The Effect of Child Health Insurance
Access on Schooling
Evidence from Public Insurance Expansions
Sarah R. Cohodes
Daniel S. Grossman
Samuel A. Kleiner
Michael F. Lovenheim
Although a sizable literature analyzes the effects of public health insurance
programs on short-run health outcomes, little prior work has examined their
long-term effects. We examine the effects of public insuranceexpansions among
children in the 1980s and 1990s on their future educational attainment. We find
that expanding health insurance coverage for low-income children increases
the rate of high school and college completion. These estimates are robust to
only using federal Medicaid expansions and mostly are due to expansions
that occur when the children are not newborns. Our results indicate that the
long-run benefits of public health insurance are substantial.
Sarah R. Cohodes is an assistant professor of education and public policy at Teachers College, Columbia
University. Daniel S. Grossman is an assistant professor of economics at West Virginia University.
Samuel A. Kleiner is an assistant professor of policy analysis and management at Cornell University and a
faculty research fellow at the National Bureau of Economic Research. Michael F. Lovenheim is an associate
professor of policy analysis and management at Cornell University and a faculty research fellow at the
National Bureau of Economic Research. The authors are grateful to Tal Gross, Kosali Simon, and Diane
Schanzenbach for helpful comments and guidance on all simulated eligibility calculations. They also thank
seminar participants at Cornell University, Harvard University, the 2014 American Economic Association
Annual Meeting, and the 2015 Association for Education Finance and Policy Annual Meeting as well as David
Autor, Amitabh Chandra, Larry Katz, and Aaron Yelowitz for helpful feedback on earlier versions of this work.
This paper represents a combination of two prior working papers: “The Impact of Medicaid Expansion for
Children on Their Educational Attainment”by Sarah Cohodes and “The Effect of Health Care Access on
Schooling: Evidence from Public Insurance Expansions”by Samuel Kleiner, Michael Lovenheim, and Daniel
Grossman. Cohodes gratefully acknowledges support from a Harvard University grant from the
Multidisciplinary Program in Inequality and Social Policy. The data used in this article can be obtained
beginning six months after publication through three years hence from Michael Lovenheim, 102 MVR Hall,
Ithaca, NY 14853; email: firstname.lastname@example.org.
[Submitted October 2014; accepted April 2015]
ISSN 0022-166X E-ISSN 1548-8004 ª2016 by the Board of Regents of the University of Wisconsin System
Supplementary materials are freely available online at: http://uwpress.wisc.edu/journals/journals/
THE JOURNAL OF HUMAN RESOURCES 51 3
Whether and how to provide access to affordable healthcare for low-
income Americans has become a central policy issue in the United States. The im-
portance of this issue is underscored by the intense debate surrounding the passage and
implementation of the 2010 Affordable Care Act (ACA), one of the largest expansions
of public health insurance in U.S. history. Most individuals from low-income house-
holds obtain medical insurance through Medicaid. Since its inception in 1965, Medicaid
has gone through repeated expansions that have greatly increased the scope of the
program as well as the public sector’s role in health insurance provision. As a result, over
50 percent of children in the United States currently are eligible for publicly provided
health insurance through this program,
and health insurance coverage is high among
this population (DeNavas-Walt et al. 2013).
The expansions that generated this high level of coverage were expensive. In 2012,
total state and federal spending on Medicaid was $415.2 billion (Henry J. Kaiser Family
Foundation 2014), which makes it the largest government program that targets low-
The substantial public funds devoted to providing health insurance
to low-income children, as well as recent debates over the value of such insurance that
surrounded the passage of the ACA, highlight the importance of understanding what
benefits, if any, accrue to individuals due to health insurance access when they are
The effect of Medicaid expansions on access to healthcare and on subsequent
child health has been studied extensively (Currie and Gruber 1996a, 1996b; Moss and
Carver 1998; Baldwin et al. 1998; Cutler and Gruber 1996; LoSasso and Buchmueller
2004; Gruber and Simon 2008), typically showing that Medicaid expansions increase
access to healthcare, decrease infant mortality, and improve childhood health. Fur-
thermore, these expansions and Medicaid access more generally have been linked to
a lower likelihood of bankruptcy and to less medical debt (Gross and Notowidigdo
2011, Finkelstein et al. 2012). Notably, this literature has focused almost exclusively
on the short- or medium-run effects of Medicaid on health and financial outcomes.
Such effects are of considerable policy importance, but without an understanding of
how Medicaid eligibility when young impacts long-run outcomes, it is difficult to
fully assess the impact of this large government program. Estimating the long-run
effects of Medicaid has received very little attention in the literature and is the focus of
We provide the first evidence on how expanding health insurance for children
throughout their youth influences their eventual educational attainment. Our analysis
focuses on education for several reasons. First, there is a strong argument from human
capital theory that the improvements in child health and increased financial stability
associated with Medicaid could have large effects on educational attainment. Second,
1. Throughout this paper, we refer to “public health insurance”and Medicaid synonymously. Publicly pro-
vided health insurance also includes State Children’s Health Insurance Plans (SCHIP). Medicare, however, is
not included in our definition of public health insurance for purposes of this paper.
2. As a point of reference, total expenditure on food stamps (SNAP) in 2012 was $78.4 billion, and spending on
Temporary Aid for Needy Families (TANF) was $31.4 billion. Total Medicare expenditure was $536 billion,
which highlights that the Medicare and Medicaid/SCHIP programs are of roughly similar size.
728 The Journal of Human Resources
cohorts affected by the Medicaid increases we study have been exposed to persistently
high returns to human capital investment (Autor 2014; Autor, Katz, and Kearney
2008). Thus, examining the effects of Medicaid expansions on long-run educational
attainment is of considerable policy interest.
Similar to prior work on Medicaid, we exploit the expansions of Medicaid and the
State Children’s Health Insurance Program (SCHIP) that took place in the 1980s and
1990s to examine how the educational attainment of these children was affected by
access to these programs. We use data on 22–29-year-olds born between 1980 and 1990
from the 2005–12 American Community Survey (ACS) that allow us to match each
respondent to his or her state of birth. We then use data from the March Current
Population Survey (CPS) to calculate Medicaid eligibility by age, state, year, and race
that we link to our ACS sample. With these data, we follow the method of simulated
instrumental variables pioneered by Currie and Gruber (1996a, 1996b) and Cutler and
Gruber (1996) to account for the fact that the demographic composition of a statemay be
endogenous to Medicaid eligibility rules. By using a fixed sample to calculate eligibility,
the model is identified using eligibility rule changes only.
We make several contributions to the literature. First, we estimate the effect of health
insurance access among both young and school-aged children on their long-run edu-
cational attainment. Second, we focus on Medicaid eligibility throughout one’s child-
hood rather than just at birth. Virtually all of the prior work on Medicaid expansions
focuses on point-in-time eligibility, particularly eligibility at birth (Levine and Schan-
zenbach 2009, Currie and Gruber 1996b).
From a policy perspective, focusing on
eligibility at older ages is important because of the large amount spent on providing
health insurance to non-newborn children. We present direct evidence that focusing just
on point-in-time eligibility at birth provides an incomplete characterization of the effect
of Medicaid on educational attainment. Our results show that it is repeated exposure
throughout one’s childhood that impacts these long-run outcomes, which has not been
Third, we are able to examine heterogeneous effects by the age at which a child is
exposed to Medicaid expansions. There is a sizable body of research demonstrating a
link between fetal health as well as the provision of fetal healthcare services on future
educational outcomes (Figlio et al. 2014, Levine and Schanzenbach 2009, Currie and
Gruber 1996b), but the effect of children’s access to health insurance on their educa-
tional attainment has not been studied. Socioeconomic disparities in educational out-
comes begin at young ages and largely persist throughout the lifecycle (Carneiro and
Heckman 2002, Todd and Wolpin 2007). Our study provides insight into the ages at
which Medicaid expansions have the largest long-run impacts on children in order to
help close these educational gaps.
Fourth, we develop a new robustness test that allows us to assess the extent to which
state-level Medicaid eligibility expansions are endogenously related to underlying
trends in outcomes. Specifically, we isolate the variation in state Medicaid eligibility that
3. Currie, Decker, and Lin (2008) present suggestive evidence that exposure to Medicaid expansions when
young lead to better health in adolescence, which suggests there could be an effect on educational attainment as
well. In a related study, Brown, Kowalski, and Lurie (2015) use IRS tax data to show that the eligibility
expansions in the 1980s led to higher earnings by the time individuals reached the age of 31. Their work does
not examine educational attainment, but their results and ours strongly complement one another.
Cohodes, Grossman, Kleiner, and Lovenheim 729
comes from changes in federal rules. These changes impact states differentially based on
their preexisting welfare eligibility rules. Importantly, these expansions are unlikely to
be related to outcome trends in any one state, which makes these estimates robust to
state-specific educational attainment trends. This is a particularly important strategy
in this paper because of our focus on average eligibility during one’s youth. There are
no sharp breaks across cohorts in childhood eligibility, but rather continuous increases
the size of which are based on one’s state and year of birth. This makes our estimates
potentially more sensitive to state-specific trends than those of the prior literature that
focuses on point-in-time eligibility. Our development and use of the federal eligibility
instrument, in addition to our use in some specifications of state-specific time trends,
provides evidence that our estimates are not being influenced by secular trends. That our
estimates are similar when only using federal eligibility suggests as well that state
Medicaid expansions are not endogenous, which helps validate the large body of work
that uses them.
Finally, we contribute to the literature by showing that our results are insensitive to
using current state versus state of birth. Because there are few data sets that include state
of birth, most long-run analyses are forced to use current state as a proxy for childhood
exposure. This is problematic if there is endogenous mobility related to Medicaid eli-
gibility. Our estimates are inconsistent with such mobility, and thus our findings expand
the possibilities for examining long-run Medicaid effects using other data sets that only
contain current state of residence.
