Working Paper
WP 2016-341
Lifetime Consequences of Early and Midlife
Access to Health Insurance: A Review
Étienne Gaudette, Gwyn C. Pauley, and Julie Zissimopoulos
Project #: UM16-Q1
Lifetime Consequences of Early and Midlife
Access to Health Insurance: A Review
Étienne Gaudette
University of Southern California
Gwyn C. Pauley
University of Southern California
Julie Zissimopoulos
University of Southern California
March 2016
Michigan Retirement Research Center
University of Michigan
P.O. Box 1248
Ann Arbor, MI 48104
www.mrrc.isr.umich.edu
(734) 615-0422
Acknowledgements
The research reported herein was performed pursuant to a grant from the U.S. Social Security
Administration (SSA) funded as part of the Retirement Research Consortium through the University of
Michigan Retirement Research Center (RRC08098401-08). The opinions and conclusions expressed are
solely those of the author(s) and do not represent the opinions or policy of SSA or any agency of the
Federal Government. Neither the United States Government nor any agency thereof, nor any of their
employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the
accuracy, completeness, or usefulness of the contents of this report. Reference herein to any specific
commercial product, process or service by trade name, trademark, manufacturer, or otherwise does not
necessarily constitute or imply endorsement, recommendation or favoring by the United States
Government or any agency thereof.
Regents of the University of Michigan
Michael J. Behm, Grand Blanc; Mark J. Bernstein, Ann Arbor; Laurence B. Deitch, Bloomfield Hills; Shauna Ryder
Diggs, Grosse Pointe; Denise Ilitch, Bingham Farms; Andrea Fischer Newman, Ann Arbor; Andrew C. Richner,
Grosse Pointe Park; Katherine E. White, Ann Arbor; Mark S. Schlissel, ex officio
Lifetime Consequences of Early and Midlife
Access to Health Insurance: A Review
Abstract
This article reviews the literature on how health insurance affects health and economic outcomes
in the United States prior to automatic Medicare eligibility at age 65, with the aim of providing a
snapshot of the breadth of the existing evidence. A targeted approach was used to identify and
review experimental or quasi-experimental articles deemed most likely to identify the causal
impact of health insurance. Results were systematically reviewed by outcome category–ranging
from mental health to education—and population of interest—ranging from prenatal to
preretired. The effects of health insurance on economic outcomes remain inconclusive despite
being well-studied, while evidence on the relationship between health insurance and several
aspects of health has strengthened over the last decade.
Citation
Gaudette, Étienne, Gwyn C. Pauley, and Julie Zissimopoulos. 2016. “Lifetime Consequences of
Early and Midlife Access to Health Insurance: A Review.” Ann Arbor, MI. University of
Michigan Retirement Research Center (MRRC) Working Paper, WP 2016-341.
http://www.mrrc.isr.umich.edu/publications/papers/pdf/wp341.pdf
1. Introduction
It is not hard to imagine why health insurance could be important for both individual health and
economic outcomes. The channels through which health is affected are intuitive. For example, by
improving access to care, health insurance may improve use of preventive care, leading to better
disease prevention and better health. When diseased, insured individuals may rely on health
insurance to obtain critically needed curative care, improving outcomes. Additionally, mental
health may improve, either through the channels described above or because just having health
insurance reduces the stress of a potentially costly health shock. On the other hand, economic
outcomes are clearly affected by access to health insurance, both private or employer sponsored
and public insurance. One mechanism through which this may occur is through improved health;
if healthier individuals are more productive, health insurance may increase employment, wages
or educational outcomes. By decreasing the cost of health care, health insurance may also free up
household budget constraints. A more obvious impact of health insurance may be on labor-force
participation. Due to the complicated relationship between health insurance and labor supply in
the United States, health insurance may act by inducing individuals to participate in the labor
force. In 2014 in the United States, the focus of this study, about 49% of individuals were
insured through employer-sponsored health insurance (ESHI). 1 However, public health insurance
is often available to individuals with low incomes, which may create a disincentive to work.
Thus, health insurance has the potential to affect individuals in a variety of ways. Further, it is
clear that health insurance may affect individuals throughout the entire life cycle.
However, how to estimate the causal effect of health insurance on health and economic outcomes
is not obvious. For example, people who value health insurance may be inherently sicker. Thus,
a simple observational study comparing individuals who are enrolled in health insurance to those
who are uninsured might find that health insurance makes individuals less healthy. In addition to
reverse causality, omitted variable bias could also be present. For example, consider ability,
which is unobserved in many data sets. Individuals with higher latent ability may be more likely
to have health insurance, possibly due to their participation in the labor force, as well as be better
able to maintain their health. Again, a simple comparison of the insured and uninsured in the
presence of this omitted variable would bias estimates of health insurance on health upward.
Given the examples above, it is clear that in order to answer the question of how health insurance
affects health or economic outcomes, researchers must control for the endogeneity of health
insurance.
A randomized control trial is the gold standard in inference and could potentially shed light on
the effects of health insurance. An experiment ideally designed to measure the causal impact of
health insurance would sample individuals at conception; randomly allocate them to insured and
uninsured groups; maintain them in these groups for the remainder of their life; and observe their
periodic and cumulative outcomes. While such an experiment is impossible to conduct in real
1
See http://kff.org/other/state-indicator/total-population/ for more information.
life, we know of three large-scale experiments that were conducted in relation to health
insurance.
The Rand Health Insurance Experiment, conducted from 1974 to 1982, came close to that
ideal.[1-5] This large-scale experiment randomized 5,809 people age 61 and younger into one of
14 different health insurance plans, and, for 3 to 5 years, followed their health care utilization,
spending, and health. Plan generosity varied greatly–from a free health care plan to plans with a
95% deductible. In effect, these extremes allowed for the first credible estimation of the causal
impact of total insurance, with very few caveats (an example of such caveats is the limit in
family out-of-pocket spending in the 95% deductible plan, unavailable to the uninsured). Results
from this experiment constitute some of the best evidence on the impact of health insurance on
health.
More recently, there was a large-scale experiment carried out in Oregon (the Oregon Health
Insurance Experiment) that consisted of a lottery to be eligible to apply for Oregon Health Plan
(OHP), public health insurance.[3, 6-8] Specifically, between January 28 and February 29, 2008
individuals could enter a lottery and winners would be invited to apply for OHP. Approximately
90,000 individuals entered the lottery and more than 35,000 won. However, of these winners just
about 30% were actually enrolled in OHP, either because they did not actually finish the
application in time or because they did not qualify for the program. Regardless, the lottery serves
as a randomized control trial and winning the lottery has been used as an instrument for being
enrolled in public health insurance.
Finally, the Accelerated Benefits Demonstration was conducted from 2007 to 2010 with Social
Security Administration funding to evaluate the impact of early access to health benefits for new
Social Security Disability Insurance (SSDI) beneficiaries. SSDI beneficiaries are typically
required to wait 24 months before becoming eligible for Medicare coverage, and, while they can
obtain insurance on their own, often are uninsured during this period. The demonstration
randomized 2,000 new beneficiaries into a control and two treatment groups: the AB group
received immediate health care benefits; and the AB Plus group received health care benefits and
additional services, notably help to navigate the health care system.[9-11] This experiment
provides insightful results regarding the impact of insurance for disabled populations, which
have significant health care needs.
In addition to randomized experiments, researchers have used several policy changes (so-called
“quasi-experiments” or “natural experiments”) to attempt to measure the causal impact of health
insurance on economic outcomes and health. Key examples include Medicaid eligibility
expansions, notably since the 1980s; the 2006 Massachusetts Health Care reform, which aimed
for state-wide, near-universal coverage; and, most recently, the national implementation of the
Patient Protection and Affordable Care Act, which expanded the objectives of the Massachusetts
Reform at the national level. There were also smaller changes to state programs or mandates that
have been exploited. For example, changes to Hawaii employer mandates, TennCare
(Tennessee), and Badger Care (Wisconsin) have all been used as natural experiments.
In this paper, we review the literature on how health insurance affects health and economic
outcomes in the United States. We selected studies whose sample consists of individuals
younger than 65, as we want to focus on the effects of health insurance before automatic
Medicare eligibility. Using a targeted approach, we identified and reviewed experimental or
quasi-experimental articles deemed most relevant for this review. We focus on seven economic
outcomes:labor-force participation, earnings, wages and other related labor-market outcomes,
welfare participation, education, savings and asset accumulations, household well-being, and
delayed care due to costs; and seven health outcomes: mortality, self-reported health, risk factors
such as obesity and high blood pressure, limitations and functional status, preventable
hospitalizations, chronic conditions, and mental health. For each outcome, we present a table
that describes each study identified including a brief description of the results and how the
authors arrived at the estimates. In addition to the difficulty in establishing believable methods to
estimate the effects of health insurance, it is not clear that health insurance should affect all
individuals the same. Thus, it is important to note who was affected by the study. For each
outcome, we summarize the findings and note where more research is needed.
In the remainder of this paper, we document these articles’ key findings by outcome category
and population of interest, with the aim of providing a snapshot of the diversity of the existing
evidence. In the next section, we discuss how we searched for and selected the articles. We then
present our economic and health findings.
2. Approach
A challenge in conducting this review was the very large body of work that documents
associations between health insurance and outcomes without addressing the issue of the
endogeneity of insurance. While these studies can be informative to some degree, as Levy and
Meltzer noted, “…we cannot count on observational studies to provide insight into the causal
effect of health insurance”.[12] Unfortunately, results from this literature are overwhelmingly
more prevalent than the causal literature when using a traditional keyword search approach.
To maximize the probability of identifying the articles susceptible of identifying causal impact of
insurance, we used the following approach. We started with a relatively recent pillar of the
causal health insurance literature–Levy and Meltzer’s 2008 review. The review and the articles it
cited were added to a database of potentially relevant articles for this review. We then used the
Google Scholar website’s “cited by” option to identify articles that cited the review, which we
also considered likely candidates to be relevant for our exercise. 2 We added to our registry those
2
As of the writing of this report, 126 articles are identified by Google Scholar as citing the review.
with titles indicative of relating to the impact of nonelderly health insurance on health or
economic outcomes in the U.S. For instance, articles with titles indicating health care utilization
as the only outcome were not retained, while articles with more general titles, such as “The
Long-term Effects of Early Life Medicaid Coverage,” [13] were added and considered for the
review.
We analyzed the abstract of each article so identified to assess 1) if it studied health or economic
outcomes; 2) if it used experimental or quasi-experimental methods; and 3) if it targeted the nonelderly U.S. population. We identified about 275 papers. Of these papers, we chose 170 as being
most relevant, and included about 115 in this paper. With each new paper identified as relevant,
we repeated the process of considering references cited by the paper (using the article’s
References section) and that cited the paper (using Google Scholar), until several subsequent
articles yielded no new article.
Due to the large number of articles studying the impact of health insurance on health and
economic outcomes, we had to select which ones were most notable to be included in this study.
To do this, we first divided articles by outcome and population of interest. If there were less than
five articles in each cell, we included all of the articles. If there were more than five, we picked
the five most significant studies based on several inclusion criteria. First, we gave preference to
papers that addressed the endogeneity of health insurance. Second, we included papers that were
published rather than in “working paper” status. Third, we noted the number of papers that cited
the paper in question. This criterion of course biases toward the inclusion of older papers. Thus,
we occasionally included newer papers with fewer citations if they were deemed novel in their
approach. In select cases, we included more than five studies if some studied private and some
public insurance.
3. Economic Outcomes
3.1 Labor-Market Participation
The interaction between health insurance and labor-market participation is one of the most
important aspects of health insurance. The Affordable Care Act has renewed interest in this
relationship, because of both the employer and individual mandate. Because of this, there has
been an abundance of work whose purpose is to answer the question of how health insurance
affects labor-market participation. 3 We identified several distinct groups that were studied in the
literature: young adults, working age adults, at-risk adults, married adults, single mothers,
pregnant women, childless adults, and other unique populations (breast cancer patients for
example).
3
There is perhaps an even larger body of literature which considers job lock, see Gruber and Madrian Health
Insurance, Labor Supply, and Job Mobility and the many papers that are cited in this review. In addition selfemployment, entrepreneurship, and retirement have also been studied. For the purpose of this study, we focus only
on how health insurance affects labor-force participation, both on the intensive and extensive margin.
Identification of how young adults were affected by health insurance primarily comes from the
dependent mandate of the Affordable Care Act, which stipulated that individuals be allowed to
stay on their parent’s insurance plan until the age of 26, regardless of student status. Studies that
examine how this affected young adults’ labor-force participation are mixed, with some finding
the mandate affected men differently than women. Outcomes studied included being in the labor
force, working full-time, part-time, and hours worked.
