Assessing the New Federalism is a multi-year Urban Institute project designed to analyze the devolution of responsibility for social programs from the federal government to the states, focusing primarily on health care, income security, job training, and social services. Researchers monitor program changes and fiscal developments. In collaboration with Child Trends, Inc., the project studies changes in family well-being. The project aims to provide timely, nonpartisan information to inform public debate and to help state and local decisionmakers carry out their new responsibilities more effectively.
Key components of the project include a household survey, studies of policies in 13 states, and a database with information on all states and the District of Columbia, available at the Urban Institute's Web site. This paper is one in a series of occasional papers analyzing information from these and other sources.
The nonpartisan Urban Institute publishes studies, reports, and books on timely topics worthy of public consideration. The views expressed are those of the authors and should not be attributed to The Urban Institute, its trustees, or its funders.
Contents
Counting the Uninsured: A Review of the Literature
Although most researchers agree that the number of uninsured is increasing, they often disagree on the actual number of uninsured—or even how the uninsured should be defined and measured. For example, although the most widely cited estimate of the number of uninsured nonelderly individuals in America is about 41 million, which is based on the March 1997 Current Population Survey (CPS), there is debate over whether this estimate is closer to the number of uninsured at a point in time or the number of uninsured throughout the year. Moreover, estimates of the uninsured using alternative data sources, or using CPS data that have been adjusted to account for underreporting of Medicaid, vary from the basic CPS estimate by as much as one-half.
This paper reviews the literature on the recent estimates of the uninsured and why the estimates from various databases differ. We review the estimates of the uninsured based on the following databases: the March CPS, the Survey of Income and Program Participation (SIPP), the National Health Interview Survey (NHIS), the National Medical Expenditure Panel Survey (MEPS), and the Community Tracking Study (CTS). The analyses presented here draw heavily from previous and ongoing work sponsored by the Department of Health and Human Services (DHHS), Office of the Assistant Secretary for Planning and Evaluation (OASPE).1 Although the analyses encompass measuring the health insurance status of all persons, many of the examples presented in this paper relate specifically to children because they have been the focus of much of the recent literature. This is relevant given the recent passage of the State Children's Health Insurance Program (CHIP), which provides states with $24 billion in federal matching funds over the next five years to provide insurance to uninsured children in low-income families. Nevertheless, the lack of health insurance is a problem that affects adults and children alike, and the measurement issues presented in this paper pertain to both.
Measuring the Uninsured
Published estimates of the number of uninsured will differ depending on the design of the survey
used for the estimate and whether the data are adjusted in any way. There are several aspects of
survey design that can affect final estimates of the uninsured and health insurance status in
general. Some aspects, such as the definition of uninsured, will have predictable effects on
insurance rates. For example, one would expect the NHIS to have higher uninsured rates than the
CPS because the NHIS purports to measure those uninsured at a point in time whereas the CPS
purports to measure those uninsured throughout an entire year. Other aspects, though, such as
sample design and survey administration, may affect uninsured rates in significant but
unpredictable ways. The following are some of the aspects of survey design that can differ and
may have significant effects on insurance status:
- Coverage. What are the characteristics of the sample frame? What were the response
rates? What, if anything, is known about the characteristics of nonrespondents?
- Instrumentation. How were the insurance questions phrased and sequenced? How many
insurance questions were asked? What types of skip patterns were imposed? How were the
uninsured identified (for example, as an actual category or as a residual of those not reporting any
other types of coverage)? What was the recall period? How were multiple types of insurance
handled? How was dependent coverage captured—by proxy or by imputation?
- Survey Administration. What was the mode of administration—in-person or via
telephone? How were proxies used? What was the level and scope of interviewer training,
especially with regard to the administration of insurance questions?
- Estimation. How were the weights developed? What types of imputations were performed
on the insurance questions, including both logical and statistical? If imputations were not
performed, were those with nonresponse to insurance questions excluded from the estimates?
In addition to survey design, differences in how the data are interpreted or adjusted can also have
significant effects on insurance status. For example, estimates of the uninsured done by the Urban
Institute using CPS data are lower than estimates by other groups using the same data because the
Urban Institute adjusts its estimates for the underreporting of Medicaid in the CPS.
The primary goal of this paper is to present side by side the various estimates of the uninsured in
the recent literature to demonstrate the degree to which they differ. A secondary goal is to
describe some of the characteristics of the various survey designs in order to explain some of the
differences. Readers should be cautioned, though, that a more thorough analysis of each survey
would have to be undertaken to determine more precisely why estimates differ.
CPS Estimates of the Uninsured
The most commonly cited estimates of the number of uninsured children—those produced by the
Census Bureau, the Congressional Budget Office (CBO), the U.S. General Accounting Office
(GAO), the Employee Benefit Research Institute (EBRI), and the Urban Institute—use the March
CPS as their source. The CPS, which is the source of the official government statistics on
employment and unemployment, is a nationally representative monthly survey of approximately
57,000 households in the United States. The sample is based on the civilian noninstitutionalized
population of the United States, which includes persons living in households and group quarters
(e.g., college dormitories and rooming houses) but does not include residents of institutions (e.g.,
homes for the aged) and persons living abroad. As well as being nationally representative, the
sample is also representative of each of the 50 states and the District of Columbia, although for
most states the samples are too small for precise state-level estimates.
The main purpose of the survey is to collect, by means of personal interviews, information on the
employment status of the population during the survey month. In addition, supplemental questions
are regularly added to the core questionnaire on topics such as health, education, income, and
previous work experience. The March CPS contains supplemental questions on the health
insurance status of each person in the household in the prior calendar year. Specifically,
respondents are asked whether they had any of various types of private or public health insurance
in the previous year. Respondents are permitted to report more than one type of health insurance
coverage, although it is impossible to tell from the data whether persons with multiple types of
coverage had the coverage concurrently or at different times during the previous year.
Respondents are never asked directly whether they were uninsured in the previous year. Instead,
estimates of the uninsured are calculated as a residual—that is, the uninsured are all
those who do not report having some type of coverage in the previous year. As a result, the
uninsured are those without any coverage throughout the previous year. However, some
researchers believe that the CPS estimates of the uninsured are too high and, thus, that many
respondents may be reporting their health insurance status as of the interview date. This and other
issues pertaining to estimates of the uninsured according to the CPS are discussed below.
The two principal classes of estimates of the uninsured using the CPS are
(1) those done by the Census Bureau, CBO, GAO, and EBRI; and (2) those done by the Urban
Institute. These estimates are summarized in table 1.
The Urban Institute's estimates of the uninsured differ from the other estimates because they adjust for the underreporting of Medicaid in the CPS.
