Evidence from the ACA Medicaid Expansion

33 Pages·2017·0.51 MB·English

Moving for Health Insurance:
∗
Evidence from the ACA Medicaid Expansion
Patrick S. Turner
University of Colorado - Boulder
March 2017
Preliminary Draft - Not for Citation
Abstract
This paper extends the welfare magnets literature by using the 2014 expansion of Medi-
caid to test for welfare migration. The Patient Protection and Affordable Care Act (ACA)
mandated the expansion of Medicaid. However, a Supreme Court decision overturned this
provision and allowed states to opt-out. As a result, childless adults with incomes up to 138%
of the federal poverty level are eligible for Medicaid in states that have expanded coverage and
are not eligible at any income level in the majority of states that opted to not expand. Using
a difference-in-differences strategy which compares the log estimated low-income population
of expansion and non-expansion states, before and after Medicaid expansion, I find that the
low-income population increased by 1% in expansion states after 2012 relative to what one
would expect given pre-existing trends and post-decision population changes in non-expansion
states. This result is robust to the use of state-level synthetic control.
∗
Turner: [email protected]. IwouldliketoacknowledgehelpfulcommentsandguidancefromFrancisca
Antman, Tania Barham, Brian Cadena, and Terra McKinnish.
1 Introduction
The2012SupremeCourtdecisionallowingstatestoopt-outoftheMedicaidexpansioninthePatient
Protection and Affordable Care Act (ACA) induced significant cross-state variation in eligibility
for Medicaid. As a result, adults without children are eligible for Medicaid up to 138% of the
federal poverty level in the 28 states that have expanded coverage. On the other hand, these same
adults would not be eligible at any income level in the majority of the non-expansion states.1 An
important policy question is then whether low-income households will migrate across state borders
in order to receive public health insurance. That is, do individuals in low(no)-eligibility states tend
to move to high-eligibility states to receive benefits? Where low-income households locate have
important effects on local labor markets. For instance, welfare-induced migration could shift the
skill composition of workers, receipts of other welfare programs could increase, and human capital
accumulation could be affected through negative spillovers from changes in the socio-economic
distribution of students in the public-school system. Using a difference-in-differences strategy which
compares the log estimated low-income population of expansion and non-expansion states, before
and after Medicaid expansion, I test for evidence of welfare-induced migration in the context of the
Medicaid program.
The ACA introduced broad changes to health insurance coverage in the United States. An
important feature of the legislation was to increase coverage rates for all citizens. The legislation
allowed individuals with pre-existing conditions to be covered under new policies, increased the
age in which young adults could maintain coverage from their parents, and created tax-based
incentives for individuals to acquire coverage and employers to provide coverage. Importantly, the
original legislation provided significant coverage expansions to low-income individuals. States were
required to extend Medicaid coverage to childless adults, among other household types, up to 138%
of the federal poverty line. However, this measure was struck down by the Supreme Court as
unconstitutional in 2012, allowing states to opt-out of expanding Medicaid eligibility.
1Even though adults without children are not eligible for Medicaid, the ACA does provide individuals with a
subsidy on the purchase of health insurance in the marketplace. An individual above 100% of the poverty level is
eligible for a subsidy.
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Using individual level data from the American Community Survey, I estimate the effect of Med-
icaid expansion on household locational choice. Specifically, I construct estimates of low-income
population counts at the state level using time-invariant characteristics of individuals. Then, I
implement a difference-in-differences strategy which compares population trends in expansion and
non-expansion states before and after the expansion of Medicaid. With this strategy, I find re-
sults consistent with the welfare magnets hypothesis. I confirm the results from the difference-in-
differences strategy with a synthetic control approach which allows the data to construct a set of
weights for non-expansion states so that population trends prior to Medicaid expansion match more
closely. Finally, I use an approach similar to McKinnish (2005) an compare differences in Medicaid
take-up in border regions to interior regions within a state, before and after Medicaid expansion.
In all three approaches, I find results consistent with welfare-induced migration. I estimate that
the low-income population in expansion is about 1% than what one would expect in the absence of
Medicaid expansion. However, back of the envelop calculations suggest that the size of the effect
does little to alleviate the gap in coverage caused by the Supreme Court ruling.
