Physician Responses to a Medicaid Price Shock: Evidence from Florida’s Fee Bump

Adam Leive, PhD**

University of Virginia

Michael R Richards, MD PhD MPH Vanderbilt University

D Sebastian Tello Trillo, PhD

University of Virginia

** Corresponding Author Assistant Professor, Frank Batten School of Leadership & Public Policy University of Virginia L050 Garrett Hall, 235 McCormick Road Charlottesville, VA 22904 Phone: (434) 924-0049 Email: leive@virginia.edu JEL Codes: H41; H51; I13; I18; J22; J44; J48 Acknowledgments The authors thank the Florida Agency for Health Care Administration for providing excellent data sources. The AHCA was not responsible for any analyses or interpretations of the data. The authors are also grateful to Daniel Polsky, Ashley Swanson, Matthew Grennan, Scott Harrington, and seminar participants at the University of Pennsylvania and Vanderbilt University for helpful feedback. The authors have no conflicts of interest to disclose, and all remaining errors and opinions belong solely to the authors.

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Physician Responses to a Medicaid Price Shock: Evidence from Florida’s Fee Bump

Abstract

The Affordable Care Act has brought millions more into Medicaid coverage, while

experimenting with related policies—including a temporary “fee bump” for primary care

services. The ultimate effects of a potentially large, but ephemeral, Medicaid price shock

are unclear a priori. Thus, we use detailed physician data from the state of Florida to

empirically document the policy’s impact. Primary care physicians (PCPs) are attracted

to more generous fees, especially those with a hospital-based practice. Doubling payment

rates leads them to increase their acceptance of new Medicaid patients by 10-13% across

the state, but only among those already participating in the program. The incentive does

not draw new PCPs into the Medicaid market. Employers seem to simultaneously induce

greater labor supply from their clinicians to accommodate more Medicaid business.

However, these changes to physician behavior do not reduce Medicaid patients’ reliance

on emergency department care, even for primary care sensitive conditions. While

politically popular in recent years, policies that temporarily adjust physician payments

may be an inefficient use of public resources.

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INTRODUCTION

Courtesy of the Affordable Care Act (ACA), the Medicaid program has surpassed

Medicare as the largest health insurer in the United States (US), enrolling nearly 73

million individuals by 2016.1 However, Medicaid’s recent growth and current size do not

guarantee enrollee access to care. The program still represents fewer than 20% of all US

health care dollars spent,2 and has a less favorable provider fee schedule when compared

to other payers. The influence of Medicaid pricing on provider participation and patient

access is also well documented in the empirical literature and known to policymakers

(Baker and Royalty 2000; Brunt and Jensen 2014; Coburn, Long, and Marquis 1999;

Cohen and Cunningham 1995; Cunningham and Nichols 2005; Decker 2007, 2012; Han

2013; Mitchell 1991; Perloff, Kletke, and Fossett 1995; Rhodes et al. 2014; Richards and

Polsky 2016; Richards et al. 2016b; Rosenbaum 2014; Showalter 1997; Sloan, Mitchell,

and Cromwell 1978; Sommers, Paradise, and Miller 2011). As a result, the ACA coupled

its federally funded Medicaid expansions with a federally funded boost to Medicaid

payments starting in 2013, with the hope of inducing more providers to accept Medicaid

patients.

The resulting policy¾known as the Medicaid “fee bump” ¾contained three

salient features. First, the more generous payments lasted just two years, expiring

December 31, 2014 unless individual states sustained them using their own funds.

Second, the payment changes were restricted to care administered by primary care

1 Enrollment numbers provided by the Kaiser Family Foundation (www.kff.org) “State Health Facts” data. 2 See CMS spending figures for the 2014 fiscal year, available here: https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html

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providers, leaving most providers and their billable activities untouched.3 And finally, the

temporary reimbursements were benchmarked to Medicare prices (French et al. 2016;

Polsky et al. 2015; Wilk and Jones 2014; Zuckerman, Skopec, and McCormack 2014).

Equalizing state Medicaid payments with nationally set Medicare rates created wide

variation in the magnitude of the price shock due to states’ differing Medicaid generosity

at baseline. Some primary care providers experienced relatively small financial

improvements while their counterparts in other states saw eligible Medicaid

reimbursements double in size (Zuckerman and Goin 2012; Zuckerman et al. 2014).

Yet, even in the presence of large payment increases, the temporary nature and

limited scope of the incentives raises doubt on their potential effectiveness (Ginsburg

2011; Goodson 2010; Wilk and Jones 2014). For instance, primary care physicians

(PCPs) may be unwilling to devote more effort toward a particular market segment at the

expense of serving other patient-payer groups, especially when capacity constrained. Any

returns from adjusting their practice to take advantage of higher payments (e.g.,

increasing labor supply or reducing care delivered to other patients) would be short-lived

and have to be balanced against the costs of doing so; Medicaid is also notorious for

administrative burdens and reimbursement delays, which erodes the appeal of higher fees

(Cunningham and O'Malley 2009; Long 2013). Additionally, a known transient incentive

could be relatively weak within an industry often reliant upon repeat customers (i.e., an

ongoing provider-patient relationship) and accompanying revenue streams. The marginal

Medicaid patient may not be a one-off transaction but instead a permanent fixture in the

provider’s patient panel going forward—making current and future payment levels

3 Primary care providers were determined eligible if they were board certified in family medicine, general internal medicine, or pediatrics or if they demonstrated that at least 60% of their Medicaid delivered services were for primary care.

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relevant for provider decision-making. For these reasons, how providers would respond

to this specific policy lever is an open empirical question.

To date, only a single published study has assessed the impact of the recent

Medicaid fee bump on primary care access (Polsky et al. 2015). Polsky and colleagues

(2015) use a “secret shopper” methodology across 10 US states and find providers

increase their willingness to see new Medicaid patients after payments increased in

comparison to a pre-policy year.4 Their study additionally demonstrates a dose-response

relationship across states, with larger price shocks corresponding to greater access

improvements. Several important questions on this policy remain, however, including

whether providers accommodating more Medicaid business were already treating

Medicaid patients, how providers adjusted their labor supply, and whether the policy led

to changes in consumer health.

In this paper, we comprehensively study how physicians respond to the Medicaid

fee bump using a rich set of administrative data collected on the universe of Florida

physicians. Importantly, we benefit from measures of Medicaid participation on the

extensive (i.e., program participation) as well as intensive (i.e., size of the Medicaid

panel) margins. We are also able to document heterogeneity in any changes by practice

setting and then investigate mechanisms by which physicians accommodate additional

Medicaid patients (e.g. changes in labor supply and time use patterns). Beyond supply-

side responses, we also examine downstream consequences for patient health by focusing

on Emergency Department (ED) utilization among Medicaid beneficiaries.

4 The “secret shopper” approach relied on trained field staff randomly calling primary care practices posing as prospective patients. Callers did not actually attend visits, but merely asked over the phone if they could be scheduled for one.

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Using a difference-in-differences research design, we empirically show that PCPs

are responsive to higher Medicaid prices. We document a robust effect across several

specifications and analytic strategies; however, the positive participation change is

modest, especially when juxtaposed to the state’s extensive price shock. Certain PCPs

also drive much of the observed behavior change—namely, those with a hospital or

health system based practice show the largest absolute increase in their willingness to

take on new Medicaid patients. All other PCPs do so to a lesser extent. Irrespective of

practice setting, the policy effects do not widely vary by local Medicaid market size. We

also see suggestive evidence of a simultaneous ramp up of labor supply (in the form of

patient care hours) once the fee bump is in place, which is indicative of at least one

channel for accommodating more Medicaid business without disrupting care to other

patient-payer groups. That said, the incentive appears to only influence PCPs’ intensive

margin; it fails to attract more PCPs into the Medicaid market.

Our results are consistent with the findings from Polsky et al. (2015) as well as a

causal interpretation for the payment initiative’s effects on Medicaid access. Yet, we find

no evidence that Medicaid beneficiaries subsequently lower their reliance on ED-based

care. ED utilization appears unchanged after the fee bump is active, and the same is true

for enrollees suffering from a host of primary care sensitive conditions, for which proper

outpatient chronic disease management would plausibly prevent acute exacerbations and

related emergency care needs. Our findings, in turn, reveal the challenges of optimally

influencing provider behavior through a large, but temporary, Medicaid price mechanism.

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HEALTH CARE MARKET CONTEXT

Our study’s setting is Florida. Beyond offering data well suited for our empirical tasks,

this specific context provides several analytic advantages. First, Florida’s Medicaid fee

bump was one of the largest in the country. Primary care services enjoyed a more than

100% markup during the ACA payment intervention (Zuckerman and Goin 2012).

Second, the fee bump did not coincide with a Medicaid expansion. The state is one of the

remaining 19 US states to opt out of this component of the ACA following the 2012

Supreme Court ruling.5 This decision is evident in Figure 1. Florida’s Medicaid program

has grown since 2008 but is fairly stable during the period when many other states

experienced dramatic increases in enrollment. Therefore, we can use the Florida

experience to isolate the access effects of a large but temporary payment shock from any

concomitant expansion of the Medicaid market.

