Non-response Bias in the 2013 CPS ASEC Content Test 

Matthew W. Brault 

U.S. Census Bureau* 

4600 Silver Hill Road, Washington, DC 20233 

Proceedings of the 2013 Federal Committee on Statistical Methodology (FCSM) Research Conference 

Abstract 

In March 2013, the Census Bureau fielded a content test to evaluate new income and health insurance questions for 

the Current Population Survey (CPS) Annual Social and Economic Supplement. The response rate to the survey was 

43.1 percent, which raised questions about bias in key estimates. The design of the Content Test was similar to that 

of the American Time Use Survey (ATUS), which also suffered from low response. However, research exploiting 

the sample design showed that non-response did not greatly bias key ATUS estimates (Abraham, Maitland, and 

Bianchi 2006). Using the ATUS analysis as a framework, I modeled survey response, contact, and cooperation rates 

from sample characteristics obtained during households’ last CPS interview. Results suggest that age, educational 

attainment, and household size appear to have the greatest effect on response. Non-response appears to bias 

uninsurance in general; employer-sponsored insurance and Medicare specifically; and wage, retirement, and interest 

income recipiency, but has little effect on mean income amounts. For instance, non-response exerted a 2.8 

percentage point downward bias on the percentage of households with a person uninsured for the full year. 

Introduction 

Rising levels of non-response in national surveys present a growing potential problem for producing unbiased 

estimates about the nation’s social and economic characteristics (Singer 2006, Groves 2006, Brick and Williams 

2013). While much has been written about non-response in production surveys, less has been discussed in the 

context of content tests. Non-response in field tests is a threat to prudent risk management because decisions to 

move forward with questionnaire design changes may be based on biased estimates. For surveys that produce 

official economic indicators, content tests often use the magnitude of the estimates as criteria for whether to move 

forward with changes to questions. 

Non-response contributes to the bias of estimates when the characteristics of non-respondents differ from those of 

respondents in ways correlated with the desired measure (Groves 2006). The amount of bias is a function of both the 

size of the difference between respondents and non-respondents and the level of non-response in the survey. 

Because the difference between respondents and non-respondents may not always be known, non-response rates 

have historically been viewed as the standard indicator for bias. Along these lines, the Office of Management and 

Budget established standards for statistical surveys, requiring an analysis of non-response bias when the unit 

response rate is below 80 percent (Office of Management and Budget 2006). While this threshold is relatively 

arbitrary, it forces agencies to conduct necessary analyses. Furthermore, simply publishing a response rate as the 

indicator of data quality can be misleading as there is low correlation between response rates and non-response bias 

(Davern 2013, Skalland 2011). 

This paper examines the impact of non-response on measures of health insurance coverage and income from the 

2013 Current Population Survey Annual Social and Economic Supplement (CPS ASEC) Content Test1

. Because 

*

 This paper is released to inform interested parties of ongoing research conducted by Census Bureau staff and to encourage 

discussion of work in progress. Any views expressed on methodological, statistical, or operational issues are those of the author 

and are not necessarily shared by the U.S. Census Bureau. Please direct all comments or questions to Matthew Brault via email 

(matthew.w.brault@census.gov) or phone (301-763-9112). 

1

 Data are subject to error arising from a variety of sources. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see the documents available at http://www.reginfo.gov/public/do/PRAViewDocument? 

ref_nbr=201211-0607-002. 

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criteria for adopting the new questions involve the magnitude of estimates about income sources and coverage types, 

non-response bias is of particular concern. Levels of non-response in the test were higher than initially expected, 

which will likely affect the efficiency of key estimates. While little can be done ex post to address the problems of 

small sample size, weighting adjustment can be done to minimize the effect of non-response bias. 

Data 

The 2013 CPS ASEC Content Test was a field test conducted to analyze proposed revisions to questions about 

income and health insurance coverage in the CPS ASEC. The design of the Content Test, however, was analogous to 

how the American Time Use Survey (ATUS) is conducted: using retired sample from the Basic Monthly Current 

Population Survey (CPS). The Content Test sample was constructed from retired CPS samples that were not selected 

for the ATUS or eligible for selection for the production CPS ASEC survey. 

