The 2019 CE Data Quality Profile 

September 9, 2020

Clayton Knappenberger, Yezzi Lee, Lucilla Tan 

Consumer Expenditure Surveys Program Report Series 
Contents

Overview............................................................................................................................................................................................1

Highlights..........................................................................................................................................Error! Bookmark not defined.

1. Final disposition rates of eligible sample units (Diary and Interview Surveys)........................................................................3

2. Records Use (Interview Survey)...................................................................................................................................................8

3. Information book use (Diary and Interview Surveys)..............................................................................................................10

4. Expenditure edit rates (Diary and Interview Surveys).............................................................................................................14

5. Income imputation rates (Diary and Interview Surveys).........................................................................................................18

6. Respondent burden (Interview Survey)....................................................................................................................................22

7. Survey mode (Interview Survey)................................................................................................................................................24

8. Survey Time (Diary and Interview Surveys)..............................................................................................................................26

Conclusion.......................................................................................................................................................................................29
Consumer Expenditure Surveys - 2019 Data Quality Profile | 1

Overview 

In keeping with Statistical Policy Directory No. 1, covering the Fundamental Responsibilities of Federal Statistical Agencies, 

the Consumer Expenditure Surveys Program (CE) is committed to producing data that are of consistently high statistical 

quality, i.e., accurate, objective, relevant, timely, and accessible. CE provides data users with a variety of metrics to assist 

them in evaluating overall data quality. Official tables provide standard errors, BLS provides response rates for all its 

household surveys (including CE), the program publishes data comparisons with other household survey estimates as well 

as the results of nonresponse bias studies, and the datasets contained in the public-use microdata provide variables and 

flags necessary for users to create their own quality measures. 

The Data Quality Profile (DQP) provides a comprehensive set of metrics that are timely, routinely updated, and 

accessible to users. For data users, the DQP metrics are an indication of quality, and cover both the CE Quarterly Interview 

Survey (CEQ) and the CE Diary Survey (CED). For internal stakeholders, they can signal areasfor survey improvements. 

Since the quality of survey estimates is affected by errors that can occurthroughoutthe survey lifecycle, we expectthat 

the set of DQP metrics will evolve over time as the CE continually researches methods to monitor and improve data 

quality. For each metric, a brief description is provided along with the results, which are tabulated and graphed. The DQP 

Reference Guide (Knappenberger, Lee, and Tan, 2020) provides detailed descriptions of the metrics, computations, and 

methodology.

The metrics are reported in quarterly format, where the quarter is the three-month period in which the survey 

data were collected. For example, “2019q1” refers to all surveys collected in the months of January, February, and March 

of 2019. Where annual rates are used to describe metric trends in this report, the annual rate was computed as the 

weighted average of quarterly rates from the same calendar year. 

Note: All the data in this report were collected in 2019, prior to the suspension of in-person interviews in 2020 

due to the COVID-19 virus, also known as novel coronavirus.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 2

Highlights

In this section, we highlightsome of themetric trends from 2017 to the 2019 collection period. Subsequent 

sections describe the individual metrics with detailed data tables.

Trends that are encouraging

 Declining rates of model-based imputation drove down income imputation rates for both CED and CEQ

(Section 5).

 The increase in expenditure allocation rates in the CEQ beginning in 2017 is offset by an equal decline in 

the expenditure imputation rates (Section 4).1 

 Overall expenditure edit rates declined from 2017 to 2019 due to declines in allocation rates in both CED 

and CEQ, and to declines in imputation rates in CEQ (Section 4).

Trends that cause concern

 CED and CEQ response rates continued to decline (Section 1). This is largely attributable to the continuing 

rise in refusal rates for both surveys. 

 Perceived burden (Section 6) increased for the CEQ (Section 8). 

 Information Book usage declined for both CED and CEQ. A significant portion of CEQ cases report not 

having access to an Information Book at all(Section 3).