Table 1. CPS Estimates of the Uninsured by State
| Source |
Data |
Time Period |
|
Universe |
Number
(millions) |
Percent |
|
|
Census Bureau, EBRI, CBO, and GAO, and others |
1996 CPS |
1995 |
Uninsured throughout 1995 (or point-estimate, depending on interpretation of CPS definition of uninsured.) |
Children ages < 17
Children ages < 18
Adults ages 18-64
Nonelderly ages 0-64
All persons
|
9.8
10.5
30.5
40.3
40.6 |
13.8
14.0
19.0
17.4
15.4 |
| Urban Institute |
1996 CPS |
1995 |
Uninsured throughout 1995 (or point-estimate, depending on interpretation of CPS definition of uninsured). Adjusted for the Medicaid undercount in the CPS using the TRIM2 model. |
Children ages < 17
Children ages < 18
Adults ages 18-64
Nonelderly ages 0-64
|
6.9
7.6
28.8
35.7 |
9.8
10.3
18.0
15.5 |
Census Bureau, CBO, GAO, and EBRI Estimates
Beginning with the March 1995 CPS, when the health insurance questions were revised to
eliminate the possibility of respondent inconsistencies, the Census Bureau (Bennefield 1996a),
CBO (Bilheimer 1997), GAO (1997), and EBRI (Fronstin 1996a) began publishing identical
estimates of the number of uninsured.2 Using the March 1996
CPS, they found the following:3
Children ages 0 to 17: 9.8 million uninsured (13.8 percent of all children)
Children ages 0 to 18: 10.5 million uninsured (14.0 percent of all children)
Adults ages 18 to 64: 30.5 million uninsured (19.0 percent of all adults)
All persons ages 0 to 65: 40.3 million uninsured (17.4 percent of all persons)4
None of these organizations adjusted their estimates for the underreporting of Medicaid in the
CPS.
The Urban Institute's Estimates
The Urban Institute's estimates of the uninsured differed from others because the Institute
adjusted for the underreporting of Medicaid in the CPS. The Institute used its Transfer Income
Model (TRIM2), a microsimulation model, to test for Medicaid eligibility among nonreporters of
Medicaid and then selected individuals to participate so that the size of the resulting Medicaid
population in the model matched Health Care Financing Administration (HCFA) administrative
data according to age and disability status of all persons ever enrolled in Medicaid in a given year.
Using the TRIM2 model with March 1996 CPS data, the Institute found the following:
Children ages 0 to 17: 6.9 million uninsured (9.8 percent of all children)
Children ages 0 to 18: 7.6 million uninsured (10.3 percent of all children)
Adults ages 18 to 64: 28.8 million uninsured (18.0 percent of all adults)
All persons ages 0 to 65: 35.7 million uninsured (15.5 percent of all persons)
The estimate of 6.9 million uninsured children in 1995 is 30 percent lower than the CPS estimates
that include no adjustment for the underreporting of Medicaid. In all, the Institute simulated 2.9
million children to participate in Medicaid who reported no health insurance coverage in the CPS.
Researchers debate whether the Institute's adjustment for the underreporting of Medicaid yields
improved estimates of the uninsured. The Institute's adjustment may overcompensate for the
underreporting because the adjustment is based on administrative estimates of the number of
persons ever enrolled in Medicaid during the year, while CPS estimates of the uninsured are
probably a mix of those uninsured at a point in time and those uninsured throughout the previous
year. Also, even though the Institute adjusts for Medicaid underreporting, it makes no adjustment
to reported private employment coverage, which could also be underreported. This is important
because the uninsured are calculated as a residual; therefore, accurate estimates of the uninsured
require accurate estimates of coverage for all other types of insurance. Despite these potential
problems, the fact remains that Medicaid appears to be underreported in the CPS and therefore
will affect most estimates of the uninsured in one way or another. The issues of underreporting of
Medicaid and whether the CPS estimates may reflect those enrolled at a point in time are
described in more detail below.
CPS Health Insurance Measurement Issues
When interpreting estimates of the uninsured done by researchers using the CPS, it is important to
understand that these estimates are affected by various measurement issues. These issues include
the following: the time frame of the CPS measures of health insurance, Medicaid underreporting,
imputation of health insurance coverage, and undercoverage of the sampled population.
Time Frame
If respondents answer the CPS health insurance questions as intended—that is, as coverage at any
time during the previous year—then estimates of the uninsured should be interpreted as those
without coverage throughout the previous year. However, some researchers believe that the CPS
estimates of the uninsured are too high and, thus, that respondents may be reporting their health
insurance status as of the interview date. Swartz (1986) compared CPS estimates of the uninsured
with estimates from three other surveys that asked respondents about their health insurance
coverage as of the interview date and found that the CPS estimates more closely resembled the
point-in-time estimates of these surveys.5 BO also considers
its CPS-based estimates of the uninsured to be closer to a point-in-time estimate rather than an
estimate of those uninsured throughout the previous year (Bilheimer 1997).
There is some evidence, though, that some CPS respondents interpret the questions correctly and
report their status as of the previous year. For example, in 1995, 15 percent of children enrolled in
Medicaid according to the CPS also reported coverage by private health insurance (Fronstin
1996a). These children are probably not reporting their current status, since it is unlikely that this
many children would be covered by Medicaid and private insurance at the same time. Instead,
some were probably covered by private insurance for part of the year and Medicaid for part of the
year.
Other evidence also suggests that many respondents interpret the questions correctly. For
example, Kronick (1989)6 found that private employer-sponsored health insurance coverage in the CPS is more consistent with employment status in the
previous year than in the interview month. In addition, the first round of the Medical Expenditure
Panel Survey (MEPS), which asked respondents whether they were uninsured continuously from
January 1, 1996, to their interview date three to six months later (and links their responses to
employment-related data), provided estimates that were slightly higher than the CPS (Beauregard
et al. 1997).7 If the CPS were a point-in-time estimate, then
the MEPS estimate should have been lower than the CPS. This suggests, at a minimum, that the
CPS is not strictly a point-in-time estimate.
In another analysis, Bennefield (1996c) compared longitudinal data from the SIPP with the
standard health insurance data from the CPS and with data from experimental questions on the
March 1995 CPS that asked about current health insurance status. Bennefield's results indicated
that CPS respondents interpreted the standard health insurance questions correctly and provided
their health insurance status as of the previous year. However, he found that respondents may
have had recall problems and failed to report some coverage and, as a result, the CPS estimates of
the uninsured looked more like point-in-time estimates. Some researchers, though, doubt the
usefulness of the experimental health insurance questions on the CPS because they yielded
extremely large numbers of uninsured.8
Long and Marquis (1996) compared the March 1993 CPS estimates of the uninsured in 10 states
with the findings from the Robert Wood Johnson Foundation (RWJF) Family Health Insurance
Survey. The RWJF survey was administered to approximately 2,000 families each in Colorado,
Florida, Minnesota, New Mexico, New York, North Dakota, Oklahoma, Oregon, Vermont, and
Washington during 1993. The uninsured and those covered by Medicaid were oversampled. The
content includes considerable detail on insurance status—both current and throughout the
previous year. Across the 10 states included in the RWJF survey, the CPS estimate of the
uninsured for all persons (14.7 percent) fell between the RWJF estimate of the currently uninsured
(15.7 percent) and the uninsured throughout the previous year (12.2 percent). Long and Marquis
also examined each state individually and found that for 9 of 10 states, the CPS measure fell
between the RWJF current and throughout-the-previous-year measures; in the remaining state, the
CPS estimate was above the RWJF estimate of the currently uninsured by only 0.2 percentage
point. Long and Marquis concluded that using the CPS as if it were a measure of the currently
uninsured generally will understate estimates of the uninsured at a point in time.
The conclusion we draw from this evidence is that the CPS probably contains a mixed bag of
reporting—that is, some respondents report health insurance status during the previous year,
some report it as of the interview date, and some fail to report it altogether—which, in the end,
yields estimates that are somewhere between a point-in-time and an uninsured-throughout-the-year estimate.
