What Other Programs Can Teach Us: Increasing Participation in Health
January 2003, Vol 93, No. 1 | American Journal of Public Health 67-74
Dahlia K. Remler, PhD and Sherry A. Glied, PhD
The authors are with the Department of Health Policy and Management,
Mailman School of Public Health, Columbia University, New York, NY.
Correspondence: Requests for reprints should be sent to Dahlia K.
Remler, Department of Health Policy and Management, Mailman School of
Public Health, Columbia University, 600 West 168th St, 6th Floor, New
York, NY 10032 (e-mail:
Many uninsured Americans are already eligible for free or low-cost
public coverage through Medicaid or Children’s Health
Insurance Program (CHIP) but do not "take up" that coverage.
However, several other public programs, such as food stamps
and unemployment insurance, also have less-than-complete take-up
rates, and take-up rates vary considerably among programs.
This article examines the take-up literature across a variety
of programs to learn what effects nonfinancial features, such
as administrative complexity, have on take-up. We find that
making benefit receipt automatic is the most effective means
of ensuring high take-up, while there is little evidence that
stigma is important.
A RECURRING PUZZLE IN incremental insurance expansion is that
many uninsured Americans—4.7 million children in 1996—are
already eligible for free public coverage through Medicaid or
Children’s Health Insurance Program (CHIP).
Understanding why they do not take up this coverage is
critical if further insurance expansions are to fulfill their
promise. Analysts cite the stigma attached to public
programs, the time needed to participate, the difficulty of
the forms and process procedures, lack of interest in health
coverage, and lack of information about the availability of
particular programs as reasons for less-than-complete
take-up. While some qualitative, self-reported evidence
indicates that these factors do matter,
there is virtually no quantitative evidence available that
would tell us how much they matter.
One way to get a better understanding of the magnitude of these
impediments to take-up is to look outside health insurance.
Many public programs have low take-up rates. Indeed, food stamps,
unemployment insurance, and Aid to Families With Dependent
Children (AFDC; now TANF) all have take-up rates similar to
that of Medicaid (Table 1).
In trying to improve health insurance programs, it is worth
seeing what lessons can be learned from the take-up of other
FACTORS THAT MIGHT INFLUENCE TAKE-UP
Take-up refers to participation in a program among those who
are eligible. Our policy interest in how to design programs
that have greater take-up drives our interest in the underlying
factors that influence take-up. Therefore, although there are
many individual characteristics (such as education) that are
predictors of take-up, these characteristics per se are not
our focus, although they are relevant for informing program
design. To examine evidence from a variety of sources, including
individual predictors of take-up, we need a conceptual framework
on the fundamental factors that influence take-up.
Conceptually, we expect eligible people to take up a program
if its benefits to them exceed its costs. Benefits depend on
the value the program provides to the recipient. Even for cash
programs, where benefits would appear obvious, the value to
potential recipients still depends on the size of the benefit
relative to their general level of wealth and income and the
opportunities forgone through participation (such as work income
for welfare programs). For programs such as education or housing
vouchers, assessing the value of benefits is even more
complicated, since tastes for education and housing clearly
vary. For health insurance, benefits depend on the potential
recipient’s health status, belief in the usefulness of health
care, attitudes toward financial risk, and access to
alternative sources of medical care (such as public
The costs of program participation have been described by several
writers, including Craig17
and Dion and Pavetti.
Working from their frameworks, we characterize the possible
influences of take-up (other than financial cost) as program
benefits, inconvenience, stigma,
In this article, we examine the literature of take-up across
health insurance and other programs, including a wide variety
of both public and private programs, to learn what we can about
the magnitude of different nonfinancial impediments to take-up.
Our purpose is not to summarize fully or do justice to each
of the articles we examine. Rather, we seek to extract from
each article information about the effects of nonfinancial program
characteristics on take-up. In many cases, the relevant portion
of an article may be a single sentence or table entry. Thus,
our approach is akin to that of a meta-analysis, although we
cannot do a formal meta-analysis because of insufficient structure
and commonality across both programs and estimation strategies.