Non-ATUS-selected Hispanic households from 8 months (Aug.-Oct. 2010, Aug.-Oct. 2011, and Aug.-Sept. 2012) 

and non-Hispanic households from 5 months (Nov.-Dec. 2010, and Nov. 2011 - Jan 2012) of outgoing rotations 

from the CPS were selected to build a sample of 22,508 housing units. The size of the sample was chosen in order to 

obtain an estimated 15,000 completed interviews, the size sufficient to detect a 0.5 percentage point difference in the 

uninsured rate (Clement 2012). Units were contacted via Computer-assisted Telephone Interviews (CATI) by 

Census Bureau field representatives from the Tucson, AZ, Jeffersonville, IN, and Hagerstown, MD call centers. 

Of the 22,508 sample cases, 9,195 complete interviews were conducted, 12,145 cases were deemed eligible for 

collection but were not completed, and 1,168 were deemed non-eligible (Table 1). The unweighted response rate 

was 43.1 percent. The telephone call centers were able to contact 59.2 percent of sample units and obtained 

completed interviews from 72.8 percent of those contacted. 

An advantage to using the retired CPS sample to construct this test is that each housing unit’s information from its 

last CPS interview is available to help determine the characteristics of those who did not respond to the Content 

Test. The characteristics of the eligible sample are shown in Table 2. Overall, the eligible sample tended to look like 

households overall. For example, households with a reference person aged 65 or older made up 23.3 percent of the 

sample, not statistically different from the 22.9 percent of households that had a householder aged 65 or older from 

the 2012 American Community Survey.2 

Methods 

When dealing with non-response, it is appropriate to define how response is calculated. The American Association 

for Public Opinion Research provides various standards for calculation. In this paper, response rates reflect the 

AAPOR RR2 standard: the percentage of complete and sufficient partial interviews divided by the sum of 

completes, refusals, noncontacts, unknown eligibles, and other situations (The American Association for Public 

Opinion Research 2011). Contact rates reflect the AAPOR CON1 standard, and cooperation rates reflect the 

AAPOR COOP2 standard. The RR2 rate is the product of the CON1 and COOP2 rates. 

Because the Content Test used a similar sample design as the ATUS, lessons can be learned from studies about nonresponse in that survey. A paper from Abraham, Maitland, and Bianchi (2006) looking at the impact of non-response 

bias on ATUS estimates used previous CPS interview characteristics to model response, contact, and cooperation to 

the survey. I use that paper as a framework for structuring this analysis. Accordingly, I use a logit model to predict 

response, contact, and cooperation to the Content Test. Also like Abraham, et al., I use propensities from the model 

to adjust estimates to assess the impact of non-response on key estimates. 

All direct estimates and model parameters are estimated using sample weights. The sample weights are derived from 

each unit’s base weight (inverse probability of selection) adjusted for the 8:5 ratio of months with Hispanic/NonHispanic households and for the removal of the ATUS sample.3 Variances are calculated using the successive 

differences replication method. 

2

 The comparison here and all other comparisons made in this paper have been tested for statistical significance at the 90 percent 

confidence level. 

3

 8 households (5 eligible and 3 ineligible) had zero weights and thus were excluded from weighted analyses. 

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For household characteristics -- like presence of children or number of people in household -- the linkage from the 

Content Test to the CPS is straightforward. For person-level characteristics – like age, sex, race, etc. – I use the 

characteristics of the individual listed as “reference person” for the household, if valid. If not valid, I use the 

characteristics of the adult in the household whose person-weight is equal to the household-weight. From these 

values, I identify characteristics for each sample unit that include age, sex, race/origin, citizenship status, marital 

status, presence of children, disability status, educational attainment, employment status, family income, and size of 

household. 

Households with different demographic and family characteristics are known to have different levels of income and 

health insurance coverage. Consequently, to the extent that these demographic and family characteristics are 

correlated with non-response, estimates of income and health insurance coverage would be biased. For instance, 

households with people aged 65 and over are more likely to have income from Social Security and retirement 

sources while being covered by Medicare (DeNavas-Walt, Proctor and Smith 2013). In addition, this age group 

frequently has higher response rates to surveys (Voigt, Koepsell and Dalling 2003). 

Differences in characteristics can affect response through two pathways. First is the contact pathway, which reflects 

those household characteristics that make it difficult for organizations like the Census Bureau to contact them (i.e. 

pick up the phone, answer the door, etc.). For phone surveys, the existence of caller ID has increasingly changed the 

characteristics of those less likely to answer the phone as households are able to implicitly “refuse” the survey. 

