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The Impacts of Physician Payments on
Patient Access, Use, and Health∗
Diane Alexander†
Federal Reserve Bank
of Chicago
Molly Schnell‡
Northwestern University
and NBER
July 14, 2019
Abstract
We examine how the amount a physician is paid influences who they are willing to see.
Exploiting large, exogenous changes in Medicaid reimbursement rates, we find that
increasing payments for new patient office visits reduces reports of providers turning
away beneficiaries: closing the gap in payments between Medicaid and private insurers
would reduce more than two-thirds of disparities in access among adults and would
eliminate disparities among children. These improvements in access lead to more office
visits, better self-reported health, and reduced school absenteeism. Our results demonstrate that financial incentives for physicians drive access to care and have important
implications for patient health.
JEL: H51, H75, I13, I14, I18, I24
Keywords: physician behavior, financial incentives, primary care rate increase, fee
bump, Medicaid, Affordable Care Act, school absenteeism
∗
We thank Marguerite Burns, Janet Currie, Pinar Karaca-Mandic, Anthony Keats, Ilyana Kuziemko,
Nicole Maestas, Melissa McInerney, Matthew Notowidigdo, Heather Sarsons, Hannes Schwandt, Jessica Van
Parys, Laura Wherry, and participants in seminars at Stanford University, University of Illinois at Chicago,
University of Zurich, the Ifo Institute for Economic Research, the 2017 CSWEP CeMENT workshop, the
2017 Society of Labor Economists Annual Meeting, the 2017 Midwest Health Economics Conference, the
2017 Barcelona GSE Summer Forum, the 2017 International Health Economics Association Congress, the
2018 NBER Program on Children Meeting, and the 2018 and 2019 American Society of Health Economists
Annual Conferences. Leah Plachinski provided outstanding research assistance. Generous financial support
from the Griswold Center for Economic Policy Studies and the Center for Health and Wellbeing at Princeton
University is gratefully acknowledged. This paper was previously circulated under the title “Closing the Gap:
The Impact of the Medicaid Primary Care Rate Increase on Access and Health.” Any views expressed in
this paper do not necessarily reflect those of the Federal Reserve Bank of Chicago or the Federal Reserve
System. All errors are our own.
†
dalexand@frbchi.org; 230 South LaSalle Street, Chicago, IL 60604
‡
schnell@northwestern.edu; 2211 Campus Drive, Evanston, IL 60208
I
Introduction
Understanding how physicians respond to financial incentives is key for designing policies to
control health care costs while maintaining quality of care. Prior work illustrates the impact
of payment levels on treatment choices, decisions over treatment intensity, and adoption
of new technologies, suggesting that higher fees lead providers to do more once a patient
is through their door (Clemens and Gottlieb, 2014; Coey, 2015; Gruber et al., 1999; Rice,
1983; Yip, 1998). However, to what extent financial incentives influence extensive margin
decisions governing who physicians are willing to see—and whether these decisions affect the
health of patients—remains an open question.
This question is particularly important in light of significant disparities in access to care
between the publicly and privately insured: in 2009, office-based physicians were 35 percent
less likely to accept new patients covered by Medicaid than those covered by private insurance
(Decker, 2012, 2013; MACPAC, 2011). Since Medicaid historically pays physicians less than
two-thirds of what Medicare and private insurers pay for the same services, these disparities
in access could be driven by differences in payment generosity (Zuckerman and Goin, 2012).
Alternatively, this preference for the privately insured could be driven by complex patient
needs, payment delays, and high denial rates that are known to plague the Medicaid system
(Cunningham and O’Malley, 2009; Gottlieb et al., 2018; Long, 2013; Niess et al., 2018; Sloan
et al., 1978). Faced with little causal evidence that low payment levels are to blame for
disparities in access to care, policy makers often lower Medicaid payments in response to
economic downturns and budgetary shortfalls (MACPAC, 2015; Smith et al., 2004).
In this paper, we exploit exogenous variation in reimbursement rates to estimate the
effects of physician payment levels on patient access, use, and health. Most of our identifying
variation comes from a federal mandate that required states to increase their Medicaid
payments to match federally regulated Medicare levels for select primary care services in
2013 and 2014.1 As states traditionally had wide latitude in setting their Medicaid payments,
1
Designated in Section 1202 of the Affordable Care Act (ACA), the rate increase was federally funded
1
reimbursement rates varied dramatically across states before the primary care rate increase
went into effect. While Medicaid payments for select primary care services increased by an
average of 60 percent as a result of the mandate, rates more than doubled in eleven states
and were unchanged in two.
We find that increased physician reimbursement causes statistically and economically
significant improvements in access to care. Combining a new database of state-level Medicaid
reimbursement rates for new patient evaluation and management services from 2009 to 2014
with measures of access from the restricted-access National Health Interview Survey (NHIS)
files, we estimate that a $10 increase in Medicaid payments reduces reports of doctors telling
adult Medicaid beneficiaries that they are not accepting new patients or their insurance by
13 and 11 percent, respectively.2 Among children covered by Medicaid, a $10 increase in
Medicaid payments leads to a 25 percent decrease in parents reporting having trouble finding
a doctor for their children. Notably, we find little evidence that these improvements in access
among Medicaid beneficiaries are offset by negative spillovers to the privately insured. Our
results indicate that closing the gap in payments between private insurance and Medicaid—
a $45 increase in Medicaid payments for the median state—would close over two-thirds of
disparities in access for adults and would eliminate such disparities among children.
If Medicaid beneficiaries eventually receive treatment despite difficulties accessing care,
increased payments could reduce search costs but have no impact on the use of services or
health among patients. However, we find that increased reimbursement rates lead to greater
usage and improved health among beneficiaries. Again using data from the NHIS, we find
that a $10 increase in Medicaid payments leads to a 1.4 percent increase in the probability
that beneficiaries visited a doctor in the past two weeks and a 1.1 percent increase in the proband was intended to ease the absorption of new Medicaid enrollees entering through the ACA’s Medicaid
expansions by encouraging primary care physicians to participate in Medicaid (Blumenthal and Collins,
2014). The primary care services covered by the mandate included evaluation and management services and
vaccine administration provided by physicians in family medicine, general internal medicine, and pediatric
medicine.
2
Compared to the average baseline payment of $76, these improvements in access imply payment elasticities of physician willingness to accept new adult Medicaid beneficiaries of 0.83 to 1.01.
2
ability that beneficiaries report being in very good or excellent health.3 Using self-reported
data on school absences from the NHIS and administrative data on school attendance from
the National Assessment of Educational Progress (NAEP), we further find that a $10 increase in Medicaid payments leads to a 14 percent decrease in chronic absenteeism due to
illness or injury and a 2 percent decrease in chronic absenteeism overall.
When the federally mandated rate increase expired at the end of 2014, 34 states chose to
return to their previous payment levels (MACPAC, 2015). This provides us with a second
round of changes in reimbursement to exploit.4 Using data from 2013 to 2015, we find
that the reduction in reimbursement rates following the expiration of the federal mandate
had effects of similar magnitudes—but opposite signs—as the primary care rate increase
itself. This suggests that many of the improvements in access, use, and health that Medicaid
beneficiaries experienced when payments increased were lost when payments returned to
their previous levels, providing clear evidence of the importance of reimbursement rates in
driving physician behavior.
Of course, changes in Medicaid payments stemming from the primary care rate increase
did not occur in isolation. The U.S. health care system in general, and Medicaid in particular,
experienced many other changes over our sample period. Most relevant for our analysis, 27
states and the District of Columbia expanded their Medicaid programs in 2014 to include
coverage for low-income, childless adults. Although the timing is similar, our results are not
confounded by the 2014 Medicaid expansions for at least four reasons. First, we find similar
effects of changing reimbursement rates in states that did and did not expand their Medicaid
programs under the ACA. Second, we find the strongest effects on access and health among
children, whose eligibility was unaffected by the Medicaid expansions.5 Third, we estimate
3
Among beneficiaries, these estimates imply an elasticity of office visits (self-reported good health) with
respect to Medicaid payments of 0.11 (0.08).
4
The decision not to extend the augmented payments may have depended on a state’s experience during
the federal mandate. However, we find that states that ultimately did and did not extend the higher payments
experienced similar improvements in outcomes as a result of the primary care rate increase.
5
Recent work demonstrates that children’s use of preventive services increases when Medicaid eligibility
is extended among adults (Venkataramani et al., 2017). While such spillovers could influence our estimates
of child health, it is unlikely that an improvement in cost-sharing among adults would make it easier for
3
similar effects when we truncate the sample period to exclude the 2014 Medicaid expansions.
Finally, as noted above, we estimate similar effects of reimbursement rates using variation
in payments stemming from the expiration of the federal mandate in 2015, a year after the
majority of Medicaid expansions had gone into effect.
While economists, public health researchers, and policy makers have long been interested
in the effects of program generosity on access, use, and health among beneficiaries, causal
analyses have been hampered by two important data limitations. First, before the primary
care rate increase, most states had not made large changes to their Medicaid reimbursement
rates in the last decades, and those that had chose to do so voluntarily.6 Previous research on
program generosity has therefore had to rely on cross-sectional associations that likely suffer
from omitted variable bias, case studies of single fee changes that may be confounded by time
trends, and difference-in-difference models in which treatment is potentially endogenous.7 In
contrast, we exploit a federal mandate that induced large, exogenous changes in Medicaid
reimbursement rates across the United States.
Second, the rise of Medicaid managed care that began in the early 1990s has made it
difficult to know how much physicians are actually reimbursed through Medicaid. In a feefor-service system, state Medicaid programs pay providers a fixed amount for each service
they provide. Although time consuming, these payment rates can be collected by contacting
each state (as we do in this study).8 Under managed care, in contrast, states typically pay
managed care organizations (MCOs) a fixed amount per beneficiary to provide all covered
parents to find physicians willing to see their children.
6
Physician reimbursement rates in Medicare offer even less variation, as changes are made to a single,
nationwide fee schedule. Furthermore, Medicare reimbursement rates for physicians have remained essentially
the same for the past decade and will remain largely unchanged until at least 2025 under the Medicare Access
and CHIP Reauthorization Act of 2015.
7
Cross-sectional studies: Cohen (1993); Cohen and Cunningham (1995); Hadley (1979); Long et al. (1986);
Mitchell (1991); Showalter (1997); Sloan et al. (1978). Case studies: Adams (1994); Coburn et al. (1999);
Fanning and de Alteriis (1993); Fox et al. (1992); Gruber et al. (1997). Difference-in-difference models:
Atherly and Mortensen (2014); Baker and Royalty (2000); Buchmueller et al. (2015); Callison and Nguyen
(2017); Chen (2014); Decker (2007, 2009); Shen and Zuckerman (2005).
8
We note, however, that previous work relies almost exclusively on payment rates from infrequently
published secondary sources such as the Urban Institute’s Medicaid-to-Medicare fee index or the American
Academy of Pediatrics Medicaid Reimbursement Reports.
4
services, and MCOs then pay providers. While over 60 percent of Medicaid beneficiaries are
enrolled in comprehensive risk-based managed care plans, states—and in turn researchers—
have little knowledge about how or how much MCOs actually pay physicians for the services
that they provide.
Importantly, the primary care rate increase required states to raise their Medicaid payments to achieve parity with Medicare levels for both their fee-for-service and managed care
programs.9 We therefore know exactly how much physicians were reimbursed under Medicaid
in 2013 and 2014. To estimate payments in the pre- and post-periods, we combine quarterly fee-for-service rates collected directly from each state’s Medicaid office with state-level
Medicaid managed care to fee-for-service reimbursement ratios and data on the fraction of
beneficiaries enrolled in each system in each state. We are thus able to examine the effects of
changing physician payments on the entire Medicaid system, rather than just on the rapidly
shrinking fee-for-service portion.
Our work contributes to an ongoing debate on the effects of the Medicaid primary care
rate increase on access to care. An early audit study found that the federal mandate was
associated with increases in appointment availability among Medicaid patients in ten states
(Polsky et al., 2015).10 In contrast, recent work by Decker (2018) found that Medicaid
acceptance rates in an annual survey of 1,500 physicians did not increase during the primary
care rate increase. Using claims data on office visits among a convenience sample of 11
percent of primary care physicians, Mulcahy et al. (2018) also found no association between
the rate increase and office visits among Medicaid beneficiaries. This previous work has
relied on small, selected samples and included limited—if any—information on the size of
the rate increase in given states.11
9
As outlined in Appendix A.3, the Centers for Medicare and Medicaid Services (CMS) required states
to submit proposals outlining how the increased reimbursements would be incorporated into the state’s
capitation payments made to MCOs in 2013 and 2014.
10
Candon et al. (2018) replicates the analysis following the end of the mandate in 2015 and finds that
appointment availability declined in the sampled states that did not extend the increased payments.
11
The studies by Decker (2018) and Mulcahy et al. (2018) further incorporate implementation delays that
were associated with the rate increase. Since increased payments were made retroactively in states that experienced such delays, we would expect the behavior of physicians—who are largely not credit constrained—to
5
In contrast, we use comprehensive data that cover every state and exploit continuous
variation in the magnitude of payment increases across states. Notably, we demonstrate that
the effects of the federal mandate scale linearly with the size of the payment increase. This
highlights that simple before-after designs—which average treatment effects across states
that experienced payment increases of 0 to over 200 percent as a result of the mandate—
lead to estimates that mask the true relationship between reimbursement rates and access
to care. Additionally, we look beyond access alone and find that improvements in access
resulting from increased payments lead to increased use and improvements in health.