Another rationale for examining the experience of a single state is there is often

important heterogeneity across states in other dimensions of Medicaid. For example,

states differ in the penetration of Medicaid managed care, which may independently

influence provider participation. In Florida, roughly three-quarters of Medicaid patients

were enrolled in managed care plans prior to the fee bump. The state also mandated that

the remainder of Medicaid enrollees move into managed care plans during the late spring

and summer of 2014 (Hu and Mortensen 2016).6 Although this additional event took

place during some of the final months of the fee bump, the change was not isolated to

PCPs (i.e., it affected all specialties). Importantly, the fee bump also applied to physicians

5 See the status of all US states, including the District of Columbia, from the Kaiser Family Foundation “Status of State Action on the Medicaid Expansion Decision” KFF State Health Facts, updated January 1, 2017. 6 The Hu and Mortensen (2016) article provides a detailed summary of the managed care change.

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billing Medicaid fee-for-service as well as those contracting with Medicaid managed care

plans.

DATA

Our empirical analyses are primarily supported by four administrative data sets. The first

is Florida’s registration system for Medicaid providers, which records their participation

in Medicaid tied to the billing system. The second dataset is the Florida Department of

Health Practitioner Profile database, which serves as a denominator for all licensed

physicians. The third is a detailed workforce survey of state’s physicians that includes

information on physician behavior and labor supply outcomes relevant to the payment

policy. The physician-level workforce data allows us to estimate a fee bump effect using

a variety of econometric approaches while capturing important heterogeneity across

physicians. Finally, the fourth data source encompasses the state’s universe of ED

encounters, which can at least partially inform any substitution of care settings as well as

effective chronic disease management after more generous Medicaid fees are made

available.

Medicaid Provider Registration System

All Florida providers¾physicians, nurse practitioners, hospitals and other organizations,

etc. ¾must formally register with the state Medicaid program to submit claims for

reimbursement. We use the resulting Provider Master List of Florida Medicaid to

determine the timing of physicians’ Medicaid participation decisions. Specifically, the

data include each physician’s license number and date of Medicaid enrollment (the date

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the physician became initially eligible to bill Medicaid). This publicly available dataset is

continually updated as new providers enroll and captures the provider extensive margin

decision of interest (i.e., to participate in Medicaid at all). Via the licensure information

we are also able to classify physicians by specialty (e.g., PCPs versus other specialty

types). One drawback of the registration data is that it is updated frequently and therefore

does not capture physicians who have left the Medicaid program and are consequently

purged form the database; however, we do not expect this to bias our inferences in any

meaningful way. For example, if the fee bump restrains Medicaid market exits, then this

will still be evident in a higher proportion of physicians participating during those years.

Practitioner Profile Database

Florida law requires physicians and other health care practitioners to submit information

regarding license status, geographic location, training experiences, and certifications to

the Division of Medical Quality Assurance. The Department of Health makes this

information publicly available via the Practitioner Profile Database. We use this data to

construct the universe of Florida physicians to then serve as our denominator for

constructing Medicaid program participation rates.

Physician Workforce Survey

We use the Florida Physician Workforce Survey to gather additional information about

individual physicians licensed in Florida from 2009 to 2015. This annual survey is

mandatory for physicians and completed upon initial license granting or license renewal

(occurring every two years). In merging these data with the Practitioner Profile data

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discussed above, the survey’s match rate exceeds 95%. A given survey is completed in

either January of the corresponding year or in the preceding few months of the prior year

(e.g., respondents to the 2015 survey would have filled out the questionnaire in late 2014

or January 2015). We use all actively practicing (i.e., non-retired) physicians currently

working in the state.

The key survey question for our study is whether the physician is currently

accepting new Medicaid patients. This intensive margin response captures important

information regarding whether the physician’s panel is likely expanding. While physician

access can be measured in a variety of ways (Sommers and Kronick 2016), and we

admittedly do not observe the share of each provider’s payer mix belonging to Medicaid

(Geissler, Lubin, and Marzilli Ericson 2016), this question complements the data from

the Medicaid registration database. The survey also asks a similar question for accepting

new Medicare patients, as well as basic demographic characteristics, the physician’s

medical specialty, the practice setting (e.g., hospital versus non-hospital based), and labor

supply measures pertaining to the practice (e.g., hours allocated to patient care and

administrative matters).7

Table 1 displays the pooled extensive and intensive margin Medicaid

participation rates for select specialty groupings using our two key data sources.8 As

expected, the rates of physicians having some level of Medicaid exposure are far higher

than the rates for currently accepting new Medicaid patients (i.e., expanding their patient

panel). The gap is particularly large for PCPs as well as medicine subspecialties when

7 For both public insurance survey questions, the rate of missing responses is only 8% among those currently licensed and practicing in Florida. This level of completeness is generally as good or better than other survey variables as well and stable over time. 8 These specific specialty groupings will be explicitly used in our analytic strategies described below.

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looking across columns 1 and 2 in Table 1—roughly 20-percentage point differences for

each. Table 2 briefly summarizes the Florida physician workforce according to specialty

groupings seen in Table 1. Across specialties, the average age is slightly above 50, and

the make-up is predominantly male. Rates of hospital or health system based practice

vary substantially by group, and uniformly, physicians are much more willing to accept

the marginal Medicare patient as opposed to Medicaid.

In certain subsequent analyses, we will further augment our workforce data with

detailed Medicaid enrollment data for the county in which a given physician operates her

primary practice.9 We can then use the size of the local Medicaid market as a proxy for

potential Medicaid exposure.

Emergency Department Discharge Data

Our health care utilization analyses use the universe of discharge data from the State of

Florida’s Agency for Health Care Administration (AHCA). The AHCA directs and serves

the Florida Medicaid program and performs licensing functions for all health care

facilities. It also collects, maintains, and distributes extensive health care utilization data

pertaining to the state.

We use all emergency department discharges in the state at the quarterly level

from the first quarter of 2009 through the third quarter of 2015.10 Emergency department

encounters represent a key source of information on downstream health and medical care

consumption for our context. First, ED visits typically reflect a consumer-initiated pattern

of utilization as opposed to provider-driven use. Second, EDs are considered inefficient

9 The enrollment data are provided by the Decision Resources Group (DRG). 10 We exclude the fourth quarter of 2015, which includes an administrative change to the data (transitioning from ICD9 to ICD10) that necessarily challenges comparability to previous data quarters.

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settings for the delivery of primary care services but will be the target of patients with

insufficient access to ambulatory-based primary care and/or suffering from poor disease

management (Hoot and Aronsky 2008; Kellerman and Weinick 2012; Laditka, Laditka,

and Probst 2005; Oster and Bindman 2003; Starfield, Shi, and Macinko 2005). Thus,

meaningful improvements in patients’ outpatient care access should spillover onto ED

clinical settings, at least among those with chronic conditions amenable to primary care

intervention. Without any changes in the demand for ED services, actual consumer

welfare effects from the temporary price change are more doubtful.

EMPIRICAL STRATEGIES FOR MEDICAID ACCEPTANCE

Extensive Margin Response

We begin our analyses by examining program participation rates among different

physician specialties. We stratify all Florida physicians into three key specialty

groupings: (1) adult primary care (2) pediatrics and (3) a composite group of all other

physicians (i.e., not adult PCPs or pediatricians). The first two specialties are eligible for

the Medicaid fee bump but are also operating in distinct physician services markets, with

differing payer mixes. Thus, we assess any behavior change among adult PCPs and

pediatricians separately. The third group serves as our control comparison group for our

main empirics, meaning we will benchmark any behavior change among PCPs against

the evolution in outcomes for the average non-PCP. However, we also create three

additional subgroups from the composite group: obstetrics and gynecology (OBGYN),

medicine subspecialists, and surgeons.11

11 Medicine subspecialties include general cardiology, endocrinology, gastroenterology, hematology and oncology, pulmonary medicine, nephrology, sports medicine, and rheumatology.

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For completeness, and as a robustness check, we leverage the alternative

comparison groups (i.e., the three subgroups previously listed) to ensure our inferences

are not sensitive to our choice of controls. Specifically, OBGYNs can and sometimes do

function as primary care providers, but they were left out of the 2013-2014 payment

incentive.12 This exclusion and subsequent protest by the affected providers suggest that

they may serve as a useful comparator in isolation. Medicine subspecialists completed a

primary care residency before subspecializing through fellowship training. Continuity of

care is also common among these subspecialty fields, just as it is with primary care, and

many of their patients suffer from the same types of chronic conditions (e.g., diabetes,

heart disease, or respiratory disease) that PCPs are accustomed to seeing in their

outpatient practices. Finally, surgical fields and those that select into them are likely to be

quite different from the marginal medical trainee entering primary care. However, if these

differences are fixed, then identification can still hold when using this alternative

comparison group. Importantly, and irrespective of the strengths and weaknesses of any

one particular control group, our goal is to ascertain that our inferences are not dependent

on our choice of comparator.