To study how adults’ labor-force participation is affected by health insurance, a variety of
approaches have been taken. We include more than five studies in this section, because some
have studied private insurance while others have studied public health insurance, and the
incentives are very different. For example Thurston [14] and Buchmueller, DiNardo, and Valletta
[15] use employer mandates in Hawaii as variation while others use the Massachusetts health
reform as exogenous variation, while Mathur et al. [16] exploit the Affordable Care Act. Early
work suggests there was little or no effect of the ACA on labor supply or part-time work.
Similar to the young adults, results for adults are mixed and extremely inconclusive.
We also include at-risk adults, who are more likely to be affected by changes in public health
insurance provisions. Kaestner et al. study nondisabled adults with a high school degree or less,
while Gooptu et al. study individuals with incomes below 138% of the federal poverty level
(FPL). It should be noted that these are potentially endogenous decisions. However, neither study
finds that labor supply, particularly working part-time, was affected by Medicaid expansions.
The fourth group that we distinguish is married adults. We group these studies separately
because of the intrahousehold effects—married individuals are able to gain health insurance
through their spouse and there may be cross-spouse effects. Typically, instrumental variables
have been used as a method to deal with the exogeneity of spouse’s health insurance. More
recently, California’s mandate that gay and lesbian partners be treated the same as same-sex
partners has also been exploited. In general, studies have found that intrahousehold effects are
important and having access to health insurance through a spouse decreases labor supply, as
measured by full-time work, part-time work, and the number of hours.
The fifth group that we consider is single mothers. This is a unique group, because initially
Medicaid was tied to welfare (AFDC) participation, which was only available to single mothers
and their children. The studies examining single mothers, while plentiful, are not decisive. For
example, Yelowitz [17] found that single mothers were very responsive to changes in Medicaid
eligibility, while Ham and Shore-Sheppard, using the same data as Yelowitz with a more flexible
specification, find that there was no significant effect on labor-force participation. This is more
or less consistent with what Meyer and Robinson [18] found. Using more recent Medicaid
expansions, both Montgomery and Navin [19] and Pohl [20] find that Medicaid expansions
increased labor-force participation, but results on the number of hours are mixed. Finally, Moffitt
and Wolfe [21], in a seminal study, and Pohl [20] suggest that mothers who value health
insurance more because of higher expected medical spending are more responsive to changes in
health insurance.
Less well studied are pregnant women and childless adults. Childless adults are a particularly
important group, because they are the ones most affected by the Affordable Care Act. All three
papers that we identified found that childless adults may adjust their labor supply in order to gain
health insurance, although it should be noted that we were able to identify just one nationally
representative study on childless adults [22]. The only study that we identified studying
pregnant women found that increases in Medicaid eligibility decreased the probability of
employment, labor-force participation, and hours worked [23].
Last, we identified three papers that examined special groups that did not fit into our categories
above. For example, Bradley et al. [24] and Page [25] study groups that are less healthy, such as
women with a breast cancer diagnosis and individuals who received a kidney transplant. We also
identified papers that studied SSI and DI recipients (Coe and Rupp [26]) and veterans (Boyle and
Lahey [27]). While these groups may be important, they are not representable and may be
difficult to generalize. Each of these studied did find that individuals’ labor supply was
responsive to health insurance.
3.2 Wages, Earnings, and Other Labor-Market Outcomes
Health insurance could affect wages for two main reasons. The first is because of compensating
differentials, and the second is through improved health. Similar to how we grouped labormarket outcomes, we created eight different groups: individuals who were covered in covered in
childhood and are in the labor force later in life, young adults, adults, married adults, single
adults, single mothers, pregnant women, and childless adults.
Very recently, there have been several studies examining how health insurance during childhood
affects later-in-life outcomes. One reason we could expect lasting effects of health insurance is if
health insurance during childhood improved health, which can be thought of as a stock. Although
Boudreaux, Golberstein, McAlpine [28] find that the introduction of Medicaid had no effect on
wages later in life, both Brown, Kowalski, and Lurie [29] and Miller and Wherry [13] use later
expansions of the program and find that it had substantial effect on outcomes later in life. The
Brown paper is unique because they have access to administrative IRS data and can look at
cumulative earnings, as well as taxes paid.
The effect of health insurance on young adults is not well studied, but with the ACA mandating
coverage of dependents, we expect that many researchers will study this in the near future.
The largest group studied is adults of preretirement age. Notably, the Oregon Health Insurance
Experiment found that being enrolled in Medicaid had no effect on earnings or whether or not
the individual was above 100% FPL. This is the most credible evidence that we have. However,
Kolstad and Kowalski [30] found that there was a compensating differential. The difference in
results may be due to who was affected by the Massachusetts mandate and Medicaid expansions
in Oregon [31].
The remaining groups are less well studied and results vary. For example, we found two papers
that studied childless adults. Dague, DeLeire, and Leininger [32] study childless adults in
Wisconsin who applied for public health insurance just before and after the program was closed
and found that results were sensitive to the specification. Garthwaite et al. [33] study public
health insurance in Tennessee, which covered childless adults, and compare outcomes to other
Southern states and find that there was no effect on wages.
3.3 Welfare Participation
Closely related to labor-force participation and earnings is enrollment in government welfare
programs. Rather than group by study sample, we present our results here based on welfare
program. We consider Aid for Families with Dependent Children/Temporary Assistance for
Needy Families (AFDC/TANF), Supplemental Nutrition Assistance Program (SNAP or food
stamps), Social Security and Disability Insurance, Workers Compensation, and the Earned
Income Tax Credit (EITC).
There is a large literature considering the effects of Medicaid on AFDC participation. This is
partly because early on, Medicaid eligibility was tied to AFDC enrollment. Seminal papers by
Blank [34], Winkler [35], and Moffitt and Wolfe [21] all consider how Medicaid affected AFDC
enrollment. Although the findings vary, there is some evidence that how women value insurance
changes welfare participation and that private insurance is valued differently than public.
Yelowitz [17] exploited the decoupling of Medicaid from AFDC and found large effects, but
Ham and Shore-Sheppard [36] found that he evaluated AFDC incorrectly and that Medicaid
eligibility had no effect on AFDC participation.
The relationship between health insurance and food stamps is especially important for
policymakers. The most compelling evidence about this relationship comes from the Oregon
Health Insurance Experiment [6], which found that Medicaid had a significant impact on the
probability of receiving food stamps. as well as the amount received, likely due to learning about
the program. Yelowitz [37] also found that Medicaid expansions increased food stamp
participation. In a different approach, Miller and Wherry [13] found that being insured during
childhood actually decreased the likelihood of food stamp receipt later in life.
The other programs: SSI and DI, Workers Compensation, and EITC are less well studied. The
Oregon Experiment also found that there was no effect of public health insurance on SSI or DI
receipt, but this is in contrast to other studies we found. The effect of health insurance on
Workers Compensation is even more varied. Last, we found one study that evaluated health
insurance and EITC receipt. Brown, Kowalski, and Lurie [29] found that being eligible for
Medicaid early in life was associated with smaller EITC payments later in life for both men and
women and a decrease in the likelihood of receiving EITC for women.
3.4 Education
Next, we turn our attention to education. There are two main groups studied in the literature. The
first is children. The literature has considered two main consequences of health insurance for
children. The first is concurrent enrollment and the second is long-run effects. Young adults have
also been studied in the context that access to health insurance may change the opportunity cost
of education for individuals. For example, the ACA mandates that children up to the age of 26 be
allowed to stay on the health insurance of their parents, regardless of student status.
Results on effects on education that occur at the time of being insured suggest that health
insurance may improve test scores in young children (Levine and Schanzenbach [38]). Results
on the long-term effects are also not fully developed, but there is evidence that being insured
during childhood improves educational attainment, as found by Cohodes et al. [39] in a recent
paper. However, Brown, Kowalski, and Lurie [29] find this effect concentrated on women. How
dependent mandates affect young adult education is not clear, as some studies found the
mandates did not affect the probability of being a student [40], while others did [41].
3.5 Savings and Asset Accumulation
Health insurance may also affects savings and asset accumulation through freeing up resources,
which may induce families or individuals to save more. Alternatively, if public health insurance
programs have asset tests, this may discourage savings. Thus, the relationship between health
insurance and savings/asset accumulation also is theoretically ambiguous, particularly for public
health insurance programs. In a seminal paper, Starr-McCluer [42] find that households with
insurance have higher savings than those without insurance. Lee [43] finds evidence that this
holds using ESHI expansions under the ACA. However, when public health insurance is
considered, results differ. For example, Gruber and Yelowitz [44] show that Medicaid
significantly decreases savings, while Maynard, and Qiu [45] show that this is concentrated in
the middle quantiles of wealth, and Gittleman [46] shows that if current Medicaid dollars are
used as an instrument rather than total expected spending, there is no effect of Medicaid on
savings.
3.6 Household Well-being
Similar to savings and asset accumulation, household well-being has been considered as an
outcome. This is a more subjective measure than other economic outcomes than we have
discussed. Results for this section are encouraging, but are dependent on the measure used to
determine household well-being. For example Leininger et al. [47], Gross and Notowidigo [48]
and Sommers and Oellerich [49] find that Medicaid (or State Children’s Health Insurance
Program—SCHIP) is associated with increased non-health consumption, decreased probability
of declaring bankruptcy, and pulling people out of extreme poverty. However, Schmidt, ShoreSheppard, and Watson [105] find that Medicaid had no effect on food insecurity in households
with children, but note that this may be due to large standard errors and call for more research to
be done. Additionally, the Oregon Health Insurance Experiment found that there Medicaid was
not associated with any change in the probability of declaring any bankruptcy [8]. However, as
the authors point out, this is a very low probability event to begin with. It should be noted that
none of the studies consider how private insurance affects household wellbeing.
3.7 Delayed Care Due to Costs
Last, we present results for delayed care due to costs. We present these results based on
households with children, young adults, adults, and childless adults. It is interesting to note that
although public health insurance covers more children than adults, there is relatively little work
studying how health insurance affects delayed care for these households. However, every study
that we identified did find decreases in delayed care for this population. Turning to the adult
population, we include one study that considers private health insurance (Pauly [50]). All of the
papers looking at the adult population find a decrease in delayed care. Most notably, the Oregon
Experiment finds there was a large decrease, about 3.6 percentage points or 55%, in the
probability of refusing treatment due to medical debt in the past six months. Again, although
childless adults are the ones most affected by the ACA, this group has not been studied enough.
However, in the one study we were able to identify, Guy Jr. [51] found that eligibility in both the
traditional cost-sharing structure and increased cost structure decreased the likelihood of going
without care.
4. Health Outcomes
With the approach described in Section 2, we identified 66 experimental and quasi-experimental
articles investigating the impact of health insurance on health, 39 of which were published after
Levy and Meltzer’s influential review [12]. The articles investigated a variety of different
outcomes, the majority of which could be grouped along seven relatively broad categories:
mortality, self-reported health, risk factors, health limitations and functional status, preventable
hospitalizations, chronic conditions, and mental health. Applying the criteria described in Section
2 to these outcome categories, we retained 35 articles in our analysis. 4 In Table 2, we document
the most salient findings stemming from each article, as well as a brief summary of the articles’
study approaches to account for health insurance endogeneity. Many articles address a variety of
outcomes, and thus appear more than once in the table.
4.1 Mortality
The ultimate health outcome, mortality has been widely studied by the causal health insurance
literature. In children, three articles using similar approaches found significant effects of
Medicaid eligibility on infant and children mortality [52-54]. The most recent of these studies
found that gains in children are limited to external-cause mortality (e.g., accidents), and are not
significant in natural-cause mortality (e.g., diseases) [54].
Among adults, studies mostly found no significant impact of insurance when considering the
overall populations targeted by reforms [8, 11, 55], and significant results were found when
studying high-risk groups. In particular, the Rand Health Experiment showed no impact of free
care in the overall sample, but showed a 10% reduction among individuals classified as having
high health risks [56]. The authors attribute this finding to better blood pressure control among
hypertensives in the free-care group. Similarly, studies of HIV-positive populations and trauma
patients indicate that insurance prevents mortality for those at high risk of dying [57-59]. We
note that the trauma studies do not explicitly account for insurance endogeneity, but instead
assume that the unexpected and life-threatening nature of the trauma is such that eventual
outcomes are plausibly caused by insurance status.
4
In this process, we removed from the analysis articles that study outcomes, such as dental health, which are
relevant but do not fit in the categories.