More broadly, our work relates to the literature that studies the effects of insurance coverage itself on the use of medical services and health outcomes. Using both randomized
controlled trials (Baicker et al., 2013; Finkelstein et al., 2012; Manning et al., 1987) and
natural experiments (Card et al., 2008; Currie and Gruber, 1996a,b; Sommers et al., 2012),
researchers have documented that having health insurance increases the use of health care
services and improves health. Recent work further demonstrates the impacts of health insurance on educational attainment, labor market outcomes, and financial hardship (Baicker
et al., 2013, 2014; Brown et al., 2015; Cohodes et al., 2016; Finkelstein et al., 2012; Gross
and Notowidigdo, 2011). Our work complements these studies by demonstrating that the
positive impacts of having health insurance will be mediated by program generosity.
The rest of the paper proceeds as follows. We provide an overview of our data in Section
II. Section III introduces our empirical strategy and examines the impacts of increased payments on access to care, use of services, and health. Section IV probes the robustness of these
findings. Section V examines the effects of the reduction in reimbursement rates resulting
from the end of the federal mandate. Section VI discusses mechanisms and concludes.
respond when the augmented payments went into effect on January 1, 2013. In fact, even though some states
took until May 2013 to release the increased payments (MACPAC, 2015), our event studies demonstrate that
physician behavior responded equally in 2013 and 2014. Incorporating payment delays therefore biases the
results towards zero because some of the “pre-period” in such specifications was actually treated.
6
II
Data
We use four main data sources to document how physician reimbursement rates affect access
to primary care, frequency of office visits, and health among patients. To measure physician
reimbursement, we construct a new dataset containing Medicaid payments for new patient
evaluation and management services for all states from 2009 to 2015. To measure patient
access, use, and health, we use the NHIS. Finally, to corroborate the NHIS outcomes related
to schooling, we use data on school absences and test scores from the NAEP. These datasets
are supplemented with information from the Health Resources and Services Administration’s (HRSA) Area Resource Files (ARF) to control for spatial and temporal differences in
sociodemographics and health care resources.
II.A
Medicaid Reimbursement Rates
Our primary explanatory variable is the amount that Medicaid pays physicians for new patient evaluation and management services across states and over time. Under a fee-for-service
system, there are five Medicaid reimbursement rates for these services, each corresponding
to a specific length and complexity of visit (current procedural terminology (CPT) codes
99201-99205).12 We obtained historical payment data for these five codes by contacting the
Medicaid offices of all 50 states and the District of Columbia.13 Our main results use reimbursement rates associated with the most commonly billed new patient evaluation and
management code over our sample period: new patient office visits of mid-level complexity
(CPT code 99203).14 Given the strong correlation between Medicaid payments for CPT
12
Some states have modifier codes that allow for different payment rates depending on patient characteristics, such as age. Although ideally we would incorporate modifier codes for children, in practice this is
difficult due to varying age group carve-outs across states (e.g., “pediatric only” or under age 15, 20, or 21)
and restrictions on the level of aggregation allowed for merging variables within the NHIS. We therefore use
base rates in all states but note that Medicaid payments for children will on average be slightly higher.
13
We have complete payment data for 44 states and the District of Columbia. Appendix A.2 outlines the
methodologies used to impute payment rates for the six states with partial payment histories.
14
Of new patient visits billed to Medicare in 2009, the relative billing frequencies across CPT codes 9920199205 were 3 percent, 19 percent, 43 percent, 27 percent, and 8 percent, respectively (Levinson, 2012). Our
results are robust to using a billing frequency–weighted average across the five reimbursement rates for new
7
codes 99201-99205 within states over time (see Figures A1 and A2), all of our results are
robust to using payments for these alternative CPT codes.
The amount physicians are paid under fee-for-service Medicaid does not tell the full
story, however, as over half of Medicaid beneficiaries are enrolled in managed care. We
take managed care into account by creating an expected Medicaid payment measure that
combines the state-level fee-for-service data with state-level managed care to fee-for-service
payment ratios and state-level Medicaid managed care enrollment shares.15 In particular,
we first use Medicaid managed care to fee-for-service payment ratios from the Government
Accountability Office (GAO) for evaluation and management services performed in doctors’
offices to calculate Medicaid managed care payments from the fee-for-service rates.16 Using
data from CMS on the percentage of Medicaid beneficiaries enrolled in managed care annually
in each state (shown in Figure A3), we then define expected Medicaid payments at the statequarter level as the enrollment-weighted average of Medicaid fee-for-service and managed care
payments.17
Both the initial geographic variation in Medicaid payment rates and the changes over
our sample period are substantial. Figure 1 plots our constructed measure of Medicaid
patient visits. Unfortunately, analogous reports are not available for Medicaid.
15
As previously noted, the primary care rate increase applied to both fee-for-service and managed care
Medicaid programs. Appendix A.3 provides an overview of how states incorporated the increased reimbursement rates into their payments to MCOs in 2013 and 2014.
16
These payment ratios come from a GAO report documenting the difference between managed care and
fee-for-service payments under Medicaid at the state level in 2010 (GAO, 2014). The report provides payment
ratios for two de-identified states and eighteen identified states (Arizona, California, Connecticut, Florida,
Georgia, Indiana, Michigan, New Jersey, New Mexico, New York, Ohio, Pennsylvania, Rhode Island, South
Carolina, Texas, Virginia, Washington, Wisconsin). We use the recorded ratio for the states in the report
and the median of 5 percent more under managed care for the missing states. Our results are robust to only
using the states in the GAO report and to imputing missing states with the mean.
17
FFS
That is, letting Rsqy
denote the Medicaid fee-for-service reimbursement rate in state s in quarter q of
MC
R
year y, RF F S
denote the managed care to fee-for-service payment ratio under Medicaid in state s in
s,2010
MC
2010, and %Bsy
denote the fraction of Medicaid beneficiaries enrolled in a managed care plan in state s in
year y, the expected Medicaid reimbursement rate in each state-quarter before and after the primary care
rate increase is approximated by
MC
R
MC
FFS
MC
FFS
˜
Rsqy = (1 − %Bsy ) · Rsqy + %Bsy · Rsqy ·
RF F S s,2010
8
payments at the state-quarter level from 2009 to 2015.18 In the first quarter of 2009, the
expected Medicaid payment for a new patient office visit of mid-level complexity ranged
from $37 in Minnesota to $160 in Alaska. Few states made meaningful changes to their
reimbursement rates in the next three years: between 2009 and 2012, Medicaid payments for
new patient office visits increased by an average of only $4.27 across states, with more than
half of states making no changes to their payment schedules for evaluation and management
services. This persistence of payments is one of the difficulties that previous work has faced
in measuring how physicians respond to changes in program generosity.
When the primary care rate increase went into effect in 2013, the range tightened, with
states paying physicians between $101 (Alabama) to $171 (Alaska).19 Figure 2 provides
maps of Medicaid payments across the United States in 2009, 2012, and 2013. The shading
in the maps reflects state-level quintiles of Medicaid payments as defined in 2009. As shown
in Figure 2, the primary care rate increase was sufficient to push all states into the top
quintile of reimbursement rates from 2009.
When the primary care rate increase was initially passed, it was unclear whether federal
funding for the increased payments would extend beyond 2014. In the end, the funding
was not extended, and in 2015, 34 states chose to return to their previous payment levels
(see Figure A4). While this provides another large change in payment rates, states may
have made this decision based on their experience during the primary care rate increase
(MACPAC, 2015). Thus, in our main analysis we do not use variation in Medicaid payments
stemming from the expiration of the federal mandate. Instead, we examine the effects of this
reverse experiment on outcomes separately and explore the potential endogeneity concerns.
Although the federal government mandated that states increase select Medicaid payments to primary care providers starting on January 1, 2013, many states experienced im18
Refer to Figure A2 for plots of quarterly fee-for-service Medicaid payments for CPT codes 99201-99205
from 2009 to 2015 for each state.
19
The remaining variation across states comes from two sources. First, Medicare payment levels vary
across locations due to adjustments for geographic and market area differences. Second, Alaska and North
Dakota maintained Medicaid payment rates that exceeded federally mandated Medicare levels over the
sample period.
9
plementation delays (MACPAC, 2015). We do not incorporate state-level variation in the
implementation of the primary care rate increase into our Medicaid payment variable; that
is, we use the payment rates reported by the state as effective in each month and year.
Because states with implementation delays were required to retroactively pay physicians the
difference between the amount paid and the enhanced Medicaid rate, we believe that the
behavior of physicians—who are largely not credit constrained—should respond at the start
of the rate increase rather than when the higher payments were actually released.
Finally, we can only expect physician behavior to respond to increased payments if
providers are aware of changes in reimbursement. We note that the primary care rate increase
was covered widely by news outlets: for example, The Washington Post published an article
on December 21, 2012—before the federal mandate went into effect—titled “Obamacare is
about to give Medicaid docs a 73 percent raise” (The Washington Post, 2012). As Medicaid
payments more than doubled for primary care providers in some states, it is reasonable that
physicians would take notice of the change, and thus there is scope for physician behavior
to respond.
II.B
National Health Interview Survey
The NHIS is the largest in-person household survey that tracks health care access, health
care utilization, and health outcomes across the United States. While many surveys measure
health patterns, the NHIS is well-suited for our study for two reasons. First, in contrast to
most other surveys, the NHIS is large enough to be used for state-level estimates. This
allows us to exploit state-level variation in Medicaid reimbursement rates over time. Second,
while many surveys ask respondents whether they have health insurance, the NHIS collects
information on insurance provider. This allows us to look separately at patients with private
insurance and Medicaid beneficiaries.
We use outcomes from three NHIS sample components in our analysis: the family file,
the sample child file, and the sample adult file. The family component collects demographic
10
information and answers to basic questions (e.g., health status) for all members of a family.
The sample child and sample adult components each sample one child and one adult in the
family and ask a longer list of more detailed questions (e.g., days of school or work missed
in the past year). Sample sizes are thus more limited when working with questions asked in
the sample child or sample adult files relative to the full family sample.
To measure access to health care services, we consider whether respondents report difficulty with doctors either not accepting new patients or not accepting their insurance.20 For
children, we further consider indicators denoting whether parents report having difficulty
finding a doctor to see their child and whether their child has a usual place of care. To
measure use of health care services, we consider whether respondents report having had an
office visit in the past two weeks. As seen in Table 1, Medicaid beneficiaries and the privately
insured have a similar likelihood of visiting a doctor in the past two weeks. However, those
covered by Medicaid are more than twice as likely to report difficulties finding physicians
who are willing to accept them as new patients.
Policies targeting health care access do so with the hope that improving access will
improve health. To examine whether higher reimbursement rates lead to better health,
we consider indicators denoting whether people rate their health as excellent/very good or
fair/poor. We further consider the number of work days adults report having missed and the
number of school days parents report their child having missed in the past year. Notably, the
NHIS asks specifically about school absences due to illness or injury, allowing us to focus on
the category of absences most likely to be sensitive to changes in access to primary care. We
focus on chronic absenteeism when considering school absences, which we define as missing
fourteen or more days of school in the past year.21
20
The exact survey questions used are outlined in Appendix A.1. All questions were asked throughout
our full sample period except those asking whether children and adults had trouble finding a doctor, which
started in 2011.
21
Chronic absenteeism is linked to low academic achievement, including test scores, test score growth, and
on-time graduation rates (Buehler et al., 2012; Connolly and Olson, 2012; Schanzenbach et al., 2016; Spradlin
et al., 2012; Utah Education Policy Center, 2012). Unlike average absenteeism, rates of chronic absenteeism
vary widely across schools, and all but thirteen states use chronic absenteeism in their accountability system
under the Every Student Succeeds Act (Bauer et al., 2018).
11
As shown in Table 1, baseline differences in health between Medicaid beneficiaries and
the privately insured are large. Compared to respondents with private insurance, Medicaid
beneficiaries are almost three times more likely to report being in fair or poor health, and
children covered by Medicaid are twice as likely to be chronically absent.
To account for differences in demographics and the availability of medical resources across
locations and over time, we control for individual demographics from the NHIS and countylevel characteristics from the ARF. Table 2 reports summary statistics for individual and
county-level characteristics by insurer. Relative to the privately insured, Medicaid beneficiaries have lower income and education levels, live in larger families, are less likely to be
married, and are more likely to be black or Hispanic. Respondents covered by Medicaid also
live in poorer, more densely populated areas.
Although much of the NHIS data is publicly available, geographic identifiers for areas smaller than Census regions are restricted. In order to link our outcome measures to
state-level variation in Medicaid reimbursement rates and county-level health resources, we
obtained access to confidential state and county identifiers. All of our analyses using the
NHIS are therefore conducted in a Census Research Data Center.