To find take-up studies outside health care, we conducted searches
at the end of 2001 of Econlit, PAIS International, and Social
Science Abstracts using the keywords "take-up," "takeup," and
"program participation." We also conducted searches using the
term "enrollment" but found few relevant hits. The searches
resulted in a total of 345 hits from Econlit, 94 from PAIS,
and 152 from Social Science Abstracts. The overwhelming majority
of studies were eliminated because examination of the title
or abstract revealed that the subject was not the take-up of
public or private programs but rather, for example, industry
"take-up" of a particular new technological innovation, job
take-up, and so forth. We also tried searching PsychInfo and
Sociological Abstracts but found no relevant studies when
searching with the same keywords. We also asked colleagues,
including those on list servers in the policy field, about
studies and searched the Web sites of various policy research
organizations. This last method enabled us to find many
articles not in the peer-reviewed literature, although of
course we could have missed other relevant articles. We
obtained and read roughly 100 articles.
For each article, we searched for any evidence of the quantitative
magnitude of nonfinancial program features. Although many articles
examined the level of take-up and even predictors, we restricted
our attention to those that were informative about program
features or the mechanisms that affected take-up. We also
restricted our attention to those studies that considered the
magnitude of these effects, although we did not require them
to have quantitative data. These exclusion criteria
eventually reduced the relevant articles to the 37 discussed.
In many cases, the main focus of a study was not the effects
of program features on take-up, but nonetheless information
relevant for our review was in the article.
For each article, we then determined the method used to identify
the effect. As in a formal meta-analysis, the quality of the
identification of an estimated effect is critical to the emphasis
placed on that estimated effect. We included in our review
randomized controlled experiments, longitudinal studies that
exploit variation in program features over time owing to a
natural experiment, longitudinal studies that exploit
observational variation over time, and crosssectional
studies. In a few cases, we also included the results of
Only one randomized controlled study was identified that examined
impediments to take-up. To examine the impact of information
on the take-up of food stamps, Daponte et al.
sampled low-income people eligible on the basis of income and
family size. Half the sample was randomly assigned to be
fully screened for eligibility and informed about the program
and the other half was randomly assigned to be controls.
Four studies used natural exogenous variation over time or across
groups to examine impediments to take-up. Madrian and Shea
examined a change in the way one company administered its 401(k)
plan. Before the policy change, employees had to actively elect
to be in the program, filling out forms and making allocation
decisions. Following the policy change, employees had to actively
decline to participate in the 401(k) plan. If employees failed
to decide, they received the default payroll deduction of 3%
and the default allocation. Anderson and Meyer
used longitudinal data to show how unemployment insurance
take-up fell after benefits became subject to income tax and
to estimate the size of the effect of tax rate on take-up.
Garrett and Glied
compared child supplemental security income (SSI) take-up
before and after a Supreme Court ruling affecting eligibility
and identified the effect of program benefit through
state-level variation in SSI benefits. Yelowitz
used the introduction and evolution of the Qualified Medicare
Beneficiary (QMB) program and its variation across states to
identify how changes in eligibility for Medicaid as
supplemental Medicare insurance affected take-up of this
A related approach is to use variation over time in take-up
and its correlation with variation in other covariates to
elucidate influences. Blank and Card10
examined the correlation between state-level take-up of
unemployment insurance with the generosity of those benefits
and the unionization rate (thought to be a proxy for
decomposed variation over time in state-level AFDC take-up
rates into variation explained by benefit generosity and
demographics and interpreted the residual as due to cultural
A much larger number of studies (including those by McGarry,
Blundell et al.,
Blank and Ruggles,
Diehr et al.,
and Stuber et al.)
have examined the correlation of individuals’ characteristics
with their decision to participate in a particular program.
These studies relied on variation in the value of the same
program characteristic across individuals. For example, the
cost of spending time at administrative offices will vary
from person to person owing to differences in wages, work
opportunities, childcare responsibilities, and so on. If time
spent enrolling is an important barrier, then people with
high time costs will have lower take-up rates than those with
low time costs, all else held equal.