To construct our analytic data for extensive margin participation, we use the

Medicaid registration data to determine the exact month a given physician becomes an

official part of the state health insurance program and then combine this information with

our complete counts of a given provider type for a given year from the Practitioner

12 Many state Medicaid managed care and primary care case management programs recognize OBGYNs as PCPs. Popular media and trade press reports also reveal strong objections to their fee bump exclusion among OGBYN providers. For example, see the ACOG’s “Medicaid to Medicare: Primary Care Pay Bump”, available here: www.acog.org/About-ACOG/ACOG-Departments/Government-Relations-and-Outreach/Medicaid-To-Medicare

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Profile Database. The latter serves as a denominator for us to generate rates of program

participation from 2009-2016 for each specialty group.

To formally test this physician behavior margin, we construct a physician-level

(monthly) panel of Medicaid participation spanning this same analytic period. The

outcome is a binary indicator for participation status in particular month (m) and year (t)

for a given physician (i). We are then able to evaluate the policy’s effect in a difference-

in-differences (DD) framework. The estimating equation is straightforward:

Yimt =α + β(Primary)i + γ (FeeBump)mt +ς (AfterBump)mt +δ1(Primary × FeeBump)imt +δ 2 (Primary × AfterBump)imt + ε imt

(1)

Primary is equal to one for our treated adult PCPs (family medicine and internal

medicine), and FeeBump is an indicator for the 2013 and 2014 years. AfterBump is an

indicator for 2015 and 2016. The two delta parameters capture any differential changes in

Medicaid enrollment among PCPs relative to our control specialties. The first parameter

is assessing any fee bump effect, while the second allows for any observed differential to

reverse (or persist) once Medicaid prices have returned to baseline levels. Thus, the sign

and size of (δ 2 ) in comparison to (δ1 ) is informative for attributing any changes in PCP

behavior to the fee bump policy. Our preferred specification uses all other unaffected

physicians (i.e., excluding pediatricians) as the control group, but we also re-estimate the

model using our alternative comparison specialties.

Our second specification is as follows:

Yimt =α + β(Primary)i + γ (FeeBump)mt +ς (AfterBump)mt +δ1(Primary × FeeBump)imt +δ 2 (Primary × AfterBump)imt +ϕXi +τ + ε imt

(2)

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The model is merely adding some additional physician-level covariates (i.e., year began

practicing and city where current practice is located), along with a linear time trend. The

standard errors for Equations 1 and 2 are clustered at the physician level.

Intensive Margin Response

Our analysis of intensive margin physician behavior leverages both the repeated cross-

sectional and longitudinal elements of the workforce survey to permit a more flexible and

complete empirical approach.13 Doing so enables us to test the robustness of our findings

across specifications and analytic strategies as well as distinguish changes in prevailing

(i.e., market-wide) Medicaid acceptance rates due to behavior changes among incumbent

physicians as opposed to physicians newly entering the market (i.e., establishing their

first practice in Florida).

Difference-in-differences

Our initial and most parsimonious analysis uses the data as a repeated cross-section, plots

the trends in Medicaid acceptance for primary care and non-primary care physicians from

2009 to 2015, and then estimates the corresponding specification:

Yi =α +ϕ(Primary)i +ς (Post)i +δ (Primary × Post)i + ε i (3)

Mirroring our extensive margin empirics, ‘Primary’ is equal to one for physicians in the

specialties of family medicine and internal medicine (i.e., adult PCPs) and is zero for all

other unaffected physician specialties.14 ‘Post’ is set to one for the year 2013, when the

13 As mentioned above, the survey is mandatory for each license renewal, which happens every two years in Florida. Thus, we can track the same physicians over time using survey-provided identifiers. 14 We exclude pediatricians for reasons previously discussed – i.e., their physician service markets and exposure to Medicaid at baseline are typically very different from the average adult primary care physician.

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fee bump was legislated to become active, and for each subsequent year. We do note that

the 2015 survey information represents an ambiguous case for our setting. As previously

mentioned, 2015 respondents may have completed the survey in late 2014, and all survey

responses had to be completed by January 2015 (i.e., at the very start of the new year

when the fee bump would have just been terminated). For these reasons, we maintain a

qualified interpretation around any changes observed for this specific survey year. The

same issue also holds for the 2013 wave. The blending of late 2012 and January 2013

responses may attenuate the PCP response, or alternatively, reflect some degree of

anticipatory effects. We formally deal with this issue in Section 4.2.2.

The interaction term in Equation 3 represents the DD estimate of interest. A

positive coefficient for the delta parameter would be consistent with the Medicaid fee

bump increasing access to primary care physicians. As a robust check, we estimate a

second model that includes physician demographic information (age, gender) and a linear

time trend. Equation 3 is as follows:

Yi =α + λ(Primary)i + ξ(Post)i +δ (Primary × Post)i +θXi +τ + ε i (4)

We also stratify our physicians by their environmental circumstances related to prevailing

size of the Medicaid market. We divide all Florida counties into above and below the

median for number of Medicaid beneficiaries in the county in 2012 (the year prior to fee

bump implementation) and then re-estimate Equations 3 and 4.

We do display their trends for Medicaid acceptance though for completeness and to bolster our inferences related to adult primary care physicians.

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Physician fixed effects

As previously mentioned, the approaches in Sections 4.2.1 and 4.2.2 are capturing

market-wide behavior change and inform whether or not the prevailing willingness to

take on additional Medicaid business has differentially changed for PCPs after the fee

bump is initiated. To better understand any aggregate intensive margin changes as well as

assess the robustness of our cross-sectional findings, we exploit the longitudinal element

of our workforce survey and introduce physician fixed effects (FE) into our DD

estimation described above (Equation 4, specifically). The DD parameter is then

reflecting changes in Medicaid acceptance behavior within an individual physician (i.e.,

switching from restricting further Medicaid patient panel growth to accepting new

Medicaid patients after the fee bump).

Event study analysis

Next, we implement an event study style analysis, which allows each individual year to

have an independent, differential effect on PCPs relative to all other unaffected physician

specialists. The model takes the form:

Yi =α + β(Primary)i + λ j yearjj=2010

2015

∑ +δ k (Primary × yeark )k=2010

2015

∑ +θXi +τ + ε i (5)

We also parallel Equation 4 from above and include individual physician covariates and a

linear time trend in this specification. Equation 5 has the advantage of testing for pre-

treatment changes as well as isolating each post-period year, including the 2014 survey

year, which does not share the partly ambiguous timing features belonging to the 2013

and 2015 waves. We can also observe if, and to what degree, the policy effects evolve

over time.

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Falsification test

To further bolster our inferences, we also re-estimate our main specifications from

Equations 3 and 4 (as well as with physician fixed effects) using acceptance of new

Medicare patients as our outcome variable. These models serve as quasi-placebo tests to

assess any differential PCP behavior not directly linked to the Medicaid fee bump. While

this analytic exercise can help support our interpretations of a fee bump effect, we do

acknowledge that some might argue that there are potential indirect effects if Medicaid

payment policies impose externalities on Medicare patients—e.g., as argued in Brunt and

Jensen (2014). Thus, we view the subsequent findings as a useful but imperfect

falsification test.

For all intensive margin regression models described in Section 4.2, we cluster

our standard errors at the physician level to account for repeated observations over time

and to correct for heteroskedasticity in our binary outcomes of interest. We also found

these to often be more conservative standard error estimates (e.g., compared to higher

level clustering and not clustering at all), and importantly, our inferences are never

sensitive to the choice of standard error estimation in these data.

MEDICAID ACCEPTANCE RESULTS

Extensive Margin Provider Participation

Figure 2 displays our Medicaid enrollment trends by specialty group. The two weakest

participation rates in the years preceding the fee bump belong to adult PCPs and

surgeons. Generally less than 40% of potential providers are enrolled from these

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specialties in 2009-2012. And although the adult PCPs enter the Medicaid market at an

increasing rate over time, a similar growth trajectory is seen for the other groups as well.

Moreover, there is no obvious uptick in PCP participation once the fee bump is active—

all increases are smoothly sloped—and the trend continues two years after the fee bump

concluded. The plot for adult PCPs generally tracks our comparison groups (e.g. all non-

PCP specialties and OBGYNs) in the pre-treatment period; though, there is some trend

divergence in the year prior to fee bump implementation for medicine subspecialties and

surgeons.

Table 3 provides the DD results for extensive margin participation. The “PCP x

Fee Bump” coefficient is negatively signed in both models (columns 1 and 2) but also

close to zero. This implies that adult PCPs’ Medicaid participation is not obviously

responding to the fee bump policy. The “PCP x After Bump” coefficient corresponding to

the two years after the fee bump ended is similarly small. These findings expectedly map

to the smooth trends evident in Figure 2. Appendix Table A1 shows the results from our

exercises using different control group specialties. Columns 2 and 3 actually demonstrate

strongly negative estimates; however, as previously mentioned, these two specialties

seem to violate the parallel pre-treatment trend requirement for our research design.