We find consistent evidence that Medicaid exposure when young increases later
educational attainment. Our baseline estimates suggest a ten percentage point increase
in average Medicaid eligibility between the ages of zero and 17 decreases the high
school dropout rate by 0.4 of a percentage point, increases the likelihood of college
enrollment by 0.3 of a percentage point, and increases the four-year college attainment
rate (BA receipt) by 0.7 of a percentage point. These estimates translate into declines
in high school noncompletion of about 4 percent, increases in college enrollment of
0.5 percent, and increases in BA attainment of about 2.5 percent relative to the sample
means. In separate estimates by race, we find that the high school completion effects
are larger among nonwhites, while the college enrollment and completion rate impacts
are largest among white children. However, both groups experience substantial in-
creases in educational attainment due to Medicaid expansions that occurred during
Our results on heterogeneity by age at the time of expansion, while imprecise, suggest
that Medicaid expansions among children aged four to eight are the most important. We
also find evidence that expansions among teens aged 14–17 increase educational at-
tainment, though interestingly, there is little effect of expansions for children at birth or in
their first few years of life. These findings highlight the importance of examining child-
hood eligibility rather than point-in-time eligibility and suggest that there are sizable
returns to covering older children in Medicaid.
Overall, our results point to large effects of Medicaid expansions for children on their
eventual educational attainment. These effects are particularly important because lower-
income families are most affected by Medicaid and SCHIP expansions, and it is children
from these families that have exhibited the most sluggish growth in educational at-
tainment over the past 30 years (Bailey and Dynarski 2011). Our estimates suggest that
730 The Journal of Human Resources
the long-run returns to providing health insurance access to children are larger than just
the short-run gains in health status.
The rest of this paper is organized as follows: Section II describes the public health
expansions we use in our analysis, and Section III reviews the literature on the effects of
health insurance on health and family finances as well as the literature examining the
links between health, family resources, and educational outcomes. Section IV provides
a description of the data. We outline our empirical strategy and detail our results in
Sections V and VI, respectively, before concluding in Section VII.
II. Medicaid and Public Health Care Expansions
The Medicaid program was introduced in 1965 and phased in mostly
over the late 1960s as a health insurance component for state-based cash welfare
programs that targeted low-income, single-parent families. Beginning in the mid-
1980s, the Medicaid program was slowly separated from cash welfare, first by ex-
tending benefits to low-income children in two-parent families and then by raising the
income eligibility thresholds for two groups: children and pregnant women (Gruber
2003, Gruber and Simon 2008).
Since the 1980s, Medicaid has been expanded to many
low-income families who did not previously qualify due to their income levels, family
composition, and/or labor force participation. As a result of these expansions, by the
mid-1990s, most children in America below the poverty line, and all young children
below 133 percent of the poverty line, were eligible for Medicaid. In certain states, their
parents were as well.
Importantly, for most of these expansions, states could choose to implement the
expansion based on their own eligibility preferences. By the early 1990s, states were
required to cover all children below 100 percent of the poverty line and children younger
than age six below 133 percent of the poverty line. Many states opted to provide
more generous coverage, however, for which the federal government would provide
matching funds up to a certain threshold. In 1997, Congress passed the State Children’s
Health Insurance Plan (SCHIP), which was one of the largest expansions of public
health insurance to date. SCHIP provided matching funds to states to expand coverage
to children from households with incomes below 200 percent of the poverty line. Prior to
SCHIP, states were permitted to cover children up to 200 percent of the poverty line, but,
without federal matching funds very few states did so.
In this paper, we exploit these expansions in Medicaid generosity in the 1980s and
1990s that were phased in at different times, and with different generosity levels across
states, to identify the effect of Medicaid eligibility on long-run educational attainment.
Thus, our identification strategy uses both state-level variation, which assumes the
timing of state eligibility changes is exogenous with respect to underlying trends in
educational attainment of residents, and federal variation to explicitly test the robustness
of our estimates to the assumption that the state Medicaid variation is exogenous.
4. For more details on Medicaid expansions, see Currie and Gruber (1996a), Gruber (2003), and Gruber and
Cohodes, Grossman, Kleiner, and Lovenheim 731
III. Previous Literature
The effect of Medicaid eligibility on education flows through two main
potential channels: better health due to Medicaid enrollment as wellas higher household
resources stemming from the insurance protection provided by Medicaid. There is a
large literature that shows Medicaid expansions both increase medical care usage and
improve health among children and adults (Currie and Gruber 1996a, 1996b; Currie
2000; Kaestner et al. 2000, 2001; Almeida, Dubay, and Ko 2001; Banthin and Selden
2003; Dafny and Gruber 2005; Buchmueller et al. 2005).
To the extent that health
enters into the education production function, the health effects of Medicaid expansions
could lead to higher educational attainment among affected children.
How are such changes in child health from Medicaid expansions predicted to affect
educational attainment? Surprisingly little work has been done on this question. While
much existing research has documented that better fetal health translates into better
educational and adult outcomes (Miller and Wherry 2014; Figlio et al. 2014; Almond and
Mazumder 2011; Almond, Edlund, and Palme 2009; Almond 2006; Black, Devereaux,
and Salvanes 2007; Oreopoulos et al. 2008; Royer 2009), very little research estimates
how childhood health after birth impacts such outcomes. Currie et al. (2010) find that
children with health problems in early childhood have poorer long-run health, a higher
likelihood of being on social assistance, and lower educational outcomes. Case, Fertig,
and Paxson (2005) and Case, Lubotsky, and Paxson (2002) both show that worse health
in childhood is negatively associated with long-run outcomes such as health, educational
attainment, and labor market outcomes.
Cox and Reback (2013) as well as Lovenheim, Reback, and Wedenoja (2014) ex-
amine the effect of access to healthcare services on educational attainment using the
rollout of school-based health centers in the United States. The former study finds that
center openings lead to higher attendance rates, while the latter shows they cause lower
teen birth rates but do not affect high school dropout rates. The students treated by these
centers are typically in high school, so the differences between these estimates and the
large effects of health found by researchers examining younger children may be due
potentially to heterogeneity in the effects of health at different times during childhood.
Another main channel through which Medicaid can influence educational attainment
is through its effect on family resources. Recent work has suggested that public health
insurance successfully shelters low-income families from financial risk associated with
negative health shocks (Gross and Notowidigdo 2011, Dave et al. 2013, Finkelstein
et al. 2012). Thus, Medicaid expansions better the financial position of households,
which much prior work demonstrates can positively affect educational investments
(Dahl and Lochner 2012, Lovenheim 2011, Michelmore 2013).
While we provide the first analysis in the literature of the long-run effects of Medicaid
on educational attainment, there are two papers in the literature that are closely related to
ours. The first is Levine and Schanzenbach (2009), which analyzes the effect of Med-
icaid and SCHIP expansions at birth on future educational achievement as measured by
5. Levy and Meltzer (2008) provide a recent review of this literature.
6. See Almond and Currie (2011) for a comprehensive overview of the fetal origins hypothesis and Eide and
Showalter (2011) for evidence on the effect of health on human capital outcomes throughout the life cycle.
732 The Journal of Human Resources
state-level National Assessment of Educational Progress (NAEP) scores. This paper is
typical of the literature in its focus on point-in-time eligibility (at birth) rather than
eligibility over a period of one’s childhood. They examine differences in Medicaid
expansions by state and the differences between age cohorts in a triple difference
framework. Their results suggest that a 50 percentage point increase in Medicaid
eligibility corresponds to a 0.09 standard deviation increase in reading test scores, but
there are no effects on math scores.
Our analysis is distinguished from theirs along several dimensions. First, we focus on
the effects of expanding health insurance throughout one’s youth. This question is par-
ticularly important given the amount of money spent in the United States on providing
healthcare to nonnewborn children through Medicaid.
Indeed, our results indicate that
expanding eligibility to nonnewborns is an important driver of the long-run effects of
Medicaid; estimates using point-in-time eligibility at birth show little effect of Medicaid
on educational attainment. Second, we examine effects on long-run educational attain-
ment rather than on test scores at younger ages. A growing body of evidence suggests that
the effects of given educational interventions on test scores are poor predictors of their
effects on the longer-run outcomes that are of greater interest, such as educational at-
tainment and earnings (Ludwig and Miller 2007,Chetty et al. 2011, Deming et al. 2013).
The second related work is a currently unpublished working paper by Brown,
Kowalski, and Lurie (2015). They use IRS tax data to examine the effect of Medicaid
expansions on earnings throughout a child’s early life. They find results that are highly
complementary to our own: Medicaid eligibility increases from 0–18 are associated
with higher earnings, lower EITC receipt, and higher labor force participation. That they
obtain these estimates on a different data set using somewhat different cohorts is notable.
Together, our results point to large effects of Medicaid expansions on the long-run
outcomes of affected children.
We use three sources of data in our analysis of the effects of insurance
expansions on educational attainment. Below, we describe these sources of data as well
as the construction of the variables that we use in our investigation.
A. Medicaid Eligibility Data
Our Medicaid eligibility data are constructed using the March Current Population
Survey (CPS) for the years during which the 1980–90 birth cohorts are between the ages
7. If health insurance among school-aged children did not positively affect these children, ostensibly the
government could only offer Medicaid to pregnant women and households with very young children.
8. Much of this evidence suggests that it is particularly problematic to use effects on contemporaneous test
scores to predict long-run outcomes. Levine and Schanzenbach (2009) examine effects on the NAEP scores of
fourth and eighth graders, which themselves are longer-run test score outcomes. Furthermore, instructors are
unlikely to manipulate NAEP scores endogenously with respect to Medicaid eligibility rates, which would not
necessarily be the case for contemporaneous test scores used to evaluate a given educational intervention.