2

 

1 This simultaneous increase in the allocation rate and decrease in the imputation rate resulted from a change to 

how CE processes cable, internet, and telephone bills. Since these services are often lumped into a single plan, CE 

asks respondents to break the total bill downinto cable, internet, and telephone costs. If a respondent was unable 

to do this, CE previously used hot-deck imputation to replace the reported total bill along with the component 

costs. This was done to ensure that the component costs added up to equal the total bill, but the process 

needlessly discarded reported data. CE now preserves the total bill amount by allocating it to the different 

components using proportions calculated from complete reports. 

2 CE provides respondents with an information book to assist them while participating in one of the surveys. Both 

the CED and CEQ Information Books provide response options for demographic questions and the income bracket 

response options. The CEQ information book also provides examples of expenditures for each section.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 3

1. Final disposition rates of eligible sample units (Diary and Interview Surveys)

Final disposition rates of eligible sample units reportthe final outcome of field staff’s survey participation 

recruitment effort. The CE classifies the final outcome of eligible sample units into four main categories: completed 

interview, nonresponse due to refusal, nonresponse due to noncontact, and nonresponsedue to otherreasons. 

Completed interviews reclassified to a nonresponse by CE staff are included within the other nonresponse category 

and presented in the nonresponse reclassification tables (Table 1.2 and 1.4). More information on the nonresponse reclassification edit, along with information on how we calculate response rates can be found in the DQP 

Reference Guide(Knappenberger, Lee, and Tan, 2020). 

Low response rates, examined with other indicators, may indicatenon-response bias of an expenditure 

estimate if the nonresponse is correlated with that expenditure category. In addition, higher response rates are 

preferred for more precise estimates. We present unweighted response ratesin this report.

CED 

 Response rates droppedfrom an annual rate of 58 percentin 2017 to an annual rate of 53 percent in 

2019. 

 The largest annual drop in response rates was in 2013andis attributed to the shutdown of the federal 

government.

 Refusal rates increasedby about 3.7 percentage pointsfrom an annual rate of 24.2 percent in 2017 to an 

annual rateof almost 28 percent in 2019.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 4

Table 1.1 CED: distribution of final dispositions for eligible CUs (unweighted)

Row percentage

Quarter Number of

eligible CUs

Interview Refusal Noncontact Other 

Nonresponse

2017q1 4,972 57.1 24.8 6.0 12.0

2017q2 5,054 59.4 23.3 5.5 11.8

2017q3 4,916 59.1 23.3 5.1 12.5

2017q4 5,168 56.3 25.3 6.8 11.6

2018q1 5,032 55.5 25.0 6.9 12.7

2018q2 5,015 55.5 25.9 6.4 12.2

2018q3 5,014 57.8 24.8 6.2 11.2

2018q4 5,072 51.5 27.9 7.3 13.3

2019q1 4,926 54.2 28.5 4.9 12.4

2019q2 5,082 53.4 27.2 6.1 13.2

2019q3 5,020 54.7 25.8 6.1 13.4

2019q4 5,216 48.9 29.9 7.6 13.5
Consumer Expenditure Surveys - 2019 Data Quality Profile | 5

Table 1.2 CED: prevalence of nonresponse reclassifications (unweighted)

Nonresponse reclassifications

Quarter Number of

eligible CUs

Number of 

other 

nonresponse

Number of

CUs

Other

nonresponse (%)

Eligible 

CUs (%)

2017q1 4,972 596 225 37.8 4.5

2017q2 5,054 595 250 42.0 4.9

2017q3 4,916 615 283 46.0 5.8

2017q4 5,168 601 227 37.8 4.4

2018q1 5,032 637 227 35.6 4.5

2018q2 5,015 613 241 39.3 4.8

2018q3 5,014 564 247 43.8 4.9

2018q4 5,072 677 205 30.3 4.0

2019q1 4,926 613 232 37.8 4.7

2019q2 5,082 673 243 36.1 4.8

2019q3 5,020 673 229 34.0 4.6

2019q4 5,216 706 188 26.6 3.6

CEQ 

 Response rates dropped from an annual rate of 61 percent in 2017 to an annual rate of 54 percentin 

2019.