When compared to OBGYNs in isolation, our PCPs are 2.6-percentage points more likely

to enroll in the Medicaid program while the fee bump is active. Yet, an equally large

positive differential is present when the fee bump has already expired (column 1 of

Appendix Table A1), which speaks against the payment increase being responsible for

the 2013-2014 change. An appropriately cautious view of our extensive margin findings

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is that the price shock had little or no effect on adult PCPs propensity to enter the

Medicaid market.15

Intensive Margin Provider Participation

Figures 3 and 3.1 plot the acceptance of new Medicaid patients by year across our treated

and control physician groups for three analytic samples: all Florida markets, markets with

relatively high shares of Medicaid patients, and markets with relatively low shares of

Medicaid patients. For all analytic groups, adult PCPs have lower receptivity to more

Medicaid business at baseline, but there is a clear narrowing of this access gap after the

fee bump is in place. We also see the outcomes for the two groups tracking each other in

parallel during the pre-implementation period—supporting the DD identification

strategy.16

Table 4 presents the difference-in-differences regression models that directly map

to Figure 3 and rely on our estimating Equations 3 and 4. All three specifications reveal a

precisely estimated and positive policy effect on PCP behavior, which comports with our

prior figures and is robust to the addition of physician-level demographics and a linear

time trend as well as physician fixed effects (columns 2 and 3). The findings in column 3

of Table 4 also reveal that we observe switching behavior in the data (i.e., PCPs with

15 Appendix Figure A1 demonstrates pediatricians’ high rates of Medicaid provider enrollment and smooth increase over time, reaching above 80% in recent years. We have also estimated versions of our extensive margin models using physician-level fixed effects; however, because of how the analytic data are constructed, this expectedly does not offer meaningful differences in terms of interpretations. 16 Appendix Figure B1 shows that the alternative control groups’ trends almost perfectly follow each other, and Appendix Figure B2 reveals that pediatricians do not obviously respond to the fee bump. 75-80% of them were accepting new Medicaid patients already, and that largely continues after the fee bump policy. Also, formally testing for a fee bump effect among pediatricians in our DD regression models reinforces this interpretation (results available by request).

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established practices are moving from not accepting new Medicaid patients in the pre-

period to allowing their panels to expand in the post-period).17

Overall, Florida PCPs in are approximately 4-5-percentage points more likely to

accept new Medicaid patients once the payment policy is in place (Table 4), which is a

10-13% increase over their pre-fee bump level (40% as seen in Figure 3). Appendix

Tables B1-B3 display the results using our alternative control groups. The data patterns

are identical across these additional tables, only with slight changes in the DD coefficient

magnitudes.18 Appendix Table B4 offers results when we stratify Florida counties by the

relative size of their Medicaid population, which corresponds to Figure 3.1. The policy

impact is similar in columns 1-4, though the absolute magnitude of the effect is slightly

larger among Florida counties with a Medicaid market size below the median (columns 3

and 4), but in a triple-differences specification, we cannot reject that the two effects are in

fact the same (results available by request). The relative changes to baseline (pre-fee

bump) Medicaid patient acceptance do differ by Medicaid market size. The overall access

increase is roughly 10% for physicians working in Medicaid-dense areas; however, there

is about an 18% gain in markets where Medicaid is a smaller segment of the payer mix.19

Appendix Table B5 presents the individual treatment-year interaction estimates

from our event study specification (Equation 5). The results reinforce the data patterns

17 Note, the estimate size in column 3 of Table 4 is smaller than the prior columns in part because the FE model is not capturing the willingness of new market entrants to take new Medicaid patients (i.e., it is capturing the behavior of incumbent physician firms). We have run a separate model that only examines the behavior of new market entrants, and there is an increase in the willingness to see new Medicaid patients among adult PCPs entering the market during the fee bump period (results available by request). 18 We have also estimated generalized DD models with full sets of physician specialty and year fixed effects. The policy coefficients are virtually identical to the simpler two group, two period setup seen in Table 4 (results available by request). 19 The average share of the insured population belonging to Medicaid in 2012 is 27% for counties above the median and 18% for counties below the median. Across all Florida counties, the range of Medicaid percentages spans roughly 13% to 40%, so we do not observe counties with extremely large or negligible Medicaid populations.

21

seen in the simpler DD models and corresponding descriptive figures. Of note, in our

descriptive trends and formal regression models, the policy response appears in the 2013

survey year but also sustains itself, if not intensifies, through the 2015 wave – the year

Florida payment rates fell back to pre-policy levels. This pattern could be consistent with

a legacy effect of the Medicaid fee bump whereby providers do not immediately dial

back their Medicaid exposure once the policy expires. However, it is also possible that

the 2015 data point is capturing 2014 behavior (given the timing of the survey—late fall,

through January each year) and/or discrepancies between individual physician reporting

and the behavior of firms (i.e., hospitals and health systems or physician groups) that

employ them. For physicians that are a part of a larger organization, the firm may have

initiated internal policies to reduce its Medicaid exposure immediately after the fee bump

was terminated (e.g., instruct clinic schedulers and managers to limit further Medicaid

entry into their patient panels). However, the patient-facing physicians may not be fully

aware of these changes (by January 2015) since they may not be the residual claimant on

the marginal revenue generated from their patient care duties and/or responsible for

patient scheduling. Across all three specifications in Appendix Table B5, the 2014

interaction has a strongly positive and precise coefficient, which is informative since all

respondents to this survey wave would have been exposed to the fee bump.20

Table 5 parallels the analyses and structure of Table 4, with the exception of the

outcome change (i.e., focusing on new Medicare patient acceptance). Adult PCPs are

again less likely than other physician specialties to be taking on new publicly insured

patients at baseline, but they demonstrate no change in behavior after the fee bump is

20 Additionally, if we drop the 2013 and 2015 survey waves (i.e., only use responses from late 2013 and early 2014 for our post-period), all of our DD findings from the Table 4 specifications still hold, which is also reassuring for our inferences.

22

implemented. Across all three specifications, the DD estimate is close to zero and even

signed in the opposite direction within the physician FE specification (column 3)—

though still small in magnitude. At a minimum, there is no indication of spillover effects

onto the Medicare market from the enhanced Medicaid payments. The results from our

quasi-placebo test further support the interpretation from Table 4 that PCPs are indeed

responding to the targeted reimbursement increase.

Uniformly, the results in 5.2 portray a precise increase in PCP intensive margin

Medicaid participation that seems attributable to the fee bump policy, but the extent of

behavior change is also modest.

EMPLOYMENT HETEROGENEITY, MECHANISMS, AND MEDICAID

PERCEPTIONS

Heterogeneity by Physician Employment Type

We conclude our analyses with the workforce data by implementing some

complementary but also descriptive exercises.

First, other research has shown that physicians catering more toward the Medicaid

market tend to also practice as employees of a hospital or health system (Richards,

Nikpay, and Graves 2016a; Sommers et al. 2011). Since we observe whether a given

physician’s primary practice is based within a hospital or health system in the workforce

data, we conduct some ancillary regression models that partition respondents by practice

setting (i.e., hospital versus non-hospital jobs).

Table 6 displays this additional heterogeneity when using our estimating

Equations 3 and 4. Columns 1 and 2 reveal a DD effect that is more than twice as large

23

for hospital/health system PCPs, which suggests that the policy impact seen in Table 4 is

primarily driven by this subset (and minority) of PCPs. However, when comparing the

DD estimates to the pre-policy average acceptance rates for these subgroups (reported in

the footnote of Table 6), the relative increases are almost identical in magnitude (10%

and 9%, respectively). In this way, PCPs with hospital-based jobs are responsible for

much of the effect’s size, but all adult PCPs in Florida seem to be altering their practice

behavior to a similar degree when taking into account their baseline (and low) levels of

new Medicaid patient acceptance.

Complementary Labor Supply Adjustments

Second, observing any increase in PCP willingness to treat Medicaid patients begs the

question as to how they incorporate more of these patients into their existing panels in the

short-run. For example, a study set in Michigan found some offsetting decreases in the

willingness to see new privately insured patients following the state’s Medicaid

expansion (Tipirneni et al. 2015). While we cannot characterize their service flows to all

patient-payer groups, we can capture their weekly allocation of time to billable effort and

other tasks. In turn, we implement identical regression models (Equations 3 and 4) used

for our primary outcome on two additional measures of labor supply: working more than

50 hours per week in direct patient care and spending more than 5 hours per week

performing administrative duties.21 Spending more time with patients would be consistent

with increasing clinical output supplied to the market in a way that does not disadvantage

21 The survey questions from which these outcomes are drawn from do not readily lend themselves to more fine-grain time use measures. Therefore, we have to rely on these coarser measures. The patient care indicator variable corresponds to roughly the top quintile of the distribution. The administrative work indicator variable corresponds to approximately being above the median in the variable’s distribution.

24

other patient-payer groups. And as previously discussed, the Medicaid program is known

for significant administrative requirements (and sometimes hurdles) for providers; thus,

more paperwork and related activities could follow an increase in Medicaid exposure.