These refusals often cannot be distinguished from noncontacts because of unavailability. The second pathway of 

non-response is the explicit refusal. In this pathway, the household answers the phone but then does not participate, 

either before the survey begins or after a few questions are asked (i.e. insufficient partials). The factors that lead to 

noncontacts may not be the same as the ones that lead to nonparticipation. For example, households with individuals 

who travel extensively for work may not be home resulting in lower contact propensity, but may not be any more or 

less likely to participate if contact is made. Because of this, I model contact and participation rates separately and 

combine the predicted propensities to form a likelihood of response. 

Key estimates from the Content Test are estimated at the household level because that is the unit of analysis for the 

response models. Generally, if any member of a household has a particular characteristic (is uninsured, has 

Medicare, earns wage income) then the household is considered to possess the characteristic. 

Results 

Table 3 presents weighted response rates, contact rates, and cooperation rates for households by various 

characteristics. Overall, 41.3 percent of households responded. Contact was made with 57.9 percent of households 

and 71.4 percent of those with whom contact was made cooperated with the survey. These differed for households 

of various characteristics. 

The response rate for younger households – those with a reference person under age 30 – was 28.1 percent and the 

response rate rose with each successive age group. For those 30 to 44 years, 45 to 54 years, and 55 to 64 years, the 

response rates were 32.8 percent, 37.7 percent, and 48.3 percent, respectively. Households with a reference person 

age 65 or older had the highest response rate at 54.0 percent. The 25.9 percentage point difference between the 

oldest and youngest groups was driven by the contact rates for these households. The difference in the contact rates 

for the 65 and older and 30 and under groups was 32.6 percentage points while the difference in cooperation rates 

was 5.0 percentage points (Table 3). 

On other demographic characteristics, households with male, non-Hispanic White, and Native born citizen reference 

persons were more likely to respond than females, other race/origin groups, and foreign born noncitizens 4 

, 

respectively. Households with widowed reference persons were more likely to respond than other marital statuses, 

however, that may be reflecting the older population of this group. Likewise, households without children, those 

with a reference person with a disability, and those not in the labor force were more likely to respond than the other 

statuses within each category. 

4

 The response rate for native citizens was not different from that of naturalized citizens. 

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Table 4 shows the average marginal effects from logit models on response, contact, and cooperation. After 

controlling for other specified characteristics, households with a reference person aged 65 or older were 23.4 

percentage points more likely to respond than those with a reference person aged 30 or younger. The older group 

was also 27.5 percentage points more likely to make contact and 8.0 percentage points more likely to cooperate. 

With regard to other demographic characteristics, female households were 2.3 percentage points less likely to 

respond than male households, although there was no statistically significant difference in cooperation. NonHispanic Black households were 11.0 percentage points less likely to respond than non-Hispanic White households. 

Hispanics were 3.9 percentage points less likely to respond than non-Hispanic Whites. Marginal differences in the 

response rates for race/origin groups appear attributable to the rates of contact, considering that marginal differences 

in cooperation were not significant or marginally significant for most groups. 

Furthermore, households with more education had with greater rates of response, contact, and cooperation than the 

less educated reference groups. Households with family income above $40,000 were more likely to respond than 

those with less income or those who did not report income. There was no statistically significant difference in the 

response of households with incomes of $40,000 to $74,999 and those with $75,000 or more. Households with a 

reference person not in the labor force were marginally more likely to respond than employed households, driven by 

an increased likelihood of contact. They were not any more or less likely to cooperate if contacted. 

Married couple households were the most likely to respond of the marital statuses, not different from widowed 

households, after controlling for other factors. Households with a reference person who reported being separated 

were the least likely to respond. These response propensities were driven by differences in the likelihood of contact. 

There were no statistically significant differences in cooperation. 

After controlling for marital status, large households – those with 5 or more people – were less likely to respond 

than single person households. There were no statistical differences on contacts; rather, the size of households 

impacted the propensity to cooperate. Five or more person households were 8.6 percentage points less likely to 

cooperate once contact is made than single person households. 

Using the predicted propensities for contact and cooperation as adjustment factors for non-response, I reweight the 

Content Test data and present household-level estimates of health insurance and income. Figure 1 shows that after 

adjusting for non-response, the percentage of households with a full-year uninsured person was 2.8 percentage 

points higher than the estimate before adjusting. This may be driven by the decrease in the percentage of households 

with a Medicare beneficiary and the small decrease in the percentage with a person covered by employer-sponsored 

insurance. 