II.C
National Assessment of Educational Progress
To examine whether increased payments to physicians lead to better educational outcomes
among low-income children, we supplement self-reported days of missed school from the
NHIS with administrative data from the NAEP. The NAEP is a congressionally mandated
assessment that provides information on reading and mathematics performance in grades 4
and 8 every other year in all states. Not all schools are tested in each wave, although the
schools and students participating in NAEP are selected to be representative of all schools
nationally and of public schools at the district level.
Importantly for our work, the NAEP reports the fraction of children who missed 0, 1–2,
3–4, 5–10, and 11 or more days of school (for any reason) in the month preceding their
12
national assessment exam.22 As in the NHIS, we focus on the fraction of chronically absent
children. When considering absences over the period of a month (rather than over the course
of a year, as in the NHIS), chronic absenteeism is commonly defined as three or more days of
missed school (KewalRamani et al., 2007; Ginsburg et al., 2014; Schanzenbach et al., 2016).
Figure A5 shows the distribution of absences averaged over math and reading assessments
in grades 4 and 8. There are large differences by grade, with a larger fraction of students
reporting zero absences in the past month in grade 4 than in grade 8.
As was seen in the NHIS data, there are large differences in the number of absences by
socioeconomic status. Although we do not observe whether children are covered by Medicaid
in the NAEP data, we can identify children that are eligible to receive free school meals.
Like Medicaid, free school lunch is a means-tested program; according to income eligibility
limits for each program, all children who are eligible for free school meals are also eligible
for Medicaid (but not vice versa).23 In grade 4, 53 percent of children ineligible for free
lunch missed zero days in the past month compared to 44 percent among students eligible
for free lunch. The discrepancy in school absences by free lunch eligibility is similar in grade
8, though fewer children report zero absences in both groups.
In all grades and subjects, average test scores are monotonically decreasing in the number
of school days missed in the past month (see Figure A6). Given the negative correlation
between absences and test scores, it is possible that test scores could be affected by changes
in access to primary care. We therefore also look at the effects of physician reimbursement
on average state-level performance on national math and reading assessments.
22
While the NAEP data does not include information on absences due specifically to illness or injury (as
in the NHIS), most school absences—particularly among young children—are attributable to either acute
illnesses such as respiratory infections and gastroenteritis or chronic childhood diseases such as asthma
(Ehrlich et al., 2014; Neuzil et al., 2002; Wiseman and Dawson, 2015). As these conditions are commonly
treated in a primary care setting, overall school absenteeism may be responsive to changes in access to
primary care.
23
Across the United States, children in households with income at or below 130 percent of the federal
poverty level are eligible to receive free meals at school (FRAC, 2018). Medicaid eligibility requirements
vary by state, although the federal minimum income limit for children’s health coverage is 138 percent of
the federal poverty level with a median income limit of 149 percent across states (Kaiser Family Foundation,
2018).
13
III
Physician Payments and Patient Access, Use, and
Health
The summary statistics in Table 1 demonstrate that those covered by Medicaid face greater
difficulty accessing health care services and have worse health than the privately insured. To
investigate whether differences in physician reimbursement contribute to these differences
in outcomes, we examine the effects of changes in physician payments under Medicaid on
access, use, and health among beneficiaries. Sections III.A through III.C consider a range of
outcomes from the NHIS, while Section III.D turns to educational outcomes from the NAEP.
We focus on the impacts of the increase in Medicaid payments stemming from the onset of
the primary care rate increase in 2013 throughout Section III; Section V considers the effects
of the reduction in Medicaid payments following the expiration of the federal mandate in
2015.
III.A
Raw Data
We begin by examining patterns in the raw data. To do so, we divide states into deciles based
on the size of the payment increase that they experienced under the Medicaid primary care
rate increase. Figure 3 plots the average change in various outcomes in the two years after the
payment increase (2013–2014) versus the two years before (2011–2012) against the average
payment increase in each decile. We plot two lines for each outcome—one for Medicaid
beneficiaries and one for privately insured patients—that depict the best fit line through
these points. We adjust the outcomes such that higher values denote better outcomes; an
increasing slope therefore indicates that larger payment increases are associated with larger
improvements in a given outcome.
Across a range of measures, we see that Medicaid beneficiaries in states with larger
increases in Medicaid payments saw greater improvements in access, frequency of office
visits, and health. For example, in the upper left subplot of Figure 3 we see that Medicaid
14
beneficiaries in states in the lowest decile of payment increases (average increase of $17.43)
experienced little change in the probability of having an office visit in the past two weeks
following the payment increase, whereas Medicaid beneficiaries in states in the highest decile
of payment increases (average of $88.41) experienced an average increase of nearly 6 percent.
Notably, across most outcomes there is no association between changes in Medicaid payments
and changes in outcomes among privately insured patients; that is, the line is flat.
III.B
Event Studies
To examine the timing of the effects and to control for differences across individuals and
locations, we estimate event study specifications. In particular, letting ∆P ayments =
P ayments,2013Q1 − P ayments,2012Q4 denote the change in Medicaid payments resulting from
the primary care rate increase in state s, we estimate the following equation:
Outcomeicsy = β0 + βy ∆P ayments ∗ λy + γXi + δZcy + λs + icsy
(1)
where Outcomeicsy denotes an outcome for Medicaid beneficiary i living in county c in state
s in year y; Xi and Zcy are vectors of individual and county characteristics (listed in Table
2), respectively; and λs and λy are state and year fixed effects, respectively. By scaling the
association between time and the outcome by the extent of the treatment, this specification
exploits the full variation in Medicaid payments induced by the primary care rate increase.
As in the raw data analysis, we adjust the outcomes such that higher values are indicative
of better outcomes. We use the sample weights provided in the NHIS and cluster standard
errors by state.
Figure 4 plots the βy s from Equation (1). The coefficients before the primary care rate
increase—β̂2009 through β̂2012 —are statistically indistinguishable from zero, indicating that
the outcomes were stable before the federal mandate. Following the rate increase, however,
there are persistent, significant increases in many of the outcomes. For example, the bottom
15
left subplot indicates that Medicaid beneficiaries saw improvements in physicians’ willingness
to accept new patients when Medicaid reimbursement rates increased in 2013 and 2014.
The effects are immediate for most outcomes, although there is some evidence that health
effects—such as patients reporting their health as excellent or very good—accrue over time.24
III.C
Regression Analysis
Figure 4 demonstrates that increased Medicaid payments lead to improved outcomes among
Medicaid beneficiaries. To quantify the effects of physician reimbursement on access, use,
and health, we estimate the following specification:
Outcomeicsqy = β0 + β1 P aymentsqy + γXi + δZcy + λs + λqy + icsqy
(2)
where Outcomeicsqy denotes an outcome for respondent i living in county c in state s in
quarter q of year y, P aymentsqy is our primary measure of Medicaid payments in state s in
quarter q of year y (see Section II.A), λqy are quarter-year fixed effects, and all other variables
are defined as in Equation (1).25 We divide payments by $10 such that β1 represents the
effect of a $10 increase in Medicaid payments.26 For the outcomes covering a retrospective
time period of twelve months, the payment variable is the average Medicaid payment over
the past four quarters; for all other outcomes we use the average payment in the quarter
of the interview. Since we include state and quarter-year fixed effects, our main coefficient
24
Coefficients for 2013 may also be smaller because the reference periods for some questions include part
of the pre-period. For example, when respondents are asked about difficulty finding a doctor over the past
twelve months, respondents who were interviewed before the end of 2013 will include some of their experience
from before the rate increase. Table A1 lists the reference window for each outcome.
25
Recall from Figure 1 that some states made minor adjustments to their Medicaid payments between 2009
and 2012 (over our sample window but before the federally mandated primary care rate increase). Most of
our estimates of Equation (2) include these changes, although Figure 5 confirms that our results are robust
to excluding variation in payments from before the federal mandate.
26
As shown in Figure 3, the relationship between increases in Medicaid payments and changes in our
outcome variables is approximately linear. We therefore prefer a linear specification both because it is
suggested by the data and because it allows for the coefficients to be easily interpreted as the effect of a $10
increase in payments. We can, however, use a specification in which we consider log(P aymentsqy ) on the
right-hand side. We note that the elasticities implied from both specifications are quantitatively similar.
16
of interest, β1 , is identified by changes in Medicaid payments within states over time. As
before, all regressions use the sampling weights provided by the NHIS, and standard errors
are clustered by state.
Results from estimation of Equation (2) are presented in Table 3. The first three columns
of each panel show the effects of changes in Medicaid payments on survey respondents covered
by Medicaid. Looking first to columns (1) and (2) of Panel B, we see that a $10 increase in
Medicaid payments leads to a 0.54 percentage point decrease in the probability that parents
report difficulty finding a doctor to see their child covered by Medicaid and a 0.36 percentage
point decrease in the likelihood that the child has no usual place of care (reflecting decreases
relative to the mean of 25 and 11 percent, respectively). Among adult Medicaid beneficiaries,
a $10 increase in Medicaid payments causes both a 0.82 percentage point reduction in the
probability of being told that a physician is not accepting new patients and a 0.89 percentage
point reduction in the probability of being told that one’s insurance is not accepted (decreases
of 13 and 11 percent of the mean, respectively; see columns (1) and (2) of Panel C). Notably,
these improvements in access lead to more use: in the full sample, a $10 increase in Medicaid
payments increases the probability that respondents covered by Medicaid had an office visit
in the past two weeks by 0.28 percentage points (1.4 percent relative to the mean; column
(1) of Panel A).
In addition to improved access and use, increases in Medicaid payments lead to better
health among the program’s beneficiaries. A $10 increase in physician reimbursement reduces
the probability that beneficiaries report being in fair or poor health by 0.31 percentage
points (1.8 percent of the mean; column (2) of Panel A) and increases the probability that
beneficiaries report being in excellent or very good health by 0.62 percentage points (1.1
percent of the mean; column (3) of Panel A). Among young children covered by Medicaid, a
$10 increase in Medicaid payments reduces the probability of being chronically absent due
to illness or injury by 0.65 percentage points (a decrease of 14 percent of the mean; column
17
(3) of Panel B).27 There is no reduction in illness-related chronic absenteeism among older
children covered by Medicaid (column (4) of Panel B); this is likely due to the fact that
absences among older children are less closely tied to health (Ehrlich et al., 2014; Neuzil et
al., 2002; Wiseman and Dawson, 2015). We further find no reduction in days of work missed
among adult Medicaid beneficiaries (column (3) of Panel C).
To get a sense of what these effects imply for the typical state under the primary care rate
increase, we consider the effects of an increase in Medicaid payments of $35—the median
increase in Medicaid payments across states from the fourth quarter of 2012 to the first
quarter of 2013. Multiplying the point estimates in Table 3 by 3.5, we see that an increase
of $35 in physician reimbursement under Medicaid leads to a 5.0 percent increase in the
probability of having visited a doctor’s office in the past two weeks, a 6.2 percent decrease in
the probability of being in fair or poor health, and a 3.9 percent increase in the probability of
being in very good or excellent health among beneficiaries. Applying the same calculations
to the access measures further indicates that the Medicaid primary care rate increase nearly
eliminated parents having trouble finding doctors for their Medicaid-covered children and
approximately halved these difficulties for adult beneficiaries in the median state.
We can compute elasticities by comparing the effects of a $10 increase in Medicaid payments in percentage terms to the corresponding percent change in Medicaid payments implied
by a $10 increase.28,29 As reported in column (4) of Table 4, our results imply elasticities
with respect to Medicaid payments of physician willingness to accept new adult Medicaid
patients of 0.83, office visits among beneficiaries of 0.11, and self-reported good health among
beneficiaries of 0.08. The implied elasticities for access among children are even more pro27
We find similar results when we consider a continuous measure of school absences rather than an indicator
for chronic absenteeism. As shown in Table A2, a $10 increase in Medicaid payments leads to an average
reduction of 0.23 days of school missed due to illness or injury per year among young children covered
by Medicaid, a 6.4 percent reduction relative to the mean. We find no effects of Medicaid payments on
illness-related school absences for older children covered by Medicaid or for children with private insurance.
28
Compared to the average baseline Medicaid payment of $76 for a new patient office visit of mid-level
complexity, a $10 increase in payments corresponds to a 13.2 percent increase.
29
Alternatively, we can calculate elasticities by including payments in logs instead of in levels in Equation
(2). The elasticities from this alternative specification are very similar to those reported in Table 4.
18
nounced, suggesting that physicians are more elastic to payments for children. Although
billing difficulties known to plague the Medicaid system should not depend on beneficiary
age (Cunningham and O’Malley, 2009; Gottlieb et al., 2018), providers report that adult
Medicaid beneficiaries have a wider breadth of needs, which makes managing their cases
more difficult than those of children or patients with private insurance (Long, 2013; Niess et
al., 2018).30 It is therefore reasonable that physician behavior would be more responsive to
Medicaid payments for children.
While we find strong evidence that increasing physician reimbursement under Medicaid
improves access and health among the program’s beneficiaries, there is little evidence of
spillovers to the privately insured. The last three columns of Table 3 present analogous
estimates for privately insured respondents, who may be indirectly affected by Medicaid
patients becoming relatively more attractive to physicians. However, we find no change in
access, use, or health among the privately insured when Medicaid payments increase, with
the exception of parents having slightly more trouble finding a doctor for their children
(an increase of 0.13 percentage points, significant at the 10 percent level). Not only are
the coefficients nearly all statistically insignificant despite large sample sizes, but across
all outcomes the point estimates are much smaller than those observed among respondents
covered by Medicaid.