Nevertheless, it is not at all clear that effects on NAEP scores would translate into higher educational attain-
ment, which underscores the importance of our analysis.
Cohodes, Grossman, Kleiner, and Lovenheim 733
of zero and 17. We construct two eligibility measures using state and year information
on eligibility rules similar to those used in Gross and Notowidigdo (2011) and Gruber
and Simon (2008).
Eligibility calculations are based on the household’s income, the
age and number of children in the household, and the gender and unemployment status
of the head of household.
The first Medicaid eligibility measure we construct is the proportion of households of
a given race (white, nonwhite) with children of age iin state sand year twho are eligible
for Medicaid, where i2(0‚1‚...‚17). Thus, for example, we calculate the proportion
of households with five-year-olds in New York who are eligible for Medicaid in each
year between 1985 (the 1980 birth cohort) and 1995 (the 1990 birth cohort). We cal-
culate eligibility separately by child’s race due to the strong correlation between race and
Medicaid eligibility: A given change in eligibility rules is likely to impact nonwhites
differently than whites even though the Medicaid rules themselves are race neutral.
These calculations allow us to measure the proportion of children of each age and race
group that are Medicaid-eligible in each state and in each year between 1980 and 2007.
As described below, our outcome data span the years 2005–12 and include the 1980–90
birth cohorts. These cohorts are between the ages of 22–29 in 2005–12, which is why
our CPS sample ends in 2007 (when the 1990 birth cohort is 17).
We use three-year
moving averages of calculated eligibility instead of yearly eligibility because the small
sample sizes in the CPS within each age-race-state cell lead to measurement error in
eligibility. While this measurement error is not problematic for our instrumental vari-
ables strategy, using one-year eligibility likely would attenuate the OLS estimates
considerably. This makes comparisons between our OLS and IV estimates less infor-
Furthermore, the use of three-year moving averages is standard in the recent
Medicaid literature that employs simulated instrument methods (Gruber and Simon
2008; Gross and Notowidigdo 2011; DeLeire, Lopoo, and Simon 2011), which facil-
itates comparisons between our estimates and those in prior work. Aside from making
the estimates more precise, our use of these moving averages has little effect on the
results. We refer to this measure of Medicaid eligibility as “actual eligibility.”
Actual eligibility varies within states over time due to changes in eligibility rules,
changes in demographic composition, and changes in the economic circumstances of
households. In order to isolate the variation in Medicaid eligibility due only to eligibility
rule changes, we follow the method first used in Currie and Gruber (1996a, 1996b) and
Cutler and Gruber (1996) and calculate “simulated fixed eligibility,”which is the pro-
portion of the population in each state, age, race, year cell that would be eligible for
Medicaid, calculated using a fixed national sample that does not vary across states or
over time. We use the 1986 CPS and calculate the share of this fixed population with a
9. We are extremelygrateful to Tal Gross and Kosali Simon for providing us with the computer code that forms
the basis for our eligibility calculations.
10. We haveconducted extensive sensitivity analyses using different birth cohort ranges and ACS age ranges.
Our results are not very sensitive to the age range or birth cohorts used. These sensitivity analyses are available
from the authors upon request.
11. This method necessitates the use of CPS data through 2009 (which contains 2008 income information) to
enable the construction of our three-year moving average. In Table A9, we show our estimates are robust to
using one-year averages, although as expected the OLS estimates are attenuated. And in Table A7, we show
they are robust to dropping all states that include cell sizes for zero-to-17 eligibility that come from under 100
observations (3.3 percent of the sample). Online appendix tables are available at http://jhr.uwpress.org/.
734 The Journal of Human Resources
child of age iin year tand race rthat would be eligible for Medicaid in each state using
that state’s Medicaid eligibility rules in that year, adjusting family income for inflation
using the Consumer Price Index for All Urban Consumers. Critically, this sample does
not vary by demographic characteristics across states or over time and thus is unaffected
by state-specific trends in population or economic conditions that relate to both eligi-
bility and coverage (such as a state-level recession). Finally, we collapse these estimates
into unique state-year-age-race cells that yield the proportion of the fixed sample eli-
gible for Medicaid in each cell.
Our baseline estimates include Medicaid eligibility variation coming from federal
Medicaid expansions, state decisions about whether they will provide more generous
benefits than required by federal law, and the timing of state expansions. Among these
sources of variation, the one that is most worrisome is the timing of state expansions
because state expansion decisions may be endogenous with respect to underlying trends
in educational attainment. Thus, we also construct measures of Medicaid eligibility
that only are a function of federal rules. Federal Medicaid rules have different impacts
on states due to preexisting state-level AFDC policies. Hence, we fix AFDC rules in
each state as of 1980 and then calculate the three-year moving average of actual eligi-
bility as well as yearly fixed simulated eligibility for each age, race, and state that would
occur only due to changes in federal regulations governing Medicaid eligibility
thresholds. Put differently, our federal eligibility measures yield state-year-age-race
eligibility that would occur if no states provided more generous Medicaid access than
required under federal law. The reason this is not simply a cohort-based analysis, then, is
that the effect of federal rules varies by state according to (fixed) welfare policies. By
design, this source of Medicaid eligibility variation is unlikely to be correlated with any
decisions states can make regarding Medicaid eligibility.
Trends in our Medicaid eligibility measures, both overall and by race, are shown in
Figure 1. For each birth cohort, we show the average eligibility between the ages of zero
and 17 to which the cohort was exposed. The panels of the figure show, for the 1980–90
birth cohorts, actual eligibility that is a function of both state and federal rules as well as
eligibility that uses only federal rules. As demonstrated in Figure 1, there was a dramatic
rise in Medicaid eligibility that took place across the birth cohorts we study. Overall,
average eligibility rates over the course of childhood increased 172 percent betweenthe
1980 and 1990 birth cohorts. Much of this was the nonlinear increase in eligibility
that came from the 1990 federal Medicaid expansion that extended eligibility to all
children born after September 30, 1983, in families up to 100 percent of the poverty line.
In Panels B and C of Figure 1, we show that the proportional increases experienced
between whites and nonwhites were similar, but the higher baseline eligibility rates
among nonwhites in 1980 led to much higher eligibility among the 1990 cohort than
among the 1980 cohort. In our data, over 50 percent of nonwhites born in 1990 were
eligible for Medicaid over the course of their childhood, while less than 30 percent of
whites were eligible among this birth cohort.
Figure 1 also shows that the trends in overall eligibility track the trends in federal
eligibility closely, especially after the 1984 birth cohort, which highlights the impor-
tance of federal Medicaid policies for identification. The simulated eligibility trends are
very close to the actual trends as well. Thus, most of the aggregate pattern in Medicaid
eligibility is due to policy changes rather than demographic shifts in the U.S. population.
Cohodes, Grossman, Kleiner, and Lovenheim 735
1980 1982 1984 1986 1988 1990
Actual Eligibility Simulated Eligibility
Federal Eligibility Simulated Federal Eligibility
Panel A: Full Sample
1980 1982 1984 1986 1988 1990
Actual Eligibility Simulated Eligibility
Federal Eligibility Simulated Federal Eligibility
Panel B: White Sample
Medicaid Eligibility by Birth Cohort and Race
Notes: The figure shows average eligibility of 0–17-year-olds by birth cohort calculated using 1980–2007 CPS
data combined with state by year Medicaid eligibility rules. Eligibility is calculated separately for whites and
nonwhites. Simulated fixed eligibility is calculated by applying state-by-year rules to 1986 CPS data. Federal
eligibility uses only federal Medicaid rules, applied to each state using fixed 1980 AFDC rules
736 The Journal of Human Resources
B. Educational Attainment
The main outcome data we use come from the 2005–12 American Community Survey
(ACS). The ACS was designed to replace the census, and thus the variables and design
across the two surveys are almost identical. The sample for our analysis consists of birth
cohorts from 1980–90 who are between 22 and 29 years old in 2005–12. Thus, for each
individual in our sample, we observe eligibility in his or her birth state at each age
between zero and 17. Table 1 shows the birth cohorts included in our analysis sample at
each age and year. The top row shows the ACS (calendar) year, and the column shows the
age of the respondent. For example, in the 2008 ACS, observations of 25-year-olds come
from the 1983 cohort. This table illustrates that we do not observe each birth cohort in
each ACS survey due to our constructed age cutoffs. For example, 29-year-olds are
observed in 2009–12 and come from the 1980–83 birth cohorts only, whereas 25-year-
olds come from the 1980–87 birth cohorts and are included in each of the ACS years in
this analysis. Our use of 1980 as the earliest birth cohort is driven by our lack of
information about state-specific Medicaid eligibility pre-1980, which makes it infeasible
to use earlier birth cohorts.
Furthermore, we examine individuals only up to age 29,
since by age 29 most education has been completed (Bound, Lovenheim, and Turner
2010). Including older individuals would reduce the number of calendar years in which
we can identify eligibility for such respondents.
We calculate, for each respondent, indicators for whether the person did not complete
high school, whether she attended any college, and whether she obtained a bachelor’s
1980 1982 1984 1986 1988 1990
Actual Eligibility Simulated Eligibility
Federal Eligibility Simulated Federal Eligibility
Panel C: Nonwhite Sample
Figure 1 (continued)
12. We also note that Medicaid eligibility was very low pre-1980 and there were few expansions. Thus, our
focus on birth cohorts between 1980 and 1990 captures most of the policy-driven variation in Medicaid
exposure that has occurred since the program’s inception.
Cohodes, Grossman, Kleiner, and Lovenheim 737
We collapse the data to birth cohort, state of birth, survey year, race
(white/nonwhite) means for all variables, using the individual census weights. We then
link each birth cohort, state-of-birth, race, and survey year cell to the Medicaid eligibility
means discussed in Section IVA.
