# The Hidden Subsidy of the Affordable Care Act††thanks: We thank Tracy
Miller, Brian Blase, and Drew Gonshorowski for helpful comments.

Liam Sigaud
Mercatus Center at George Mason University. Email<EMAIL_ADDRESS>Markus Bjoerkheim
Mercatus Center at George Mason University. Email:
<EMAIL_ADDRESS>Vitor Melo
Clemson University and Knee Regulatory Research Center. Email:
<EMAIL_ADDRESS>
(September 1, 2024)

###### Abstract

Under the ACA, the federal government paid a substantially larger share of
medical costs of newly eligible Medicaid enrollees than previously eligible
ones. States could save up to 100% of their per-enrollee costs by
reclassifying original enrollees into the newly eligible group. We examine
whether this fiscal incentive changed states’ enrollment practices. We find
that Medicaid expansion caused large declines in the number of beneficiaries
enrolled in the original Medicaid population, suggesting widespread
reclassifications. In 2019 alone, this phenomenon affected 4.4 million
Medicaid enrollees at a federal cost of $8.3 billion. Our results imply that
reclassifications inflated the federal cost of Medicaid expansion by 18.2%.

_JEL Classification: H71, H77, I13, I18_

_Keywords: Political Incentives, State Spending, Medicaid, Affordable Care
Act_

##  1 Introduction

Medicaid is the single largest source of health insurance in the United
States. It provides coverage to an estimated 85 million people and costs the
federal government and states approximately $750 billion per year (Mitchell et
al., 2023). The Affordable Care Act (ACA) of 2010 made substantial changes to
the program, including permitting states to expand Medicaid eligibility to all
non-elderly adults with incomes up to 138 percent of the federal poverty level
(hereafter, the “new adult group"). States that opted to expand Medicaid
received enhanced federal matching funds for the new adult group. From 2014 to
2019, states covered a median of about 40% of the medical costs for the
original Medicaid population,111Throughout this paper, we use the term
“original Medicaid population” or “original Medicaid enrollees” to refer to
those, such as poor children and people with disabilities, who were eligible
for Medicaid under pre-ACA eligibility rules. but at most 7% of the medical
costs of enrollees in the new adult group. These provisions implied that the
median state could save at least 82.5% of their per-enrollee costs by
reclassifying members of the original Medicaid population into the new adult
group. We define “reclassification" as enrollment in the new adult group when,
in the absence of the ACA, the individual would been a part of the original
Medicaid population.222This definition accommodates several forms of
reclassification, ranging from deliberate, improper actions by state Medicaid
administrators to legitimate shifts in enrollment stemming from natural life
cycle events. These possibilities are discussed in more detail in Sections 2.3
and 5. Given that Medicaid expenditures represent about one-fifth of states’
general fund expenditures on average (MACPAC, 2017), reclassifications could
represent a substantial hidden subsidy from the federal government to states.

We examine the existence and extent of the ACA’s hidden subsidy by
investigating the effect of expansion on enrollment in the original Medicaid
population and estimating the fiscal impact of these enrollment changes. To
quantify possible reclassifications, we examine the change in the original
Medicaid population in expansion states relative to non-expansion states.
Drawing on state administrative records from 2014 to 2019 and leveraging
variation in the implementation of Medicaid expansion across states and time,
we find that Medicaid expansion is associated with an average decline of 9.93%
in the number of original Medicaid enrollees. In 2019 alone, this represents
4.4 million fewer original Medicaid enrollees.

Since a Medicaid beneficiary’s reclassification is primarily an administrative
matter concerning states’ requests for federal reimbursement, Medicaid
enrollees would likely have no knowledge of how they were being classified.
Nor would reclassifications necessarily affect enrollees’ coverage or
benefits. In some cases, such reclassifications were permitted under the ACA
and subsequent federal rulemaking.333We discuss these dynamics in more detail
in Section 2.3. In other cases, states may have deliberately reclassified
enrollees in violation of federal law. States may also have incorrectly
classified people into the new adult group due to carelessness or poor
training of case managers. Irrespective of their legal status, such
reclassifications are financially attractive to states.

Reclassifications, and the associated hidden subsidy, had substantial fiscal
implications for states and the federal government. Our results indicate that
the fiscal impact of Medicaid expansion on the U.S. Treasury would have been
substantially lower if the effect we document had not occurred. Our estimates
imply that the federal government distributed $52.9 billion to states from
2014 to 2019 as a result of these reclassifications, including $8.3 billion in
2019 alone. Based on these results, we revise CBO’s estimates of the federal
fiscal impact of Medicaid expansion and find that reclassifications increased
federal costs by 18.2% (Fritzsche et al., 2019).

While many factors influence Medicaid enrollment, we argue that rival
explanations are inadequate to account for our results. Where possible, we
subject alternative theories to empirical scrutiny (see Sections and 5.1 and
5.4). For example, we account for possible state-level changes to income
thresholds for Medicaid eligibility, as well as changes in other
administrative practices in Medicaid. We discuss other competing explanations
— including other changes to the health care system introduced by the ACA,
such as the availability of premium tax credits for certain low- and middle-
income households — in greater detail in Section 5.2. In our view, none
provide a convincing explanation for the enrollment patterns that unfolded
from 2014 to 2019. We conclude, therefore, that reclassifications played a key
role in shrinking the size of the original Medicaid population in expansion
states, relative to what it would have been absent the reform.

Previous research based on household surveys show that Medicaid expansion
triggered a robust “woodwork effect" — i.e., large increases in enrollment
among already-eligible people “coming out of the woodwork" (Frean et al.,
2017; Hudson and Moriya, 2017; Gruber and Sommers, 2019; Sacarny et al.,
2022). This increase in awareness of Medicaid eligibility and enrollment of
already-eligible people suggests that expanding Medicaid would increase the
original population relative to a counterfactual where a state does not
expand. However, our analysis of state administrative records indicates the
opposite effect, suggesting that states engaged in large-scale
reclassifications from the original Medicaid population to the new adult group
that swamped the magnitude of the “woodwork effect." Thus, our results should
be considered a lower-bound estimate of reclassifications.

In addition to exposing a previously overlooked fiscal effect of the ACA, our
work sheds light on several facts related to Medicaid expansion that have not
been well understood: 1) Despite larger-than-expected enrollment, the fiscal
impact of Medicaid expansion on states has been small (Sommers and Gruber,
2017; Gruber and Sommers, 2020). In fact, for some states, Medicaid expansion
appears to have been a net fiscal benefit (Levy et al., 2020; Simpson, 2020).
The hidden subsidy we document helped states offset the direct costs of
Medicaid expansion; 2) Projections of the size of the new adult group have
been greatly exceeded (Blase, 2016). Reclassifications from the original
Medicaid population to the new adult group, which was not contemplated by
forecasters, may be an important mechanism behind these discrepancies; and 3)
Per enrollee spending on the new adult group has been substantially higher
than expected, and the ratio of per enrollee spending in the new adult group
to other non-elderly adults on Medicaid has also exceeded actuarial
expectations (Truffer et al., 2013; Truffer et al., 2018). Our results are
consistent with this pattern. Members of the original Medicaid group tend to
have higher medical spending than members of the new adult group, so
reclassifications from the former to the latter would tend to increase per
enrollee spending in the new adult group.

##  2 Background and Policy Context

###  2.1 The Affordable Care Act and Medicaid Financing

The expansion of Medicaid, arguably the centerpiece of the ACA’s coverage
provisions, has been responsible for a substantial decline in the uninsured
rate among working-age Americans (Butler, 2016). The implications of this
expansion for the health system and population health have been extensively
studied (Buchmueller et al., 2016; Peng, 2017; Huh, 2021; Zhang and Zhu, 2021;
Neprash et al., 2021; Miller et al., 2021; Nikpay, 2022), adding to other work
on the effects of eligibility changes in public health insurance programs (De
La Mata, 2012; Arenberg et al., 2024). To date, forty states and the District
of Columbia have expanded Medicaid under the ACA. In 2019, 12 million members
of the new adult group were enrolled in Medicaid, accounting for about 16.2%
of total enrollment in the program.

Less is known about the impact of reforms made under the ACA to Medicaid’s
financing structure. Although Medicaid is operated by the states, the federal
government contributes the majority of the program’s funding. Contributions by
states accounted for approximately 31 percent of total Medicaid spending in
2021; the federal government paid 69 percent. Still, Medicaid represents a
large and growing share of state budgets. In 2016, Medicaid accounted for
nearly 20 percent of states’ general fund expenditures, roughly double the
program’s share in the early 1990s (MACPAC, 2017). Medicaid is, by a wide
margin, the most prominent example of fiscal federalism in the U.S.

Most federal Medicaid dollars are distributed to states on the basis of a
formula that provides more assistance to states with low per capita personal
income relative to the national average. The Federal Medical Assistance
Percentage (FMAP), the share of Medicaid benefit spending reimbursed by the
federal government, generally ranges from 50 percent (the statutory minimum)
to about 77 percent, depending on the state. Over the decades, however,
federal rules governing Medicaid funding have grown complex, with special
treatment given to certain groups and service categories. The largest
deviation from the traditional FMAP structure relates to Medicaid’s expansion
under the ACA to cover all low-income, non-elderly adults. Expansion states
receive an FMAP rate for these enrollees that substantially exceeds the FMAP
rate for most other Medicaid-eligible populations.444Other exceptions to the
traditional FMAP rate include, e.g., enhanced federal matching for family
planning services, smoking cessation programs for pregnant women, certain
immunizations, and certain women with breast or cervical cancer. These carve-
outs represent a very small proportion of total Medicaid spending, partly
because the eligible populations are narrowly defined and partly because the
FMAP rate enhancement is typically small. Therefore, we ignore these nuances
for the purposes of our analysis.

From 2014 to 2016, the federal government paid 100% of the medical costs of
the new adult group, declining to 95% in 2017, 94% in 2018, 93% in 2019, 90%
in 2020, and remaining at 90% in perpetuity. These enhanced federal
reimbursement rates, which, unlike the FMAP rates for most of the rest of the
Medicaid population, are not dependent on state average income, were designed
to ease the fiscal burden on states and increase political support for the
law.

