Using Administrative Records to 

Improve the Nonresponse 

Weighting Procedure in the

Consumer Expenditure Survey: 

Follow Up Analysis

February 27, 2024

Barry Steinberg (U.S. Bureau of Labor Statistics), Stephen Ash

(U.S. Bureau of Labor Statistics), and John Voorheis (U.S. 

Bureau of Census)

Any conclusions expressed herein are those of the authors and do not necessarily 

represent the views of the U.S. Census Bureau. All results were approved for release by 

the U.S. Census Bureau, authorization number CBDRB-FY2022-CES019-004.
2

Using Administrative Records to Improve

the Nonresponse Weighting Procedure in the

Consumer Expenditure Survey: Follow Up Analysis

Barry Steinberg (U.S. Bureau of Labor Statistics), Stephen Ash (U.S. Bureau of 

Labor Statistics), and John Voorheis (U.S. Bureau of Census)

1. Introduction

The Consumer Expenditure Surveys (CE) are the most detailed source of spending data 

collected directly from U.S. consumers. The CE consist of two components: an Interview 

Survey, collected quarterly from a random sample of consumer units (CUs) across the 

U.S.; and a Diary Survey, collected daily for a two-week period from another random 

sample of CUs across the U.S.1 2 The CUs in the samples are representative of all CUs in 

the U.S., and they are assigned weights that add up to the total number of CUs in the U.S. 

Unfortunately, some of the CUs in the sample do not participate in the surveys because 

they are unable to be contacted, refuse to participate, etc. Noninterview adjustments are 

made to the weights of the responding CUs to mitigate the effects of nonresponse.

Both the Interview and the Diary surveys began using IRS’s publicly available ZIP-code 

income data in their noninterview adjustments with 2014 data. In that process, the IRS’s 

average reported income per ZIP-code was assigned to each CU in the sample and used 

as a proxy for the individual household’s (HH) income. In this report, the effect of IRS’s 

publicly available ZIP-code income versus its non-public Title 26 HH income on CE’s 

noninterview adjustments is analyzed.

Although IRS income data is used in both CE surveys, only the Interview Survey’s data 

is analyzed in this report. There are three reasons. First, the Interview Survey is the 

source of most of CE’s expenditures. Over 80 percent of CE’s expenditures come from 

the Interview Survey, which makes its impact on total expenditures greater than that of 

the Diary Survey. Second, the impact of ZIP-code income versus HH income is expected 

to be similar in both surveys, because the sample addresses for both surveys come from 

the same sampling frame. And third, this report is a follow-up study of two other studies, 

and those two other studies examined only the Interview Survey’s data.

1 Technically, the two CE samples are random samples of residential addresses, with every CU at 

the addresses being included in the surveys. Hence, the samples can be thought of as random 

samples of CUs as well, in a two-stage sampling process.

2 Consumer units (CUs) are similar to households (HHs). A HH is a group of related family 

members and all the unrelated people, if any, living together in a housing unit. A person living 

alone, or a group of unrelated people sharing a housing unit, is also considered to be a HH. By 

contrast, a CU is a group of people living together in a housing unit who are related by blood, 

marriage, adoption, or some other legal arrangement; who are unrelated but pool their incomes to 

make joint expenditure decisions; or is a person living alone or sharing a housing unit with other 

people but who is financially independent of the other people. The key difference between CUs 

and HHs is the financial relationship between the people living in the housing unit. The people in 

a CU are financially interdependent on each other, while the financial relationship between the 

people living in a housing unit does not figure into the definition of a HH. Most HHs have only 

one CU, so the terms are often used interchangeably.
3

Administrative records are nonpublic data collected by other government agencies and 

surveys that are available at the U.S. Census Bureau for researchers to use after signing a 

strict confidentiality agreement. With the availability of the Census Bureau’s own 

administrative records, Brummet et al. (2018) suggested that those administrative records 

“have the potential to improve the accuracy and quality of statistics produced from the 

CE.” Steinberg et al. (2020) found that replacing the current ZIP-code income from the 

IRS with the HH Adjusted Gross Income (AGI) also from the IRS had little effect on the 

2014 estimates of mean total expenditures for the Interview Survey.

