Are the Current Population Survey
Uninsurance Estimates Too High? An
Examination of the Imputation Process
Michael Davern, Holly Rodin, Lynn A. Blewett, and
Kathleen Thiede Call
Research Objective. To determine whether the imputation procedure used to re-
place missing data by the U.S. Census Bureau produces bias in the estimates of health
insurance coverage in the Current Population Survey’s (CPS) Annual Social and
Economic Supplement (ASEC).
Data Source. 2004 CPS-ASEC.
Study Design. Eleven percent of the respondents to the monthly CPS do not take the
ASEC supplement and the entire supplement for these respondents is imputed by the
Census Bureau. We compare the health insurance coverage of these ‘‘full-supplement
imputations’’ with those respondents answering the ASEC supplement. We then com-
pare demographic characteristics of the two groups and model the likelihood of having
insurance coverage given the data are imputed controlling for demographic charac-
teristics. Finally, in order to gauge the impact of imputation on the uninsurance rate we
remove the full-supplement imputations and reweight the data, and we also use the
counter-factual simulation that no cases had the full-supplement imputation.
Population Studied. The noninstitutionalized U.S. population under 65 years of age
Data Extraction Methods. The CPS-ASEC survey was extracted from the U.S.
Census Bureau’s FTP web page in September of 2004 (http://www.bls.census.gov/
Principal Findings. In the 2004 CPS-ASEC, 59.3 percent of the full-supplement im-
putations under age 65 years had private health insurance coverage as compared with
69.1 percent of the nonfull-supplement imputations. Furthermore, full-supplement im-
coverage in multivariate models with demographic controls. Both our reweighting
strategyand our counterfactual modeling show that the uninsuredrateisapproximately
one percentage point higher than it should be for people under 65 (i.e., approximately
2.5 million more people are counted as uninsured due to this imputation bias).
Conclusions. The imputed ASEC data are coding too many people to be uninsured.
The situation is complicated by the current survey items in the ASEC instrument
r Health Research and Educational Trust
allowing all members of a household to be assigned coverage with the single press of a
button. The Census Bureau should consider altering its imputation specifications and,
more importantly, altering how it collects survey data from those who respond to the
Implications for Policy Delivery or Practice. The bias affects many different pol-
icy simulations, policy evaluations and federal funding allocations that rely on the CPS-
Primary Funding Source. The Robert Wood Johnson Foundation.
Key Words. Health insurance coverage, current population survey, annual social
and economic supplement, hotdeck imputation, item nonresponse, missing data
The U.S. Census Bureau’s Annual Social and Economic Supplement (ASEC)
to the Current Population Survey (CPS) provides the most visible estimate of
the number of uninsured people in the United States. The ASEC has become
the survey of record for estimates of health insurance coverage because it
produces both national and state estimates of health insurance coverage,
makes its micro data available to analysts within 6 months after the data are
collected, contains a wealth of demographic information (including family
structure and income), and releases its detailed report on an annual basis
(Blewett et al. 2004). The ASEC estimates of health insurance coverage are
widely used in academic research literature and media outlets, and it is the
survey to which all other surveys are compared for coverage measurement.
The ASEC estimates of health insurance coverage are used for a variety
of purposes. The Congressional Budget Office makes use of the ASEC to help
score legislation (Glied, Remler, and Zivin 2002), and states use the ASEC to
monitor progress in determining the success of the State Children’s Health
children in each state (Davern, Blewett et al. 2003). The ASEC is also used to
allocate three to four billion dollars per year to states to fund SCHIP based, in
part, on the number of low income uninsured children in each state and the
number of low-income children in each state (Davern, Blewett et al. 2003).
AddresscorrespondencetoMichael Davern,Ph.D., AssistantProfessor, Division ofHealthPolicy
and Management, University of Minnesota, School of Public Health, 2221 University Avenue,
S.E., Minneapolis, MN. Holly Rodin, Ph.D., is with the Service Employees International Union,
St. Paul, MN. Lynn A. Blewett, Ph.D., Associate Professor, and Kathleen Thiede Call, Ph.D.,
Associate Professor, are with the Division of Health Policy and Management, University of Min-
nesota, School of Public Health, Minneapolis, MN.