In particular, we calculate average eligibility for each
birth cohort (c) in each survey year (t), state of birth (s), and race (r) over their childhood
(1) eligibilityscrt =1
where eligscirt is the average Medicaid eligibility in birth state s, cohort c, survey year t,
and of race rwhen the birth cohort was age i.
We construct an identical measure using fixed simulated eligibility:
(2) fs eligibilityscrt =1
where fs eligscirt is simulated Medicaid eligibility that is calculated using a constant
sample from the 1986 CPS, as described above.
Descriptive tabulations of the analysis data for the full sample and by race group are
shown in Table 2. In the full sample, the average respondent is 25, and about 68 percent of
the respondents are white. The gender and age composition of the sample varies little across
race groups. Furthermore, the educational attainment of nonwhites is much lower than that
of whites, while average Medicaid eligibility is much higher for nonwhites. Both of these
patterns reflect the strong correlation between socioeconomic status and race, which
highlights the potential importance of any effect of Medicaid eligibility on educational
attainment to help address gaps in educational outcomes between whites and nonwhites.
Birth Cohorts by Age in Each ACS Year
Age 2005 2006 2007 2008 2009 2010 2011 2012
22 1983 1984 1985 1986 1987 1988 1989 1990
23 1982 1983 1984 1985 1986 1987 1988 1989
24 1981 1982 1983 1984 1985 1986 1987 1988
25 1980 1981 1982 1983 1984 1985 1986 1987
26 1980 1981 1982 1983 1984 1985 1986
27 1980 1981 1982 1983 1984 1985
28 1980 1981 1982 1983 1984
29 1980 1981 1982 1983
13. Our measure of high school completion includes GEDs, which is potentially problematic if Medicaid
eligibility shifts students from obtaining a traditional high school diploma to a GED given the low returns to
GED receipt found in the literature (Heckman and LaFontaine 2006). In 2008 and after, however, the ACS asks
directly about GED completion. We showin Table 4 that our main high school completion results are not being
driven by GEDs.
14. Public insurance expansions can potentially alter the character of medical care for both individuals who
experience a change in insurance coverage and also those who do not (Finkelstein 2007). Because we adopt an
aggregated cohort-based empirical approach, we allow for the presence of these “spillovers”within cohorts.
738 The Journal of Human Resources
Summary Statistics for Analysis Samples
Variable Name All White Nonwhite
No high school 0.094 0.071 0.143
(0.048) (0.029) (0.045)
No high school or GED 0.126 0.102 0.176
(0.054) (0.038) (0.050)
At least some college 0.656 0.694 0.572
(0.086) (0.062) (0.071)
College graduate 0.265 0.309 0.172
(0.108) (0.096) (0.065)
Age 25.001 25.031 24.936
(2.156) (2.155) (2.157)
Male 0.504 0.508 0.497
(0.039) (0.032) (0.049)
White 0.683 1.0 0.0
(0.466) (0.0) (0.0)
Black 0.143 0.0 0.451
(0.266) (0.0) (0.290)
Hispanic 0.123 0.0 0.386
(0.230) (0.0) (0.255)
Other race 0.052 0.0 0.163
(0.108) (0.0) (0.135)
Age zero to 17 three-year average
Age zero to 17 average fixed
Simulated Medicaid eligibility
Age zero to 17 three-year federal
average Simulated Medicaid
Age zero to 17 average federal fixed
Simulated Medicaid eligibility
Observations 5,494 2,754 2,740
Source: Author’s tabulations from the 2005–12 ACS.
Notes: The samples consist of 1980–90 birth cohorts aged 22–29, for whom we observe Medicaid eligibility
in every year in their birth state from age zero to 17. All tabulations were done using ACS sample weights.
Standard deviations are shown in parentheses. Average eligibility is calculated using three-year moving
averages. The GED tabulations only include ACS years 2008–12. Federal Medicaid eligibility is calculated
using federal rules only, interacted with 1980 state AFDC rules as described in the text.
Cohodes, Grossman, Kleiner, and Lovenheim 739
V. Empirical Methodology
In order to motivate our empirical models, it is helpful first to consider the
ideal experiment one would use to identify the effect of Medicaid on long-run outcomes.
Similar to the lottery for access to Oregon’s Medicaid program (Finkelstein et al. 2012), the
most credible way to estimate the program effects of interest would be to randomly assign
eligibility for Medicaid to families with children of different ages. Such eligibility would
last throughout the remainder of the child’s schooling years, unless the household finances
made them ineligible. With a long enough panel, we then could simply compare educa-
tional attainment among children who were randomly assigned Medicaid eligibility rel-
ative to those who were not. One also could calculate the effect o f Medicaid coverage using
randomized eligibility as an instrument (Finkelstein et al. 2012).
While such an experiment would identify the effect of Medicaid eligibility over one’s
childhood, in practice such an analysis is not currently feasible. The Oregon experiment
did not target children,
and there is no other randomized Medicaid experiment of
which we are aware. However, we can exploit the changes in both state and federal
Medicaid eligibility rules that occurred over the 1980s and 1990s to approximate this
experimental ideal. Because these policy changes never make Medicaid eligibility less
generous, once a child’s family is eligible for Medicaid in a state, he or she remains
eligible for the duration of childhood unless the family’s income or assets rise suffi-
ciently. As we argue below, the variation in eligibility on which we focus is unrelated to
demographic differences across individuals or to secular trends in educational attain-
ment. Thus, these eligibility expansions mirror the assignment mechanism one would
use in the ideal experiment.
We exploit the state and federal Medicaid eligibility expansions that occurred since
1980 using a difference-in-difference model that estimates how within-state changes in
Medicaid eligibility across cohorts over their childhood impacted their educational
attainment. Specifically, we estimate models of the following form:
(3) Yscart =b0+b1eligibilityscrt +b2Xsart +crs +drt +hra +escart‚
where Yscart is the educational outcome (high school noncompletion rate, college at-
tendance rate, or college graduation rate)
in state-of-birth s, birth cohort c, age a,of
race r, in survey year t. The variable eligibility
comes from Equation 1 above and
denotes the mean fraction of individuals of a given race and in a given birth cohort and
birth state who were eligible for Medicaid.
In the baseline specification, the vector Xsart consists only of an indicator for whether
the observation is for the nonwhite sample or not. As we discuss below, we then include
in Xsart some measures of potential confounding policies. In the baseline specification,
the model includes as well a set of race-by-age fixed effects (hra), race-by-state-of-birth
15. Adult access to Medicaid through the Oregon lottery might influence indirectly children’s outcomes
through family financial stability or better parental health. However, the experiment occurred too recently to
test its effects on children’s long-run outcomes.
16. The “some college”outcome contains both college dropouts and those who receive an Associates Degree
(AA). In Table A8, we showestimates that use “Associates Degree”rather than “Some College.”The estimates
are very similar in showing little effect of Medicaid eligibility on whether an individual obtains an AA.
740 The Journal of Human Resources
fixed effects (crs), and race-by-calendar year fixed effects (drt).
The race-by-age fixed
effects in particular are important because they account for the fact that older individuals
have more time to complete their education and that this age pattern might be different
across whites and nonwhites. The race-by-state fixed effects control for fixed differ-
ences across states that are correlated with both Medicaid eligibility and educational
attainment, such as the higher education structure and the industrial mix in the state,
which we allow to vary by race as well. The race-by-year fixed effects account for any
economy-wide shocks that could be correlated with prior Medicaid expansions and that
might be different across racial groups.
The coefficient of interest in Equation 3 is b1. It thus is important to clarify the sources
of variation identifying this parameter, conditional on other controls in the model. As
discussed above, we are exploiting variation from Medicaid eligibility expansions over
the course of one’s childhood. With the inclusion of state fixed effects, we are focusing
on within-state changes in eligibility across cohorts and relating these to within-state
changes in educational attainment. That is, within each state, we are using the fact that
Medicaid eligibility for older cohorts is lower than that for younger cohorts, and thus
we are essentially comparing across cohorts within states to identify b1. When we
pool all states, we are averaging these within-state effects together. Furthermore, the
time-varying nature of the Medicaid expansions across states allows us to partial out age
effects from calendar year effects.
As a result, our identifying variation comes from
cross-cohort changes in childhood Medicaid eligibility within each state as well as
cross-state variation in the timing of eligibility expansions.
Equation 3 incorporates a potentially restrictive set of assumptions about the cross-
state variation we use, namely that the state and age fixed effects are constant across
calendar years. We can relax this assumption by including race-state-year and race-age-
year fixed effects in the model:
(4) Yscart =b0+b1eligibilityscrt +b2Xsart +crst +hrat +escart:
In Equation 4, including the age-by-year-by-race effects allows for any national birth
cohort-specific shocks that could impact educational attainment or for more recent
cohorts to obtain their degrees later. State-by-year-by-race fixed effects account for any
state macroeconomic changes that could influence contemporaneous educational at-
tainment. While Equation 4 is more flexible, it also is much more demanding of the
data, which leaves us with less statistical power. As a result, these estimates tend to be
Both Equations 3 and 4 are identified off of the fact that states expanded their Med-
icaid eligibility rules differentially across cohorts and the fact that the timing and size of
17. Henceforth, we will refer to “state fixed effects”and “state-of-birth fixed effects”synonymously.
18. If we estimated this model using one state, we could not estimate both age and year fixed effects.The reason
is that, within a state, birth cohort fully describes the treatment intensity and birth cohort and age-year inter-
actions are perfectly collinear with each other.
