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Nonresponse Bias in the Consumer Expenditure Survey: A Case Study

Susan L. King1, Jennifer Edgar2, Jeffrey M. Gonzalez2, Boriana Chopova1, and Lucilla Tan1 

The Consumer Expenditure Interview Survey (CE) is a nationwide survey conducted by the U.S. Bureau of Labor 

Statistics to estimate the expenditures made by American households. The response rate for the survey has varied 

between 74.5 and 78.6 percent from 2002 to 2007.

Key words: Continuum of resistance, intermittent respondent, missing data, panel survey, proxy nonrespondent

 Response rates in this range indicate a risk for nonresponse bias. 

This paper is a synthesis of four studies undertaken to study whether nonresponse introduces bias into the survey 

estimates. The four studies are: (1) A comparison of response rates between subgroups of the survey's sample, (2) A 

comparison of respondent demographic characteristics between the CE and the American Community Survey, (3) 

An analysis of nonresponse bias using 'harder-to-contact' respondents as proxies for nonrespondents, and (4) An 

analysis of nonresponse bias using intermittent respondents and attritors as proxies for nonrespondents. 

Collectively, the studies show no meaningful bias in the survey's estimates even though the nonresponse is not 

missing completely at random.

1. Introduction

The purpose of this paper is to present a case study that investigated nonresponse bias in the Consumer Expenditure 

Interview Survey (CE). The CE is a nationwide household survey conducted by the U.S. Bureau of Labor Statistics 

(BLS) to estimate the expenditures made by American households. It is a federal statistical survey with a response 

rate ranging from 74.5 percent to 78.6 percent between collection years 2002 and 2007. Low response rates are a 

concern because they indicate risk for nonresponse bias. The CE is a rotating panel survey in which approximately 

15,000 households are visited each quarter of the year, and each household is contacted for an interview every three 

months for five consecutive quarters. Expenditure information from the first interview is not used in the CE’s 

 

1 Consumer Expenditure Survey, U.S. Bureau of Labor Statistics, 2 Massachusetts Ave. NE, Washington DC 20212. 

Email: king.susan@bls.gov

2 Office of Survey Methods Research, U.S. Bureau of Labor Statistics, 2 Massachusetts Ave. NE, Washington DC 

20212. Email: edgar.jennifer@bls.gov 
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published estimates. Instead, the first interview is used only for inventory and ‘bounding’ purposes, which 

addresses a common problem in which survey respondents tend to report expenditures to have been made more 

recently than they were actually made. Only expenditure information from the second through fifth interviews is 

used in the published estimates. 

To evaluate nonresponse bias in the CE, four studies were completed: 

1. A comparison of response rates between subgroups of the survey’s sample.

2. A comparison of socio-demographic characteristics to an external data source (the American Community 

Survey, or ACS)

3. An analysis of nonresponse bias using ‘harder-to-contact’ respondents as proxies for nonrespondents.

4. An analysis of nonresponse bias using intermittent respondents and attritors (survey dropouts) as proxies 

for nonrespondents. 

These studies were designed to answer the questions: (1) Are the data in the CE missing completely at random 

(MCAR)? (2) What are the demographic characteristics of the nonrespondents? and (3) What is the level of 

nonresponse bias in the CE?

The first step in most nonresponse bias studies is to understand the missing data mechanism. Data are said to be 

‘missing completely at random’ if the mechanism that produces the missing values is unrelated to the values of the 

data themselves (Little and Rubin 2002). More precisely, data are ‘missing completely at random’ if their pattern of 

‘missing-ness’ is independent of the data’s actual values and the values of any other variables. Another mechanism 

for generating missing data is ‘missing at random’ (MAR). Data are said to be ‘missing at random’ (MAR) if the 

probability of data missing for a particular variable is unrelated to the value of that variable after controlling for 

other variables in the analysis. MAR is a weaker assumption than MCAR and it is impossible to verify whether the 

MAR condition is satisfied. The values of the missing data are unknown and thus a systematic comparison of 

observations with and without the missing variable is not possible (Allison 2002).

Nonresponse bias is often associated with the data not being MCAR. One common method of determining whether 

the data are MCAR is to examine differences between respondents and nonrespondents on variables that are 

collected for both groups and that are associated with the values of the survey data. Any differences suggest that the 
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missing data may not be MCAR. Frame and stratification variables are frequently used to test for MCAR in this 

approach (e.g., Brick and Bose 2001; Dixon 2001 and 2004; Duncan and Hill 1989; Pearl and Fairley 1985; and 

Purdie et al. 2002). Another common approach is to compare characteristics of respondents to characteristics of an 

external population. Again, differences between the two groups suggest that the missing data may not be MCAR. 

The first two studies followed these approaches, but all four studies included MCAR analyses.

After answering the MCAR question, the next step is to estimate the amount of nonresponse bias in the survey’s 

expenditure estimates. Since by definition no data are collected from nonrespondents, one approach is to partition 

the respondent sample into subgroups that exhibit different nonresponse bias characteristics (Groves 2006), and then 

select one of the subgroups to serve as ‘proxies’ for the nonrespondents. Once a set of proxy nonrespondents is 

selected, a dataset can be created with expenditure and socio-demographic information that is representative of the 

nonrespondents, and the level of nonresponse bias can be estimated. The third and fourth studies in this report use 

this approach. The third study uses ‘harder-to-contact’ respondents as proxy nonrespondents and the fourth study 

uses intermittent respondents and attritors as proxy nonrespondents. Both studies draw on the ‘continuum of 

resistance’ theory. Sample units are ordered by the amount of interviewer effort exerted to obtain a completed 

interview, and those requiring the most effort are chosen to serve as the proxy nonrespondents (Groves 2006). In 

both of these studies nonresponse bias was computed for total expenditures and 13 expenditure subcategories, 

ranging from regular monthly expenses such as housing payments to infrequent and highly variable expenses such 

as those for education.

2. Methodology: Common Approaches Across Studies

2.1. Data

For comparability of results, the four studies used a common data file for analysis. This common file includes 15 

months of data, from April 2005 through June 2006. Data on respondent contacts used in two of the studies come 

from the Contact History Instrument (CHI), an interviewer completed instrument which collects data on every 

contact attempt made for each CE case. The CHI data includes information about the date, time, method, and 

outcome of each contact. The unit of analysis in these studies is the consumer unit (CU), which in most cases is a 

household. The common data file consists of one record per wave (or interview) per CU for Waves 1, 2, and 5. 
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Waves three and four were excluded for simplicity, and with the expectation that any bias found would be consistent 

in these waves. 