These effects have large implications for disparities in access to care between the publicly
and privately insured. Column (3) of Table 4 reports baseline disparities in our outcome
measures between Medicaid beneficiaries and patients with private insurance. Columns (6)
through (8) show how much of this disparity is reduced by increasing Medicaid payments by
$10, $35, and $45, respectively. As shown in column (7), increasing Medicaid payments by
$35—the median increase under the primary care rate increase—reduces disparities in reports
of doctors telling adult patients that they are not taking new patients or their insurance by
30
Niess et al. (2018) surveyed 806 physicians in Colorado to assess their beliefs and attitudes about adult
Medicaid patients. Eighty-six percent of respondents had an unfavorable attitude toward adult Medicaid
beneficiaries, with respondents most likely to agree that “socially complicated” and “medically complicated”
described a typical adult Medicaid patient.
19
64 and 55 percent, respectively. Closing the gap in payments between private insurance and
Medicaid—a $45 increase in Medicaid payments for the median state at baseline31 —closes
over 80 percent of the gap in reports of doctors not taking new adult patients and over
two-thirds of the gap in reports of doctors not taking an adult patient’s insurance. Because
providers are more elastic to payments for children, it is easier to close gaps in access: as
shown in column (9) of Table 4, it would take an increase in Medicaid payments of about
$26 on average to eliminate disparities in access between children with private insurance and
children with Medicaid.32
III.D
Educational Outcomes in the NAEP
All of the measures in the NHIS, including days of school missed, are self reported. To corroborate our finding that increased reimbursement rates reduce absenteeism among children—
and to examine whether these reductions in absenteeism lead to improvements in test scores—
we use administrative data from the NAEP.
Here, we use a specification similar to Equation (2) but at the state-year level:
Outcomesy = β0 + β1 P aymentsy + λs + λy + sy
(3)
where Outcomesy denotes an average schooling outcome in state s in year y, and λs and λy
31
We calculate the median difference in reimbursement rates between private insurance and Medicaid at
baseline by combining private insurance to Medicaid payment ratios for evaluation and management services
from the GAO with our data on Medicaid payments. The GAO data that we use documents the difference
between private insurance and Medicaid payments for 32 states in 2010 (GAO, 2014).
32
As shown in column (7) of Table 4, the median increase in Medicaid payments of $35 under the federal
mandate was sufficient to close more than the disparity in outcomes between children with private insurance
and children with Medicaid; this suggests that children on Medicaid were more attractive to physicians than
children with private insurance after the rate increase. As noted in footnote 12, state Medicaid programs
often pay slightly more for children than for adults. The median payment increase of $35 will therefore
close more of—or may even go beyond—the gap in payments between Medicaid and private insurance for
children. Furthermore, we note that we do find some, albeit weak, evidence of negative spillovers to children
with private insurance as a result of the Medicaid primary care rate increase: as shown in Table 3, a $10
increase in Medicaid payments increases reports of parents having trouble finding a doctor to see their child
with private insurance by 0.13 percentage points, or 16 percent relative to the mean (significant at the 10
percent level).
20
are state and year fixed effects, respectively.33 As all state assessments take place between
January and March, P aymentsy is the expected Medicaid payment in state s in the first
quarter of year y. Standard errors are again clustered by state.
Results from estimation of Equation (3) are presented in Table 5. Panel A shows the
effects of changes in Medicaid payments on outcomes among students who qualify for free
lunch, our proxy for Medicaid eligibility in the NAEP. For low-income children in the 4th
grade, a $10 increase in Medicaid payments reduces the fraction of students who missed three
or more days in the past month for any reason by 0.49 percentage points (2 percent relative
to the mean; column (3)) and increases the fraction of students with zero absences by 0.46
percentage points (1 percent relative to the mean; column (1)). Columns (1) and (3) in Panel
B show that no such pattern is present for children who do not qualify for free lunch (and
are thus unlikely to be covered by Medicaid).34 Similar patterns are present for students in
grade 8, though the point estimates are less precise. The larger effects in grade 4 relative
to grade 8 likely reflect the fact that absences for younger children are more closely tied to
health status, whereas absences for older children are more likely for non-health-care related
reasons, such as truancy (Ehrlich et al., 2014; Neuzil et al., 2002; Wiseman and Dawson,
2015).
As school absenteeism is strongly linked to test scores, it is possible that these reductions
in absenteeism lead to improvements in student performance. However, we find no effects of
increased physician reimbursement under Medicaid on average state-level scores on national
math and reading assessments (Table A3). It is possible that more prolonged improvements
in attendance are necessary to cause improvements in student performance.
33
There was little variation in most state-level characteristics over our sample period. As we include
state fixed effects, our results are therefore quantitatively very similar if we include a range of time-varying,
state-level controls. Our results are further robust to weighting by population.
34
Recall that the NAEP covers absences for any reason whereas the NHIS asks specifically about school
absences due to illness or injury. It is therefore not surprising that we find larger effects in percentage terms
when considering school absenteeism in the NHIS than in the NAEP.
21
IV
IV.A
Robustness
2014 Medicaid Expansions
In 2014, 27 states and the District of Columbia expanded their Medicaid programs to extend
coverage to low-income, childless adults. Although the timing of these expansions and the
Medicaid primary care rate increase were similar, we are confident that our results are not
confounded by the Medicaid expansions for several reasons. First, recall that states were
required to raise their Medicaid payments to match Medicare levels for select primary care
services beginning in January 2013, a year before most of the expansions. As shown in
Figure 4, most of the effects of the rate increase are realized before 2014. Second, we find
the largest effects on access and health among children, whose eligibility was unaffected
by the expansions. Third, as shown in Section V below, we estimate similar effects of
reimbursement rates on patient outcomes using variation in payments stemming only from
the federal mandate expiring in 2015, a year after the majority of Medicaid expansions had
gone into effect.
Nevertheless, we conduct three additional analyses to verify that our results are not
confounded by the Medicaid expansions. In particular, we estimate Equation (2) including
only: (1) the years before the 2014 Medicaid expansions (2009–2013), (2) states that did
not expand their Medicaid programs in 2014, and (3) families with children. The top rows
of each subfigure in Figure 5 compare the estimates from our main sample with the results
from these subsample analyses. Looking first to the results using data from 2009–2013 only,
we see that our estimates are remarkably consistent when we exclude 2014. While some of
our estimates lose precision when we only consider states that did not expand Medicaid,
the general pattern of results is consistent with our main findings. Finally, we see that—if
anything—our results are often stronger among households with children.
Taken together, these results provide strong evidence that we are identifying changes in
access, use, and health driven by changes in program generosity, not program eligibility.
22
IV.B
Medicaid Payment Variable
As outlined in Section II.A, we create expected Medicaid payment rates by combining: (1)
state-level reimbursement rates under fee-for-service Medicaid collected directly from state
Medicaid offices, (2) state-level Medicaid fee-for-service to managed care payment ratios from
the GAO, and (3) state-level Medicaid managed care enrollment shares from CMS. While
we have Medicaid fee-for-service rates and Medicaid managed care enrollment shares for all
states, the GAO report only provides payment ratios for 20 states.35
In our main analysis, we use the median payment ratio among states in the GAO report
(5 percent more under Medicaid managed care) for states that are not in the GAO data.
To probe the robustness of our results to this imputation, we replicate our main results: (1)
imputing states that are not in the GAO report with the mean payment ratio of 14 percent
more under Medicaid managed care, (2) only using states in the GAO report, and (3) only
using variation stemming from Medicaid fee-for-service payment rates.
Results from these additional analyses are presented in Figure 5. Across all outcomes,
imputing missing states with the mean payment ratio instead of the median has very little
impact on the results. Narrowing the sample to only the 20 states in the GAO report tends
to decrease the precision of our estimates, but the magnitudes of the effects are very similar
to our primary specification. Finally, despite the fact that nearly 60 percent of Medicaid
beneficiaries are enrolled in managed care, our results are very similar if we only consider
fee-for-service reimbursement rates. This is not surprising given that most of the residual
variation in our measure of Medicaid payments when controlling for state and time fixed
effects comes from changes in fee-for-service payments within states over time.36
35
As previously noted, we have complete payment information for 44 states and the District of Columbia.
Appendix A.2 outlines the methodologies used to impute payment rates for the six states with incomplete
payment histories. Given that only a few imputations are required, our results are robust to only using
non-imputed data and to using alternative imputation strategies.
36
Recall that variation in our Medicaid payment variable comes from three sources: (1) time-series variation
in state-level fee-for-service payments, (2) cross-sectional variation in state-level Medicaid managed care to
fee-for-service payment ratios, and (3) time-series variation in the fraction of Medicaid beneficiaries enrolled
in managed care in each state. With the inclusion of state fixed effects, residual variation in payments
comes only from (1) and (3). While the fraction of Medicaid beneficiaries enrolled in managed care varies
23
Recall that our primary payment variable includes all variation in state-level Medicaid
payments for new patient office visits of mid-level complexity between 2009 and 2014. As
previously noted, states that adjusted their reimbursement rates before the federal mandate
chose to do so voluntarily, and thus the payment changes may be endogenous. The final
row in each subfigure in Figure 5 replicates our main results using variation in Medicaid
reimbursement rates stemming only from the federally mandated primary care rate increase.
To do so, we impute state-level reimbursement rates from 2009 through the third quarter of
2012 with the relevant payment rate from the fourth quarter of 2012. As the overwhelming
majority of variation in Medicaid reimbursement rates between 2009 and 2014 was driven
by the federal mandate in 2013 (see Figure 1), our results are nearly identical if we ignore
variation in payments between 2009 and 2012.
IV.C
Triple Difference Model
In our preferred empirical specification, we conduct analyses separately among Medicaid
beneficiaries and patients covered by private insurance. We look separately at these two
groups, rather than using the privately insured as a control group, as changes in relative
reimbursement rates could influence the treatment of individuals with private insurance.
If, for example, increases in Medicaid payments lead physicians to see fewer patients with
private insurance, then a triple difference strategy using patients with private insurance as
a “control” group would overestimate improvements among Medicaid beneficiaries.
However, as this strategy has been used previously when examining the impacts of
changing reimbursement rates (see, for example, Atherly and Mortensen, 2014; Callison
and Nguyen, 2017; and Shen and Zuckerman, 2005), we provide estimates from a triple
within a state over time (see Figure A3), the vast majority of our identifying variation comes from changes
in fee-for-service payments.
24
difference model for comparison. In particular, we estimate the following equation:
Outcomeicsqy = β0 + β1 P aymentsqy + β2 I {M edicaidi }
+β3 P aymentsqy ∗ I {M edicaidi }
(4)
+γXi + δZcy + λs + λqy + icsqy
where I {M edicaidi } is an indicator denoting whether respondent i is a Medicaid beneficiary
and all other variables are defined as in Equation (2). We only include patients with private
insurance and Medicaid beneficiaries; that is, we exclude those without insurance and those
covered by Medicare.
Table 6 shows that the pattern of results from the triple difference model is very similar
to that found using only Medicaid beneficiaries in a difference-in-difference framework. The
similarity is not surprising given the minimal evidence of spillovers on the privately insured
that we saw in Table 3.
V
Expiration of the Primary Care Rate Increase
Both the federally mandated Medicaid primary care rate increase and the accompanying
federal funding expired at the end of 2014.
Beginning in 2015, states could therefore
choose either to maintain the payments at higher levels—and pay for the higher payments
themselves—or revert to their original payments. As shown in Figure A4, 34 states chose not
to extend the increased payments; in these states, Medicaid reimbursement rates returned
to their December 2012 levels in January 2015.
While the reduction in Medicaid reimbursement rates resulting from the end of the federal
mandate provides a second round of changes in reimbursement, the decision not to maintain
the higher payments could be endogenous. Notably, states that experienced greater success
under the federally mandated rate increase—that is, states in which the rate increase led
to larger improvements in access, use, and health among Medicaid beneficiaries—may have
25
been more likely to extend the increased rates.37 To examine whether the primary care rate
increase had smaller impacts in states that chose not to maintain the higher payments, we
replicate our analysis using only the subset of states that did not extend the increased rates
beyond 2014. Although we divide the sample by a decision made at the end of 2014, we
only use variation in Medicaid payments stemming from the onset of the primary care rate
increase in 2013; that is, we consider the effects of the primary care rate increase switching
on in states that ultimately decided to switch it off.
As shown in the top two rows of each subfigure in Figure 6, states that chose not to
extend the higher payments saw improvements in outcomes during the federal mandate that
were similar in magnitude to those experienced by the average state.38 While there is some
evidence that states that did not extend the increased payments experienced smaller improvements in access, the point estimates for effects on use and health suggest that these
states experienced similar—and if anything larger—improvements in downstream outcomes
during the primary care rate increase. This suggests that states chose to return their payments to previous levels despite significant improvements in outcomes resulting from the
increased payments.
There are a number of reasons a state’s decision over whether to extend the payments
may have been unrelated to its experience during the federally mandated rate increase.