In 2014, when the new adult group was reimbursed by the federal government at
a rate of 100%, the FMAP rate for the original Medicaid population ranged from
50% to 73%, depending on the state; 27 states received FMAP rates below 60%.
In 2019, despite the FMAP rate for the new adult group declining to 93%, the
gap with the original population’s FMAP rate remained large. That year, FMAP
rates ranged from 50% to 76%, with 26 states received FMAP rates below 60%.
Moreover, since states that opted to expand Medicaid tend to have higher
average incomes than non-expansion states, this group has disproportionately
low FMAP rates for its original population, resulting in an even larger spread
between the FMAP rate for the new adult group and the FMAP rate for the
original Medicaid population.

To put the difference in federal support between the new adult group and the
original Medicaid population in perspective, the Coronavirus Aid, Relief, and
Economic Security (CARES) Act of 2020, which provided additional Medicaid
resources to states during the COVID-19 pandemic, increased the traditional
FMAP rate by a mere 6.2 percentage points — roughly one-fifth the size of the
FMAP rate spread established by the ACA.

Previous research has shown that states are responsive to changes in federal
Medicaid funding (Grannemann and Pauly, 1983). Adams and Wade (2001) find that
states succeed in substituting federal funds for state revenues, resulting in
a reduction in state tax burdens for Medicaid. Leung (2022) exploits a kink in
the match rate formula to estimate that a percentage point increase in the
federal Medicaid match raises per-enrollee spending by 3% to 6%. Bundorf and
Kessler (2022) estimate that the ACA’s enhanced FMAP rate led states to
increase spending per original Medicaid enrollee by approximately 15%, showing
that state Medicaid spending is sensitive to the magnitude of the federal
subsidy. We extend this work by examining how states reacted to the
unprecedented fiscal incentives to reclassify enrollees embedded within the
ACA’s Medicaid expansion.

###  2.2 The Woodwork Effect

Economists have long recognized and sought to document the spillover effects
of reforms to social assistance programs (Bartik, 2002; Baicker, 2005;
Grabowski, 2006; McInerney et al., 2017; Carey et al., 2020). Expanding public
programs to cover a new group of people tends to increase enrollment among
those who were already eligible under the pre-expansion eligibility criteria.
This phenomenon — known as the “woodwork” or “welcome mat" effect — may be
particularly strong when a program’s expansion is widely publicized. Millions
of Americans are eligible for Medicaid but are not enrolled in the program
(Sommers and Epstein, 2011). Although foregoing Medicaid coverage may be a
deliberate choice for some individuals, administrative barriers and lack of
awareness of program rules may play a decisive role in many cases. The passage
of the ACA, of which the expansion of Medicaid was a core component, generated
substantial media coverage and considerable public interest. Many states
advertised Medicaid expansion on billboards and in TV and radio ads, urging
the public to check their eligibility (Artiga and Stephens, 2013). Moreover,
the ACA instituted other policy changes, such as tax incentives to obtain
health insurance and measures to streamline the Medicaid application process,
that likely contributed to the woodwork effect.

The most reliable evidence of woodwork effects in Medicaid predates the ACA.
Sonier et al. (2013) estimates that health reforms adopted in Massachusetts in
2006, which align closely to key design features of the ACA, provoked large
woodwork effects that substantially increased Medicaid enrollment. Sacarny et
al. (2022) leverage data from the Oregon Health Insurance Experiment, in which
Medicaid eligibility was determined by lottery, and calculate a short-run 6%
increase in child enrollments when adults in their household gained access to
Medicaid.

Several studies have sought to quantify the woodwork effect in the context of
the ACA using household survey data, typically drawn from the American
Community Survey, an annual, large-scale survey of U.S. households. Frean et
al. (2017) estimate that fully half of the impact on coverage attributable to
Medicaid expansion in 2014-2015 came from the woodwork effect. The woodwork
effect was found to be large in both expansion and non-expansion states.
Hudson and Moriya (2017) find that the ACA induced a large woodwork effect
among children. They estimate that 710,000 low-income children gained Medicaid
coverage through the woodwork effect in 2014-2015. McInerney et al. (2021)
present evidence of a woodwork effect among seniors dually eligible for
Medicare and Medicaid. They estimate the ACA increased Medicaid enrollment in
this population by 4.4%.

Since care provided to the original Medicaid population was reimbursed by the
federal government at an FMAP rate of only 50% to 77% between 2014 and
2019,555The statutory maximum traditional FMAP rate is 83%, but in practice no
state’s traditional FMAP rate exceeded 77% during the 2014-2019 period. the
size of the woodwork effect is – at least theoretically – a key parameter in
estimating the impact of Medicaid expansion on states’ budgets. In certain
states, these costs were expected to be non-trivial (Price and Saltzman,
2013).

However, our analysis of state administrative records suggests that previous
research on Medicaid expansion and the woodwork effect, while perhaps an
accurate reflection of the gains in Medicaid coverage among previously
eligible individuals, should not be used to calculate the state fiscal costs
of Medicaid expansion. This discrepancy arises because of trends in how states
classified Medicaid enrollees when seeking federal reimbursement for program
expenses. We find that a large number of individuals who otherwise would
likely have been reported as belonging to the original Medicaid population
were reclassified into the new adult group in expansion states.

###  2.3 Reclassification of Enrollees to the New Adult Group under the ACA

The ACA created several channels for states to shift Medicaid enrollees who
would otherwise have be classified in the original Medicaid population (and
been reimbursed at the traditional FMAP rate) to the new adult group under the
enhanced FMAP rate.

First, adult Medicaid enrollees who were not eligible for full benefits prior
to the ACA’s passage (e.g., individuals receiving family planning services
under waivers granted by CMS or individuals eligible under special Medicaid
rules for the “medically needy") could be transferred to the new adult group
under the ACA, and states could receive the enhanced FMAP rate for care
provided to these individuals.

Second, the ACA created opportunities for individuals to join the new adult
group prior to experiencing a health event (e.g., pregnancy or a disabling
injury) that would otherwise have made them eligible for the original Medicaid
population. For example, a woman may qualify for the new adult group and
enroll in Medicaid before becoming pregnant. During her pregnancy, states are
allowed to maintain her classification in the new adult group and receive
enhanced FMAP rates for her pregnancy-related care. Similarly, enrollees in
the new adult group who become disabled may remain in the new adult group. In
the counterfactual where Medicaid expansion had not occurred, many pregnant
women and people with disabilities would presumably have joined the original
Medicaid population. Instead, Medicaid expansion “siphons off" some of these
enrollees, resulting in lower enrollment in the original Medicaid population
and larger federal subsidies to states.

In addition to the mechanisms described above, which were authorized under the
ACA, it is possible that states may have – knowingly or unknowingly –
reclassified enrollees in violation of federal laws and regulations. Medicaid
administrative tasks, including eligibility verification, data management, and
reporting to CMS, are almost entirely controlled by states with minimal
federal oversight. Moreover, CMS exerts little meaningful pressure on states
to correct errors in eligibility classifications or deficiencies in data
management practices. A recent report by the Government Accountability Office
noted: “While CMS is generally required to disallow, or recoup, federal funds
from states for eligibility-related improper payments…, it has not done so for
decades… [I]n July 2017, CMS issued revised procedures through which it can
recoup funds for eligibility errors, beginning in fiscal year 2022” (Yocom,
2020). Consequently, during our entire post-treatment period (2014-2019),
states faced no financial sanctions for eligibility errors.

The failure to properly determine enrollees’ eligibility is widespread in
Medicaid (Blase and Albanese, 2022). Audits of state Medicaid records carried
out by the U.S. Department of Health and Human Services provide direct
evidence that misclassifications of Medicaid enrollees – including individuals
who should be classified in the original Medicaid population being reported as
belonging to the new adult group – occur on a fairly large scale.
Investigations conducted in 2018 and 2019 in California, New York, and
Colorado (all of which expanded Medicaid in 2014) suggest that as many as
28.3% of individuals classified as new adult group enrollees may be ineligible
(Levinson, 2018; Chiedi, 2019, 2018), a figure that matches closely with our
estimates. Based on the Payment Error Rate Measurement (PERM) system, CMS
estimated in 2019 that improper eligibility determinations accounted for 8% of
federal Medicaid payments, amounting to approximately $32.3 billion (Centers
for Medicare & Medicaid Services, 2019).

##  3 Data

We construct a balanced state-level panel of the original Medicaid population
from 2006 to 2019. We exclude later years because the COVID-19 pandemic, and
the government response to the public health crisis, substantially affected
Medicaid enrollment and altered states’ fiscal incentives. Most importantly,
states paused their normal eligibility redetermination processes from early
2020 to early 2023, leading to a nationwide surge in Medicaid enrollment.
Other temporary policies included a 6.2 percentage point increase in states’
traditional FMAP rates, which narrowed the FMAP spread between the traditional
FMAP rate and the enhanced FMAP rate. It would be difficult to disentangle the
enrollment effects of Medicaid expansion from the effects of these forces.
Moreover, we believe the future of Medicaid is more likely to resemble the
2014-2019 period than the anomalous pandemic years, so focusing on the pre-
pandemic period is likely to yield more valuable insights.