There are 2 problems with this approach. First, as in all
observational studies, it is difficult to separate
correlation from causality. Cross-individual variation in the
measured size of nonfinancial barriers may be influenced by
individual benefits. For example, potential recipients may
become informed about a program because they expect to
receive high benefits. Second, it is difficult to draw
specific inferences from individual characteristics. Consider
education. Education probably lowers the cost of gathering
information. It also probably lowers the cost of filling out
forms, predicts future income, and is correlated with asset
levels, which are imperfectly measured in the data set. Thus,
the observation that education is a significant predictor of
take-up does not provide clear answers to which nonprice features
influence take-up and what the sizes of those effects are.
describes the quantitative evidence on the impact of each of
the potential nonprice influences on take-up. Since not all
authors used the same categories that we do, we reclassified
variables where necessary.
TABLE 2— Evidence of Nonprice Effects on Take-Up, by
Method of Identification
Statistical Significance and Effect Size
Program Benefit–Statistically Significant Effects
Anderson and Meyer26 (1997)
Longitudinal with exogenous variation
A 1.0–1.5 percentage point decrease in take-up from 10% decrease
in after-tax benefits.
Blank and Card10 (1991)
A 1% increase in the state replacement rate causes a 1.6%
increase in the take-up rate.
Housing benefits in UK
Blundell et al.29 (1988)
A 0.52 percentage point increase per 1% increase in benefit size.
Medicaid as supplemental insurance
Elderly with chronic functional limitations 4 times likelier to
take up Medicaid as supplemental insurance.
Effect could be interpreted as owing to better information about
program because of greater contact with medical providers.
Garrett and Glied27 (2000)
Longitudinal with natural experiment
Change in eligibility rules results in a 0.427 percentage point
increase in take-up per $100 increase in maximum SSI benefit.
Value of the benefit is identified by the extent that a higher
SSI benefit increases the take-up effect of the eligibility
Participation rose by 11 percentage points from an increase in
benefits to a national minimum of 65% of the poverty line.
Program Benefit–Insignificant, No Significance
Test Provided, or Both
Food stamps and AFDC
Blank and Ruggles9 (1996)
No statistical test. Length of eligibility or "need" an important
determinant of take-up.
Longitudinal analysis used in study, but the effects of interest
for us were identified cross-sectionally.
Earned income tax credit
Author states that magnitude is consistent with substantial
effect but is not statistically significant.
Medicaid as Medigap (QMB)
Longitudinal with natural experiment
Insignificant. Change in eligibility rules results in a 0.427
percentage point increase in take-up per $100 increase in maximum
Value of benefit is identified by the extent that hospitalization
increases the take-up effect of the eligibility expansion.
Program Benefit–Reverse Sign, Significant Results
Subsidized health insurance
Diehr et al.30 (1996)
Sign the reverse of what was expected.
Those with poorer health status and greater prior health care
usage less likely to take up insurance.
Income support (UK)
Unobserved inconvenience costs could be as much as 20% of benefit
Indirect proxies for inconvenience could proxy for other factors.
Madrian and Shea25 (2000)
Longitudinal with natural experiment
Statistically significant 49 percentage point increase in 401(k)
participation due to change to presumptive enrollment.
Dramatic effect, but it may be due more to psychological factors
than literal convenience.
Mixed statistical significance. Car owner: insignificant; same
MSA: marginally significant; poor health: significant.
Car ownership, same MSA, and poor health all considered proxies
for convenience. No marginal effects calculated.
Earned income tax credit
Statistically significant. Having no state income tax system
lowers take-up by 7.6 percentage points.
Stuber et al.31 (2000)
Statistically significant. Perceiving forms as long and
complicated implies 1.8 times less likely to take up Medicaid.
Perceiving hours as inconvenient implies 1.7 times less likely to
Stigma and Cultural Attitudes–Statistically
Daponte et al.24 (1998)
Survey questioning those who are eligible but not receiving
6.3% of eligibles not receiving cite stigma as a reason.
