Fan, Y. and M. Xiao (2015). Competition and subsidies in the deregulated US local telephone industry. The

RAND Journal of Economics 46 (4), 751–776.

Freedman, S., H. Lin, and J. Prince (2018a). Does competition lead to agglomeration or dispersion in EMR

vendor decisions? Review of Industrial Organization 53 (1), 1–23.

Freedman, S., H. Lin, and J. Prince (2018b). Information technology and patient health: Analyzing outcomes,

populations, and mechanisms. American Journal of Health Economics 4 (1), 51–79.

Fudenberg, D. and J. Tirole (1985). Preemption and rent equalization in the adoption of new technology.

The Review of Economic Studies 52 (3), 383–401.

Gowrisankaran, G. (1995). A Dynamic Analysis of Mergers. Ph. D. thesis, Yale University.

Gowrisankaran, G. and J. Stavins (2004). Network externalities and technology adoption: Lessons from

electronic payments. RAND Journal of Economics 35 (2), 260–276.

Gowrisankaran, G. and R. J. Town (1997). Dynamic equilibrium in the hospital industry. Journal of

Economics & Management Strategy 6 (1), 45–74.

Hotz, V. J. and R. A. Miller (1993). Conditional choice probabilities and the estimation of dynamic models.

The Review of Economic Studies 60 (3), 497–529.

Hotz, V. J., R. A. Miller, S. G. Sanders, and J. A. Smith (1994). A simulation estimator for dynamic models

of discrete choice. The Review of Economic Studies 61 (2), 265–289.

Hsu, J., J. Huang, V. Fung, N. Robertson, H. Jimison, and R. Frankel (2005). Health information technology

and physician-patient interactions: impact of computers on communication during outpatient primary care

visits. Journal of the American Medical Informatics Association 12 (4), 474–480.

Igami, M. (2017). Estimating the innovator’s dilemma: Structural analysis of creative destruction in the

hard disk drive industry, 1981-1998. Journal of Political Economy 125 (3), 798–847.

Jha, A. K., C. M. DesRoches, E. G. Campbell, K. Donelan, S. R. Rao, T. G. Ferris, A. Shields, S. Rosenbaum,

and D. Blumenthal (2009). Use of electronic health records in U.S. hospitals. New England Journal of

Medicine 360 (16), 1628–1638.

56

Jin, G. (2005). Competition and disclosure incentives: an empirical study of HMOs. The RAND Journal of

Economics 36 (1), 93–112.

Karaca-Mandic, P., R. J. Town, and A. Wilcock (2017). The effect of physician and hospital market structure

on medical technology diffusion. Health Services Research 52 (2), 579–598.

Laflamme, F. M., W. E. Pietraszek, and N. V. Rajadhyax (2010). Reforming hospitals with IT investment.

Technical report, McKinsey on Business Technology.

Lee, J., J. S. McCullough, and R. J. Town (2013). The impact of health information technology on hospital

productivity. The RAND Journal of Economics 44 (3), 545–568.

Lin, H. (2015). Quality choice and market structure: A dynamic analysis of nursing home oligopolies.

International Economic Review 56 (4), 1261–1290.

Lin, J. (2019a). Strategic complements or substitutes: The case of adopting Electronic Medical Records

(EMRs) by U.S. hospitals. SSRN Working Paper 3099638.

Lin, J. (2019b). To coordinate with or differentiate from your neighbor: The adoption of Electronic Medical

Records by hospital systems. SSRN Working Paper 3189469.

Makuc, D. M., B. Haglund, D. D. Ingram, J. C. Kleinman, and J. J. Feldman (1991). Health service areas

for the United States. Vital and health statistics. Series 2, Data evaluation and methods research (112),

1–102.

Mashaw, J. L. (1994). Improving the environment of agency rule-making: An essay on management, games,

and accountability. Law and Contemporary Problems 57, 185–257.

Maskin, E. and J. Tirole (1988). A theory of dynamic oligopoly, i: Overview and quantity competition with

large-fixed costs. Econometrica 56 (3), 549–569.

Mazzeo, M. J. (2002). Product choice and oligopoly market structure. The RAND Journal of Eco-

nomics 33 (2), 221–242.

McCullough, J. S., S. T. Parente, and R. Town (2016). Health information technology and patient outcomes:

the role of information and labor coordination. The RAND Journal of Economics 47 (1), 207–236.

McGarity, T. O. (1992). Some thoughts on “deossifying” the rule-making process. The Duke Law 41,

1384–1462.

Miller, A. R. and C. Tucker (2014). Health information exchange, system size and information silos. Journal

of health economics 33, 28–42.

Miller, A. R. and C. E. Tucker (2009). Privacy protection and technology diffusion: The case of electronic

medical records. Management Science 55 (7), 1077–1093.

Miller, A. R. and C. E. Tucker (2011). Can health care information technology save babies. Journal of

Political Economy 119 (2), 289–324.

57

Newey, W. K. (1987). Efficient estimation of limited dependent variable models with endogenous explanatory

variables. Journal of Econometrics 36 (3), 231–250.

Pakes, A. and P. McGuire (1994). Computing Markov-perfect Nash equilibria: numerical implications of a

dynamic differentiated product model. The RAND Journal of Economics 25 (4), 555–589.

Pakes, A., M. Ostrovsky, and S. Berry (2007). Simple estimators for the parameters of discrete dynamic

games (with entry/exit examples). The RAND Journal of Economics 38 (2), 373–399.

Pesendorfer, M. and P. Schmidt-Dengler (2008). Asymptotic least squares estimators for dynamic games.

The Review of Economic Studies 75 (3), 901–928.

Reinganum, J. F. (1981). Market structure and the diffusion of new technology. The Bell Journal of

Economics 12 (2), 618–624.

Rust, J. (1987). Optimal replacement of GMC bus engines: An empirical model of harold zurcher. Econo-

metrica 55 (5), 999–1033.

Ryan, S. P. (2012). The costs of environmental regulation in a concentrated industry. Econometrica 80 (3),

1019–1061.

Rysman, M., T. Simcoe, and Y. Wang (2019). Differentiation strategies in the adoption of environmental

standards: LEED from 2000-2014. Management Science forthcoming.

Schmidt-Dengler, P. (2006). The timing of new technology adoption: The case of MRI. Manuscript.

Seim, K. (2006). An empirical model of firm entry with endogenous product-type choices. The RAND

Journal of Economics 37 (3), 619–640.

Shaked, A. and J. Sutton (1982). Relaxing price competition through product differentiation. The Review

of Economic Studies 49 (1), 3–13.

Suzuki, J. (2013). Land use regulation as a barrier to entry: Evidence from the Texas lodging industry.

International Economic Review 54 (2), 495–523.

Tauchen, G. (1986). Finite state Markov-chain approximations to univariate and vector autoregressions.

Economics Letters 20 (2), 177–181.

Tay, A. (2003). Assessing competition in hospital care markets: the importance of accounting for quality

differentiation. RAND Journal of Economics 34 (4), 786–814.

Tucker, C. (2008). Identifying formal and informal influence in technology adoption with network external-

ities. Management Science 54 (12), 2024–2038.

58