37
Alternatively, states that did not extend the increased payments could have had less pronounced effects because providers in those states knew that the payments would be temporary (and physician-patient
relationships tend to last for many years). However, it was difficult to forecast whether the rates would
ultimately be extended. Notably, it was not announced until late 2014 that the increased payments would
not receive federal funding in 2015; if the federal funding were to have persisted, it seems likely that all states
would have maintained the higher rates. Furthermore, the states that chose to extend the increased payments
show diversity in geography, demographics, and political affiliations (see Figure A4), so it is unlikely that
providers could have predicted whether the payments would be extended based on state-level characteristics
alone.
38
Ideally, we would estimate our primary specification separately among states that did and did not extend
the payments. However, because only sixteen states opted to extend the payments, we lose precision and
run into potential disclosure violations when we limit the sample to this subset of states. Alternatively, we
can estimate an augmented version of Equation (2) that includes a state-level indicator denoting whether a
state extended the increased payments beyond 2014 and an interaction between the payment variable and
this indicator. As shown in Table A4, the interaction is not significant for any outcome other than days
of work missed, demonstrating that states that did and did not ultimately extend the increased payments
experienced similar effects from the mandated rate increase.
26
First, federal funding for the increased payments expired with the mandate. Budgetary
considerations could therefore have led states to lower payments even if they were aware of
the implications for the health care of Medicaid beneficiaries. Second, until this point, little
comprehensive evidence has existed to demonstrate that the primary care rate increase had
significant impacts on access, use, and health among Medicaid beneficiaries. Notably, a small
survey of Medicaid officials, plan administrators, and provider organizations conducted by
the Medicaid and CHIP Payment and Access Commission in the summer of 2014 suggests
that states believed that the primary care rate increase had little impact on access to primary
care (MACPAC, 2015).
We therefore consider the effects of the primary care rate increase expiring in 2015 on
access, use, and health among Medicaid beneficiaries. To do so, we estimate a specification
analogous to Equation (2) that instead exploits variation in payments stemming only from
the federal mandate expiring in 2015. Although the variation comes from payment decreases,
the estimated coefficients again represent the effects of a $10 increase in Medicaid payments.
As shown in the bottom row of each subfigure in Figure 6, the effects of reimbursement rates
using variation stemming from the expiration of the primary care rate increase look similar,
although slightly smaller, than the effects of reimbursement rates using variation stemming
from the onset of the primary care rate increase. While we lose precision when considering
a subset of states and years, comparing the point estimates between the final two rows in
each subfigure—which hold the sample of states constant—suggests that the rate increase
switching off had effects of similar magnitudes on access and use but smaller effects on health
relative to the rate increase switching on. This could be because there is more persistence
in health than physician and patient behavior.
It is notable that we find similar effects of physician payments using variation stemming
from a payment increase and a payment decrease. One could imagine that providers would
adjust their practice in response to a payment increase in ways that would persist in the
face of subsequent payment decreases. For example, providers might pay the fixed cost to
27
enroll as a Medicaid provider or invest in learning to deal with the complexities of Medicaid
billing. Additionally, providers might establish relationships with Medicaid patients that
would be difficult to abandon if payments were to subsequently decline. Although limited
in precision, our results instead suggest that many of the improvements in access, use, and
health that Medicaid beneficiaries experienced when payments increased were lost when
payments returned to their previous levels.
VI
Discussion and Conclusion
While it is known that financial incentives matter in health care, increasing reimbursement
rates may not make physicians more willing to accept new patients for at least two reasons.
First, factors other than low payments may lead providers to restrict access for certain
patients. In the case of Medicaid, for example, payment delays, high denial rates, and
complex patient needs may make treating beneficiaries unattractive regardless of relative
payment levels (Cunningham and O’Malley, 2009; Gottlieb et al., 2018; Long, 2013; Niess
et al., 2018; Sloan et al., 1978). Second, capacity constraints limit the number of patients
that providers can see. With a fixed number of hours in the day, access to health care will
necessarily be rationed when the supply of providers has not kept pace with growing demand.
In contrast, we find that changes in physician reimbursement have meaningful effects on
access to care for patients. Exploiting large, exogenous changes in physician reimbursement
rates for primary care visits under Medicaid, we estimate that an increase in Medicaid
payments of $35—the median increase across states over the federally mandated primary
care rate increase—reduced the probability that adult Medicaid beneficiaries were told that
a physician was not accepting their insurance by 3.1 percentage points, or 38 percent of the
mean. Compared to the average Medicaid payment of $76 for a new patient office visit of
mid-level complexity before the rate increase, our estimates imply an elasticity of physician
willingness to accept adult Medicaid patients with respect to reimbursement of 0.83.
28
These improvements in access among Medicaid beneficiaries have large implications for
disparities in access to care. Before the primary care rate increase, 8.2 percent of adult Medicaid beneficiaries reported being told that a provider was not accepting their insurance, as
compared to only 2.5 percent among adults with private insurance. Our results demonstrate
that increasing Medicaid reimbursement rates by $45—enough to close the median gap in
payments between Medicaid and private insurers—would reduce disparities in access to care
by at least 70 percent. Our results are even more pronounced among children, for whom we
find that closing the gap in physician payment rates between Medicaid and private insurance
would eliminate disparities in access.
We further find that improving access leads to increased use and better health among
Medicaid beneficiaries. Increasing Medicaid payments by $35 increases the probability that
program beneficiaries had an office visit in the past two weeks by 5.0 percent and increases
the probability that they report being in excellent or very good health by 3.9 percent. The
implied elasticity of self-reported health with respect to outpatient care is consistent with
the literature using exogenous variation in health insurance coverage: when Medicaid was
extended to low-income adults using a lottery in Oregon, those who gained insurance saw
a 50 percent increase in office visits and were 25 percent more likely to report being in
excellent or very good health (Baicker et al., 2013; Finkelstein et al., 2012). We further find
that increased access to primary care reduces school absenteeism among young children: a
$35 increase in Medicaid payments leads to an average reduction of 0.79 days missed per
year due to illness or injury, or 22 percent of the mean, and reduces illness-related chronic
absenteeism by nearly 50 percent.
An outstanding question is how physicians are able to absorb new patients when reimbursement rates increase. If physicians are capacity constrained, they could increase the
number of Medicaid patients they see either by substituting away from patients with private
insurance or by decreasing their appointment length per patient. We find little evidence
that increasing Medicaid payments negatively impacts access among patients with private
29
insurance, suggesting that physicians do not respond to increased Medicaid payments by
substituting away from the privately insured on the extensive margin. Furthermore, we find
no health effects among the privately insured, whereas decreased appointment length may
result in worse provision of care.
If physicians are not capacity constrained, they could increase the number of Medicaid
patients they see by increasing their total hours worked. Although we cannot look at physician labor supply directly in our data, we can divide counties by those with and without
shortages of primary care providers as defined by the HRSA. If some physicians are capacity
constrained, we would expect increased Medicaid payments to have smaller effects in areas
where providers have little scope to take on new patients. However, as shown in Table A5,
we find no evidence of differential effects between counties that are and are not designed as
primary care shortage areas. This suggests that some providers in areas with and without
an adequate supply of providers have scope to increase the number of patients they see.
Understanding how physicians accommodate more patients when payments increase is an
important area for future work.
The difficulties that Medicaid patients face accessing care is commonly attributed to a
combination of complex patient needs, billing complications, and low reimbursement rates.
This has led policy makers, practitioners, and researchers alike to argue that increasing
reimbursement rates alone will not be enough to improve the provision of care to Medicaid
beneficiaries (Goroll, 2018). In contrast, we find that the majority of differences in access
between Medicaid beneficiaries and privately insured patients are driven by differences in
reimbursement. Not only does increasing Medicaid reimbursement rates improve access, but
these improvements in access translate into meaningful improvements in self-reported health
and school absenteeism among the program’s beneficiaries. While it is well-known that
financial incentives matter in health care, they appear to matter even more than previously
thought.
30
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36
VII
Figures
100
0
50
Medicaid Payments
150
200
Figure 1: State-Level Medicaid Payments over Time
2009
2010
2011
2012
2013
2014
2015
Average across states
Notes: The above figure depicts Medicaid payments at the state-quarter level from 2009 to 2015. As defined
in footnote 17, the payments are beneficiary-weighted averages of Medicaid fee-for-service and managed care
payments for new patient office visits of mid-level complexity; patterns are qualitatively robust to using
Medicaid payment rates for alternative billing codes. The top two lines are Alaska (1) and North Dakota
(2); the bottom two lines in 2009 are New Hampshire (50) and Minnesota (51).
37
Figure 2: Maps of State-Level Medicaid Payments
2009
2012
2013
Notes: The above maps depict Medicaid payments for each state in 2009 (first year in sample period), 2012
(year before rate increase), and 2013 (first year of rate increase). As defined in footnote 17, the payments
are beneficiary-weighted averages of Medicaid fee-for-service and managed care payments for new patient
office visits of mid-level complexity; patterns are qualitatively robust to using Medicaid payment rates for
alternative billing codes. The colors reflect quintiles of reimbursement levels in 2009; following the primary
care rate increase, all states had Medicaid payments that were in the highest 2009 quintile.
38
15
15
15
30
30
30
60
60
45
60
Accepting New Patients
45
No Trouble Finding MD
45
75
75
75
Office Visit in Past Two Weeks
90
90
90
15
15
15
45
60
75
45
60
75
30
45
60
75
Accepts Patient’s Insurance
C. Adult Subsample
30
Usual Place of Care
B. Child Subsample
30
Health: Not Poor or Fair
A. Full Sample
90
90
90
Increase in Medicaid Payments
15
15
15
45
60
75
30
30
60
45
60
Work Days
45
75
75
<14 School Days Missed (Age ≤ 10)
30
Health: Excellent or Very Good
Figure 3: Changes in Access, Use, and Health by Size of Payment Increase
90
90
90
Notes: The above figures plot the average change in each outcome between 2011–2012 and 2013–2014 across deciles of state-level Medicaid payment
increases. The dots represent changes among Medicaid patients; the solid black line is the best fit line through these points. The dashed line depicts
the best fit line for changes among patients with private insurance. Outcomes are adjusted such that higher values denote better outcomes. School
days missed reflect absenteeism due to illness or injury over the previous year; the exact survey questions and the corresponding reference windows
for all outcomes are outlined in Appendix A.1.
Change Following Payment Increase
.06
.04
.02
0
−.02
.01 .02
−.02 −.01 0
.1
.05
0
−.05
.04
.02
0
−.02
.02
0
−.02
.04
0
−.02
.02
.1
.05
0
−.05
.01 .02 .03
−.01 0
5
0
−5
−10
39
2011
2012
2013
2011
2012
2013
2010
2011
2012
2013
Accepting New Patients
2010
No Trouble Finding MD
2010
2014
2014
2014
2009
2009
2009
2011
2012
2013
2011
2012
2013
2010
2011
2012
2013
Accepts Patient’s Insurance
C. Adult Subsample
2010
Usual Place of Care
B. Child Subsample
2010
Health: Not Poor or Fair
2014
2014
2014
2009
2009
2009
2011
2012
2013
2010
2010
2012
2011
2012
Work Days
2011
2013
2013
<14 School Days Missed (Age ≤ 10)
2010
Health: Excellent or Very Good
2014
2014
2014
Notes: The above figures plot the coefficients and 95% confidence intervals on year indicators interacted with state-level changes in Medicaid payments
induced by the primary care rate increase from estimation of Equation (1). Outcomes are adjusted such that higher values denote better outcomes.
Outcomes with missing coefficients in 2009 and 2010 were only added to the survey in 2011. School days missed reflect absenteeism due to illness or
injury over the previous year; the exact survey questions and the corresponding reference windows for all outcomes are outlined in Appendix A.1.
2009
2009
2009
Office Visit in Past Two Weeks
A. Full Sample
Figure 4: Event Study: Effects of Medicaid Payments on Access, Use, and Health Among Medicaid Beneficiaries
Estimated Coefficient
.01
.005
0
−.005
.01
.005
0
−.005
.01
0
−.01
.01
.005
0
−.01 −.005
.01
.005
0
−.01 −.005
.02
.01
0
−.01
.005 .01 .015
−.005 0
.02
.01
0
−.01
2
1
0
−1
−2
40
41
−.005
0
.005
.01
−.015
−.01
−.005
0
.005
0
.005
−.02
−.01
0
.01
Not Accepting Patient’s Insurance
−.005
Not Accepting New Patients
−.01
C. Adult Subsample
−.015
Blank
−.01 −.008−.006−.004−.002 0
No Usual Place of Care
−.01
Trouble Finding MD
.01
B. Child Subsample
.005
Blank
0
Health: Poor or Fair
Office Visit in Past Two Weeks
−.005
A. Full Sample
Blank
0
Blank
.005
.01
.015
−2
−1
0
1
Work Days Missed
Blank
−.02 −.015 −.01 −.005
0
2
.005
14+ School Days Missed (Age ≤ 10)
−.005
Health: Excellent or Very Good
Blank
Notes: Each dot in the above figures depicts the estimated effect of a $10 increase in Medicaid payments for the subsample listed on the y-axis
from estimation of Equation (2). Each coefficient comes from a separate regression. The horizontal bars depict 95% confidence intervals for each
coefficient. The dashed vertical line in each subplot displays the coefficient estimate from the main sample (as reported in Table 3), which includes
years 2009–2014 and imputes missing payment ratios with the median across all states in the GAO report. School days missed reflect absenteeism
due to illness or injury over the previous year; the exact survey questions and the corresponding reference windows for all outcomes are outlined in
Appendix A.1.