Mixed significance; other welfare programs highly statistically
significant; South (cultural proxy), not significant.
No marginal effects calculated.
Cross-sectional structural model estimation
Statistically significant; Stigma is a structurally identified
and unitless function of race, education, and family size.
Interpretation as stigma is problematic.
Medicaid as long-term care insurance
Comparison of the distribution of time to spend down with the
distribution of times to spend down predicted by a separate survey
of assets and income
Longer times to spend down than are predicted by assets, implying
that residents are receiving asset transfers to avoid Medicaid.
Interpretation as stigma is problematic. Effect could be due to
fear of worse treatment because of lower provider payments for
Stigma and Cultural Attitudes–Insignificant and No
Stuber et al.31 (2000)
Horan and Austin19 (1997)
Small sample size; poor proxies for stigma
Informational Barriers–Statistically Significant
Blank and Card10 (1991)
A 1% increase in the state unionization rate causes a 0.67%
increase in the take-up rate.
Unionization is a poor proxy for informational barriers.
Daponte et al.24 (1998)
36 percentage point increase in take-up due to information
Preintervention distribution of information appears to be
endogenous: those with greatest potential benefit unlikely to be
Supplemental grant support (social fund) (UK)
Huby and Whyley35 (1996)
Those who have heard about program from friends or family are 7.4
times more likely to apply.
Qualified Medicare Beneficiary program (QMB)
Neumann et al.36 (1995)
20 percentage point increase in take-up due to awareness; 60% of
those eligible and with knowledge of program take up; 40% of those
eligible and unaware of program take up.
Medicare beneficiaries merged with Medicare and QMB and Medicare
data. Beneficiaries asked about awareness of program. Substantial
take-up by those unaware of program suggests importance of providers
Stuber et al.31 (2000)
Confusion about Medicaid eligibility rules implies 1.8 times less
likely to take up.
Medicaid as Medigap (QMB)
Greater effect of lagged eligibility than current eligibility
indicates the possible effect of learning over time.
Relative contributions of lagged eligibility indicate role of
learning over time.
Many studies found that the size of potential benefits affects
participation. The size of the benefit matters most when measured
over the period of participation. For example, Blank and Ruggles,using longitudinal data, found that women who ended up with
shorter spells of unemployment were much less likely to sign
up for unemployment insurance when they initially became eligible.
The larger the benefits, the more likely potential recipients
are to overcome other barriers and sign up for a program. Daponte
found that potential recipients were more likely to be
informed about food stamp benefits the larger the size of the
benefits for which they were eligible. Anderson and Meyer
found that take-up fell with the taxation level of unemployment
benefits. Blundell et al.
found that higher housing benefits in the United Kingdom were
associated with higher take-up. Garrett and Glied
found that higher SSI benefits were associated with larger
increases in take-up due to eligibility expansions. Ettner
found that elderly people with chronic functional limitations
were 4 times likelier to take up Medicaid than those without
such limitations. Table 2
gives further examples of the impact of the value of
The importance of benefit size is also apparent in the many
studies that looked at enrollment in linked programs (not shown
in Table 2).
For example, take-up of food stamps is greater when receipt
is automatic upon enrollment in the AFDC program than when
eligible people must apply for food stamps separately.
Take-up of Medicaid fell when the program became delinked from
welfare during welfare reform.
Take-up of welfare, which automatically provides people with
Medicaid, is, in turn, greater among people who expect high
medical costs than among healthier applicants.
Take-up of SSI, which ensures Medicaid eligibility, increases
with health care expenditures.
However, differences do not always matter. For example, Yelowitz
examined participation in the QMB program, which serves poor
Medicare beneficiaries and pays both the $50 monthly premium
and any service-related co-payments, including the $760 deductible
payable only by those hospitalized. He found that being
hospitalized, and therefore subject to the deductible, does
not make eligible QMB beneficiaries more likely to take up
this supplemental Medicare insurance. In another example,
found that the size of the earned income tax credit is not
statistically significantly associated with take-up. Diehr et
found in a survey that those with poorer health status are
less likely to take up subsidized insurance.