In each record, there were CU-level variables as well as variables for respondent characteristics. In the event of a 

noninterview, the respondent characteristics were missing except for interviewer-recorded values on member race, 

CU size, and housing tenure. These interviewer-recorded values are often based on speaking to neighbors or from 

the interviewer’s inference. The sample size of the common data file is shown in Table 1.

Table 1. Sample size of common data file by wave

Wave

Completed 

Interview

Type A Noninterview Total 

Eligible 

Refused Cases

No one 

Home

Temporarily 

Absent Other

1 9,795 1,640 459 166 391 12,451

2 9,432 2,017 407 138 378 12,372

5 9,547 2,225 279 100 354 12,505

Total 28,774 5,882 1,145 404 1,123 37,328

Records for other CU members were added to the common data file for member-level analyses in the second study, 

‘Comparison of the Characteristics of CE Respondents to External Data.’

Variables collected in the CHI were added to the common data file for analysis in the third study, ‘Harder-toContact Respondents as Proxies for Nonrespondents.’ These variables provide information about the number of 

times an interviewer contacted the CU prior to completing the interview.

Several studies required Wave 1 demographic data. Since Wave 1 is primarily a bounding interview and is not used 

in published estimates, the data are not subjected to the regular cleaning, editing, and variable creation activities that 

are performed on data in Waves 2 through 5. To use these data in the common data file, Wave 1 demographics were 

processed following the production specifications used in Waves 2 through 5. Slight modifications were necessary 

for race and housing tenure (owner or renter) variables. The values for race and housing tenure created for this 

series of studies were tested against those generated from production data for Waves 2 through 5. For each wave, 
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the match rates between the created code and the production code for race and tenure were over 99%, while the 

match rates for the size of the CU ranged from 85% to 89%.

2.2. Weighting

The CE’s sample design is a nationwide probability sample of addresses. The selection of addresses for the survey 

begins with the definition and selection of geographic areas called “primary sampling units” (PSUs), which are 

small groups of contiguous counties. CE’s sample in 2006 consisted of 91 PSUs randomly selected to represent the 

entire nation. Within each of these PSUs a systematic sample of addresses is drawn. Most addresses consist of one 

CU, but some addresses have more than one CU. Each interviewed CU represents itself as well as other CUs and 

therefore must be weighted to properly account for all CUs in the population. The U.S. Census Bureau selects the 

sample and provides the base weights, which are the inverse of the CU’s probability of selection. A Primary 

Sampling Unit (PSU) is a geographic area of several connected counties. The average number of counties in a PSU 

is five. Each CU in a PSU has the same base weight. BLS makes three types of adjustments to the base weights: an 

adjustment if the field representative finds multiple housing units where only a single housing unit was expected, a 

noninterview adjustment; and a calibration adjustment. These weight adjustments are made to each individual CU. 

The noninterview adjustment accounts for nonresponse by increasing the weight of the respondents in sociodemographic classes that are associated with nonresponse. Calibration adjusts the weights to Census population 

controls in order to account for frame under-coverage. The noninterview and calibration adjustments are more 

significant than the adjustment for multiple housing units.

All of the studies use base weights. The two proxy nonrespondent studies and the study comparing response rates 

across subgroups only used base-weighted data, but the study comparing CE respondents to external data study used 

all three weights (base weights, noninterview adjustment weights, and the final calibration weights).
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2.3. Relative Nonresponse Bias Equation

For the estimates of nonresponse bias in the two proxy nonrespondent studies, relative nonresponse bias was 

computed instead of an estimate of nonresponse bias. The reason is that the dollar amounts vary substantially across 

expenditure categories; thus, making comparisons among them difficult. Relative nonresponse bias is a more 

appropriate statistic for comparisons across categories. The following formula was used to compute relative 

nonresponse bias, denoted as Re ( ) R lBias Z , in the base-weighted sample mean: 

( ) ( ) Re R NR R NR

R

T T

R T

T

B Z N Z Z

lBias Z

Z N Z

Z Z

Z

− = = = −          (1)

where:

is the nonresponse bias in the base-weighted respondent sample mean;

is the base-weighted respondent mean of expenditures;

is the base-weighted all CU mean of expenditures;

is the base-weighted proxy nonrespondent mean of expenditures;

is the base-weighted number of proxy nonrespondent CUs; and 

is the base-weighted total number of CUs.

2.4. Variance Estimation

CE uses Balanced Repeated Replication (BRR) to calculate variance estimates. In this method, the PSUs are 

divided into 43 strata and the sample units within each stratum randomly assigned to one of two half samples. The 

full sample variance is calculated using the calibration-adjusted weights from completed interviews (Wolter 1985). 

There are several issues in using BRR in this project. Any change, addition or deletion, of consumer units requires 

re-estimating the noninterview and calibration adjusted weights. In addition, calibration weights are not calculated 

for Type A noninterviews, which occur when no interview is completed at an occupied eligible housing unit. The 

( ) R B Z

R Z

T Z

NR Z

NNR

N
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information collected from Type A Interviews is used to calculate the nonresponse adjusted weights for completed 

interviews. Therefore, in this project, estimates of means and frequencies were made using two procedures from 

SAS®

The variance for relative nonresponse bias does not have a closed-form solution, so an estimate was calculated for 

each expenditure category using the random groups method (Wolter 1985). With the number of random groups 

equal to 10, the variance formula is as follows:

9, designed for complex surveys, PROC SURVEYMEANS and SURVEYFREQ. 

( ) ( ) ( )2 10

,

1

1 ˆ var

10 10 1 rg j k j j

k

θ θ θ

=

= − −

  ∑ (2)

where:

is the respondent sample mean on expenditure category j for random group k;

is the total sample mean on expenditure category j for random group k;

is the relative bias on expenditure category j for random group k; and

 is the average of the relative bias on expenditure category j over all 10 random groups.

2.5. Significance Test

A 95% confidence interval of the relative nonresponse bias of expenditures was computed as follows:

9,0.975 var ( ) ˆj rg j θ ± ⋅ t θ

 (3)

where:

is the full sample estimate of the relative nonresponse bias on expenditure category j;

, ,

ˆ

Rk j Z

, ,

ˆ

Tk j Z

,, ,,

,

, ,

ˆ ˆ

ˆ ˆ

Rk j Tk j

k j

Tk j

Z Z

Z

θ − =

10

,

1

1 ˆ

10 j k j

k

θ θ

=

 = ∑

, ,

,

ˆ ˆ

ˆ ˆ

Rj Tj

j

T j

Z Z

Z

θ − =
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is the 97.5th

is the random groups variance estimator described above.

percentile of a t-distribution with 9 degrees of freedom; and

2.6. Categorical Data

We compared respondents and proxy nonrespondents on categorical socio-demographic characteristics. For these 

comparisons, the test statistic was the adjusted Rao-Scott chi-square, a modified version of the Pearson chi-square, 

which accounts for the complex sample design (SAS Institute Inc. 2004). The null hypothesis for the two-way 

comparisons in our analyses is that there is no association between response status and subgroup. The null 

hypothesis for the two-way comparisons in our analyses is that there is no association between response status and 

subgroup. The null hypothesis for the one-way comparisons is that the respondent distribution for a characteristic 

from the CE is statistically equivalent to the corresponding distribution for the population obtained from the ACS.