19. Note that we do not control for race-by-state-by-age fixed effects. Thus, some of the identifying variation
could be coming from fixed differences across ages within a state. However, this would require the existence of
shocks to specific ages (but not birth cohorts) in a state that happen to be correlated with Medicaid eligibility
differences. We have estimated models using these fixed effects, and the results are qualitatively similar (if
somewhat less precise). We do not include them in the analysis because there is little economic justification for
these controls. Furthermore, note that the estimates that use only federal variation would be unaffected by any
Cohodes, Grossman, Kleiner, and Lovenheim 741
these changes varied across states. These models therefore are difference-in-difference
specifications, where the treatment dosevaries across different cohorts depending on the
state and year of birth, as well as depending on one’s race. As discussed in Section IV,
this variation comes from two sources: The first is rule changes that expand Medicaid
eligibility to different age groups within each state, and the second is demographic shifts
that expand the proportion of individuals who meet preexisting eligibility criteria.
For our analysis, the second source of variation is potentially problematic even
conditional on the fixed effects. If there are demographic changes that affect the pro-
portion of people eligible for Medicaid, these changes are likely to be correlated with
educational attainment. Our limited set of demographic controls cannot fully account
for such changes, although demographic changes that expand Medicaid eligibility most
likely generate a negative bias in estimating the effect of Medicaid on educational
attainment. We therefore use an instrumental variables strategy that is robust to de-
mographic shifts. This IV strategy amounts to using fs_eligibility from Equation 2 as an
instrument for eligibility. Because fs_eligibility is based on eligibility rules in each year
using a fixed sample of individuals from the 1986 CPS, it is only affected by eligibility
rule changes over time within states.
Similar to any difference-in-difference analysis, there are two main assumptions we
invoke. The first is that Medicaid expansions are not correlated with underlying trends
in educational attainment across cohorts at the state level. A particular concern for our
identification strategy would be if Medicaid expansions are occurring in states that are
becoming more affluent. Then, even simulated fixed eligibility changes would be posi-
tively correlated with underlying trendsin educational attainment. We do not believe such
a situation is likely, however, since states probably would be more compelled to expand
Medicaid eligibility due to increased, not decreased, demand for public insurance. This is
a common identification assumption that has been invoked repeatedly in the Medicaid
literature (Currie and Gruber 1996a, 1996b; Cutler and Gruber 1996; Gross and Noto-
widigdo 2011; Gruber and Simon 2008). The second assumption underlying our iden-
tification strategy is that there are no other state-level policies that are correlated with
Medicaid expansions that themselves might affect educational attainment.
We provide an extensive set of robustness checks to provide additional confidence
that our results are not being driven by endogenous state Medicaid eligibilityexpansions
or by other policies. First, in some specifications, we control in Xsart for average state
EITC amounts between the ages of zero and 17 for each cohort. Prior work linking EITC
policies to educational outcomes suggests EITC generosity could be a confounding
factor if it is correlated with Medicaid generosity.
We also control for average school
spending per pupil in the years in which each cohort was five to 17, separately by urban,
rural and suburban districts. Although there is a tenuous link between school expen-
ditures and education outcomes (see Hanushek 2003 for an overview of this literature),
recent work has linked school spending increases from school finance reforms to better
long-run outcomes (Jackson, Johnson, and Persico 2014). We view these factors as the
ones that are most likely to produce confounding effects, but our estimates that control
for these policies provide evidence that this is not the case.
20. See Michelmore (2013) for an overview of state-level EITC laws. We thank Kathy Michelmore for
providing us with these data.
742 The Journal of Human Resources
We provide more direct evidence that endogenous state Medicaid expansions are not
biasing our estimates by using only federal Medicaid eligibility rules as discussed in
Section IVA. The race-by-state-of-birth fixed effects control for the fixed differences in
AFDC rules across states, and the identifying variation in the federal model comes
solely through the fact that federal rule changes have differential impacts on states due to
preexisting AFDC policies. Thus, there is no scope in these models for endogenous state
decisions regarding Medicaid, and to the extent we obtain similar results using this
variation, it will provide confidence in the validity of the results that use state Medicaid
variation. This is the first paper to provide estimates using only federal eligibility
variation, so these results are of interest in their own right insofar as they help validate
the widely employed assumption that state Medicaid expansions are exogenous.
We also conduct robustness tests that include race and state of birth specific linear
trends across birth cohorts. These models are identified off of the nonlinear increases
in Medicaid eligibility that followed from state and federal law changes, and they help
guard against any upward bias from correlated secular trends in educational attainment
and Medicaid eligibility. We further provide a robustness check in which we randomly
assign observed eligibility levels across age-state-year cells. Overall, our estimates are
robust to using variation in Medicaid eligibility from different sources and to the ad-
dition of controls for other policies affecting low-income populations. These findings
support the validity of our identification strategy.
Because errors are unlikely to be independent within states of birth over time, we
cluster all standard errors at the state-of-birth level. All estimates also are weighted using
sample weights provided in the ACS.
A. Main Results
Table 3 presents the main results from our estimation of Equations 3 and 4. Each cell in
the table comes from a separate regression, with Panel A showing results that use all
Medicaid eligibility and Panel B showing results using only federal eligibility. The first
column in the table presents the first stage, which shows how a change in fixed simulated
eligibility translates into actual eligibility. The table also shows the effect of actual
Medicaid eligibility (“OLS”) and fixed simulated eligibility (“RF”for reduced form) on
high school noncompletion, college enrollment, and four-year college completion, as
well as the associated IV estimates.
Across outcomes and the specifications shown in different rows, we find consistent
evidence that Medicaid eligibility when young increases educational attainment.
Focusing on the baseline IV results in Row 1, a ten percentage point increase in Med-
icaid eligibility reduces high school noncompletion by 0.39 of a percentage point,
increases college enrollment by 0.35 of a percentage point, and increases BA attainment
by 0.66 of a percentage point. The high school and college completion estimates are
statistically significantly different from zero at the 5 percent level. Relative to the mean
attainment rates shown in Table 2, these estimates translate into a 4.1 percent decline in
high school dropouts, a 0.5 percent increase in college enrollment, and a 2.5 percent
increase in BA receipt. As shown in Figure1, there was a 24 percentage point increase in
Cohodes, Grossman, Kleiner, and Lovenheim 743
The Effect of Average Medicaid Eligibility During Childhood on Educational Attainment
No High School Some College College Plus
Specification First Stage OLS RF IV OLS RF IV OLS RF IV
Panel A: All eligibility
1. Baseline 0.927*** -0.030** -0.036** -0.039** 0.023 0.032 0.035 0.019 0.061** 0.066**
(0.111) (0.014) (0.014) (0.015) (0.018) (0.022) (0.025) (0.017) (0.029) (0.033)
2. EITC and school
0.966*** -0.023 -0.036** -0.038** 0.024 0.033 0.034 0.016 0.067** 0.069**
(0.076) (0.015) (0.015) (0.015) (0.019) (0.021) (0.022) (0.020) (0.030) (0.032)
3. EITC, school spending,
R-S-Y and R-A-Y FE
0.943*** 0.000 -0.019 -0.021 0.045 0.076* 0.081* 0.042 0.096 0.102
(0.111) (0.020) (0.024) (0.024) (0.031) (0.042) (0.042) (0.028) (0.064) (0.064)
4. Baseline +R-S-Y
and R-A-Y FE
0.890*** -0.001 -0.022 -0.025 0.010 0.087 0.099 0.036 0.080 0.091
(0.170) (0.020) (0.025) (0.027) (0.033) (0.059) (0.071) (0.026) (0.066) (0.070)
Panel B: Federal eligibility
5. Baseline 0.212*** -0.030** -0.012*** -0.055*** 0.023 0.002 0.011 0.019 0.017*** 0.078***
(0.030) (0.014) (0.004) (0.021) (0.018) (0.007) (0.032) (0.017) (0.006) (0.028)
6. EITC and school
0.210*** -0.023 -0.011*** -0.054** 0.024 0.002 0.011 0.016 0.016*** 0.077***
(0.032) (0.015) (0.004) (0.021) (0.019) (0.007) (0.033) (0.020) (0.006) (0.027)
Source: Authors’estimation of Equations 3 and 4 in the text using 22–29-year-old respondents from the 2005–12 ACS.
Notes: Each cell in the table comes from a separate regression (N=5480). The “OLS”columns refer to models that use a three-year moving average of actual eligibility as the
independent variable, and the “RF”columns refer to models that use fixed simulated eligibility as the independent variable. The “IV”columns refer to models that instrument
for actual eligibility using fixed simulated eligibility. All estimates include an indicator for the cell being nonwhite or not as well as race-by-age, race-by-calendar year, and
race-by-state of birth fixed effects. Rows 3 and 4 include race by state of birth by calendar year (R-S-Y) fixed effects and race by age by calendar year (R-A-Y) fixed effects as
shown in Equation 4. Standard errors clustered at the state-of-birth level are in parentheses: *** indicates significance at the 1 percent level, ** indicates significance at the 5
percent level, and * indicates significance at the 10 percent level.
744 The Journal of Human Resources
average eligibility during childhood between the 1980 and 1990 birth cohorts. Our
estimates suggest this change would have reduced high school noncompletion by 10.0
percent, increased college enrollment by 1.3 percent, and increased college completion
by 6.0 percent.
To put these effects in perspective, it is helpful to compare them to educational at-
tainment trends over this period. Murnane (2013) shows that high school graduation rates
increased by about six percentage points between the 1980 and 1990 birth cohorts.
Because our estimates show that a 24 percentage point increase in Medicaid would
increase high school completion by 0.94 percentage points, this implies that 15.6 percent
of this increase can be attributed to Medicaid expansions. Our tabulations from the
Current Population Survey indicate that college completion rates among 23-year-olds
between the 1980 and 1990 birth cohorts increased by 4.8 percentage points. A 24
percentage point Medicaid eligibility increase would increase BA attainment by 1.6
percentage points using the baseline results, which implies that Medicaid expansions can
explain 33.3 percent of the overall BA attainment increases over this period.