Table 7 presents these ancillary results for physician labor supply and time

allocation. Panel A of Table 7 suggests more time devoted to patient care for PCPs after

the fee bump started, but the overall correlations in columns 1 and 2 are completely

driven by hospital or health system based physicians. The fee bump is associated with a

7-percentage point increase in the likelihood of allocating more than 50 hours per week to

direct patient care for those with a hospital or health system practice setting (Panel A,

columns 3 and 4).22 No similar behavior change is seen for other PCPs (Panel A, columns

5 and 6). However, this latter group only increased their willingness to accept new

Medicaid patients by 3-percentage points, and our measure of patient care hours is unable

to capture finer adjustments in labor supply (which may correspond to the smaller

increase in Medicaid acceptance). We do see that all PCPs (i.e., regardless of practice

setting) seem to be more likely to have a relatively high share of their weekly work time

allocated to administrative tasks after the fee bump (Table 7, Panel B). These associations

could reflect higher processing and billing paperwork burdens related to taking on

additional Medicaid patients, but the patterns could also be coincidental.23

22 We also note that further data probing revealed that these PCPs reporting more weekly clinical hours are the same PCPs that newly reported taking on more Medicaid patients during the policy intervention period. 23 Of note, we have also explored trends in retirement behavior for PCPs relative to all other unaffected specialties. There is little to suggest that the Medicaid fee bump policy altered retirement decisions (e.g., delaying retirement) for PCPs. The trends are quite stable over our study period (results available by request).

25

Reasons for Not Accepting New Medicaid Patients

Finally, in Table 8, we display the breakdown of reasons for not accepting new Medicaid

patients before and after the fee bump. All four reasons provided in the survey are

trending in a similar direction for PCPs and all other physicians. Insufficient

compensation is becoming less of a barrier over time, while capacity issues and

administrative burdens are becoming more influential. That said, the decline in PCPs

justifying their Medicaid refusal due to prevailing reimbursements is larger and from a

slightly lower base (meaning an even larger relative decline) than non-PCPs. Again, this

descriptive finding seems to at least align with the policy’s timing and intent.

EMERGENCY DEPARTMENT UTILIZATION

We conclude our empirics by focusing on the downstream consequences for actual

patient care. Accommodating more Medicaid business means little if it does not generate

improved care delivery and health outcomes for the involved patients. Additionally, even

in the absence of increasing Medicaid output on the intensive margin, one could argue

that higher payments may lead to higher quality visits for existing Medicaid patients (e.g.,

more time spent with these patients and more regular follow-up by PCPs) and related

health benefits. To explore such possibilities, we specifically examine Medicaid

beneficiaries’ reliance on ED-based care.

Using our discharge data, we first construct an overall trend in the number of ED

encounters for Florida Medicaid enrollees at the quarterly level from 2009-2015. Given

that physician behavioral change is only found among adult PCPs, we limit to encounters

where the patient is at least 18 years of age. We also restrict these encounters to episodes

26

where the patient was discharged to home following treatment (i.e., did not require an

inpatient stay or transfer to another care facility). Generally, more than 90% of all ED

encounters in a given quarter conclude in this manner, and we believe such episodes

reflect less severe cases that should be the most sensitive to outpatient disease

management. We then perform an identical exercise for various primary care sensitive

conditions presenting in the ED: cardiac-related chest pain, diabetes-related

complications or glucose control issues, uncontrolled hypertension, and chronic

respiratory disease.24 The resulting time series of ED utilization can then qualitatively

reveal if consumption has changed during the fee bump period.

In an attempt to more formally test for changes in ED use, we implement a DD

analysis that compares Florida residents (treated group) to non-residents (control group)

that happen to seek ED care while within the state. Our outcome of interest is whether a

given ED patient has Medicaid insurance. Florida is well known as a vacation and event

destination, and 5% of all Florida ED utilization during our analytic period is from

patients residing outside of the state. Additionally, 6% of non-resident ED care is paid for

by Medicaid (i.e., a Medicaid program from some other state).25 Medicaid beneficiaries

from other states are unlikely to have an established primary care source within Florida

and would arguably be unlikely to seek one during a transient stay. Therefore, non-

residents are plausibly unable to benefit from a fee bump access effect on Florida’s adult

PCPs, and their ED use is more likely to reflect unavoidable encounters.26 We can then

24 The chronic respiratory encounters are those linked to asthma and chronic obstructive pulmonary disease (COPD) exacerbations. All disease-specific encounters are identified using the primary ICD-9 code listed for each discharge. 25 The range of states spans the entire US (i.e., it is not limited to Medicaid beneficiaries from a particular state). 26 Florida PCPs also would not receive the Florida-specific Medicaid payment bonus for enrollees that are part of a different state’s Medicaid program.

27

benchmark changes in Florida residents’ ED payer mix (i.e., shifts in the share belonging

to Medicaid) against non-residents’ payer mix to identify a fee bump policy effect on ED

consumption. We again do this for the full analytic population and then restricted to those

presenting with a chronic disease exacerbation.

The estimating equation is as follows:

Medicaidi =α +φ(resident)i + γ (bump)i + λ(after)i +δ1(resident × bump)i +δ 2 (resident × after)i +ςXi +τ + ε i

(6)

Medicaid is a binary indicator for having Medicaid insurance, resident is equal to one for

all Florida residents, and bump is equal to one for all encounters from 2013-2014.

Equation 6 also captures any differential changes occurring after the fee bump policy

sunsets. Specifically, after is set to one for all encounters taking place in 2015. The first

delta parameter recovers the policy effect, while the second delta parameter tests for any

symmetrical change once the policy is turned off. Paralleling our prior analyses, we also

estimate Equation 6 with and without patient covariates (X) and a linear time trend (τ );

all standard errors are clustered at the county level in these analyses.

Figure 4 plots the total ED encounters paid for by the Florida Medicaid program

over time and stratified by the relative size of the county-level Medicaid market

(according to the patients’ county of residence). Use of ED-based care by Florida

Medicaid patients follows a linear trajectory from 2009 through 2015 and likewise among

those living in areas with relatively higher and lower Medicaid population densities.

Figure 4.1 offers an identical pattern when we examine those with chronic care needs in

isolation. The total volume of visits is expectedly much lower, but the trajectory matches

what is seen for the full population (Figure 4). Table 9 presents the regression results.

Across all four specifications, the fee bump effect estimate is small (though precise) and

28

signed in the wrong direction. At a minimum, we can rule out any substantive reduction

in ED care consumption by Florida Medicaid patients while the physician pay increase is

active. Appendix Figure C1 displays the share of ED episodes devoted to Medicaid for

our treatment and control groups over time. There is some predictable seasonality among

non-residents (i.e., peaks during the summer travel months), but non-residents’ reliance

on Medicaid insurance is fairly stable during our analytic period (5-6% cases).

Importantly, the trends across the two groups confirm the inferences from the regression

estimates in Table 9 and validate the DD identification strategy.

Considering the utilization trends from Figures 4 and 4.1 and the DD results in

Table 9, there is no compelling evidence that Medicaid patients rely less on the ED

during a time of heightened primary care payments—including those suffering from

chronic illness. Thus, our universe of Florida ED discharges does not reveal any obvious

disease management benefits or substitution of care settings (i.e., from higher cost EDs to

lower cost primary care clinics) for Medicaid enrollees.

DISCUSSION

Using several administrative data sets in a setting that benefits from a large price shock in

the absence of a Medicaid expansion, we find evidence consistent with the fee bump

policy modestly increasing Medicaid participation among Florida adult PCPs. The

response is concentrated on the intensive margin (i.e., acceptance of new Medicaid

patients), rather than the extensive margin (whether to enroll as a Medicaid provider at

all), and supports the inferences drawn by the only other published study on this specific

policy. In fact, Polsky et al. (2015) show an 18% increase in new Medicaid patient

29

acceptance for New Jersey—a state with a reimbursement jump almost identical to

Florida. Even with a very different data source, we observe a 10-13% increase in Florida

markets, which corresponds reasonably well to their findings.27 Florida’s Medicaid

reimbursements prior to the fee bump were also among the lowest nationally, suggesting

our estimates could be interpreted as upper bounds, and relatedly, the implied intensive

margin labor elasticity is only around 0.1, which further underscores the limited behavior

change the policy was able to elicit.

We also augment our findings by exploring heterogeneity across Medicaid

markets and physician practice attributes. Our intensive margin effect sizes are typically

largest among physicians with hospital or health system based jobs, which maps to a

prior conjecture made by Sommers et al. (2011). During the early days of ACA rollout,

the authors posited that hospital owned primary care practices might have an easier time

absorbing new Medicaid business. Our DD estimates align with this view, and

importantly, we see these same providers devoting more labor supply to direct patient

care when the fee bump is initiated. This complementary finding reveals a mechanism

whereby providers can increase the flow of services to the Medicaid market without

disrupting care delivery to other payer groups. In short, they can either reduce leisure by

working longer hours or substitute away from other non-revenue generating tasks.

Another extension of our work that goes beyond Polsky et al. (2015) is the

inclusion of ED utilization data to capture downstream health consequences for patients.

