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Federal Reserve Bank of Chicago

Check Up Before You Check Out:
Retail Clinics and Emergency Room Use
Diane Alexander, Janet Currie,
and Molly Schnell

REVISED
July 2018
WP 2017-11
Working papers are not edited, and all opinions and errors are the
responsibility of the author(s). The views expressed do not necessarily
reflect the views of the Federal Reserve Bank of Chicago or the Federal
Reserve System.

Check Up Before You Check Out:
Retail Clinics and Emergency Room Use

Diane Alexander
Federal Reserve Bank of Chicago
dianealexand@gmail.com
Janet Currie
Princeton University
jcurrie@princeton.edu
Molly Schnell
Stanford Institute for Economic Policy Research
molly.k.schnell@gmail.edu
July 2018

We thank participants in seminars at Princeton University, the University of Chicago, the 2017 Highland Health
Economics Symposium, and the 2018 American Society of Health Economists Annual Meeting for helpful
feedback. We also thank the staff at the New Jersey Department of Health for their assistance in accessing the data
used in this paper. The Center for Health and Wellbeing at Princeton University provided generous financial
support. All views expressed are those of the authors and cannot be attributed to the Federal Reserve Bank of
Chicago, the Federal Reserve System, or the New Jersey Department of Health.

Abstract
The retail clinic is an innovation that has the potential to improve competition in health care
markets. Given concern about inefficient use of the emergency room (ER) increasing health care
costs, we use all ER visits in New Jersey from 2006-2014 to examine the impact of retail clinics
on ER usage. We find that people residing close to an open clinic are 4.1-12.3 percent less likely
to use an ER for preventable conditions and for minor acute conditions. Our estimates suggest
annual cost savings of over $70 million from reduced ER usage if retail clinics were readily
available across New Jersey.

1. Introduction
The U.S. health care market is often viewed as one in which competition does not work well: it is
increasingly concentrated (Gaynor and Townsend, 2011); prices are not transparent; and many
studies highlight imperfections and asymmetries in the information available to providers,
patients, and insurers. These shortcomings are thought to entail large efficiency and welfare
losses both from unnecessary procedures and delayed interventions, including costly emergency
room (ER) visits that could have been avoided with proper preventive care.
Policy makers have long sought to increase competition in health care markets. For
example, a joint commission of the Federal Trade Commission (FTC) and the Department of
Justice (DOJ) recommended adopting measures to increase competition including increasing
transparency in pricing and lowering barriers to entry into primary care for allied health
professions (FTC and DOJ, 2004). In fact, expanded scope of practice laws allowing nurse
practitioners to practice outside a doctor’s office are thought to have been a key policy leading to
the rise of retail clinics (Carton et al., 2016). In turn, retail clinics have been leaders in promoting
price transparency.1
Retail clinics first appeared in 2000 and have since grown rapidly, with over 2,000 clinics
operating in 41 states and Washington D.C. in 2015 (NCSL, 2017). Retail clinics are generally
located within retail stores, such as pharmacies or “big-box” outlets like Wal-Mart. They offer a
limited range of services and are staffed by nurse practitioners or physician assistants. Retail
clinics are typically open seven days a week, have extended hours in the evenings, and do not
require appointments. Prices are posted on line and may be a quarter to a third less expensive

The commission recognized that competition is not a panacea. They state that “Competition cannot provide its full
benefits to consumers without good information and properly aligned incentives. Moreover, competition cannot
eliminate the inherent uncertainties in health care, or the informational asymmetries among consumers, providers,
and payers. Competition also will not shift resources to those who do not have them” (FTC and DOJ, 2004).

than the price a doctor would charge for the same services (Mehrotra et al., 2009; Tu and Cohen,
2008; Thygeson et al., 2008). Thus, retail clinics compete with doctors’ offices for basic primary
care services by offering lower and more transparent prices, shorter waiting times, and
convenience (Ahmed and Fincham, 2010; Wang et al., 2010).
In this paper, we use the universe of visits to ERs in New Jersey between 2006 and 2014
to examine the impact of retail clinics on ER use. We focus on ER use because it is thought that
many cases that end up in the ER reflect inefficient allocations of medical care, and reducing the
unnecessary use of emergency services has long been a focus on public policy.2 Lowering the
monetary and time costs of care might result in higher consumption of primary care and
subsequent improvements in health which could be reflected in fewer ER visits. Retail clinics
may also divert some patients from ERs, particularly for relatively minor conditions that arise
outside of normal office hours when doctors’ offices are typically closed, resulting in cost
savings. On the other hand, professional physician organizations have expressed fears that retail
clinics might sell unnecessary services or products, provide lower quality, or disrupt continuity
of care, leading to higher costs and worse health outcomes including additional ER visits (AMA,
2007; AAP, 2014; AAFP, 201). In light of these concerns, the American Medical Association
has called for greater regulation of store-based clinics.
In order to identify the effects of retail clinics on ER usage, we use a difference-indifference framework. In particular, we compare ER visits among residents living 0 to 2 miles
from any site where a clinic ever operated (“near”), to those among residents who live 2 to 5

For example, Kellermann and Weinick (2012) discuss Washington state’s attempt to reduce Medicaid costs by
restricting the use of ERs for non-urgent care. They argue that the policy ultimately failed because inappropriate use
of ERs reflected lack of access to other sources of primary care. This is exactly the sort of deficiency that retail
clinics could possibly remedy.

miles from such a site (“far”), when the clinic is operating and when it is not.3 Our identifying
assumptions are therefore that those who live closest to a clinic are most likely to use it, and that
ER visits would have shown similar trends in both distance bands in the absence of the opening
and/or closing of a retail clinic. We exclude patients who live further than 5 miles away from
sites where a retail clinic ever operated over our sample period because patients in unserved
areas are quite different than those in areas where retail clinics locate. To absorb any timeinvariant differences across neighborhoods, all of our specifications include a fixed effect for
each retail clinic location.
We consider three classes of conditions: (1) conditions that frequently result in ER visits
but which could have been prevented by adequate primary care; (2) relatively minor conditions
which could nevertheless lead to an ER visit in the absence of an open or convenient doctor’s
office or retail clinic; and (3) a control group of conditions that are normally only treated in the
ER and cannot be prevented by improved primary care. The first category of conditions—severe
yet preventable conditions—includes ER visits for influenza and complications of diabetes.
These conditions are common reasons for ER visits and both flu shots and routine diabetes care
are explicit foci of retail clinics. The second category—relatively minor conditions—includes
sprains and strains, urinary tract infections, conjunctivitis, upper respiratory tract infections, ear
infections, and sore throat. Finally, placebo conditions that cannot be treated in a retail clinic
include fractures, poisonings, and childbirth.
We find that residents who live close to an open clinic are 12.3 percent less likely to go to
the ER for influenza and 4.1 percent less likely to use the ER for complications of diabetes. They
are also between 4.7 and 11.4 percent less likely to go to the ER for relatively common, minor

Results are robust to using 0 to 1 vs. 1 to 3 mile bands.

conditions. As predicted, retail clinics do not have any statistically significant effect on ER visits
for fractures, poisonings, or childbirths.
Our estimates suggest that retail clinics result in annual cost savings of $817,492 per
100,000 people from reduced ER use alone. Scaled to the population of New Jersey in 2010
(8,791,894), these estimates suggest a potential cost savings from ER visits of over $70 million
annually. The bulk of these savings come from reductions in visits for the two preventable
conditions we consider: Influenza and diabetes.
Two previous studies have suggested that retail clinics may increase health care costs by
encouraging more primary care visits outside of the ER (Sussman et al., 2013; Ashwood et al.,
2016). 4,5 It is unclear whether cost savings from reduced ER visits completely offset increases
in health care cost from additional visits to retail clinics. Since New Jersey does not have an “all
payer claims” data base, it is in fact impossible to ask how outpatient visits have changed over
time. However, we calculate that it would take over 700,000 annual visits to retail clinics in New
Jersey, costing $100/visit, to offset the estimated savings from reduced ER usage. Furthermore,
to the extent that preventing illness is socially beneficial, even when such illnesses do not result
in an interaction with the healthcare system, the fact that retail clinics reduce the burden of
preventable diseases may swing the balance of welfare calculations in favor of regulatory
changes that promote competition from retail clinics.

One recent paper from the medical literature also looks at the effect of retail clinics on low acuity emergency room
visits, and finds little to no effect (Martsolf et al., 2017). Our analysis differs from Martsolf et al. (2017) in a number
of ways that could explain the discrepancy. For example, Martsolf et al. (2017) focus on the ratio of low acuity ER
visits to other ER visits. If ER visits for both low acuity visits and other preventable conditions fall (as we argue),
then they could find no effect or even a positive effect of retail clinics, as both the numerator and the denominator
are affected by retail clinics. In contrast, we look at ER visits per capita. Our data are also richer, including both a
finer level of geographical detail. Finally, we include ER visits that result in an admission to hospital which are
excluded in Martsolf et al.
5 Using a similar identification strategy to Martsolf et al. (2017), Hollingsworth (2014) shows that the number of
retail clinics within 5 miles of a Florida hospital in 2012 is associated with fewer ER visits for bronchitis in 2012
relative to 2006.

Our paper improves on previous work in four ways. First, we consider the universe of
patients who ever use an ER rather than a subset of patients who are covered by a particular
insurance plan. This feature of our data ensures that we capture all of the changes in ER
utilization that occur as a result of retail clinic operation. Second, we consider ER visits for an
immunization preventable disease (influenza), a prevalent chronic condition (diabetes), low
acuity conditions like sprains and strains, as well as a range of placebo conditions that are not
treated in retail clinics and should therefore be unaffected, like childbirth.6 This range of
conditions allows us to obtain a more complete picture of the impact of retail clinics on ER usage
than previous work. Third, given the length of our panel, we are able to exploit both openings
and closings of retail clinics. This rich variation in timing allows us to distinguish the effects of
retail clinics from underlying trends in ER use. Finally, our difference-in-difference framework
does not require us to match patients on observables. Our estimates are therefore not subject to
the selection biases that result when matching does not perfectly control for differences between
those who use retail clinics and those who do not.
This paper proceeds as follows. In Section 2, we provide additional background on retail
clinics and discuss the categories of conditions that we consider. Section 3 introduces a
conceptual framework that highlights the predicted impacts of retail clinic expansion on ER
visits for preventable and non-preventable conditions of different severities. We discuss the
datasets used in our analysis in Section 4. Section 5 outlines our empirical specification, and
results are provided in Section 6. Section 7 discusses and concludes.