Medicaid Underreporting
One potential weakness of the CPS is that the number of persons reporting Medicaid is lower than
the number of persons ever enrolled in Medicaid in a given year according to administrative data
from HCFA—the agency that administers the Medicaid program. This problem is often referred
to as "underreporting" and can lead to overestimates of the uninsured if many of those that appear
uninsured are actually enrolled in Medicaid.9
Underreporting is thought to occur because survey respondents may not admit to being covered
due to the stigma associated with public assistance programs, because they are not currently
receiving health services, or because they may not realize they are enrolled in Medicaid. Another
possibility is that respondents who are enrolled in a Medicaid managed care plan report being
enrolled in private managed care. If so, then the problem of Medicaid underreporting could get
worse as more states adopt Medicaid managed care programs.
In 1995, 36.7 million nonelderly individuals were enrolled in Medicaid at some point during the
year according to HCFA, a 21 percent difference from the CPS estimate of 29 million. As shown
in table 2, Medicaid underreporting has become worse in the past few years: 19.8 percent in 1994
and 15.5 percent in 1993 (Fronstin 1997b; HCFA 1996).10
Medicaid underreporting for children ages 0 to 17 follows the same general trend as that for all
nonelderly individuals, although the underreporting rate is slightly higher. In 1995, for example,
16.5 million children were enrolled in Medicaid according to the CPS versus 21.4 million
according to HCFA data—22.9 percent underreporting (table 2; Fronstin 1997a; HCFA 1996).
The extent of Medicaid underreporting in the CPS is difficult to determine because of some
confounding factors. On the one hand, the above comparisons are only valid if the CPS Medicaid
question was answered as intended—that is, whether one was ever enrolled in Medicaid during
the previous year. However, the CPS probably provides something in between a point-in-time and
an ever-enrolled estimate. As a result, the above comparisons probably overstate Medicaid
underreporting somewhat.11 On the other hand, Medicaid
underreporting may be understated somewhat because the Census Bureau does some recodes to
the CPS data that assign Medicaid to persons who may not actually be covered by Medicaid.
Specifically, all those reporting Indian Health Service coverage, "other government" coverage,
and "other" coverage were recoded to Medicaid, which resulted in an additional 1.6 million
persons ages 0 to 64 reporting Medicaid in the March 1996 CPS (6 percent of the 29 million
persons reporting Medicaid). These issues need to be investigated further before firm conclusions
can be drawn concerning the extent to which Medicaid is underreported in the CPS.
Imputation of Health Insurance Coverage
Estimates of the uninsured based on the CPS and other surveys can also vary because of the
different ways that these surveys deal with nonresponse to the health insurance items. All survey
data have some degree of nonresponse to the health insurance questions (and most of the other
questions as well). Some survey data retain the missing data and have a coverage category called
"unknown."Other surveys exclude these persons from tabulations and reweight the remaining
persons to a population control total. Finally, some surveys impute data for the missing values.
The CPS uses a statistical method called "hot decking" to impute insurance (or no insurance) to
persons with missing health insurance data. In addition, the CPS also logically imputes Medicaid
to children under age 21 in families where either the householder or spouse reports being covered
by Medicaid. In addition, all adult Aid to Families with Dependent Children (AFDC) recipients
and their children, and Supplemental Security Income (SSI) recipients living in states that legally
require Medicaid coverage of all SSI recipients, were also assigned Medicaid coverage. It is the
logical imputation of Medicaid that makes the CPS differ from most other surveys. Of the 29
million persons ages 0 to 65 with Medicaid on the March 1996 CPS, 16 percent (4.7 million) had
Medicaid logically imputed.12
We do not know how estimates of the uninsured and Medicaid underreporting would have been
affected had the CPS not logically imputed Medicaid, for some of these people would have ended
up with Medicaid through statistical imputations. In any case, the effect of logical imputation
should be considered when comparing estimates of the uninsured.
Table 2. CPS Estimates of Medicaid Enrollment, Nonelderly and Children,
1992 to 1995 (Numbers in Millions)
| |
1992 |
1993 |
1994 |
1995 |
|
|
Nonelderly Enrollees (Ages 0-64) |
|
|
|
|
| CPS |
26.5 |
29.0 |
28.7 |
29.0 |
| HCFA |
31.4 |
34.3 |
35.8 |
36.7 |
| Underreporting Percent |
15.6 |
15.5 |
19.8 |
21.0 |
| |
| Child Enrollees (Ages 0-17) |
|
|
|
|
| CPS |
15.1 |
16.7 |
16.1 |
16.5 |
| HCFA |
18.4 |
20.2 |
21.0 |
21.4 |
| Underreporting Percent |
17.9 |
17.3 |
23.3 |
22.9 |
Sources:CPS enrollment numbers from EBRI (Fronstin 1997a and 1997b); HCFA enrollment numbers from 2082.
Notes: Child enrollees = number of children ages 0-14 plus one-half of the children ages 15-20 (HCFA does not report number of children ages 0-17 in the 2082).
HCFA data represent those ever enrolled during the year and include the institutionalized. CPS data are best interpreted as in between a point in time and those ever enrolled during the year and do not include the institutionalized.
That the number of children with Medicaid fell from 1993 to 1994 in the CPS may be an artifact of the mid-decade shift in the sample framework for the CPS (Swartz 1997).
Undercoverage of the Population
According to the Census Bureau (Bennefield 1995), all demographic surveys, including the CPS
and the SIPP, suffer from undercoverage of the population. Undercoverage results from missed
housing units in the sampling frame and missed persons within sampled households. The Census
Bureau estimated that the overall CPS and SIPP undercoverage rate is about 7 percent and that
undercoverage varies with age, sex, and race. It reported that for some groups, such as 20- to 24-year-old black males, the undercoverage rate is as high as 27 percent. The Census Bureau noted
that even though its weighting procedures partially correct for the bias due to undercoverage, the
final impact of undercoverage on estimates is unknown. This problem could bias estimates of the
uninsured if the groups that are missed in the survey are either disproportionately insured or
disproportionately uninsured.
SIPP Estimates of the Uninsured
The SIPP is a multipanel longitudinal survey of adults in a sample of approximately 20,000
households selected to be representative of the noninstitutionalized resident population of the
United States. We focus on data from the 1990, 1991, and 1992 SIPP panels. These panels
followed sampled adults for approximately two-and-a-half years, interviewing them either in
person or by telephone every four months.13 During each
SIPP interview (called a wave), household-, family-, and person-level information is collected for
each of the previous four months on income, labor force activity, program participation (such as
AFDC, Food Stamps, and Medicaid), and health insurance status.
The value the SIPP adds to analyses of the uninsured is that it allows researchers to examine the
dynamic aspects of the uninsured that are not apparent in static estimates. For example, Swartz
and McBride (1990) pointed out that data collected at a point in time from a population with
dynamic movements are more likely to contain people who are in long spells without health
insurance, even though most people have fairly short spells (this phenomenon is described in more
detail below).14 In short, static data may present a myopic
picture of the uninsured. The SIPP's longitudinal data, in contrast, can present a more complete
picture of the uninsured by answering questions such as:
- How many are uninsured in at least one month of a given year?
- How many are uninsured throughout a given year?
- How does the number of uninsured in a one-year period compare with that of a two-year
or more period?
- What is the average duration of all spells of uninsurance? How does this compare to the
average duration for all those uninsured at a point in time?