Figures 2 and 3 show the effect of non-response on estimates of income. The model based adjustment does little to 

the means of reported household wage income, retirement income, and interest income (Figure 2). Each unadjusted 

mean was within the 90 percent confidence interval of the adjusted mean. However, the adjusted percentage of 

households with wage income was 2.6 percentage points higher than the estimate without the adjustment. The 

percentage with retirement income dropped from 14.2 to 10.9 percent and the percentage with interest income fell 

2.0 percentage points. 

5 

Discussion 

Non-response bias in content tests is particularly problematic for managing the risks associated with moving forward 

with a set of new questions in a survey. The CPS ASEC provides important economic data about income, poverty, 

and health insurance coverage of people and households in the United States. Therefore any changes to that survey 

must be made carefully as so not to misrepresent the nature of those characteristics. Based on the results presented 

here, there does appear to be significant differences between the populations of households that responded to the 

2013 Content Test and those that did not. These differences, combined with the sizeable rate of non-response, result 

in non-ignorable bias in the estimates of the percentage of households with specific types of health insurance (and 

coverage overall) and those with specific sources of income. 

5

 Because the adjusted and unadjusted estimates use the same underlying sample, estimates are statistically different from each 

other. This statement is one way to suggest that there is no meaningful difference resulting from the weighting adjustment. 

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Of the various factors that contribute to this bias, age, educational attainment, and household size appear to be the 

driving factors. Older populations are more likely to have Social Security income, receive retirement income, and 

have coverage through Medicare. Those with more education are more likely to have earnings, earn more, receive 

earnings from assets, and be covered by private sources of health insurance. Large households are more likely to 

have multiple income sources, which would affect household income. 

Of these factors, age may, in practice, be the least worrisome. While the non-response adjustment did not take age 

into account, the Content Test final weights used age-based controls (CPS second stage controls) to make 

adjustments. These adjustments would diminish the impact that age-based differentials on response might have on 

key measures. Further analysis would be needed to determine whether the final weights do in fact sufficiently make 

the adjustment. 

Lastly, the analysis presented here is only a limited examination of the impact of non-response on income and health 

insurance estimates. There may be many other factors not included here that are correlated with response and the 

key measures which would contribute to bias. Likewise, I only looked at a few income and health insurance 

measures; a more complete examination of income sources and the aggregate amounts is needed, as is an 

examination for other health insurance coverage types. 

References 

Abraham, Katharine G, Aaron Maitland, and Suzanne M Bianchi. "Nonresponse in the American Time Use Survey: 

Who is Missing From the Data and How Much Does It Matter." Public Opinion Quarterly 70, no. 5 (2006): 

676-703. 

Brick, J Michael, and Douglas Williams. "Explaining Rising Nonresponse Rates in Cross-Sectional Surveys." The 

ANNALS of the American Academy of Political and Social Science 645 (2013): 36-59. 

Clement, Lisa A. "Specifications for selecting Household Identifiers for the March 2013 Content Test of the ASEC 

Survey - REVISED." Memorandum for David E. Adams, Chief, Current Population Surveys Programming 

Branch. Washington, DC: U.S. Census Bureau, October 12, 2012. 

Davern, Michael. "Nonresponse Rates are a Problematic Indicator of Nonresponse Bias in Survey Research." Health 

Services Research 48, no. 3 (2013): 905-912. 

DeNavas-Walt, Carmen, Bernadette D Proctor, and Jessica C Smith. Income, Poverty, and Health Insurance 

Coverage in the United States: 2012. Current Population Reports, P60-245, U.S. Census Bureau, 

Washington, DC: U.S. Government Printing Office, 2013. 

Groves, Robert M. "Nonresponse Rates and Nonresponse Bias in Household Surveys." Public Opinion Quarterly 

70, no. 5 (2006): 646-675. 

Office of Management and Budget. "Standards and Guidlines for Statistical Surveys." September 2006. 

http://www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/statpolicy/standards_stat_surveys.pd 

f (accessed May 30, 2013). 

Singer, Eleanor. "Introduction: Nonresponse Bias in Household Surveys." Public Opinion Quarterly 70, no. 5 

(2006): 637-645. 