Several studies used proxies, such as having a car or filing
a related form, to assess the effects of inconvenience on participation.
As already discussed, the interpretation of such proxies is
often problematic. (Studies with proxies whose interpretation
is highly problematic are not included in Table 2.)
Moreover, the effects of these proxies are frequently
statistically insignificant. Their magnitudes, however, may
be nonnegligible. One study of welfare benefits in Britain
estimated that the aggregate magnitude of inconvenience costs
could be as much as 20% of the total benefit for the average
Stuber et al.
found that those who perceived the applications as long and
complicated were 1.8 times less likely to take up Medicaid
and that those who felt that the application hours were
inconvenient were 1.7 times less likely.
Presumptive enrollment, which eliminates inconvenience costs,
has an enormous effect on take-up. In the Madrian and Shea
study of a company’s 401(k) plan policy change, moving
from voluntary to automatic enrollment resulted in an increase
in the participation rate from 37% to 86% among employees with
less than a year of tenure. The value of automatic enrollment
is also clear in the studies of newly delinked benefits. When
enrollment into Medicaid and food stamps was an automatic
corollary of welfare receipt, many more who were potentially
To compare studies, we adopted a definition of stigma that includes
psychological feelings of shame or a social sense of disrespect
associated with program participation. Studies used a range
of proxies for attitudes and stigma. The proxies were hard to
interpret, and the results were generally weak. This finding
is consistent with the interviews that Daponte et al.
conducted with people eligible for food stamps who had been
informed of their eligibility and yet had not signed up. Only
1 of the 16 households in this group replied with a reason
related to stigma; most said that it was not worth the
trouble for the small benefit. Stuber et al.
found that all stigma measures were insignificantly related
to take-up of Medicaid.
Only one quantitative study found evidence consistent with stigma.
compared the time a sample of nursing home residents took to
"spend down" to become eligible for Medicaid long-term care
coverage with the time the assets of a different sample of
nursing home residents would have been predicted to last. He
found that the actual times to spend down were longer than
those predicted by the assets and incomes of the elderly in
nursing homes, implying that the elderly were receiving transfers
to avoid the stigma of participating in a public program. Whether
this effect is a "true" stigma effect or reflects fear of worse
treatment by providers who receive less payment for Medicaid
residents than for private residents is unclear.
Cross-sectional analyses typically have weak proxies for the
effects of information, such as educational attainment, and,
perhaps in consequence, find weak results. Those that use survey
information on whether and how people have learned about the
program do find that information matters (see Huby and Whyley
for an example). These studies are, however, vulnerable to the
objection that knowledge about the program may be a function
of expected benefits. Thus, the experimental study by Daponte
is particularly valuable here. They found that information
does increase take-up of the food stamps benefit; 0% of those
eligible but not already on food stamps who were not informed
of the benefit by researchers took up food stamps, while 36%
of those who were informed took up the benefit and another 10%
said they planned to.
Other cross-sectional studies also found effects of providing
information. Stuber et al.
found that those confused about eligibility rules were 1.8
times less likely to take up Medicaid. Neumann et al.
found from a survey of Medicare beneficiaries matched with
Medicare data that while awareness of the QMB program was
correlated with take-up of that program, many of those unaware
of the program were actually enrolled. Presumably, providers
looking to avoid bad debt for beneficiaries’ share are
an important impetus behind take-up. Yelowitz
found that being eligible for QMB in the previous period made
take-up more likely, possibly indicating some
learning-over-time effect. Kenney and Haley
found that "88% of all low-income uninsured children had
parents who had heard of either the Medicaid program or the
SCHIP program," implying that knowledge of the program was
not a major barrier. They also found that 18% of such parents
thought, possibly mistakenly, that their children would not
LESSONS FROM VARIATION ACROSS PROGRAMS
As Table 1
suggests, there is very large variation in take-up rates
across programs. This variation can also help inform our
understanding of what drives take-up. One very striking pattern
emerges from the table. Those programs for which no "extra action"
is required—Medicare part A, Medicare part B, and
employer-sponsored insurance—have the highest take-up rates.