Are the CPS Uninsurance Estimates Too High?2039
Because of their many uses, the ASEC uninsurance estimates have been
scrutinized over the years, especially as they tend to be higher than most
surveys that ask about health insurance coverage at a single point in time
(Lewis, Ellwood, and Czajka 1998; Fronstin 2000; Short 2001; Congressional
Budget Office 2003). Some of the national surveys that measure health in-
surance coverage include the National Health Interview Survey (NHIS), the
Household Component of the Medical Expenditure Panel Survey (MEPS-
HC), and the Survey of Income and Program Participation (SIPP) (Blewett
et al. 2004). The ASEC estimate of the number of people with no health
insurance for the entire previous calendar year, is typically higher than the
full-year uninsurance estimates produced using data from these other
surveys. In addition, the ASEC full-year uninsurance estimates are even
higher than many ‘‘point-in-time’’ uninsurance estimates from other surveys
(Congressional Budget Office 2003; Czajka 2005; Peterson 2005). Several
authors have offered potential reasons why the health insurance estimates
differ across the various federal surveys, including: differences in sample
frame; sample selection and population coverage; mode of survey ad-
ministration; survey operationalization of the concept of uninsurance; misre-
porting by respondents in the survey; and data processing (Lewis, Ellwood,
and Czajka 1998; Fronstin 2000; Short 2001; Congressional Budget Office
One of the least explored reasons why the ASEC differs from other
surveys is the impact of missing data imputation on health insurance coverage
estimates from the ASEC. Davern et al. (2004) explicitly examined this issue
ASEC biases the state estimates of uninsurance. Some states had higher and
some states had lower rates of coverage due to bias, but the national rate was
unbiased (Davern et al. 2004).
In this paper we focus explicitly on the national estimate of uninsurance
to explore whether the imputation process used by the Census Bureau ex-
plains the higher uninsurance estimates found in the ASEC relative to other
national surveys. Specifically, we examine whether there is a significant dif-
ference in estimates of uninsurance for those cases that have health insurance
data imputed (11 percent of the ASEC sample) and those that do not. We
begin by describing the imputation methodology the Census Bureau uses for
the ASEC health insurance items and why we think the current methodology
may impact the overall rates of coverage to produce an upward bias in the
national estimates of uninsurance.
2040HSR: Health Services Research 42:5 (October 2007)
ASEC MISSING DATA AND HEALTH INSURANCE
TheCPSis a monthlysurvey conducted by the CensusBureau and sponsored
by the U.S. Bureau of Labor Statistics. The ASEC supplement is added to the
has a rotating panel design in which sampled households are in the sample for
4 months, then rotate out for 8 months, and then rotate back in for an ad-
ditional 4 months. The major exception to this rotation is that November
respondents in their eighth month of interviewing may be re-interviewed if
they meet certain criteria (children or minority members in the household).
They are recontacted to take the CPS and ASEC supplement again
between February and March (U.S. Census Bureau 2002; Davern, Beebe
et al. 2003).
Missing data in the form of item nonresponse is a common problem in
survey research (Groves et al. 2002). Missing data results when someone re-
fuses to answer a survey item, an entire supplement, or an entire survey.
Item missing data also results when respondents ‘‘do not know’’ an answer
to a question. An estimated 11 percent of the CPS monthly core survey
respondents do not respond to the entire ASEC supplement. These 11
percent have their entire ASEC supplement values imputed. We refer to
these cases as ‘‘full-supplement imputations’’ and they are the focus of our
Statisticians have developed a wide range of techniques for dealing with
item nonresponse (e.g., Kalton 1983; Kalton and Kasprzyk 1986; Little and
Rubin 1987; Rubin 1996; Heeringa, Little, and Raghunathan 2002; Marker,
Judkins, and Winglee 2001). Most of the techniques use information from the
completed cases to impute a model-based estimate to the cases with missing
data. The Census Bureau uses ‘‘hotdeck’’ imputation to replace item nonre-
sponse in all of its household surveys (e.g., CPS, SIPP, decennial census, and
the Survey of Program Dynamics). Hotdeck is a type of model-based imput-
ation by which a respondent’s valid value for a specific variable is assigned to
another respondent that does not have a valid value for the variable. The
respondent with the valid value is called a ‘‘donor’’ and the one with a missing
valueis calleda‘‘recipient.’’For example, ifthedonor is15 yearsold,then the
recipient (respondent with missing age) is given a value of 15 and the donor
maintains the age of 15. Donors and recipients are matchedtogether based on
age, and work status (for additional information see David et al. 1986; Mason,
Are the CPS Uninsurance Estimates Too High?2041
Lesser, and Traugott 2001; Marker, Judkins, and Winglee 2001; Davern et al.