As one might expect, as the reference period for SIPP estimates of the uninsured lengthens, the
percent uninsured throughout decreases while the percent uninsured in at least one month
increases. As shown in table 3, estimates of uninsured children in 1993 versus the 32-month
period from early 1991 through mid-1993 illustrate this point:15
- 6.5 percent of children ages 0 to 18 were uninsured throughout 1993 (Bilheimer 1997),
while only 3.2 percent of children ages 0 to 17 were uninsured throughout the 32-month period
(Bennefield 1995).
- 15.5 percent of children ages 0 to 18 were uninsured at least one month in 1993
(Bilheimer 1997), while 29.0 percent were uninsured at least one month throughout the 32-month
period (Bennefield 1995).
Thus, for a given reference period, the percentage of children uninsured throughout is
considerably less than the percentage uninsured in at least one month. This simply suggests there
is substantial churning among uninsured children. From the examples above, 6.5 percent were
uninsured throughout 1993 versus 15.5 percent uninsured at least one month.16 The evidence of churning is even greater as the reference
period increases: 3.2 percent were uninsured throughout the 32-month period versus 29.0 percent
for at least one month. In short, although a substantial number of children are uninsured at a point
in time (about 14 percent according to the CPS), the SIPP data tell us that the problem of
uninsured children is even more widespread—over a two-and-a-half-year period almost one-third
of all children will be uninsured at some point (Swartz 1994).17
Presented in table 4 are various estimates of the uninsured for all persons ages 0 to 64. These
estimates, as well as those for children only presented in table 3, are provided simply to give an
overall picture of the uninsured according to the SIPP. We do not attempt to compare and
contrast these estimates with one another because SIPP estimates can vary based on the specific
files and methodology used, and most researchers do not publish their precise methodology.
Table 3. SIPP Estimates of Uninsured Children by Source
| Time Period |
Universe |
Estimate Definition |
Number
(millions) |
Percent |
Source |
Panel |
|
| 1990 |
Children ages <18 |
Average monthly uninsured |
11.1 |
16.2 |
The Lewin Group (1997, Draft) |
not cited |
| |
Children ages <17 |
Point estimate of uninsured in wave 1 of 1990 panel (October 1989 to April 1990) |
— |
13.3 |
Urban Institute
(Blumberg et al. 1997) |
1990 |
| 1991 |
Children ages <18 |
Average monthly uninsured |
11.5 |
16.5 |
The Lewin Group (1997, Draft) |
not cited |
| 1992 |
Children ages <17 |
Point estimate of uninsured in wave 8 of 1990 panel (October 1989 to April 1990) |
— |
13.3 |
Urban Institute
(Blumberg et al. 1997) |
1990 |
| |
Children ages <18 |
Average monthly uninsured |
12.4 |
17.2 |
The Lewin Group (1997, Draft) |
not cited |
| 1993 |
Children ages <18 |
Uninsured throughout
Uninsured at any given time
Uninsured at least one month
Average monthly uninsured |
—
—
—
13.0
|
6.5
13.5
15.5
17.9 |
CBO (Bilheimer, 1997)
The Lewin Group (1997, Draft) |
1992
not cited |
| Time Period |
Universe |
Estimate Definition |
Number
(millions) |
Percent |
Source |
Panel |
|
| 24-month period from February 1991 to January 1993 |
Children ages <17 |
Uninsured throughout
Uninsured at least one month
|
3.0
20.5 |
—
— |
Families USA (1997) |
1991 |
|
|
Children ages <17 uninsured at least one month |
Uninsured 12 months or longer |
9.6 |
47 |
|
|
| 32-month period from early 1991 through mid-1993 |
Children ages <17 |
Uninsured throughout
Uninsured at least one month |
2.2
19.6 |
3.2
29.0 |
Census (Bennefield 1995) |
1991 |
| 28-month period from early 1992 through 1994 |
Children ages <17 |
Uninsured at least one month
Median number of months insured |
—
4.0 |
30.0
— |
Census (Bennefield 1996b) |
1992 |
SIPP Health Insurance Measurement Issues
The SIPP asks respondents whether they were covered by employer- or union-sponsored
insurance, other private health insurance, Medicare, military health care, or Medicaid. Like the
CPS, estimates of the uninsured using the SIPP are calculated as a residual—that is, the uninsured
are those who do not report receiving coverage of any type. Unlike the CPS, though, SIPP
respondents are asked about health insurance coverage in each month of the four-month reference
period.
The SIPP may also underreport Medicaid. For example, HCFA administrative data show that 35.7
million persons were ever enrolled in Medicaid in 1992. In comparison, Bennefield (1996c)
calculated that 12.3 percent of all persons, or approximately 30.5 million persons, reported
Medicaid for at least one month in 1992 based on the SIPP—an underreporting of about 15
percent. Therefore, the number of uninsured based on the SIPP may be overestimated somewhat,
assuming that private health insurance is reported accurately (or, at least, not overreported). Also
like the CPS, the SIPP suffers from undercoverage of the population in general. According to the
Census Bureau, though, the final impact of undercoverage on estimates is unknown.
Various SIPP estimates of the uninsured, even those for the same time period, may not be
comparable because there are a number of different alternatives for analyzing a given time period
based on the SIPP. Some examples of these alternatives are as follows:
- Because SIPP panels overlap, researchers often have a choice of SIPP panels for a given
time period, or researchers can combine SIPP panels.
- The weights researchers use will depend on the length of the time period analyzed and the
specific SIPP file used. Researchers may use calendar year weights, panel weights, or wave-specific weights.
- Some researchers may have used sophisticated duration estimates, while others may have
used simple slice-in-time analyses.
This last point deserves further explanation. Researchers' estimates of the uninsured using the
SIPP can vary substantially depending on whether they use duration estimates for all spells
observed over a period of time or whether they simply examine spells in progress at a
point in time. Examining spells in progress at a point in time is useful if you want to
understand the characteristics of those uninsured at a point in time. However, Swartz and
McBride (1990) pointed out that uninsured spells in progress at a point in time are
disproportionately long spells, whereas most spells are actually fairly short. Using data from the
1984 panel of the SIPP, Swartz and McBride demonstrated this phenomenon by comparing the
distribution of spell lengths for all persons for whom they could observe a spell beginning in the
SIPP with the distribution of spell lengths among persons whose spells were in progress at a point
in time. Using a survival analysis technique, they found that half of all observable spells ended
within five months, and another 16.5 percent ended within five to eight months. Only 15 percent
of all spells lasted more than two years. In contrast, among spells in progress at a point in time, 58
percent lasted more than two years and only 13 percent ended within five months.