4.2 Self-Reported Health
For two reasons, self-reported health is by far the most commonly studied outcome. Firstly, it has
been consistently shown to successfully convey valuable information about respondents’ health
[60, 61]. Secondly, and plausibly most important, is that it can be measured easily through
survey questionnaires, unlike many health indicators.
However, the self-reported health variables studied are quite variable in the literature, making
cross-study comparisons difficult. For instance, it is not clear that Sommers et al.’s finding of a
5% decrease in the probability of reporting “less than excellent health” after the Massachusetts
reform [62] invalidates Zhu et al.’s finding of no significant change in the probability of
reporting “fair or poor health” after the same reform [63]. Perhaps because of such differences in
definitions, self-reported health findings in Table 1 present no strong support for an impact of
insurance: for each study finding a significant association among an age group, another finds no
effect. An exception concerns younger adults, which showed significant improvements for two
different self-report measures after implementation of the Affordable Care Act’s dependant
coverage provision [64, 65]. Overall, more consistent measures are needed to allow for a better
assessment of the impact of insurance.
4.3 Risk Factors
Studies of the impact of insurance on risk factors focused on blood pressure and obesity, two key
heart disease predictors, and health behaviors. Notably, better blood pressure management
among insured individuals is a staple finding of the Rand Health Experiment [3, 56], but this
result has not been replicated in the Oregon Health Experiment [7]. Findings also indicate that
prenatal Medicaid eligibility is associated with a lower probability of obesity in young adulthood
[65], but childhood eligibility has no protective effect [28, 66]. More recently, the Affordable
Care Act’s dependant coverage provision was associated with a lower BMI in young adults, but
also with riskier drinking habits [65]. The majority of reviewed studies found no impact of
insurance on healthy behaviors [4, 7, 56, 67].
4.4 Health limitations and functional status
In childhood and young adulthood, the existing literature found either no or small impact of
insurance on several health limitation measures [5, 13, 68, 69]. In adulthood, results from two of
the three randomized experiments showed sizeable effects. In the Accelerated Benefits
demonstration, SSDI beneficiaries receiving health benefits had a 21% decline in the probability
of having a survey score indicative of meeting the Social Security Administration’s disability
definition [11]. In the Oregon Health Experiment, Medicaid enrollment was associated with an
8% increase in the amount of reported days not impaired by health [8]. These results appear to
contradict the absence of findings in the Rand Health Experiment, [56] but this divergence could
be due to the different populations studied. Namely, the Rand Health Experiment randomized a
somewhat nationally representative sample, while the other experiments studied disability
insurance beneficiaries and a low-income population.
4.5 Preventable hospitalizations
Hospital utilization studies are potentially informative of health status, but we recognize the
challenges of such an interpretation. Since the price of hospital use and other care are lower for
insured individuals, insurance may lead to more (or less) hospital visits and overnight stays
without impacting health. For instance, among adults, the Massachusetts health reform was
found to lead to higher all-cause readmissions and readmissions for chest pain and substance
abuse [70]. We cannot interpret these as indicating that insurance worsens the health of adults,
since the variation may be explained by a response to the decline in the price in hospital care. In
childhood, similarly, high hospital use among the uninsured may be an optimal response to high
relative prices of primary care in nonhospital settings. Thus, findings that insurance reduces
childhood emergency care [71, 72] should be interpreted cautiously.
However, as Kaestner et al. argue, ambulatory care sensitive hospitalizations are plausibly a
better objective health indicator, since they are “…sensitive to better primary care and greater
medical intervention”[73]. Both they and Aizer [74] found that Medicaid eligibility and
enrollment is associated with declines in ambulatory care among children, suggesting that
Medicaid improves child health.
4.6 Chronic conditions
All studies with outcomes related to chronic condition in children identified by this review
reported a positive impact of insurance. Two studies’ findings indicate that Medicaid eligibility
in childhood may prevent the onset of chronic conditions [13, 28], while a third found a
reduction in asthma attacks, albeit significant at the 10 percent level [71].
Among adults, results from the Rand Health Insurance Experiment showed that hypertensives in
the free plan reduced their diastolic blood pressure levels in comparison with the cost-sharing
plans, with highest differences found in low-income people [3, 56]. This finding is consistent
with Lurie et al., who estimated that termination from public insurance among medically
indigent hypertensives caused a 15 mm Hg increase relative to a control group [75]. Studies did
not find a significant impact of insurance on blood glucose control among diabetics [4, 75], but
found an important increase in the probability of a diagnosis after two years of public insurance
eligibility [7].
4.7 Mental health
Mental health is among the most studied aspects of the impact of health insurance on health.
Existing evidence reveals no impact in young age. Only two studies identified by this review
have investigated childhood mental health, and found no significant impact on the Kessler 6
measure of psychological distress, mental health-related hospitalizations, and a standardized
mental health score [5, 13].
In adulthood, results are more mixed. All three randomized studies of health insurance have
investigated mental health in adulthood, and two of them found no or small impact of insurance.
Rand Health Experiment data showed no impact of being in the free health-care plan in
comparison with the cost-sharing plans on a standardized score [56], on worry about one’s
children’s health [5], and on worry about one’s own health conditions (except for a category
including chronic bronchitis and emphysema) [4]. Similarly, data from the Accelerated Benefits
demonstration project presented no significant impact of receiving health benefits in comparison
with the control group; unless these benefits were supported by other services, including help
navigating the health-care system, as was the case in the AB Plus treatment group [11].
However, most of the recent literature found that insurance is significantly associated with
mental health improvements. A study of the Oregon Health Experiment, the third randomized
study, showed that Medicaid enrollment was associated with lower probability of positive
depression screening, higher health scores based on a standardized survey, and increased selfreported days in good mental health [7, 8]. These findings are compatible with those of several
quasi-experimental studies investigating the Massachusetts Health Reform [70, 76, 77],
Medicaid expansions [78], and the Affordable Care Act’s dependent coverage provision [64].
Based on the literature alone, it is quite unclear why the latter studies diverge from the Rand
Health Experiment and the Accelerated Benefits demonstration project. In the case of the Rand
Experiment, populations studied (the latter studies all focus on low-income populations or other
factors that may have changed the impact of health insurance since the 1970s, such as the higher
costs of care as a share of disposable income, may explain some of the divergence.
5. Conclusion
This paper aimed to shed light on the causal impact of insurance on economic and health
outcomes by conducting a thorough review of the existing evidence, focusing on studies with
experimental and quasi-experimental designs.
The effects of health insurance on economic outcomes are well studied, but not conclusive.
There is some evidence that individuals choose their labor supply based on health insurance, but
it depends how one measures labor supply, as well as the population studied. Similarly with
wages, there is no resounding evidence that health insurance increases or decreases earnings. We
identified welfare participation as an understudied area. In particular, more on the long-term
effects of health insurance on welfare take-up is needed. Additionally, there is variation across
states in enrollment processes for Medicaid and other welfare programs that should be noted and
studied. There is evidence that health insurance affects education, particularly during childhood.
Household wellbeing may improve due to health insurance, but the measure is important.
Additionally, it appears that households are less likely to delay care due to costs because of
insurance.
Patterns of the relationship between health insurance and several aspects of health have been
strengthening over the last decade, but uncertainty remains about their overall importance on
health. Results from several studies suggest that insurance improves child mortality in lowincome populations and mortality in high-risk adults, but it was not shown to impact mortality in
the overall population. Evidence suggests that insurance does not impact risk factors such as
glucose and cholesterol levels, exercise, smoking, and drinking, but leads to improved blood
pressure management, especially among hypertensives, and less health limitations in adulthood.
Analyses of the impact of insurance on subjective health are quite inconclusive, but inconsistent
definitions confound cross-study comparisons of this outcome. Finally, most recent work on its
impact on mental health have shown positive effects, but such appear to contradict the Rand
Health Insurance Experiment, which remains the most compelling source of causal evidence.
A limitation of the literature—and this review—is that it tends to implicitly treat being insured as
a dichotomous state. Because of this, we provide limited insight about the importance of health
insurance design and quality, which are likely to introduce variations in insurance coverage
response. In the future, we believe that research should focus on heterogeneity in insurance plans
and individual responses. As data become better and better, finding heterogeneous effects should
be easier. Additionally, the field would benefit from more replication studies. We noted at least
two studies that could not be replicated, either because different data sources were used or
because the measure of health insurance was not robust. Given the current effervescence in this
area of research, we are optimistic that many of these gaps will be addressed in the coming years.
Tables and Figures
Table 1. Main Findings from the Experimental and Quasi-Experimental Literature on the Impact of Health Insurance on
Economic Outcomes, by Outcome Category
1.1 Labor-Force Participation
Population of
interest
Study
Study design
Finding
Antwi et al., 2013
[79]
Difference-in-difference comparing individuals most
likely to be affected based on age to those above and
below them in age
• No effect on probability of being employed, but
reduced prevalence of full-time work by 2 ppt (5.8%)
and reduced hours of work by about 3%.
• No change in rates of job change.
22-35 year olds,
possibly affected
by mandates earlier
Dillender, 2014
[80]
Triple difference comparing changes for affected ages
after the reforms relative to slightly older ages in states
that implement the reform relative to those that do not.
• Women saw 1.2 ppt decline in labor-force
participation. No change for men. Men saw 1.7 ppt
decline in full-time employment, but no change for
women.
19-29 year olds
Depew, 2015 [40]
Triple-difference comparing age criteria in states and
adoption across states
• No effect on extensive margin for males or females.
• 3.7 ppt (5.67%) decrease in FT employment for
females and 1.9 ppt (2.44%) decrease for males.
Females decreased hours by 3.7% and males by 1.9%.
24-28 year olds
who are not
married
Dahlen, 2015 [81]
Regression discontinuity design comparing 24-26 year
olds to 27-28 year olds
•Aging out of provision increased employment for men
by 8 ppt and increased the probability of being in the
labor force by 10ppt. No change for women
19-29 year olds
Heim, Lurie, &
Simon, 2015 [82]
Triple difference comparing young adults had access to
benefits, age, and pre-post law
• Effects on employment (as measured by filing a tax
return and receiving a W-2) were not statistically
different from 0.
Thurston, 1997
[14]
Difference-in-difference comparing HI to rest of
country
• No evidence of a change in hours worked
Young Adults
16-29 year olds,
except 26
Adults
Working
individuals, ages
18-65
Ages 18-65 who
were not self
employed
Buchmueller,
DiNardo, and
Valletta, 2011
[15]
Difference-in-difference comparing Hawaii to other
states
• Interested in LR effects of the law.
• Increase in low hour (<20 hrs/wk) jobs in Hawaii of
1.4 ppt compared to other states, concentrated in
quintiles with lowest concentration of ESHI.
• No effect on probability of employment.
Ages 16+
Dubay, Long, &
Lawton, 2012
[83]
Difference-in-difference comparing Massachusetts to
similar states as well as rest of country
• Little or no effect on private-sector employment or
hours worked even when looking at results by firm
size.
Ages 18-64 who
were not self
employed
Dillender,
Heinrich, and
Houseman, 2015
[84]
Difference-in-difference comparing outcomes in MA
before and after reform to rest of country while
controlling for unemployment rates
• No change in part time work for full population, but
when constrained to individuals without bachelor's
degrees, find a 1.9 ppt (9.8%) increase in the
probability of working part time hours.
Employed
individuals under
the age of 65
Moriya, Selden, &
Simon, 2016 [85]
First difference w state and year FE
• No increases in 25-29 hrs/wk or decreases in 30-34
hrs/wk in 2014 or 2015. Does not seem to vary
significantly across firm size, but employees of large
firms did decrease working 30-34 hrs/wk by .25 ppt in
2015.
• No significant change in involuntary part-time work
• 25-29 hrs/week may have increased for individuals
with no more than a HS diploma, but trend pre-dated
ACA, for older workers (60-64) slight increase in 2529 hrs/week.
Employed in the
private sector,
nonagricultural
Mathur et al.,
2016 [16]
Difference-in-difference comparing workers most
likely to be close to min wage and industries likely to
be affected to others
•No significant effect on odds of working 25-29 vs 3135 hrs.
Ages 18-64
Kolstad and
Kowalski, 2012
[30]
Difference-in-difference using MA reform as
exogenous variation in ESHI
• The change in hours is equal to a decrease of .96
hrs/week.
Difference-in-difference comparing states that
expanded Medicaid to those that did not and synthetic
• No effects on employment at time of interview, usual
hrs/wk worked or working 30 or more hrs/wk
At Risk Adult Populations
High school degree
or less
Kaestner et al.,
2015 [86]
control approach
Adults with less
than 138% FPL
Gooptu et al.,
2016 [87]
Difference-in-difference comparing states that
expanded Medicaid to those that did not
• No significant effect on transitioning from employed
to unemployed our out of labor force, job switching, or
switching from FT to PT.