Although properly specified imputation can alter basic distributional
summary statistics (means and variances) compared with the statistics calcu-
lated using complete cases only, it should not transform the relationships
among variables. If there was a relationship between two variables in the
reported data it should remain the same in the imputed data, and no new
relationships should appear after the imputation. The basic idea of model-
make an informed guess as to what the actual response would have been. In
assessing accuracy of datasets with imputed hotdeck values for estimating
demographic characteristics, a critical question is whether there is a relation-
ship between having an imputed value and the concept being imputed after
controlling for the covariates used to impute the data. If this relationship is
strong after controlling for the variables used in the imputation process, there
is evidence that bias may have been introduced. To examine whether this is
occurring in the ASEC data we explore three questions: (1) is there a differ-
encebetweentheimputedcasesand the nonimputedcaseswithrespect tokey
demographic variables and health insurance coverage?; (2) is there a differ-
ence in the probability of being uninsured after controlling for covariates used
to impute health insurance coverage?; and (3) is the difference in the prob-
ability of being uninsured between those cases with and without imputed data
enough to produce a substantively significant difference in the national un-
WHY IMPUTED CASES MAY BE DIFFERENT FROM
The ASEC imputation specification for private insurance coverage has two
stages. The first is to impute whether each person is a private health insurance
policyholder (done separately for self-purchased and employer sponsored
is to impute whether the policyholder has a ‘‘family plan’’ or an ‘‘individual
is extended to other specific family members. If the person is imputed to have
an individual plan, then no dependent coverage is extended to other family
members in the household.
2042 HSR: Health Services Research 42:5 (October 2007)
The private insurance coverage imputation for the ASEC data has two
limitations that we believe are working together to create a significant prob-
whether or not a person is a private coverage policyholder (U.S. Census
Bureau 1998). This is a problem because one-person families are more likely
to be private health insurance policyholders after controlling for other cova-
riates. As a result, we expect too few policyholders to be imputed in one-
person families, and therefore too little private health insurance coverage
among people living in one-person families.
After the private insurance coverage policyholder is imputed, then fam-
ily or individual coverage is imputed. When a person is imputed to be a
policyholder with family coverage, the specification then extends this private
coverage to specific relatives living in the same household. Dependent cov-
erage in the imputation process is not extended to a nonchild or nonspouse of
the policyholder (U.S. Census Bureau 1998). Although theoretically correct,
this is problematic because this limitation is not enforced in the computer-
to everyone within the household regardless of relationship. Specifically, re-
spondentsareasked if‘‘anyoneelse inthe householdiscoveredbythistypeof
Census Bureau’s field representative can enter ‘‘A’’ (for ‘‘all’’) regardless of the
relationship among the people being assigned coverage. Therefore some
nondependent household members may inappropriately be assigned de-
pendent coverage during the survey interview. Because of this difference
between the survey interview and the imputation specifications, we expect
less dependent health insurance coverage in the imputed data than in the
We use the 2004 ASEC supplement to the CPS for this analysis. Data were
collected from 77,149 households representing 213,241 individuals; the
household response rate for the monthly portion of the CPS was 84 percent
(U.S. Census Bureau 2004). In this paper we limit our analysis to ASEC re-
there is bias in the national estimates of uninsurance. The first analysis shows
Are the CPS Uninsurance Estimates Too High?2043
general demographic characteristics of all people in the ASEC under the age
of 65. We conduct two independent sample t-tests comparing the demo-
graphic characteristics of the full-supplement imputations to the nonfull-sup-
plement imputations. The key variables of interest are health insurance
coverage as well as other demographic characteristics. The second analysis
shows the percent of households in which everyone in the household has
coverage by household size. This analysis is further broken into full-supple-
ment imputation households——defined as a household in which any one per-
son in the household is a full-supplement imputation——versus all others.2
The third analysis uses a multinomial logistic regression model with the
dependent variable taking on one of three values indicating whether the per-
son was coded: (1) to be uninsured; (2) to have any public coverage; and (3) as
having private coverage only. If the respondent indicates both private and
public insurance, the person is coded as having public insurance.3The key
covariate of interest in this model is whether the person was a full-supplement
We use the following variables in our model as they are used in the
Census Bureau’s health insurance coverage hotdeck routine: age, veteran sta-
tus, employment, earnings, employer size, self-employment, family labor
covariates including race, ethnicity, education, family size, and citizenship in
an attempt to see if they are able to explain away the relationship between
being an imputed case the probability of being uninsured.
term for being under 19 years of age and being a full-supplement imputation.