This paper is related to the broader “welfare magnets” literature which explores migration in-
duced by cross-state differences in welfare benefits. Most work focuses on cross-state variation in
cash welfare programs - Aid to Family with Dependent Children (AFDC) and Temporary Assis-
tance for Needy Families (TANF). In examining migration of natives, McKinnish (2005, 2007) finds
evidence that individuals move across borders to receive higher cash benefits. Fiva (2009) finds sim-
ilar evidence in Norway. In considering the locational choice of immigrants in particular, Buckley
(1996), Borjas (1999), and Dodson III (2001) all find evidence that immigrants disproportionately
locatetohighbenefitstates. However, theirworksuffersfromeconometricproblemsassociatedwith
relying on cross-sectional data (Buckley, 1996; Dodson III, 2001) or time-series problems (Borjas,
1999) by not being able to control for state-level unobservables or macroeconomic trends changing
over time, respectively. Kaushal (2005) overcomes these difficulties by exploiting cross-state vari-
ation in welfare eligibility introduced by the Personal Responsibility and Work Opportunity Act
(PRWORA). The author finds no evidence for the “welfare magnets” hypothesis with respect to
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immigrant welfare migration.
Despiteawideliteratureexaminingthemagneticincentivesofcashbenefits, littleworkconsiders
whether households relocate to gain public health insurance coverage. Chatterji and Li (2016)
briefly consider the topic of migration in their paper which considers the effects of early (pre-2014)
Medicaid expansions on disability program takeup. 2 My work contributes to the literature by
exploring the possible effects of vast eligibility differences induced by the ACA. Thus, this paper
fits into the broader literature considering the effects of ACA. Furthermore, whether households
decide torelocateis of greater importancewithin the field oflabor economics. Changes in household
behavior could affect the skill composition of the labor supply, demographic characteristics of peers
in the education system, tax burden of state residents, among others. Furthermore, the ability for
households to “vote with their feet” might mean that national take-up rates could be higher than
expected despite low eligibility levels in some states. Finally, whether or not individuals have health
insurance coverage could affect long-term health status which has implications for human capital
accumulation and employment outcomes.
The paper proceeds as follows. Section 2 explains Medicaid expansion and introduces the basic
theoretical framework for thinking about welfare induced migration. Section 3 discusses the data
used for analysis and the empirical strategy. Section 4 presents results. Section 5 provides a
concluding discussion.
2 Theoretical Framework
2.1 Medicaid Expansion
The U.S. experienced a significant overhaul of its health insurance system with the signing of the
ACA in March, 2010. Various provisions of the law sought to increase health insurance coverage
across the country. For instance, insurers are no longer able to deny coverage based on pre-existing
2There are some papers that consider migration induced or reduced by non-cash welfare programs. For example,
Barham and Kuhn (2014) considers if individuals stay in rural Bangladesh in response to expansion in healthcare
supply and Verdugo (2014) looks at the effect of public housing supply on immigrant locational choice in Europe.
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conditionsandyoungadultsareeligibletoremainontheirparent’sinsuranceplanuntiltheageof26.
However, two important mechanisms were put in place to increase health insurance coverage rates:
expanding the populations eligible for Medicaid and the creation of health insurance exchanges to
improve access to private insurance.
The initial intent of the ACA was to expand Medicaid eligibility to all individuals whose house-
holdincomeplacesthembelow138%ofthefederalpovertyline. Thefederalgovernmentisresponsi-
blefor100%oftheincreaseincostforexpandingeligibilityfortheinitialyearsafterimplementation.
Beyond 2020, the federal government will still cover 90% of the costs from expansion. However,
many states felt the required expansion to be unconstitutional. In 2012, the Supreme Court ruled in
NFIB v. Sibelius that Congress did not have the constitutional right to dictate that states expand
Medicaid. As a result, states were given the right to opt-out of that particular provision in the law.
The Supreme Court ruling created significant cross-state variation in Medicaid eligibility levels
between non-expansion and expansion states. Importantly, childless adults in most non-expansion
states are ineligible for Medicaid, regardless of household income. These same individuals would be
eligible up to 138% in expansion states. Table 1 specifies the states used in analysis by expansion
states. In total, 24 states are included that expanded Medicaid on January 1, 2014 and 21 states
chose not to expand Medicaid at any time since. Six states are removed from analysis. Alaska and
Hawaii are excluded because they are not in the continental United States. Indiana, Michigan, New
Hampshire, and Pennsylvania are excluded because they expanded Medicaid after the initial date.
Figure 1 documents the sharp increase in Medicaid take-up in 2014 due to Medicaid expansion.
The dashed line plots the share of the adult population with Medicaid in non-expansion states. In
the period 2008-2015, this share slightly increases from about 7 percent to 10 percent. The solid line
plots the same outcome for the set of states that expanded Medicaid on January 1, 2014. Adults
in these states were more likely to be covered by Medicaid prior to expansion. However, this gap
increases in 2014 by almost 4 percent. Since there are certain groups that are eligible in some states
but not others, individuals may reap important welfare benefits by migrating across state borders.