Table 4. SIPP Estimates of Uninsured for All Persons Ages 0 1/N 64
| Time Period |
Universe |
Estimate Definition |
Number
(Millions) |
Percent |
Source |
Panel |
|
| 1991 |
All persons |
Uninsured throughout |
— |
7.0 |
Bennefield 1996c |
1991 |
| |
| 1992 |
All persons |
Uninsured throughout |
— |
7.6 |
Bennefield 1996c |
not cited |
| |
|
Uninsured throughout |
18.1 |
7.2 |
Census (Bennefield 1995) |
1991 |
| |
|
Uninsured first quarter (point estimate) |
— |
14.8 |
Bennefield 1996c |
not cited |
| |
|
Uninsured at least one month |
50.7 |
20.3 |
Census (Bennefield 1995) |
1991 |
| |
|
|
|
|
|
|
| Time Period |
Universe |
Estimate Definition |
Number
(Millions) |
Percent |
Source |
Panel |
|
| 1993 |
All persons |
Uninsured throughout |
19.4 |
— |
Census (Bennefield 1996b) |
1992 |
| |
|
Uninsured throughout |
— |
7.7 |
Bennefield 1996c |
not cited |
| |
|
Uninsured first quarter (point estimate) |
— |
14.6 |
Bennefield 1996c |
not cited |
| |
|
Uninsured at least one month |
53.6 |
21.2 |
Census (Bennefield 1996b) |
1992 |
| |
|
|
|
|
|
|
| 1994 |
All persons |
Uninsured first quarter (point estimate) |
— |
14.5 |
Bennefield 1996c |
not cited |
| |
|
|
|
|
|
|
| Time Period |
Universe |
Estimate Definition |
Number
(Millions) |
Percent |
Source |
Panel |
|
| 32-month period from early 1991 through mid-1993 |
All persons |
Uninsured throughout |
9.7 |
4.0 |
Census (Bennefield 1995) |
1991 |
|
Uninsured at least one month |
64 |
26.5 |
|
|
| |
|
|
|
|
|
|
| 28-month period from early 1992 through 1994 |
All persons |
Uninsured throughout |
11.9 |
4.8 |
Census (Bennefield 1996b) |
1992 |
|
Uninsured at least one month |
66.6 |
27.0 |
|
|
|
Median number of months uninsured |
5.7 |
— |
|
|
SIPP versus CPS Estimates of the Uninsured
Bennefield (1996c) compared the SIPP and CPS estimates of the uninsured and offered
explanations as to why they seem to differ. Bennefield compared the CPS estimates of the
uninsured for 1991, 1992, and 1993 with two types of estimates from the SIPP: (1) the SIPP first
quarter average monthly estimates for 1992, 1993, and 1994, which can be considered point-in-time estimates; and (2) the SIPP estimates of those uninsured throughout the year for 1991, 1992,
and 1993. He chose the SIPP first quarter average monthly estimates for his SIPP point-in-time
estimates because they correspond with March, the month in which the CPS collects data about
the previous year. Bennefield found that the CPS estimates are more similar to the SIPP point-in-time estimates than the uninsured-throughout-the-year estimates, which are typically cited as the
evidence that CPS respondents were reporting their current health insurance status (
table 5). He
found uninsured rates of 14 to 15 percent for all persons for both the CPS estimate and the SIPP
point-in-time estimate.18 In contrast, he found uninsurance
rates of 7 to 8 percent for the SIPP uninsured-throughout-the-year estimates.19
Bennefield showed that the estimate of the uninsured throughout a given year using the SIPP was
substantially lower than CPS estimates because the SIPP has substantially more persons reporting
private health insurance coverage. For example, according to the SIPP, 81 percent of
persons reported private health insurance at any time during 1993. In contrast, only about 72
percent reported it at a point in time in the SIPP in 1993, which was substantially closer to the 70
percent private coverage estimate from the CPS. Unlike estimates of private insurance, estimates
of government-sponsored health insurance were generally consistent across time frames and
surveys—the CPS Medicaid coverage rates were 11 to 12 percent for the periods analyzed, and
both the insured at any time during the year and point-in-time SIPP Medicaid coverage rates were
9 to 13 percent for the periods analyzed. It is not clear what conclusions should be drawn from
the fact that private health insurance coverage accounted for much of the difference between the
CPS and the SIPP uninsured-throughout-the-year estimates. On the one hand, if recall problems
were to blame for higher CPS estimates of the uninsured compared with SIPP, then respondents
seemed to be more likely to fail to recall private insurance than public insurance. Such an
explanation is plausible if those publicly insured are more likely than those privately insured to
have coverage throughout the year.
Even though the CPS estimates of the uninsured are more widely cited, Census Bureau officials
suggest that SIPP may be better suited to measure health insurance information for a number of
reasons.20 First, the SIPP may have less recall error than the
CPS because it has a shorter recall period (four months for the SIPP versus over one year for the
CPS). Second, respondents may be more likely to answer the SIPP health insurance questions
because the questions are somewhat more detailed (for instance, they ask about Medicare plans A
and B and ask to see Medicare and Medicaid cards) and are better positioned at the beginning of
the interview. Third, the SIPP attempts to interview each person in the household age 15 and
over, whereas the CPS interviews only one person, who may not provide accurate information on
all household members. Finally, the SIPP is especially designed to measure program participation
(such as Medicaid), whereas the CPS is primarily a labor force survey. The principal drawbacks of
the SIPP, though, are that the data are not as timely as the CPS (the 1993 SIPP panel is the most
recent available) and the sample frame is not representative at the state level for analyses of
programs such as the CHIP.21
Table 5. Health Insurance Status of All Persons:
CPS versus SIPP for Various Years
| |
|
1992
(or Q1 1994) |
1992
(or Q1 1993) |
1991
(or Q1 1992) |
|
| Percent Uninsured |
| | | | | |
| CPS |
|
15.3 |
|
14.7 |
|
14.1 |
|
| SIPP Annual |
|
7.7 |
|
7.6 |
|
7.0 |
|
| SIPP Point-in-Time |
|
14.5 |
|
14.6 |
|
14.8 |
|
| |
|
|
|
|
|
|
|
| Percent with Private Coverage |
|
|
|
|
|
|
|
| CPS |
|
70.2 |
|
71.1 |
|
72.2 |
|
| SIPP Annual |
|
81.2 |
|
81.7 |
|
83.4 |
|
| SIPP Point-in-Time |
|
71.9 |
|
72.4 |
|
72.7 |
|
| |
|
|
|
|
|
|
|
| Percent with Medicaid |
|
|
|
|
|
|
|
| CPS |
|
12.2 |
|
11.2 |
|
10.7 |
|
| SIPP Annual |
|
12.9 |
|
12.3 |
|
11.5 |
|
| SIPP Point-in-Time |
|
11.3 |
|
10.2 |
|
9.4 |
|
Source: Bennefield (1996c).
Other Estimates of the Uninsured
The CPS and the SIPP are the most commonly used surveys to measure the health insurance
status of individuals, primarily because of their rich economic and demographic data, and
repetition on a regular basis. Nevertheless, other surveys that measure the health insurance status
of individuals are gaining prominence. Below, we present the estimates from three of these
surveys: (1) the National Health Interview Survey, (2) the Medical Expenditure Panel Survey, and (3) the
Community Tracking Study. For each survey, we present their estimates of the uninsured and then
give some detail, if available, on the survey design and health insurance measurement issues. The
uninsured estimates for these surveys are presented in
table 6.
National Health Interview Survey
The National Health Interview Survey (NHIS) is a continuing nationwide survey of the U.S.
civilian noninstitutionalized population designed to be the principal source of information on the
health of the population of the United States.22 The survey is
conducted by the National Center for Health Statistics (NCHS). According to NHIS data, there
was a monthly average of 11.5 million uninsured children ages 0 to 17 in 1994 (NHIS 1996). This
should be considered a point-in-time estimate of the uninsured because the reference period for
the health insurance questions was the month prior to the interview month. That this estimate is
17 percent higher than the CPS estimate of the uninsured supports the view that the CPS is
somewhat lower than a point-in-time estimate of the uninsured.