3. Individual Studies

3.1. Comparison of Response Rates Across Subgroups

This study examined the response rates among socio-demographic subgroups that could be identified for both 

respondents and nonrespondents. The goal was to determine whether the survey’s respondents and nonrespondents 

had the same socio-demographic characteristics. The subgroups analyzed were: region of the country (Northeast, 

Midwest, South, West), ‘urbanicity’ (urban, rural), type of PSU, housing tenure (owner or renter), and housing 

values for owners and renters.

Base-weighted response rates were calculated for these subgroups separately for Waves 1, 2, and 5 of the survey. 

They answer the question “What percent of the survey’s target population do the respondents represent?” Baseweighted response rates are defined as the sum of base-weighted interviewed units divided by the sum of baseweighted interviewed units plus the units with Type A noninterviews.

9,0.975 t

var ( ) rg j θ


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Base-weighted response rate ( )

i i

i

ii i

i

w I

wI A = +

∑

∑ (4)

where:

wi = base weight for the ith

i I

consumer unit (CU);

= 1 if the ith

i A

CU is a completed interview, and 0 otherwise; and

= 1 if the ith

Ideally, for this type of analysis, there should be no missing values for the subgroups of interest. Missing values 

may distort the response rates within a subgroup, especially if the cases with missing values differ from the complete 

cases. Therefore, this analysis was restricted to subgroups with no missing values for all the eligible cases. The 

available variables were primarily geographic frame variables. Most likely, other variables also contribute to 

response propensity.

CU is a Type A noninterview, and 0 otherwise.

Table 2 shows the weighted response rates for the subgroups in Waves 1, 2, and 5. For each subgroup, the sample 

size and response rate are given by wave. The results show that response rates differ within all of the subgroups

examined. In particular, statistically significant differences (p<0.05) were found in the following pairwise 

comparisons within the subgroups:

 across the regions, CUs in the Northeast and West have lower response rates than those in the Midwest and 

South;

 across the types of PSU, CUs in metropolitan Core Based Statistical Areas (CBSAs) with a population of 

more than 2 million people have lower response rates than those in other types of PSUs;

 renters in the third and fourth housing value quartiles have lower response rates than renters in the first and 

second quartiles in the unit and area frames, with a similar trend among homeowners; and

 CUs in urban areas have lower response rates than those in rural areas.
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In general, response rate differences within the subgroups suggest that the data are not MCAR because the 

respondent and nonrespondent CUs are not simple cross sections of the original sample.

Although there is evidence of an association between housing tenure and survey participation, there were no 

statistically significant differences between the pair-wise comparisons of owners and renters in Waves 2 and 5. 

Respondents who do not own or rent their homes had significantly higher response rates, though the number of these 

‘other’ respondents is very small. These findings generally held across Waves 1, 2, and 5.
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Table 2. Subgroup response rates by wave, CE

Wave 1 Wave 2 Wave 5

Subgroup n

Response

Rate % n

Response

Rate % n

Response

Rate %

Overall 12,451 78.9 12,372 76.4 12,505 76.5

Region1,2,5

Northeast 2,468 75.1 2,468 74.8 2,332 73.8

Midwest 2,871 81.2 2,841 78.6 2,875 78.0

South 4,116 80.3 4,086 77.1 4,038 79.4

West 2,996 77.4 2,977 74.5 3,260 72.8

Type of PSU1,2,5

A Metropolitan CBSAs > 2 million people 6,197 75.7 6,127 73.5 6,462 73.4

X Metropolitan CBSAs < 2 million 4,326 81.2 4,355 78.9 4,135 79.6

Y Micropolitan CBSAs 1,308 83.1 1,286 79.6 1,304 79.6

Z Non-CBSA (‘rural’) areas 620 80.9 604 78.0 604 77.0

Unit and Area Frames

Housing value - Renters1,2,5

Quartile 1-2 1,947 80.4 1,901 79.2 1,241 78.4

Quartile 3-4 1,891 76.0 1,910 75.0 1,171 74.6

Housing value - Homeowners2,5

Quartile 1-2 4,228 79.8 4,239 77.2 2,869 78.3

Quartile 3-4 3,399 78.7 3,406 75.2 2,191 75.1

Urbanicity1,2,5

Urban 10,217 78.2 10,159 75.9 10,054 75.7

Rural 2,234 81.4 2,213 78.6 2,451 79.4

Housing tenure*2,5

Owner 6,446 -- 8,395 75.8 8,515 76.2

Renter 3,071 -- 3,872 77.3 3,908 77.0

Other 93 -- 105 92.6 82 88.4

1, 2, 5

* Wave 1 response rates for housing tenure are not displayed due to missing values.

Indicates a statistically significant difference (p<0.05) was found for the computed Rao-Scott adjusted chi-square statistic for the test of no association 

between survey participation and subgroup in Waves 1, 2, and 5, respectively.
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3.2. Comparison of CE Respondents to External Data

Another common approach to analyzing nonresponse is to compare the distribution of socio-demographic 

characteristics of respondents to that of a recent census or other ‘gold standard’ survey (Groves 2006). A ‘gold 

standard’ survey is one whose estimates are considered very accurate. Any significant differences between CE and 

the census or ‘gold standard’ survey suggest that respondents in CE are not representative of the target population, 

and thus the missing data in the CE are probably not MCAR.

The ‘gold standard’ survey chosen for this study was the 2005 American Community Survey (ACS). The ACS 

satisfied three important criteria: its estimates are considered to be very accurate; it has key socio-demographic 

variables available; and it was conducted in a time period very close to that which was used to analyze the CE. The 

ACS is a mandatory survey with a response rate of 97% and a coverage rate of 95% (Census Bureau 2006). The 

ACS data used in this study were obtained from published tables on the Census website.

CE data were weighted three ways for comparisons with the ACS: with base weights, noninterview adjustment 

weights, and final calibration weights. Comparisons were made for all three stages of weighting. Since all of CE’s 

weights are CU-level weights and all of the ACS’s weights are person-level weights, we made the results of the two 

surveys comparable by deriving frequency distributions for person-level characteristics in the CE from CU-level 

weights.