Main sample
Truncated sample: 2009−2013
Non−expansion states
Families with children
Missing MMC ratios imputed with mean
Only states in GAO report
Only fee−for−service payments
Only variation from federal mandate
Blank
Main sample
Truncated sample: 2009−2013
Non−expansion states
Missing MMC ratios imputed with mean
Only states in GAO report
Only fee−for−service payments
Only variation from federal mandate
Blank
Main sample
Truncated sample: 2009−2013
Non−expansion states
Families with children
Missing MMC ratios imputed with mean
Only states in GAO report
Only fee−for−service payments
Only variation from federal mandate
Blank
Figure 5: Robustness: Effects of Medicaid Payments on Access, Use, and Health Among Medicaid Beneficiaries
42
−.015
−.01
−.005
0
.005
−.015
−.01
−.005
0
.005
Not Accepting Patient’s Insurance
.002 .004
Not Accepting New Patients
0
C. Adult Subsample
−.006 −.004 −.002
Blank
0
.005
No Usual Place of Care
0
Trouble Finding MD
−.005
B. Child Subsample
−.01
Blank
.002 .004 .006 .008
−.008 −.006 −.004 −.002
0
Health: Poor or Fair
Office Visit in Past Two Weeks
−.002
A. Full Sample
Blank
0
Blank
.005
.01
.015
−2
−1
0
1
Work Days Missed
Blank
−.02 −.015 −.01 −.005
0
2
.005
14+ School Days Missed (Age ≤ 10)
−.005
Health: Excellent or Very Good
Blank
Notes: Each dot in the above figures depicts the estimated effect of a $10 increase in Medicaid payments for the subsample listed on the y-axis from
estimation of Equation (2). Although the third row in each subfigure exploits payment decreases, the estimated coefficients still represent the effects of
a $10 increase in Medicaid payments. Each coefficient comes from a separate regression. The horizontal bars depict 95% confidence intervals for each
coefficient. The dashed vertical line in each subplot displays the coefficient estimate from the main sample (as reported in Table 3), which includes
years 2009–2014 and imputes missing payment ratios with the median across all states in the GAO report. School days missed reflect absenteeism
due to illness or injury over the previous year; the exact survey questions and the corresponding reference windows for all outcomes are outlined in
Appendix A.1.
Turning off: 2013−2015
Turning on: 2012−2014
Turning on: 2012−2014
Blank
Turning off: 2013−2015
Turning on: 2012−2014
Turning on: 2012−2014
Blank
Turning off: 2013−2015
Turning on: 2012−2014
Turning on: 2012−2014
Blank
Figure 6: Effects of Payment Increases versus Payment Decreases
43
0.055
0.075
4.929
0.021
0.031
3.433
4.428
0.044
0.064
0.196
0.174
0.566
0.016
0.022
3.730
0.008
0.021
2.887
3.221
0.022
0.032
0.174
0.061
0.729
0.062
0.082
5.010
0.022
0.034
3.516
4.745
0.046
0.070
0.197
0.176
0.562
0.017
0.025
3.711
0.008
0.022
2.933
3.302
0.023
0.034
0.175
0.062
0.726
0.049
0.069
4.798
0.020
0.026
3.292
3.895
0.042
0.053
0.194
0.169
0.573
Medicaid
(5)
0.015
0.020
3.767
0.008
0.019
2.792
3.058
0.021
0.028
0.172
0.059
0.734
Private
(6)
Rate Increase (2013–2014)
Notes: The reported statistics are weighted using the sample weights provided in the NHIS. School days missed reflect absenteeism
due to illness or injury over the previous year; the exact survey questions and the corresponding reference windows for all outcomes
are outlined in Appendix A.1.
Not accepting new patients
Not accepting patient’s insurance
Work days missed
C. Adult Subsample
Trouble finding a doctor
No usual place of care
School days missed (age ≤ 10)
School days missed (age > 10)
14+ school days missed (age ≤ 10)
14+ school days missed (age > 10)
B. Child Subsample
Office visit in past two weeks
Health: poor or fair
Health: excellent or very good
A. Full Sample
Private
(4)
Medicaid
(3)
Medicaid
(1)
Private
(2)
Baseline (2009–2012)
Entire Sample (2009–2014)
Table 1: Summary Statistics: NHIS Outcome Measures
VIII
Tables
Table 2: Summary Statistics: Individual and County Controls
All
Medicaid
Private
Demographics
Male
Average age
Black
Hispanic
U.S. citizen
0.489
0.373
0.132
0.167
0.927
0.439
0.242
0.252
0.296
0.936
0.489
0.384
0.097
0.101
0.959
Education
< High school
High school or GED
Some college
Associate’s degree
Bachelor’s degree
Master’s, professional, or Ph.D.
0.135
0.255
0.190
0.107
0.181
0.097
0.307
0.307
0.179
0.079
0.049
0.013
0.058
0.218
0.194
0.120
0.246
0.139
Family structure
Married
Live with partner
No children
1 child
2 children
3 children
4 children
5+ children
0.582
0.055
0.479
0.176
0.191
0.099
0.036
0.019
0.400
0.049
0.229
0.193
0.243
0.185
0.090
0.059
0.666
0.045
0.503
0.179
0.197
0.086
0.025
0.010
Income and wealth
Welfare
Homeowner
Income to poverty
Income to poverty
Income to poverty
Income to poverty
0.127
0.334
0.138
0.166
0.250
0.299
0.483
0.645
0.475
0.285
0.109
0.025
0.035
0.223
0.036
0.097
0.286
0.436
1,126,919
2,010
53,749
0.083
286,546
0.093
234
876
401
3,254
1,284,943
3,087
50,031
0.087
362,920
0.118
265
969
447
3,750
1,050,948
1,834
55,408
0.081
255,757
0.096
222
838
386
3,037
603,074
96,128
338,174
Individual-level controls
line:
line:
line:
line:
<1
1-1.99
2-3.99
4+
County-level controls
Population
Population density
Median income
Unemployment rate
Medicaid eligibles
Expansion state (2014)
Number of pediatricians
Number of primary care doctors
Number of nurse practitioners
Number of hospital beds
Observations
Notes: All statistics are in percentages unless otherwise specified; some categories do not sum to one
due to missing responses. Individual-level statistics are weighted using the sample weights provided
in the NHIS. County-level controls come from the HRSA’s Area Resource Files.
44
Table 3: Effects of Medicaid Payments on Access, Use, and Health
A. Full Sample
Medicaid
Private
(1)
Office Visit
(2 Weeks)
(2)
Health
≤ Fair
(3)
Health ≥
Very Good
(4)
Office Visit
(2 Weeks)
(5)
Health
≤ Fair
(6)
Health ≥
Very Good
0.0028*
(0.0017)
-0.0031*
(0.0017)
0.0062***
(0.0023)
-0.0011
(0.0010)
0.0002
(0.0006)
0.0020
(0.0017)
Observations
R2
96,017
0.071
96,067
0.296
96,067
0.232
337,506
0.036
337,903
0.079
337,903
0.138
Baseline mean
0.197
0.176
0.562
0.175
0.062
0.726
Medicaid payments
($10s)
B. Child Subsample
Medicaid
Private
(1)
Trouble
Finding
MD
(2)
No Usual
Place of
Care
(3)
(4)
14+ School 14+ School
Absences
Absences
(Age≤10) (Age≥11)
(5)
Trouble
Finding
MD
(6)
No Usual
Place of
Care
-0.0054***
(0.0015)
-0.0036*
(0.0018)
-0.0065**
(0.0030)
0.0019
(0.0038)
0.0013*
(0.0007)
0.0000
(0.0010)
0.0012
(0.0028)
0.0003
(0.0026)
Observations
R2
16,752
0.016
21,221
0.022
6,665
0.036
6,766
0.047
26,277
0.007
33,994
0.029
10,079
0.025
14,337
0.021
Baseline mean
0.022
0.034
0.046
0.070
0.008
0.022
0.023
0.034
Medicaid payments
($10s)
C. Adult Subsample
Medicaid
(7)
(8)
14+ School 14+ School
Absences
Absences
(Age≤10) (Age≥11)
Private
(1)
Not
Accepting
New Patients
(2)
Not
Accepting
Patient’s
Insurance
(3)
Work Days
Missed
(4)
Not
Accepting
New Patients
(5)
Not
Accepting
Patient’s
Insurance
(6)
Work Days
Missed
-0.0082***
(0.0015)
-0.0089***
(0.0030)
-0.1638
(0.3737)
0.0005
(0.0006)
-0.0007
(0.0007)
0.1047
(0.1147)
Observations
R2
14,806
0.037
14,805
0.039
6,298
0.076
79,812
0.006
79,802
0.009
76,971
0.009
Baseline mean
0.062
0.082
5.010
0.017
0.025
3.711
Medicaid payments
($10s)
Notes: Observations are at the individual level; standard errors are clustered by state. All regressions include state and quarter-year fixed effects and all individual and county-level controls listed in Table 2 (with
age in five-year bins). Regressions are weighted using the sample weights provided in the NHIS. School days
missed reflect absenteeism due to illness or injury over the previous year; the exact survey questions and the
corresponding reference windows for all outcomes are outlined in Appendix A.1. Fewer children report days
of missed school in the past year relative to other child outcomes because a child must be at least five years
old to be asked this question. Similarly, only adults with employment histories are asked how many days of
work they missed in the past year.
45
46
0.062
0.082
0.022
0.034
0.046
0.197
0.176
0.562
0.017
0.025
0.008
0.022
0.023
0.175
0.062
0.726
(2)
(1)
-0.045
-0.057
-0.014
-0.012
-0.023
-0.022
-0.114
0.164
(3)
Disparity:
(2) - (1)
-0.0082
-0.0089
-0.0054
-0.0036
-0.0065
0.0028
-0.0031
0.0062
(4)
-1.01
-0.83
-1.87
-0.81
-1.08
0.11
-0.13
0.08
(5)
Effect of Implied
$10 Increase Elasticity
0.18
0.16
0.39
0.30
0.28
NA
0.03
0.04
(6)
$10
0.64
0.55
1.35
1.05
0.99
NA
0.10
0.13
(7)
$35
0.82
0.70
1.74
1.35
1.27
NA
0.12
0.17
(8)
$45
Share of Disparity Closed by
$54.88
$64.04
$25.93
$33.33
$35.38
NA
$367.74
$264.52
Increase
to Erase
Disparity
(9)
Notes: The above table displays the effects of changes in Medicaid payments on disparities in outcomes between Medicaid beneficiaries and privately
insured patients. Columns (1) and (2) reproduce the averages from Table 1; column (3) is the difference in these means. Column (4) reproduces our
main estimates from Table 3. Column (5) is the implied elasticity of each outcome with respect to physician payments; this is computed using a median baseline payment of $76 under Medicaid for an office visit of mid-level complexity ((5) = [(4)/(1)]/(10/76)]). Columns (6)–(8) show how much of
the disparity from column (3) would be reduced by increasing Medicaid payments by $10, $35 (median payment increase from federal mandate), and
$45 (median payment difference between Medicaid and private insurance), respectively. Column (9) shows the implied increase in Medicaid payments
that would be required to eliminate the disparity in each outcome between Medicaid patients and patients with private insurance ((9) = [(3)/(4)]∗10).
Office visits are excluded from columns (6)–(9) because Medicaid beneficiaries have more office visits than patients with private insurance.
Not accepting new patients
Not acc. patient’s insurance
C. Adult Subsample
Trouble finding MD
No usual place of care
14+ school days missed
B. Child Subsample
Office visit (2 weeks)
Health: poor or fair
Health: excellent or very good
A. Full Sample
Private
Medicaid
Baseline Means
Table 4: Effects of Medicaid Payments on Disparities between Publicly and Privately Insured
Table 5: Effects of Medicaid Payments on School Absences: NAEP
A. Free Lunch Eligible
0 days missed (%)
3+ days missed (%)
(1)
Grade 4
(2)
Grade 8
(3)
Grade 4
(4)
Grade 8
0.0046***
(0.0012)
0.0041
(0.0025)
-0.0049***
(0.0011)
-0.0032*
(0.0016)
Observations
R2
150
0.906
150
0.889
150
0.859
150
0.868
Baseline mean
0.444
0.381
0.243
0.267
Medicaid payments ($10s)
B. Free Lunch Ineligible
0 days missed (%)
3+ days missed (%)
(1)
Grade 4
(2)
Grade 8
(3)
Grade 4
(4)
Grade 8
0.0037
(0.0020)
0.0024
(0.0020)
-0.0014
(0.0014)
-0.0018
(0.0013)
Observations
R2
150
0.808
150
0.871
150
0.778
150
0.838
Baseline mean
0.536
0.468
0.162
0.168
Medicaid payments ($10s)
Notes: Data are from the 2009, 2011, and 2013 National Assessment of Educational Progress and represent
the percentage of students missing a given amount of school (for any reason) in the month preceding their
national math and reading assessments. Observations are at the state-year level; standard errors clustered by
state. All regressions include state and year fixed effects. Results are robust to weighting by state population.