Skalland, Benjamin. "An Alternative to the Response Rate for Measuring a Survey's Realization of the Target 

Population." Public Opinion Quarterly 75, no. 1 (2011): 89-98. 

The American Association for Public Opinion Research. Standard Definitions: Final Dispositions of Case Codes 

and Outcome Rates for Surveys. 7th edition. AAPOR, 2011. 

Voigt, Lynda F, Thomas D Koepsell, and Janet R Dalling. "Characteristics of Telephone Survey Respondents 

According to Willingness to Participate." American Journal of Epidemiology 157, no. 1 (2003): 66-73. 

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Table 1. Outcome codes, categorized 

Outcome Description 

Number of 

sample units 

(Households) 

Percentage 

(of eligible 

cases) 

Completed responses 9,195 43.1 

Type A non-interviews 12,145 56.9 

 Contacts 3,435 16.1

 Refusals 3,366 15.8 

Other contacts 69 0.3

 Noncontacts 8,710 40.8

 Eligible noncontacts 3,456 16.2 

Unknown eligibility 5,254 24.6 

Type B non-interviews (ineligible) 1,168 --

Source: U.S. Census Bureau, 2013 CPS ASEC Content Test 

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Table 2. Characteristics of the sample (weighted) 

Characteristic Percentage of 

Households SE 

Age1 

Under 30 years 10.7 0.27 

30 to 44 years 25.6 0.35 

45 to 54 years 21.0 0.33 

55 to 64 years 19.4 0.31 

65 years or older 23.3 0.33 

Sex 

Male 50.0 0.36 

Female 50.0 0.36 

Race/Origin 

Non-Hispanic White 76.1 0.38 

Non-Hispanic Black 8.2 0.27 

Hispanic or Latino 9.7 0.29 

Other non-Hispanic 6.0 0.21 

Citizenship Status 

Native citizen 88.1 0.30 

Naturalized citizen 6.1 0.20 

Not a citizen 5.8 0.22 

Marital Status 

Married 54.6 0.43 

Widowed 10.0 0.21 

Divorced 14.3 0.29 

Separated 2.6 0.13 

Never married 18.5 0.30 

Presence of Children 

Children present 29.2 0.40

 Child under 6 years present 11.9 0.30

 Child 6 to 17 years present 23.1 0.37 

No children present 70.8 0.40 

Disability Status 

With a disability 13.2 0.27 

No disability 86.8 0.27 

Educational Attainment 

Less than HS Diploma 10.4 0.27 

HS Diploma or equivalent 29.2 0.37 

Some College/Associate's Degree 28.7 0.39 

Bachelor's degree 19.8 0.35 

Master's degree or higher 11.8 0.28 

Employment Status 

Employed 60.2 0.40 

Unemployed 4.6 0.17 

Not in labor force 35.2 0.39 

Family Income 

Income not reported 22.1 0.41 

Under $20,000 15.1 0.33 

$20,000 to $39,999 19.4 0.32 

$40,000 to $74,999 21.5 0.33 

$75,000 or more 21.8 0.36 

Number of People in Household 

1 25.0 0.33 

2 35.9 0.39 

3 15.7 0.32 

4 13.4 0.30 

5 or more 10.0 0.28 

1

 Person characteristics are that of the household reference person, if eligible. 

Source: U.S. Census Bureau, 2013 CPS ASEC Content Test 

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Table 3. Direct Estimates of Response, Contact and Cooperation Rates 