Table 3 presents a comparison of the distribution of selected respondent socio-demographic characteristics between 

CE and the ACS. The variables compared were: gender, age, race, educational attainment, household size, tenure, 

the number of rooms in the dwelling unit, housing value, rent, and CU income. Statistically significant differences 

(p<0.05) were found between the two distributions for all comparisons and all types of weighting with only two 

exceptions: calibration-weighted age and housing.

In addition to the different respondent characteristics, several other factors may contribute to making the differences 

statistically significant. First, the extremely large sample sizes make statistical significance likely even if the 

differences are not substantively meaningful. Second, CE and the ACS collect data differently. The two surveys 

use different data collection modes, and they use different question wording. As a result, the validity of the 

comparison to the ACS is limited by the extent to which the survey designs are truly comparable. Third, each CU 

was treated independently even though a CU can appear multiple times in the data if the CU participates in more 
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than one wave of the survey. This also has the potential to artificially magnify the differences between the 

demographic distributions of CE and the ACS.

The majority of the percentages shown in Table 3 had differences smaller than six percentage points, meaning that 

CE and ACS distributions are very similar on these variables. However, larger differences were found for race and 

rent: there were higher percentages of whites and higher percentages of monthly rents under $500 among Interview 

Survey respondents than among ACS respondents, indicating that the Interview data are probably not MCAR.

In short, the first study found that the data are not MCAR, and this study provided further evidence to substantiate 

that conclusion.
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Table 3. A comparison of selected characteristics of respondents in the CE and the American Community Survey

CE Interview CE Interview

ACS BaseWeighted

NoninterviewWeightedCalibrationWeighted ACS BaseWeightedNoninterviewWeightedCalibrationWeighted

Gender (%) 1,2,3 Number of rooms in dwelling unit (%)1,2,3

 Male 49.0 48.2 48.2 48.2 1 1.0 1.0 1.0 1.1

 Female 51.0 51.8 51.8 51.8 2 3.2 2.0 2.0 2.1

Age (%)1,2 3-4 26.9 22.8 23.0 23.6

 Under age 25 34.5 35.4 34.4 34.8 5-6 40.8 39.5 39.4 39.5

 25-34 13.5 12.4 12.2 13.4 7-8 20.2 24.0 23.9 23.5

 35-44 15.0 14.7 14.6 14.7 9+ 8.0 10.7 10.6 10.3

 45-54 14.6 14.3 14.5 14.5 Owner occupied housing value (%)1,2,3

 55-64 10.5 10.7 11.1 10.5 <$50,000 9.1 7.5 7.4 7.6

 65-74 6.4 6.7 7.1 6.3 $50,000 to $99,999 18.1 16.2 16.3 16.5

 75 and over 5.7 5.8 6.1 5.7 $100,000 to $149,999 17.4 16.6 16.7 16.8

Race (%)1,2,3 $150,000 to $199,999 13.7 14.2 14.4 14.4

 White 74.8 82.3 83.3 81.4 $200,000 to $299,999 15.1 17.2 17.0 17.0

 Black 12.5 11.4 10.4 12.5 $300,000 to $499,999 15.4 16.6 16.6 16.5

 Other 12.7 6.3 6.3 6.1 $500,000 to $999,999 9.3 9.6 9.5 9.3

Education attainment* (%) 1,2,3 $1,000,000+ 2.0 2.1 2.1 2.0

Less than high school 15.8 16.3 16.1 16.0 Monthly rent (%)1,2,3

High school graduate 29.6 28.1 28.0 28.0 Less than $500 20.5 38.7 38.5 38.4

Some college/Assoc degree 27.5 28.1 28.1 28.3 500- <750 29.1 29.4 29.4 29.6

College graduate 27.2 27.4 27.7 27.7 750- <1,000 22.0 16.1 16.4 16.4

Household size (%)1,2,3 1,000- <1,500 16.1 9.8 9.9 9.8

 1 person 27.1 28.2 29.3 29.4 1,500+ 6.3 4.2 4.3 4.2

 2 persons 33.3 31.9 32.6 31.9 CU income (%)1,2,3

 3 persons 16.0 15.9 15.2 15.6 <$10,000 8.7 10.3 10.2 10.4

 4+ persons 23.6 24.1 22.9 23.1 $10,000 to $14,999 6.2 7.6 7.6 7.6

Housing tenure (%)1,2 $15,000 to $24,999 12.0 12.6 12.6 12.6

 Owner 66.9 68.0 68.8 67.4 $25,000 to $34,999 11.5 14.3 14.3 14.3

 Renter 33.1 32.0 31.2 32.6 $35,000 to $49,999 15.1 15.1 15.2 15.2

$50,000 to $74,999 18.9 16.4 16.4 16.5

$75,000+ 27.7 23.7 23.7 23.4

1, 2, 3

* respondent age ≥ 25

Indicates a statistically significant difference (p<0.05) was found for the computed Rao-Scott adjusted chi-square statistic for the test of no difference in 

distributions between the ACS and the CE base-weighted, noninterview-weighted, and calibration-weighted, respectively
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3.3 ‘Harder-to-Contact’ Respondents as Proxies for Nonrespondents

The third study uses ‘harder-to-contact’ respondents as proxy nonrespondents. It draws on a theory known as the 

‘continuum of resistance’ to identify appropriate respondents to serve as proxy nonrespondents. This theory 

suggests that sample units can be ordered across a continuum by the amount of interviewer effort exerted in order to 

obtain a completed interview (Groves 2006). This approach is limited by the extent that the proxy nonrespondents 

are like actual nonrespondents. Other studies (Lin and Schaeffer 1995) have suggested that there are significant 

differences between ‘harder-to-contact’ respondents and nonrespondents.

In past research, ‘harder-to-contact’ respondents were classified along the ‘continuum of resistance’ using the total 

number of contact attempts, the length of time the sample unit was in the field, and the last five percent of completed 

interviews (Bates and Creighton 2000; Traugott 1987; Lin and Schaeffer 1995; Ellis, Endo and Armer 1970). The 

weakness with these indicators is in attributing the contact difficulty to the correct party, the interviewer or the 

respondent. For instance, the interviewer’s schedule or personal preferences for when to work on a case may affect 

the length of time the sample unit is in the field. Also, the total number of attempts may include scheduling 

appointments and other preliminary activities prior to actually attempting an interview.

Using data collected in the CHI, we defined respondents to be ‘harder-to-contact’ when over 45 percent of the 

contact attempts resulted in noncontacts. This cut-off for ‘hard to contact’ respondents was selected to yield a 

response rate slightly under 80 percent, which is similar to CE’s actual response rate during the time period covered 

by the data. Also, this measure controls for some of the weaknesses cited above because it standardizes the amount 

of effort exerted by an interviewer to make contact across all sample units.