The results in Table 3 represent the effect on educational attainment (the intent-to-
treat) of exposure to Medicaid eligibility throughout one’s childhood. From a policy
perspective, this is a parameter of interest because the government cannot compel the
takeup of Medicaid. It also is the parameter on which much of the Medicaid literature
However, it is of interest as well to understand how enrollment in Medicaid
affects educational attainment (the treatment effect on the treated). This is a difficult
calculation because we lack the ability to track how average eligibility in one’s child-
hood relates to Medicaid takeup during childhood. The existing estimates on takeup in
the literature are not the appropriate “first stages”in our context, as they provide the
contemporaneous effects on enrollment, where we would need an estimate of the effect
on takeup over one’s entire childhood to scale our results.
In order to estimate the treatment on the treated effect, we use the marginal takeup rate
of 0.156 calculated by Gruber and Simon (2008) for the period 1996–2002 and assume
this rate represents a yearly “risk”of taking up Medicaid. That is, we assume that in each
year of childhood, 15.6 percent of the eligible population that has not yet taken up
Medicaid does so. For example, at age zero, 15.6 percent of eligibles will have taken up
Medicaid and 84.4 percent will have not, and at age one, [15.6 +(15.6*84.4)] =28.8
percent will have taken it up and 71.2 percent will have not (and so forth). This method
implies that for a child eligible at birth, he or she has a 95 percent chance of being on
Medicaid at some point before the age of 18. Because children are made eligible at
different ages, we calculate theassociated likelihoodof taking up Medicaid conditional on
first being eligible at each age between zero and 17. We then average over these takeup
estimates by age and find that expanding eligibility increases the likelihood a child takes
up Medicaid at some point during childhood by 71.4 percent. This average takeup
estimate matches the average Medicaid takeup rate of 73 percent quite closely (Currie
2004). Thus, treating marginal takeup rates as a constant risk of Medicaid enrollment
reconciles the evidence on low marginal but high average takeup rates, which provides
some validation for the method we use to calculate treatment on the treated effects.
21. The other relevant papers that use simulated instruments to examine effects on child or family outcomes,
namely Levine and Schanzenbach (2009), Currie and Gruber (1996b), and Gross and Notowidigdo (2011),
only report these intent-to-treat estimates.
Cohodes, Grossman, Kleiner, and Lovenheim 745
We calculate the treatment effect on the treated by dividing our IV parameter estimates
for eligibility by the 71.4 percent takeup rate. These calculations allow us to interpret our
results from the standpoint of an individual becoming eligible for Medicaid (eligibility
changing from zero to one) rather than from the standpoint of a policymaker who can
expand eligibility by a given percentage among the state population. Treatment on the
treated estimates shows that enrolling in Medicaid decreases the likelihood of dropping out
of high school by 5.5 percentage points and leads to a 9.2 percentage point increase in the
likelihood of completing a BA. To put the magnitude of these results in perspective, they
are similar to the estimated effects on educational attainment of attending a higher-quality
high school (Deming et al. 2014) and of Head Start (Garces, Thomas, and Currie 2002).
Rows 2–4 of Table 3 show our conclusions are largely robust to adding additional
controls for EITC and school spending (Row 2). In Rows 3 and 4, the addition of race-
state-year and race-age-year fixed effects reduces precision considerably. For the high
school noncompletion outcome, the magnitudes of the point estimates decline, while for
the collegegraduation outcome, the point estimates increase. However, in bothcases, they
are qualitatively similar to the baseline estimates, and the confidence intervals include
the baseline estimates. Overall, adding controls for other potentially confounding policies
as well as a large array of fixed effects do not change the conclusion that Medicaid-
eligibility expansions had sizable positive effects on long-run educational attainment.
Table 3 also demonstrates that the OLS and IV results are quite different from each
other. The OLS estimates in Panel A show Medicaid eligibility increases are associated
with smaller high school dropout declines (in absolute value) and with smaller college
completion increases. These results are suggestive that the bias from failing to account
for the correlation between demographics and Medicaid eligibility would cause one to
find a smaller effect of Medicaid on educational attainment.
As discussed in Section V, an important identification concern with the estimates that
use state-level policy variation is that this variation is potentially correlated with secular
trends in educational outcomes. This is especially relevant in this study relative to the rest
of the Medicaid literature since we are using average Medicaid eligibility over one’s
childhood. As a result, there are no sharp breaks in average eligibility that we can exploit.
In Panel B of Table 3, we thus show estimates using only federal Medicaid eligibility that
are unlikelyto be correlated with the trends associated with any one state. Focusing on the
baseline estimates in Row 5, we find that increases in Medicaid eligibility reduce high
school dropout and increase college enrollment and completion. Comparing the estimates
in Row 5 to the baseline results in Row 1, the point estimates for the reduced form are
smaller in absolute valuewhen only the federal variation is used. As the IVestimates show,
this difference mostly reflects the smaller first stage. In Panel A, the first-stage estimates
are around 0.9, suggesting that a ten percentage point change in fixed simulated eligibility
is associated with a nine percentage point change in actual eligibility.
As expected, the
link betweenfederal Medicaid rules and actual eligibility ismuch weaker because we are
ignoring state responses to the federal regulation changes. However, the first stage for the
federal variation still is sizable in magnitude and is statistically significant from zero at the
1 percent level.
22. Our first-stage estimates are similar to what has been found in prior work. Cutler and Gruber (1996) report a
first stage of 0.84 for children and 0.95 for women, while Gross and Notowidigdo (2011) have an implied first-
stage estimate of 0.61.
746 The Journal of Human Resources
The Effect of Average Medicaid Eligibility During Childhood on Educational Attainment, Separating GED and HS Diplomas, 2008–12
No High School
or High School
Stage OLS IV OLS IV OLS IV OLS IV
Panel A: All eligibility
1. Baseline 0.927*** -0.020 -0.047** -0.023 -0.043** 0.009 0.047 0.025 0.085**
(0.115) (0.018) (0.019) (0.020) (0.020) (0.020) (0.037) (0.019) (0.042)
2. EITC and school spending 0.959*** -0.013 -0.046*** -0.015 -0.042** 0.021 0.039 0.021 0.087**
(0.078) (0.019) (0.018) (0.020) (0.020) (0.022) (0.027) (0.022) (0.041)
3. EITC, school spending
R-S-Y and R-A-Y FE
0.944*** 0.010 -0.023 0.003 -0.016 0.032 0.079** 0.025 0.099
(0.110) (0.021) (0.024) (0.023) (0.028) (0.028) (0.039) (0.031) (0.071)
4. Baseline +R-S-Y
and R-A-Y FE
0.889*** 0.009 -0.027 0.003 -0.022 -0.003 0.100 0.020 0.090
(0.173) (0.020) (0.027) (0.023) (0.029) (0.030) (0.072) (0.027) (0.075)
Panel B: Federal eligibility
5. Baseline 0.210*** -0.020 -0.075*** -0.023 -0.075*** 0.009 -0.004 0.025 0.086***
(0.030) (0.018) (0.025) (0.020) (0.026) (0.020) (0.038) (0.019) (0.030)
6. EITC and school spending 0.206*** -0.013 -0.073*** -0.015 -0.073*** 0.021 0.000 0.021 0.087***
(0.031) (0.019) (0.026) (0.020) (0.026) (0.022) (0.041) (0.022) (0.031)
Source: Authors’estimation of Equations 3 and 4 in the text using 22–29-year-old respondents from the 2008–12 ACS.
Notes: Each cell in the table comes from a separate regression (N=3957). The “OLS”columns refer to models that use a three-year moving average of actual eligibility as the
independent variable, and “IV”columns refer to models that instrument for actual eligibility using fixed simulated eligibility. All estimates include an indicator for the cell
being nonwhite or not as well as race-by-age, race-by-calendar year, and race-by-state of birth fixed effects. Rows 3 and 4 include race by state of birth by calendar year (R-S-
Y) fixed effects and race by age by calendar year (R-A-Y) fixed effects as shown in Equation 4. Standard errors clustered at the state-of-birth level are in parentheses: ***
indicates significance at the 1 percent level, ** indicates significance at the 5 percent level, and * indicates significance at the 10 percent level.
Cohodes, Grossman, Kleiner, and Lovenheim 747
Comparing the IV estimates from similar models across panels shows that using the
federal-only variation produces results that are quantitatively and qualitatively similar
to the estimates that use state variation as well. For high school noncompletion in the
baseline specification (Row 1), the estimates indicate a ten percentage point eligibility
increase during childhood reduces dropout by 0.39 of a percentage point using all
Medicaid variation, and it reduces dropout by 0.55 of a percentage point using only
federal variation (Row 5). For college enrollment, the estimates in Row 5 are smaller
than those in Row 1, and they are inconsistent with all but a small increase in college
attendance. Finally, for college completion, the IV coefficients across panels of Table 3
show very similar effects of Medicaid eligibility expansions. Comparisons of Rows 2
and 6 show that our estimates using federal variation are comparable when including the
EITC and school spending controls as well.
That these two models yield similar esti-
mates of the effect of changes in Medicaid eligibility among children on long-run edu-
cational attainment supports our use of all Medicaid variation, as it suggests state Medicaid
eligibility variation is not endogenous with respect to long-run educational outcomes.
A final potential concern with the results in Table 3 is that the high school completion
variable groups GED and high school diploma recipients together. Starting in 2008,
the ACS began asking separately about high school diploma and GED receipt, and in
Table 4 we present estimates using 2008–12 data where we separate high school
diploma nonreceipt from diploma and GED nonreceipt. As the table demonstrates, the
effects are extremely similar across the two measures of high school completion, sug-
gesting that our baseline estimates do not obscure potential shifts between traditional
diplomas and GEDs. In addition, the some college and college plus estimates are similar
in the 2008–12 sample, if somewhat larger among all outcomes. These results suggest
our estimates are not driven by the particular sample period we chose.