Medicare does not require any sign-up. People are
automatically enrolled when they reach age 65. They receive a
form that they must return if they wish to decline
part B coverage. Thus, it requires positive action to avoid
part B, while everyone eligible receives part A no matter
what. Employer-sponsored insurance is through payroll
deduction and is generally performed automatically by the workplace
benefits office. The earned income tax credit, which does not
require extra paperwork for those already filing income tax
returns, also has a very high take-up rate. In contrast, other
programs that do require extra action have much lower take-up
Second, programs that have complex eligibility criteria, such
as asset tests, appear to have more variable take-up across
studies than simpler programs. Whether survey or administrative
data are used appears to affect measured take-up rates. One
of the key findings in many studies across programs is that
estimated take-up rates are often a function of how eligibility
is measured. Studies typically find both false positives (people
who collect benefits but appear ineligible) and false negatives
(people who do not collect benefits but appear eligible). Agencies
evaluating eligibility make mistakes. More importantly, studies
are based on surveys that allow only an imperfect assessment
of individual eligibility.
For example, many surveys do not collect information on
assets, but many programs have asset limits on participation.
Studies that compare take-up both with and without
incorporating asset information find large differences in
estimated take-up. In one study examining take-up of SSI by
the elderly, the measured take-up rate for some groups increased
by 60% after asset limits were included in the eligibility
Daponte et al.
found that after they performed a more accurate eligibility
screening test on their sample (including assets and
deductions), only about half of those families that initially
seemed eligible for food stamps were, in fact, eligible. These
results suggest that take-up rates in programs with complex
eligibility criteria may not be nearly as low as the rates
calculated by researchers using survey data. Survey data may
not be sufficiently rich to capture all eligibility features,
leading to underestimates of true take-up.
Our review suffers from several limitations. First, we may have
missed relevant articles, particularly in the non–peerreviewed
literature or those whose primary focus was elsewhere but
nonetheless contained relevant lessons. Second, our system of
categorizing barriers to take-up in a functional way to guide
program design could obscure relevant patterns. For example,
language barriers could result in low take-up because of poor
information, inconvenience, and cultural barriers, but
focusing on the language issue rather than its consequences
might be more informative for program design. Finally, our
general conclusions could obscure population heterogeneity in
the determinants of take-up. Informational barriers may be
more important for some groups while stigma could be more
important to others. Program design should be sensitive to
regional and population variation. Despite these limitations,
we can draw several conclusions about the literature and what
The low take-up of health insurance programs is troubling to
those concerned with insurance expansions. The limited extent
of quantitative information available on barriers to take-up—even
when we cast the net to include all social welfare and related
programs—makes it very difficult to know how to design
More research is greatly needed—especially experimental or
quasi-experimental research that can be used to draw measurable
and plausibly causal inferences about how such features as
administrative complexity, renewal rules, and organizational
structure affect participation. New studies must incorporate
carefully developed measures of program characteristics,
including qualitative features.
Nonetheless, looking across individuals and programs, several
conclusions can be drawn. First, the size of a benefit—measured
over time—is the most consistently important predictor
of participation. One reason for low take-up of some coverage
expansions may be that many spells of uninsurance are short
and people do not anticipate a great benefit over time. Longer
periods of coverage might lead to higher participation. Second,
information can help, but how much information people absorb
is related to potential benefits. Third, although the evidence
is very limited, stigma generally does not seem to be important,
with the one exception of Medicaid as long-term care insurance.
Fourth, mismeasurement of eligibility may be an important
contributor to poor take-up numbers. Finally and most
strikingly, reducing individual administrative barriers seems
to have little effect, but moving from voluntary to automatic
coverage is extremely effective. Looking broadly across many
programs, it seems clear that automatic enrollment is the
best way to increase take-up.
We gratefully acknowledge funding from the Commonwealth Foundation
project on Workable Solutions.
We thank Jason Rachlin for very able literature searching.
Accepted for publication September 2, 2002.
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