As an example, consider the contact history of a CU that had 6 contact attempts:

Contact attempt

1. No one home

Classification

Noncontact

2. No one home Noncontact

3. Got answering machine/service Noncontact

4. No one home Noncontact

5. Respondent too busy, appointment set Contact

6. Complete case – ready to transmit Contact
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In this example, 6 contact attempts were made, resulting in 2 contacts and 4 noncontacts, for a 67% noncontact rate. 

Since the noncontact rate is greater than 45%, this particular CU was classified as ‘harder-to-contact.’

In this study respondents and proxy nonrespondents were compared at each wave of the survey on the following 

socio-demographic characteristics: gender, marital status, race/ethnicity, age, educational attainment, household 

tenure, Census Region (Northeast, Midwest, South and West), urbanicity (urban or rural) and CU size. Table 4 

shows these comparisons. For example, in Wave 1 people who were 25-34 years old made up 15.1% of the 

respondents and 19.9% of the proxy nonrespondents. Differences such as this suggest that the data may not be 

MCAR with respect to some socio-demographic characteristics (respondent age, race, educational attainment, 

marital status, CU size, household tenure, and census region).
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Table 4. Demographic characteristics of respondents and proxy nonrespondents (‘harder-to-contact’) in CE by wave

Demographic Characteristic

Wave 1 Wave 2 Wave 5

Respondents Proxies Respondents Proxies Respondents Proxies

Gender (%)

Male 41.7 41.5 40.0 42.0 38.8 38.8

Female 58.3 58.5 60.0 58.0 61.2 61.2

Age (%)1, 2, 5

Under age 25 9.8 8.6 7.1 7.6 7.3 7.2

25-34 15.1 19.9 15.4 19.8 14.2 18.3

35-44 19.3 21.7 20.2 23.1 20.0 22.4

45-54 19.4 21.1 19.7 20.5 20.3 21.6

55-64 15.9 15.1 16.0 15.8 16.4 14.8

65-74 10.5 7.2 11.1 7.5 11.3 8.4

75 and over 10.0 6.3 10.5 5.7 10.4 7.3

Race (%)1

White 83.4 82.6 83.4 83.3 82.6 82.7

Black 11.1 12.6 11.2 11.5 11.4 12.3

Other 5.5 4.8 5.4 5.2 6.0 5.0

Educational Attainment (%)1, 2

Less than high school 15.1 14.1 15.5 13.1 15.3 13.9

High school graduate 25.7 23.9 26.1 25.1 26.1 27.2

Some college or Associate’s degree 32.1 32.0 31.2 33.4 32.0 30.2

College graduate 27.2 30.1 27.2 28.3 26.6 28.7

Marital Status (%)1, 2, 5

Not Married 45.1 52.0 45.6 50.5 45.4 49.4

Married 54.9 48.0 54.4 49.5 54.6 50.6

CU Size (%)1, 2, 5

1 27.5 31.9 27.8 29.9 27.7 29.8

2 32.3 30.6 31.9 31.3 32.2 31.2

3 15.7 16.4 15.1 17.0 15.3 18.3

4+ 24.5 21.1 25.1 21.8 24.8 20.6

Household Tenure (%)2

Owner 68.4 66.3 68.8 64.9 68.4 67.9

Renter 31.6 33.7 31.2 35.1 31.6 32.1

CU Income (%) (imputed)

<$10,000 - - 9.7 8.6 8.5 8.2

$10,000 to $14,999 - - 6.8 5.8 7.8 6.1

$15,000 to $24,999 - - 13.3 11.3 12.4 12.0

$25,000 to $34,999 - - 12.5 13.1 12.0 14.6

$35,000 to $49,999 - - 13.7 14.1 15.5 15.9

$50,000 to $74,999 - - 16.9 19.2 17.4 17.6

$75,000+ - - 27.1 27.9 26.3 25.6

Census Region (%)1, 2, 5

Northeast 18.6 17.1 18.4 20.3 18.5 17.9

Midwest 24.6 24.9 24.5 24.9 23.7 25.2

South 34.1 39.4 34.9 36.4 36.0 39.5

West 22.7 18.6 22.2 18.4 21.8 17.4

Urbanicity (%)

Rural 20.9 21.2 20.9 20.8 23.2 24.3

Urban 79.1 78.8 79.1 79.2 76.8 75.7

1, 2, 5 Indicates a statistically significant difference (p<0.05) between the respondents and nonrespondents 

for the demographic characteristic at the particular interview (1, 2, or 5)
Page 18

In addition to answering the MCAR question, this study also estimated the relative nonresponse bias for total 

expenditures and for 13 expenditure subcategories: alcoholic beverages, apparel and services, cash contributions, 

education, entertainment, food, health care, housing, personal care, personal insurance and pensions, reading, 

tobacco and smoking supplies, and transportation. See Appendix A for a description of the expenditure categories. 

Table 5 shows the relative nonresponse bias estimates with their 95% confidence intervals for Waves 2 and 5.

When ‘harder-to-contact’ respondents were used as proxy nonrespondents, no evidence of nonresponse bias was 

found for total expenditures. The relative nonresponse bias for total expenditures was similar for both waves:

 -0.14% with a 95% confidence interval of (-1.40%, 1.12%) for Wave 2 and -0.10% with a 95% confidence interval 

of (-1.18%, 0.98%) for Wave 5. The nonresponse bias was not significant for most of the subcategories as indicated 

by the inclusion of zero in the confidence interval. The exceptions were reading materials and health expenditures 

in both Waves 2 and 5. Reading materials had a nonresponse bias of 3.82% (0.51%, 7.13%) for Wave 2 and 3.41% 

(0.28%, 6.54%) for Wave 5. The magnitude of nonresponse bias is similar for both waves. The magnitude of the 

bias was similar for both waves for health expenditures: 3.68% (1.80%, 5.56%) for Wave 2 and 3.21% (0.88%, 