Wives 25-54 years
old, and husbands
were not nonworking, excluding
couples on public
insurance during
the past year
Buchmueller and
Valletta, 1999
[88]
Observational- multinomial logit hour hours worked
and insurance; difference-in-difference comparing
across wives’ insurance status, but with-in same hours
category- no control for exogeneity of husband's offer
of HI
• Much less likely to work both PT and FT relative to
not working in DD specifications.
• 11 ppt reduction (26%) in FT & receiving insurance if
husband does (relative to husbands not offered ins.)
This is concentrated mainly on women with children.
• 1.2 ppt reduction in PT & receiving insurance if
husband does.
Married
households where
both partners are
19-64 years old and
at least on spouse
is employed
outside of
household
Royalty and
Abraham, 2006
[89]
Difference-in-difference with IV for spouse's health
insurance with spouse’s age and education and
difference out effect paid sick leave to isolate effect of
spouse's insurance
• 10 point increase in probability of husband having
insurance offer is associated with 1 pt decrease in wife
working full time (35 hrs+) and having offer and 1.5 pt
decrease in wife working 20+ hrs/week with offer.
• 10 point increase in probability of wife having
insurance offer is associated with 2.1 point decrease in
husband working FT (35 hrs+) with offer and 1.9 point
decrease in working 20+ hrs/week with offer.
Excludes couples
where husband is
not working, wife
is younger than 25
or older than 54,
Medicare/Medicaid
recipients, selfemployed wives
Kapinos, 2009
[90]
Difference-in-difference with IV for husband's
insurance offer with husband's unions status and firm
size and difference out effect having a pension to
isolate effect of husband's insurance
• Husband's insurance offer has no effect on hours
worked, but wives whose husband has HI offer are
16% less likely to work and suggests effect has been
increasing in magnitude over time; wives whose
husband has HI offer are 23% less likely to work full
time and effect has been again suggestive evidence that
this has been increasing in magnitude over time.
25-64 years of age
Buchmueller and
Carpenter, 2012
[91]
Differences in differences comparing partnered gay
men (women) to non-partnered gay and straight men
(women)
• No change in partnership or employment for gay
men, but lesbian women were 7.6 ppt (14%) more
likely to be in a partnership and 7.1 ppt less likely to be
working FT.
Married Adults
Couples where
both members are
30-65
Dillender, 2015
[92]
Triple differences comparing before and after states
extended legal recognition, between same-sex and
married opposite-sex couples.
• LFP fell by 7.9 ppt (9%) for women, likelihood of
both members being in the LF fell by 12.2 ppt,
likelihood of one member in labor force increased by
10.2 ppt, no change in neither member in LF. These
are concentrated in women with young children. No
changes for men.
Females ages 1864 with 1+ child
less than 18
Winkler, 1991
[35]
Use form of state Medicaid generosity as exogenous
variation in valuation with two-step estimate to correct
for selection in hours decision
• Medicaid generosity decreases employment until
control for region and urban/rural
• 10% increase in Medicaid generosity causes avg
female employment probability to fall .9-1.3 pp
• 10% increase in Medicaid generosity causes
employment probability to fall .61 (Miss) - 2.1 (DC) pp
• Medicaid generosity has no effect on hours worked
for female heads
Females ages 1864 with 1+ child
less than 18
Moffitt and
Wolfe, 1992 [21]
Use individual valuation of Medicaid and private
insurance to account for heterogeneity in health
• Increase in valuation of Medicaid/private insurance
increases/decreases AFDC participation, but effect of
private insurance is larger.
•Women with highest values of Medicaid are driving
results
• Increase in value of Medicaid coverage of $50 (~1/3)
increases AFDC participation by 2 pp (5.9%) and
reduces emp. rates by 5.5 pp. Results for increasing
priv. ins valuation are opposite size and almost double
in size
• If every woman who worked was insured, 3.5 pp
reduction in AFDC and 7.6 pp increase in employment
rate
Females ages 1855 with children
less than 15, not
receiving Medicare
or military health
ins., not reporting a
handicap or ill
health, and not a
Yelowitz, 1995
[17]
Medicaid expansions of late 1980s and early 1990s as
exogenous variation in eligibility
• Decoupling Medicaid and AFDC increased laborforce participation by .9 ppt, or 1.4% and reduced
AFDC caseload by 3.5%. These results are
concentrated on ever married women.
Single Mothers
veteran
See Yelowitz,
above
Ham and ShoreSheppard, 2005
[36]
Medicaid expansions of late 1980s and early 1990s as
exogenous variation in eligibility
• Increasing Medicaid eligibility had no effect on laborforce participation (working 1+ week in last year) or on
the number of hours worked using a Heckman
selection model.
Females ages 1865 with at least one
child less than15
years of age
Montgomery and
Navin, 2000 [19]
Medicaid expansions of late 1980s and early 1990s as
exogenous variation in eligibility, excluding spending
on disabled & elderly
• Medicaid expenditures have no effect on employment
or hours worked or LFP when controlling for state FE.
• Expansion of Medicaid is important- increase
eligibility by 25% increases LFP by .034 ppt but no
effect on hours.
Women ages 19-44
and not in school,
not ill or disabled
in previous year
Meyer and
Rosenbaum, 2001
[18]
Difference-in-difference comparing single mothers to
single childless women with variation in changes
across time and states in how families are treated
• Medicaid had no sig. effect on work as measured by
probability of working last week or probability of
working at all last year and small negative effects on
hours worked.
Females ages 1855 with children
Pohl, 2015 [20]
Estimate partial equilibrium static discrete choice
model with labor supply and insurance choice for
mother & kid using exogenous variation in Medicaid
eligibility across states and times; simulate changes
when Medicaid is expanded & subsidies are introduced
• Labor supply increases by 4.5% at extensive margin
and 2.2% at the intensive margin. These changes are
largest for single mothers with medical conditions.
Dave et al., 2015
[23]
Reduced form using variation in public health
insurance across states and time as exogenous
variation
•10 ppt increase in Medicaid eligibility is associated
with a 2 ppt decrease in probability of being employed
in the past year, a 1.8 ppt decrease in labor-force
participation in the past year, and no significant change
in weeks worked in the past year. It also reduced
weekly hours by 3.9% and conditional weekly hours by
.9%. These results are concentrated on women with
less than a high school degree.
Pregnant Women
Women who gave
birth from 19851996 when they
were between 18
and 39
Childless Adults
Individuals age 19
to 64 with family
income ≤ 300%
FPL who worked at
least one wk last yr
Guy Jr., Atherly,
& Adams, 2012
[22]
Difference-in-difference comparing individuals in
states that expanded access to childless adults to those
in states that did not
• Public health insurance eligibility is associated with a
2.2 percentage point decrease in full-time employment,
a 0.8 percentage point increase in the likelihood of
part-time employment, and a 1.4 percentage point
increase in the likelihood of not working. Effects are
stronger for those who are older (50-64) and in worse
health
21-64 year olds
with a Bachelor
degree or less and
not in armed forces
Garthwaite et al.,
2014 [33]
Difference-in-difference comparing TN to other
southern states & triple-diff focusing on childless
adults
• Increase in employment of 2.5 ppt after
disenrollment, which is concentrated among childless
adults who saw a decrease of 4.6 ppt. (6%). Evidence
that change in labor supply is happening along the
extensive margin. Results are larger for older
individuals (40-64).
Non-elderly, nondisabled, childless
adults
Dague, DeLeire,
& Leininger, 2014
[32]
Regression discontinuity in WI comparing applications
just before freeze to applications just after freeze as
well as propensity-score matching diff-in-diff
• Public insurance reduced the likelihood of
employment by 2.4-5.9 ppt or 6.1 to 10.6 depending on
specification.
• Decrease in earnings of $200-$210/quarter (2010$) in
RD and increase of $70-$120 in propensity score.
Larger for older individuals.
• No evidence that changes are driven by transition to
self-employment.
Women diagnosed
with breast cancer
Bradley et al.,
2007 [24]
First difference comparing women who have insurance
from their own jobs to women who are insured through
their husband
• Having insurance, even if husband was offered
insurance, increased probability of working and work
more hours 12 and 18 months after diagnosis.
Individuals who
received a kidney
transplant
Page, 2011 [25]
Difference-in-difference comparing individuals in low
income treatment group to those in high (more likely to
have private ins)
• 8ppt decrease in labor-force participation for low
income treatment group 1 year after transplant. Effect
jumps to 22.85 after correcting for proxy.
Other Groups
SSI and DI
recipients
Coe and Rupp,
2013 [26]
Difference-in-difference using state-level variation in
the access and affordability of health care for disabled
individuals in both the non-group and the Medicaid
markets.
• Medicaid buy- in programs have a positive, but small,
effect on earning, increasing the likelihood of positive
earnings by about 0.2-0.5 percentage points. Medicaid
generosity seems to have different effects on different
program participants: the likelihood of earning among
DI-only beneficiaries is lower, by 0.3 percentage
points, in states with high Medicaid coverage, while
SSI-only beneficiaries are more likely to have positive
earnings by 0.5 percentage points. These effects are
larger for sicker individuals.
Married couples
with male veteran
where husband is
between 55 & 64,
wife was not a
veteran, no active
military personal
Boyle and Lahey,
2016 [27]
Difference-in-difference comparing wives of male
veterans and non-veterans before and after VA benefits
expansion
• For married men, 2.3% increase in not working,
14.7% increase in PT employment, no change in selfemployment.
• For wives, 3-4% increase in probability of working,
avg hours/week increases by about .5 hr, no change in
hrs conditional on working, log earnings increase by
3%. These results are driven by wives with HS degree
or less and those with wealth below the median.
1.2 Earnings, Wages, and Other Labor Market Outcomes
Population of
interest
Children
Children ages 05 not from AZ
Study
Study design
Finding
Boudreaux,
Golberstein,
McAlpine, 2016
[28]
Use timing of introduction of Medicaid as exogenous
variation in exposure
• No statistically significant effect on income to poverty
ratio, decile of family wealth, or economic index for
the low income (<150% FPL target population)
Children whose
parents filed
taxes every year
from 1996-when
child turned 18
Brown, Kowalski,
& Lurie, 2015 [29]
Simulated IV using variation in eligibility across states
and time as instrument for total years of eligibility
• Each additional year of eligibility from birth to age 18
increases cumulative tax payment by $247 for women,
but results are not significant for men. Pooled together,
1 std dev increase in Medicaid eligibility increases tax
payments by 3.6%.
• Each additional year of eligibility from birth to age
18, women earn $656 more from age 19-28, but no
significant effect on men's earnings.
Individuals born
between 1979
and 1993 who
are 18+ and not
born in AZ
Miller and Wherry,
2015 [13]
Simulated IV using variation in eligibility across states
and time as instrument for prenatal eligibility, and at
different ranges 1-4, 5-9, 10-14, 15-18
• 10 ppt increase in prenatal eligibility associated with
increase in personal income of about $285 (2013
dollars). Using log of income, 10 ppt increase in
prenatal eligibility increase average income by 1.31.5ppt. Also ages 5-9 increases log income, but by
smaller amount, .3-.4 ppt.
Dillender, 2014
[80]
Triple difference comparing how outcomes changed for
affected ages after the reforms relative to slightly older
ages in states that implement the reform relative to
those that do not.
• No significant change in wages for men when
controlling for education. Women saw a increase of
2.2-2.4% for being previously treated and 2.5-2.8% for
currently treated.
Baicker et al, 2014.
[6]
Oregon Experiment: IV for being enrolled in Medicaid
using lottery as instrument; also show reduced form
results
• No statistically significant impact of Medicaid on
amount of individual earnings or whether individual
earnings are above the FPL for 2009 labor market
activity.
Young Adults
Ages 25-35 who
were possibly
affected by
dependent
mandates
Adults
Adults ages 1964
Working
individuals, ages
18-65
Thurston, 1997
[14]
Difference-in-difference comparing HI to rest of
country
• Wages in industries most affected by mandate shrunk
relative to other industries in HI, but grew relative to
same industries in the US.
Working
individuals, ages
18-65, not self
employed
Buchmueller,
DiNardo, &
Valletta, 2011 [15]
Difference-in-difference comparing Hawaii to other
states
• Interested in LR effects of the law.
• No detectible difference in wages.
Ages 18-64
Kolstad and
Kowalski, 2012
[30]
Difference-in-difference using MA reform as
exogenous variation in ESHI
• Compensating differential is -$1.35/hr which amounts
to -$2812/year.