 The dip in response rates in 2013 is attributed to the shutdown of the federal government.

 Refusal rates increasedby about 7 percentage pointsfrom an annual rate of 29 percent in 2017 to an 

annual rateof almost 36percent in 2019.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 6

Table 1.3 CEQ: distribution of final dispositions for eligible CUs (unweighted) 

Row percentage

Quarter Number of 

eligible CUs

Interview Refusal Noncontact Other 

nonresponse

2017q1 10,113 61.4 28.7 5.3 4.6

2017q2 9,988 61.8 28.0 5.5 4.6

2017q3 9,954 61.2 28.9 5.1 4.9

2017q4 10,138 59.2 30.7 5.7 4.4

2018q1 10,077 58.7 31.1 5.7 4.5

2018q2 10,075 58.6 31.1 5.5 4.8

2018q3 10,053 57.4 32.6 5.5 4.5

2018q4 10,161 54.8 34.7 5.5 5.0

2019q1 10,108 55.6 34.3 4.8 5.2

2019q2 10,075 54.5 35.5 5.0 5.0

2019q3 10,036 53.2 36.5 5.6 4.8

2019q4 10,170 51.6 36.8 6.1 5.5
Consumer Expenditure Surveys - 2019 Data Quality Profile | 7

Table 1.4 CEQ: prevalence of nonresponse reclassifications (official tables, unweighted) 

Nonresponse reclassifications

Quarter Number of

eligible CUs

Number of 

other nonresponse

Number of 

CUs

Other 

nonresponse (%)

Eligible 

CUs (%)

2017q1 10,113 467 1 0.2 0.01

2017q2 9,988 462 21 4.6 0.21

2017q3 9,954 487 21 4.3 0.21

2017q4 10,138 445 15 3.4 0.15

2018q1 10,077 454 1 0.2 0.01

2018q2 10,075 486 1 0.2 0.01

2018q3 10,053 450 8 1.8 0.08

2018q4 10,161 504 5 1.0 0.05

2019q1 10,108 528 8 1.5 0.08

2019q2 10,075 499 2 0.4 0.02

2019q3 10,037 477 8 1.7 0.08

2019q4 10,170 563 13 2.3 0.13

 
Consumer Expenditure Surveys - 2019 Data Quality Profile | 8

2. Records Use (Interview Survey)

This metric measures the proportion of respondents who used records whileanswering the CEQ survey questions.

Examples of records include but are not limited to receipts, bills, checkbooks, and bank statements. Records use is

retrospectively recorded by the interviewer at the end of the interview. Past research has shown that respondents

who use expenditure records reported more items with lower missingness (Abdirizak, Erhard, Lee, and McBride, 

2017),so a higher prevalence of records use is desirable.

CEQ 

 Records usage temporarily rose in 2016for Wave 1 respondents, and this is likely a result of a field test 

conducted during this period that gave a subset of respondentsmonetary incentivesto use records. 

 From 2017 to 2019, records use has been stable across interview waves.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 9