Some recent research finds access and health benefits from ACA-related expansions

27 Unlike Florida, New Jersey expanded Medicaid during this same period, which may account for some of the difference seen across the two studies. Admittedly, some states in their data show increases that are much higher (e.g., 25% across their two data waves). The average relative change in Medicaid acceptance in their study is 13% over the 10 state sample (see Polsky et al. 2015).

30

(French et al. 2016), including reductions in avoidable hospitalizations following a pre-

ACA Medicaid expansion in Wisconsin (DeLeire et al. 2013). However, we see no

obvious health improvements due to Florida’s fee bump. The use of the ED remains on

an upward climb for Medicaid enrollees, and those suffering from chronic conditions do

not witness drops in their acute exacerbations that subsequently require emergent

intervention. Both patterns suggest persistent suboptimal disease management and/or

access to primary care for Medicaid patients.

There are several potential explanations for the discrepancy between the

physician- and patient-focused elements. First, physicians’ reported increase in their

willingness to see new Medicaid patients may not have translated to more primary care

services actually supplied to Medicaid patients. While we cannot rule out this possibility,

Polsky and colleagues’ (2015) data suggest otherwise since they measured real world

appointment granting to fake patients. This explanation is also at odds with our collage of

evidence (e.g., greater patient care hours during the policy period).28 Alternatively, the

incentives’ targeted nature (i.e., restricted to primary care) may have undermined some of

the potential disease management benefits. If primary care needs to be a conduit to

specialty care for those with extensive health issues, the missing financial incentive for

the latter providers could blunt the policy impact. Others have demonstrated the typically

high barriers to specialty care for Medicaid patients and/or lack of coordination between

primary care and specialty providers, with some state programs seeking innovative ways

to lower these access hurdles (Felland, Lechner, and Sommers 2013; Takach 2011). If

28 That said, this style of incentive program does not explicitly target the demand-side (e.g., encouraging check-ups among enrollees or engaging in patient steering to new providers), which may further dampen any desired access effects since Medicaid patients may not be seeking out new matches to or more frequent encounters with local primary care providers (who are now more willing to see them because of the payment change).

31

joint provider production (i.e., a more team-oriented approach across provider types) is

necessary for optimal management of complex patients, then incentives with a narrow

scope can be less helpful.

More simply, the small changes in Medicaid acceptance may have been

inadequate to engender detectable population health effects for patients newly gaining

PCP access, and a temporary and unconditional transfer for continuing to serve existing

Medicaid patients may do little to benefit patients with established PCP access. Recall,

the enhanced revenue stream was not conditional upon achieving performance metrics or

population health objectives. In fact, PCPs with at least one Medicaid patient in their

panel prior to implementation would receive a positive income shock even if their flow of

services maintained the status quo. They were also not subjected to any financial

penalties if their patients’ health stayed the same or even deteriorated after the fee

increase. Such a design does not properly incentivize better clinical effort overall nor

does it encourage providers to allocate more resources to patients with the largest

potential payoff (e.g., those with poorly managed chronic conditions). Relatedly, for

multi-product firms (e.g., multi-specialty groups or health systems) that offer a wide

range of patient care services, linking payments to volumes may not induce a primary

care member of the firm to manage complex patients in a way that lowers demand for

other (non-primary care) services. In the example of a health system without capitated

contracts, the firm can collect higher payments for primary care services but also retain

any marginal revenue from ED visits and hospitalizations from these same Medicaid

patients, which creates a misaligned incentive structure.

32

In summary, our analyses should help inform any cost-benefit considerations

related to this particular payment policy and expectations around any similarly designed

initiative in the future. While some lament a recent Supreme Court decision barring

providers from seeking legal recourse to avoid Medicaid payment cuts and/or to spur

payment increases (Huberfeld 2015; Rosenbaum 2014), it may be worth considering if

efforts (and additional resources) geared toward fee-for-service provision are in fact

optimal policy levers. Our study highlights some of the challenges for inducing

meaningful changes to physician behavior through sizable but ephemeral price shocks.

Doing so can risk inefficient use of public resources, and if the health benefits from

improved primary care access require longer time durations for more aggressive and

consistent provider intervention, then transient incentives hold little promise, even if they

engender a short-run supply-side response.

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MAIN FIGURES Figure 1: Florida Public Insurance Enrollment from 2008-2016

Source: Decision Resources Group (DRG) proprietary data

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

2008 2010 2012 2014 2016

Medicaid Enrollment Medicare Enrollment

Figure 2: Physician Medicaid Program Participation from 2009-2016 by Specialty

Source: Florida Department of Health Practitioner Profile Database (denominator) and the Florida Medicaid Provider Master List (numerator) – updated as of December 2016

Medicaid fee bumpin effect

010

2030

4050

6070

80Pe

rcen

t of p

hysic

ians

enr

olle

d wi

th M

edica

id

-48 -36 -24 -12 0 12 24 36 48Months from Jan 2013

Adult Primary Care Other specialtiesOBGYN Medical subspecialtiesSurgery

Figure 3: Acceptance of New Medicaid Patients from 2009-2015

Source: Florida physician workforce data. “PCP” is family medicine and internal medicine physicians. “All Others” captures any other physician specialty, excluding general pediatrics.

Medicaid Fee Bump

.3

.4

.5

.6

.7Fr

actio

n Ac

cept

ing

New

Med

icaid

Pts

2009 2010 2011 2012 2013 2014 2015Year

PCP All Others

Figure 3.1: Acceptance of N

ew M

edicaid Patients by Relative M

edicaid Market Size in the State of Florida

Source: Florida physician w

orkforce data. “PCP” is fam

ily medicine and internal m

edicine physicians. “All O

thers” captures any other physician specialty, excluding general pediatrics. “H

igh” Medicaid m

arkets are counties with a M

edicaid share of the insured population above the median in 2012, “Low

” reflects counties below

the median

Medicaid Fee Bum

p

.4 .5 .6 .7

Fraction Accepting New Medicaid Pts

20092010

20112012

20132014

2015Year

PCP - High Medicaid Share

All Others - High Medicaid Share

Medicaid Fee Bump

.25 .35 .45 .55 .65

Fraction Accepting New Medicaid Pts

20092010

20112012

20132014

2015Year

PCP - Low Medicaid ShareAll Others - Low Medicaid Share

Figure 4: Total Volume of Medicaid ED Encounters in Florida 2009-2015

Source: AHCA universe of ED discharges, limited to Florida residents over the age of 17. Market size stratification is based on being a Florida county above or below the median share of insured individuals enrolled in Medicaid as of 2012.

Medicaid Fee Bump

010

0000

2000

0030

0000

4000

00To

tal E

D V

isits

Bel

ongi

ng to

Med

icai

d

2009q1 2010q1 2011q1 2012q1 2013q1 2014q1 2015q1Year

Full State Large Medicaid MarketsSmall Medicaid Markets

Figure 4.1: Total Volume of Medicaid ED Encounters for Chronic Disease Exacerbation in Florida 2009-2015

Source: AHCA universe of ED discharges, limited to Florida residents over the age of 17. Market size stratification is based on being a Florida county above or below the median share of insured individuals enrolled in Medicaid as of 2012. Analytic sample restricts to individuals with an ICD9 code linked to a chronic disease for their primary reason for ED presentation. Chronic illnesses include: COPD, asthma, diabetes, hypertension, and cardiac-related chest pain.

Medicaid Fee Bump0

5000

1000

015

000

2000

0ED

Vis

its B

elon

ging

to M

edic

aid

- Chr

onic

ally

Ill

2009q1 2010q1 2011q1 2012q1 2013q1 2014q1 2015q1Year

Full State Large Medicaid MarketsSmall Medicaid Markets

APPENDIX FIGURES

PART A: Health Practitioner Data Appendix Figure A1: Physician Medicaid Program Participation by Specialty

Source: Florida Department of Health Practitioner Profile Database (denominator) and the Florida Medicaid Provider Master List (numerator) – updated as of December 2016

Medicaid fee bumpin effect

010

2030

4050

6070

80Pe

rcen

t of p

hysic

ians

enr

olle

d wi

th M

edica

id

-48 -36 -24 -12 0 12 24 36 48Months from Jan 2013

Pediatrics Other specialtiesOBGYN

PART B: Physician Workforce Data Appendix Figure B1: Medicaid Acceptance Trends for Alternative Control Groups

Appendix Figure B2: Medicaid Acceptance Trends for Alternative Controls and Pediatricians

Medicaid Fee Bump

.4.5

.6.7

.8Fr

actio

n Ac

cept

ing

New

Med

icaid

Pts

2009 2010 2011 2012 2013 2014 2015Year

OBGYN MedSubSurgeons

Medicaid Fee Bump

.4.5

.6.7

.8.9

1Fr

actio

n Ac

cept

ing

New

Med

icaid

Pts

2009 2010 2011 2012 2013 2014 2015Year

Gen Peds OBGYNMedSub Surgeons

PAR

T C

: Em

ergency Departm

ent Discharge D

ata A

ppendix Figure C1: Shares of ED

Encounters Belonging to M

edicaid 2009-2015 Based on Florida R

esidency Status

Source: AH

CA

universe of ED discharges for those over age 17. Second figure restricts to those presenting to the ED

due to chronic disease exacerbation.