Olson, 2002 [93]
IV for health insurance coverage of wife using
husband's firm size and union status
• Wives with health insurance earn about .20 log points
lower wage than they would if they took a job without
health insurance.
Lluis and
Abraham, 2013
[94]
Individual level FE; also instrument for lagged wages
using lagged skills and health and for current choice of
benefits with past benefits
• Being offered health insurance is associated with a
decrease in wages of 1.8%. Results are sensitive to
specification.
Hamersma, 2013
[95]
Individual FE/IV of distance between twice lagged
earnings and Medicaid threshold for lagged distance
• Medicaid & SCHIP threshold had no effect on
earnings for workers.
• When studying heterogeneity in response, finds no
change in monthly hours, but there is some evidence
that workers with earnings below Medicaid threshold
experience higher earnings growth when Medicaid
threshold is increased. For example, worker who was
$300 below threshold is predicted to have improved
earnings growth rate of about 15% for $100 increase in
Medicaid threshold.
Married adults
Full time wives
with hourly wage
of at least $2.00
Single adults
Employed FT
(30+ hrs/wk), not
married, not
receiving health
insurance
through other
sources
Single mothers
Ages 18-55 with
no more than a
HS degree, up to
5 children under
age 19, not
receiving
disability
benefits for
themselves
Pregnant women
Women who
gave birth from
1985-1996 when
they were
between 18 and
39
Childless Adults
Dave et al., 2015
[23]
Reduced form using variation in public health
insurance across states and time as exogenous
variation
• There was no change in log wages conditional on
working. These results are concentrated on women with
less than a high school degree.
Non-elderly,
non-disabled,
childless adults
Dague, DeLeire, &
Leininger, 2014
[32]
RD comparing applications just before freeze to
applications just after freeze as well as propensity-score
matching diff-in-diff
• Decrease in earnings of $200-$210/quarter (2010$) in
RD and increase of $70-$120 in propensity score.
Larger for older individuals.
21-64 year olds
with a Bachelor
degree or less,
not in armed
forces
Garthwaite et al.,
2014 [33]
Difference-in-difference comparing TN to other
southern states & triple-diff focusing on childless
adults
• No significant change on wages.
1.3 Welfare Participation
Population of
interest
AFDC/TANF
Female headed
houses w
children
Study
Study design
Finding
Blank, 1989 [34]
Use form of state Medicaid generosity as exogenous
variation in valuation
• Medicaid value had no effect on AFDC participation.
Female age 1864 headed
houses with 1+
child younger
than 18
Winkler, 1991 [35]
Use form of state Medicaid generosity as exogenous
variation in valuation with two-step estimate to correct
for selection in hours decision
• Replicate Blank's findings that Medicaid has
insignificant effect on AFDC participation when use
market value of Medicaid. However, when use
expenditures per dollar of state personal income as
measure of Medicaid, increases participation in AFDC.
Female age 1864 headed
houses with 1+
child younger
than 18
Moffitt and Wolfe,
1992 [21]
Use individual valuation of Medicaid and private
insurance to account for heterogeneity in health
• Increase in valuation of Medicaid/private insurance
increases/decreases AFDC participation, but effect of
private insurance is larger.
•Women with highest values of Medicaid are driving
results
• Increase in value of Medicaid coverage of $50 (~1/3)
increases AFDC participation by 2 pp (5.9%). Results
for increasing priv. ins valuation are opposite size and
almost double in size
• If every woman who worked was insured, 3.5 pp
reduction in AFDC and 7.6 pp increase in employment
rate
Female headed
houses (ages 1855) with children
< 15, not
receiving
Medicare or
military health
ins., no handicap
or ill health, not
Yelowitz, 1995
[17]
Medicaid expansions of late 1980s and early 1990s as
exogenous variation in eligibility
• Decoupling Medicaid and AFDC reduced AFDC
caseload by 3.5%. These results are concentrated on
ever married women.
a veteran
Ham and ShoreSheppard, 2005
[36]
Medicaid expansions of late 1980s and early 1990s as
exogenous variation in eligibility
• Increasing Medicaid eligibility had no effect on AFDC
participation. .
Decker and Selck,
2012 [96]
OLS using the timing of Medicaid introduction across
states as exogenous variation
• Medicaid introduction increased AFDC caseloads by
3% in first year, 9% in second year, and 13% in third
year. Permanent caseloads increased by almost 16%
•Medicaid introduction increased chance of female heads
participating in AFDC by 6.9 ppt or 16%. Ultimately,
increases by about 12 ppt, or 28% .
Adults ages 1964
Baicker et al., 2014
[6]
Oregon Health Insurance Experiment: IV for being
enrolled in Medicaid using lottery as instrument; also
show reduced form results
• Winning the lottery increased probability of receiving
food stamps by 2.5 ppt (4%) and increases unconditional
annual household food stamp benefits by $73, or $3000
in annual benefits for new beneficiaries. The effects of
being on Medicaid are about 4x larger. Probability of
being newly covered by SNAP increases in first 3
months and continues to increase in subsequent 3 month
increments out to 12-15 mo.
Nonelderly
households
Yelowitz, 1996
[37]
Medicaid expansions
• Marginal effect of expanding eligibility was to increase
FS participation by .58 pp or 7.5% increase in FS
caseload
• True effect of expansions was to increases FS
participation by .22 pp (Medicaid explains 10% of FS
growth)
Individuals born
between 1979
and 1993 who
are 18+ and not
born in AZ
Miller and Wherry,
2015 [13]
Simulated IV using variation in eligibility across states
and time as instrument for prenatal eligibility, and at
different ranges 1-4, 5-9, 10-14, 15-18
• 10 ppt increase in Medicaid prenatal eligibility
decreased probability of having Food Stamp benefits by
.6%.
See Yelowitz,
above
Female headed
houses w
children
Food Stamps
SSI & DI
Adults ages 1964
Baicker et al., 2014
[6]
Oregon Health Insurance Experiment: IV for being
enrolled in Medicaid using lottery as instrument; also
show reduced form results
• No statistically significant effect on SSDI or SSI
benefit receipt. Possible evidence of increase in
probability of receipt of TANF but results are small and
not robust.
18- to 64-yearolds non single
parent
households,
white or African
American not in
AZ, not
including women
younger than 45
Yelowitz, 1998
[97]
Medicaid expansions; IV is average Medicaid
expenditure for blind SSI recipients
• Increasing Medicaid expenditure by $1000, increases
SSI participation by .0537 ppt, or 13% of increase in SSI
participation
• For low permanent-income group, Medicaid explains
about 20% of growth in SSI for this group
Ages 18-64 b/w
October 2004 &
Sept. 2009 in
MA and other
states in NE
census division
Maestas et al.,
2014 [98]
Difference-in-difference comparing MA to other states
in NE census division
• Disability applications increased by 3% (.08/1000
working age residents) compared to neighboring states in
2008, but disappears in 2009. This is primarily driven
by SSDI only applications.
• Low-insurance counties saw decrease in applications of
.06 working age residents in 2007 and 2008, even though
SSDI-only applications increased by .04 in 2008.
• SSDI-only applicants filed on .5-1 month later (on
avg.) in low-insurance counties and 1-2 mo earlier in
high-insurance counties
Workers Compensation
NLSY sample
born between
1957 and 1964
Lakdawalla,
Reville, &
Seabury, 2007 [99]
Observational but individual FE, controlling for
industry, state, establishment size
• Employer offer of HI is associated with .4-1 ppt
increase in probability of workplace injury
• Injured workers in firms that offer HI 14-17 ppt more
likely to file a WC claim but actually having HI is
associated with 4-6 ppt increase in probability of filing a
WC claim
TX individuals
within 2 years of
26th birthday
Dillender, 2015
[100]
RD exploiting jump in coverage through parents health
insurance after age 26
• No significant change in claims after age 26 but
number of bills paid for by WC increases 8.1%, driven
by strain and sprain bills as well as number of
occupational disease bills
MEPS sample
Li., 2015 [101]
Structural
• Calibrated general equilibrium model suggests that
ACA will decrease percentage of working-age people
receiving DI by .3pp and increase LFP by .2 ppt
Brown, Kowalski,
& Lurie, 2015 [29]
Simulated IV using variation in eligibility across states
and time as instrument for total years of eligibility
• Each additional yr of eligibility from birth to age 18,
women receive $109 less in EITC by age 27 & men
receive $41 less. Women are 1.7% less likely to collect
EITC, but there is no effect on extensive margin for men.
EITC
Children whose
parents filed in
every tax year
from 1996-when
child turns 18
1.4 Education
Population of
interest
Children
4th and 8th graders
Study
Study design
Finding
Levine and
Schanzenbach,
2009 [38]
Simulated IV for eligibility using Medicaid expansions
of the 1980s and 1990s as variation with tripledifferences
• No effect on test scores separately in 4th or 8th grade,
but 50 percentage point increase in PHI eligibility at
birth increases reading test scores by .091 sd (3 points.)
Individuals born
between 1980 and
1990
Cohodes et al.,
2015 [39]
Simulated IV for eligibility using Medicaid expansions
of the 1980s and 1990s as variation
• 10 ppt increase in average Medicaid eligibility
between 0-17 decreases high school dropout rate by .4
ppt (4%), increases likelihood of college enrollment by
.3 ppt (.5%), and increases 4-year college attainment
rate by .7 ppt (2.5%). These effects are not driven by
eligibility between birth and age 3.
Children whose
parents filed in
every tax year
from 1996-when
Brown, Kowalski,
& Lurie, 2015 [29]
Simulated IV using variation in eligibility across states
and time as instrument for total years of eligibility
•Female eligibles were more likely to have attended
college at ages 20-22. At age 20, one additional year of
eligibility increased likelihood of having ever attended
college by .40 ppt. Results for men are not significant.
child turns 18
Individuals born
between 1979 and
1993 who are 18+
and not born in
AZ
Miller and Wherry,
2015 [13]
Simulated IV using variation in eligibility across states
and time as instrument for prenatal eligibility, and at
different ranges 1-4, 5-9, 10-14, 15-18
• 10 ppt increase in prenatal eligibility increased
probability of graduating high school by .2 ppt (.2%).
Corresponds to coverage raising graduation rate by
7.3%. No significant effects on probability of attending
some college or receiving a college degree.
Children ages 0-5
not from AZ
Boudreaux,
Golberstein,
McAlpine, 2016
[28]
Use timing of introduction of Medicaid as exogenous
variation in exposure
• No statistically significant effect on years of
education, income to poverty ratio, decile of family
wealth, or economic index for the low income (<150%
FPL target population)
Jung, Hall, and
Rhoades, 2013
[102]
Observational
Ages 25-35 who
were possibly
affected by
dependent
mandates
Dillender, 2014
[80]
Triple difference comparing how completed education
changes for affected ages after the reforms relative to
slightly older ages in states that implement the reform
relative to those that do not.
• Men who were 18 or younger at the time of the
reform gain 0.173 years of education on average by the
time they are older than 25, were 2.5 ppt more likely to
have completed college by the time they are 26, and
were 2.8 ppt more likely have attended some college,
and increased completing high school by 1.5 ppt.
Women saw very little effect on education- increased
high school graduation by 1.6 ppt.
Ages 19-29
Depew, 2015 [40]
Triple-difference comparing age criteria in states and
adoption across states
• No change in being a student, married, or having
children.
Ages 19-22
Yaskewich, 2015
[41]
Difference-in-difference comparing Pennsylvania to
New Jersey
• College enrollment in NJ was not statistically
different than PA for full sample.
• Upper-income households (300%+ FPL) saw
decrease of 8.6-9.3 ppt (14.4 and 27.0%) in college
Young Adults
Ages 17-23
• Availability of parental health insurance increases the
probability of being a full time student by 22%
decreases the probability of being a part-time student
by 2.6%, and decreases the probability of not enrolling
in college by 19.4%.
• When sample consists only of students, the
representative student is 6.5% more likely to enroll
full-time when parental health insurance is available.
enrollment. Households where the YA adult lived at
home and the parent worked in a small firm saw larger
effects.
1.5 Savings and Asset Accumulations
Population of
interest
Study
Study design
Finding
Households
where head is
not retired and
younger than 65
Starr-McCluer, 1996
[42]
Jointly estimate wealth and insurance coverage to try to
control for selectivity of health insurance using share of
households heads in area who work for organizations
with 100+ employees as instrument
•Households with insurance have significantly higher
savings than households without coverage.