B. Educational Attainment Results by Age at Expansion
While these results indicate a beneficial overall effect of Medicaid expansions on edu-
cational attainment, from a policy perspective, it is important to discern whether it matters
if one measures eligibility at a point-in-time (typically birth) relative to over one’s
childhood, as well as whether there are effects of health insurance access at different ages.
In Table 5, we present IVestimates that control for eligibility at birth (similar to what was
done in Levine and Schanzenbach 2009 and Currie and Gruber 1996b). Using both
Equations 3 and 4, we find very little evidence that Medicaid eligibility at birth is
associated with long-run educational attainment. With the exception of our college
completion measure (and only when we include the full range of fixed effects), the rest of
the estimates are small in magnitude and either are “wrong-signed”or are not statistically
significant. This finding is suggestive that the test score gains found by Levine and
Schanzenbach (2009) do not translate into higher educational attainment. However, when
we add in eligibility at ages zero to 17, we find that Medicaid eligibility does lead to more
23. We do not present federal variationresults that include race-state-year and race-age-year fixed effects. Due
to the limited amount of variation in federal Medicaid eligibility, including these fixed effects yields large
standard errors that make the resulting estimates uninformative. Furthermore, the goal of using the federal
variation is to find a source of variation that is unlikely to be related to state trends. As a result, there is little
theoretical justification for including the race-state-year and race-age-year fixed effects in these models.
748 The Journal of Human Resources
education among affected cohorts. It is the eligibility at older ages that is responsible for
this relationship; eligibility at birth continues to be uncorrelated withlong-run educational
The age zero and age one to 17 estimates are statistically different from each
other at the 10 percent level for no high school and at the 5 percent level for BA plus in the
first column. But when we add in the fixed effects in the second column, the loss of
precision makes these estimates not statistically different from each other (although they
still remain qualitatively different from each other).
Table 6 expands upon the finding that the age at which one experiences Medicaid
eligibility might matter for long-run outcomes. In this table, we estimate the effects of
eligibility using ages zero to three, ages four to eight, ages nine to 13, and ages 14–17. These
IV Estimates of the Effect of Average Medicaid Eligibility at Birth and During
Childhood on Educational Attainment
Age Zero Age Zero, One to 17
Medicaid Age Eligibility Baseline FE Baseline FE
No high school
Age zero eligibility -0.006 -0.011 -0.004 -0.008
(0.011) (0.013) (0.011) (0.013)
Age one to 17 eligibility -0.038*** -0.022
Age zero eligibility -0.016 -0.001 -0.017 -0.011
(0.012) (0.014) (0.012) (0.017)
Age one to 17 eligibility 0.032 0.100
Age zero eligibility -0.024* 0.046* -0.026* 0.039**
(0.013) (0.019) (0.013) (0.019)
Age one to 17 eligibility 0.062* 0.070
Source: Authors’estimation of Equations 3 and 4 in the text using 22–29-year-old respondents from the 2005–
Notes: All estimates include an indicator for the cell being nonwhite or not, race-by-age fixed effects, race-by-
calendaryear fixed effects, and race-by-state of birth fixed effects. “FE”estimates are from Equation 4 and include
race by state of birth by calendar year (R-S-Y) fixed effects and race by ageby calendar year(R-A-Y) fixed effects.
Standard errors clustered at the state-of-birth level are in parentheses: *** indicates significance at the 1 percent
level, ** indicates significance at the 5 percent level, and * indicates significance at the 10 percent level.
24. The one exception is again for the BA Plus outcome when estimating Equation 4. Here, we see a positive
effect of eligibility at birth on college completion. But, the effect of eligibility at ages one to 17 still is larger
(although also less precisely estimated).
Cohodes, Grossman, Kleiner, and Lovenheim 749
categories are selected to correspond to those Medicaid eligibility age categories delin-
eated in Currie et al. (2008) as well as to correspond roughly to different schooling levels
(preschool, elementary school, etc.). Panel A shows results from the baseline specifica-
tion (Equation 3), while in Panel B we include our full set of fixed effects (Equation 4).
While the results are somewhat imprecise, they show evidence that eligibility at
ages zero to three has little impact on educational attainment. For high school com-
pletion, it is eligibility at ages four to eight that is the most important.
IV Estimates of the Effect of Average Medicaid Eligibility During Childhood
on Educational Attainment, by Age at Eligibility
Age Range No High School Diploma Any College College Plus
Panel A: Baseline model
0–3-0.011 0.019 -0.003
(0.014) (0.015) (0.017)
4–8-0.030** 0.013 0.037**
(0.010) (0.014) (0.014)
9–13 0.007 -0.025* -0.026
(0.010) (0.015) (0.017)
14–17 -0.012 0.061** 0.069**
(0.008) (0.011) (0.016)
Panel B: Baseline +R-S-Y and R-A-Y FE
0–3 0.003 -0.010 0.053
(0.020) (0.030) (0.036)
4–8-0.030 0.032 0.051
(0.020) (0.026) (0.057)
9–13 0.003 0.012 -0.002
(0.012) (0.029) (0.022)
14–17 -0.013 0.072** 0.025
(0.015) (0.028) (0.025)
Source: Authors’estimation of Equations 3 and 4 in the text using 22–29-year-old respondents from the 2005–
Notes: Each cell in the table comes from a separate regression (N=5480). All estimates include an indicator for
the cell being nonwhite or not, race-by-age fixed effects, race-by-calendar year fixed effects, and race-by-state
of birth fixed effects. Estimates in Panel B come from Equation 4 and also include race by state of birth
by calendar year (R-S-Y) fixed effects and race by age by calendar year (R-A-Y) fixed effects. Standard
errors clustered at the state-of-birth level are in parentheses: *** indicates significance at the 1 percent level,
** indicates significance at the 5 percent level, and * indicates significance at the 10 percent level.
25. This is not to say that this insurance has no effect as they age. Indeed, one reason why Medicaid expansions
among younger children might be more effective is because they are likely to be eligible for a longer proportion
of their childhood. Of course, this does not explain why expansions among very young children do not affect
750 The Journal of Human Resources
completion, the estimates are less consistent across panels. Focusing on Panel B,
eligibility at all ages except nine to 13 are positively related to BA attainment.
But in Panel A, only eligibility during teenage years impacts college completion.
We also find evidence of a college enrollment effect due to eligibility expansions
among teenagers. At least some of this effect may be due to reproductive services
that can be purchased with Medicaid (Lovenheim, Reback, and Wedenoja 2014;
Kearney and Levine 2009). Taken together, the results from Tables 5 and 6 dem-
onstrate that estimates of Medicaid eligibility at birth provides an incomplete
characterization of how Medicaid affects educational attainment and that eligibility
among older, school-aged children is particularly important for driving attainment
C. Educational Attainment Results by Race
Thus far, we have estimated models that pool effects across racial groups. But, given
persistent racial disparities in educational attainment, heterogeneous effects by race
are of considerable interest. In Tables A1 and A2,
we estimate our models separately
for whites and nonwhites, respectively. For whites, the effects on high school non-
completion are negative, but they are smaller in absolute value than in the pooled
model and they are not statistically significant at conventional levels. There is a larger
effect of Medicaid on college completion for whites, although these estimates also are
not statistically significantly different from zero at conventional levels. The effect is
on the order of 1.0 to 1.3 percentage points for each ten percentage point increase in
Medicaid eligibility. There is a sizable, positive effect on college completion for
whites using the federal variation as well. While these point estimates are large—
suggesting a 2.5 percentage point increase from a ten percentage point Medicaid-
eligibility increase—they are consistent with observed increases in white college
completion across these cohorts.
Among nonwhites, the effects on high school noncompletion are larger, particularly
in the baseline model. High school noncompletion is reduced by 0.46 percentage points
for each ten percentage point increase in Medicaid eligibility.
There also is evidence
of a positive college completion effect on the order of 0.4 of a percentage point for each
ten percentage point increase in eligibility. However, as we show in Table A6, the BA
estimates for nonwhites are not robust to the inclusion of state-specific birth cohort
trends. Overall, theseresults are consistent with a larger effect of Medicaid eligibility on
higher education completion for whites and a larger effect on high school completion
26. All online appendices can be found at http://jhr.uwpress.org/.
27. CPS tabulations indicate that college completion rates among white 23-year-olds increased by 6.4 per-
centage points between the 1980 and 1990 birth cohorts. White Medicaid eligibility expanded by 19 percentage
points across cohorts, which would increase BA attainment rates by 4.75 (=0.25*0.19*100) percentage points.
This is 74 percent of the total BA attainment increase over this period.
28. It is notable that these estimates become much smaller in absolute value when we include the full set of
fixed effects. However, they also become much less precise such that the baseline estimates are still within the
95 percent confidence intervals. Furthermore, the estimates using federal variation show a large effect of
eligibility increases on high school completion.
Cohodes, Grossman, Kleiner, and Lovenheim 751
Other than for the college completion estimates using federal eligi-
bility variation, the estimates by race are not statistically different from each other,
D. Robustness Checks
In this section, we present several additional robustness checks that yield additional
insight into the validity of our central identifying assumption—namely, that there are not
differential underlying trends in educational attainment correlated with public health
insurance eligibility expansions. First, in Table 7, we present results from the models
presented in Table 3 that also include state-specific linear birth cohort trends, separately
by race. If there are differential trends in educational attainment correlated with Med-
icaid expansions, these results should yield substantively different results from our
baseline model. For the high school graduation rate estimates, the results are extremely
similar to baseline. However, adding state-specific linear time trends reduces the college
completion estimates that include state-level eligibility. As shown in Tables A5 (whites)
and A6 (nonwhites), this average result is mostly due to the fact that there is a large effect
of Medicaid eligibility on whites when including state-specific linear time trends but no
effect on nonwhites. These results also highlight that the federal variation estimates are
robust to including state-specific linear time trends. Thus for whites, there continues to
be a large effect of eligibility expansions on college completion, while for nonwhites the
effects of eligibility expansions are localized to high school completion.