The research presented in this paper is a follow-up to Steinberg et al 2020 (which used 

2014 data) using 2015 and 2016 CE Interview Survey results. Using the 2015-2016 data

and the same metrics, the results were very comparable to those in Steinberg et al. 2020,

which had little effect on the estimates of the mean total expenditures for the Interview 

Survey.

2. Background and Literature Review

Within the calculation of the Interview Survey sample weights, CE adjusts the weights of 

the respondents to account for the nonrespondents. This noninterview adjustment uses the 

traditional cell adjustment method where all the consumer units in the sample are 

partitioned into 192 cells using variables based on a few demographic characteristics3. The 

weights of the respondents in a cell are then increased to account for the nonrespondents 

by multiplying them by an adjustment factor equal to the inverse of the cell’s weighted 

response rate.

The variables used by a nonresponse adjustment are critical to its effectiveness. Having 

good nonresponse variables can reduce nonresponse bias, which can be large when the 

respondents and nonrespondents have different characteristics. A successful nonresponse 

adjustment depends on having variables that are available for both respondents and 

nonrespondents and, as suggested by Vartivarian and Little (2002a, 2002b), are correlated

with the propensity to respond, the estimate of interest, or both. Finding good variables is

difficult due to limited information on nonrespondents4. 

Prior to 2014, the nonresponse adjustment for the CE Interview Survey used the following 

variables: region of country (Northeast/South/Midwest/West), Consumer Unit size (1, 2, 

3-4, and 5+ persons), housing tenure (owner/renter), race (Black/Non-Black), and rotation 

group (1-4) which identifies the quarter of the year where the CU is first introduced. The 

variables are summarized in Table 1.

3 Steinberg, B. (2016). U.S. Bureau of Labor Statistics requirement “Phase 2 Interview Production 

Requirement – Weighting, 2017 Release, version 1.0,” dated October 22, 2016.

4 While characteristics such as age and education are not available for nonrespondents, others, 

such as region of country are available, as the interviewer has information about the address of the 

nonresponding consumer unit(s) within the CU at that address, but not information about the 

persons residing therein.
4

Table 1: Nonresponse Adjustment Variables Used in the CE Interview Survey

1986 – 2013 2014 – present

1 Region of country Region of country

2 Consumer Unit size Consumer Unit size

3 Housing tenure Number of contact attempts 

4 Race IRS Adjusted Gross Income (AGI) by ZIP-Code

5 Rotation Group 

Sabelhaus et al. (2013) examined CE’s response rates by income. Taken from their paper, 

Figure 1 shows their main result.

CE had approximately 6,300 zip codes in the 2006-2010 sample and each CU in their zip 

code had an IRS publicly available adjusted gross income (AGI) associated with it. Each 

CU with its AGI was sorted from lowest AGI to highest AGI and the percentiles were 

created after sorting. Response rates associated with each of these percentiles were then 

calculated and plotted on Figure 1 and appear as 100 dots. The vertical axis refers to the 

response rate and the horizontal axis the income percentile from 1 to 100.

Figure 1. Response Rates for CE Interview Survey by Income Percentile: 2006-2010. 

From Sabelhaus, Johnson, Ash, Swanson, Garner, and Henderson. (2013). “Is the 

Consumer Expenditure Survey Representative by Income?,” (NBER Working Paper No. 

19589, October 2013).

- In Figure 1, we see that response rates differ by income. HHs in higher income 

ZIP-codes had lower response rates, and HHs in lower income ZIP-codes had 

higher response rates. In general, Sabelhaus et al. (2013) drew three main 

conclusions. First, high-income HHs were under-represented and low-income 

households were over-represented in the CE Survey. Second, high-income HHs 

under-reported both their incomes and their expenditures. Third, CE’s 

noninterview adjustment should have accounted for the different response 

propensities by income, but they did not.
5

As a result of preliminary work eventually published as Sabelhaus et al. (2013), Dumbacher 

et al. (2012) conducted research on the variables used in the noninterview adjustment.

5

Their research suggested keeping some of the old variables but replacing others, including 

income. Table 1 above summarizes the variables before and after the change in 2014.