Households with
only one family,
head ages 18-64,
no members
older than 64,
state uniquely
identified in
SIPP
Gruber and
Yelowitz, 1999 [44]
Simulated IV using total Medicaid dollars with
eligibility and value of Medicaid as exogenous
variations
• $1000 increase in Medicaid eligible dollars decreases
odds of having positive assets by .81%, and wealth
holdings fall by 2.51% conditional on having positive
net wealth. For the Medicaid eligible population, these
values are 4.2% and 12.8% respectively.
• Medicaid program lowers asset holdings by between
25 and 32 cents for each dollar of eligibility, which
amounts to lowered wealth holdings between $1293
and $1654. Expansions between 1984 and 1993
lowered wealth holdings by $567 to $722.
• Having an asset test more than doubles the reduction
in assets.
• For each $1000 in eligibility, non-durable
expenditures rise by .82%. For the eligible population,
this is 4.2%, or $538 (1987$).
See Gruber and
Yelowitz, above
Maynard and Qiu,
2009 [45]
Simulated quantile IV with eligibility and value of
Medicaid as exogenous variations
• $1000 increase in Medicaid eligible dollars drops
median net worth by 5.47% (gt mean reported in
Gruber and Yelowitz)
• The effect of Medicaid dollars on assets is U-shaped
in net-worth quantiles. No significant effect on lower
quantiles (0-.2) but increase monotonically in
magnitude and significance until .6 quantile, and then
increase in magnitude.
• Households in very bottom quantiles of net-worth do
not respond to asset tests, while those in the middle do.
No restrictions
other than valid
wealth data
Gittleman, 2011 [46]
Simulated IV using current Medicaid dollars with
eligibility and value of Medicaid as exogenous
variations
• Using same instrument as Gruber, Yelowitz, find
reduction of wealth holdings of 28.0% for Medicaid
eligibles. In aggregate, this is .8% reduction in wealth.
• Using current Medicaid dollars (as opposed to total)
find no effect. Also show evidence that G-Y is driven
by second-order interactions and depends on time
period selected.
Heads of
households ages
19-50 years of
age
Lee, 2016 [43]
Triple difference comparing households with ESHI
living with dependent child age 19-25 to control group
with child outside of mandated ages
• Households with dependents ages 19-25 & ESHI
increased shares of stocks in financial portfolio by 4.2
percentage points after ACA mandate with no
significant reduction in shares of bonds or assets in
interest-bearing accounts.
1.6 Household Wellbeing
Population of
interest
Study
Study design
Finding
Adults ages 19-64
Finkelstein et al.,
2012 [8]
Oregon Health Insurance Experiment: IV for being
enrolled in Medicaid using lottery as instrument
• No significant effect on probability of declaring
bankruptcy, judgements, or liens.
Adults ages 21-64
without an
advanced degree
Gross and
Notowidigdo, 2011
[48]
Simulated IV with Medicaid expansions of 1990s
providing exogenous variation
• 10 ppt increase in eligibility for Medicaid reduces
personal bankruptcies by 8%.
Noninstitutionalized
Americans
Sommers and
Oellerich, 2013
[49]
Propensity-score matching individuals with Medicaid
coverage to those without Medicaid coverage (either
private insurance or uninsured)
• In 2010, Medicaid kept 2.1 million Americans out of
poverty and 1.4 million out of extreme poverty.
Without Medicaid, total OOP spending would increase
from $376 to $871 per Medicaid enrollee and family
income would drop from 149% to 143% of FPL.
Citizens in 41 states
Flavin, 2015 [103]
Difference-in-difference comparing low income
citizens in states that expanded Medicaid to states that
did not
• Moving from non-expansion state to an expansion
state is associated with more than 1/2 std dev increase
in SWB
General Households
Households with Children
Households with
children younger
than 18
Leininger et al.,
2010 [47]
Simulated IV with eligibility under CHIP providing
exogenous state-level variation in access to health
insurance
• Eligibility associated with increase in non-health
consumption of $5477, which is concentrated in
transportation and retirement/pension savings.
Noninstitutionalized
with incomes below
300% FPL
Saloner, 2013
[104]
Simulated IV with eligibility under CHIP providing
exogenous state-level variation in access to health
insurance
• CHIP did not decrease food security or housing
problems, even for low-income sub-sample.
Reference person
18-64, unmarried
with at least 1 never
married child
younger than 18
Schmidt, ShoreSheppard, &
Watson, 2015
[105]
Simulated IV with Medicaid eligibility across states as
exogenous variation
• Medicaid did not have a statistically significant
impact on food insecurity, but should be researched
further.
1.7 Delayed Care Due to Cost
Population of
interest
Study
Study design
Finding
Households with Children
Nondisabled
parents, ages 1864
Busch and
Duchovny, 2005
[106]
Simulated IV using variation in eligibility across states
and years
• .29 ppt increase in probability that one did not forego
needed care due to cost.
Children younger
than 18
Miller, S., 2012
[107]
Difference-in-difference comparing MA to other states
in Northeast region
• .9 ppt decrease in forgone medical care because of
cost.
Nonelderly adults
with at least one
child and incomes
≤ 138% FPL
McMorrow et al.,
2015 [78]
Use Medicaid threshold that exploit exogeneity across
states and time in eligibility
• Reduced delays in care due to cost in past 12 months
by 3.1 ppt, unmet need for prescription meds due to
cost by 3.1ppt, and decreased unmet need for mental
health care due to cost by 2.0 ppt.
Wallace and
Sommers, 2015
Difference-in-difference comparing those who were
• Proportion of young adults unable to see physician
Young Adults
Ages 19-34
[108]
affected by dependent mandate to those who were not
because of cost declined by 1.9 ppt.
Adults ages 19-64
Finkelstein et al.,
2012 [8]
Oregon Health Insurance Experiment: IV for being
enrolled in Medicaid using lottery as instrument
• Decrease of 3.6 ppt (55%) in probability that refused
treatment because of medical debt in past six months
Women with
incomes ≥ 125%
FPL
Pauly, 2005 [50]
Instrument for health insurance coverage using size of
firm and marital status
• Large decrease in going without care needed for
health, but hard to interpret because of categorical
nature of explanatory and dependent variables.
Ages 18-64
Long, 2008 [109]
Difference between outcomes before and after MA
reform
• Decrease in not getting needed care in the past year,
especially for adults with income <300% FPL,
decrease in not getting needed care because of cost,
which was almost doubled for adults with income
<300% FPL.
Ages 18-64
Zhu et al., 2010
[63]
Difference-in-difference comparing MA to rest of New
England
• Cost related barriers improved for MA compared to
New England for whites and blacks but not Hispanics,
for individuals above 300% FPL and below 100% FPL.
Ages 18-64
Pande et al., 2011
[110]
Difference-in-difference comparing MA to other New
England states
• MA residents were 6.6 ppt more likely to forgo care
because of cost, which was concentrated on the
disadvantage subpopulation.
Ages 18-64
Sommers et al.,
2015 [111]
First difference comparing pre and post-ACA as well as
Difference-in-difference comparing pre and post-ACA
adults above and below 138% of the poverty level
•Inability to afford care decreased 5.5 ppt when
comparing pre and post-ACA but was not statistically
significant in difference-in-difference specification.
Guy Jr., 2010 [51]
Difference-in-difference comparing childless adults
eligible for expansions to childless adults not eligible
but above 300% FPL
•10 ppt increase in eligibility for programs with
increased cost sharing lead to .22 ppt increase in
likelihood of not forgoing needed care due to costs.
For the traditional cost sharing, the result was an
increase of .28 ppt.
Adults
Childless Adults
Ages 19-64
Table 2. Main Findings from the Experimental and Quasi-Experimental Literature on the Impact of Health Insurance on
Health, by Outcome Category
2.1 Mortality
Population of
interest
Study
Study design
Finding*
Currie and Gruber,
1996 (JPE) [52]
Instrumental variable regressions using the simulated
fraction of women age 15-44 eligible for Medicaid in
each state and year in the event of pregnancy as an
instrument for individual eligibility
8.5% decline in the infant mortality rate associated
with the increase in Medicaid eligibility between 1979
and 1992
Children ages 1-17
Howell et al., 2010
[54]
Instrumental variable using the fraction of a fixed
group of children who would be eligible for health
insurance to instrument for individual Medicaid and
SCHIP eligibility
2% reduction in external-cause mortality for each 10
percentage points increase in Medicaid/SCHIP
eligibility. No impact on natural-cause mortality when
considering year fixed effects
Children (age
range not
mentioned)
Currie and Gruber,
1996 (QJE) [53]
Instrumental variable two stage least square regressions
using the fraction of a fixed group of children who
would be eligible for health insurance to instrument for
individual Medicaid and SCHIP eligibility
0.13 percentage point reduction in all-cause mortality
for each 10 percentage points increase in the fraction of
children eligible for Medicaid, corresponding to a
relative decline of 3.4% and an estimated 5.1%
reduction in child mortality due to rise in eligibility
between 1984 and 1992
Brook et al., 1983
[56]
Randomized experiment: comparison of individuals
randomly assigned to one of 14 insurance plans, with
copays ranging from 0% (free health care) to 95%, and
followed 3 to 5 years during the Rand Health Insurance
Experiment
10% reduction in probability of mortality among highrisk individuals due to being in the free plan versus
cost-sharing plans. No impact in the overall sample
Children
Infants
Adults
Individuals age 1461 at enrollment
Social Security
Disability
Insurance
beneficiaries ages
18-54
Weathers and
Stegman, 2012
[11]
Randomized experiment: comparison of newly enrolled
Social Security Disability Insurance beneficiaries
randomly assigned to receiving health benefits
packages (treatment) to individuals that remained in the
24-month waiting list for Medicare benefits (control)
during the Accelerated Benefits demonstration project
No impact of treatment on mortality rates
Adults ages 19-64
Finkelstein et al.,
2012 [8]
Randomized experiment: Instrumental variable
comparison of Oregonians enrolled in Medicaid after
selection in a 2008 lottery drawing (treatment) and
Oregonians who signed up for the lottery but were not
selected (control) using lottery selection as an
instrument for coverage
No impact on mortality 16 months after the lottery
Adults ages 20-64
Sommers et al.,
2012 [112]
Difference-in-difference comparing childless adults
with income below 100% of the federal poverty line in
Arizona, Maine, and New York, which expanded
Medicaid to cover low-income childless adults in 2001
and 2002 (treatment), to neighbouring non-expansion
states (control) before and after the expansions
19.6 per 100,000 reduction in mortality, corresponding
to a relative decline of 6.1%; 41.0 reduction for nonwhites vs. 14.0 reduction for whites; 30.4 reduction at
ages 35-64 vs. no reduction at ages 20-34; 22.2
reduction in counties with poverty rate ≥ 10% vs. 11.3
in counties with poverty rate < 10%
Sommers et al.,
2014 [62]
Difference-in-difference comparing mortality rates in
Massachusetts counties (treatment) to propensity-score
matched counties in other states before and after the
Massachusetts health reform.