Table 2.1 CEQ: prevalence of records use among respondents

Row percentage

Quarter Wave Number of

eligible CUs

Used Did not use Missing

response

2017q1 Wave 1 1,557 51.3 47.4 1.3

2017q1 Waves 2 & 3 3,078 55.1 44.3 0.6

2017q1 Wave 4 1,573 50.3 49.0 0.7

2017q2 Wave 1 1,573 52.7 46.5 0.8

2017q2 Waves 2 & 3 3,003 50.9 48.6 0.5

2017q2 Wave 4 1,601 56.2 43.3 0.5

2017q3 Wave 1 1,581 49.1 50.1 0.8

2017q3 Waves 2 & 3 2,933 45.8 53.6 0.6

2017q3 Wave 4 1,576 53.2 46.0 0.8

2017q4 Wave 1 1,592 48.2 50.5 1.3

2017q4 Waves 2 & 3 2,935 49.2 50.3 0.5

2017q4 Wave 4 1,477 49.2 50.1 0.7

2018q1 Wave 1 1,501 53.7 45.2 1.1

2018q1 Waves 2 & 3 2,951 49.5 50.0 0.5

2018q1 Wave 4 1,464 52.7 46.4 0.9

2018q2 Wave 1 1,529 50.2 48.7 1.1

2018q2 Waves 2 & 3 2,884 47.4 52.0 0.6

2018q2 Wave 4 1,486 50.1 49.4 0.5

2018q3 Wave 1 1,494 50.3 48.9 0.9

2018q3 Waves 2 & 3 2,815 48.8 50.9 0.4

2018q3 Wave 4 1,464 48.9 50.2 0.9

2018q4 Wave 1 1,399 53.3 45.7 0.9

2018q4 Waves 2 & 3 2,782 48.7 50.8 0.4

2018q4 Wave 4 1,390 51.5 47.4 1.1

2019q1 Wave 1 1,465 55.2 43.8 1.0

2019q1 Waves 2 & 3 2,730 51.1 48.4 0.5

2019q1 Wave 4 1,428 52.7 46.9 0.4

2019q2 Wave 1 1,443 51.6 47.6 0.8

2019q2 Waves 2 & 3 2,653 51.7 47.9 0.4

2019q2 Wave 4 1,397 53.6 45.5 0.9

2019q3 Wave 1 1,401 50.1 48.7 1.2

2019q3 Waves 2 & 3 2,651 49.0 50.2 0.8

2019q3 Wave 4 1,285 51.3 48.1 0.6

2019q4 Wave 1 1,318 53.0 46.2 0.8

2019q4 Waves 2 & 3 2,637 48.8 51.0 0.2

2019q4 Wave 4 1,293 53.1 46.3 0.5
Consumer Expenditure Surveys - 2019 Data Quality Profile | 10

3. Information Book use (Diary and Interview Surveys)

The Information Book is a recall aide the interviewer can provide the respondent. There are separate Information 

Books for each survey, and eachprovides the response options for demographic questions and the income bracket 

response options. In addition, the CEQ Information Book provides examples that can clarify the kinds of 

expenditures that each section/item code is intended to collect. The Information Book usemetric measures the 

prevalence of Information Book use among respondents during their interviews. For interviews conducted over the 

phone, the Information Book is typically not directly available to the respondent (although examples from it may 

be read over the phone), so this metric should be interpreted in conjunction with the rising prevalence of 

telephone interviews described in Section 7. At the end of the interview, the interviewer is asked how often the 

respondent used the Information Book. Using the Information Book canimprove reporting quality by clarifying 

concepts with concrete examples, and help recall. Therefore, higher rates of Information Book usage are preferred.

CED 

 The prevalence of Information Book use among CED respondents has declined over 3 percentage points 

from an annual rateof about 43 percent in 2017to about 40 percent in 2019.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 11

CEQ 

 Information Book usein Wave 1 declined from an annual rateof about 51.5 percentin 2017 to 47.5

percent in 2019. 

 The rate of Wave 1 respondents who did not have access to the Information Book increased from an 

annual rateof 31.2percentin 2017to 34.6 percent in 2019. 

 In subsequent waves, the rate of Information Book use during interviews were at least 10 percentage 

points lower than in Wave 1, and about half of respondents did not have access to the Information 

Booklet.