However, we could be concerned that other provisions within ACA that are in effect in non-
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expansion states might ease the burden of getting health insurance coverage within this population.
Indeed, the ACA implemented what is commonly known as the individual mandate requiring in-
dividuals to purchase coverage. Specifically, individuals are required by law to maintain health
insurance coverage or face a tax penalty. Indeed, as Figure 2 shows, insurance coverage rates im-
proved in both expansion and non-expansion states. To ease the ability to find affordable insurance,
the law created state-level health insurance exchanges. The exchanges provides a clearer market
structure for individuals to shop and compare various plans. Additionally, certain individuals above
100% of the federal poverty line are eligible for subsidies to cover a large portion of the cost of
insurance. However, the alteration of the law from its original intent has left a “coverage gap”
for many low-income individuals. In this context, Medicaid is the only affordable option for this
population, when available.
2.2 Welfare Migration
In what follows, I adapt McKinnish (2005) which provides a suitable framework for considering
welfare migration. Imagine that there two bordering states each separated into two regions: the
interior and the border. The region which borders the neighboring state is the border county and
the other region is the interior county. Within each state, Medicaid eligibility is a uniform policy.
However, the eligibility level has the potential to change discretely at the state border. States can
either set policy to be a “high” eligibility state or a “low” eligibility state. In this setup, four border
types could exist: high-high, high-low, low-high, and low-low. For instance, the high-high type is
a high eligibility state which borders another high eligibility state. Remaining characteristics of a
region which might influence population flows are assumed to be constant across regions within a
state.
Consider an individual’s decision to migrate. There are various components which impact the
optimal location. For instance, wages, local amenities, social networks, and public benefits are
all inputs in an individual’s utility in the region. As assumed above, wages and amenities are
constant across a state, or at least observable to the econometrician, and given the importance of
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local labor markets, we should not expect to see large wage differentials across neighboring regions.
Furthermore, since public benefits are set at the state level, individual’s receive the same benefit
receipt regardless of region choice within a region. Importantly, eligibility has the potential to
vary across the state border. In the four border types listed above, high-low and low-high borders
could provide an incentive for a household to migrate from the low to the high state. If individuals
decide to move, this would be observed as an increase in the population at-risk of being eligible for
Medicaid in states that set higher eligibility levels. Similarly, the size of this population would fall
in low eligibility states. I will explore this possibility by comparing differences in population trends
in non-expansion and expansion state, before and after Medicaid expansion.
However, important social networks and information costs might make it more difficult to move
across multiple regions or even be aware of policies in neighboring states. For this reason, we should
expect to see more migration between border counties relative to interior-interior migration. That
is, marginal migrants who find the benefits of Medicaid exceed the cost of migration will choose to
relocate to a border county since the cost will be strictly smaller than choosing the interior county
in the neighboring state. Although, there may be some migration from the low-eligibility interior
county to the high-eligibility border county, I expect this amount to be smaller given increased
costs from leaving behind key support networks and information costs of moving a farther distance.
Finally, the welfare magnet mechanism should be stronger when the gap in eligibility levels is wider
betweenneighboringstates. Thismechanisminformsatripledifferenceapproachwhichcomparesan
outcomevariableofinterestbetweenborderandinteriorcountieswithinnon-expansionorexpansion
state, before and after Medicaid expansion.
3 Methodology
This section discusses the data used in analysis and lays out the empirical strategy to explore the
presence welfare magnets in the setting of public health insurance. The approach is two-tiered: (i) a
between-state analysis which compares changes in population levels/trends of the at-risk Medicaid
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population before and after Medicaid expansion between expansion and non-expansion states and
(ii)awithin-stateanalysiswhichcomparesdifferencesinMedicaidtake-upbeforeandafterMedicaid
expansion between geographies that border a neighboring state to those that do not.
3.1 Data
Data come from the 2005-2015 American Community Survey (ACS) compiled by IPUMS-USA
(Ruggles et al., 2015). The ACS is an annual household survey collected by the U.S. Census Bureau
and represents an annual 1% sample of the entire United States. The survey contains detailed data
on economic, demographic, and geographic characteristics of individuals. Specifically, the analysis
in this paper relies on an individual’s age, year of birth, location of residence, income, level of
education, and type of health insurance coverage. Additionally, I use the individual-level data to
construct average wages of particular sectors (manufacturing, retail, and construction) to use as
controls.
The between-state analysis of this paper relies on population trends. I construct state-level
population estimates of the low-income population for 2005-2015.3 I restrict the sample to native-
born individuals not living in group quarters and not working in the armed forces. The ACA had a
provision which allows individuals to remain on their parent’s health insurance until the age of 26.