It is possible the impact will vary by whether the person is a child or an adult
because children are much more likely to obtain dependent coverage than to
be a policyholder. Several of the variables are not collected for children, and
we have coded children to not be married, be out of the labor force, and have
less than a high school degree.
In our final analysis we estimate the impact of the full-supplement im-
putation on the uninsurance rate, private coverage rate, and public coverage
rate. The impact is assessed in two distinct ways. First, in order to give the
multinomial logistic regression results a meaningful scale we use the recycled
predictions methodology to obtain estimated coverage rates (Graubard and
Korn 1999; StataCorp 2001; Kronick and Gilmer 2002). The recycled pre-
dictions approach uses the actual values for a respondent (e.g., black, male,
three-person family) to determine the probability of having the various
coverage types (e.g., public or private) under a counterfactual scenario. We
2044 HSR: Health Services Research 42:5 (October 2007)
alter only one variable in the counterfactual scenario to observe its marginal
impact on an individual’s probability of coverage. We sum these new altered
counterfactual person probabilities for various types of coverage to get the
adjusted overall rate of coverage under the scenario. The counterfactual re-
cycled predictions analysis gives everyone the value of ‘‘not full-supplement
imputation.’’ These recycled rates allow us to control for everything else we
include in our model (e.g., demographic characteristics and key covariates)
while tryingto isolatetheeffect of being a full-supplement imputation. We use
this analysis to answer the central question: holding other covariates constant,
what is the impact of full-supplement imputation on coverage rates?
of uninsurance and the rate of private health insurance coverage by reweight-
ing the ASEC data after removing all of the full-supplement imputation cases.
control totals from the entire ASEC by race, ethnicity, gender, age, and pov-
erty status. This analysis helps to further answer the question of what would
happen to the ASEC data if the full-supplement cases were treated as non-
respondents, as opposed to having their ASEC items fully imputed.4We
compare the reweighted and recycled predictions estimates to the standard
ASEC estimates to gauge the impact of imputation on the health insurance
Table 1 shows the basic demographics of those cases with full-supplement
imputations compared with everyone else in the ASEC sample. The full-
supplement imputations make up 10.8 percent of the ASEC data and they are
significantly less likely to have private insurance coverage: 59.3 percent com-
pared with69.1percentforallothercases. There is no significant difference in
public coverage between the full-supplement imputations compared with all
other cases.5The full-supplement imputations also have significantly higher
Among other contrasts, the full-supplement imputations are also less likely to
be under 19 years of age, less likely to be working, more likely to be black and
less likely to be white, and are more likely to be out of the labor force.
Table 2 compares health insurance coverage rates for those in house-
holds where any one person in the household is a full-supplement imputation
and those in households without any full-supplement imputations. As expect-
Are the CPS Uninsurance Estimates Too High?2045
Persons o65 Years of Age by Full Supplement Imputation Status
Insurance Status and Other Demographic Characteristics of
Primary family member
Not family member
Primary family member
oHigh school degree
High school graduate
College or postgraduate
Not U.S. born
2046 HSR: Health Services Research 42:5 (October 2007)
ed, the findings are consistent across household size; households with at least
one person with a full-supplement imputation have lower rates of coverage.
For example, a person living alone who is a full-supplement imputation is
Out of labor force
Total percentage and counts
Source: 2004 Current Population Survey——Annual Social and Economic Supplement
a Full Supplement Imputation
Percent of Persons o65 Years of Age in Households Where Every-
No Full Supplement
at Least One Full
between No Full
Source: 2004 Current Population Survey——Annual Social and Economic Supplement
Are the CPS Uninsurance Estimates Too High?2047
10 percent more likely to be uninsured; those living in two-person full-sup-
plement households are 18.3 percent more likely to have at least one unin-
sured households member; and so forth. The percent difference between the
four- and five-person households falling in the middle.