Our data comes from two sources. We obtain data for the years 2006-13 from
issue briefs published by the Kaiser Family Foundation. These data were
compiled by Health Management Associates, a research and consulting firm,
based on internal state Medicaid enrollment records.666CMS does not publicly
release state-level Medicaid enrollment data for the years 2006-2013. The
figures reported represent “point-in-time” monthly Medicaid enrollment counts
for the month of June each year (enrollment for the month of December of each
year was also reported but not used in our analysis). Every person with
Medicaid coverage was counted as an enrollee with the exception of family
planning waiver enrollees and pharmacy plus waiver enrollees. No adjustment
was made for other persons who were enrolled in Medicaid categories with less
than full coverage. Therefore the enrollment figures include a small number of
individuals who are covered by Medicaid only for emergency services or
services related to breast and cervical cancer, and persons with Medicare and
Medicaid dual eligibility enrolled as Qualified Medicare Beneficiaries (QMBs),
Specified Low-Income Medicare Beneficiaries (SLMBs) or Qualified Individuals
(QIs) for whom Medicaid pays a portion of Medicare premiums, copays, and
deductibles. Persons in state-only health coverage programs and Medicaid
expansion CHIP enrollees not funded by Medicaid are excluded. Our second
source of data, which covers the 2014-19 period, is Medicaid enrollment
reports submitted by states to the Centers for Medicare & Medicaid Services
(CMS) through the Medicaid Budget and Expenditure System (MBES).777Both
sources capture only individuals whose coverage is funded through Medicaid
(Title XIX of the Social Security Act); children and young adults funded
through CHIP are excluded. Post-ACA enrollment information is a count of
unduplicated individuals enrolled in the state’s Medicaid program at any time
during each month in the quarterly reporting period. The enrollment data
identifies the total number of Medicaid enrollees and, for states that have
expanded Medicaid, provides specific counts for the number of individuals
enrolled in the new adult group.888The new adult group consists of two
distinct populations: newly-eligible and non-newly-eligible. Non-newly-
eligible enrollees are a small, special class of Medicaid recipients already
enrolled in Medicaid when the ACA was passed. To calculate the number of
enrollees in the original Medicaid population, we subtract the number of newly
eligible enrollees from the total number of Medicaid enrollees. This will tend
to bias our results against finding a decline in the size of the original
Medicaid enrollment, since it is possible that states have reclassified
enrollees from the original Medicaid population to the non-newly-eligible
group (Bundorf and Kessler, 2022). Enrollment figures for the month of June
were used for each year analyzed. We define our dependent variable as the
natural log of the number of individuals in the original Medicaid population.

The use of two different datasets to track Medicaid enrollment across time is
not ideal, since differences in how each dataset is collected and compiled
could potentially influence our results. In our case, this concern is
compounded by the fact that the endpoints of each dataset coincide with the
beginning of treatment for the largest cohort of states. However, we know of
no alternative source of publicly available yearly Medicaid enrollment figures
at the state level. Moreover, in Section 5.4 we perform several empirical
tests to determine whether our approach affects the main results; we find no
such evidence.

Many factors affect the size of the original Medicaid population. We explore a
range of specifications with a range of state-level covariates that capture
differences in Medicaid program rules, political conditions, demographics, and
the state of the economy. We account for income eligibility thresholds for key
subgroups within the original Medicaid population (children and parents),
using data from the Kaiser Family Foundation. More stringent eligibility
thresholds would tend to reduce the size of the original Medicaid population.
Since Medicaid enrollment tends to be counter-cyclical, in some specifications
we control for the state unemployment rate, the state poverty rate, the
maximum level of welfare (TANF) benefits for a family of three, or the state
food insecurity rate, all of which measure economic distress; these variables
come from the University of Kentucky’s Center for Poverty Research. We also
consider the demographic composition of the state population (proportion non-
White), since Medicaid enrollment varies across racial groups, as well as the
size of the state population (both from the Census Bureau). Finally, in some
specifications we use data from the University of Kentucky’s Center for
Poverty Research to adjust for the political party of the chief executive
(governor for states and mayor for the District of Columbia) to account for
potential differences in how the Medicaid program is administered. We control
for baseline values of our state covariates in the last period before Medicaid
expansion was implemented. The path of the original Medicaid population (in
the absence of expansion) likely depends on these covariates, so a conditional
parallel trends assumption may be more plausible than an unconditional
parallel trends assumption.

We present descriptive statistics (means and standard deviations) of our
outcome variable, as well as all state-level covariates, in Table 1. Expansion
and control states are broadly similar across several dimensions, including
population size, racial diversity, and economic performance. Unsurprisingly,
expansion states are substantially more likely to have a Democratic governor,
provide more generous TANF benefits, and have higher income limits for parents
on Medicaid.

Table 1: Descriptive Statistics

| Expansion States | Control States
---|---|---
Variable |  Mean |  SD |  Mean |  SD
|  (1) |  (2) |  (3) |  (4)
Original Medicaid Population (ln) |  13.26 |  1.119 |  13.33 |  1.114
Eligibility Limit, Children (Proportion of FPL) |  2.563 |  0.583 |  2.231 |  0.385
Eligibility Limit, Parents (Proportion of FPL) |  1.167 |  0.537 |  0.542 |  0.354
State Unemployment Rate (%) |  5.888 |  2.192 |  5.543 |  2.230
Governor’s Political Party (1 = Dem) |  0.569 |  0.496 |  0.189 |  0.392
State Population (ln) |  15.08 |  1.055 |  15.24 |  0.980
Non-White (Proportion of State) |  0.211 |  0.142 |  0.232 |  0.128
Maximum TANF Benefits ($) |  485.5 |  163.6 |  347.9 |  139.1
Food Insecurity (Proportion of State) |  0.132 |  0.0338 |  0.150 |  0.0329
Poverty Rate |  12.46 |  3.464 |  13.62 |  3.260
Observations |  476 |  |  238 |

  * •

Notes: This table presents descriptive statistics for variables used in our
analysis, split by state Medicaid expansion status. Expansion states consist
of 33 states (and the District of Columbia) that expanded Medicaid under the
ACA before the end of 2019. Control states consist of 17 states that had not
expanded Medicaid by the end of 2019. FPL refers to the Federal Poverty Level
(in 2019, approximately $25,750 for a family of four). Sources: Medicaid
original population is drawn from issue briefs published by the Kaiser Family
Foundation and enrollment reports submitted by states to CMS. Unemployment
rates are from the Bureau of Labor Statistics. Medicaid eligibility limits are
from the Kaiser Family Foundation. State population and proportion of
population non-White come from the Census Bureau. The political party of the
governor (or mayor, in the case of the District of Columbia), poverty rates,
TANF benefits, and food insecurity rates come from the University of
Kentucky’s Center for Poverty Research.

##  4 Empirical Strategy

To identify the effect of Medicaid expansion on enrollment, we leverage
variation in the adoption of Medicaid expansion across geographies and time,
comparing trends between states that opted to expand Medicaid under the ACA
and states that did not. Since non-expansion states did not experience a
relative change in their FMAP rates to cover different groups of Medicaid
recipients, they represent a natural control group to test our
reclassification hypothesis. Historically, two-way fixed effects (TWFE)
regressions have served as the workhorse models for estimating causal effects
in the context of staggered policy adoption. However, recent studies have
shown that the TWFE estimator can yield inconsistent and misleading estimates
of the average treatment effect on the treated (ATT) in the presence of
treatment effect heterogeneity between groups or across time (De Chaisemartin
and d’Haultfoeuille, 2020; Borusyak et al., 2021; Callaway and Sant’Anna,
2021; Goodman-Bacon, 2021; Imai and Kim, 2021; Sun and Abraham, 2021).

These concerns related to TWFE models apply to our setting, in which states
expanded Medicaid at different times (the first expansions in our data occur
in 2014 and the last occurs in 2019; see Table A1 in the Appendix for details
on the treatment timing of individual states).999For this reason, the
preliminary descriptive evidence presented in Figure 1 and Table 2 in Section
5 is based on simple comparisons of the initial expansion cohort of states
(i.e., the 25 states (including the District of Columbia) that expanded
Medicaid in January 2014) and the 17 states that did not expand Medicaid by
the of 2019, when our sample ends. In these exhibits, we exclude the nine
states that expanded Medicaid between February 2014 and December 2019. To
overcome these limitations, in our main results we implement the robust
difference-in-differences estimator proposed by Callaway and Sant’Anna (2021).
This approach allows us to retain all states in our sample, including those
that expanded Medicaid after the initial cohort in January 2014. The Callaway
Sant’Anna method delivers consistent ATT estimates even in the presence of
arbitrary heterogeneous treatment effects by shutting down problematic 2
$\times$ 2 difference-in-differences comparisons between newly treated and
already treated states. We implement the augmented inverse-probability
weighting estimator described in Callaway and Sant’Anna (2021), in which both
the treatment and outcome are modeled; recovering consistent estimates depends
only on correctly specifying one of the models.

For our comparison group, we use only states that did not expand Medicaid
before the end of 2019, when our sample ends (i.e., “never-treated" states).
An alternative approach would be to include “not yet" treated states in the
comparison group. We choose to restrict the comparison group to “never-
treated" states for several reasons. First, our data includes a relatively
large number of 17 “never-treated" states and a relatively small number of
late-expanding states (i.e., those that would serve as additional controls
under the “not yet" option). Second, “never-treated" states are broadly
similar to treated states, with geographic representation in the South, West,
and Midwest. Third, the economic conditions during early and late treatments
differ. Fourth, the parallel trends assumption is different between the two
choices, and its interpretation is more straightforward when the comparison
group is limited to “never-treated" states (Callaway and Sant’Anna, 2021).

There are a variety of ways to represent the results from the Callaway and
Sant’Anna (2021) estimator. In our main results (Table 3), we focus on the
overall ATT, which is a (simple) weighted average of each $ATT(g,t)$, where
$g$ denotes the treatment group and $t$ denotes the year. This calculation
aggregates the ATTs within all treatment groups and time periods. In Figure 2
we also present dynamic ATTs across treatment event time. In addition to
highlighting treatment effects with respect to length of exposure to
treatment, this dynamic specification allows us to assess the plausibility of
the parallel trends assumption.