Ashwood et al. (2016) note that it may be important to consider conditions that can be prevented through adequate
primary care rather than only treatment for minor illnesses, but they do not do this.

2. Background
Retail clinics are highly concentrated among just a few retailers: CVS MinuteClinics and
Walgreen’s Healthcare Clinics make up 75 percent of the market nationwide (market shares of
50 and 25 percent, respectively) with Kroger, Wal-Mart, and Target accounting for most of the
rest (NCSL, 2016).7 In recent years, Wal-Mart and Target began to exit the retail clinic business;
at the end of 2015, CVS acquired all of Target's pharmacies and in-store clinics for $1.9 billion.
In New Jersey, ShopRite closed all six of its locations between 2008 and 2012, while CVS
opened at least two new stores in every year between 2011 and 2014.
Retail clinics are predominately staffed by nurse practitioners (NPs). Under New Jersey’s
scope of practice laws, NPs must be supervised by a doctor both to practice and to prescribe
medication. However, the supervising doctor is not required to be on site. In practice, NPs adhere
to a manualized practice handbook outlining protocols and refer to their supervising physician
only when a situation requires further guidance. The use of NPs is a key difference between
retail clinics and urgent care clinics. The later can be difficult to distinguish from traditional
doctors’ offices, since they are staffed by doctors and often take appointments in advance. In
fact, for regulatory purposes urgent care clinics are treated as doctor’s offices.
Prices charged by retail clinics are on average between 25-33 percent less expensive than
the prices charged by physicians’ offices for the same services (Mehrotra et al., 2009; Tu and
Cohen, 2008). These cost savings are likely to be salient to consumers as retail clinics are used
primarily by younger adults and families who are more likely to be uninsured and to pay out of
pocket than those using other health care providers (ICPSR, 2010). However, most retail clinic

CVS owns and operates all of its clinics while Walgreens outsources clinics to health care groups.

visits are still covered by insurance, with about 70 percent of retail clinic customers being
insured (compared to 90 percent for patients visiting primary care physicians).
Despite lower and more transparent prices, patients cite convenience (i.e. time costs) as
the main reason for using retail clinics (Weinick et al., 2010). Almost all New Jersey retail
clinics are open on weekends and weekday evenings, and every retail clinic in New Jersey has a
pharmacy on site (see Table 1). Patients are generally seen on a first come, first served basis,
although some clinics allow patients to make an appointment or “get in line” online before
arriving at the clinic.
Retail clinics are very transparent about the types of medical care they can and cannot
provide. Services include treating minor illnesses such as urinary tract infections, ear infections,
conjunctivitis, and sore throat (see http://www.cvs.com/minuteclinic/services/minor-illnesses/Nd8Z3a3jkZd5); minor injuries such as sprains and strains; immunizations including influenza;
and health screenings such as diabetic glucose screenings. Retail clinics do not have imaging
equipment or intravenous drips and are not equipped to handle fractures, childbirth, or lifethreatening emergencies such as poisonings.
In what follows, we examine the impact of retail clinics on ER visits for three sets of
conditions: (1) emergent, preventable; (2) primary care treatable; and (3) emergent, not
preventable. These three severity categories are in the spirit of those used in Billings et al. (2000)
and Taubman et al. (2014). Our classification focuses on diagnoses that are both known to be
treated in retail clinics and where there is little ambiguity in severity class.8 An overview of these
condition categories and how their treatment relates to the services provided by retail clinics is

The Billings et al. (2000) classification algorithm starts with very detailed medical records to determine what
percentage of each diagnosis would fall into each severity category. The algorithm then applies this information to
discharge records, where individual cases are classified probabilistically based on the percentage of cases with each
discharge diagnosis that fell into each category in the more detailed data.

given below.
Emergent, Preventable Conditions
The first group of conditions that we consider includes ER visits for influenza and complications
of diabetes. We refer to this group as “emergent, preventable” since these conditions can be
prevented with adequate primary care but often result in ER visits once they develop.
While visits for influenza represent a relatively small fraction of ER visits in New Jersey
(0.21 percent; see Table 2), they are particularly important from a public health perspective. For
each person whose influenza is sufficiently severe to be treated at a hospital, there are many
more cases that result in visits to doctors’ offices and an even larger number of cases that did not
result in contact with health care providers but may have caused days missed from work or
school.9 Moreover, there is a non-trivial risk of death from influenza. According to the Centers
for Disease Control (CDC), annual U.S. deaths related to influenza ranged from 12,000 to 56,000
between 2010 and 2014 (CDC, 2018).
Retail clinics are particularly well placed to increase influenza vaccination rates and in
turn to decrease the severity of seasonal outbreaks. One of the most common reasons adults give
for foregoing the vaccine is that they forgot or “didn't get around to it” (Harris et al., 2010).
When adults do get immunized, they receive the vaccination from many different sources,
suggesting that convenience plays an important role.10 Located within high foot traffic stores,
retail clinics are convenient and advertise to remind their shoppers to get a flu shot. According to
one large retailer, over half of those immunized did not intend to get a flu shot when they entered

It is estimated that for every flu hospitalization there are approximately 5.6 ER visits, 66 cases which sought
medical care, and around 149 cases total (Kostova et al., 2013; Uscher-Pines and Elixhauser, 2013).
10
According to the CDC (2012), while most children receive flu shots at doctors’ offices or health centers (65
percent and 19 percent, respectively), more adults get vaccinated at pharmacies, stores, and workplaces than at a
doctor's office.

the store (Sifferlin, 2013). Anecdotal evidence further suggests that NPs routinely offer flu shots
to retail clinic patients at the end of each visit. According to Uscher-Pines et al. (2012), vaccines
were administered in 40 percent of visits to retail clinics from 2007 to 2009, with 95 percent of
the vaccinations being for influenza.11
In addition to offering immunizations, retail clinics are increasingly advertising that they
can provide monitoring for common chronic conditions, such as diabetes. Unlike influenza,
complications of diabetes make up a large share of ER visits. In New Jersey, 8.74 percent of ER
visits had diabetes listed as either a primary or secondary diagnosis (see Table 2). If properly
managed, diabetes should not result in ER visits, and thus a reduction in ER visits for diabetes
represents evidence of an improvement in primary care.
According to the U.S. National Library of Medicine’s MedlinePlus,12 recommended care
for diabetics includes two to three visits per year to monitor blood pressure, weight, and blood
glucose levels (using A1C tests) and to check for any infections or loss of feeling in the feet.
Diabetics must also monitor cholesterol and check for protein in the urine. The necessity for
frequent visits for routine monitoring, combined with the many supplies (insulin, other drugs
such as metformin, needles, testing strips) that must be purchased from pharmacies, make
diabetics a natural market for retail clinics.
Primary Care Treatable Conditions
The second set of conditions that we consider includes the following minor conditions: urinary
tract infection, conjunctivitis, upper respiratory tract infections, sore throat, ear infection, and

55 percent of the vaccinations were administered to adults aged 18 to 64. Patients who visit retail clinics
specifically to receive influenza vaccinations are older and less likely to be black or Hispanic relative to the retail
clinic patient population as a whole (Lee et al., 2009). This pattern likely reflects national differences in vaccination
rates between racial and ethnic groups: vaccination rates for non-Hispanic whites are much higher than for blacks or
Hispanics.
12
Available at https://medlineplus.gov/ency/patientinstructions/000082.htm; last accessed July 2018.

sprains and strains. We chose these six categories because they together account for the largest
share of ER visits among minor illnesses and injuries (12.36 percent of ER visits in New Jersey
over our sample period; see Table 2) and are all explicitly listed online as treated at CVS
MinuteClinics (the majority of retail clinics in New Jersey; see Table 1).
Emergent, Non-Preventable Conditions
The third set of conditions that we examine are placebo conditions that retail clinics do not treat
and that are not likely to be prevented by routine preventive care: fractures, childbirth, and
poisonings. On their list of services, CVS MinuteClinics specifically tell patients with suspected
poisonings not to seek care at their clinics. Furthermore, conversations with NPs at CVS
MinuteClinics in New Jersey suggest that practitioners immediately send patients who arrive
with a suspected broken bone to the ER. Finally, while retail clinics do provide limited family
planning services, it is unlikely that retail clinics affect aggregate fertility patterns.13 We
therefore do not expect retail clinics to have any impact on the use of ERs for these services.

3. Conceptual Framework
In this section, we consider where patients choose to receive care. This decision depends on both
the availability of different treatment options (ER, doctor's office, retail clinic) and the severity
of the patient's condition. We start by assuming that there are no retail clinics, and ask how the
availability of a primary care doctor influences ER usage. We then introduce retail clinics to ask
how ER usage is affected by the presence of this new treatment option, both when a primary care
doctor is available and when a primary care doctor is unavailable.

While a short-term prescription for birth control can be obtained at a retail clinic, retail clinics are not intended to
be a regular source of care for reproductive health. Anecdotal evidence suggests that NPs in New Jersey advise
patients to follow up with an OB-GYN whenever a prescription for birth control is administered.