5.54%) for Wave 5. It is worth noting that these two categories represent only 0.29% and 6% of total spending, so 

their impact on total nonresponse bias is very small.
Page 19

Table 5: Estimates of relative nonresponse bias in CE expenditure categories using harder to contact respondents as 

proxy nonrespondents

Expenditure Category

Share of total 

expenditures 

(%)*

Harder-to-contact respondents as proxy nonrespondents

Wave 2 Wave 5

Relative 

Bias %

Lower 

95% CI

Upper 

95% CI

Relative 

Bias %

Lower 

95% CI

Upper 

95% CI

Total Expenditures -0.14 -1.40 1.12 -0.10 -1.18 0.98

Alcoholic Beverages 0.81 -0.96 -3.74 1.82 -2.82 -6.18 0.54

Apparel and Services 2.88 0.96 -3.48 5.40 0.37 -1.85 2.59

Cash Contributions 3.90 2.09 -2.60 6.78 0.73 -2.02 3.48

Education 1.90 -3.74 -10.83 3.35 1.86 -6.08 9.80

Entertainment 5.10 0.49 -2.49 3.47 0.40 -2.43 3.23

Food 13.40 0.19 -0.83 1.21 0.76 -0.06 1.58

Health 6.00 3.68 1.80 5.56 3.21 0.88 5.54

Housing 33.13 -0.28 -1.40 0.84 -0.89 -1.94 0.16

Personal Care 0.63 0.32 -2.24 2.88 0.59 -0.91 2.09

Personal Insurance 10.32 -0.51 -2.72 1.70 -0.68 -2.36 1.00

Reading Materials 0.29 3.82 0.51 7.13 3.41 0.28 6.54

Tobacco 0.72 -0.27 -4.07 3.53 -1.51 -3.43 0.41

Transportation 19.12 -1.96 -5.04 1.12 -0.53 -3.59 2.53

* Note that this column does not sum to 100% since some expenditure items are not listed in one of these 

subcategories.

3.4. Pattern of Participation while in Sample

The fourth study is based on the premise that we can learn about nonrespondents by examining panel survey 

respondents who failed to complete the entire series of interviews (Reyes-Morales 2003 and 2007). In this study, 

the proxy nonrespondents are attritors (survey dropouts) and intermittent respondents. They were compared to 

complete respondents on socio-demographic variables and expenditures. 

The study was based on a single cohort of data, CUs that had their first interview in April-June 2005, their second 

interview in July-September 2005, and their last (5th) interview in April-June 2006. The cohort had 3,071 unique 
Page 20

CUs out of which 2,468 were used in the study. 1

The set of 2,468 usable CUs was divided into three groups according to their pattern of participation in Waves 2 

through 5 while they were in the sample:

 Using the common data file described earlier, demographic 

characteristics were imputed for the nonrespondents with a technique similar to the ‘last observation carried 

forward’ (Verbeke and Molenberghs 2000). In this technique, any missing values for a particular CU were imputed 

by copying the values that were recorded for that CU in a previous interview. The technique assumes that the 

demographic characteristics do not change from one wave to the next.

1. Complete respondents: These are CUs that participated in the survey in all contiguous waves of the 

survey period.

2. Attritors: These are CUs that participated in the first wave for which they were eligible, and possibly 

completed the second and third waves (if eligible) but then refused to participate in all subsequent 

waves for which they were eligible. For example, if the pattern of participation was (1, 2, 2, 3) or (1, 

1, 2, 2), then that CU would be classified as an attritor.2

3. Intermittent respondents: These are CUs that participated in at least one wave among all the waves for 

which they were eligible. For example, if the pattern of participation was (3, 2, 1, 2), then that CU 

would be classified as an intermittent respondent.

The set of complete respondents had 1,941 CUs (78.6%), the set of intermittent respondents had 347 CUs (14.1%), 

and the set of attritors had 180 CUs (7.3%).

After accounting for CE’s complex sampling design, the three response groups were compared using the Rao-Scott 

chi-square and Wald statistics on various socio-demographic variables, including household tenure (owner or 

renter), marital status, gender, respondent age, race, Hispanic origin, CU size, educational attainment, region, and 

urbanicity. Statistically significant differences were found between intermittent respondents and complete 

respondents with respect to age and Hispanic origin, while statistically significant differences were found between 

 

1 The 603 CUs excluded from this study were excluded for the following reasons: 332 CUs did not participate in the 

survey or did not provide enough information to count as a completed interview; 231 CUs dropped out of the survey 

after the first interview; and 40 CUs were ineligible for an interview. 2 Here ‘1’ indicates a completed interview, ‘2’ indicates a Type A noninterview, and ‘3’ indicates ineligibility for the 

sample.
Page 21

attritors and complete respondents only with respect to age (Table 6). The difference in sample size between 

attritors and intermittent respondents, may have resulted in a loss of statistical power to detect demographic 

differences. This may be the cause of the lack of statistical difference between attritiors and respondents , rather 

than substantive differences between the groups. 

For relative nonresponse bias computations, we combined attritors and intermittent respondents to form one group 

of proxy nonrespondents and averaged expenditures across Waves 2 through 5 for each expenditure category (Table 

7).

When attritors and intermittent respondents were used as proxy nonrespondents, the relative nonresponse bias for 

total expenditures was not statistically different from zero, -0.54% (-2.31%, 1.24%). However, three expenditure 

subcategories showed some evidence of nonresponse bias: entertainment, 3.46% (0.35%, 6.57%), personal 

insurance, 3.82% (1.72%, 5.93%) and transportation, -5.65% (-9.11%, --2.19%). These subcategories represent 5%, 

10%, and 19%, respectively, of total expenditures and are different from the expenditure subcategories identified in 

the ‘harder-to-contact’ study.
Page 22

Table 6. Demographic characteristics of respondents and proxy nonrespondents (attritors and intermittent respondents) in CE

Proxy nonrespondents Proxy nonrespondents

Demographic Characteristic

Respondents

(n=1,941)

Intermittent

(n=347)

Attritors

(n=180)

Demographic 

Characteristic

Respondents

(n=1,941)

Intermittent

(n=347)

Attritors

(n=180)

Gender (%) CU Size (%)

Male 41.1 46.1 38.9 1 person 30.6 25.6 35.0

Female 58.9 53.9 61.1 2 persons 31.1 30.3 29.4

Age (%)I,A 3 persons 15.1 15.0 16.1

Under age 25 7.8 6.2 9.5 4+ persons 23.2 29.1 19.4

25-34 16.8 17.2 22.9 Household Tenure (%)

35-44 20.8 25.1 24.0 Owned 62.7 64.3 56.1

45-54 19.9 21.9 21.8 Rented 35.4 34.9 41.7

55-64 15.4 17.5 9.5 Other 1.9 0.9 2.2

65-74 10.6 6.5 7.3 CU Income(%) (imputed)