##  5 Results

###  5.1 Main Results

In this section, we discuss our empirical results. Before turning to more
sophisticated statistical methods, we present graphical evidence of
longitudinal trends. Figure 1 plots the change in the size of the original
Medicaid population (measured in individuals enrolled in June of each year),
contrasting states that expanded in January 2014 with those that had not
expanded by the end of 2019. For each cohort and year, we sum enrollment
across all states. For ease of comparison, for both cohorts we express the
change in enrollment relative to 2013, the last pre-expansion year. From 2006
to 2013, both cohorts tracked closely together. For both groups, enrollment in
2006 was approximately 21-24% lower than in 2013. From 2013 to 2014, the first
treated year, both cohorts continued to follow very similar growth paths, with
expansion states showing slightly larger gains in enrollment. Beginning in
2015, however, the cohorts began to diverge. Non-expansion states continued to
experience positive enrollment growth in 2015 and 2016, before declining
gradually through 2019 — broadly consistent with how one would expect Medicaid
enrollment to evolve given the strengthening state of the national economy
during this period and the national reach of the “woodwork effect" triggered
by Medicaid expansion. Meanwhile, states that expanded Medicaid in January
2014 reported negative enrollment growth in 2015 and 2016, followed by a small
rebound in 2017 and 2018 and renewed decline in 2019. In total, from 2013 to
2019, enrollment in the original Medicaid group declined by 1.7% in states
that expanded in January 2014. Over the same period, non-expansion states
reported a 19.2% increase in enrollment.

Figure 1:  Original Medicaid Population Enrollment (% Change Compared to 2013)
0.5Notes: This figure plots the change in the size of the original Medicaid
population in non-expansion states and in states that expanded Medicaid in
January 2014. Values are normalized to zero in 2013, the last pre-expansion
year. We use enrollment figures for the month of June in each year. The
vertical dashed line denotes the implementation of the ACA’s Medicaid
expansion. The January 2014 expansion cohort consists of 25 states (including
the District of Columbia). The non-expansion cohort consists of 17 states that
have not expanded Medicaid under the ACA (as of April 2024) as well as states
that expanded after 2019. The remaining nine states expanded Medicaid in a
staggered fashion between February 2014 and December 2019; for simplicity, we
omit these states from the graph. Data was compiled by the authors from Kaiser
Family Foundation issue briefs (for the years 2006-2013) and reports from the
Medicaid Budget and Expenditure System (for the years 2014-2019); see section
3 for more details. We define the original Medicaid population as total
Medicaid enrollment minus the number of enrollees reported by states as “newly
eligible" under the ACA.

Next, using the same data, we formalize this comparison by deriving simple
difference-in-differences estimates of Medicaid expansion’s effect on
enrollment in the original Medicaid population. Table 2 compares changes in
the state-reported size of the original Medicaid population in the pre-
treatment period (2006-2013) and the post-treatment period (2014-2019) between
the cohort of states that expanded in January 2014 and states that had not
expanded by the end of 2019. In the pre-treatment period, the mean level of
enrollment in the original Medicaid population in expansion states was 1.10
million, while the mean in non-expansion states was 0.92 million. In the post-
treatment period, the mean in expansion states grew to 1.35 million, while the
mean in the non-expansion cohort increased to 1.23 million. Hence, our simple
difference-in-differences calculation implies that, on average, the original
Medicaid population would have been larger by nearly 58,000 enrollees (or
4.29%) in expansion states in the absence of the expansion.

Table 2: Simple Difference-in-Differences Estimate

| Pre-treatment | Post-treatment | Difference (Pre/Post) | Difference-in-Differences
---|---|---|---|---
_State Cohort_ |  |  |  |
Jan. 2014 expansion states | 1,101,718 | 1,350,646 | +248,928 | -57,894
Non-expansion states | 923,996 | 1,230,818 | +306,822 |

  * •

Notes: This table compares the average level of enrollment in the original
Medicaid population between pre- and post-treatment periods and expansion and
non-expansion states. We use enrollment figures for the month of June in each
year. The January 2014 expansion cohort consists of 25 states (including the
District of Columbia). The non-expansion cohort consists of 17 states that
have not expanded Medicaid under the ACA (as of April 2024) as well as states
that expanded after 2019. The remaining nine states expanded Medicaid in a
staggered fashion between February 2014 and December 2019; for simplicity, we
omit these states from our calculations. Data was compiled by the authors from
Kaiser Family Foundation issue briefs (for the years 2006-2013) and reports
from the Medicaid Budget and Expenditure System (for the years 2014-2019); see
section 3 for more details. We define the original Medicaid population as
total Medicaid enrollment minus the number of enrollees reported by states as
“newly eligible" under the ACA.

While informative, the comparisons presented in Table 2 have three important
shortcomings. First, they ignore potentially confounding factors. Second, they
omit late-expanding states (i.e., those that expanded between February 2014
and December 2019). Third, they conceal the dynamic effects of Medicaid
expansion across different treatment periods. In Table 3 and Figure 2, we
address each of these limitations by implementing the difference-in-
differences estimator described in Callaway and Sant’Anna (2021). Since our
dependent variable is the log of enrollment in the original Medicaid
population, our regression coefficients can be interpreted as (approximate)
percent changes. To obtain a baseline, in column (1) of Table 3 we drop late-
expanding states and estimate the model without controls. The coefficient does
not attain statistical significance (p = 0.11) but is similar in magnitude to
our implied estimate in Table 2. Each of the other specifications presented in
Table 3 include all states and account for the staggered adoption of Medicaid
expansion across time. Column (2) shows the no-controls specification with all
states. Once again, the coefficient is similar to the implied estimate from
Table 2 but is not statistically significant (p = 0.13). In column (3), we
present our preferred specification, adding controls for the Medicaid income
eligibility threshold for parents, the political party of the governor, and
the state unemployment rate. These variables account for a range of possible
sources of confounding. The Medicaid income eligibility threshold for parents
reflects changes to eligibility affecting the original Medicaid population. We
also adjust for the political party of the chief executive because Democratic
and Republican governors may administer their Medicaid programs differently,
in ways that are difficult to explicitly capture (e.g., the level of outreach
to eligible populations). Finally, the state unemployment rate helps to
isolate our estimates from the impact of economic shocks on Medicaid
enrollment. The magnitude of the coefficient in column (3) is large and
statistically significant at the 1% level, indicating that Medicaid expansion
leads to a 9.93% decline in the size of the original Medicaid population.

In columns (4) to (9) we present a range of alternative specifications using
our preferred specification, column (3), as a baseline. Column (4) adjusts for
the Medicaid income eligibility threshold among children (rather than
parents), another major sub-group of the original Medicaid population. In
column (5), we use the poverty rate as a proxy for state economic conditions,
rather than the unemployment rate. Column (6) adds the log of state population
to adjust for inter-state shifts in population. Column (7) adds the proportion
of the state population that is non-white. In Column (8), to account for the
fact that the generosity of safety-net programs may have spillover effects on
enrollment in other programs (Schmidt et al., 2019), we add the maximum level
of TANF benefits for a family of three. Finally, column (9) shows the effect
of using the food insecurity rate rather than the unemployment rate to measure
economic distress. All alternative specifications yield similar results.

As a basis for later computations, we use the coefficient given in column (3),
which is approximately in the middle range of our estimates.

Table 3: Effects of Medicaid Expansion on Enrollment in the Original Medicaid
Population

| (1) |  (2) | (3) |  (4) | (5) |  (6) | (7) |  (8) | (9)
---|---|---|---|---|---|---|---|---|---
ATT | -0.0595 |  -0.0505 |  -0.0993*** |  -0.0675** |  -0.1064*** |  -0.0912** |  -0.1021** |  -0.0638* |  -0.0895*
| (0.0370) |  (0.0336) | (0.0348) |  (0.0320) | (0.0369) |  (0.0382) | (0.0428) |  (0.0331) | (0.0348)
Gov’s Political Party |  |  | ✓ |  ✓ | ✓ |  ✓ | ✓ |  ✓ | ✓
Elig. Limit, Parents |  |  | ✓ |  | ✓ |  ✓ | ✓ |  ✓ | ✓
Unemployment Rate |  |  | ✓ |  ✓ |  |  ✓ | ✓ |  ✓ |
Elig. Limit, Children |  |  |  |  ✓ |  |  |  |  |
Poverty Rate |  |  |  |  | ✓ |  |  |  |
ln(State Population) |  |  |  |  |  |  ✓ |  |  |
Non-White (% of State) |  |  |  |  |  |  | ✓ |  |
TANF Benefits |  |  |  |  |  |  |  |  ✓ |
Food Insecurity Rate |  |  |  |  |  |  |  |  | ✓
N | 602 |  714 | 714 |  714 | 714 |  714 | 714 |  714 | 714

  * •

Notes: This table shows estimates of the ATT of Medicaid expansion on the size
of the original Medicaid population (logged) across a range of models, all of
which use the staggered difference-in-differences estimator described by
Callaway and Sant’Anna (2021). The comparison group is "never" treated units.
The specification in column (1) is without controls and includes only states
that expanded in January 2014. All other models include all states. Standard
errors (clustered by state) are reported in parentheses. * $p<0.10$, **
$p<0.05$, *** $p<0.01$.

Figure 2 plots the dynamic treatment effects derived from our preferred
specification (column (3) in Table 3). The event study is generally supportive
of the parallel trends assumption, showing little evidence of differential
trends in the periods preceding the expansion of Medicaid. We also note the
magnitude of the effect, with the exception of the first year of Medicaid
expansion’s implementation (Year 0 in Figure 2), is statistically significant
and roughly constant throughout the post-expansion period.

Figure 2:  Dynamic Effects of Medicaid Expansion on Enrollment Notes: This
plot shows dynamic effects across event time, based on our preferred
specification (column (3) in Table 3). Bars represent 95% confidence
intervals. The vertical dashed line represents the implementation of Medicaid
expansion. We use enrollment figures for the month of June in each year. Data
was compiled by the authors from Kaiser Family Foundation issue briefs (for
the years 2006-2013) and reports from the Medicaid Budget and Expenditure
System (for the years 2014-2019); see section 3 for more details. We define
the original Medicaid population as total Medicaid enrollment minus the number
of enrollees reported by states as “newly eligible" under the ACA.