With the traditional cell adjustment method, all of the variables used in the noninterview 

adjustment need to be categorical. Therefore, continuous variables, like income, must be 

made categorical in some way. For the IRS ZIP-code AGI variable, based on the distinct 

horizontal lines representing response rates in Figure 1 from Sabelhaus et al. (2013), CE 

decided to classify three groups for the IRS Zip-code AGI variable: top 10%, middle 80%, 

and bottom 10%. The three categories are based on the horizontal lines in Figure 1 from 

Sabelhaus et al. (2013). For brevity, we refer to the categorical variable defined by IRS 

AGI by ZIP-code as simply ZIP-code income for the remainder of the paper.

The Census Bureau’s administrative records are a valuable source of information for the 

CE because it has IRS HH-level income. Brummet et al. (2018) examined the use of 

these administrative records and their relationship to response rates. One of their 

conclusions conveys evidence of systematic survey non-response across the income 

distribution by linking responding and non-responding households to administrative 

records by address. Non-responding sample units were much more likely to be richer than 

responding households, measured either by wage and salary income or by broader AGI. 

Furthermore, nonresponse rates were higher at the top of the income distribution, and 

high income non-responding sample units were substantially richer even than high 

income responding sample units. This indicates that very high-income individuals were

systematically less likely to respond to the CE. Given their conclusions, Brummet et al. 

(2018, p. 45) suggested the following:

These results suggest potential ways forward to improve income statistics produced 

from the CE. In particular, both the PIK and MAFID based analysis suggest areas 

where current imputation and weighting procedures may be producing less than 

optimal outcomes. Incorporating the linkage of administrative records into these 

production processes has the potential to improve the accuracy and quality of statistics 

produced from the CE.

Krieger et al. (2019) also compared the set of nonresponse variables from 2013 and prior 

to those for 2014 and forward and showed the newer variables (which included ZIP-code 

income) made some improvement in stratifying the sample HHs by response propensity, 

but they did not noticeably affect estimates of mean total expenditures.

Steinberg et al. (2020) defined a new variable based on IRS HH income from the Census 

Bureau’s administrative records. This new variable was constructed with the same three 

categories that define ZIP-code income but uses IRS HH income in place of ZIP-code 

income. For brevity, we refer to the categorical variable defined by the nonpublic HH-level 

IRS AGI as HH income. Their research examined the effect of replacing ZIP-code income 

with HH income on the noninterview adjustment, on the final weights, and on the estimates 

5

Sabelhaus et al. presented this work in December, 2011 at the CRIW/NBER Conference before 

the work was formally published. The paper, Is the Consumer Expenditure Survey Representative

by Income? was formally presented at the National Bureau of Economic Research in October 

2013.
6

of mean total expenditures for the 2014 Interview Survey. They found that “the increase in 

expenditure estimates were not significantly higher.”

This research is a follow-up to Steinberg et al. (2020) in which a new variable was defined 

based on the IRS HH income from the Census Bureau’s administrative records. This new 

variable was constructed as done in Steinberg et al. (2020). After deriving HH income and 

incorporating it into the noninterview adjustment, a similar analysis as in Steinberg et al. 

(2020), was then conducted which included: 

- 1) How much information is lost by using ZIP-code income rather than HH 

income? This is discussed in section 5.

- 2) How much do the noninterview adjustment factors and the final weights change 

by replacing ZIP-code income with HH income? This is discussed in section 6.;

and 

- 3) How much do the estimates of mean total expenditures change by replacing 

ZIP-code income with HH income? This was our main objective and is discussed 

in section 7.

Lastly, we mention that other authors, including Garner (2009) and Sabelhaus et al. (2013), 

suggest that CE’s estimates of mean total expenditures are under-reported, so results that 

show an overall increase in mean expenditures could be considered an improvement in data 

quality.