75 and over 8.6 5.6 5.0 <$10,000 9.6 8.4 11.7

Race (%) $10,000 to $14,999 7.4 7.2 4.4

White 81.6 78.7 80.6 $15,000 to $24,999 12.9 10.4 16.1

Black 11.8 14.7 13.3 $25,000 to $34,999 12.3 13.6 10.6

Other 6.6 6.6 6.1 $35,000 to $49,999 15.4 14.2 22.2

Hispanic Origin (%)I $50,000 to $74,999 17.6 18.2 15.0

Hispanic 11.3 17.3 10.6 $75,000+ 24.8 28.0 20.0

Non-Hispanic 88.7 82.7 89.4 Census region

Educational Attainment (%) Northeast 18.6 16.4 22.8

Less than high school 15.3 16.0 15.2 Midwest 23.7 21.3 21.7

High School graduate 26.2 25.6 24.7 South 34.0 38.9 29.4

Some college or 

Associate’s degree 

32.8 31.4 38.2 West 23.7 23.3 26.1

Bachelor’s or higher 25.7 27.0 21.9 Urbanicity (%)

Marital Status (%) Urban 82.5 84.4 86.1

Married 51.1 54.5 45.0 Rural 17.5 15.6 13.9

Not married 48.9 45.5 55.0

‘I’ indicates a statistically significant difference (p<0.05) between complete respondents and intermittent respondents, and ‘A’ between complete 

respondents and attritors.
Page 23

Table 7. Estimates of relative nonresponse bias in CE expenditure categories using intermittent respondents and 

attritors as proxies for nonrespondents

Expenditure Category Share of total 

expenditures 

(%)*

Intermittent respondents and 

attritors as proxy nonrespondents

Across waves

Relative 

Bias %

Lower 

95% CI

Upper 

95% CI

Total Expenditures -0.54 -2.31 1.24

Alcoholic Beverages 0.81 -2.37 -9.09 4.36

Apparel and Services 2.88 -0.01 -5.24 5.21

Cash Contributions 3.90 4.24 -0.98 9.46

Education 1.90 -2.27 -11.81 7.28

Entertainment 5.10 3.46 0.35 6.57

Food 13.40 0.09 -1.35 1.53

Health 6.00 1.03 -2.76 4.82

Housing 33.13 -0.56 -2.26 1.14

Personal Care 0.63 1.95 -0.74 4.65

Personal Insurance 10.32 3.82 1.72 5.93

Reading Materials 0.29 4.22 -1.16 9.60

Tobacco 0.72 2.51 -1.79 6.81

Transportation 19.12 -5.65 -9.11 -2.19

* Note that this column does not sum to 100% since some expenditure items are 

not listed in one of these subcategories.
Page 24

4. Findings Across Studies

4.1. MCAR

Using socio-demographic data from respondents and nonrespondents, all four studies conclude that either the 

respondents are significantly different from the complete sample or that they are significantly different from the 

nonrespondents. In either case, these findings suggest that the data are not MCAR. (See Tables 2, 3, 4, and 6.)

4.2 Characteristics of Nonrespondents

All four studies provide insights into the characteristics of CE nonrespondents. Table 8 synthesizes the results from 

the four studies. In Table 8, a characteristic is shown as being under-represented (or over-represented) if that 

characteristic was under-represented (or over-represented) in at least three of the four studies. For example, race 

was analyzed in three studies (the comparison to external data, and the two proxy nonrespondent studies), and all 

three of them found blacks to be under-represented (see Tables 3, 4, and 6). Therefore, we conclude that blacks are 

more likely than other races to be CE nonrespondents. Likewise, age was analyzed in three studies, and all three of 

them found people in the oldest age group to be over-represented, so we conclude that people over 55 years old are 

more likely than people in younger age groups to be CE respondents.

4.3. Estimates of Relative Nonresponse Bias for Expenditure Categories

Of the four studies, only the two proxy nonrespondent studies estimate nonresponse bias. Both proxy 

nonrespondent studies found no evidence of nonresponse bias in the total expenditures category, and no evidence of 

nonresponse bias in most of the thirteen expenditure subcategories examined. Although both studies found some 

evidence of nonresponse bias in a few of the expenditure subcategories, they did not find it in the same 

subcategories. Since the two studies defined ‘proxy’ nonrespondents differently, some variation in the results was 

expected, but taken together the results suggest that nonresponse bias is probably not a major issue in the CE (see 

Tables 5 and 7).
Page 25

Table 8. Summary of characteristics of respondents across studies

Study

Characteristic

Response Rate 

across subgroups

Comparison to 

External Data

‘Harder-to-contact’

as nonrespondents

Attritors & 

intermittent 

respondents as 

nonrespondents Conclusion

Gender — males underrepresented inconclusive inconclusive inconclusive

Age — 55+ overrepresented

55+ overrepresented65+ overrepresented55+ overrepresented

Race — blacks underrepresented

blacks underrepresentedblacks underrepresentedblacks underrepresented

Educational 

attainment — 

HS graduate, some 

college & higher 

over-represented

HS graduate or less 

over-represented

HS graduate overrepresented inconclusive

Housing tenure renters overrepresented

renters underrepresentedrenters underrepresented inconclusive inconclusive

Marital Status — — not married underrepresented inconclusive inconclusive

Census region Midwest overrepresented — 

Midwest & South 

under-represented; 

West overrepresented

Midwest overrepresented inconclusive

Urbanicity urban underrepresented — inconclusive urban underrepresented inconclusive

CU size — 1 person CU overrepresented

2-person & 4+ 

person CUs overrepresented

2-person CUs overrepresented inconclusive

— indicates characteristic was not studied
Page 26

5. Conclusion

The CE is a federal statistical survey that has a response rate below 80 percent. Each of the four studies was 

designed to analyze nonresponse in CE by answering one or more of the following questions: (1) Are the data in the 

CE MCAR? (2) What are the demographic characteristics of the nonrespondents? and (3) What is the level of 

nonresponse bias in the CE? All four studies shed light on the first two questions, while the two proxy 

nonrespondent studies addressed the third question.

All of the studies found that the data are not MCAR. The study comparing response rates by subgroup addressed the 

MCAR question by looking for differences in response propensities on variables that were available for both 

respondents and nonrespondents. Statistically significant differences were found by region of the country, 

urbanicity, and housing tenure. Likewise, the study comparing respondent demographic characteristics to the 

American Community Survey’s population found statistically significant differences for most of the variables 

examined. The study using ‘harder-to-contact’ respondents as proxy nonrespondents found statistically significant 

differences for age, marital status, CU size, and region of the country for all waves, and statistically significant 

differences for race, educational attainment, and household tenure for some waves. The study using intermittent 

respondents and attritors as proxy nonrespondents found statistically significant differences by age in both groups of

proxy nonrespondents, and for Hispanic origin in the set of ‘intermittent’ proxy nonrespondents. Because 

statistically significant differences were found in each of these studies, we conclude that the data are not MCAR.