All of the intuition presented below can be displayed graphically using value functions
that depict the net benefit of care (benefit - cost) as a function of the patient's severity. In
drawing these curves, we make three sets of assumptions. First, we assume that the value of
treatment is weakly increasing in the severity of the patient's condition. Second, as there are
bounds on both the costs and benefits of treatment, we assume that the value function is either
concave or S-shaped.14 Finally, we assume that retail clinics are the most valuable treatment
option (in terms of benefit minus costs) for patients with low-severity conditions, doctors' offices
are the most valuable treatment option for patients with mid-severity conditions, and ERs are the
most valuable treatment option for patients with high-severity conditions.15
Without retail clinics
In the absence of retail clinics, ER usage is determined by both the availability and the relative
costs and benefits of receiving emergency versus primary care. When a primary care doctor is
not available, either because it is after hours or because appointments are limited, patients will go
to the ER only if the net value of receiving emergency care is greater than zero.16 Since the value
of receiving care is weakly increasing in the severity of the patient's condition, only patients with
severities exceeding some threshold will find it beneficial to go to an ER. This result is displayed

As drawn below, we assume that there is an inflection point in the value function for care received in either an ER
or a doctor's office: while the marginal value of treatment is increasing at an increasing rate from low-severity to
mid-severity conditions, the marginal value of treatment is increasing at a decreasing rate from mid-severity to highseverity conditions.
15 These relative values derive from underlying assumptions about the relative costs and benefits of receiving care in
each location. In terms of costs, the evidence suggests that retail clinics are the lowest cost option, either because of
direct monetary costs or time costs, whereas ERs are the most expensive option. In terms of benefits, since retail
clinics only treat low-severity conditions, the benefits of receiving treatment for more severe conditions are greater
at doctors' offices and ERs. Finally, since many high-severity conditions require emergency care, ERs are the most
beneficial option for high-severity conditions.
16
Recent work by Bruni et al. (2016) in Italy suggests that extending hours of primary care availability alone can
generate significant reductions in the use of the ER.

graphically in Figure 1: When neither a retail clinic nor a primary care doctor is available, only
patients with severities exceeding d will go to the ER (case 1).
When a primary care doctor is available but there is no retail clinic (case 2 in Figure 1),
two things change: (1) more patients receive care and (2) fewer patients go to the ER. More
patients receive care because it is beneficial for patients with relatively low-severity conditions
to receive primary care but not emergency care (“market expansion”; patients with severities
between b and d in Figure 1). If some of these patients receive preventive care, such as flu shots,
then ER visits will also decline in the future from fewer patients developing high-severity
conditions (“prevention”; some fraction of severities greater than d in Figure 1). Finally, as it is
more valuable for mid-severity patients to receive primary care than emergency care, even
though mid-severity patients would go to an ER in the absence of available primary care, fewer
patients go to the ER in the current period (“substitution”; patients with severities between d and
f in Figure 1).17 Note that since the value of receiving emergency care exceeds the value of
receiving primary care for high-severity conditions, high-severity patients will go the ER
regardless of whether primary care is an option (patients with severities exceeding f in Figure 1).

With retail clinics
How do retail clinics affect who receives treatment and in which health care setting the treatment
is received? The impacts of retail clinics on the market when a primary care doctor is either
unavailable or available are depicted in cases 3 and 4 of Figure 1, respectively. As with the

Providing evidence of this substitution, Buchmueller et al. (2006) find that hospital closures in LA shifted some
care to doctors’ offices.

introduction of a primary care doctor discussed above, retail clinics affect the market through
three mechanisms: market expansion, prevention, and substitution.
Since retail clinics are more valuable than doctors' offices and ERs for low-severity
conditions, more patients will receive care in the current period when a retail clinic is present (in
Figure 1, patients with severities between a and b if a primary care doctor is available or between
a and d if a primary care doctor is unavailable). If some of these patients receive preventive care,
then a fraction of these patients will avoid developing high-severity conditions that would
require emergency care in the future.
Finally, the entrance of a retail clinic will cause substitution between different types of
health care providers. If a primary care doctor is available, low-severity patients will substitute
from doctors' offices to retail clinics (patients with severities between b and c). If a primary care
doctor is unavailable, mid-severity patients will substitute from ERs to retail clinics (patients
with severities between d and e). Note that since the value of receiving treatment from a doctor's
office or ER exceeds the value of receiving care at a retail clinic for more severe conditions, the
presence of retail clinics does not affect the provision of care for these patients.
The theoretical framework outlined above delivers three testable predictions about ER
visits:
1. Prevention: Fewer visits to ERs for emergent, preventable conditions. ER visits for highseverity conditions that can be prevented through adequate primary care should decrease
when a retail clinic opens if retail clinics effectively expand consumption of preventive care.
2. Substitution from ERs for primary care treatable conditions: ER visits for low-severity
conditions that can be treated at either an ER, a doctor's office, or a retail clinic should
decrease when a retail clinic opens.

3. No substitution from ERs for emergent, non-preventable conditions: ER visits for highseverity conditions that cannot be prevented by primary care and are not usually treated in a
doctor’s office or retail clinic should stay the same when a retail clinic opens.
In addition to predictions about the number of ER visits for conditions of various types,
our theoretical framework delivers predictions on the average severity of conditions that continue
to be treated in an ER in the presence of a retail clinic. Since patients with relatively low-severity
primary care treatable conditions will substitute from ERs to retail clinics when a retail clinic
opens, we expect the remaining primary care treatable cases that are seen in the ER to be of
higher severity than before the retail clinic opened. However, since there is no reason to believe
that increased prevention will affect the severity of patients who nevertheless develop emergent
conditions, we do not expect the remaining emergent, preventable cases that are seen in the ER
to be of a systematically different severity than before the clinic opening. Similarly, since ER
cases for emergent, non-preventable conditions should be unaffected by the presence of a retail
clinic, the average severity of these cases treated in an ER should remain the same after a retail
clinic opens.

4. Data
Data for this study come from two main sources: (1) The location and operation dates of retail
clinics in New Jersey from 2006 to 2014 are from Merchant Medicine, and (2) data on all visits
to New Jersey ERs over the same time period are from the New Jersey Department of Health.
We supplement these data with information from the 2010 Census and the five-year pooled
(2008-2012) American Community Survey (ACS).
The data from Merchant Medicine include the geocoded locations of all retail clinics in

New Jersey and each clinic’s opening and/or closing dates. A total of 55 retail clinics operated in
New Jersey at some point over our sample period: two clinics opened before 2006 and 53 opened
between 2006 and 2014. By 2014, 18 retail clinics had closed. The majority of clinic openings
occurred in 2006, 2007, 2008, and 2011, and there was an increase in closures during the great
recession (Figure 3).18 There is a seasonal pattern to openings and closings, with openings
frequently occurring towards the end of the year and closings concentrated in March. Figure 2
shows the locations of all the clinics in these data, whether they opened or closed during our
sample period, and the ownership of the pharmacies. The clustering of locations along the I-95
corridor reflects the distribution of New Jersey’s population.
The hospital discharge data come from the New Jersey Uniform billing records. These
records are compiled by the state from information that all general medical and surgical hospitals
are required to submit about every individual encounter with a patient. We include all records
where there is an ER revenue code on the billing record; some of these visits resulted in
admission to the hospital whereas others did not.19 In most cases, the patient was seen in the ER
and then sent home. Importantly, these data include the address of each patient. We use this
information to extract each patient’s residential census block group using ArcGIS.
We create a panel at the retail clinic-week level by linking the retail clinic and ER data
geographically. For each retail clinic in these data, we create two distance groups: (1) a near
(“treated”) group that consists of census block groups with centroids within 2 miles of the retail
clinic and (2) a far (“control”) group that consists of census block groups with centroids between

In interviews with retail clinic staff, we were told that some retail clinics closed over this time period because of
difficulties retaining practitioners. The high demand for NPs outside of retail clinics, combined with the requirement
that practitioners work nights and weekends, makes it difficult for some clinics to retain their providers.
19
Some ER discharge data only includes information on ER visits resulting in admission. Our data includes all ER
visits regardless of whether the visit resulted in an admission.

2 and 5 miles from the retail clinic.20,21 The ER data are then collapsed into retail clinic-weekdistance group cells so that for each retail clinic-week we have the number of ER visits per
100,000 people residing within 0 to 2 and 2 to 5 miles of the clinic.22 Patients who reside more
than 5 miles from a retail clinic are not considered in our analysis. As there is no obvious choice
for how to define the distance bands, for robustness we replicate our results using distance bands
of 0 to 1 miles for “near” and 1 to 3 miles for “far.”
As can be seen in the first panel of Table 3, the demographic characteristics of those
living more than 5 miles from a retail clinic location are different than those living in the other
two distance groups. In particular, the population of the areas more than 5 miles from a clinic are
poorer, older, and more rural than either of the other two distance groups, and thus we do not
think these areas serve as a good control group for the areas closest to where clinics are opening.
The second panel of Table 3 shows that in the first quarter of our data (when only two retail
clinics were operating in New Jersey) the block groups more than 5 miles from a retail clinic also
had more weekly ER visits per 100,000 residents across diagnosis groups. By dropping these
areas from our analysis, we create a control group that is more similar to the treatment group.
Figure 4 displays the resulting distance groups geographically.
Despite being more similar than block groups 5 or more miles away, the treatment and
control groups are not entirely identical.23 The treatment block groups are wealthier, more
densely populated, and have a lower fraction of black residents than the control groups. To

Some households fall within the catchment area of two retail clinics. In these cases, households are counted in
both retail clinic groups.
21
An alternative “treated” group would be visits occurring outside of normal business hours. Unfortunately, we do
not observe the exact time stamp in the discharge data.
22
Population is from the 2010 Census and is aggregated from the census block group level. We note that intercensal
population estimates are not available at the block group level.
23
Table 3 further highlights that there is no difference in average distance to the closest ER across treatment and
control groups.

control for remaining differences in local demographics, we take the population-weighted
average across block group-level demographics from the ACS within each distance group. We
include these local demographic measures in all of our regressions and explore the key
assumption of parallel pre-trends across the treatment and control groups using event study
graphs.
As introduced in Section 2, our primary outcome measures are the number of ER visits
for conditions within three categories: (1) emergent, preventable; (2) primary care treatable; and
(3) emergent, not preventable. The particular conditions included in each category are chosen
based on conditions that are actually treated in retail clinics (urinary tract infections,
conjunctivitis, upper respiratory tract infections, pharyngitis, otitis externa/media, sprains and
strains, influenza, and diabetes), and conditions that are never treated in retail clinics and thus
serve as controls (fractures, poisonings, and births). The ICD-9 codes used to define each
diagnosis category are provided in Table 2.
All visits are categorized based on the primary diagnosis code with the exception of
diabetes. In order to capture complications associated with poor disease management, we include
visits with diabetes listed in any diagnosis field when coding diabetes visits (up to nine diagnoses
can be listed for each visit).24 We treat diabetes differently from the other diagnoses because it is
a chronic disease with a high comorbidity burden that often complicates the management of
other conditions. While diabetes can be controlled in an outpatient setting with adequate primary
care, unstable diabetes is associated with a wide range of conditions that can result in
hospitalizations.