8.2 per 100,000 reduction in all-cause mortality,
corresponding to a relative decline of 3.9%; relative
decline of 4.5% in health care-amenable mortality;
larger reduction among Hispanics and non-whites
(significant at the 10 percent level)
Kaestner, 2016
[55]
Replication of Sommers et al., 2014 using
randomization inference methods to estimate
significance levels of treatment effects
No impact on mortality
Goldman et al.,
2001 [113]
Two-equation parametric model of insurance and
mortality using a set of policy variables including the
generosity of Medicaid and measures of the generosity
of Aids Drug Assistance Programs as an instrument for
insurance
71 to 85% reduction in 6-months mortality among HIV
patients due to insurance
High-risk groups
HIV-positive
patients (age range
not mentioned)
Gunshot trauma
patients (age range
not mentioned)
Dozier et al., 2010
[58]
Comparison of in-hospital mortality rates of insured
and uninsured individuals following gunshot trauma
Uninsured patients had higher odds of mortality (OR
2.2)
Car accident
patients (age range
not mentioned)
Doyle, 2005 [114]
Comparison of mortality rates of insured and uninsured
individuals with auto insurance following car accidents
Uninsured patients had a 1.5 percentage points higher
mortality rate; corresponding to a 39% relative increase
over the mean mortality rate of 3.8%
*: Reported findings are significant at the 5 percent level or below unless stated otherwise
2.2 Self-Reported Health
Population of
interest
Study
Study design
Finding*
Children
Prenatal and
children ages 0-18
Miller and Wherry,
2015 [13]
Instrumental variable estimations using the simulated
fraction of individuals who would be eligible given
eligibility policy to instrument for individual Medicaid
eligibility
Eligibility at ages 5-9 associated with an increased
probability of reporting very good or excellent health at
ages 19-35. Prenatal eligibility and eligibility at ages 14, 10-14 and 15-18 have no effect
Children ages 0-8
Currie et al., 2008
[115]
Instrumental variable estimation using the fraction of a
fixed group of children who would be eligible for
health insurance to instrument for individual Medicaid
and SCHIP eligibility
Eligibility at ages 2, 3 and 4 associated with a reduced
probability that parents report their child's health as
less than excellent at ages 9-17 (ages 2 and 4
significant at the 10 percent level). No impact of
eligibility at ages 0, 1, and 5-8
Children age 0-13
at enrollment
Newhouse and
Rand Corporation,
1993 [5]
Randomized experiment: comparison of individuals
randomly assigned to one of 14 insurance plans, with
copays ranging from 0% (free health care) to 95%, and
followed 3 to 5 years during the Rand Health Insurance
Experiment
No impact of being in the free plan versus cost-sharing
plans on a parent-assessed standardized health
perception score
Children ages 0-17
Miller, 2012 [107]
Difference-in-difference comparison of trends in
Massachusetts (treatment) to other Northeast region
states (control) before and after the Massachusetts
health reform
5.5 to 6 percentage points increase in the likelihood
that child's health is reported as excellent (relative
increase of 10%)
Children ages 5-18
De la Mata, 2012
[66]
Regression discontinuity design using thresholds in
income eligibility for Medicaid across states
No impact of contemporary, one-year lagged, and 5year lagged eligibility on the probability of reporting
excellent health
Children ages 9-17
Currie et al., 2008
[115]
See above
No impact of contemporary eligibility on the
probability that parents report child health as less than
excellent
Young adults
Adults ages 19-25
Chua and
Sommers, 2014
[64]
Difference-in-difference comparison of adults ages 1925 (treatment) to adults ages 26-34 (control) before and
after the implementation of the Affordable Care Act's
dependent coverage provision
6.2% increase (95% CI, 3.2-9.3) in the probability of
reporting excellent physical health
Adults ages 23-25
Barbaresco et al.,
2015 [69]
Difference-in-difference comparison of adults ages 2325 (treatment) to adults ages 27-29 (control) before and
after the implementation of the Affordable Care Act's
dependent coverage provision
Increased probability of reporting excellent health. No
impact on the probability of reporting very good health
and the number of self-reported days not it good
physical health
Pauly, 2005 [50]
Instrumental variable linear probability models using
the size of the firm in which household members work
and marital status as instruments for private insurance
coverage of non-poor young women
No impact on the probability of reporting fair or poor
health
Individuals age 1461 at enrollment
Brook et al., 1983
[56]
Same as Newhouse and Rand Corporation, 1993 above
No impact of being in the free plan versus cost-sharing
plans on a standardized health perception score
Social Security
Disability
Insurance
beneficiaries ages
18-54
Weathers and
Stegman, 2012
[11]
Randomized experiment: comparison of newly enrolled
Social Security Disability Insurance beneficiaries
randomly assigned to receiving health benefits
packages (treatment) to individuals who remained in
the 24-month waiting list for Medicare benefits
(control) during the Accelerated Benefits
demonstration project
10.8 percentage points reduction in the probability of
reporting poor health for the AB group (health
benefits) compared to control after 12 months; no
additional impact of being in the AB plus group (health
benefits package and additional services) compared to
the AB group
Adults ages 18-64
Zhu et al., 2010
[63]
Difference-in-difference comparison of trends in
Massachusetts (treatment) to New England states
(control) before and after the Massachusetts health
reform
No impact on the probability of reporting fair or poor
health
Sommers et al.,
2015 [111]
Difference-in-difference comparison of adults with
income below 138% of the federal poverty line in
Medicaid expansion states (treatment) and in nonexpansion states (control) before and after the
Affordable Care Act first enrollment period in October
2013
No impact on the probability of reporting fair or poor
health
Adults
Young women
(age range not
mentioned) with
incomes ≥ 125%
FPL
Adults ages 19-64
Finkelstein et al.,
2012 [8]
Randomized experiment: instrumental variable
comparison of Oregonians enrolled in Medicaid after
selection in a 2008 lottery drawing (treatment) and
Oregonians who signed up for the lottery but were not
selected (control) using lottery selection as an
instrument for coverage
13 percentage points increase in the probability of
reporting good, very good or excellent health 16
months after the lottery, corresponding to 25% of the
control group mean; 1.3 days increase in the reported
number of days in good health in the past 30 days
Sommers et al.,
2012 [112]
Difference-in-difference comparison of childless adults
with income below 100% of the federal poverty line in
Arizona, Maine, and New York, which expanded
Medicaid to cover low-income childless adults in 2001
and 2002, (treatment) to neighbouring non-expansion
states (control) before and after the expansions
2.2 percentage points increase in the probability of
reporting "excellent" or "very good" health,
corresponding to a relative increase of 3.4%. 2.5
percentage points increase among the population ages
35-64, against no significant impact in the population
ages 19-34. No significant impact on non-white and
Hispanic populations
Baicker et al., 2013
[7]
Same as Finkelstein et al., 2012 above
7.8 percentage points increase in the probability of
reporting health as the same or better than last year 25
months after the lottery. No significant impact on
Medical Outcomes Study 8-Item Short-Form Health
Survey physical-component score
Adults ages 20-64
Sommers et al.,
2014 [62]
Difference-in-difference comparison of adults in
Massachusetts counties (treatment) and in propensityscore matched counties in other states before and after
the Massachusetts health reform.
1.8 percentage points decrease in the probability of
reporting less than excellent health, corresponding to a
5% relative decrease
Adults ages 21-65
Lurie et al., 1984
[75]
Difference-in difference comparison of medically
indigent Californians ages 21 to 65 that were
terminated from Medi-Cal in 1983 (treatment) to
beneficiaries that were not terminated because of the
nature of their medical needs (control) at termination
and six months after
Terminated indigents showed an 8.0 points decline in
their General-health perceptions score, against 0.7
point in the control group.
Hadley, 2007
[116]
Logistic regression of short term health outcomes of
insured and non-insured individuals following a trauma
Uninsured individuals were more likely to report a
much worse health status 3.5 months following
condition onset (9.8% vs 6.7% uninsured; OR 0.86)
High-risk groups
Trauma patients
younger than 64
*: Reported findings are significant at the 5 percent level or below unless stated otherwise
2.3 Risk Factors
Population of
interest
Study
Study design
Finding*
Children
Prenatal and
children ages 0-18
Miller and Wherry,
2015 [13]
Instrumental variable estimations using the simulated
fraction of individuals who would be eligible given
eligibility policy to instrument for individual Medicaid
eligibility
1.7 percentage points reduction in the likelihood of
obesity at ages 19-35 for each 10 percentage points
increase in prenatal eligibility, corresponding to an 8%
decrease over the sample mean. Eligibility at ages 1-18
have no effect on adult body mass index and the
probability of obesity
Children ages 0-5
Boudreaux et al.,
2016 [28]
Regression model approach using cross-state variation
in timing of Medicaid adoption to measure the fraction
of months a person was exposed to Medicaid during
early childhood
No impact of exposure to Medicaid during childhood
on body mass index at ages 25-54
Children ages 5-18
De la Mata, 2012
[66]
Regression discontinuity design using thresholds in
income eligibility for Medicaid across states
One-year lagged eligibility associated with an
increased probability of being obese. No impact of
contemporary and 5-year lagged eligibility
Barbaresco et al.,
2015 [69]
Difference-in-difference comparison of adults ages 2325 (treatment) to adults ages 27-29 (control) before and
after the implementation of the Affordable Care Act's
dependent coverage provision
Increase in the probability of being a risky drinker.
Decrease in body mass index. No consistent impact on
smoking status, drinks per month, obesity, exercise and
pregnancy
Brook et al., 1983
[56]
Randomized experiment: comparison of individuals
randomly assigned to one of 14 insurance plans, with
copays ranging from 0% (free health care) to 95%, and
followed 3 to 5 years during the Rand Health Insurance
Experiment
0.7 mm Hg diastolic blood pressure reduction due to
being in the free plan versus cost-sharing plans in the
overall sample (significant at the 10 percent level). No
impact of being in the free plan versus cost-sharing
plans on smoking status, weight, and cholesterol
Keeler et al., 1987
[4]
Same as Brook et al., 1983 above
No impact of being in the free plan versus cost-sharing
plans on blood glucose, level of physical activity and
monthly alcohol consumption in the overall group
Young adults
Adults ages 23-25
Adults
Individuals age 1461 at enrollment
Adults ages 18-64
Courtemanche and
Zapata, 2014 [77]
Difference-in-difference ordered probit regression
comparing adults in Massachusetts (treatment) and in
states that did not conduct health reforms (control)
before and after the Massachusetts health reform
Reduction in body mass index. No impact on smoking
and minutes of exercise per week
Adults ages 19-64
Baicker et al., 2013
[7]
Randomized experiment: instrumental variable
comparison of Oregonians enrolled in Medicaid after
selection in a 2008 lottery drawing (treatment) and
Oregonians who signed up for the lottery but were not
selected (control) using lottery selection as an
instrument for coverage
No impact on blood pressure, cholesterol, glycated
hemoglobin, and Framingham risk score measures 25
months after the lottery
*: Reported findings are significant at the 5 percent level or below unless stated otherwise
2.4 Health limitations and functional status
Population of
interest
Study
Study design
Finding*
Children
Prenatal and
children ages 0-18
Miller and Wherry,
2015 [13]
Instrumental variable estimations using the simulated
fraction of individuals who would be eligible given
eligibility policy to instrument for individual Medicaid
eligibility
No impact of prenatal eligibility and eligibility at ages
1-18 on the probability of health limitations at ages 1935
Children age 0-13
at enrollment
Newhouse and
Rand Corporation,
1993 [5]
Randomized experiment: comparison of individuals
randomly assigned to one of 14 insurance plans, with
copays ranging from 0% (free health care) to 95%, and
followed 3 to 5 years during the Rand Health Insurance
Experiment
No impact of being in the free plan versus cost-sharing
plans on a standardized role functioning score that
indicates play, school, and usual activities limitations
due to poor health
Children ages 0-17
Lykens and
Jargowsky, 2002
[68]
Regression models using geographic variations in the
estimated proportion of children eligible during
Medicaid expansions as the variable of interest
0.03 and 0.06 days reductions in reported number of
bed days and restricted activity days in the previous
two weeks, respectively, for each 10 percentage points
increase in Medicaid eligibility (both significant at the
10 percent level). Reduction in the average number of
acute health conditions for non-Hispanic whites and
Hispanics (the latter significant at the 10 percent level).
No effect on school loss days and acute conditions of
blacks
Children ages 5-18
De la Mata, 2012
[66]
Regression discontinuity design using thresholds in
income eligibility for Medicaid across states
No impact of contemporary, one-year lagged, and 5year lagged eligibility on the number of school days
missed due to illness
Barbaresco et al.,
2015 [69]
Difference-in-difference comparison of adults ages 2325 (treatment) to adults ages 27-29 (control) before and
after the implementation of the Affordable Care Act's
dependent coverage provision
No impact on the number of self-reported days with
physical limitations
Young adults
Adults ages 23-25
Adults
Individuals age 1461 at enrollment
Brook et al., 1983
[56]
Same as Newhouse and Rand Corporation, 1993 above
No impact of being in the free plan versus cost-sharing
plans on 1) a standardized physical functioning score
that indicates the degree of limitations in personal selfcare, mobility, or physical activity; and 2) a
standardized role functioning score that indicates
limitations at work, school, or conducting housework
activities due to poor health
Social Security
Disability
Insurance
beneficiaries ages
18-54
Weathers and
Stegman, 2012
[11]
Randomized experiment: comparison of newly enrolled
Social Security Disability Insurance beneficiaries
randomly assigned to receiving health benefits
packages (treatment) to individuals who remained in
the 24-month waiting list for Medicare benefits
(control) during the Accelerated Benefits
demonstration project
11.0 percentage points decline in the probability of
having a SF-36 score indicating likely meeting the
Social Security Administration's definition of disability
for the treatment groups compared to the control group,
corresponding to 21% of the control group mean.