3. Data Description

This paper uses CE Interview Survey data collected from February 2015 through December 

2016. (January 2015 was the transition month between data collected under the 2000 and 

2010 sample designs, while the next months described were all collected under the 2010 

design.) The data include both respondents (households that completed interviews) and 

nonrespondents (households that could not be contacted or refused to give interviews). The 

data exclude residential addresses that are not occupied and nonresidential addresses. For 

2015, the data include information from 36,692 eligible cases, which consist of 23,574 

respondents and 13,118 nonrespondents. For 2016, the data include information from 

40,375 eligible cases, which consist of 25,441 respondents and 14,934 nonrespondents. 

CE’s sample of addresses was originally drawn from the Census Bureau’s Master Address 

File (MAF), which is the Census Bureau’s official list of all residential addresses in the 

U.S. Therefore, every address in CE’s sample has a MAF identification number (a MAFID) 

linking it back to the MAF. Then the addresses on the MAF are linked to the IRS’s 

administrative records using the same process that Brummett et al. (2018) used, a process 

that is described in the next section.

4. Data Linkage

As is shown by Brummett et al. (2018; pp. 4-8), record linkage between CE’s data and 

IRS’s data was also done in a two-step process. The first step linked CE’s households to 

the Master Address File by their MAFIDs that linked CE’s households to their addresses. 

Then, the second step linked those addresses to the IRS’s dataset by the addresses on the 

two files. 
7

The process of linking addresses on the two files was accomplished by first running them 

through two different software packages designed to clean up the addresses and put them 

in a standard format that matched addresses on the U.S. Postal Service’s Delivery Sequence 

File. One was a commercially available software package called “SAS® Dataflux,” and the 

other was a software package developed by the Census Bureau’s Geographic Division to 

accomplish the same task of cleaning addresses. Both software packages corrected minor 

misspellings; standardized words and abbreviations like “Road” and “Rd.” or “Street” and 

“St.”; appended the ZIP-code’s 4-digit extension if needed; and so forth. Then after the 

addresses on the two files were cleaned up and put in the standard format of the U.S. Postal

Service’s Delivery Sequence File, the two files were merged. The resulting data showed 

nearly 70 percent of CE households were successfully linked to at least one tax return with 

an adjusted gross income (AGI).

After linking the data, the software addressed other problems to ready the data for use. This 

included fixing problems like duplicate tax years for an address and other data anomalies, 

before deeming it to be ready for use. For example, sometimes an address had more than 

one tax return. When that happened, the AGIs from all the tax returns were summed 

together (up to a maximum of six) to represent the address’s total AGI. Addresses with 

more than six tax returns were treated as non-linkages since they were suspected of being 

apartment buildings or addresses with data linkage problems, and not individual 

households.

Of course, a linkage rate of 70 percent means 30 percent of the households still needed to 

have an income value imputed for them. The imputation was done by assigning them the 

average AGI for their ZIP-code using IRS’s publicly available data, as is done in CE’s 

production weighting process.

5. Comparing ZIP-Code Income with HH Income

The first objective of the research was to determine how much information is lost by using 

ZIP-code income rather than HH income. Tables 2 and 3 compare the household counts by 

the three income groups of HH and ZIP-code income for 2015 and 2016, respectively.

Table 2. Comparison of 2015 Household and ZIP-Code Income

Household Income Total Bottom 10% Middle 80% Top 10%

ZIP-Code

Income

Bottom 10% 1,450 2,250 40 3,740

Middle 80% 2,080 25,300 1,900 29,280

Top 10% 150 1,840 1,750 3,740

Total 3,680 29,390 3,690 36,760

Table 3. Comparison of 2016 Household and ZIP-Code Income

Household Income Total Bottom 10% Middle 80% Top 10%

ZIP-Code

Income

Bottom 10% 1,550 2,480 40 4,070

Middle 80% 2,300 27,850 2,200 32,350

Top 10% 200 2,130 1,700 4,030

Total 4,050 32,460 3,940 40,450
8

Tables 2 and 3 show the ZIP-code income and the HH income agreed for 77.5 percent (= 

(1,450 + 25,300 + 1,750)/36,760) of the HHs in 2015. Similarly, they agreed for 76.9

percent (= (1,550 + 27,850 + 1,700)/40,450) of the HHs in 2016. We suggest that this is a 

fairly high percentage of agreement, which means that using ZIP-code income does not 

result in the loss of too much information, since over 75 percent of the sample units would 

have been put into the same nonresponse cell using either HH or ZIP-code income.