We offer two possible explanations for the absence of a clear effect in the
first treated year (Year 0 in Figure 2), which for the vast majority of states
corresponds to 2014. First, there is evidence that the woodwork effect in
expansion states was particularly strong in 2014 (Hill et al., 2014; Frean et
al., 2017), which would have counteracted, and perhaps fully offset, the
effects of reclassifications on the number of people in the original Medicaid
population. The woodwork effect likely waned in later years, partly due to the
mechanical decline in the number of eligible-but-not-enrolled individuals and
partly because by mid-2015, some states had already shifted away from large-
scale community outreach efforts to raise awareness about Medicaid (Artiga et
al., 2015). Second, it may have taken time for state policymakers and
administrators to adapt to the new bureaucratic structures established by the
ACA and scale-up reclassification efforts.

A range of additional checks are shown in the Appendix. Table A2 gives results
weighted by each state’s 2013 Medicaid population to ensure that our findings
are not unduly influenced by small states; dynamics effects of the main
specification, with weights, are shown in Figure A1. Weighting yields similar,
or slightly larger, effects. In Table A3 we show the sensitivity of our main
specification to different choices in defining the treatment group.
Specifically, we show the effects of dropping late-expanding states (i.e.,
those that expanded Medicaid after January 2014), states that implemented
early ACA expansions during 2010-2012, and states that covered low-income
childless adults prior to the ACA’s passage in 2010. We report coefficients
from both weighted and unweighted models. All specifications remain
statistically significant at the 5% or 1% level, and seven out of nine
alternative samples yield treatment effects larger than our main estimate.
Finally, in Table A4, we limit the sample to states that expanded in January
2014 and present the same set of specifications as in Table 3; dynamics
effects of the main specification with this narrower sample are shown in
Figure A2. Results are consistent.

###  5.2 Other ACA-Related Factors

Before turning to the fiscal implications of our empirical results, we
consider several alternative explanations to our reclassification hypothesis
and argue that no other explanation can plausibly account for the large
decline in the original Medicaid population in expansion states relative to
non-expansion states over the 2014-2019 period. In the discussion that
follows, we focus on major provisions of the ACA (other than Medicaid
expansion) that had substantial effects on the U.S. health care system.

Premium Tax Credits — The ACA created a system of tax subsidies (in the form
of premium tax credits, or PTCs) to help lower- and moderate-income Americans
purchase private health insurance on the non-group market. During our sample
period, households in expansion states were eligible for PTCs if their income
fell between 138% and 400% of the FPL. In non-expansion states, the households
earning between 100% and 400% of the FPL were eligible for PTCs. The lowest-
income households received more generous subsidies, with premium contributions
capped at 2% of their annual income. For some original Medicaid enrollees,
transitioning to private coverage — possibly perceived as being higher quality
than Medicaid — may have been appealing. Yet there is little reason to think
that such transitions are driving our results. First, under federal law
individuals eligible for Medicaid are not eligible for PTCs, so this
hypothesis requires millions of households to have strategically adjusted
their income or other characteristics to gain PTC eligibility. Second, this
hypothesis requires PTC-induced transitions from Medicaid to ACA plans to have
been substantially larger in expansion states than in non-expansion states.
Yet despite more than 8 million Americans signing up for ACA plans during the
2013-2014 open enrollment period (Frank, 2014), we see no differential effect
on original Medicaid enrollment that year (see Figure A2). Third, we note that
the individual mandate was eliminated in 2019; yet we detect strong effects
that year.

The ACA’s Individual Mandate — Under the ACA, most Americans were required to
maintain health coverage or face a financial penalty. If this mandate had more
“bite" in non-expansion states than expansion states, it might account for the
divergence in original Medicaid enrollment between the two groups of states.
However, this explanation is tenuous for two reasons: First, the mandate would
essentially have augmented the “woodwork effect" — drawing even more eligible-
but-not-enrolled people onto Medicaid. But as we discuss in Section 2.2,
survey-based studies do not support the view that the “woodwork" was
substantially larger in non-expansion states. Second, since the individual
mandate was enforced by the IRS, a federal agency, there is no reason to
believe that residents of non-expansion states experienced more vigorous
enforcement. Third, if the individual mandate had played an important role in
causing the divergence in the growth of the original Medicaid population from
2014 to 2019, one would expect Figure 1 to show accelerating growth in both
expansion and non-expansion states, with non-expansion states rising faster.
In reality, the divergence stems from stagnating growth among expansion
states, not particularly rapid growth in non-expansion states.

“Silver Loading" — In the fall of 2017, the Trump administration stopped
reimbursing health insurers operating in the ACA’s exchanges for cost-sharing
reductions (CSRs) that certain lower-income households are entitled to. The
decision caused temporary disruption to the individual health insurance
market, but ultimately resulted in lower premiums for millions of consumers on
the exchanges as insurers built the cost of CSRs into premiums, triggering
larger PTCs (Aron-Dine, 2019; Fiedler, 2021). By making exchange coverage more
affordable, “silver loading" may have led some people to transition off
Medicaid and on to private plans. We consider this implausible. First, the
cessation of federal CSR payments that became the impetus for “silver loading"
did not occur until October 2017. Therefore, it cannot explain the clear
effects we find in 2016 and 2017 (see Figure A2).101010Recall that our data on
Medicaid enrollment represents the month of June in each year (see Section 3),
so our estimates for 2017 precede the elimination of federal CSR payments.
Second, as mentioned above, individuals eligible for Medicaid are not eligible
for PTCs, so this hypothesis assumes that millions of households reacted to
“silver loading" by altering their income or other characteristics to become
eligible for PTCs. Moreover, Aron-Dine (2019) notes that “silver loading" was
least beneficial for people with incomes between 100 percent and 200 percent
of the poverty level — implying that the group for whom such strategic
behavior may have been the most feasible had the least incentive to do so.
Third, this hypothesis requires “silver loading" to have had a substantially
larger effect on Medicaid enrollment in expansion states than in non-expansion
states. Based on state-level estimates of the number of consumers affected by
“silver-loading" (Aron-Dine, 2018), we see little evidence that this was the
case.

###  5.3 Fiscal Impact of Reclassifications

Using our estimates of the effects of Medicaid expansion on enrollment in the
original Medicaid population, we now turn to back-of-the-envelope calculations
of its fiscal impact on states and the federal government. For the purposes of
deriving quantitative fiscal estimates, we assume that all those who would
otherwise have been enrolled in the original Medicaid population were
reclassified into the new adult group.111111Despite this assumption, we likely
still underestimate the number of original Medicaid enrollees reclassified to
the new adult group, since research using household survey data indicates that
the woodwork effect induced by the ACA was larger in expansion states than in
non-expansion states. As a result, non-expansion states are likely to
underestimate the counterfactual level of enrollment in the original Medicaid
population in expansion states. See Section 2.2 for more details. Because of
the difference in FMAP rates applicable to the original Medicaid population
and the new adult group, reclassifications represent a substantial federal
subsidy to states. We approximate the size of the subsidy for each state and
year using

$\textit{Y}_{i,t}=\text{estimated enrollees
reclassified}_{i,t}\times\text{FMAP rate spread}_{i,t}\times\text{per enrollee
expenditures}_{t}$ (1)

where $Y_{i,t}$ represents the reclassification-related Medicaid subsidy
received by state $i$ in year $t$, estimated enrollees reclassified represents
the difference between actual enrollment in the original Medicaid population
and our estimated counterfactual enrollment,121212We use our coefficient from
column (3) in Table 3 (–0.0993) to derive the counterfactual enrollment levels
in each state. To do so, we multiply actual enrollment in a given state and
year by $\frac{1}{1-0.0993}$ = 1.1102. FMAP rate spread is the difference
between the traditional FMAP rate and the enhanced FMAP rate,131313Over our
sample period, the mean FMAP rate spread among expansion states was 0.39; the
median was 0.43. and per enrollee expendiutures is the national average of
expenditures per non-elderly adult Medicaid enrollee (expressed in constant
2019 dollars), excluding the new adult group.141414We obtain per enrollee
expenditures from annual reports compiled by the Medicaid and CHIP Payment and
Access Commission (MACPAC). Estimates are available for 2013, 2018, and 2019.
Estimates were not published for 2014, 2015, 2016 or 2017. To estimate per
enrollee expenditures in the missing years, we perform a linear interpolation
using 2013 and 2018 as endpoints. We aggregate per enrollee expenditures up to
the national level because of data quality concerns with state-level
estimates. All years are converted to 2019 dollars using the CPI. The
estimated annual per enrollee expenditures (in 2019 dollars) rose from $4,612
in 2014 to $4,908 in 2019.

Our results are presented in Table 4. The fiscal impact of the
reclassifications we document is substantial. Our estimates imply that $52.9
billion in additional federal funding was distributed to states from 2014 to
2019 based on these reclassifications. Over that period, approximately 26.2
million reclassifications (measured as enrollee-years) may have occurred
across all expansion states. Since Medicaid expansion was adopted in a
staggered fashion over our sample period, however, the cumulative totals are
somewhat distorted by the fact that some states expanded Medicaid in later
years. To address this, Table 4 also shows estimates for 2019, the last year
in our sample.151515Despite more states belonging to the expansion cohort in
2019 than in previous years, the total state subsidy in 2019 ($8.3 billion) is
slightly smaller than the average annual subsidy
($\frac{\mbox{\$52.9}}{\mbox{6 years}}$ = $8.8 billion) over the 2014-2019
period because the enhanced FMAP rate declined from 100% in 2014 to 93% in
2019. That year, the original Medicaid population had approximately 4.4
million fewer beneficiaries as a result of expansion, resulting in $8.3
billion in subsidies to states, assuming these were reclassified into the new
adult group. For context, federal Medicaid expenditures totaled $405 billion
in fiscal year 2019. Therefore, we calculate that the ACA’s hidden subsidy may
have accounted for approximately 2.0% of federal Medicaid outlays that year.