Characteristic Total Unweighted 

Households 

Weighted Estimates 

Response 

Rate SE 

Contact 

Rate SE 

Cooperation 

Rate SE 

Total 

Age1 

22,508 41.3 0.4 57.9 0.4 71.4 0.5 

Under 30 years 2,693 28.1 1.1 41.2 1.1 68.2 1.7 

30 to 44 years 5,240 32.8 0.8 46.9 0.8 70.0 1.2 

45 to 54 years 4,502 37.7 0.9 54.6 0.9 69.0 1.2 

55 to 64 years 4,528 48.3 0.9 65.8 0.8 73.4 1.0 

65 years or older 

Sex 

5,545 54.0 0.8 73.8 0.7 73.2 0.8 

Male 11,315 43.2 0.5 59.7 0.5 72.3 0.6 

Female 

Race/Origin 

11,193 39.4 0.5 56.0 0.5 70.3 0.7 

Non-Hispanic White 17,575 44.7 0.5 61.7 0.4 72.4 0.5 

Non-Hispanic Black 1,530 26.3 1.3 40.4 1.4 65.0 2.2 

Hispanic or Latino 2,009 30.7 1.3 45.8 1.3 66.9 1.9 

Other non-Hispanic 

Citizenship Status 

1,394 35.8 1.6 52.4 1.6 68.3 2.1 

Native citizen 20,184 41.9 0.4 58.3 0.4 71.9 0.5 

Naturalized citizen 1,165 39.7 1.8 59.2 1.6 67.0 2.0 

Not a citizen 

Marital Status 

1,159 33.0 1.7 49.3 1.7 66.9 2.3 

Married 11,653 43.3 0.5 61.4 0.5 70.5 0.6 

Widowed 2,346 48.4 1.2 67.9 1.2 71.3 1.3 

Divorced 3,422 41.0 1.0 55.3 1.0 74.1 1.3 

Separated 559 26.5 2.3 38.6 2.4 68.8 3.9 

Never married 

Presence of Children 

4,528 33.7 0.9 46.5 0.9 72.6 1.3 

Children present 5,269 32.2 0.8 48.0 0.9 67.0 1.2 

 Child under 6 years present 2,248 31.0 1.3 45.9 1.4 67.5 1.8 

 Child 6 to 17 years present 4,087 32.3 0.9 48.9 1.0 66.1 1.4 

No children present 

Disability Status 

17,239 45.0 0.4 61.9 0.4 72.8 0.5 

With a disability 3,194 44.7 1.1 61.2 1.1 73.0 1.2 

No disability 

Educational Attainment 

19,314 40.8 0.4 57.3 0.4 71.1 0.5 

Less than HS Diploma 2,313 34.1 1.2 52.3 1.2 65.3 1.8 

HS Diploma or equivalent 6,618 38.6 0.7 56.7 0.7 68.0 0.9 

Some College/Associate's Degree 6,451 39.8 0.7 55.9 0.8 71.2 0.8 

Bachelor's degree 4,483 45.4 0.9 60.8 1.0 74.6 1.0 

Master's degree or higher 

Employment Status 

2,643 51.1 1.2 65.4 1.1 78.1 1.3 

Employed 13,589 39.0 0.5 54.3 0.5 71.8 0.6 

Unemployed 951 33.2 1.7 48.5 1.8 68.5 2.6 

Not in labor force 

Family Income 

7,968 46.3 0.7 65.2 0.6 71.0 0.8 

Income not reported 5,040 36.5 0.8 59.5 0.8 61.3 1.1 

Under $20,000 3,514 34.2 1.0 48.3 1.0 70.8 1.5 

$20,000 to $39,999 4,462 41.4 0.9 57.0 0.9 72.8 1.1 

$40,000 to $74,999 4,854 44.8 0.8 59.5 0.8 75.2 0.9 

$75,000 or more 

Number of People in Household 

4,638 47.5 0.9 61.9 0.9 76.7 1.0 

1 6,378 44.4 0.7 59.4 0.7 74.7 0.9 

2 8,585 45.7 0.6 63.1 0.5 72.4 0.6 

3 3,184 37.9 1.0 53.4 0.9 71.0 1.4 

4 2,520 34.8 1.2 51.9 1.2 67.0 1.6 

5 or more 1,841 31.6 1.4 50.0 1.3 63.1 2.0 

1

 Person characteristics are that of the household reference person, if eligible. 

Source: U.S. Census Bureau, 2013 CPS ASEC Content Test 

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Table 4. Average Marginal Effects on Response, Contact, and Cooperation Propensities 

Characteristics Response Contact Cooperation 

Age (ref. group: under 30 years) 

30 to 44 years 0.0390*** 0.0311** 0.0421* 

(0.0139) (0.0144) (0.0215) 

45 to 54 years 0.0813*** 0.1003*** 0.0325 

(0.0157) (0.0169) (0.0230) 

55 to 64 years 0.1633*** 0.1942*** 0.0569** 

(0.0174) (0.0175) (0.0232) 

65 years or older 0.2342*** 0.2747*** 0.0801*** 

(0.0182) (0.0172) (0.0233) 

Race/Hispanic origin (ref. group: White, non-Hispanic) 

Non-Hispanic Black -0.1095*** -0.1221*** -0.0433* 

(0.0159) (0.0157) (0.0229) 