In line with standard procedure, the counts in Tables 2 and 3 are rounded in accordance 

with Census Bureau Disclosure Review Board guidelines.

6 Because of rounding, for 2015 

data, there are 36,760 households in this table as opposed to 36,692 stated earlier, and for 

2016 data, there are 40,450 households in this table as opposed to 40,375 stated earlier.

6. Impact on Weights

The second objective of the research was to determine how much the nonresponse 

adjustment factors and final weights would change at the CU level. If the nonresponse 

factors and final weights do not change from the production values, then we would expect 

the estimates of expenditures not to change substantially either.

After replacing the ZIP-code income with the HH income, the effect on the nonresponse 

adjustment factors and the final weights was substantial. In 2015, the average 

nonresponse adjustment factor was 1.57, which corresponded to a response rate of 63.7 

percent (1.57=1/0.637). After replacing the ZIP-code income with the HH income, the 

average nonresponse adjustment factor remained at 1.57. However, the values for the 

individual CUs in the sample changed substantially. Half of their values increased, and 

half of their values decreased, with the average absolute change in their values being 

0.13. Any change over 5 percent of the average nonresponse adjustment factor (i.e., 

anything over 0.08=1.57x0.05) was considered substantial by the authors.

Likewise, in 2016, the average nonresponse adjustment factor was 1.59, which 

corresponded to a response rate of 62.9 percent (1.59=1/0.629). Again, after replacing the 

ZIP-code income with the HH income, the average nonresponse adjustment factor 

remained at 1.59. However, the values for the individual CUs in the sample changed 

substantially. Half of their values increased, and half of their values decreased, with the 

average absolute change in their values being 0.13. Again, that change was considered 

substantial by the authors.

CE’s “final weights” are defined as the survey’s base weights, multiplied by a nonresponse 

adjustment factor, and then a calibration adjustment factor. That is, CE "final weights" 

(FW) are defined as: FW = BW x NAF x CAF, where BW are the survey base weights; 

NAF is a nonadjustment factor; and CAF is a calibration adjustment factor. Calibration 

adjustment factors are applied to make the estimates from CE consistent with population 

totals for certain demographic estimates as estimated from the Current Population Survey.

For our research, the final weights were computed two different ways. The first approach 

used the base weights provided by the Census Bureau and the nonresponse adjustment 

factors that were used by BLS in production (those based on ZIP-code level income 

categories). The second approach used the base weights provided by the Census Bureau 

6 The Census Bureau Disclosure Review Board (DRB) guidelines were not provided but they have 

a system of rounding guidelines that performed these calculations.
9

and the nonresponse adjustment factors that were customized for our research (those based 

on HH level income categories). Then both sets of nonresponse-adjusted base weights were 

sent through the same calibration algorithm described above to produce two sets of final 

weights.

After producing the final weights with the HH income, the average final weight in 2015

was 19,955. That means each respondent household in the survey represented 19,955

households in the U.S. population, itself plus 19,954 other households that were not 

selected for the survey or were selected for the survey but did not participate in it. The

average absolute value of the change in the final weights, between those produced with 

ZIP-code level income categories and those produced with HH level income categories,

was 3,181, which is 15.9 percent of the average final weight. It was more than a difference 

of simple rounding. For 2016, the average final weight was 20,354. The absolute value of 

the change was 1,634, which is 8.0 percent of the average final weight. These values were

less than those in 2015, but more than simple rounding.

7. Impact on Estimates of Expenditures

The third and most important objective of our research was to examine the effect of IRS 

HH-level data on expenditure estimates. In general, the expenditure estimates using IRS 

ZIP-code income and IRS HH income were not much different. For example, as Table 4 

below shows, for the “All Items” expenditure category the Interview Survey estimates were 

only 0.48 percent larger for 2015 and 0.19 percent larger for 2016. Table 4 also compares

some of the larger summary variables for the 2015 and 2016 estimates of mean 

expenditures derived from ZIP-code income and HH income.