Table 3 shows the results from the multinomial logistic regression mod-
el. The coefficient for the full-supplement imputation cases is a strong pre-
dictor of whether the respondent is uninsured versus having private health
insurance coverage. The full-supplement imputation cases are 2.2 times more
likely to have private health insurance coverage relative to being uninsured
after controlling for other important covariates. In addition, the interaction
effect representing those full-supplement imputations (relative risk ra-
tio50.67) from one-person families shows that the impact of being a full-
supplement imputation is somewhat reduced for this group. This is because
they are not impacted by the survey process that allows all household mem-
bers to have the same coverage because there are no other household mem-
The public insurance coverage estimates were also significant. Full-sup-
plement imputation cases were more likely to be uninsured than to have
public insurance coverage. The relative risk ratio was approximately 1.69 and
significant, and for full-supplement children it was 0.65 and significant. We
believe that full-supplement imputation cases are much more likely to be
uninsured because fewer cases are being imputed to have private health in-
surance coverage. Because the multinomial logistic regression contains inter-
action effects whose coefficients are difficult to interpret in isolation, we
performed additional analyses.
In Table 4 we present the unadjusted ASEC estimates of private cov-
erage, public coverage, and uninsurance for people under 65 years of age. We
also break this table into children under age 19 and adults aged 19–64 and
report the results from two adjustment methods. The first adjustment is the
reweighted data and the second are the ‘‘model-based’’ recycled prediction
estimates. Both of these techniques (reweighting and recycled predictions)
yield strikingly similar results. The national unadjusted ASEC estimate of
percent, and the reweighted results are slightly higher, at 69.1 percent. The
rates forany publicinsurance coveragedo not vary among thethree methods.
The unadjusted ASEC national estimate of uninsurance is 17.6 percent,
2048 HSR: Health Services Research 42:5 (October 2007)
for Having Private Coverage versus Being Uninsured and Having Public
Coverage versus Being Uninsured for Persons o65 Years of Age
Multinomial Logistic Regression Relative Risk Ratios (RRR)
RRRStandard Error RRRStandard Error
Full supplement imputation
Full supplement imputation ? one person family
Full supplement imputation ? under 19years
Excellent to good health
American Indian only
Born U.S. citizen
Unemployed/out of labor force
High school graduate or less
College or post graduate
Employer size 25–499
Employer size 500–999
Employer size 1,0001
Source: 2004 Current Population Survey——Annual Social and Economic Supplement
FPL, federal poverty level.
Are the CPS Uninsurance Estimates Too High?2049
compared with 16.7 percent using the ‘‘model-based’’ recycled predictions
and 16.6 percent using the reweighted data. The results within each age stra-
tum follow the same pattern observed for the total population——compared
with the unadjusted estimates, the re-weighted and recycled prediction esti-
mates are higher for private coverage, lower for uninsurance, and similar for
We find evidence that the Census Bureau estimates of health insurance cov-
erage are biased with respect to the full-supplement imputations. We estimate
that the biastranslates to roughly 2.5 million less uninsured or 6 percent of the
total number of uninsured for those people under 65 years of age.6This
magnitude of difference is similar to the adjustment made to the 1999 unin-
surance estimate resulting from the addition of a new health insurance ver-
ification question (Nelson and Mills 2001).
The goal of imputation is to use available data to make an informed
estimate of what the missing value should be. Imputation should not alter or
introduce new relationships among the variables (Davern et al. 2004). Our
results show that cases with no imputed values differ significantly from the
Estimates of Health Insurance Coverage for Persons o65 Years of Age——
Comparisons of the Reweighted and Model-Based Predictions for
Model based CPS (if none imputed)
Children under 19 years of age
Model based CPS (if none imputed)
Adults 19–64 years of age
Model-based CPS (if none imputed)
Source: 2004 Annual Social and Economic Supplement to the Current Population Survey
CPS, current population survey.
2050HSR: Health Services Research 42:5 (October 2007)
full-supplement imputation cases with respect to health insurance coverage.
impute health insurance coverage. Our results demonstrate that bias in health
insurance coverage estimates is introduced by the current imputation speci-
fications used by the Census Bureau.
Following from these results we recommend that the Census Bureau
alter its imputation specifications to eliminate this bias. An important first step
in this process is to use family size when imputing private health insurance
policyholder status. Adjusting the specifications for who is allowed to receive
should reflect the reported data and should not be used to enforce rules that
are not enforced in the reported data. As currently designed, the ASEC in-
strument may allow for too much dependent coverage to be reported among
the household is covered under the policy. With one press of a button de-
pendentcoveragecanbeassignedto peoplein the householdwhomaynotbe
eligible to receivehealth insurancecoveragethrough a specific plan. Itis quite
possible that this assigns dependent coverage to people outside of the family
health insurance unit who may not, in fact, be eligible for that coverage. To
further complicate the picture, there is evidence from administrative data that
a fair number of people actually receiving dependent health insurance cov-
erage benefits do not, in fact, qualify (i.e., are not part of the family health
insurance unit). Companies such as Ford and Northwest Airlines have inves-
tigatedtheir healthinsurance coverage roles toremove ineligible people, such
as older children and ineligible unmarried partners, whohad been enrolled in
the plan. In both cases about 10 percent of the people obtaining insurance
from the companies were found to not be eligible (Appleby 2004; Cummins
and Fedor 2005). As a result we think it is important to actually ask who is
covered without necessarily restricting dependent coverage to specific family
members as the imputation does.