47
Table 6: Effects of Medicaid Payments: Triple Difference Model
A. Full Sample
Medicaid payments ($10s)
1{Medicaid}
Medicaid payments * 1{Medicaid}
Observations
R2
Baseline mean
B. Child Subsample
(1)
Office Visit
(2 Weeks)
(2)
Health:
Poor or Fair
(3)
Health: Excellent
or Very Good
-0.0003
(0.0009)
0.0690***
(0.0107)
-0.0007
(0.0008)
0.0001
(0.0005)
0.1137***
(0.0073)
-0.0024***
(0.0007)
0.0022
(0.0015)
-0.1359***
(0.0136)
0.0035**
(0.0015)
430,800
0.040
431,244
0.128
431,244
0.163
0.179
0.082
0.698
(1)
Trouble
Finding MD
(2)
No Usual
Place of Care
(3)
14+ School Days
Missed (Age≤10)
(4)
14+ School Days
Missed (Age≥11)
-0.0006
(0.0008)
0.0267***
(0.0054)
-0.0018***
(0.0005)
-0.0011
(0.0008)
0.0043
(0.0065)
-0.0009
(0.0007)
-0.0008
(0.0023)
0.0376***
(0.0109)
-0.0023**
(0.0012)
0.0018
(0.0022)
0.0541***
(0.0160)
-0.0032*
(0.0016)
Observations
R2
42,541
0.010
54,602
0.021
16,544
0.023
21,473
0.026
Baseline mean
0.013
0.026
0.029
0.042
Medicaid payments ($10s)
1{Medicaid}
Medicaid payments * 1{Medicaid}
C. Adult Subsample
(1)
Not Accepting
New Patients
(2)
Not Accepting
Patient’s Insurance
(3)
Work
Days Missed
0.0002
(0.0007)
0.0691***
(0.0123)
-0.0042***
(0.0011)
-0.0010
(0.0008)
0.0887***
(0.0118)
-0.0046***
(0.0011)
0.0964
(0.1094)
1.9949*
(1.1576)
-0.1241
(0.1108)
Observations
R2
94,162
0.016
94,150
0.020
83,054
0.009
Baseline mean
0.022
0.031
3.785
Medicaid payments ($10s)
1{Medicaid}
Medicaid payments * 1{Medicaid}
Notes: Observations are at the individual level; standard errors are clustered by state. All regressions include state and quarter-year fixed effects and all individual and county-level controls listed in Table 2 (with
age in five-year bins). Regressions are weighted using the sample weights provided in the NHIS. School days
missed reflect absenteeism due to illness or injury over the previous year; the exact survey questions and the
corresponding reference windows for all outcomes are outlined in Appendix A.1. Fewer children report days
of missed school in the past year relative to other child outcomes because a child must be at least five years
old to be asked this question. Similarly, only adults with employment histories are asked how many days of
work they missed in the past year.
48
For Online Publication
A
Data Appendix
A.1
Outcomes
Table A1: Overview of Data Sources used for Outcome Measures
Outcome
Data Source
Years Available
Look Back
Period
Payment
Variable
Sample
Office visit
NHIS
2009-2015
Past 2 weeks
Avg. rate in
interview
quarter
Full sample
Health: excellent or very good
NHIS
2009-2015
Not specified
Avg. rate in
interview
quarter
Full sample
Health: poor or fair
NHIS
2009-2015
Not specified
Avg. rate in
interview
quarter
Full sample
Trouble finding MD
NHIS
2011-2015
Past 12
months
Avg. rate over
past 12 months
Child
subsample
No usual place of care
NHIS
2009-2015
Not specified
Avg. rate over
past 12 months
Child
subsample
School days missed
NHIS
2009-2015
Past 12
months
Avg. rate over
past 12 months
Child
subsample
Not accepting new patients
NHIS
2011-2015
Past 12
months
Avg. rate over
past 12 months
Adult
subsample
Not acc. patient’s insurance
NHIS
2011-2015
Past 12
months
Avg. rate over
past 12 months
Adult
subsample
Work days missed
NHIS
2009-2015
Past 12
months
Avg. rate over
past 12 months
Adult
subsample
School absences
NAEP
2009, 2011,
2013
30 days before
test
Avg. rate in
first quarter
4th and 8th
grade math
and reading
Test scores
NAEP
2009, 2011,
2013
Testing occurs
in Q1
Avg. rate in
first quarter
4th and 8th
grade math
and reading
49
National Health Interview Survey questions
• Full Sample (from Family File)
– During the last two weeks, did {person} see a doctor or other health care professional at a doctor’s office, a clinic, an emergency room, or some other place? (Do
not include times during an overnight hospital stay.)
– Would you say {person’s} health in general is excellent, very good, good, fair, or
poor?
• Child Subsample
– During the past 12 months, did you have any trouble finding a general doctor or
provider who would see {sample child}?
– Is there a place that {sample child} usually goes when {he/she} is sick or you
need advice about {his/her} health?
– During the past 12 months, that is, since {12-month ref. date}, about how many
days did {sample child} miss school because of illness or injury?
• Adult Subsample
– During the past 12 months, were you told by a doctor’s office or clinic that they
would not accept {sample adult} as a new patient?
– During the past 12 months, were you told by a doctor’s office or clinic that they
would not accept {sample adult}’s health care coverage?
– During the past 12 months, about how many days did {sample adult} miss work?
A.2
Medicaid Payments
We collected data on fee-for-service reimbursement rates directly from state Medicaid offices.
The data have two components: (1) standard fee-for-service rates applicable in 2009–2015 for
50
all providers and (2) augmented fee-for-service rates applicable in 2013–2014 (and 2015, depending on the state) for qualifying physicians in family medicine, general internal medicine,
and pediatric medicine. In constructing our state-quarter panel of payments, we use standard
rates in 2009–2012, augmented rates in 2013–2014, and either the standard or augmented
rates in 2015 depending on whether a given state extended the primary care rate increase.
44 states and the District of Columbia provided us with complete, quarterly rate information
used to construct this panel. For the remaining six states, we use the following procedures
to impute missing rate information:
• California: We only have the standard rates for 2009 and 2015. As the standard rates
were the same in 2009 and 2015, we assume that they did not change over this period
and pull forward the standard rates to 2012.
• Hawaii: We only have the standard rates for 2009, 2012, and 2015. As the standard
rates were the same in 2009 and 2012, we assume that they did not change over this
period and pull forward the standard rates to 2011.
• New Mexico: We are missing standard rates for January–November 2009. The rates
changed over this period; we impute the missing months with the rate in the nearest
month with non-missing rate information.
• Utah: We are missing standard rates for January–May 2009 and July–December 12.
We impute the missing months with the rate in the nearest month with non-missing
rate information.
• South Dakota: Standard rates are not archived, so we only have standard rates for 2015.
We impute standard rates from 2009–2012 such that the change in reimbursement rates
between each quarter and 2015 reflects the average change in reimbursement rates for
neighboring states (MT, ND, MN, IA, NE, WY) over the same period.
• Tennessee: We have no micro-data on reimbursement rates, as the state only uses
51
Medicaid managed care. However, we were told by the state that average reimbursements increased by 44 percent as a result of the primary care rate increase. We impute
reimbursement rates for Tennessee in 2013 and 2014 by averaging the 2013 and 2014
augmented rates for neighboring states (MO, KY, VA, NC, GA, AL, MS, AR). We
then apply the 44 percent increase from 2012 to 2013 to impute the rates for 2012.
For 2009–2012 and 2015, we calculate the average change in physician payments across
neighboring states in the relevant period and apply this rate change to Tennessee over
the same window.
A.3
Medicaid Managed Care
The primary care rate increase applied to both Medicaid fee-for-service and Medicaid managed care programs. While states could just increase fee-for-service reimbursement rates
for the covered services to comply with the mandated higher rates, determining how to increase reimbursement rates for physicians treating patients enrolled in Medicaid managed
care was more complicated. To ensure that Medicaid managed care programs complied with
the rate increase, each state’s Medicaid program was required to submit proposals to CMS
that outlined methodologies for:
1. Identifying the proportion of the capitation payments made by the state to its contracted MCOs in 2009 that was spent on each of the applicable primary care services,
as well as the per-unit cost of each of these services. These baseline costs were used to
calculate the refunds that each state’s Medicaid program was eligible to receive from
the federal government in 2013 and 2014.
2. Developing a “model” that incorporated the increased fees for primary care services
into the state’s 2013 and 2014 capitation payments to MCOs. It was recommended
that states implemented one of three types of models:
• Model 1: “Full-risk prospective capitation” in which states incorporated increased
52
fees directly into their capitation payments to MCOs for 2013 and 2014
• Model 2: “Prospective capitation with risk-sharing that incorporates retrospective
reconciliation” in which increased fees were built into states’ capitation payments
for 2013 and 2014 (similar to Model 1), but capitation payments were to be
adjusted at the end of an agreed-upon time period to reflect actual utilization
and costs (states and MCOs engage in “retrospective reconciliation”)
• Model 3: “Non-risk reconciled payments for enhanced rates” in which states’ initial
capitation payments to MCOs for 2013 and 2014 did not incorporate increased
fees. Instead, MCOs submitted encounter data to the state at the end of the
quarter, year, etc., and the state sent an additional payment to the MCOs to
cover the costs of the increased fees.
CMS had to sign off on each state’s methodology for determining the 2009 rates and on
its plan for implementing the rate increase for eligible physicians treating managed care
enrollees. According to CMS, at least 21 states opted to receive the increased funding in
lump-sum payments based on encounter data (Model 3). The rest of the states incorporated
the increased fees directly into their capitation payments (Models 1 and 2); most of these
states did not engage in any retrospective reconciliation based on actual utilization data.
53
B
Supplementary Tables and Figures
0
Percent Increase from 2009 to 2013
75
150
225
Figure A1: Increases in Medicaid Payments from 2009 to 2013 Across New Patient Codes
0
75
150
Percent Increase from 2009 to 2013 (CPT 99203)
CPT 99201
CPT 99202
CPT 99204
225
CPT 99205
Notes: The above figure displays state-level changes in Medicaid payments for CPT codes 99201, 99202,
99204, and 99205 from 2009 to 2013 versus state-level changes in Medicaid payments for CPT code 99203
(the CPT code used in the majority of our analyses) over the same period. The black line is the 45-degree
line. The figure excludes CPT codes 99204 and 99204 for New Jersey; payments for these codes increased
by 308 percent and 404 percent, respectively, while payment for CPT code 99203 increased by 169 percent.
54
2009
CPT 99201
2011
2013
Wisconsin
West Virginia
2013
South Dakota
South Carolina
2011
North Carolina
Missouri
Mississippi
New York
Kentucky
Kansas
2009
Florida
District of Columbia
2015
Alaska
Alabama
2015
2009
2011
2015
2009
CPT 99202
2013
Wyoming
Tennessee
North Dakota
Montana
Louisiana
Georgia
Arizona
2011
2013
Texas
Ohio
Nebraska
Maine
Hawaii
Arkansas
2015
2011
2013
CPT 99203
2009
Utah
Oklahoma
Nevada
Maryland
Idaho
California
2015
2009
2011
2015
2009
2013
Virginia
Pennsylvania
New Jersey
Michigan
Indiana
Connecticut
2011
CPT 99204
2013
Vermont
Oregon
New Hampshire
Massachusetts
Illinois
Colorado
Figure A2: Medicaid Payments Across New Patient Codes by State
2015
2009
2013
2015
CPT 99205
2011
Washington
Rhode Island
New Mexico
Minnesota
Iowa
Delaware
Notes: The above figure displays Medicaid payments by states for CPT codes 99201, 99202, 99203, 99204, and 99205 from 2009 to 2015. The payments
for each CPT code in each state are normalized to their level in the first quarter of 2009.
Percent Increase from First Quarter of 2009
0 100 200 300 400
0 100 200 300 400
0 100 200 300 400
0 100 200 300 400
0 100 200 300 400
0 100 200 300 400
0 100 200 300 400
55
.8
.6
.4
.2
0
Medicaid Beneficiaries Enrolled in MMC (%)
1
Figure A3: State-Level Medicaid Managed Care Penetration over Time
2009
2010
2011
2012
2013
2014
2015
Average across states
Notes: The above figure depicts the fraction of Medicaid beneficiaries enrolled in comprehensive risk-based
managed care in each state from 2009 to 2015. The black line depicts the national average. Data for 2009
through 2014 come from CMS; data for 2015 comes from the Henry J. Kaiser Family Foundation. In 2014,
eleven states had less than one percent of their Medicaid beneficiaries enrolled in managed care plans: Alabama, Alaska, Arkansas, Connecticut, Idaho, Maine, Montana, North Carolina, Oklahoma, South Dakota,
and Wyoming. In the same year, nine states had more than 85 percent of their Medicaid beneficiaries enrolled
in such plans: Arizona, Delaware, Hawaii, Kansas, Kentucky, New Hampshire, New Jersey, Tennessee, and
Washington.