Second, in Table 8, we show the mean and the 2.5th and 97.5th percentiles from 500
simulations that randomly assign Medicaid eligibility and fixed simulated eligibility
across age-state-year cells. That is, we take combinations of actual and fixed simulated
eligibility, and as a pair randomly assign them to different age-state-year cells, separately
by race. This assignment is done with replacement. Both for the baseline model and for
the model including race-state-year and race-age-year fixed effects, the average estimates
are very close to zero. Furthermore, the nonparametric confidence intervals suggest these
null estimates are precisely estimated. This robustness check suggests the results pre-
sented in Table 3 are due to the specific way the Medicaid eligibility expansions were
rolled out over time within states. When we randomly assign eligibility levels, they are no
longer meaningfully related to educational attainment.
In Tables A7–A11, we also present results that explore the sensitivity of our results to
several modeling assumptions we have made throughout the analysis. In Table A7, we
estimate our models excluding the small states that generate fewer than 100 observations
for an underlying age-race-cohort-year eligibility calculation. The results are virtually
identical to those in Table 3. Table A8 replaces the some college outcome with whether
an individual earns an Associates Degree (AA). We fail to find an effect of Medicaid
expansions on AA attainment, which supports our finding that the main impacts of
Medicaid on educational attainment come through high school and BA completion. In
Table A9, we use one-year instead of three-year Medicaid eligibility. Again, our results
are very similar to baseline.
29. In Tables A3 and A4, we also show estimates by gender. Although the estimates are somewhat noisy, they
suggest a high school completion effect exists for both males and females, while the college enrollment and
completion results are isolated to males.
752 The Journal of Human Resources
The Effect of Average Medicaid Eligibility During Childhood on Educational Attainment, Including State of Birth Linear Time Trends
No High School Some College College Plus
Specification First Stage OLS RF IV OLS RF IV OLS RF IV
Panel A: All eligibility
1. Baseline 1.009*** -0.033** -0.043*** -0.042*** 0.011 -0.019 -0.019 0.028 0.026 0.026
(0.059) (0.016) (0.015) (0.015) (0.022) (0.029) (0.028) (0.022) (0.025) (0.024)
2. EITC and school spending 1.013*** -0.032** -0.041*** -0.040*** 0.007 -0.016 -0.016 0.022 0.035 0.034*
(0.059) (0.016) (0.015) (0.014) (0.020) (0.027) (0.026) (0.018) (0.022) (0.020)
3. EITC, school spending, R-S-Y
and R-A-Y FE
1.019*** -0.009 -0.031 -0.029 0.046 0.000 -0.002 0.014 0.005 0.006
(0.148) (0.033) (0.048) (0.043) (0.040) (0.062) (0.054) (0.026) (0.049) (0.043)
4. Baseline +R-S-Y
and R-A-Y FE
1.033*** -0.003 -0.023 -0.021 0.044 -0.000 -0.003 0.017 0.013 0.014
(0.147) (0.032) (0.047) (0.041) (0.039) (0.060) (0.052) (0.027) (0.046) (0.040)
Panel B: Federal eligibility
5. Baseline 0.220*** -0.033** -0.013*** -0.059*** 0.011 0.004 0.019 0.028 0.018*** 0.082***
(0.032) (0.016) (0.004) (0.021) (0.022) (0.007) (0.030) (0.022) (0.006) (0.026)
6. EITC and school spending 0.223*** -0.032** -0.012*** -0.054** 0.007 -0.000 0.000 0.022 0.011* 0.049**
(0.032) (0.016) (0.004) (0.021) (0.020) (0.007) (0.030) (0.018) (0.006) (0.025)
Source: Authors’estimation of Equations 3 and 4 in the text using 22–29-year-old respondents from the 2005–12 ACS.
Notes: Each cell in the table comes from a separate regression (N=5480). The “OLS”columns refer to models that use a three-year moving average of actual eligibility as the independent
variable, and the “RF”columns refer to models that use fixed simulated eligibility as the independent variable. The “IV”columns refer to models that instrument for actual eligibility using
fixed simulated eligibility. All estimates include an indicator for the cell being nonwhite or not as well as race-by-age, race-by-calendar year, and race-by-state of birth fixed effects. Rows
3 and 4 include race by state of birth by calendar year (R-S-Y) fixed effects and race by age by calendar year (R-A-Y) fixed effects as shown in Equation 4. Estimates also include race by
state of birth linear time trends. Standard errors clustered at the state-of-birth level are in parentheses: *** indicates significance at the 1 percent level, ** indicates significance at the
5 percent level, and * indicates significance at the 10 percent level.
Placebo Test with Randomly Assigned Medicaid Eligibility
No High School Graduation Some College BA
RF IV RF IV RF IV
Baseline 0.0001 0.0001 -0.0004 -0.0005 2.87e–5 2.31e–5
(-0.006, 0.007) (-0.007, 0.007) (-0.011, 0.010) (-0.013, 0.012) (-0.010, 0.011) (-0.011, 0.012)
& R-A-Y FE
0.0002 0.0002 -0.0004 -0.0005 0.0001 0.0001
(-0.008, 0.007) (-0.009, 0.008) (-0.012, 0.011) (-0.014, 0.013) (-0.010, 0.011) (-0.011, 0.012)
Source: Authors’estimation of Equations 3 and 4 in the text using 22–29-year-old respondents from the 2005–12 ACS.
Notes: We randomly assign age-state-year eligibility and fixed simulated eligibility, as a pair, across different age-state-year cells. This is done separately by race. We conduct
500 separate simulations for each outcome, both including and excluding state of birth by calendar year (R-S-Y) fixed effects and race by age by calendar year (R-A-Y) fixed
effects. All estimates include an indicator for the cell being nonwhite or not as well as race-by-age fixed effects, race-by-calendar year fixed effects, and race-by-state of birth
fixed effects. The table shows the mean estimate across all 500 runs, as well as the 2.5th and 97.5th percentiles in parentheses. The range in parentheses thus shows the
nonparametric 95 percent confidence interval. IV estimates are constructed by dividing the reduced form (RF) by the first stage, which also is estimated using this method.
First-stage estimates are available upon request from the authors.
754 The Journal of Human Resources
In Table A10, we assign Medicaid eligibility based on an individual’s state of residence
rather than state of birth. The results are very similar to the baseline analysis. This is
especially notable since there arevery few sources of datathat include an individual’sstate
of birth. Thus, any long-run analysis of Medicaid eligibility requires researchers to use an
individual’s current state of residence as a proxy for childhood exposure, which is
problematic if there is endogenous mobility related to Medicaid eligibility. Our estimates
are inconsistent with such mobility, and thus our findings expand the possibilities for
examining long-run Medicaid effects using other data sets that only contain current state
of residence. Finally, in order to assess whether the results are sensitive to cohort exposure
to local labor market conditions, inTable A11 we control for average unemployment rates
in each state-of-birth and for each birth cohort. The estimates change little from those in
Table 3. Overall, these results show our conclusions are robust to different ways of
constructing our analysis sample and to different modeling assumptions.
In this paper, we provide the first evidence on the effects of public
health insurance expansions on long-run educational attainment in the United States.
Overall, our results suggest large effects of childhood Medicaid expansions on eventual
educational outcomes. Our baseline estimates indicate that a ten percentage point in-
crease in Medicaid eligibility between the ages of zero and 17 decreases the likelihood
of not completing high school by approximately 4 percent and increases the four-year
college completion rate by 2.5 percent. The effects on high school completion are
largest among nonwhites, while the effects on college completion are largest for whites.
We also present evidence that public health insurance expansions when children are of
school age are closely linked with long-run educational attainment; eligibility expan-
sions beyond birth lead to higher educational attainment. To the best of our knowledge,
these are the first estimates to demonstrate the importance of health insurance eligibility
among older children, particularly as it relates to educational outcomes.
Although the public health insurance expansions that we study occurred in the past
several decades, our resultshave several implications that are important for current public
policy. First, they suggest that the long-run benefits of providing health insurance to low-
income children may be much larger than the short-run gains. Evidence pointing to the
large and growing returns to educational attainment (Autor, Katz, and Kearney 2008) as
well as the importance of education in increasing intergenerational economic mobility
(Black and Devereaux 2011, Chetty et al. 2014) suggests that the returns on the public
investments in health insurance in the 1980s and 1990s will be realized for some time.
Second, our results relate to current policy discussions over the future of the SCHIP
program, which have accompanied the larger debate over the ACA. More specifically,
the ACA prohibits states from imposing eligibility and enrollment standards for
30. In an earlier version of our paper, we present evidence using outcomes from the Youth Risk Behavior
Surveillance System (YRBSS) that better health is one of the mechanisms driving our results by showing that
Medicaid eligibility when young translates into better teen health. While our estimates from this analysis were
typically not statistically significant at conventional levels, they provide support for the idea that better health is
an important mechanism that drives at least part of the increased educational attainment we document.
Cohodes, Grossman, Kleiner, and Lovenheim 755
Medicaid and SCHIP until 2019 that were more restrictive than those in place in March
2010 (when the ACA was passed). However, there have been attempts in Congress to
repeal these provisions, which would essentially allow states to cut SCHIP benefits and
eligibility. In addition, SCHIP funding is up for reauthorization in 2015, and its passage
is far from assured. A back-of-the-envelope calculation indicates that eliminating the
SCHIP program would reduce eligibility for public health insurance by 15.4 percentage
points. Our baseline estimates suggest such a decline would increase the high school
dropout rate by six-tenths of a percentage point and would decrease the college en-
rollment rate by five-tenths of a percentage point and the college completion rate by one
percentage point. The results from this study highlight the need to account for the long-
run effects of public health insurance provision when considering changes to the pub-
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