Adults ages 18-64
Courtemanche and
Zapata, 2014 [77]
Difference-in-difference ordered probit regression
comparing adults in Massachusetts (treatment) and in
states that did not conduct health reforms (control)
before and after the Massachusetts health reform
Reduction in the probability of activity-limiting joint
pain and days with health limitations
Sommers et al.,
2015 [111]
Difference-in-difference comparison of adults with
income below 138% of the federal poverty line in
Medicaid expansion states (treatment) to and nonexpansion states (control) before and after the
Affordable Care Acts first enrollment period in
October 2013
No impact on the proportion of the last 30 days in
which activities were limited by poor health
Finkelstein et al.,
2012 [8]
Randomized experiment: instrumental variable
comparison of Oregonians enrolled in Medicaid after
selection in a 2008 lottery drawing (treatment) and
Oregonians who signed up for the lottery but were not
selected (control) using lottery selection as an
instrument for coverage
1.6 additional reported days during which poor
physical or mental health did not impair usual activity
in the past 30 days, corresponding to 8% of the control
group mean
Adults ages 19-64
*: Reported findings are significant at the 5 percent level or below unless stated otherwise
2.5 Preventable Hospitalizations
Population of
interest
Study
Study design
Finding*
Children
Prenatal and
children ages 0-18
Miller and Wherry,
2015 [13]
Instrumental variable estimations using the simulated
fraction of individuals who would be eligible given
eligibility policy to instrument for individual Medicaid
eligibility
Prenatal eligibility reduces hospitalizations related to
endocrine, nutritional and metabolic diseases, and
immunity disorders in adulthood (at ages 19-35).
Eligibility at ages 1-4 reduces all-cause hospitalizations
excluding pregnancy-related visits; eligibility at ages 518 has no effect on adult hospitalizations
Kaestner et al.,
2001 [73]
Difference-in-difference estimations comparison of
ambulatory care sensitive hospitalizations in lowincome children (treatment) to high-income children
(control) before and after Medicaid eligibility
extensions in 11 states
Reduction in the incidence of both asthma and nonasthma ambulatory care sensitive hospitalizations
among children ages 2-6 in very low income areas;
reduction in incidence of asthma ambulatory care
sensitive hospitalizations among children ages 2-6 and
7-9; no impact on non-asthma ACS hospitalizations at
ages 7-9
Dafny and Gruber,
2005 [117]
Instrumental variable estimations using simulated
eligibility stemming from changes in the proportion of
children eligible for Medicaid across states during
Medicaid expansions as an instrument for eligibility
8.4% and 8.1% increases in total hospitalizations and
unavoidable hospitalizations for each 10 percentage
points increase in eligibility, respectively; no
significant increase in avoidable hospitalizations
Aizer, 2007 [74]
Instrumental variable estimations using zip codes and
the timing and placement of outreach efforts to
increase Medicaid take-up in California in the late
1990's as an instrument for insurance
2.9 percentage points decline in ambulatory care
sensitive hospitalizations for each 10 percentage points
increase in Medicaid enrollment
Bronchetti, 2014
[71]
Instrumental variable approach using cross-state
variations in Medicaid and State Children's Health
Insurance Program eligibility expansions following
1996 as an instrument for eligibility
Reduction in hospital emergency care among first- and
second-generation immigrant children; negligible
effects among native children
Children ages 0-17
Adults
Adults ages 18-64
Wherry et al., 2015
[118]
Regression discontinuity design using the discontinuity
in the cumulative number of years a child is eligible for
Medicaid based on date of birth
Years of Medicaid eligibility at ages 0-17 are
associated with fewer hospitalizations and emergency
visits at age 25 for blacks; larger effects in low-income
neighborhoods and for utilization associated with
chronic conditions. No impact on non-blacks
Lasser et al., 2014
[70]
Difference-in-difference logistic regressions comparing
Massachusetts (treatment) to New York and New
Jersey (control) before and after the Massachusetts
health reform
Increase in the odds of 30-day all-cause readmission
(OR 1.02); readmission for non-specified chest pain
and coronary atherosclerosis (OR 1.15); and
readmission for substance related and alcohol related
disorders (OR 1.07). Decrease in 30 day readmission
for mood disorders, schizophrenia and other psychotic
disorders (OR 0.91)
*: Reported findings are significant at the 5 percent level or below unless stated otherwise
2.6 Chronic Conditions
Population of
interest
Study
Study design
Finding*
Children
Prenatal and
children ages 0-18
Miller and Wherry,
2015 [13]
Instrumental variable estimations using the simulated
fraction of individuals who would be eligible given
eligibility policy to instrument for individual Medicaid
eligibility
0.4 to 0.5 percentage point reduction in the probability
of reporting one or more chronic conditions in
adulthood (at age 19-35) for each 10 percentage points
increase in eligibility at ages 5-9. Prenatal eligibility
and eligibility at ages 1-4, 10-14 and 15-18 have no
effect on reported chronic conditions. Prenatal
eligibility is associated with fewer hospitalizations
related to diabetes and obesity; eligibility at ages 1-18
had no effect
Children ages 0-5
Boudreaux et al.,
2016 [28]
Regression model approach using the fraction of
months a person was exposed to Medicaid during early
childhood, estimated using cross-state variation in
timing of Medicaid adoption, as a variable of interest
For low-income individuals, full exposure to Medicaid
during childhood decreased hypertension and a chronic
condition index composed of hypertension, heart
disease, diabetes and obesity in adulthood (ages 25-54).
No impact on heart disease and diabetes. No impact on
chronic diseases for moderate-income individuals
Children ages 0-17
Bronchetti, 2014
[71]
Instrumental variable approach using cross-state
variations in Medicaid and State Children's Health
Insurance Program eligibility expansions following
1996 as an instrument for eligibility
2 percentage points reduction in the likelihood of
asthma attacks in the last 12 months (significant at the
10 percent level)
Hadley, 2007 [116]
Logistic regression of short term health outcomes of
insured and non-insured individuals following the
onset of chronic conditions
Higher proportions of uninsured individuals reported a
much worse health status 3.5 months following
condition onset (12.3% vs 10.1% uninsured; OR 0.74)
Adults
Nonelderly
younger than 64
Individuals ages
14-61
Brook et al., 1983
[56]
Randomized experiment: comparison of individuals
randomly assigned to one of 14 insurance plans, with
copays ranging from 0% (free health care) to 95%, and
followed 3 to 5 years during the Rand Health Insurance
Experiment
1.4 mm Hg reduction in diastolic blood pressure due to
being in the free plan versus cost-sharing plans for
individuals with diastolic blood pressure > 83 mm Hg
or taking hypertension drugs at enrollment
Keeler et al., 1985
[3]
Same as Brook et al., 1983
1.8 mm Hg reduction in diastolic blood pressure due to
being in the free plan versus cost-sharing plans for
hypertensives; larger difference for low-income than
high-income hypertensives (3.5 vs. 1.1 mm Hg
reductions)
Keeler et al., 1987
[4]
Same as Brook et al., 1983
No impact of being in the free plan versus cost-sharing
plans on blood glucose in the high-risk group
Non-poor young
women (age range
not mentioned)
Pauly, 2005 [50]
Instrumental variable linear probability models using
the size of the firm in which household members work
and marital status as instruments for private insurance
coverage
No impact on the probability of reporting a chronic
condition
Adults ages 18-64
Lurie et al., 1984
[75]
Difference-in difference comparison of medically
indigent Californians ages 21 to 65 that were
terminated from Medi-Cal in 1983 (treatment) to
beneficiaries that were not terminated because of the
nature of their medical needs (control) at termination
and six months after
Six months after termination from coverage,
hypertensives showed a diastolic blood pressure
increase of 10.0 mm Hg, against a 5.0 mm Hg decrease
in the control group. There was no significant
differential impact in blood glucose control among
diabetics
Adults ages 19-64
Baicker et al., 2013
[7]
Randomized experiment: instrumental variable
comparison of Oregonians enrolled in Medicaid after
selection in a 2008 lottery drawing (treatment) and
Oregonians who signed up for the lottery but were not
selected (control) using lottery selection as an
instrument for coverage
3.9 percentage points increase in the probability of a
diabetes diagnosis 25 months after the lottery,
corresponding to 348% of the control group mean. No
impact on hypertension and hypercholesterolemia
diagnoses
*: Reported findings are significant at the 5 percent level or below unless stated otherwise
2.7 Mental Health
Population of
interest
Study
Study design
Finding*
Children
Prenatal and
children ages 0-18
Miller and Wherry,
2015 [13]
Instrumental variable estimations using the simulated
fraction of individuals who would be eligible given
eligibility policy to instrument for individual Medicaid
eligibility
No impact of prenatal eligibility and eligibility at ages
1-18 on Kessler 6 scores and mental health related
adult hospitalizations at ages 19-35
Newhouse and
Rand Corporation,
1993 [5]
Randomized experiment: comparison of individuals
randomly assigned to one of 14 insurance plans, with
copays ranging from 0% (free health care) to 95%, and
followed 3 to 5 years during the Rand Health Insurance
Experiment
No impact of being in the free plan versus cost-sharing
plans on a standardized mental health score
Chua and
Sommers, 2014
[64]
Difference-in-difference comparison of adults ages 1925 (treatment) to adults ages 26-34 (control) before and
after the implementation of the Affordable Care Act's
dependent coverage provision
4 percentage points increase (95% CI, 0.6-7.5) in the
probability of reporting excellent mental health
Brook et al., 1983
[56]
Same as Newhouse and Rand Corporation, 1993 above
No impact of being in the free plan versus cost-sharing
plans on a standardized mental health score
Keeler et al., 1987
[4]
Same as Newhouse and Rand Corporation, 1993 above
Being in the free plan versus cost-sharing plans
increases likelihood of being worried about phlegm
production, chronic bronchitis, or emphysema. No
impact on the likelihood of worrying about 10 other
conditions
Newhouse and
Rand Corporation,
1993 [5]
See above
No impact of being in the free plan versus cost-sharing
plans on parental worry about child's physiological
conditions
Children age 0-13
at enrollment
Young adults
Adults ages 19-25
Adults
Individuals age 1461 at enrollment
Parents younger
than 64 at
enrollment
Social Security
Disability
Insurance
beneficiaries ages
18-54
Weathers and
Stegman, 2012
[11]
Randomized experiment: comparison of newly enrolled
Social Security Disability Insurance beneficiaries
randomly assigned to receiving health benefits
packages (treatment) to individuals who remained in
the 24-month waiting list for Medicare benefits
(control) during the Accelerated Benefits
demonstration project
Positive mean impact of being in the AB Plus group
(health benefits package and additional services)
compared to control on the mental health score derived
from the Qualitymetrics SF-36 health instrument; 9.1
percentage points decline in the probability of having a
score indicating clinical depression. For both measures,
no impact of being in the AB group (health benefits
package only) in comparison with the control group
Adults ages 18-64
Van der Wees et
al., 2013 [76]
Difference-in-difference comparison of trends in
Massachusetts (treatment) to other New England states
(control) before and after the Massachusetts health
reform
1.5 percentage points increase in the probability of
reporting that mental health was good for at least 28 of
the last 30 days. Increases were 0.2 percentage point
higher among individuals with incomes below 300% of
the federal poverty line; and 0.1 percentage point and
0.4 percentage point higher among white residents than
black and Hispanic residents, respectively
Courtemanche and
Zapata, 2014 [67]
Difference-in-difference ordered probit regression
comparing adults in Massachusetts (treatment) and in
states that did not conduct health reforms (control)
before and after the Massachusetts health reform
Reduction in the reported days not in good mental
health
Adults ages 18-64
Lasser et al., 2014
[70]
Difference-in-difference logistic regressions comparing
Massachusetts (treatment) to New York and New
Jersey (control) before and after the Massachusetts
health reform
Decrease in 30-day readmission for mood disorders,
schizophrenia and other psychotic disorders (OR 0.91)
Parents ages 18-64
McMorrow et al. ,
2016 [78]
Linear probability models using changes in Medicaid
eligibility thresholds for low-income parents across
states during Medicaid expansions as the variable of
interest
2.3 percentage points increase in the probability of
having no or mild psychological distress based on the
Kessler scale; 2.1 percentage points decrease in the
probability of moderate psychological distress; no
impact on the probability of severe psychological
distress
Adults ages 19-64
Finkelstein et al.,
2012 [8]
Randomized experiment: instrumental variable
comparison of Oregonians enrolled in Medicaid after
selection in a 2008 lottery drawing (treatment) and
Oregonians who signed up for the lottery but were not
selected (control) using lottery selection as an
instrument for coverage
7.8 percentage points increase in the probability of
negative depression screening 16 months after the
lottery, corresponding to 10% of the control group
mean; 2 days increase in the reported number of days
in good mental health in the past 30 days
Baicker et al., 2013
[7]
Same as Finkelstein et al., 2012 above
*: Reported findings are significant at the 5 percent level or below unless stated otherwise
9.2 percentage points decrease in the probability of
positive depression screening 25 months after the
lottery; 1.2 point increase in the Medical Outcomes
Study 8-Item Short-Form Health Survey mentalcomponent score
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