The main objective of the NHIS is to monitor the health of the U.S. population through the
collection and analysis of data on a broad range of health topics. Although the NHIS has been
conducted continuously since 1957, its content has been updated every 10 to 15 years in order to
incorporate the latest population information and statistical methodology into the survey design.
The estimates presented above are from the sample design used from 1985 to 1996.
The sample design of the NHIS follows a multistage probability design that permits a continuous
weekly sampling of the U.S. population. The survey is designed so that the sample scheduled for
each week is representative of the target population, and the weekly samples are additive over
time. The weekly samples are consolidated to produce quarterly files (each consisting of data for
13 weeks). The weights are adjusted so that each quarterly file represents the U.S. population.
These quarterly files are later consolidated to produce the annual file, which is the basis of most
tabulations of the NHIS data. The yearly sample is composed of 36,000 to 47,000 households,
including 92,000 to 125,000 persons, depending upon the year. Interviews are conducted in
person by Census Bureau staff and response rates are high, ranging from 94 to 98 percent over
the years.
The NHIS questionnaire used from 1985 to 1995 contained two major parts. The first part
consisted of topics that remain relatively the same from year to year. Among these topics were the
incidence of acute conditions, the prevalence of chronic conditions, and utilization of health care
services. The second part of the NHIS consisted of special topics added as supplements to each
year's questionnaires. Between 1985 and 1995, health insurance status was only included as
periodic supplemental questions. Because supplements were not always administered to a full
year's sample, sample sizes and response rates differed somewhat from that of the full sample.
The NHIS health insurance questions typically asked about all types of insurance coverage,
including private, public, and other coverage. Like the CPS and SIPP, the uninsured are a residual
of those not reporting any other type of coverage. The NHIS did not impute health insurance
coverage—those with item nonresponses were generally coded as unknown.23 However, for most estimates made by NCHS, the unknowns
were excluded from the estimate and the remaining persons were reweighted to make up the
difference. The reweighting had the effect of assuming that those with nonresponses had roughly
the same insurance characteristics as all other respondents.
The NHIS has not been a commonly used source for health insurance status because the
supplements that asked about health insurance were not conducted on a regular basis. This may
change, though, because, beginning in 1997, health insurance questions are included as part of the
core questionnaire of the NHIS. The new health insurance questions ask whether a family has
health insurance coverage and who in the family is covered. Respondents are asked to provide the
full names of their plans and the annual amount spent on premiums, including health insurance
premiums. The redesigned survey will also have observations in every state for the first time
(although the sample will be too small to provide state-level estimates in all states), making the
NHIS a candidate for analyzing the effectiveness of CHIP programs.
Table 6. Other Estimates of the Uninsured
| Source |
Data |
Time |
Universe |
Estimate Definition |
Number
(millions) |
Percent |
|
NHIS
Correspondence
(1996) |
National Health Interview Survey
(NHIS) |
1994 |
Children ages <17 |
Uninsured defined as lacking coverage in previous month. Estimate is a 12-month average of survey responses. |
11.5 |
|
— |
|
| Beauregard et al. (1997) |
1996 Medical Expenditure Panel Survey (MEPS) |
First half of 1996 |
Nonelderly ages 0-64
Children ages <17 |
Without insurance throughout the first half of 1996. |
44.5
11.0 |
|
19.2
15.4 |
|
| Reschovsky et al. (1997) |
Community Tracking Study (CTS) |
Late 1996 / early 1997 |
Children ages <18
Nonelderly ages 0-64 |
Point estimate |
8.8
35.4 |
|
12.1
15.4 |
|
Medical Expenditure Panel Survey
The Medical Expenditure Panel Survey (MEPS), cosponsored by the Agency for Health Care
Policy and Research (AHCPR) and the National Center for Health Statistics, was designed to
yield comprehensive data that estimate the level and distribution of health care use and
expenditures, monitor the dynamics of the health care delivery and insurance systems, and assess
health care policy implications. Beauregard et al. (1997) used MEPS to develop a national
estimate of the uninsured population, defined as those who were uninsured continuously from
January 1, 1996, to their first-round interview date three to six months later.24 By this measure they found that 19.2 percent of all nonelderly
persons ages 0 to 64 (44.5 million persons) and 15.4 percent of children ages 0 to 17 (11 million
children) were uninsured.
Although these estimates appear higher than those of the CPS (17.4 percent for nonelderly and
13.8 percent for children) and other data sources, Beauregard et al. concluded that once time-period and definitional issues are considered, their estimates are consistent with the findings of the
CPS. In reaching this conclusion, though, they assumed the CPS measured the uninsured
throughout the previous year. If, instead, the CPS is viewed as a point-in-time estimate of the
uninsured, or even a mix of point-in-time and uninsured-throughout, then the MEPS and CPS
findings are not necessarily consistent, since the MEPS estimate of those uninsured throughout a
three- to six-month period should be considerably less than a point-in-time estimate. Additional
research comparing MEPS and CPS estimates of the uninsured is warranted before firm
conclusions can be drawn.
MEPS is the third in a series of national probability surveys conducted by AHCPR on the
financing and utilization of medical care in the United States. The National Medical Care
Expenditure Survey (NMCES, also known as NMES-1) was conducted in 1977, and the National Medical Expenditure Survey (NMES-2) in
1987.
The MEPS collects data from a nationally representative sample of households through a rotating
panel design. The sample of 9,400 households is a subsample of the households responding to the
1995 NHIS and is representative of the civilian noninstitutionalized population of the United
States. The data are collected through a precontact interview followed by a series of five rounds
of in-person interviews over a two-year period. As a rotating panel survey, this series of data
collection rounds is begun again each subsequent year on a new sample of households drawn from
the NHIS sampling frame to provide overlapping panels of survey data, which when combined
with other ongoing panels will provide continuous estimates of health care expenditures at both
the personal and household level. The data presented here are from the first round of the MEPS
and had an overall response rate of 78 percent.
Each MEPS interview collects information pertaining to a specific time period called the
"reference period."The reference period for the first round of the MEPS began January 1, 1996,
and ended on the date of each responding unit's first-round interview, conducted from March
through June 1996. The health insurance section of the MEPS collects information about private
and public health insurance programs. It identifies the household members covered by health
insurance and various details about their plans. For employer-sponsored coverage, a link is created to job characteristics collected in the employment section of
the questionnaire. Like most other surveys, the uninsured are a residual of those not reporting any other type of coverage. For individuals who
are uninsured at the beginning of the year, information is collected on the length of time they have
been uninsured. Additional questions clarify whether each person identified by each policy was
covered throughout the reference period.
According to AHCPR, minimal editing and imputing were done to the MEPS data. A small
number of cases reporting AFDC or SSI coverage were assigned Medicaid coverage. In addition,
Medicaid was assigned to persons who paid nothing for their other public insurance when such
coverage was through a Medicaid HMO. Some editing was also done for Medicare coverage, but
only for persons over age 65. All other coverage types were unedited and unimputed (Vistnes and
Monheit 1997). The few cases with nonresponses for all the health insurance questions were
coded as uninsured.
It is difficult to determine whether MEPS underreports Medicaid because AHCPR reports
Medicaid together with other public insurance in a single category called public insurance, which
includes Medicaid, Medicare, military health care, and other public programs (Vistnes and
Monheit 1997). Moreover, it does not include in this category anyone who might have had both
public insurance and private insurance during the reference period. Instead, these persons are
reported as being privately insured.