Additionally, individuals become eligible for Medicare at the age of 65. For these reasons, I further
restrict the sample to adults aged 26-64.
As noted above, the ACA expanded Medicaid to all individuals with modified adjusted gross
income below 138% of the Federal Poverty Level. An individual’s income can vary over time
due to economic conditions and these conditions vary across states. These state-specific economic
shocks could bias estimation if the shocks are correlated with states that expanded Medicaid. I
constructestimatesofthelow-incomepopulationofstatesforeachyearbypredictingtheprobability
that an individual’s income falls below 138% FPL using a set of time-invariant characteristics and
summing the product of the predicted probability and the ACS person weight. Specifically, I
3Priorto2005,theACSwasalessthana1%sampleofthenationalpopulation. Forthisreason,Irestrictanalysis
to 2005 and later.
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estimate education-specific probabilities using the following model:
Pr[Povertyi <= 138] = XβkEducki +ǫi (1)
allk
where i indexes the individual and k indexes an educational category (high school dropout, high
school graduate, some college, college graduate, and advanced degree). The dependent variable
is an indicator if individual i has an IPUMS provided poverty measure that is less than or equal
to 138 and Educk are a set of indicators for the individual’s highest educational attainment. The
i
coefficients, β , are the share of each education group that falls below 138 FPL. By allowing the
k
data to estimate the likelihood that an educational group is at-risk of being eligible for Medicaid,
this approach is similar in spirit to earlier work in the welfare magnets literature which constructed
comparison groups based on educational attainment (e.g., Gelbach, 2004; McKinnish, 2007).
Table A-1 presents the estimated probabilities of being in the at-risk Medicaid population by
educational attainment. Column 1 uses data from the post-Medicaid Expansion years, 2014-2015.
High-school dropouts are the most likely to fall below the threshold at 45.1 percent. Higher ed-
ucational groups are monotonically less likely to fall below the threshold with individuals with a
post-graduate degree only being 4.16 percent likely to be at risk of being eligible for Medicaid.
Using data from the years 2005-2015 show estimated probabilities that are largely unchanged and
the results are robust to using either set of probabilities.
With probabilities in hand, I am able to construct state-year population estimates of the low
income population. These estimates are created by summing, over all individuals, the product of
the predicted probability of falling below the threshold and the individual’s ACS person weight.4
The population estimate places more weight on individuals that dropped out of high school (45.1%)
and graduated high school (21.6%), but still allows changes in the college-educated population to
influence the estimates.
The within-state analysis of this paper separates individuals within a state into different regions
based on their public-use micro data area (PUMA). PUMAs are geographically contiguous areas
4Specifically, I construct population estimates in the following way: Popst =Palli(cid:0)pˆkist·ACSperwtist(cid:1)
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that with a population around 100,000 people. The analysis requires PUMAs to be categorized as
border or interior regions. Geographic data on PUMAs come from shape files provided by IPUMS.
Since PUMA borders changed beginning with the 2012 ACS, the analysis is restricted to data from
2012-2015. Border regions are defined as those regions which share a border with a neighboring
state. Interior regions are defined as those regions which only share a border with other regions
within the state or large bodies of water.
PUMAs within a metropolitan statistical area (MSA) tend to have a small geographic area.
Some regions that are only a few miles from the border would be classified as interior even though
they are in the city that borders a neighboring state. An example is St. Louis, MO which borders
Illinois. I combine all PUMAs within the same MSA, whether or not that MSA borders a state, so
that if a city borders a neighboring state, then all of the PUMAs within that metropolitan area are
coded as one border region. I will refer to regions within a state as PUMA-MSAs.
The main outcome for the within-state analysis is the share of the adult PUMA-MSA population
on Medicaid. The sample is restricted to individuals aged 26-64. The adult Medicaid share is
constructed by summing the ACS person weights of individuals with Medicaid and dividing by the
sum of the ACS person weights of all adults within the PUMA-MSA.
3.2 Empirical Strategy
The difficulty with estimating a causal relationship between state Medicaid policy and low-income
migration is that state-level policy choice is plausibly endogenous and eligibility expansion could
be a part of a package of other policy changes that are correlated with welfare migration. However
in the case of Medicaid expansion, this is less of a concern. First, the ACA was a national-level
policy so its timing does not necessarily coincide with state-level policy packages. Furthermore,
as discussed above, it was not until years after the law was passed that the Supreme Court, in an
unlikely decision, decided to overrule Medicaid expansion while leaving the remainder of the law in
effect. Thus, Medicaid expansion is the only part of the law which introduces cross-state differences
in policy. However, a careful identification strategy is still necessary to argue for causality. I begin
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