everyone in the household with a press of a button is not a good practice.
The ASEC survey instrument may allow for incorrect reporting of
dependent coverage by allowing the ‘‘A’’ option to assign coverage to all
problem. In the short run the Census Bureau should fix the imputed data to
reflect the reported data by allowing an imputation of ‘‘A’’ just as the reported
data does. However, in the long run the Census Bureau should alter its survey
instrument to eliminate the ‘‘A’’ for ‘‘all’’ option. This is especially critical
Are the CPS Uninsurance Estimates Too High?2051
because dependent coverage is of particular concern as the cost of employer-
based coverage continues to increase and the offer and take up of dependent
coverage continues to decline (Holohan 2003; Gould 2004). To get a more
are needed to clearly specify dependent coverage in the ASEC questionnaire.
Improveddatacollectionwill resultinmore reliableestimatesaswellasbetter
baseline data from which to impute missing values.
It is widely acknowledged in the research community that data from the
ASEC produce estimates of uninsurance for an entire year that are too high
documented a previously unexplored reason why these estimates may be too
high: the Census Bureau’s imputation procedure for full-supplement cases.
However, the adjusted estimates presented in Table 4 are still much higher
than the full-year uninsured estimates observed in other Census Bureau sur-
veys, such as the SIPP (Congressional Budget Office 2003; Peterson 2005).
The imputation bias discussed here is not large enough to reconcile the ASEC
estimates with other surveys measuring full-year uninsurance rates. However,
the adjusted numbers do bring the estimates closer to other estimates by
increasing the amount of private coverage and lowering the number of
The authors wish to thank Linda Bilheimer, John Czajka, Deborah Chollet,
Steve Zuckerman, Marie Wang, Chuck Nelson, Robert Mills, and Joanne
Pascal for participating in a meeting at the Mathematica Policy Research in
Their insights greatly improved our interpretation and understanding of the
issue. The authors would also like to thank Karen Soderberg for an outstand-
ing job of editing this manuscript. All remaining problems are the fault of the
authors only. Preparation of this manuscript was funded by Grant no. 38846
from The Robert Wood Johnson Foundation to the State Health Access Data
Assistance Center at the University of Minnesota, School of Public Health.
health insurance series imputed, but we do not explicitly examine these at this
2052 HSR: Health Services Research 42:5 (October 2007)
point. An additional 15 percent of respondents have only dependent health in-
surance (either privately purchased or employer sponsored) marked as imputed.
All of these imputed cases have private health insurance coverage (no one is
imputed to be uninsured). After investigation, the Housing and Household Eco-
nomic Statistics Division of the Census Bureau found that these cases were mis-
takenly marked as imputed. The Census Bureau informed us that the specification
will be altered to remove this problem.
2. For the vast majority (85 percent) of people in households with at least one full-
supplement imputation person, everyone in the household is a full-supplement
both public and private health insurance coverage than those people who respond
to the supplement. Putting those with both public and private health insurance
coverage into the public insurance category highlights the main issue regarding
imputation and private coverage. To fix this problem, the Census Bureau could
impute public coverage first and then use the imputed public coverage in the
hotdeck to impute private coverage. This will reduce the number of full-supple-
ment imputations with both types of coverage.
4. This is what is done in other surveys, such as the NHIS, to adjust for supplement
nonresponse. In the NHIS the sample child and sample adult supplements have
sample loss from the household and person portion of the interview as sampled
respondents refuse to take them. These supplements weight the responding cases
to represent the entire adult and the entire child population. Supplement refusers
do not have their full set of supplement data imputed as in the CPS-ASEC
(National Center for Health Statistics 2003).
5. See endnote 3.
6. There are estimated to be 253,621,207 million people o65 in the United States in
2004 multiplied by 1 percent is 2.5 million.
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