Medicaid Fee Bump

0 .1 .2 .3Share of ED Visits Belonging to Medicaid

2009q12010q1

2011q12012q1

2013q12014q1

2015q1Year

Florida ResidentsLiving Outside of Florida

Medicaid Fee Bump

0 .1 .2 .3ED Visits Belonging to Medicaid Among Chronically Ill

2009q12010q1

2011q12012q1

2013q12014q1

2015q1Year

Florida ResidentsLiving Outside of Florida

MAIN TABLES

Table 1: Overall Medicaid Participation Among Florida Physicians 2009-2015

Extensive Margin

Intensive Margin

Enrolled in Medicaid Program*

Accepting New Medicaid Patients**

(%) (%) PCP 64.5 44.8 All Others 73.0 61.6 OBGYN 73.8 59.2 Med Subspec 81.8 59.8 Surgeons 73.8 58.8 Pediatricians 86.3 78.1 * Source: Florida Department of Health Practitioner Profile as of December 2016 ** Source: Florida physician workforce data, pooled 2009-2015 survey waves “PCP” is family medicine and internal medicine physicians. “All Others” captures any other physician specialty, excluding general pediatrics. “OBGYN” is for obstetrics and gynecology fields, and “Med Subspec” is inclusive of general cardiology, endocrinology, gastroenterology, hematology and oncology, pulmonary medicine, nephrology, sports medicine, and rheumatology

T

able 2: Summ

ary Statistics for Physician Specialty Groups in W

orkforce Data, 2009-2015

Main Treatm

ent and Control

Alternative Control G

roups

PC

P

All O

thers O

BG

YN

M

ed Subspec Surgeons

Age

51.6 (11.8)

52.0 (11.3) 52.1 (11.8)

52.5 (10.9) 53.5 (11.6)

(%

) (%

) (%

) (%

) (%

) M

ale 69.1

79.1 61.6

85.7 92.3

New

Medicaid

44.8 61.6

59.2 59.8

58.9 N

ew M

edicare 79.6

84.5 77.7

94.0 91.0

Hospital/H

ealth Sys Based Practice

16.3 30.5

7.2 12.7

26.0 M

ore than 50 Hours Per W

eek Patient C

are 21.0

23.6 27.4

34.8 35.5

More than 5 H

ours Per Week

Adm

inistrative Duties

47.4 41.9

43.7 44.0

51.4

Source: Florida physician workforce data, pooled over entire analytic period (2009-2015). Sam

ple is restricted to non-retired physicians currently w

orking in the state. “New

Medicaid” and “N

ew M

edicare” are binary variables for reporting that the physician is currently accepting new

patients from the specific payer, respectively. “PC

P” is family m

edicine and internal medicine physicians.

“All O

thers” captures any other physician specialty, excluding general pediatrics. “OB

GY

N” is for obstetrics and gynecology fields,

and “Med Subspec” is inclusive of general cardiology, endocrinology, gastroenterology, hem

atology and oncology, pulmonary

medicine, nephrology, sports m

edicine, and rheumatology

Table 3: Medicaid Fee Bump Effect on Extensive Margin Medicaid Participation

Difference-in-Differences

(1) (2) PCP -0.101***

(0.005) -0.111***

(0.005) Fee Bump 0.152***

(0.002) 0.023***

(0.001) After Bump 0.205***

(0.002) -0.009***

(0.002) PCP x Fee Bump -0.005*

(0.003) -0.005* (0.003)

PCP x After Bump 0.008* (0.004)

0.008* (0.004)

Covariates No Yes Observations (physicians x months x years)

4,009,398 4,009,298

* P value at 0.10 ** P value at 0.05 *** P value at 0.01, standard errors in parentheses and clustered at the physician level. “PCP” is equal to one for family medicine and internal medicine physicians and zero otherwise. General pediatricians are excluded from the analytic samples. “Fee Bump Active” is equal to one for all months in 2013 and 2014. “After Fee Bump” is equal to one for all months in 2015 and 2016. The outcome is a binary variable for whether a given physician in a given month-year is currently enrolled in the Medicaid program (and therefore eligible to see Medicaid patients and receive payment from the state program). Column 2 includes the year the physician began practicing, practice city, and a linear time trend.

Table 4: Medicaid Fee Bump Effect on Intensive Margin Medicaid

Participation

Difference-in-Differences

(1) (2) (3) PCP -0.191***

(0.005) -0.194***

(0.005) -0.052***

(0.011) Post Fee Bump 0.055***

(0.002) 0.011***

(0.004) 0.001

(0.003) PCP x Post 0.056***

(0.005) 0.056***

(0.005) 0.039***

(0.005) Covariates No Yes Yes Physician FE No No Yes Observations 133,990 131,373 46,617 * P value at 0.10 ** P value at 0.05 *** P value at 0.01, standard errors in parentheses and clustered at the physician level. “PCP” is equal to one for family medicine and internal medicine physicians and zero otherwise. General pediatricians are excluded from the analytic samples. “Post Fee Bump” is equal to one for all survey years after 2012. The outcome is a binary variable for whether new Medicaid patients are currently being accepted by the physician. Column 2 includes age and sex demographics along with a linear time trend. Column 3 uses a panel data structure, with individual physician fixed effects (FE)

Table 5: Quasi-Placebo Test for Medicaid Fee Bump Effect on Medicare Participation

Difference-in-Differences

(1) (2) (3) PCP -0.051***

(0.004) -0.042***

(0.004) -0.025***

(0.010) Post Fee Bump 0.031***

(0.002) 0.028***

(0.003) 0.020***

(0.003) PCP x Post 0.003

(0.004) -0.0006 (0.004)

-0.006* (0.004)

Covariates No Yes Yes Physician FE No No Yes Observations 134,749 132,118 46,706 * P value at 0.10 ** P value at 0.05 *** P value at 0.01, standard errors in parentheses and clustered at the physician level. “PCP” is equal to one for family medicine and internal medicine physicians and zero otherwise. General pediatricians are excluded from the analytic samples. “Post Fee Bump” is equal to one for all survey years after 2012. The outcome is a binary variable for whether new Medicare patients are currently being accepted by the physician. Column 2 includes age and sex demographics along with a linear time trend. Column 3 uses a panel data structure, with individual physician fixed effects (FE)

Table 6: Medicaid Fee Bump Effect on Intensive Margin Medicaid Participation by Practice Setting

Difference-in-Differences

Hospital / Health System Based

All Others

(1) (2) (3) (4) PCP -0.136***

(0.010) -0.149***

(0.010) -0.128***

(0.005) -0.132***

(0.006) Post Fee Bump 0.068***

(0.004) 0.042***

(0.007) 0.066***

(0.003) 0.016***

(0.005) PCP x Post 0.070***

(0.011) 0.071***

(0.011) 0.030***

(0.006) 0.031***

(0.006) Covariates No Yes No Yes Observations 34,285 33,727 97,781 95,812 * P value at 0.10 ** P value at 0.05 *** P value at 0.01, standard errors in parentheses and clustered at the physician level. “PCP” is family medicine and internal medicine physicians. “Post Fee Bump” is equal to one for all survey years after 2012. The outcome is a binary variable for whether new Medicaid patients are currently being accepted by the physician. Columns 2 and 4 include age and sex demographics along with a linear time trend. Note, 71% of hospital or health system based PCPs were accepting new Medicaid patients prior to the fee bump, compared to 35% among all other PCPs

Table 7: D

escriptive Analyses for C

oncurrent Changes in T

ime U

se During the M

edicaid Fee Bum

p Period

PAN

EL

A

More than 50 Patient C

are Hours Per W

eek

Full Sam

ple H

ospital/Health System

Based

All O

thers

(1)

(2) (3)

(4) (5)

(6) PC

P x Post 0.019***

(0.005) 0.017***

(0.005) 0.067***

(0.013) 0.066***

(0.013) -0.003

(0.005) -0.005

(0.005) C

ovariates N

o Y

es N

o Y

es N

o Y

es O

bservations

137,020 134,228

36,361 35,713

99,109 97,042

PAN

EL

B

More than 5 Adm

inistrative Hours Per W

eek

Full Sam

ple H

ospital/Health System

Based

All O

thers

(1)

(2) (3)

(4) (5)

(6) PC

P x Post 0.030***

(0.006) 0.026***

(0.006) 0.033**

(0.014) 0.031**

(0.014) 0.028***

(0.007) 0.024***

(0.007) C

ovariates N

o Y

es N

o Y

es N

o Y

es O

bservations 123,612

121,114 32,178

31,602 90,101

88,248 * P value at 0.10 ** P value at 0.05 *** P value at 0.01, standard errors in parentheses and clustered at the physician level. “PC

P” is family m

edicine and internal medicine physicians. The labor supply outcom

e measures

in Panels A and B

are binary variables. Colum

ns 2, 4, and 6 include age and sex demographics along w

ith a linear tim

e trend. Only the D

D coefficient is reported for table brevity.