In contrast to the other conditions we consider, diabetes is most often recorded as a secondary—rather than a
primary—diagnosis (see Figure A.1). For example, even if diabetes is the underlying cause of a person’s heart
failure, heart failure is usually listed as the primary diagnosis with diabetes listed as a secondary diagnosis. Table
A.1 lists the most common primary diagnoses for visits in which diabetes is listed as a secondary diagnosis.
24

For influenza, we look at both the total number of visits and the number of visits by
patients in different age groups (ages 0-4, 5-17, 18-44, 45-64, and 65+). Age is particularly
important to consider when studying influenza, as there are important differences across age
groups in both the riskiness of the disease and in vaccination rates.25 Despite the CDC’s
recommendation that everyone aged six months and older get an annual flu vaccination, primeaged adults are much less likely to be vaccinated than other age groups, with vaccination rates
around 30 percent versus over two thirds for young children and older adults (CDC).
In addition to the number of ER visits, we further consider the average severity of
individuals arriving at the ER within each diagnosis category. Our main proxy for severity is the
total list charges reported for each patient.26 List charges come from a hospital’s charge master—
a list of charges for all billable items—and are not the prices paid by either insurers or patients
but rather the starting point for negotiations between hospitals and insurers. As such, list charges
are the same for all patients who receive particular services at a given hospital whereas actual
amounts paid vary depending on the individual’s insurance (with the actual price generally being
a negotiated fraction of the list price). Total list charges incurred during a visit therefore measure
how much was done to a patient and are a proxy for severity.
Total list charges are an imperfect proxy, however, since hospitals could respond to retail
clinic entry by changing their charge master. We think it is unlikely that hospitals respond in this
way, immediately. Nevertheless, we consider the number of diagnoses recorded as an alternative
severity proxy; cases with more comorbidities are on average more complicated and difficult to

Very young children and the elderly are both the most likely to die from influenza and the most likely to be
vaccinated.
26
In principle we could also use admission to the hospital as another proxy for severity. However, for the conditions
we consider very few cases result in admission, so there is little meaningful variation in this measure.

treat.27 As many primary care treatable conditions only have one diagnosis recorded, however,
the number of diagnoses is a much less sensitive measure compared to total list charges. For each
severity proxy, we create an average at the retail clinic-distance group-week level for visits in
each diagnosis category.

5. Empirical Specification
Our difference-in-difference strategy compares the number of ER visits among residents living in
areas near a retail clinic to those among residents living slightly farther away, both before and
after a retail clinic opened or closed. For each retail clinic c, there are two observations per week:
(1) the number of ER visits or average severity of patients who live within 2 miles of the retail
clinic and (2) the same outcomes for patients living between 2 and 5 miles from the retail clinic.
The second group provides a counterfactual for the group of people living near the retail clinic.
Note that we use all locations where a retail clinic ever operated over our sample period when
defining observations, whether the retail clinic was currently operating or not.
Two assumptions must hold for this research design to identify the causal effect of retail
clinics on ER use. First, it must be true that conditional on being within a 5 mile radius of a
clinic, people who live closer to a retail clinic are more likely to use it. Survey evidence supports
this assumption. According to the 2010 Health Tracking Household Survey, 76 percent of
families using retail clinics said the fact that “the location was more convenient than another
source of care” was either a major (49 percent) of minor (27 percent) factor in choosing to use a
retail clinic. In addition, Tu and Boukus (2013) report that the rate of retail clinic use was 40

As illnesses and injuries tend to be more severe for older patients, we also experimented with the average age of
patients and the fraction of patients over 80 years old. Since most visits for primary care treatable diagnoses are
made by relatively young patients, there is unfortunately little meaningful variation in this measure for these
conditions.
27

percent higher for patients living less than 1 mile from a retail clinic relative to those living 1 to
5 miles away in 2010.28
Since it is unclear exactly how far a typical consumer is willing to travel to use a retail
clinic, we repeat our main analysis using distance groups of 0 to 1 and 1 to 3 miles in place of
our primary groups of 0 to 2 and 2 to 5 miles.29 The wider distance band definitions (0 to 2 and 2
to 5 miles around a retail clinic) draw larger areas into both treatment and control groups,
reducing noise in our estimates of ER visit rates. However, if only people within 1 mile are
actually more likely than people further away to use retail clinics, then using the wider distance
band definition will lead us to underestimate the true treatment effect of retail clinics on ER use
by included untreated areas in the treatment group. Alternatively, and perhaps more likely, if the
true treated population decays with distance but is not zero by 2 miles away from the clinic,
some of the population in the “far” distance group in our main specification are also treated. If
some of the control group is in reality treated, our results will again underestimate the effects of
retail clinics on ER use.
The second assumption we need to make for our research design to identify a causal
effect is that the treatment and control groups would have shown similar trends in ER use in the
absence of a retail clinic opening or closing. In order to probe this assumption, Figures 5 through
7 plot the average number of ER visits per 100,000 people for the near (treatment) and far
(control) groups for primary care treatable conditions (Figure 5); emergent, preventable
conditions (Figure 6); and emergent, non-preventable conditions (Figure 7). Both clinic openings
and closings are considered events (with closings treated as the negative of openings with respect

The Tu and Boukus (2013) study is based on a very small sample, so exact magnitudes should be interpreted with
caution.
29 Figure A.2 demonstrates the distance bands used in both our primary analysis and robustness exercises for a
specific example: a retail clinic located in North Arlington (near Newark).
28

to event time), and month, year, and retail clinic fixed effects are first removed from the data.
The trends in ER visits between the near and far distance groups are reasonably similar before a
clinic opens (or after it closes), suggesting that the parallel trends assumption is justified. Note
that our “near” and “far” definitions refer to distance to a retail clinic, which is uncorrelated with
the distance to the nearest ER so it is perhaps unsurprising that there are similar pretrends in ER
use in the two groups.30
We estimate regressions of the following form:
(1) ER Visits/Popctd = b0 + b1I[near]cd + b2I[clinic open]ct + b3(I[near]cd* I[clinic open]ct) +

bXcd+ lc + lmonth + ectd.
where ER Visits/Popctd denotes the number of ER visits for a given diagnosis in week t per
100,000 residents who live within distance group d of retail clinic c. In some specifications, this
dependent variable is replaced by average list charges. The variable I[clinicopen]ct is an indicator
equal to one if retail clinic c is operating in week t and zero otherwise for both distance groups
associated with a clinic. The indicator I[near]cd is equal to one for observations from the near
category, regardless of whether the retail clinic is currently operating. Equation (1) further
includes month by year and retail clinic location fixed effects to flexibly account for trends in
hospital visits over time and differences across space. We also control for demographic
characteristics in each retail clinic-distance group by including Xcd, a vector of populationweighted averages of block group demographic characteristics from the ACS.31 We further

At the block group level, the correlation between the distance to the nearest open retail clinic and the nearest
hospital-based emergency room is 0.036.
31 Demographic controls include population density, fraction black, a quadratic in median household income, and
the fraction of the population in detailed age bins (5-9, 10-14,15-17, 18-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75+).

control for the distance from each retail clinic to the nearest hospital.32 Standard errors are
clustered by retail clinic, and all of our regressions are population weighted.33
The parameter of interest in Equation (1) is β3, the coefficient on the interaction term
I[near]cd * I[clinic open]ct. This coefficient captures the differential impact of an open retail
clinic on locations near the clinic relative to those further away. Given that our models include
retail clinic fixed effects, β3 is identified by changes in the operating status of a clinic (i.e., clinic
openings and closings).