Table 4: Estimated State Subsidies from Reclassifications

| (1) | (2) | (3) | (4)
---|---|---|---|---
State | Enrollees Reclassified | Subsidy (Millions of $, | Enrollees Reclassified | Subsidy (Millions of $,
| (2014-2019) | 2014-2019) | (2019) | 2019)
Alaska | 65,197 | $ 143,908,236 | 16,898 | $ 35,661,429
Arizona | 1,143,180 | $ 1,532,214,257 | 193,490 | $ 220,223,764
Arkansas | 415,174 | $ 531,401,967 | 63,461 | $ 70,049,185
California | 6,301,045 | $ 14,249,028,204 | 964,592 | $ 2,035,712,772
Colorado | 578,605 | $ 1,295,730,016 | 94,860 | $ 200,195,982
Connecticut | 451,198 | $ 1,015,806,169 | 78,068 | $ 164,758,053
Delaware | 125,772 | $ 251,946,480 | 18,527 | $ 32,234,178
District of Columbia | 123,553 | $ 159,419,572 | 21,262 | $ 24,001,881
Hawaii | 189,060 | $ 391,952,540 | 31,090 | $ 59,632,187
Illinois | 1,490,936 | $ 3,319,867,980 | 222,405 | $ 465,988,054
Indiana | 556,220 | $ 810,178,691 | 109,948 | $ 145,914,499
Iowa | 296,977 | $ 565,883,377 | 49,309 | $ 80,032,042
Kentucky | 558,725 | $ 706,783,889 | 92,468 | $ 96,802,505
Louisiana | 381,252 | $ 565,793,857 | 123,108 | $ 169,180,471
Maine | 27,384 | $ 38,277,057 | 27,384 | $ 38,277,057
Maryland | 600,868 | $ 1,353,645,229 | 101,313 | $ 213,815,775
Massachusetts | 1,148,144 | $ 2,592,755,094 | 160,450 | $ 338,620,310
Michigan | 1,138,343 | $ 1,731,004,370 | 193,986 | $ 271,820,305
Minnesota | 600,981 | $ 1,356,606,456 | 94,384 | $ 199,192,440
Montana | 69,418 | $ 101,424,056 | 17,304 | $ 23,321,446
Nevada | 252,055 | $ 390,746,502 | 41,558 | $ 57,375,621
New Hampshire | 74,226 | $ 166,749,362 | 14,187 | $ 29,940,195
New Jersey | 735,045 | $ 1,658,273,490 | 115,925 | $ 244,653,227
New Mexico | 389,011 | $ 487,757,313 | 62,712 | $ 63,835,701
New York | 3,871,472 | $ 8,735,439,467 | 635,420 | $ 1,341,015,875
North Dakota | 47,045 | $ 106,003,360 | 7,721 | $ 16,294,819
Ohio | 1,579,154 | $ 2,601,153,570 | 246,572 | $ 361,963,879
Oregon | 397,494 | $ 635,702,735 | 63,689 | $ 95,151,371
Pennsylvania | 1,146,557 | $ 2,466,059,165 | 233,785 | $ 467,571,680
Rhode Island | 150,946 | $ 332,561,427 | 25,818 | $ 51,230,964
Vermont | 122,864 | $ 253,484,030 | 18,352 | $ 35,226,675
Virginia | 137,448 | $ 290,074,709 | 137,448 | $ 290,074,709
Washington | 802,355 | $ 1,808,644,578 | 131,438 | $ 277,391,244
West Virginia | 246,826 | $ 293,702,925 | 39,591 | $ 36,258,941
Total | 26,214,527 | $ 52,939,980,131 | 4,448,523 | $ 8,253,419,233

  * •

Notes: This table reports the estimated federal payments distributed to states
based on the reclassification of Medicaid enrollees from the original
population to the new adult group. Authors’ calculations. See main text for
details. We omit states that had not expanded by 2019, when our sample period
ends.

Several strands of circumstantial evidence support the reclassification
hypothesis. Despite fears that a large woodwork effect would put substantial
strain on state budgets, subsequent analyses have revealed that the fiscal
impact of Medicaid expansion has been smaller than anticipated (Sommers and
Gruber, 2017; Gruber and Sommers, 2020), with some analyses appearing to show
that Medicaid expansion resulted in net fiscal savings in some states (Levy et
al., 2020; Simpson, 2020). Reclassifications, by allowing states to blunt the
woodwork effect and draw down additional federal Medicaid funding through the
enhanced FMAP rate, help to explain this outcome. Relatedly, enrollment in the
new adult group enrollment has exceeded projections in virtually every
expansion state (Blase and Yelowitz, 2019). Reclassifications, which were
generally not contemplated by forecasters, provide a simple explanation.
Finally, per enrollee spending on the new adult group has been significantly
higher than predicted. In 2013, CMS estimated that per enrollee costs in the
new adult group would be $3,625 per enrollee in 2016 (Truffer et al., 2013). A
subsequent report from the same source revealed that members of the new adult
group had, in fact, cost $5,959 per enrollee in 2016 (Truffer et al., 2018),
nearly two-thirds more than originally predicted. This fact is consistent with
the notion that some of the original Medicaid population — who are more costly
to insure, on average, than members of the new adult group — were reclassified
into the new adult group.

Our data provides little direct insight into the types of enrollees being
reclassified. As we discussed in Section 2.3, the ACA and subsequent federal
rulemaking established some pathways whereby certain individuals who would
otherwise have been enrolled in the original Medicaid population could be
counted in the new adult group. For example, a woman who enrolls in Medicaid
under the ACA rules and later becomes pregnant (thereby meeting eligibility
criteria for the original Medicaid population) need not be reclassified into
the original Medicaid population during her pregnancy. Similar logic applies
to a person who enrolls in the new adult group and subsequently suffers a
disabling injury that renders them eligible for Medicaid coverage under pre-
ACA eligibility rules; they need not be transferred to the original Medicaid
population for the purposes of obtaining federal reimbursements. These forces
would tend to reduce the size of the original Medicaid population gradually,
as more and more members of the new adult group experienced these health
events. Yet our results are inconsistent with this prediction. The dynamic
treatment effects we estimate indicate that the original Medicaid population
contracted suddenly in the second post-treatment year and remained relatively
stable over the succeeding four years, rather than continuing to decline.
Therefore, we conclude that the forces “siphoning off" enrollees from the
original Medicaid population likely play a minor role in explaining our
results.

On the other hand, our findings may partly be driven by reclassifications that
occurred in violation of Medicaid rules. Under federal law, states are
responsible for determining applicants’ eligibility for Medicaid, including
periodically redetermining eligibility, disenrolling individuals who are no
longer eligible, and reclassifying enrollees who may no longer meet the
criteria under one eligibility pathway but may still qualify for Medicaid
coverage through a different pathway. Yet the enhanced FMAP rates for the new
adult group offered under the ACA dramatically reduced states’ incentives to
maintain accurate Medicaid rolls. Moreover, the federal government provides
only token oversight of states’ eligibility verification procedures. According
to CMS, “When states submit their Medicaid expenditure reports, they certify
the data are accurate and CMS conducts a limited review to assess whether the
data is reasonable. The review consists of comparing the state-reported data
to other readily available information, including state-reported performance
indicators and expenditures, and follow-up with the state as needed.” Yet
states rarely face meaningful penalties for submitting incorrect enrollment
records. During the entire post-treatment period we examine (2014-2019), it
was the explicit policy of the federal government not to attempt to recoup
funds distributed to states on the basis of eligibility errors (Yocom, 2020).
Previous research has noted that the ACA’s Medicaid expansion was associated
with large increases in Medicaid coverage among adults with incomes above 138%
of the federal poverty level, suggesting that states failed to adequately
enforce eligibility rules (Courtemanche et al., 2019).

Recent federal investigations into expansion states’ Medicaid records provide
direct evidence that improper reclassifications into the new adult group are
common. In an audit of New York’s Medicaid program, investigators reviewed
eligibility documentation for a random sample of 130 Medicaid enrollees New
York had classified as belonging to the new adult group, and for whom New York
had received funding through the ACA’s enhanced FMAP rate. The audit found
that New York incorrectly claimed enhanced reimbursement for 13.8% of these
enrollees and did not provide sufficient documentation to verify that 1.5% of
these enrollees were eligible for enhanced Medicaid reimbursement (Chiedi,
2018). A similar audit in California found that 18.0% of a randomly selected
sample of enrollees in the new adult group were ineligible and 9.3% of
enrollees were potentially ineligible under ACA rules (Levinson, 2018). In
Colorado, an investigation found that 23.3% of randomly selected enrollees in
the new adult group were ineligible, while an additional 6.7% lacked
sufficient documentation to determine eligibility (Chiedi, 2019).

Following Bundorf and Kessler (2022), we extrapolate from these audits to
provide a general indication of the proportion of reclassifications that may
be improper. To do so, we use the results of the New York audit as a lower
bound (using only the proportion of enrollees – 13.8% – auditors verified as
ineligible) and the results of the Colorado audit as an upper bound (using the
proportion of enrollees – 28.3% – auditors found to be definitely or
potentially ineligible).161616Out of the 60 Medicaid beneficiaries sampled, 14
were ineligible and 4 may have been ineligible, but one person was counted in
both groups. In 2019, states reported total enrollment in the new adult group
of 12.0 million. Applying these lower and upper bounds, we find that between
1.65 million and up to 3.4 million of these enrollees may have been improper.
In light of our finding that the original Medicaid population declined by 4.4
million enrollees, these figures suggest that between 37.2% and 76.3% of all
reclassified enrollees may have been reported in violation of federal law.
These estimates should be interpreted cautiously, however, since enrollment
patterns may vary by state and over time; other expansion states may have
higher or lower misclassification rates than New York, Colorado, or
California.

###  5.4 Robustness Tests

In this section, we perform several of tests to assess the sensitivity of our
findings.

####  5.4.1 Data Quality

As explained in greater detail in Section 3, our main analysis uses two
different data sources to measure state Medicaid enrollment; one covers the
years 2006 to 2013, while the other covers the years 2014 to 2019. Since most
expansion states began implementing the reform in 2014, it is conceivable that
our findings could be an artifact of transitioning to a different data source.
This could occur if our 2014-2019 data systematically under-counted original
Medicaid enrollment in expansion states relative to non-expansion states. The
lack of a clear discontinuity between expansion and non-expansion states in
2014 — visible in Figures 1, 2, and A2 — is re-assuring. Still, we further
explore this possibility in two ways. First, we re-run the analysis using only
2014-19 data. While this restricts our sample and limits the number of pre-
treatment periods available, it obviates the need to combine different data
sources. Results of that exercise are presented in Table 5. The treatment
effect in our preferred specification (column 3) remains statistically
significant, albeit somewhat smaller in magnitude (-0.0764 instead of
-0.0993). Most specifications are no longer statistically significant at the
10% level, and estimated effect sizes generally shrink compared to our main
results. The small number of observations in our restricted sample may
contribute to a loss of statistical significance. Still, we note that all
coefficients remain negative and economically meaningful.