Community Tracking Study
The Community Tracking Study (CTS) household survey is sponsored by the Robert Wood
Johnson Foundation and conducted by the Center for Studying Health System Change. The
survey is designed to track changes in the health care system over time and to gain a better
understanding of how health system changes are affecting both consumers and providers. Using
the CTS, Reschovsky et al. (1997) estimated that at any point in time from late 1996 to early
1997 there were approximately 35.4 million uninsured nonelderly persons ages 0 to 64 (15.4
percent of all nonelderly persons) and 8.8 million uninsured children ages 0 to 18 (12.1 percent of
all children).
The CTS survey consisted of primarily telephone interviews of a sample of 33,000 households
that were representative of the contiguous 48 states. The telephone sample was supplemented by
a field sample of households without telephones. The interviews, which were conducted between
July 1996 and July 1997, gathered information on all adults and one randomly chosen child in
each household. Altogether, the survey has information on about 60,000 individuals. The overall
response rate of the survey was 65 percent.
The CTS collects information about all private and public health insurance programs that
respondents are covered by as of the interview date. Unlike all the other surveys presented here,
the uninsured in the CTS are not calculated as a residual. Instead, all those not reporting any types
of coverage are asked to verify that they are uninsured or whether they have health insurance
coverage through a plan not previously mentioned. Reschovsky et al. pointed out that the
additional insurance coverage captured through this last question helps to explain why the CTS
had lower uninsured rates than the CPS. Overall, about 2.3 million nonelderly persons reported
insurance coverage only after being asked this last question, or just under half of the 5 million-person difference between the CTS and CPS estimates of the uninsured. Reschovsky et al.
acknowledged, though, that the debate over whether the CPS is a point-in-time or period-of-time
estimate confounds comparisons between the CTS and the CPS. If the CPS is closer to an
uninsured-throughout-the-year estimate, then the CTS estimate, which is clearly a point-in-time
estimate, would be expected to be higher rather than lower than the CPS estimate.
The CTS does some minor imputation of health insurance coverage and, like the MEPS, codes as
uninsured persons with all missing or "don't know" responses to the health insurance questions.
Like most other surveys, the CTS also appears to underreport Medicaid. In 1996, 36.3 million
nonelderly individuals in the 48 contiguous states were enrolled in Medicaid at some point during
the year, according to HCFA. In contrast, 17.4 million nonelderly individuals were enrolled in
Medicaid at a point in time according to the CTS, a difference of over 50 percent. We would
expect the CTS estimate of Medicaid coverage to be less than the HCFA estimate because of
time-frame differences (CTS is a point-in-time measure and the HCFA data are for those enrolled
at any time during the year) and because the CTS does not capture Medicaid enrollees who also
have private coverage. However, we believe that these two factors alone are unlikely to lead to a
50 percent difference in the estimates.
State-Level Estimates
Several researchers have combined CPS surveys to increase the sample sizes enough to produce
state-level estimates of the uninsured. Below, we give an overview of two of these studies, one by
the Urban Institute and one by Families USA.
The Urban Institute
Winterbottom et al. (1995) combined data from the March 1991, 1992, and 1993 CPS surveys to
obtain state-level estimates of the health insurance status of individuals. Because CPS households
are interviewed for two consecutive years and Winterbottom et al. only wanted to include each
household once, they included all the observations from the 1993 CPS plus approximately half of
the observations from the 1991 and 1992 surveys. Thus, combining three years of CPS data
doubled the sample size, which reduced the sampling variance.25 Winterbottom et al. then used the Urban Institute's TRIM2
model to adjust for underreporting of Medicaid.
Winterbottom et al. found that the rate of uninsurance among children ages 0 to 17 varied by state
and region. For example, in the West South Central region—the region with the highest rate of
uninsurance—18.5 percent of children were uninsured.26 In
contrast, in the East North Central region—the region with the lowest rate of uninsurance—6.8
percent of children were uninsured.27 Winterbottom et al.
pointed out that uninsurance rates vary by region and state for a number of reasons, including the
rate of employer-sponsored insurance coverage and the rate of Medicaid coverage. Winterbottom
et al. used the following example of the uninsurance rates of all persons age 0 to 64 to illustrate
their point:
The Middle Atlantic region has the lowest rate of employer coverage among its poverty
population—only 11.5 percent have employer-sponsored coverage—significantly lower than the
15.8 percent coverage in the Mountain states. However, because the Middle Atlantic region has a
high rate of Medicaid enrollment in the poverty population—53 percent of the poor get their
primary coverage through the program—its uninsured rate of 25.1 percent is not the highest. The
Mountain States, with greater employer coverage among the poor, have a higher uninsured rate
(32.6 percent) than the Middle Atlantic region because Medicaid covers fewer of the poor in the
Mountain States region (40 percent).
Families USA
Families USA (Families USA 1997) used March 1995 and 1996 CPS data in combination with
imputation equations developed from the 1991 SIPP panel to estimate the number of children
ages 0 to 17 who were without health insurance in one or more months over the two-year period
from 1995 through 1996.28 It estimated that 23.1 million
children, or 33 percent of all children, were without health insurance in at least one month of the
two-year period from 1995 to 1996. Families USA noted that the proportion of children with gaps
in health insurance varied significantly from state to state due to differences in state economies
and residents' income, the prevalence of jobs that offer employer-based coverage, the scope of
public insurance programs (especially Medicaid), and the existence of other state health reforms.
It found the highest proportions of uninsured children in southern and southwestern states. This
supports the finding of Winterbottom et al. that the three regions with the highest proportion of
uninsured children are (1) the West South Central, (2) the South Atlantic, and (3) the East South
Central. According to Families USA, the following 10 states had the highest percentage of
children who experienced gaps in their health insurance during the period 1995 through 1996:
Texas (46 percent); New Mexico (43 percent); Louisiana (43 percent); Arkansas (42 percent);
Mississippi (41 percent); District of Columbia (39 percent); Alabama (38 percent); Arizona (38
percent); Nevada (37 percent); and California (37 percent). Families USA did not report
confidence limits for these estimates.
Counting the Uninsured: Implications for CHIP
On October 1, 1997, $4.2 billion in federal funds for fiscal year 1998 were made available to
states under CHIP to initiate and expand health insurance coverage for uninsured children in low-income families. CHIP, the new Title XXI of the Social Security Act, was established by the Balanced Budget Act of 1997 (P.L.
105-33) and provides states with $24 billion in federal matching funds over the next five years.
Under the law, states may provide coverage for children in low-income families by creating a
separate child health insurance program, expanding the Medicaid program, or a combination of
the two.
The Balanced Budget Act of 1997 also added three options for states to expand coverage of
children under Medicaid. First, states now have the option of establishing presumptive eligibility
guidelines to cover children temporarily who appear eligible for Medicaid but are not yet enrolled.
States already have a similar option for establishing presumptive eligibility guidelines for pregnant
women—approximately 30 states have exercised this option. Second, states have the option to
guarantee 12 months of coverage to children enrolled in Medicaid regardless of changes in the
child's family income. Finally, states have the option to accelerate the phase-in of Medicaid
coverage for children under age 19 in families with income below 100 percent of the federal
poverty level.