6. Results
Figures 5 and 6 show suggestive evidence of decreases in ER visits for influenza, diabetes, and a
range of primary care treatable conditions in the treatment groups relative to the control groups
in the months after a retail clinic opens. For diabetes and primary care treatable conditions, we
see a widening of the gap between the near and far distance groups after a retail clinic opens. For
influenza, the relationship between the near and far distance groups actually reverses: after a
retail clinic opens, the near distance group switches from having more ER visits to having fewer
ER visits relative to the far group. Reassuringly, Figure 7 demonstrates that no such pattern
emerges among our placebo conditions.34
To more formally examine the impact of retail clinics on ER use, we estimate Equation
(1). Results for the full sample are provided in Table 4. As can be seen in the first row, we do

The distance between the retail clinic and the nearest hospital is time varying because of hospital closings. In
principle we could run the analysis separately on areas that are near versus far from an ER, but unfortunately there is
little variation in distance to the nearest hospital across population centers in New Jersey.
33
The qualitative patterns and statistical significance of our results are unaffected by weighting. Unweighted
regression results are available upon request.
34
The null results for the placebo conditions tell us two things: (1) trends in underlying health are not changing
differentially in the treatment and control groups and (2) other changes in the provision of care that occurred over a
similar time period, such as the expansion of urgent care centers and walk-in physician clinics, do not bias our
results (as these types of clinics do treat some of the placebo conditions).

not find a statistically significant main effect of a retail clinic being open for any condition. The
main effect of being within 0-2 miles of a retail clinic is also statistically insignificant except for
childbirth, confirming that the near and far groups have very similar patterns of ER use prior to
clinic openings.
The coefficient of interest, the interaction term “Open*Near,” behaves as predicted in our
conceptual framework. Looking first to the results for emergent, preventable conditions, we see
that ER visits for influenza fall by 13.6 percent and ER visits for diabetes fall by 3.6 percent
among those near an open retail clinic (bottom panel of Table 4; columns 1 and 2). This later
estimate is only significant at the 90 percent level of confidence, however. This pattern is in
accordance with the prediction that ER visits for emergent, preventable conditions should
decrease when a retail clinic opens due to increased use of preventive services.
Recall that ER visits for primary care treatable conditions are likewise predicted to fall
among the near group when a retail clinic opens. As seen in the top panel of Table 4, we find
significant negative interactions for all of the primary care treatable conditions we examine,
indicating that people substitute away from ERs when a retail clinic is available. The reductions
in ER visits for these minor conditions range from 5.7 percent (for urinary tract infections and
sprains and strains) to 12 percent for sore throat. Finally, as predicted, we do not find any
statistically significant effect of being near an open retail clinic on the placebo conditions of
fractures, poisonings, and childbirth.
Table 5 probes the results for influenza further by estimating separate regressions by age.
The results suggest that the largest effect (a 17 percent reduction in ER visits) is among adults
aged 18-44, the group that previous work suggests is the most likely both to have low
vaccination rates and to obtain a flu shot from a retail clinic. We also find large reductions in

visits among children, suggesting either that they too get flu shots at retail clinics or that they
benefit from reduced transmission among people their parents’ age.
Figures 8, 9, and 10 show event study graphs for average list charges—our preferred
proxy for severity. While average list charges are very similar in the near and far groups prior to
the opening of a retail clinic for primary care treatable conditions, average list charges in the near
areas are higher than those in the far areas after an opening (Figure 8). This finding provides
suggestive evidence that the simplest cases in each primary care treatable condition substitute
away from ERs to retail clinics, leaving the more complicated cases in the ER.
Perhaps unsurprisingly, Figure 9 provides little evidence of substitution for influenza.
While increased vaccination should prevent flu cases from occurring in the first place, the
serious flu cases that do emerge likely require hospital care, and thus there is no reason to believe
that increased vaccination will affect the severity of the marginal patient who becomes infected
and visits the ER. However, there is again suggestive evidence of some substitution away from
ERs for the simplest diabetes cases starting around one year after the opening of a retail clinic.
Finally, Figure 10 shows that for placebo conditions like fractures and childbirth, charges remain
roughly the same in both near and far areas before and after clinic openings and closings.
Table 6 provides estimates of the effects of being near an open retail clinic on list charges
for ER visits estimated in a regression similar to Equation (1). The results here are somewhat
inconclusive. The interaction term “Open*Near” is uniformly positive, but it is only marginally
statistically significant for urinary tract infections and otitis (ear ache). There is therefore some,
albeit inconclusive, evidence that patients with less severe cases in these categories substitute

from ERs to retail clinics to receive treatment.35
Robustness
The estimated effects of retail clinics on the number of ER visits when we instead use distance
bands of 0-1 miles (near) and 1-3 miles (far) are provided in Table A.4. These estimates are
somewhat noisier than those discussed above because there are many fewer people in the “near”
band, but they show the same qualitative patterns. In particular, among those who live near an
open retail clinic, we see reductions in ER visits for both influenza and a range of primary care
treatable conditions. Our results are therefore robust to the use of alternative distance bands.
Qualified pharmacists in New Jersey have been able to administer vaccines to adults
since 2004.36 However, in May 2014, New Jersey pharmacists gained the ability to administer
the influenza vaccine to patients aged 7-17 with the permission of their parents or legal guardian
and to patients under 12 with a prescription from an authorized provider (NJ Board of Pharmacy,
2015). All of our results are robust to including an indicator for the time period during which
pharmacists were allowed to administer the flu vaccine to children or to dropping the last eight
months of our sample. Furthermore, since we find the largest effect of retail clinics on ER flu
visits for adults, we do not think that this change in the scope of practice of New Jersey
pharmacists is confounding our main results.
A potential limitation of our work is that we have been unable to obtain information on
the openings and closings of urgent care centers in New Jersey. As discussed above, urgent care

Since the distribution of list charges is very skewed to the right, Table 6 is based on data that trims the top 0.1
percent of charges. Table A.2 shows estimates from regressions using untrimmed list charges. As an alternative
approach to mitigating outliers, Table A.3 shows estimates from regressions in which list prices are first residualized
from hospital fixed effects and an indicator denoting whether the patient was admitted. In both tables we again see
suggestive evidence that less severe primary care treatable cases substitute away from ERs when a retail clinic is
available.
36 Unfortunately, our hospital data does not go back far enough to look at the effect of allowing pharmacists to
provide vaccinations on ER usage.

centers differ from retail clinics in that they are staffed, run, and often owned by doctors, and
price levels tend to be similar to doctor’s offices (Mehotra et al., 2009). They also compete more
directly with ERs in that they often offer services such as imaging and intravenous drips and can
treat conditions such as simple fractures and poisonings. Like retail clinics, however, they offer
patients convenience via walk-in appointments.
The key issue for our analysis is whether patients who live within 2 miles of a retail clinic
are also more likely to live closer to an urgent care center than those who live 2-5 miles from a
retail clinic. An analysis of the locations of urgent care centers in 2017 suggests that this is not
the case: the number of urgent care centers per square mile is quite similar in the near and far
distance bands (the locations of both are plotted in Figure A.3). Furthermore, if urgent care
centers were opening in similar locations to retail clinics at similar times, we would find
“effects” of retail clinics on fractures (which can be treated in urgent care centers but not retail
clinics), which we do not. We therefore do not believe that the presence of urgent care centers
biases our results regarding the impact of retail clinics.

7. Discussion and Conclusions:
Our study shows that retail clinics reduce ER visits both for minor conditions and for conditions
like influenza and diabetes that are preventable given adequate primary care. These findings
suggest that encouraging competition in the form of retail clinics, with their transparent prices
and convenient access, has the potential to be welfare improving. Indeed, if Figure 1 is cast in
terms of social benefits and social costs, then all of the visits between points a and b represent
clear welfare improvements because these visits have positive value but would not have taken
place in the absence of retail clinics. Likewise, the visits between points b and d have positive

social value but would not have taken place when doctor’s offices were closed in the absence of
retail clinics. On the other hand, visits that are diverted from physicians’ offices to retail clinics
represent a transfer from one group to another and may be neutral in terms of welfare
consequences. If, however, the net social cost of treatment (including costs due to congestion) is
higher in the ER than elsewhere, then visits diverted from ERs to retail clinics (visits between
points d and e in Figure 1) are likely to also be socially beneficial.
The difficulty that arises in making welfare calculations is that insured health care
consumers do not pay the full cost of their care, so the private value (benefit minus cost for the
patient) of the visit often exceeds the social value. This distortion in valuation means that
patients may consume too much health care from a social perspective, and making consumption
cheaper and easier should increase the size of this distortion.
Using our results, we can provide estimates of some of the costs and benefits of retail
clinics on the healthcare system. First, we can compute the cost savings implied by the
reductions in ER use that we observe. To do so, we use the cost data shown in Tables A.4 and
A.5. As there is some evidence that visits for primary care treatable conditions that substitute
from ERs to retail clinics are less severe, we use the 25th percentile of costs to evaluate cost
savings for these conditions. However, because improved preventive care should prevent visits
for both minor and severe emergent, preventable conditions, we use mean costs for influenza
visits and diabetes. Finally, because list prices overstate the actually amounts paid, we use an
approximate cost-to-charge ratio drawn from Medicare of one third to deflate the estimated cost
savings.
Combining these assumptions about cost with the estimated reductions in ER visits from
Table 4, we estimate that an open retail clinic reduces spending on ER visits by at least $15,721

per week per 100,000 people with convenient access to a clinic. This implies annual cost savings
of $817,492 per 100,000, or $8.80 per person. Of this amount, $7.60 is accounted for by
reductions in costs for ER visits due to influenza and diabetes.
This is likely an underestimate of the cost savings attributable to retail clinics for three
reasons. First, we only consider two important preventable conditions that are easy to track in
our data. However, increased access to primary care through retail clinic expansion likely
reduces the burden of other emergent, preventable conditions. For example, there might well be
important cumulative effects on conditions such as heart disease and stroke from more frequent
monitoring of blood pressure and cholesterol levels. Second, we only consider the effect of retail
clinics on ER visits. To the extent that we are missing savings in doctors’ offices from better
preventive care, as well as savings downstream from hospital visits such as those generated when
patients are discharged into skilled nursing facilities or to home health care, the cost benefits of
retail clinics will be greater.37 Third, as discussed above, to the extent that there is error in
drawing the boundary between “near” and “far” areas, the estimated effects of residing near an
open retail clinic will be attenuated. Further, Spetzl et al. (2013) argue that the costs of treating
patients at retail clinics could be reduced further by loosening scope of practice laws that
currently limit the services nurse NPs are allowed to provide.
According to Ashwood et al. (2016), retail clinics cause an increase in the number of
visits for “low-acuity” conditions (conditions that we refer to as primary care treatable) that cost
an additional $14 per person after netting out reductions in ER visits for these conditions.38 Our
results suggest that over half of this increased spending ($7.60) is offset by reductions in

Around 1 percent of influenza and 9 percent of diabetes related ER visits are discharged to skilled nursing
facilities.
38
Ashwood et al. (2016) do not include sprains and strains in their measure of primary care treatable conditions, and
so the increased spending of $14 is not net of savings from substitution away from ERs for these conditions.

preventable ER visits. Are these costs savings enough to overcome the costs of increased use?
While we cannot provide a definitive answer to this question, the fact that retail clinics appear to
improve preventive care and prevent disease suggests that competition from retail clinics may
well be welfare enhancing. To the extent that preventing sickness is socially beneficial, even
when illness does not result in doctor visits, such considerations may swing the balance of
welfare calculations in favor of policies promoting competition from retail clinics.