56
Figure A4: States that Extended the Medicaid Primary Care Rate Increase past 2014
Extended payment increase
Did not extend payment increase
Notes: The above map depicts whether states chose to maintain the primary care rate increase after the
federal mandate expired in 2014: shaded states extended higher Medicaid payment rates into 2015.
57
Figure A5: Distribution of School Absences by Free Lunch Eligibility: NAEP
60
Grade 4
53.6
Percent
40
44.4
30.2
20
31.3
14.5
10.4
4.2
3.9
1.6
0
5.9
0
1−2
3−4
5−10
11+
Days missed
Free lunch eligible
Ineligible
40
50
Grade 8
46.8
38.1
36.4
20
Percent
30
35.3
16.7
10
11.5
6.9
3.1
1.3
0
3.9
0
1−2
3−4
5−10
11+
Days missed
Free lunch eligible
Ineligible
Notes: The above figures display the average percentage of students who missed 0, 1–2, 3–4, 5–10, or 11+
days in the month preceding their national math and reading assessments in 2009, 2011, and 2013. Data
come from the National Assessment of Educational Progress.
58
Figure A6: Average Test Scores by School Absences: NAEP
300
Math
288.7
284.4
273.9
270.1
254.9
244.5
240.4
234.9
233.5
0
Average test scores
100
200
218.3
0
1−2
3−4
5−10
11+
Days missed
Grade 4
Grade 8
300
Reading
268.6
266.1
258.4
254.1
235.7
224.2
216.1
Average test scores
100
200
220.9
213.7
0
192.5
0
1−2
3−4
5−10
11+
Days missed
Grade 4
Grade 8
Notes: The above figures display the average test scores of students who missed 0, 1–2, 3–4, 5–10, or 11+
days in the month preceding their national assessment in 2009, 2011, and 2013. Data come from the National
Assessment of Educational Progress.
59
Figure A7: States that Expanded Medicaid
Medicaid Expansion:
None
Before 2014
2014
Notes: The above map depicts whether states expanded their Medicaid programs: the dark-shaded states
participated in the ACA Medicaid expansion in 2014, the light-shaded states expanded their Medicaid
program prior to the ACA, and the remaining states did not participate in any type of Medicaid expansion
by 2014. Data are from Leung and Mas (2018).
Table A2: Effects of Medicaid Payments on School Days Missed (Continuous Measure)
Child Subsample
Medicaid
Private
(1)
School Days
Missed
(Age≤10)
(2)
School Days
Missed
(Age≥11)
(3)
School Days
Missed
(Age≤10)
(4)
School Days
Missed
(Age≥11)
-0.2256**
(0.1022)
-0.0702
(0.2251)
0.0014
(0.0463)
0.0649
(0.1018)
Observations
R2
6,687
0.043
6,794
0.062
10,168
0.033
15,046
0.029
Baseline mean
3.516
4.745
2.933
3.302
Medicaid payments ($10s)
Notes: Observations are at the individual level; standard errors are clustered by state. All regressions include state and quarter-year fixed effects and all individual and county-level controls listed in Table 2 (with
age in five-year bins). Regressions are weighted using the sample weights provided in the NHIS. School days
missed reflect absenteeism due to illness or injury over the previous year.
60
Table A3: Effects of Medicaid Payments on Test Scores: NAEP
A. Free Lunch Eligible
Math
Reading
(1)
Grade 4
(2)
Grade 8
(3)
Grade 4
(4)
Grade 8
-0.109
(0.165)
0.353
(0.236)
0.141
(0.220)
0.0341
(0.207)
Observations
R2
150
0.925
150
0.937
150
0.903
150
0.893
Baseline mean
228.5
268.1
206.6
251.4
Medicaid payments ($10s)
B. Free Lunch Ineligible
Math
Reading
(1)
Grade 4
(2)
Grade 8
(3)
Grade 4
(4)
Grade 8
0.0114
(0.225)
0.448
(0.235)
0.116
(0.192)
0.143
(0.190)
Observations
R2
150
0.869
150
0.921
150
0.893
150
0.915
Baseline mean
250.6
294.1
232.8
273.7
Medicaid payments ($10s)
Notes: Data is from the 2009, 2011, and 2013 National Assessment of Educational Progress. Observations
are at the state-year level; standard errors clustered by state. All regressions include state and year fixed
effects. Results are robust to weighting by state population.
61
Table A4: Effects of Medicaid Payments: States that Extended the Rate Increase
A. Full Sample
Medicaid
Private
(1)
Office Visit
(2 Weeks)
(2)
Health
≤ Fair
(3)
Health ≥
Very Good
(4)
Office Visit
(2 Weeks)
(5)
Health
≤ Fair
(6)
Health ≥
Very Good
0.0026
(0.0017)
-0.0991**
(0.0374)
-0.0018
(0.0020)
-0.0028*
(0.0017)
-0.0254
(0.0395)
0.0027
(0.0023)
0.0063***
(0.0022)
-0.2376***
(0.0538)
0.0008
(0.0033)
-0.0009
(0.0010)
-0.0487***
(0.0138)
0.0019**
(0.0008)
0.0001
(0.0006)
0.0118
(0.0083)
-0.0003
(0.0005)
0.0021
(0.0017)
-0.0587**
(0.0236)
0.0010
(0.0014)
Observations
R2
96,017
0.071
96,067
00.296
96,067
0.232
337,506
0.036
337,903
0.079
337,903
0.138
Baseline mean
0.197
0.176
0.562
0.175
0.062
0.726
MC payments ($10s)
1{Extended}
Payments * 1{Extended}
B. Child Subsample
MC payments ($10s)
1{Extended}
Payments * 1{Extended}
Medicaid
Private
(1)
Trouble
Finding
MD
(2)
No Usual
Place of
Care
(3)
(4)
(5)
14+ School 14+ School Trouble
Absences Absences Finding
(Age≤10) (Age≥11)
MD
(6)
No Usual
Place of
Care
(7)
(8)
14+ School 14+ School
Absences Absences
(Age≤10) (Age≥11)
-0.0054***
(0.0015)
0.0324
(0.0209)
0.0002
(0.0011)
-0.0034*
(0.0019)
-0.0199
(0.0327)
0.0016
(0.0017)
-0.0074**
(0.0032)
0.1609
(0.0974)
-0.0074
(0.0053)
0.0010
(0.0040)
-0.0234
(0.0802)
-0.0082*
(0.0045)
0.0000
(0.0010)
0.0666***
(0.0236)
-0.0001
(0.0012)
0.0010
(0.0028)
0.0581
(0.0371)
-0.0017
(0.0019)
0.0000
(0.0027)
-0.0393
(0.0769)
-0.0024
(0.0041)
0.0012*
(0.0007)
-0.0259***
(0.0080)
-0.0002
(0.0004)
Observations
R2
16,752
0.016
21,221
0.022
6,665
0.036
6,766
0.047
26,277
0.007
33,994
0.029
10,079
0.025
14,337
0.021
Baseline mean
0.022
0.034
0.046
0.070
0.008
0.022
0.023
0.034
C. Adult Subsample
Medicaid
Private
(1)
Not
Accepting
New Patients
(2)
Not
Accepting
Patient’s
Insurance
(3)
Work Days
Missed
(4)
Not
Accepting
New Patients
(5)
Not
Accepting
Patient’s
Insurance
(6)
Work Days
Missed
-0.0084***
(0.0014)
0.1212***
(0.0410)
-0.0022
(0.0024)
-0.0088***
(0.0030)
0.0322
(0.0699)
0.0008
(0.0037)
-0.2808
(0.3704)
16.5395**
(6.0360)
-0.7897**
(0.2961)
0.0005
(0.0006)
0.001
(0.0133)
-0.0003
(0.0008)
-0.0006
(0.0007)
-0.0093
(0.0150)
0.0007
(0.0008)
0.1255
(0.1158)
-4.4484**
(1.8844)
0.1825*
(0.0972)
Observations
R2
14,806
0.037
14,805
0.039
6,298
0.076
79,812
0.006
79,802
0.009
76,971
0.009
Baseline mean
0.062
0.082
5.010
0.017
0.025
3.711
MC payments ($10s)
1{Extended}
Payments * 1{Extended}
Notes: Observations are at the individual level; standard errors are clustered by state. All regressions include state and quarter-year fixed effects and all individual and county-level controls listed in Table 2 (with
age in five-year bins). Regressions are weighted using the sample weights provided in the NHIS. School days
missed reflect absenteeism due to illness or injury over the previous year; the exact survey questions and the
corresponding reference windows for all outcomes are outlined in Appendix A.1. Fewer children report days
of missed school in the past year relative to other child outcomes because a child must be at least five years
old to be asked this question. Similarly, only adults with employment histories are asked how many days of
work they missed in the past year.
62
Table A5: Effects of Medicaid Payments: Primary Care Shortage Areas
A. Full Sample
Medicaid
Private
(1)
Office Visit
(2 Weeks)
(2)
Health
≤ Fair
(3)
Health ≥
Very Good
(4)
Office Visit
(2 Weeks)
(5)
Health
≤ Fair
(6)
Health ≥
Very Good
0.0025
(0.0018)
-0.0056
(0.0127)
0.0008
(0.0017)
-0.0030*
(0.0017)
0.0148
(0.0113)
-0.0007
(0.0014)
0.0060**
(0.0027)
-0.0242
(0.0183)
0.0011
(0.0022)
-0.0008
(0.0010)
0.0028
(0.0046)
-0.0005
(0.0006)
0.0006
(0.0006)
0.0087***
(0.0029)
-0.0009**
(0.0004)
0.0016
(0.0018)
-0.0110*
(0.0063)
0.0008
(0.0008)
Observations
R2
96,017
0.071
96,067
00.296
96,067
0.232
337,506
0.036
337,903
0.079
337,903
0.138
Baseline mean
0.197
0.176
0.562
0.175
0.062
0.726
MC payments ($10)
1{Shortage}
Payments * 1{Shortage}
B. Child Subsample
Medicaid
(1)
Trouble
Finding
MD
MC payments ($10)
1{Shortage}
Payments * 1{Shortage}
-0.0056***
(0.0017)
-0.0081
(0.0141)
0.0006
(0.0015)
Private
(2)
No Usual
Place of
Care
(3)
(4)
(5)
14+ School 14+ School Trouble
Absences Absences Finding
(Age≤10) (Age≥11)
MD
(6)
No Usual
Place of
Care
(7)
(8)
14+ School 14+ School
Absences Absences
(Age≤10) (Age≥11)
-0.0036*
(0.0020)
-0.0001
(0.0115)
0.0000
(0.0012)
-0.0052
(0.0034)
0.0065
(0.0266 )
-0.0024
(0.0028 )
0.0005
(0.0047)
-0.0187
(0.0309)
0.0026
(0.0037)
0.0012
(0.0008)
0.0010
(0.0052)
0.0002
(0.0006)
0.0002
(0.0012)
0.0028
(0.0059)
-0.0003
(0.0007)
0.0015
(0.0027)
0.0095
(0.0118)
-0.0005
(0.0014)
0.0000
(0.0028)
-0.0086
(0.0110)
0.0007
(0.0013)
Observations
R2
16,752
0.016
21,221
0.022
6,665
0.036
6,766
0.047
26,277
0.007
33,994
0.029
10,079
0.026
14,337
0.021
Baseline mean
0.022
0.034
0.046
0.070
0.008
0.022
0.023
0.034
C. Adult Subsample
Medicaid
Private
(1)
Not
Accepting
New Patients
(2)
Not
Accepting
Patient’s
Insurance
(3)
Work Days
Missed
(4)
Not
Accepting
New Patients
(5)
Not
Accepting
Patient’s
Insurance
(6)
Work Days
Missed
-0.0084***
(0.0017)
-0.0212
(0.0128)
0.0008
(0.0012)
-0.0077**
(0.0030)
-0.0002
(0.0168)
-0.0020
(0.0016)
-0.2740
(0.3170)
-2.3331
(2.8151)
0.2592
(0.3144)
0.0004
(0.0007)
-0.0026
(0.0051)
0.0002
(0.0006)
-0.0005
(0.0008)
0.0048
(0.0075)
-0.0003
(0.0008)
0.1710
(0.1273)
0.7920
(0.7211)
-0.1233
(0.0841)
Observations
R2
14,806
0.038
14,805
0.040
6,298
0.076
79,812
0.006
79,802
0.009
76,971
0.010
Baseline mean
0.062
0.082
5.010
0.017
0.025
3.711
MC payments ($10)
1{Shortage}
Payments * 1{Shortage}
Notes: Observations are at the individual level; standard errors are clustered by state. All regressions include state and quarter-year fixed effects and all individual and county-level controls listed in Table 2 (with
age in five-year bins). Regressions are weighted using the sample weights provided in the NHIS. School days
missed reflect absenteeism due to illness or injury over the previous year; the exact survey questions and the
corresponding reference windows for all outcomes are outlined in Appendix A.1. Fewer children report days
of missed school in the past year relative to other child outcomes because a child must be at least five years
old to be asked this question. Similarly, only adults with employment histories are asked how many days of
work they missed in the past year.
63
@FedFRASER