All four of these studies addressed the second question by providing insight into the characteristics of CE 

nonrespondents. Any characteristic for which a statistically significant difference was observed suggests that the 

respondent sample over-represents particular subgroups of the survey’s target population while under-representing 

other subgroups. Again, the individual study conclusions vary, but it is evident that blacks are under-represented 

among the respondents (nonrespondents tend to be disproportionately black) while those age 55 and over tend to be 

over-represented (respondents tend to be disproportionately older).

Two of the studies addressed the third question about the level of nonresponse bias in the survey’s estimates. The 

study that used ‘harder-to-contact’ respondents as proxy nonrespondents did not find any nonresponse bias in the 

total expenditures category, since its 95% confidence intervals included the number zero. In addition, the study did 

not find any nonresponse bias in eleven of the thirteen expenditure subcategories examined. The results from the 
Page 27

‘patterns of participation’ study were similar. That study did not find any nonresponse bias in the total expenditures 

category, and it did not find any nonresponse bias in ten of the thirteen expenditure subcategories examined. 

Although both proxy nonrespondent studies identified a few expenditure categories with non-zero levels of 

nonresponse bias, they identified different categories. Since some bias could be expected to occur at random, and 

since the findings across the two proxy studies were not consistent, we conclude that CE expenditure estimates are 

not subject to high levels of nonresponse bias.

No one study, taken alone, is meant to provide a definitive answer to the questions raised in this research. Each 

approach has its own strengths and weaknesses. But taken together, the four studies show that CE estimates do not 

have a significant amount of nonresponse bias, even though the respondents and nonrespondents have different 

characteristics and the data are not MCAR.

The results from these four studies support recent research on nonresponse bias that indicates nonresponse rates are 

a weak predictor of nonresponse bias and only indicate a risk for nonresponse bias. Research indicates that 

everyone has an unobservable propensity to be either a respondent or nonrespondent in a survey (Groves et al. 2006; 

Groves and Peytcheva, 2008). Other sources of bias and measurement error exist in surveys and high response rates 

may not be adequate to prevent them.

These studies provide a counterexample to the commonly held belief that if a survey’s data are not MCAR then its 

estimates are subject to nonresponse bias. From the nonresponse bias equation, the nonresponse bias in a survey 

estimate will be zero if there is complete response, or if the mean expenditure for respondents is equal to the mean 

expenditure for nonrespondents. The key for CE is the latter situation. When the data are not MCAR, as in this 

case, similar expenditure patterns for the respondents and nonrespondents could still be observed if the bias in an 

under-represented group (e.g., blacks) is counterbalanced by the same bias in an over-represented group (e.g., the 

over-55 age group). The absence of meaningful bias in the total expenditures category seems to support this 

argument. In conclusion, nonresponse bias does not appear to be a significant issue for the Consumer Expenditure 

Interview Survey.
Page 28

6. References

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Thousand Oaks, CA: Sage.

Bates, N. and Creighton, K. (2000). The Last Five Percent: What Can We Learn From Difficult/Late Interviews? 

Proceedings of the American Statistical Association, Section on Government Statistics and Section on Social 

Statistics, 120-125.

Brick, J. M., and Bose, J. (2001). Analysis of Potential Nonresponse Bias. Proceedings of the Annual Meetings of 

the American Statistical Association, [CD-ROM].

Census Bureau (2006). Using the Data: Quality Measures. Retrieved April 18, 2007 from Census Bureau Web Site 

www.census.gov/acs/www/acs-php/quality_measures_coverage_2005.php. 

Dixon, J. (2001). Relationship Between Household Nonresponse, Demographics and the Unemployment Rate in the 

Current Population Survey. Proceedings of the American Statistical Association, Section on Survey Methods 

Research, 803-806. 

Dixon, J. (2004). Using Census Match Data to Evaluate Models of Survey Nonresponse. Proceedings of the 

American Statistical Association, Section on Survey Methods Research, 3421 -3425.

Duncan, G.J., and Hill, D.H. (1989). Assessing the Quality of Household Panel Data: The Case of the Panel Study of 

Income Dynamics. Journal of Business & Economic Statistics, 7(4), 441-452.

Ellis, R.A., Endo, C.M. and Armer, J.M. (1970). The Use of Potential Nonrespondents for Studying Nonresponse 

Bias. The Pacific Sociological Review, 13(2), 103-109.

Groves, R.M. (2006). Nonresponse Rates and Nonresponse Bias in Household Surveys. Public Opinion Quarterly, 

70(5), 646-675.

Groves, R.M., Couper, M.P., Presser, S., Singer, E., Tourangeau, R., Acosta, G., and Nelson, L. (2006). Experiments 

in Producing Nonresponse Bias. Public Opinion Quarterly, 70(5), 720-736.

Groves, R.M. and Peytcheva, E. (2008). The Impact of Nonresponse Rates on Nonresponse Bias. Public Opinion 

Quarterly, 72(2), 167-189.

Lin, I.F, and Schaeffer, N. (1995). Using Survey Participants to Estimate the Impact of Nonparticipation. Public 

Opinion Quarterly, 59(2), 236-258.

Little, R. J. A., and Rubin, D. B. (2002), Statistical Analysis with Missing Data, 2nd

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Quarterly Interview Survey. Final Report, Bureau of Labor Statistics, Washington, D.C. .

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Page 30

Appendix A. Expenditure Examples for the Aggregated Expenditure Categories

Aggregated Expenditure 

Group Types of Expenditures

Alcoholic Beverages Alcohol for home consumption plus alcohol at restaurants and bars.

Apparel and Services Clothing, other apparel products, and footwear. Services including repair of shoes, 

watches, and jewelry, alterations, clothing rental, storage, and sewing materials.

Cash Contributions Cash contributions to religious organizations, educational institutions, political 

organizations

Education College, elementary, and high school tuition, books and supplies; recreational classes

Entertainment Toys, games, arts, crafts, and other entertainment

Food Food consumed at home and food consumed away from home (e.g., restaurants, take 

out and delivery, vending machines)

Health Health insurance, physician, dental, and eye care services, hospital costs, prescription 

drugs, medical equipment

Housing Owned and rented dwellings, mortgage interest, property taxes, maintenance, repairs, 

insurance, landscaping, vacation homes, lodging on out-of-town 

trips, utilities

Personal Care Personal care products and services, electric personal care appliances, personal care 

services

Personal Insurance Personal insurance, including life insurance and pensions, social security

Reading Materials Newspaper, magazine, and books

Tobacco Tobacco products and smoking supplies

Transportation Vehicle purchases, gasoline and motor oil, vehicle finance charges, maintenance and 

repairs, vehicle audio equipment, vehicle insurance, rented vehicles, public 

transportation