Table 5: Effects of Medicaid Expansion on Enrollment: 2014-2019 Sample Period

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
---|---|---|---|---|---|---|---|---
ATT |  -0.0813** |  -0.0764** |  -0.0604 |  -0.0526 |  -0.0744** |  -0.0480 |  -0.0651 |  -0.0367
| (0.0334) | (0.0339) | (0.0417) | (0.0354) | (0.0330) | (0.0419) | (0.0408) | (0.0410)
Gov’s Political Party |  | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Elig. Limit, Parents |  | ✓ |  | ✓ | ✓ | ✓ | ✓ | ✓
Unemployment Rate |  | ✓ | ✓ |  | ✓ | ✓ | ✓ |
Elig. Limit, Children |  |  | ✓ |  |  |  |  |
Poverty Rate |  |  |  | ✓ |  |  |  |
ln(State Population) |  |  |  |  | ✓ |  |  |
Non-White (% of State) |  |  |  |  |  | ✓ |  |
TANF Benefits |  |  |  |  |  |  | ✓ |
Food Insecurity Rate |  |  |  |  |  |  |  | ✓
N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150

  * •

Notes: This table shows estimates of the ATT of Medicaid expansion on the size
of the original Medicaid population (logged) across a range of models, all of
which use the difference-in-differences estimator described by Callaway and
Sant’Anna (2021) weighted by each state’s 2013 Medicaid population. The
comparison group is "never" treated units. All other models include all
states. Standard errors (clustered by state) are reported in parentheses.
$p<0.10$, ** $p<0.05$, *** $p<0.01$.

As an additional check, we compare our 2013 Medicaid enrollment data from KFF
with estimates from CMS of 2013 Medicaid and CHIP enrollment.171717 The CMS
estimates reflect the average monthly Medicaid and CHIP enrollment from July
to September 2013. Our KFF estimates reflect Medicaid enrollment in June 2013.
CMS did not release 2013 estimates for Medicaid only. The CMS data, which has
been used as a benchmark to gauge ACA-induced changes in coverage, is not
available for prior years, but this narrow overlap in 2013 provides some
insight into whether KFF and CMS estimates systematically differ. Figure A3 in
the Appendix plots the log of enrollment in each state from KFF and CMS in
2013. Nearly all states lie very close to the diagonal, indicating no large
differences between the two sources. To the extent that some states lie
slightly above the diagonal, this may be due to the fact that CMS’ data
includes CHIP enrollees, while KFF’s estimates exclude these enrollees. We
also note the absence of any clear pattern between expansion states (in blue)
and non-expansion states (in red). Overall, these results assuage concerns
that the our main findings are driven by data discrepancies.

####  5.4.2 Changes to Enrollment Practices

During our study period, some states implemented reforms to their
administrative procedures that may have reduced enrollment in the original
Medicaid population. In particular, Arbogast et al. (2024) document two major
categories of new rules: 1) increases in the stringency and frequency of
eligibility and income checks, and 2) mechanisms to automatically disenroll
beneficiaries deemed to no longer qualify for the program (e.g., cancelling
someone’s coverage without notice if a person does not respond to a request
for documentation within a certain time frame). To the extent that these
policies coincided with Medicaid expansion and may have disproportionately
affected populations in expansion states, they could influence our findings.
To address this concern, we re-estimate our main models after dropping the 13
states that implemented one or both of these policies from 2013-2019. The
results of this exercise, which we report in Table 6, are generally similar to
our main estimates; all specifications that reached statistical significance
in our main analysis remain statistically significant and some coefficients —
including our preferred specification — grow slightly in magnitude. The
dynamic treatment effects we obtain from this more limited subset of states,
shown in Figure 4, are also similar to our main results.

Table 6: Effects of Medicaid Expansion on Enrollment: Exclude Observations
from States That Imposed Administrative Burdens

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
---|---|---|---|---|---|---|---|---
ATT (S.E.) |  -0.0299 |  -0.1031** |  -0.0654** |  -0.1086** |  -0.0950* |  -0.0943* |  -0.0607* |  -0.0749**
| (0.0383) | (0.0404) | (0.0332) | (0.0429) | (0.0499) | (0.0488) | (0.0339) | (0.0378)
Gov’s Political Party |  | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Elig. Limit, Parents |  | ✓ |  | ✓ | ✓ | ✓ | ✓ | ✓
Unemployment Rate |  | ✓ | ✓ |  | ✓ | ✓ | ✓ |
Elig. Limit, Children |  |  | ✓ |  |  |  |  |
Poverty Rate |  |  |  | ✓ |  |  |  |
ln(State Population) |  |  |  |  | ✓ |  |  |
Non-White (% of State) |  |  |  |  |  | ✓ |  |
TANF Benefits |  |  |  |  |  |  | ✓ |
Food Insecurity Rate |  |  |  |  |  |  |  | ✓
N | 669 | 669 | 669 | 669 | 669 | 669 | 669 | 669

  * •

Notes: This table shows estimates of the ATT of Medicaid expansion on the size
of the original Medicaid population (logged) across a range of models, all of
which use the difference-in-differences estimator described by Callaway and
Sant’Anna (2021) excluding observations from states that had imposed
administrative burdens of more stringent eligibility checks (TX, MS, LA, ID,
IL, HI, FL, CO) and states that implemented automatic disenrollment policies
during our sample period (AR, IL, LA, MO, NC, OH, TN). For more, see Arbogast
et al. (2024). The comparison group is "never" treated states. Standard errors
(clustered by state) are reported in parentheses.
* $p<0.10$, ** $p<0.05$, *** $p<0.01$.

Figure 4:  Dynamic Effects of Medicaid Expansions on Enrollment: Exclude
Observations from States That Imposed Administrative Burdens Notes: This plot
shows dynamic effects across event time, from (column (2) in Table 6),
excluding observations from states that had imposed administrative burdens of
more frequent/stringent eligibility checks (TX, MS, LA, ID, IL, HI, FL, CO)
and states that implemented automatic disenrollment policies during our sample
period (AR, IL, LA, MO, NC, OH, TN). For more, see Arbogast et al. (2024).
Bars represent 95% confidence intervals. The vertical dashed line represents
the implementation of Medicaid expansion. We use enrollment figures for the
month of June in each year. Data was compiled by the authors from Kaiser
Family Foundation issue briefs (for the years 2006-2013) and reports from the
Medicaid Budget and Expenditure System (for the years 2014-2019); see section
3 for more details. We define the original Medicaid population as total
Medicaid enrollment minus the number of enrollees reported by states as “newly
eligible" under the ACA.

##  6 Conclusion

The expansion of Medicaid under the ACA was a significant development in U.S.
health policy. We examine a previously overlooked fiscal effect of this
reform. Although past research using household survey data has documented a
robust woodwork effect in Medicaid associated with expansion, we find no
evidence of such an effect in states’ administrative enrollment records.
Rather, we find evidence that the original Medicaid population contracted
sharply following Medicaid expansion’s implementation, defying forecasters’
expectations. We argue that this discrepancy is a mirage caused by the
reclassification of individuals who otherwise would have been counted in the
original Medicaid population to the new adult group. While these
reclassifications were a purely administrative phenomenon that did not affect
the coverage or benefits of individual Medicaid enrollees, they have had
substantial fiscal effects on states and the federal government.

Our estimates imply that these reclassifications resulted in nearly $52.9
billion in federal Medicaid payments to states from 2014-2019, including $8.3
billion in 2019 alone. The hidden subsidy we document represents a sizable
share of Medicaid expansion’s impact on federal spending. According to the
CBO, the direct federal costs of Medicaid expansion — that is, reimbursements
made to states to cover medical services for the new adult group — were $66
billion in 2019 (Fritzsche et al., 2019). This figure, however, implicitly
assumes that members of the new adult group would not have received federal
subsidies in the absence of the ACA’s expanded eligibility rules. Our results
indicate, however, that 4.4 million Medicaid enrollees classified in the new
adult group may have been counted as original Medicaid enrollees and
reimbursed at states’ traditional FMAP rate if Medicaid expansion had not
occurred. Our results imply that the federal government may have provided
$12.1 billion to states in 2019 to cover these enrollees.181818We arrive at
this result by multiplying the estimated number of reclassified Medicaid
enrollees in 2019 in each state (totalling 4.4 million) by the average per
enrollee cost nationwide in 2019 ($4,908) and the applicable state’s
traditional FMAP rate. Therefore, the federal fiscal impact of Medicaid
expansion is approximately $53.9 billion ($66 billion – $12.1 billion),
substantially smaller than CBO’s estimates suggest. On the other hand, these
downward revisions imply that reclassifications inflated the federal cost of
Medicaid expansion by 18.2%.

It is likely that similar subsidies occurred in more recent years, although
forces linked to the COVID-19 public health emergency may have changed their
magnitude. On the one hand, the temporary increase in the traditional FMAP
rate during the COVID-19 public health emergency narrowed the FMAP rate spread
with the enhanced FMAP rate, which would have reduced the size of the hidden
subsidy. On the other hand, the continuous enrollment requirement imposed
during the COVID-19 public health emergency substantially increased Medicaid
enrollment and may have increased the size of the hidden subsidy.

Our results suggest that state policymakers are sensitive to incentives
created through Medicaid’s joint financing structure. The ACA’s hidden subsidy
has had a substantial fiscal effect on the federal government and expansion
states. Accounting for strategic behavior by states is crucial for accurately
predicting the effects of policy changes to Medicaid and similar federal-state
programs. More stringent federal monitoring of states’ enrollment practices
may help to mitigate such behavior. Alternative financing methods, such as
federal block grants, could also reduce or eliminate opportunities to draw
down additional federal Medicaid funding through administrative
reclassifications.