Table 4. Comparison of 2015 and 2016 National Estimates of Mean Expenditures

Year Expenditure 

Category

The Nonresponse Adjustment Used...

Difference

Percentage 

Difference

(%)

ZIP-Code Income HH Income

Estimate Standard 

Error Estimate Standard Error

2015

All items $52,560 543 $52,810 $549 $250 0.48

Housing 17,360 194 17,410 199 50 0.29

Transportation 9,332 211 9,327 214 -5 -0.05

Food 7,851 48 7,845 47 -6 -0.08

Insurance 5,590 116 5,631 116 41 0.73

Health 4,124 54 4,132 52 8 0.19

Apparel 1,183 33 1,228 49 45 3.80

Education 1,153 78 1,209 84 56 4.86

Income before taxes 57,630 912 57,880 885 250 0.43

2016

All items 53,150 587 53,250 599 100 0.19

Housing 17,640 167 17,660 167 20 0.11

Transportation 8,802 196 8,834 198 32 0.36

Food 7,969 55 7,972 55 3 0.04

Insurance 5,974 156 5,984 157 10 0.17

Health 4,367 76 4,358 75 -9 -0.21

Apparel 1,239 41 1,246 45 7 0.56

Education 1,099 67 1,104 68 5 0.45

Income before taxes 62,780 1,547 62,810 1,547 30 0.05

We first note that the differences for the estimated mean expenditures are small. The 

differences are all less than one percent of the expenditure estimates that were computed 
10

with ZIP-code incomes. For example, for the “All Items” category, the percentage

differences were 0.48 percent and 0.19 percent for 2015 and 2016, respectively. The 

percentage differences for the other expenditure categories are also small. They are all less 

than 1%, and most of them are less than 0.50 percent. However, there were a couple of 

smaller summary variables that showed slightly higher changes in expenditures, such as 

“Apparel” and “Education” in 2015. It showed an increase of 3.80 percent and 4.86 percent

respectively due to using IRS HH income.

Additionally, the differences between the two methods were all smaller than the standard 

errors of the estimates. Because the samples are not independent, it is not appropriate to 

conduct statistical tests of the differences between their means. However, we suggest that 

appreciable differences should be larger than the standard errors, and therefore we consider 

the differences in Table 4 to be small.

Lastly, the estimate of the mean expenditure for the “All Items” category increased slightly 

for both 2015 and 2016 when weighting was changed from ZIP-code income to HH 

income. However, we suggest this increase was unimportant and negligible because it was 

small relative to the size of the estimate of expenditure. The mean expenditure increased 

slightly for a few other categories as well, but again we suggest those increases were 

unimportant and negligible because they were also small relative to the size of their 

estimates of expenditures.

At the regional level, there were similar patterns for the average annual expenditures for 

the “All Items” category that were seen at the national level. As shown in Table 5, all four 

regions saw a small increase or decrease, but the differences were all less than 1%.

Table 5. Comparison of 2015 and 2016 Estimates of Mean Expenditures

for the All Items Category by Census Region

Census 

Region

The Nonresponse Adjustment Used...

Difference

Percentage 

Difference

(%)

Year ZIP-Code Income HH Income

Estimate Standard 

Error Estimate Standard Error

2015 Northeast $56,940 1,508 $57,440 1,595 $500 0.88

Midwest 50,810 953 51,130 966 320 0.63

South 49,080 894 49,300 908 220 0.45

West 56,680 1,360 56,670 1,282 -10 -0.02

2016 Northeast 56,190 1,118 56,480 1,235 290 0.52

Midwest 50,590 917 50,700 893 110 0.22

South 49,070 1,384 49,190 1,399 120 0.24

West 60,350 791 60,240 813 -110 -0.18

8. Conclusions

Overall, HH income did not make an appreciable change to the estimates of total 

expenditures compared to ZIP-code income. It made an appreciable change to the 

nonresponse adjustment factors and the final weights, but not to the estimates of total 

expenditures. Comparing the results between ZIP-code income and HH income, the 

nonresponse adjustment factors changed on average 8.3% in 2015, and 8.0% in 2016; and 

the final weights changed on average 15.9% in 2015, and 8.0% in 2016. However, the 

estimates of total expenditure changed only 0.48% in 2015, and only 0.19% in 2016. These 

results are consistent with Steinberg et al. (2020), who found that in 2014 the nonresponse 
11

adjustment factors changed on average 8.67%, the final weights changed on average 

8.60%, but the estimate of total expenditure changed only 0.24%. These results show there 

was an appreciable change to both the nonresponse adjustment factors and the final 

weights, but not to the estimates of total expenditure.