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Figures
Figure 1: Value of Treatment by Severity of Illness and Location

Emergency room

Doctor’s office

Value (benefit - cost)

Retail clinic

e
c

Severity of condition
a

Without retail clinic
Case 1: PC MD unavailable
Case 2: PC MD available
With retail clinic
Case 3: PC MD unavailable
Case 4: PC MD available
No treatment
Treatment at retail clinic

Treatment at doctor’s office
Treatment at emergency room

Notes: The top panel of the above figure depicts the value of care (benefit - cost) as a function of patient
severity at retail clinics, doctors’ offices, and ERs. The bottom panel displays in which locations patients
of different severities choose to seek care in different states of the world (a primary care MD is available or
unavailable; a retail clinic is available or unavailable).

Figure 2: New Jersey Retail Clinics: Locations and Ownership

Already open

Opened

Opened and closed

CVS

ShopRite

Walgreens

Other

Notes: The above maps display the location, operation status between 2006 and 2014, and ownership of all
retail clinics in New Jersey. As seen in the map on the left, two clinics were open before 2006, 53 opened
between 2006 and 2014, and 18 opened and closed between 2006 and 2014. The single retail clinic not
operated by CVS, ShopRite, or Walgreens was called Simple Simon Pharmacy.

Figure 3: New Jersey Retail Clinics: Timing of Openings and Closings

Number of Clinics
10

By Year

2006

2007

2008

2009

2010

2011

Clinic Openings

2012

2013

2014

Clinic Closings

Number of Clinics
5
10

By Month

Clinic Openings

Clinic Closings

Notes: The above figures display the years and months in which retail clinics opened and closed in New
Jersey between 2006 and 2014.

Figure 4: New Jersey Retail Clinic Distance Groups: 0-2 Miles vs. 2-5 Miles

Notes: The above map displays the distance bands used in our analysis. The black block groups contain a
retail clinic. The rings of block groups are shaded lighter as one moves away from a retail clinic and depict
distances between 0 to 2 miles and 2 to 5 miles from each retail clinic.

Figure 5: Primary Care Treatable Conditions: Difference in ER Visits Between Near and
Far Distance Groups in Event Time

Difference: ER Visits per 100,000
-80
-70
-60
-50
-40

Primary Care Treatable

-24

-18

-12

-6
0
6
Event time (Monthly)

Difference: Near − Far

Notes: The above figure plots the difference in ER visits per 100,000 between the near and far distance groups
in event time for a balanced panel. Coefficients from a regression of monthly ER visits on an indicator for
near, event time dummies, and event time dummies interacted with near, as well as month by year and retail
clinic fixed effects. An event is defined as either a clinic opening or closing (clinic closings are treated inversely
to clinic openings). “Primary care treatable” is the sum of ER visits for urinary tract infections, conjuctivitis,
upper repiratory tract infections, pharyngitis, otitis media, and sprains and straings. Population weighted.

Figure 6: Emergent, Preventable Conditions: ER Visits in Event Time
Diabetes

Difference: ER Visits per 100,000
-10
-5
0
5

Difference: ER Visits per 100,000
-50
-40
-30
-20
-10

Influenza

-24

-18

-12

-6
0
6
Event time (Monthly)

Difference: Near − Far

-24

-18

-12

-6
0
6
Event time (Monthly)

Difference: Near − Far

Notes: The above figure plots the difference in ER visits per 100,000 between the near and far distance groups
in event time for a balanced panel. Coefficients from a regression of monthly ER visits on an indicator for
near, event time dummies, and event time dummies interacted with near, as well as month by year and
retail clinic fixed effects. An event is defined as either a clinic opening or closing (clinic closings are treated
inversely to clinic openings). Population weighted.

Figure 7: Emergent, Non-Preventable Conditions (Placebo): ER Visits in Event Time

-20

Difference: ER Visits per 100,000
-15
-10
-5
0

Emergent, Not Preventable

-24

-18

-12

-6
0
6
Event time (Monthly)

Difference: Near − Far

Notes: The above figure plots the difference in ER visits per 100,000 between the near and far distance groups
in event time for a balanced panel. Coefficients from a regression of monthly ER visits on an indicator for
near, event time dummies, and event time dummies interacted with near, as well as month by year and
retail clinic fixed effects. An event is defined as either a clinic opening or closing (clinic closings are treated
inversely to clinic openings). Population weighted. “Emergent, Not Preventable” is the sum of ER visits for
fractures, births, and poisonings.

Figure 8: Primary Care Treatable Conditions: List Charges in Event Time

-500

Difference: Average List Charges
0
500
1000
1500

Primary Care Treatable

-24

-18

-12

-6
0
6
Event time (Monthly)

Difference: Near − Far

Notes: The above figure plots the difference in average monthly list charges between the near and far distance
groups in event time for a balanced panel. Coefficients from a regression of average monthly list charges
on an indicator for near, event time dummies, and event time dummies interacted with near, as well as
month by year and retail clinic fixed effects. An event is defined as either a clinic opening or closing (clinic
closings are treated inversely to clinic openings). “Primary care treatable” includes ER visits for urinary
tract infections, conjuctivitis, upper repiratory tract infections, pharyngitis, otitis media, and sprains and
straings. Population weighted.

Figure 9: Emergent, Preventable Conditions: List Charges in Event Time

-24

-18

-12

-6
0
6
Event time (Monthly)

Diabetes

Difference: Near − Far

Difference: Average List Charges
-4000 -2000
0
2000 4000

Difference: Average List Charges
-10000 -5000
0
5000 10000

Influenza

-24

-18

-12

-6
0
6
Event time (Monthly)

Difference: Near − Far

Notes: The above figure plots the difference in average monthly list charges between the near and far distance
groups in event time for a balanced panel. Coefficients from a regression of average monthly list charges on
an indicator for near, event time dummies, and event time dummies interacted with near, as well as month
by year and retail clinic fixed effects. An event is defined as either a clinic opening or closing (clinic closings
are treated inversely to clinic openings).

Figure 10: Emergent, Non-Preventable Conditions (Placebo): List Charges in Event Time

Difference: Average List Charges
-3000 -2000 -1000
0
1000 2000

Emergent, Not Preventable

-24

-18

-12

-6
0
6
Event time (Monthly)

Difference: Near − Far

Notes: The above figure plots the difference in average monthly list charges between the near and far distance
groups in event time for a balanced panel. Coefficients from a regression of average monthly list charges on
an indicator for near, event time dummies, and event time dummies interacted with near, as well as month by
year and retail clinic fixed effects. An event is defined as either a clinic opening or closing (clinic closings are
treated inversely to clinic openings). “Emergent, Not Preventable” includes ER visits for fractures, births,
and poisonings.

Tables
Table 1: New Jersey Retail Clinics: Summary Statistics
(1)
All

(2)
(3)
(4)
(5)
CVS ShopRite Walgreens Other

Open Saturdays
Open Sundays
Pharmacy on Site
Clinics Shut Down

0.96
0.95
1.00
0.33

0.98
0.98
1.00
0.23

0.83
0.83
1.00
1.00

1.00
1.00
1.00
0.25

1.00
0.00
1.00
1.00

Weekday Open Time
Weekday Close Time
Saturday Open Time
Saturday Close Time
Sunday Open Time
Sunday Close Time

08:32
07:33
09:02
05:25
09:48
05:18

08:30
07:30
09:00
05:30
09:55
05:25

09:20
08:00
09:00
05:00
09:00
04:24

08:00
07:30
09:30
05:00
09:30
05:00

08:00
08:00
09:00
06:00
.
.

Clinics

Notes: The above table summarizes characteristics of all retail clinics in operation in New Jersey at some point between 2006 and 2014. The “Other” category
is Simple Simon Pharmacy.

Table 2: Overview of Condition Categories
Diagnosis group

Primary care treatable
Urinary tract infection
Conjunctivitis
URTI/sinusitis/bronchitis
Pharyngitis
Otitis externa/media
Sprains/strains
Emergent, preventable
Influenza
Diabetes
Emergent, not preventable
Fractures
Poisonings
Childbirth

ICD-9-CM
codes

Percent of
ER visits

599*, 595*
372*
460*-461*, 465*-466*, 473, 490
462*-463*, 034
380*-382*
840*-848*

1.71%
0.46%
3.15%
1.37%
1.39%
4.38%

487*-488*
249*-250*

0.21%
8.74%

800*-829*
909.0*, 909.1*, 909.5*, 995.2*, 960*-989*
DRGs 372-375

2.94%
0.53%
2.20%

Notes: The above table provides an overview of the three categories of conditions used in our
analysis. For all conditions other than diabetes, we consider a visit as being for the condition in
question only if the condition is listed as the primary diagnosis. For diabetes, we consider all visits in which diabetes is listed in any diagnosis field. The percent of ER visits reflects the total
share of ER visits in New Jersey between 2006 and 2014 with the corresponding ICD-9 codes.