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##  7 Appendix

Table A1: State Medicaid Expansion Status State |  Implementation date |  Designation in our analysis
---|---|---
Alabama |  Has not expanded |  Non-expansion
Florida |  Has not expanded |  Non-expansion
Georgia |  Has not expanded |  Non-expansion
Kansas |  Has not expanded |  Non-expansion
Mississippi |  Has not expanded |  Non-expansion
South Carolina |  Has not expanded |  Non-expansion
Tennessee |  Has not expanded |  Non-expansion
Texas |  Has not expanded |  Non-expansion
Wisconsin |  Has not expanded |  Non-expansion
Wyoming |  Has not expanded |  Non-expansion
Arizona |  1/1/2014 |  Expansion
Arkansas |  1/1/2014 |  Expansion
California |  1/1/2014 |  Expansion
Colorado |  1/1/2014 |  Expansion
Connecticut |  1/1/2014 |  Expansion
Delaware |  1/1/2014 |  Expansion
District of Columbia |  1/1/2014 |  Expansion
Hawaii |  1/1/2014 |  Expansion
Illinois |  1/1/2014 |  Expansion
Iowa |  1/1/2014 |  Expansion
Kentucky |  1/1/2014 |  Expansion
Maryland |  1/1/2014 |  Expansion
Massachusetts |  1/1/2014 |  Expansion
Minnesota |  1/1/2014 |  Expansion
Nevada |  1/1/2014 |  Expansion
New Jersey |  1/1/2014 |  Expansion
New Mexico |  1/1/2014 |  Expansion
New York |  1/1/2014 |  Expansion
North Dakota |  1/1/2014 |  Expansion
Ohio |  1/1/2014 |  Expansion
Oregon |  1/1/2014 |  Expansion
Rhode Island |  1/1/2014 |  Expansion
Vermont |  1/1/2014 |  Expansion
Washington |  1/1/2014 |  Expansion
West Virginia |  1/1/2014 |  Expansion
Michigan |  4/1/2014 |  Expansion
New Hampshire |  8/15/2014 |  Expansion
Pennsylvania |  1/1/2015 |  Expansion
Indiana |  2/1/2015 |  Expansion
Alaska |  9/1/2015 |  Expansion
Montana |  1/1/2016 |  Expansion
Louisiana |  7/1/2016 |  Expansion
Virginia |  1/1/2019 |  Expansion
Maine |  1/10/2019 |  Expansion
Idaho |  1/1/2020 |  Non-expansion
Utah |  1/1/2020 |  Non-expansion
Nebraska |  10/1/2020 |  Non-expansion
Oklahoma |  7/1/2021 |  Non-expansion
Missouri |  10/1/2021 |  Non-expansion
South Dakota |  7/1/2023 |  Non-expansion
North Carolina |  12/1/2023 |  Non-expansion
Source: Kaiser Family Foundation

Table A2: Effects of Medicaid Expansion on Enrollment: Weighted by Medicaid
Population

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
---|---|---|---|---|---|---|---|---
ATT |  -0.0819** |  -0.1195*** |  -0.0956** |  -0.0986** |  -0.1195*** |  -0.1188*** |  -0.1079** |  -0.0742*
| (0.0345) | (0.0392) | (0.0383) | (0.0405) | (0.0377) | (0.0445) | (0.0547) | (0.0394)
Gov’s Political Party |  | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Elig. Limit, Parents |  | ✓ |  | ✓ | ✓ | ✓ | ✓ | ✓
Unemployment Rate |  | ✓ | ✓ |  | ✓ | ✓ | ✓ |
Elig. Limit, Children |  |  | ✓ |  |  |  |  |
Poverty Rate |  |  |  | ✓ |  |  |  |
ln(State Population) |  |  |  |  | ✓ |  |  |
Non-White (% of State) |  |  |  |  |  | ✓ |  |
TANF Benefits |  |  |  |  |  |  | ✓ |
Food Insecurity Rate |  |  |  |  |  |  |  | ✓
N | 714 | 714 | 714 | 714 | 714 | 714 | 714 | 714

  * •

Notes: This table shows estimates of the ATT of Medicaid expansion on the size
of the original Medicaid population (logged) across a range of models, all of
which use the difference-in-differences estimator described by Callaway and
Sant’Anna (2021) weighted by each state’s 2013 Medicaid population. The
comparison group is "never" treated units. All other models include all
states. Standard errors (clustered by state) are reported in parentheses.
$p<0.10$, ** $p<0.05$, *** $p<0.01$.

Table A3: Effects of Medicaid Expansion on Enrollment in the Original Medicaid
Population: Robustness of Main Specification

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10)
---|---|---|---|---|---|---|---|---|---|---
ATT (S.E.) |  -0.0993*** |  -0.1195*** |  -0.1118*** |  -0.1246*** |  -0.0800*** |  -0.1266** |  -0.0902** |  -0.1576*** |  -0.1079*** |  -0.1798***
| (0.0348) | (0.0392) | (0.0395) | (0.0433) | (0.0350) | (0.0492) | (.0349) | (0.0578) | (0.0414) | (0.0684)
Gov Pol Party | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Elig Parents | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Unemployment | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Drop Late Exp |  |  | ✓ | ✓ |  |  |  |  | ✓ | ✓
Drop 2010-12 Exp |  |  |  |  | ✓ | ✓ |  |  | ✓ | ✓
Drop Pre-2010 Exp |  |  |  |  |  |  | ✓ | ✓ | ✓ | ✓
Weighted |  | ✓ |  | ✓ |  | ✓ |  | ✓ |  | ✓
N | 714 | 714 | 602 | 602 | 630 | 630 | 574 | 574 | 462 | 462

  * •

Notes: This table shows estimates of the ATT of Medicaid expansion on the size
of the original Medicaid population (logged) across a range of specifications,
all of which use the difference-in-differences estimator described by Callaway
and Sant’Anna (2021). Specifications that drop Late Expanders exclude the
following states from the sample: AK, IN, LA, ME, MT, NH, PA, and VA.
Specifications that drop 2010-2012 Expanders exclude the following states: CA,
CT, DC, MN, NJ, WA. Specifications that drop Pre-2010 Expanders exclude the
following states: DE, DC, MA, NY, VT. The comparison group is “never-treated"
units. All other models include all states. Standard errors (clustered by
state) are reported in parentheses.
* $p<0.10$, ** $p<0.05$, *** $p<0.01$.

Table A4: Effects of Medicaid Expansion on Enrollment Original Medicaid
Population: 2014 Cohort Only

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
---|---|---|---|---|---|---|---|---
ATT (S.E.) | -0.0595 |  -0.1118*** |  -0.0777*** |  -0.1209*** |  -0.1040** |  -0.1021** |  -0.1159** |  -0.0755**
| (0.0370) | (0.0395) | (0.0362) | (0.0417) | (0.0382) | (0.0429) | (0.0496) | (0.0353)
Gov’s Political Party |  | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Elig. Limit, Parents |  | ✓ |  | ✓ | ✓ | ✓ | ✓ | ✓
Unemployment Rate |  | ✓ | ✓ |  | ✓ | ✓ | ✓ |
Elig. Limit, Children |  |  | ✓ |  |  |  |  |
Poverty Rate |  |  |  | ✓ |  |  |  |
ln(State Population) |  |  |  |  | ✓ |  |  |
Non-White (% of State) |  |  |  |  |  | ✓ |  |
TANF Benefits |  |  |  |  |  |  | ✓ |
Food Insecurity Rate |  |  |  |  |  |  |  | ✓
N | 602 | 602 | 602 | 602 | 602 | 602 | 602 | 602

  * •

Notes: This table shows estimates of the ATT of Medicaid expansion on the size
of the original Medicaid population (logged) across a range of models, all of
which use the difference-in-differences estimator described by Callaway and
Sant’Anna (2021). The treatment group in this table is states that expanded in
2014. The comparison group is “never-treated" units. All other models include
all states. Standard errors (clustered by state) are reported in parentheses.
$p<0.10$, ** $p<0.05$, *** $p<0.01$.

Figure A1:  Dynamic Effects of Medicaid Expansions on Enrollment: Weighted by
Medicaid Population Notes: This plot shows dynamic effects across event time,
from (column (3) in Table A2), which are weighted by each state’s 2013
Medicaid population. Bars represent 95% confidence intervals. The vertical
dashed line represents the implementation of Medicaid expansion. We use
enrollment figures for the month of June in each year. Data was compiled by
the authors from Kaiser Family Foundation issue briefs (for the years
2006-2013) and reports from the Medicaid Budget and Expenditure System (for
the years 2014-2019); see Section 3 for more details. We define the original
Medicaid population as total Medicaid enrollment minus the number of enrollees
reported by states as “newly eligible" under the ACA. Figure A2:  Dynamic
Effects of Medicaid Expansion on Enrollment: 2014 Cohort Only Notes: This plot
shows dynamic effects across event time, from (column (3) in Table A4). Bars
represent 95% confidence intervals. The vertical dashed line represents the
implementation of Medicaid expansion. We use enrollment figures for the month
of June in each year. Data was compiled by the authors from Kaiser Family
Foundation issue briefs (for the years 2006-2013) and reports from the
Medicaid Budget and Expenditure System (for the years 2014-2019); see Section
3 for more details. We define the original Medicaid population as total
Medicaid enrollment minus the number of enrollees reported by states as “newly
eligible" under the ACA. Figure A3:  Medicaid Enrollment in 2013 by Data
Source Notes: This figure plots the (log) Total Medicaid Population for
December 2013 from CMS-MBES against the (log) Total Medicaid and CHIP
population in KFF reports from 2013. This graph excludes Connecticut and Maine
as data was not available for these states for December 2013 in CMS-MBES. Data
was compiled by the authors from Kaiser Family Foundation issue briefs (for
the years 2006-2013) and reports from the Medicaid Budget and Expenditure
System (for the years 2014-2019); see section 3 for more details.