Also consistent with the research of Steinberg et. al. (2020) was the agreement rate being

greater than 70 percent in weighting group classifications between HH income and ZIPcode income for all three years (70.6 percent for 2014, 77.5 percent for 2015, and 76.9

percent for 2016). These high agreement rates exhibit uniformity between the two sources

but are still a considerable amount below 100 percent since there is heterogeneity in 

household incomes within most zip-codes. Even though there was a high agreement rate, 

it was still enough to change the nonresponse factors and final weights substantially as 

mentioned above.

Our conclusions are consistent with Sabelhaus et al. (2013) and Brummet et al. (2018) who 

found an association between HH income and response. We agree that there is an

association between HH income and response, but the relationship does not appear to be 

as strong as originally thought.

References

Brummet, Q., Flanagan-Doyle, D., Mitchel, J., Voorheis, J., Erhard, L., and McBride, B. 

(2018). Investigating the Use of Administrative Records in the Consumer Expenditures 

Survey, CARRA Working Paper 2018-01.

https://www.census.gov/content/dam/Census/library/working-papers/2018/adrm/carrawp-2018.pdf

Dumbacher, B., Swanson, D., Reyes-Morales, S., Steinberg, B., Ash, S., and Castelo, D. 

(2012). U.S. Census Bureau memorandum entitled “Consumer Expenditure Surveys 2010 

Redesign Research: Noninterview and Calibration Weighting Adjustments for the 

Consumer Expenditure Surveys,” from Ruth Ann Killion to Jay Ryan, dated September 26, 

2012.

Garner, T.I., McClelland, R., and Passero, W. (2009). Strengths and Weaknesses of the 

Consumer Expenditure Survey from a BLS Perspective. Presentation at the National 

Bureau of Economic Research, Summer Institute, Conference on Research on Income and 

Wealth. Bureau of Labor Statistics, U.S. Department of Labor. Washington, DC.

https://www.bls.gov/cex/research_papers/pdf/strengths-and-weaknesses-of-theconsumer-expenditure-survey-from-a-bls-perspective.pdf

Krieger. S., McBride, B., and Wilson, T. (2019) Bureau of Labor Statistics memorandum 

entitled “Analysis Item #2 – Measuring the Effectiveness of the New Variables Used in 

Nonresponse Weighting,” from Sharon L. Krieger, Brett McBride, and Taylor J. Wilson to 

Laura Erhard, dated September 23, 2019.
12

Sabelhaus J., Johnson, D., Ash, S., Swanson, D., Garner, T., and Henderson, S. (2013). “Is 

the Consumer Expenditure Survey Representative by Income?” (NBER Working Paper No. 

19589, October 2013).

https://www.nber.org/system/files/working_papers/w19589/w19589.pdf

Steinberg, B., Voorheis, J., Reyes-Morales, S., and McBride, B. (2014). “Using 

Administrative Data to Improve Nonresponse Weighting Procedures in the Consumer 

Expenditure Survey,” JSM, Section on Government Statistics.

https://www.bls.gov/osmr/research-papers/2020/st200050.htm

Vartivarian, S. and Little, R.J.A. (2002a). On the formation of weighting adjustment cells 

for unit nonresponse. Proceedings of the Survey Research Methods Section, American 

Statistical Association.

http://www.asasrms.org/Proceedings/y2002/Files/JSM2002-000178.pdf

Vartivarian, S. and Little, R.J.A. (2002b). Does Weighting for Nonresponse Increase the 

Variance of Survey Means? Survey Methodology, 31, 161-168.

https://www150.statcan.gc.ca/n1/pub/12-001-x/2005002/article/9046-eng.pdf