Table 3: Retail Clinic Distance Groups: Block Group Summary Statistics at Baseline
Demographics

Population
Population density
Median household income
Pct. black
Pct. under 18
Pct. aged 18-54
Pct. aged 55-74
Pct. age 75 and over
Urgent care clinic density
Distance to hospital
Block Groups

(1)
All

(2)
0-1 Miles

(3)
0-2 Miles

(4)
2-5 Miles

(5)
5+ miles

1,391
9,148
75,367
14.36
23.06
50.71
19.33
6.90
0.15
2.93
6,320

1,372
12,318
84,990
7.54
21.16
54.44
17.63
6.77
0.27
2.38
260

1,361
11,463
82,236
10.83
22.13
52.49
18.68
6.70
0.19
2.36
894

1,385
9,338
76,900
15.81
23.51
51.03
18.89
6.57
0.16
2.37
3,499

1,415
7,721
69,311
13.37
22.67
49.28
20.44
7.61
0.11
4.20
1,927

(1)
All

(2)
0-1 Miles

(3)
0-2 Miles

(4)
2-5 Miles

(5)
5+ miles

13.973
4.400
38.250
14.459
15.293
41.032
1.899
73.206
26.119
4.130
21.735
6,320

19.401
3.513
28.266
10.807
10.951
36.823
1.307
62.932
24.701
3.376
25.080
260

14.377
3.808
31.919
12.479
12.240
37.608
1.632
67.636
24.767
3.987
21.483
894

13.715
4.371
37.756
14.174
15.461
39.512
1.894
70.834
25.973
4.091
21.860
3,499

14.257
4.730
42.116
15.906
16.414
45.416
2.033
80.151
27.020
4.269
21.623
1,927

ER Visits per 100,000 per Week

Urinary tract infection
Conjuctivitis
Upper repiratory infection
Pharyngitis
Otitis
Sprain/strain
Influenza
Diabetes
Fracture
Poisoning
Births
Block Groups

Notes: The above table provides baseline demographic information and outcomes for block groups in
different distance bands around retail clinics in New Jersey. Observations are at the block group level.
Baseline weekly ER visits per 100,000 are the weekly rates averaged over the first quarter of our sample.
Data is taken from both the 2010 Census and the 2008-2012 ACS.

Table 4: Retail Clinics and ER Visits
Primary Care Treatable
(3)
(4)
(5)
URI
Pharyngitis Otitis

(1)
UTI

(2)
Conjunct.

Open

0.117
(0.308)

0.228
(0.146)

1.077
(0.841)

0.189
(0.490)

0.251
(0.325)

1.104
(0.693)

Near

-0.041
(0.455)

0.151
(0.200)

-0.064
(1.032)

0.082
(0.566)

-0.138
(0.497)

1.738
(1.088)

Open*Near

-0.890∗∗
(0.390)

-0.402∗∗
(0.179)

-1.597∗
(0.879)

-1.435∗∗
(0.619)

-0.715∗
(0.421)

-2.289∗∗∗
(0.813)

Mean per 100k
Mean Pop.
R-Squared
Observations

15.615
175,121
0.521
51,480

4.011
175,121
0.454
51,480

27.152
175,121
0.732
51,480

11.942
175,121
0.661
51,480

12.140
175,121
0.642
51,480

39.662
175,121
0.679
51,480

Emergent, Preventable
(1)
(2)
Influenza
Diabetes

(6)
Sprain/strain

Placebo: Emergent, Not Preventable
(3)
(4)
(5)
Fracture
Poisoning
Births

Open

0.028
(0.120)

-0.321
(1.183)

0.216
(0.327)

0.053
(0.102)

-0.267
(0.301)

Near

0.102
(0.078)

0.816
(1.937)

0.706
(0.560)

0.054
(0.136)

-0.668∗∗
(0.282)

Open*Near

-0.251∗∗∗
(0.088)

-2.526∗
(1.441)

-0.555
(0.439)

-0.117
(0.102)

0.548
(0.425)

Mean per 100k
Mean Pop.
R-Squared
Observations

1.878
175,121
0.502
51,480

76.390
175,121
0.790
51,480

27.495
175,121
0.458
51,480

5.064
175,121
0.401
51,480

20.804
175,121
0.616
51,480

*** p<0.01, ** p<0.05, * p<0.1
Notes: Observations are at the retail clinic-distance band-week level. The independent variable in each
regression is the number of ER visits for a given condition. All regressions include month byyear and
retail clinic fixed effects and are population weighted. Additional retail clinic-distance group controls
include population density, fraction black, a quadratic in median household income, distance to nearest
hospital, and the age structure. Standard errors are clustered by retail clinic. “UTI” denotes urinary
tract infections; “URTI” denotes upper respiratory tract infections.

Table 5: Retail Clinics and ER Visits for Flu by Patient Age
(1)
(2)
(3)
(4)
(5)
(6)
All Visits Ages 0-4 Ages 5-17 Ages 18-44 Ages 45-64 Ages 65+
Open

0.028
(0.120)

-0.002
(0.582)

-0.038
(0.270)

0.051
(0.105)

0.044
(0.050)

-0.026
(0.066)

Near

0.102
(0.078)

0.594∗
(0.334)

0.272∗
(0.157)

0.057
(0.102)

-0.008
(0.039)

-0.013
(0.060)

-0.251∗∗∗
(0.088)

-0.818∗
(0.420)

-0.369∗∗
(0.176)

-0.325∗∗∗
(0.107)

-0.080
(0.065)

0.051
(0.089)

Mean per 100k 1.878
Mean Pop.
175,121
R-Squared
0.502
Observations
51,480

5.655
11,781
0.333
51,480

2.734
30,037
0.355
51,480

1.942
66,860
0.460
51,480

0.864
46,345
0.351
51,480

0.929
22,901
0.358
51,480

Open*Near

*** p<0.01, ** p<0.05, * p<0.1
Notes: Observations are at the retail clinic-distance band-week level. The independent variable
in each regression is the number of ER visits for influenza. All regressions include month by year
and retail clinic fixed effects and are population weighted. Additional retail clinic-distance group
controls include population density, fraction black, a quadratic in median household income, distance to nearest hospital, and the age structure. Standard errors are clustered by retail clinic.

Table 6: Retail Clinics and Average ER List Charges
Primary Care Treatable
(1)
UTI

(2)
Conjunct.

(3)
URTI

(4)
Pharyngitis

(5)
Otitis

(6)
Sprain/strain

Open

-330.317
(230.307)

35.972
(50.100)

79.040
(83.881)

90.786
(76.526)

9.320
(58.924)

47.755
(89.175)

Near

-458.965
(279.216)

-68.730
3.980
(107.253) (154.260)

-28.656
(105.415)

-23.843
(95.942)

-20.575
(104.054)

Open*Near

847.679∗
(504.357)

165.461
427.660
(214.460) (294.260)

234.627
(217.165)

327.419∗
(191.895)

247.593
(210.771)

Mean per 100k 12,794.461 1,220.547 3,899.715
Mean Pop.
175,121
175,121
175,121
R-Squared
0.087
0.130
0.129
Observations
51,480
51,480
51,480

1,788.249
175,121
0.153
51,480

1,829.747
175,121
0.103
51,480

2,667.661
175,121
0.383
51,480

Emergent, Preventable

Emergent, Not Preventable (Placebo)

(1)
Influenza

(2)
Diabetes

(3)
Fractures

(4)
Poisonings

(5)
Births

Open

-214.543
(997.012)

-388.686
(362.217)

-347.490
(340.134)

27.087
(429.094)

91.058
(464.000)

Near

-2,119.802
(1,315.987)

-427.187
(414.853)

-457.786
(318.818)

-625.744
(427.497)

-266.211
(221.921)

Open*Near

1,827.985
(1,246.891)

764.090
(642.152)

467.331
(455.722)

886.099
(624.454)

389.109
(379.694)

7,996.014
175,121
0.037
51,480

33,997.848
175,121
0.404
51,480

17,587.116
175,121
0.137
51,480

15,256.463
175,121
0.064
51,480

23,120.788
175,121
0.569
51,480

Mean per 100k
Mean Pop.
R-Squared
Observations

*** p<0.01, ** p<0.05, * p<0.1
Notes: Observations are at the retail clinic-distance band-week level. The independent variable in each regression is the average total list charges of ER visits for a given condition. Hospital visits with list charges
at or above the 99.99th percentile across all ER visits for a given condition are excluded. All regressions include month, year, and retail clinic fixed effects and are population weighted. Additional retail clinic-distance
group controls include population density, fraction black, a quadratic in median household income, and the
age structure. Standard errors are clustered by retail clinic. “UTI” denotes urinary tract infections; “URTI”
denotes upper respiratory tract infections.

Table 7: Retail Clinics and ER Visits: 0-1 Miles vs. 1-3 Miles
Primary Care Treatable
(1)
UTI

(2)
Conjunct.

(3)
URTI

(4)
Pharyngitis

(5)
Otitis

(6)
Sprain/strain

Open

-0.011
(0.432)

0.296
(0.214)

1.057
(0.930)

-0.038
(0.639)

0.255
(0.371)

1.230
(0.899)

Near

0.327
(0.430)

0.337∗
(0.190)

1.650∗
(0.985)

0.253
(0.611)

0.576
(0.415)

2.117∗∗
(1.000)

Open*Near

-0.663
(0.660)

-0.541∗∗
(0.225)

-2.576∗
(1.381)

-1.421
(0.864)

-0.979∗∗
(0.485)

-2.203∗
(1.158)

Mean per 100k
Mean Pop.
R-Squared
Observations

15.080
79,154
0.386
51,480

3.733
79,154
0.352
51,480

25.141
79,154
0.648
51,480

11.000
79,154
0.564
51,480

10.988
79,154
0.506
51,480

39.391
79,154
0.580
51,480

Emergent, Preventable

Emergent, Not Preventable (Placebo)

(1)
Influenza

(2)
Diabetes

(3)
Fractures

(4)
Poisonings

(5)
Births

Open

-0.248
(0.154)

-0.853
(1.450)

0.303
(0.445)

0.017
(0.158)

0.143
(0.271)

Near

0.171∗∗
(0.084)

1.798
(2.315)

0.556
(0.449)

0.181
(0.131)

-0.730∗∗
(0.288)

Open*Near

-0.305∗
(0.153)

-0.180
(2.625)

-0.171
(0.448)

0.030
(0.163)

0.706
(0.438)

Mean per 100k
Mean Pop.
R-Squared
Observations

1.803
79,154
0.162
51,480

75.452
79,154
0.727
51,480

27.523
79,154
0.304
51,480

4.990
79,154
0.238
51,480

19.199
79,154
0.226
51,480

*** p<0.01, ** p<0.05, * p<0.1
Notes: Observations are at the retail clinic-distance band-week level. In contrast to our primary analysis, the near (far) group is within 0-1 (1-3) miles of a retail clinic; residents living further than 3 miles
from a retail clinic are excluded. The independent variable in each regression is the number of ER visits
for a given condition. All regressions include month, year, and retail clinic fixed effects and are population weighted. Additional retail clinic-distance group controls include population density, fraction black,
a quadratic in median household income, and the age structure. Standard errors are clustered by retail
clinic. “UTI” denotes urinary tract infections; “URTI” denotes upper respiratory tract infections.