States have already begun to develop and submit plans to insure children under CHIP, either by
expanding their Medicaid programs, creating new state programs, or a combination of the two.29 Both implementing and evaluating these various programs
will require estimating the number of uninsured children in each state.
Implementing CHIP
Implementing CHIP requires accurate estimates of the number of uninsured children in each state
for two principal reasons. First, CHIP funds are allocated based largely on the number of
uninsured in each state. For example, for fiscal year 1998, funding was allocated based on the
number of uninsured in each state who are under 19 years of age and whose family income is at or
below 200 percent of the federal poverty level. In later years, funding will be allocated based on
the number of uninsured as well as the overall number of children with incomes below 200
percent of poverty. The official estimates of the uninsured in each state for CHIP funding will be
made by the Census Bureau using three-year averages of combined CPS data. The 1998 estimates
were made using the March 1994, 1995, and 1996 surveys. Using the CPS to make state-level
estimates of the uninsured in order to allocate CHIP funds has possible drawbacks, though.
One drawback is potential Medicaid underreporting. If Medicaid underreporting affected all states
to the same degree, then funds would generally be allocated equitably. However, researchers
suspect that Medicaid underreporting may be more prevalent in states with Medicaid managed
care programs or with higher poverty-related eligibility thresholds. If those that fail to report
Medicaid report private insurance instead, then estimates of the uninsured will not be affected.
But if they report no insurance, then estimates of the uninsured will be too high.
Another drawback is sampling variability. Although the Census Bureau pools three years of data
to make state-level estimates of the uninsured for allocating CHIP funds, pooling three years of
CPS data only doubles the sample size because the samples overlap. Moreover, since the two
samples are not independent (they tend to be pulled from the same neighborhoods), the doubled
sample size yields something less than a proportionate reduction in variance. In short, even with
the pooling of three years of data, CPS estimates of the uninsured in smaller states will still have
fairly large variances.
Although the CPS has weaknesses that could affect the allocation of CHIP funds, it still provides
the best data available for this purpose. For allocating CHIP funds, the CPS data are superior to
the other data mentioned in this paper along a number of dimensions: they are more timely, have a
larger sample size, are representative at the state level, and are updated regularly. Policymakers
may be able to improve the funding allocation method by adjusting the CPS data for Medicaid
underreporting. Policymakers could also reduce CPS estimated variances by using sophisticated
small-area estimation techniques, such as those used to allocate funding to states for the Special
Supplemental Nutrition Program for Women, Infants, and Children (WIC) (Schirm and Long,
1995). These improvements, though, would take time, and CHIP funds are already being
allocated.
The second reason that implementing CHIP requires accurate estimates of the number of
uninsured children in each state is that a state's choice to use CHIP funds to expand Medicaid or
to establish or expand an existing state child health insurance program depends, in part, on the
number of uninsured children in the state and how much it would cost to insure them. For
example, expanding Medicaid may be attractive to states because the infrastructure, such as an
existing network of providers and procedures for rate setting, is already in place. However, states
that expand Medicaid are expanding an entitlement program, which means that once eligibility
standards are set, all who meet those standards can enroll. As a result, the costs of entitlement
programs are sometimes unpredictable and a state may end up with higher financial obligations
than expected, particularly during economic downturns. In contrast, if a state establishes a new
program, it can set explicit enrollment and funding caps to ensure that the budget is not exceeded.
Therefore, in deciding how to expand health insurance coverage under CHIP, states will have to
make some estimate of their number of uninsured children and how much it would cost to insure
them.
When estimating the number of uninsured for the purposes of CHIP program planning,
policymakers should keep in mind that some groups of uninsured children are not eligible for
health insurance coverage with CHIP funds. For example, uninsured children who are already
eligible for Medicaid are specifically excluded by the legislation. This is a significant exclusion
since researchers using national survey data have found that anywhere from 21 to 45 percent of all
uninsured children may be eligible for Medicaid (table 7). In some states, the population of
uninsured children eligible for Medicaid could be even higher.
Policymakers should also keep in mind that some immigrant children may not be eligible for CHIP
(or Medicaid) because it is considered a means-tested benefit program, from which some classes
of immigrants are prohibited under the Personal Responsibility and Work Opportunity
Reconciliation Act of 1996. Specifically, permanent resident immigrant children who arrived in
the United States after enactment of CHIP (August 22, 1997) are not eligible. In addition,
undocumented immigrants are not eligible for CHIP regardless of their date of entry into the
United States. However, many children in immigrant families—both documented and
undocumented—were born in the United States and therefore are U.S. citizens and eligible for
CHIP.
In addition to affecting CHIP program planning, the number of uninsured Medicaid-eligible
children and uninsured immigrant children ineligible for CHIP can affect the allocation of CHIP
funds. CHIP funds may not be allocated equitably if estimates of the uninsured in each state
include these groups. Although some states clearly have disproportionately more immigrants (for
example, California, Florida, and Texas), it is not as clear which states have disproportionately
more Medicaid-eligible uninsured. It is plausible, though, that states with optional poverty-related
programs that cover older children and children in families with higher incomes may have more
Medicaid-eligible uninsured, since these groups tend to participate in Medicaid at lower rates than
the traditional cash-related groups. States that lack adequate outreach programs may also have
more Medicaid-eligible uninsured. In any case, given the variable nature of state Medicaid
programs, it is plausible that some states have disproportionately more Medicaid-eligible
uninsured than do others.
Table 7. Estimates of the Number of Uninsured Children Who Are Eligible for Medicaid but Not Participating by Source
|
| Uninsured Children
Eligible for Medicaid |
|
|
|
| Source |
Date |
Time Period |
Estimate Definitions and
Eligibility Criteria |
Number (millions) |
Percent of All
Uninsured |
|
| Urban Institute's Estimate Using TRIM2 Model, Which Adjusts for Medicaid Undercount (Ullman, Bruen, and Holahan 1998) |
CPS 3/95 and 3/96 |
1994-1995 |
Estimate definition: Children ages <18 |
|
|
|
|
|
|
Eligibility criteria: |
|
|
|
|
| | |
State-specific poverty related
AFDC children.
SSI children.
Medically needy children.
Assets. |
1.6 |
|
21% |
| |
| Urban Institute's Estimate Using Unadjusted CPS Data (Ullman, Bruen, and Holahan, 1998) |
CPS 3/95 and 3/96 |
1994-1995 |
Same as above. |
4.5 |
|
42% |
|
| |
|
| Uninsured Children
Eligible for Medicaid |
|
|
|
| Source |
Date |
Time Period |
Estimate Definitions and
Eligibility Criteria |
Number (millions) |
Percent of All
Uninsured |
|
| Reschovsky et al. (1997) |
CTS |
late 1996 / early 1997 |
Estimate definition: Children ages <18. |
|
|
|
|
| |
|
|
Eligibility criteria: |
|
|
|
|
| |
|
| |
State-specific poverty-related criteria only. |
3.1 |
|
35% |
| |
| Thorpe (1997b) |
CPS 3/96 |
1995 |
Estimate definition: Children ages <18 |
|
|
|
|
| |
|
Eligibility criteria: |
| |
| |
None given |
3.3 |
|
31% |
|
| |
|
| Uninsured Children
Eligible for Medicaid |
|
|
|
| Source |
Date |
Time Period |
Estimate Definitions and
Eligibility Criteria |
Number (millions) |
Percent of All
Uninsured |
|
| GAO (1996) |
CPS 3/95 |