Table 8: Reasons for Not Currently Accepting New Medicaid Patients

PCP

All Others

2010-2012 2013-2015 2010-2012 2013-2015 Compensation 69.9 48.5 75.9 62.5 Hassel 15.9 25.7 13.2 18.9 At Full Capacity 13.0 23.7 9.9 17.2 Fraud Issues 1.3 2.1 1.0 1.3 Restricts to observations the report not currently accepting new Medicaid patients and that provide a justification response within a follow-up survey question. “Hassel” includes complaints regarding billing processes as well as excessive paperwork. Note, this questions was not asked in the 2009 survey.

Table 9: Medicaid Fee Bump Effect on Adult ED Utilization for Florida versus non-Florida Residents

Diff-in-Diff

All Adult ED Encounters Chronic Disease Exacerbation

(1) (2) (3) (4) FL Resident x Bump

0.015*** (0.002)

0.016*** (0.003)

0.009*** (0.003)

0.011*** (0.003)

FL Resident x After Bump

0.003 (0.002)

0.004 (0.003)

-0.009** (0.003)

-0.008** (0.004)

Covariates No Yes No Yes Observations 34,921,596 34,921,596 1,950,406 1,950,406 *** P value at 0.01 ** P value at 0.05 * P value at 0.10, standard errors clustered on patient county. All non-Florida (FL) residents are a single cluster. FL residents are considered the treatment group, while all FL ED visitors that do not permanently reside in FL are the control, comparison group. “Bump” is equal to one in 2013-2014 and zero otherwise. “After Bump” is equal to one for 2015 observations. Only key coefficients reported for brevity. Covariates include patient age, sex, and a dummy for white race as well as a linear time trend (columns 2 and 4). Columns 3 and 4 reduce the analytic sample by restricting to individuals with an ICD9 code linked to a chronic disease for their primary reason for ED presentation. Chronic illnesses include: COPD, asthma, diabetes, hypertension, and cardiac-related chest pain.

APPENDIX TABLES

PART A: Health Practitioner Data

Appendix Table A1: Medicaid Fee Bump Effect on Extensive Margin Medicaid Participation – Using Alternative Control Groups

Difference-in-Differences

Control: OBGYN Control: Med Subspec Control: Surgeons (1) (2) (3) PCP x Active 0.026***

(0.006) -0.025***

(0.005) -0.021***

(0.006) PCP x After 0.029***

(0.008) -0.029***

(0.007) -0.072***

(0.008) Covariates Yes Yes Yes Observations (physicians x months x years)

1,383,511 1,595,844 1,458,492

* P value at 0.10 ** P value at 0.05 *** P value at 0.01, standard errors in parentheses and clustered at the physician level. “PCP” is equal to one for family medicine and internal medicine physicians and zero otherwise. “OBGYN” is for obstetrics and gynecology fields, and “Med Subspec” is inclusive of general cardiology, endocrinology, gastroenterology, hematology and oncology, pulmonary medicine, nephrology, sports medicine, and rheumatology. General pediatricians are excluded from the analytic samples. “Fee Bump Active” is equal to one for all months in 2013 and 2014. “After Fee Bump” is equal to one for all months in 2015 and 2016. The outcome is a binary variable for whether a given physician in a given month-year is currently enrolled in the Medicaid program (and therefore eligible to see Medicaid patients and receive payment from the state program). Covariates include the year the physician began practicing, practice city, and a quadratic for monthly time trend. Only key coefficients reported.

PART B: Physician Workforce Data

Appendix Table B1: Medicaid Fee Bump Effect on Intensive Margin Medicaid

Participation – Control Group: OBGYN

Difference-in-Differences

(1) (2) (3) PCP -0.173***

(0.011) -0.179***

(0.011) -0.043***

(0.016) Post Fee Bump 0.042***

(0.009) -0.020* (0.011)

-0.017** (0.009)

PCP x Post 0.069*** (0.010)

0.070*** (0.010)

0.047*** (0.008)

Covariates No Yes Yes Physician FE No No Yes Observations 43,324 42,418 15,078 * P value at 0.10 ** P value at 0.05 *** P value at 0.01, standard errors in parentheses and clustered at the physician level. “PCP” is equal to one for family medicine and internal medicine physicians and otherwise zero. “Post Fee Bump” is equal to one for all survey years after 2012. The outcome is a binary variable for whether new Medicaid patients are currently being accepted by the physician. Column 2 includes age and sex demographics along with a linear time trend. Column 3 uses a panel data structure, with individual physician fixed effects (FE)

Appendix Table B2: Medicaid Fee Bump Effect on Intensive Margin Medicaid Participation – Control Group: Medicine Subspecialties

Difference-in-Differences

(1) (2) (3) PCP -0.165***

(0.008) -0.169***

(0.008) -0.033**

(0.014) Post Fee Bump 0.074***

(0.006) 0.015* (0.009)

0.003 (0.007)

PCP x Post 0.037*** (0.008)

0.035*** (0.008)

0.024*** (0.007)

Covariates No Yes Yes Physician FE No No Yes Observations 52,290 51,276 17,977 * P value at 0.10 ** P value at 0.05 *** P value at 0.01, standard errors in parentheses and clustered at the physician level. “PCP” is equal to one for family medicine and internal medicine physicians and otherwise zero. Medicine Subspecialties is inclusive of general cardiology, endocrinology, gastroenterology, hematology and oncology, pulmonary medicine, nephrology, sports medicine, and rheumatology. “Post Fee Bump” is equal to one for all survey years after 2012. The outcome is a binary variable for whether new Medicaid patients are currently being accepted by the physician. Column 2 includes age and sex demographics along with a linear time trend. Column 3 uses a panel data structure, with individual physician fixed effects (FE)

Appendix Table B3: Medicaid Fee Bump Effect on Intensive Margin Medicaid Participation – Control Group: Surgeons

Difference-in-Differences

(1) (2) (3) PCP -0.155***

(0.010) -0.170***

(0.011) -0.034**

(0.016) Post Fee Bump 0.072***

(0.009) 0.002

(0.011) -0.012

(0.010) PCP x Post 0.039***

(0.010) 0.041***

(0.010) 0.033***

(0.009) Covariates No Yes Yes Physician FE No No Yes Observations 44,148 43,194 15,404 * P value at 0.10 ** P value at 0.05 *** P value at 0.01, standard errors in parentheses and clustered at the physician level. “PCP” is equal to one for family medicine and internal medicine physicians and otherwise zero. “Post Fee Bump” is equal to one for all survey years after 2012. The outcome is a binary variable for whether new Medicaid patients are currently being accepted by the physician. Column 2 includes age and sex demographics along with a linear time trend. Column 3 uses a panel data structure, with individual physician fixed effects (FE)

Appendix Table B4: Medicaid Fee Bump Effect on Intensive Margin Medicaid Participation

Difference-in-Differences

Large Medicaid Markets

Small Medicaid Markets

(1) (2) (3) (4) PCP -0.143***

(0.008) -0.146***

(0.008) -0.229***

(0.006) -0.232***

(0.007) Post Fee Bump 0.054***

(0.004) 0.002

(0.007) 0.056***

(0.003) 0.020***

(0.006) PCP x Post 0.047***

(0.008) 0.050***

(0.008) 0.065***

(0.007) 0.063***

(0.007) Covariates No Yes No Yes Observations 58,582 57,454 72,024 70,701 * P value at 0.10 ** P value at 0.05 *** P value at 0.01, standard errors in parentheses and clustered at the physician level. “PCP” is family medicine and internal medicine physicians. “Post Fee Bump” is equal to one for all survey years after 2012. The outcome is a binary variable for whether new Medicaid patients are currently being accepted by the physician. Column 2 includes age and sex demographics along with a linear time trend. ‘Large’ Medicaid markets are counties with a Medicaid share of the insured population above the median in 2012, ‘Small’ reflects counties below the median

Appendix Table B5: Event Study Results for Medicaid Fee Bump Effect on Intensive Margin Medicaid Participation

Event Study

Abbreviated Output Overall

Large Medicaid

Markets Small Medicaid

Markets (1) (2) (3) PCP x 2010 -0.017

(0.012) -0.004

(0.019) -0.010

(0.016) PCP x 2011 0.0004

(0.009) 0.018

(0.014) -0.001

(0.013) PCP x 2012 0.009

(0.012) 0.018

(0.018) 0.019

(0.016) PCP x 2013 0.034***

(0.010) 0.038***

(0.015) 0.043***

(0.014) PCP x 2014 0.053***

(0.012) 0.062***

(0.018) 0.063***

(0.016) PCP x 2015 0.076***

(0.010) 0.071***

(0.016) 0.091***

(0.014) Covariates Yes Yes Yes Observations 131,373 57,454 70,701 * P value at 0.10 ** P value at 0.05 *** P value at 0.01, standard errors in parentheses and clustered at the physician level. “PCP” is equal to one for family medicine and internal medicine physicians and otherwise zero. General pediatricians are excluded from the analytic sample. Only key coefficients of interest are reported for table brevity. The outcome is a binary variable for whether new Medicaid patients are currently being accepted by the physician. Covariates include age and sex demographics