Appendix
A

Supplementary Figures
Figure A.1: Diagnosis Number of Diabetes and Influenza
Influenza

20000

200000

Number of Visits
40000
60000

Number of Visits
400000
600000

80000

800000

Diabetes

4
5
6
Diagnosis Number

Notes: The above figures depict the frequency of all observed diagnosis positions for diabetes and influenza.
That is, each figure considers all ER visits in which the diagnosis in question was recorded and displays
the position in which the diagnosis appeared (i.e., “1” denotes the first diagnosis, “2” denotes the second
diagnosis, etc.).

Figure A.2: Retail Clinic Distance Group Example: North Arlington (Newark)

Notes: The above picture displays a close-up of the distance bands used in both our primary analysis and
robustness exercises for the retail clinic located in North Arlington, New Jersey. The black block group
contains the North Arlington retail clinic. The rings of block groups are shaded lighter as one moves away
from the retail clinic and depict distances of 0-1 miles, 1-2 miles, 2-3 miles, and 3-5 miles from the retail
clinic.

Figure A.3: Locations of Urgent Care Clinics in 2017

Retail clinics

Urgent care clinics

Notes: The above map displays the locations of all retail clinics in our data (triangles) and the locations of all
urgent care clinics in 2017 (circles). The urgent care clinic locations in 2017 were collected by scraping google
search results, and following up with each urgent care clinic’s website to identify locations of all branches.

Supplementary Tables
Table A.1: ER Visits for Diabetes: Most Common Primary Diagnosis

Frequency Percent Cumulative
Diabetes mellitus (250)
285,743
8.43
8.43
Respiratory system/other chest symptoms (786)
160,203
4.73
13.16
General symptoms (780)
110,206
3.25
16.41
Other chronic ischemic heart disease (414)
107,351
3.17
19.58
Heart failure (428)
99,258
2.93
22.50
Other abdomen/pelvis symptoms (789)
74,681
2.20
24.71
Other cellulitis/abscess (682)
74,015
2.18
26.89
Other urinary tract disorder (599)
65,540
1.93
28.83
Cardiac dysrhythmias (427)
62,913
1.86
30.68
Other & unspecified back disorder (724)
57,266
1.69
32.37
Complications peculiar to certain spec. proc. (996)
53,709
1.58
33.96
Acute myocardial infarct ion (410)
50,227
1.48
35.44
Pneumonia, organism unspecified (486)
50,044
1.48
36.91
Chronic bronchitis (491)
45,409
1.34
38.25
Asthma (493)
447,66
1.32
39.58
Septicemia (038)
38,190
1.13
40.70
Fluid/electrolyte disorder (276)
36,838
1.09
41.79
Symptoms involving head/neck (784)
35,801
1.06
42.85
Osteoarthrosis & allied disorders (715)
35,629
1.05
43.90
Acute renal failure (584)
33,229
0.98
44.88
Cerebral artery occlusion (434)
32,516
0.96
45.84
Renal/ureteral calculus (592)
29,667
0.88
46.71
Essential hypertension (401)
29,252
0.86
47.57
Cataract (366)
28,985
0.86
48.43
Other soft tissue disorders (729)
27,886
0.82
49.25
GI system symptoms (787)
27,513
0.81
50.06
Notes: The above table displays the most common primary diagnoses for hospital visits with any diabetes diagnosis recorded. Each row lists the description; the corresponding ICD-9 code is provided in
parentheses.

Table A.2: Retail Clinics and Average ER List Charges: Untrimmed Prices
Primary Care Treatable Emergent, Preventable Emergent, Not Preventable
(1)
Combined

(2)
Influenza

(3)
Diabetes

(4)
Combined

Open

10.096
(73.543)

-287.840
(980.343)

-320.411
(350.802)

0.569
(328.006)

Near

-104.187
(180.333)

-2,170.106 -489.617
(1,317.644) (430.300)

-402.278
(322.448)

Open*Near

474.526∗
(282.555)

1,863.661
824.956
(1,238.487) (667.641)

412.784
(449.641)

Mean per 100k
Mean Pop.
R-Squared
Observations

4,030.004
175,121
0.484
51,480

8,047.146 34,139.164
175,121
175,121
0.037
0.394
51,480
51,480

19,637.637
175,121
0.547
51,480

*** p<0.01, ** p<0.05, * p<0.1
Notes: Observations are at the retail clinic-distance band-week level. The independent variable in each regression is the average total list charges of ER visits for a given condition. All regressions include month, year, and
retail clinic fixed effects and are population weighted. Additional retail clinic-distance group controls include
population density, fraction black, a quadratic in median household income, and the age structure. Standard
errors are clustered by retail clinic.

Table A.3: Retail Clinics and Average ER List Charges: Residualized Prices

Primary Care Treatable

Open

Near

Open*Near

Mean per 100k
Mean Pop.
R-Squared
Observations

(1)
UTI

(2)
Conjunct.

(3)
URTI

(4)
Pharyngitis

(5)
Otitis

(6)
Sprain/strain

-280.392
(197.169)

29.458
(69.217)

154.586
(133.758)

52.962
(79.129)

54.008
(78.472)

-6.126
(85.581)

-677.494∗∗∗ -82.697
-131.861
(223.223) (111.229) (153.679)

-144.213
(108.170)

-104.856
(112.123)

-112.172
(114.621)

305.274
(221.984)

346.609
(222.429)

274.012
(233.502)

25.170
175,121
0.123
51,477

105.067
175,121
0.064
51,473

26.844
175,121
0.281
51,474

952.289∗∗
(397.480)
-11.948
175,121
0.044
51,476

161.852 615.495∗∗
(208.919) (302.572)
29.194
175,121
0.016
51,477

210.073
175,121
0.047
51,473

Emergent, Preventable

Emergent, Not Preventable (Placebo)

(1)
Influenza

(2)
Diabetes

(3)
Fractures

(4)
Poisonings

(5)
Births

Open

4,019.556
(2,848.726)

-22.988
(436.149)

-51.293
(263.449)

166.862
(429.721)

186.730
(466.919)

Near

-370.052
(4,649.174)

-451.070
(457.218)

-569.792∗
(294.849)

104.494
(624.785)

-258.234
(252.725)

Open*Near

-7,730.023
(5,222.984)

1,090.283
(914.503)

472.349
(526.075)

376.180
(810.051)

456.116
(500.814)

Mean per 100k
Mean Pop.
R-Squared
Observations

-1,192.490
175,121
0.046
51,480

-152.406
175,121
0.247
51,479

-33.930
175,121
0.065
51,471

155.379
175,121
0.030
51,472

36.936
175,121
0.376
51,475

*** p<0.01, ** p<0.05, * p<0.1
Notes: Observations are at the retail clinic-distance band-week level. The independent variable in each regression is the average total list charges of ER visits for a given condition residualized from hospital fixed effects and
an indicator for whether the patient was admitted. All regressions include month, year, and retail clinic fixed
effects and are population weighted. Additional retail clinic-distance group controls include population density,
fraction black, a quadratic in median household income, and the age structure. Standard errors are clustered
by retail clinic. “UTI” denotes urinary tract infections; “URTI” denotes upper respiratory tract infections.

Table A.4: Distribution of List Charges by Diagnosis
p5

p25

p50

p75

p95

Mean

Influenza
495 1054 1787 3348 29425 6688.63
Diabetes
895 3258 14527 42429 116862 32402.23
Urinary tract infection
663 1396 2934 9220 53504 11773.92
Conjunctivitis
293
513
785
1193
3145
1222.62
URTI/sinusitis/bronchitis 373
776
1335 2387 12420 3322.52
Pharyngitis
352
680
1046 1659
4446
1708.13
Otitis
322
575
913
1415
6003
1728.56
Sprains & strains
555 1034 1589 2378
7997
2666.38
Poisonings
404 1086 2847 12874 64116 14797.16
Fractures
802 1477 2523 11990 78526 17611.13
Births
9108 15311 20730 27729 42460 22939.67

Std. Dev.

Count

25927.55
53472.52
26540.98
2364.37
9624.29
3428.79
4434.3
7047.24
41404.47
53310.2
14193.1

82,676
3,389,333
660,532
178,601
1,220,564
528,145
535,466
1,673,502
204,605
1,120,181
854,560

Notes: The above table presents summary statistics for list charges by diagnosis across hospital visits.

Table A.5: CVS MinuteClinic Price List
Posted price
General medical exams
Minor illnesses
Minor injuries
Adeno test (viral pink eye)
Blood sugar test
Flu test (A/B)
Strep test (rapid)
Diabetes monitoring
Diabetes screening (glucose)
Influenza vaccine (high dose)
Influenza vaccine (seasonal)

89
89-129
89-129
25
25
35
35
79-99
59-69
69.99
44.99

Notes: The above prices come from www.cvs.com/minuteclinic; last accessed May 2017. Minor illnesses include urinary tract and bladder infections, upper respiratory infections, sore and strep throat, and earaches and ear infections. Minor injuries include sprains, strains, and
joint pain. High dose influenza vaccines contain more antigen and are
intended to create a stronger immune response; these vaccines are available for people aged 65 years and older (CDC).

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Full text of Working Papers (Federal Reserve Bank of Chicago) : Check Up Before You Check Out : Retail Clinics and Emergency Room Use, Working Paper 2017-11

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