Hispanic or Latino -0.0386** -0.0640*** 0.0149 

(0.0165) (0.0161) (0.0193) 

Other non-Hispanic -0.0591*** -0.0698*** -0.0179 

(0.0172) (0.0177) (0.0202) 

Female (=1) -0.0228*** -0.0206*** -0.0142 

(0.0074) (0.0080) (0.0099) 

Native born (=1) -0.0110 -0.0386** 0.0235 

(0.0182) (0.0177) (0.0204) 

Citizen (=1) -0.0282 -0.0155 -0.0275 

(0.0258) (0.0249) (0.0300) 

With a disability (=1) -0.0082 -0.0313** 0.0201 

(0.0130) (0.0136) (0.0147) 

Educational Attainment (ref. group: Less than HS Diploma) 

HS Diploma or equivalent 0.0264* 0.0275* 0.0141 

(0.0139) (0.0152) (0.0192) 

Some College/Associate's Degree 0.0500*** 0.0373** 0.0442**

(0.0155) (0.0167) (0.0199) 

Bachelor's degree 0.1000*** 0.0787*** 0.0805*** 

(0.0167) (0.0190) (0.0212) 

Master's degree or higher 0.1263*** 0.0952*** 0.1034*** 

(0.0168) (0.0196) (0.0217) 

Employment Status (ref. group: Employed) 

Unemployed -0.0147 -0.0039 -0.0186 

(0.0174) (0.0168) (0.0248) 

Not in labor force 0.0214* 0.0393*** -0.0060 

(0.0112) (0.0103) (0.0129) 

Family Income (ref. group: Income not reported) 

Under $20,000 0.0320** -0.0380*** 0.1030*** 

(0.0128) (0.0137) (0.0175) 

$20,000 to $39,999 0.0773*** 0.0132 0.1168***

(0.0114) (0.0112) (0.0152) 

$40,000 to $74,999 0.1016*** 0.0300*** 0.1389*** 

(0.0113) (0.0111) (0.0144) 

$75,000 or more 0.1095*** 0.0330** 0.1456*** 

(0.0135) (0.0130) (0.0170) 

Marital Status (ref. group: Now married) 

Widowed -0.0259 -0.0365** -0.0052 

(0.0161) (0.0168) (0.0205) 

Divorced -0.0312** -0.0588*** 0.0175 

(0.0129) (0.0133) (0.0158) 

Separated -0.1024*** -0.1371*** -0.0056 

(0.0278) (0.0259) (0.0405) 

Never married -0.0242* -0.0444*** 0.0183 

(0.0134) (0.0128) (0.0176) 

Household size (ref. group: 1 person households) 

2 -0.0217* -0.0040 -0.0296** 

(0.0114) (0.0109) (0.0150) 

3 -0.0345** -0.0210 -0.0307 

(0.0146) (0.0138) (0.0200) 

4 -0.0483** -0.0195 -0.0583** 

(0.0193) (0.0177) (0.0250) 

5 or more -0.0631*** -0.0198 -0.0855*** 

(0.0217) (0.0185) (0.0284) 

Observations 21,341 21,341 12,636 

Other covariates include region, metro status, presence of own children, availability and permission to use telephone for CPS 

interviews. 

Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 

Source: U.S. Census Bureau, 2013 CPS ASEC Content Test 

ひ 
Figure 1. Effect of non-response on household-level health insurance estimates. 

のな.ば のど.の

No Adjustment Model‐based	Adjustment

ぬぬ.ぱ

にぱ.ね

なに.ぱ など.ど

Uninsured	(Full‐year) Medicare Coverage (Point in Employer Sponsored

time) Insurance (Point in time)

Source: U.S. Census Bureau, 2013 CPS ASEC Content Test 

Figure 2. Effect of non-response on household income estimates (means). 

$ねは,ねなぬ $ねの,ばはは 

No Adjustment Model‐based	Adjustment

$ぬ,はぱな $に,ばどに $のぬね $ぬばは

Wage income Retirement income Interest income

Source: U.S. Census Bureau, 2013 CPS ASEC Content Test 

Figure 3. Effect of non-response on household income estimates (percentages). 

No Adjustment Model‐based	Adjustment

のぱ.ぬ のの.ば

ぬは.な ぬね.な

なね.に など.ひ

With wage income With retirement income With interest income

Source: U.S. Census Bureau, 2013 CPS ASEC Content Test 

など