Table 3.1 CED: prevalence of Information Book use among respondents

Row percentage

Quarter Number of 

eligible CUs

Used Did not use Missing

response

2017q1 2,841 45.1 50.8 4.1

2017q2 3,003 43.9 52.7 3.4

2017q3 2,904 43.6 53.1 3.2

2017q4 2,910 39.7 57.3 3.0

2018q1 2,791 42.0 54.3 3.8

2018q2 2,781 37.7 59.2 3.1

2018q3 2,896 39.5 56.5 4.0

2018q4 2,611 38.3 58.6 3.1

2019q1 2,671 42.0 54.9 3.1

2019q2 2,713 40.6 56.3 3.1

2019q3 2,745 39.2 58.1 2.7

2019q4 2,553 37.1 59.6 3.3
Consumer Expenditure Surveys - 2019 Data Quality Profile | 12
Consumer Expenditure Surveys - 2019 Data Quality Profile | 13

Table 3.2 Prevalence of Infobook use among CEQ respondents

Row percentage

Quarter Wave Number of 

eligible CUs

Used Did not use No Infobook 

available

Missing 

response

2017q1 Wave 1 1,557 49.6 15.4 33.7 1.3

2017q1 Wave 2 &3 3,078 39.6 12.7 47.1 0.6

2017q1 Wave 4 1,573 33.8 14.8 50.7 0.7

2017q2 Wave 1 1,573 53.8 17.0 28.4 0.8

2017q2 Wave 2 &3 3,003 38.9 15.4 45.2 0.5

2017q2 Wave 4 1,601 39.0 13.4 47.1 0.5

2017q3 Wave 1 1,581 52.6 16.6 30.0 0.8

2017q3 Wave 2 &3 2,933 38.3 16.2 44.9 0.6

2017q3 Wave 4 1,576 38.5 13.9 46.8 0.8

2017q4 Wave 1 1,592 50.1 15.9 32.8 1.3

2017q4 Wave 2 &3 2,935 37.1 15.4 47.0 0.5

2017q4 Wave 4 1,477 35.2 14.8 49.3 0.7

2018q1 Wave 1 1,501 50.2 16.5 32.2 1.1

2018q1 Wave 2 &3 2,951 37.2 14.5 47.7 0.5

2018q1 Wave 4 1,464 34.4 13.9 50.9 0.9

2018q2 Wave 1 1,529 47.5 17.7 33.6 1.1

2018q2 Wave 2 &3 2,884 36.4 16.3 46.7 0.6

2018q2 Wave 4 1,486 34.5 16.8 48.1 0.5

2018q3 Wave 1 1,494 48.1 20.6 30.5 0.9

2018q3 Wave 2 &3 2,815 36.8 15.9 47.0 0.4

2018q3 Wave 4 1,464 33.9 14.9 50.3 0.9

2018q4 Wave 1 1,399 49.0 17.3 32.8 0.9

2018q4 Wave 2 &3 2,782 35.6 15.9 48.1 0.4

2018q4 Wave 4 1,390 32.4 16.7 49.9 1.1

2019q1 Wave 1 1,465 46.3 15.8 36.9 1.0

2019q1 Wave 2 &3 2,730 36.2 14.0 49.3 0.5

2019q1 Wave 4 1,428 32.8 14.6 52.2 0.4

2019q2 Wave 1 1,443 49.5 17.3 32.4 0.8

2019q2 Wave 2 &3 2,653 35.6 15.9 48.1 0.4

2019q2 Wave 4 1,397 33.9 16.7 48.5 0.9

2019q3 Wave 1 1,401 47.5 18.0 33.3 1.2

2019q3 Wave 2 &3 2,651 35.6 15.2 48.4 0.8

2019q3 Wave 4 1,285 35.0 13.8 50.6 0.6

2019q4 Wave 1 1,318 46.7 16.5 35.9 0.8

2019q4 Wave 2 &3 2,637 33.7 14.9 51.2 0.2

2019q4 Wave 4 1,293 32.3 15.3 51.9 0.5
Consumer Expenditure Surveys - 2019 Data Quality Profile | 14

4. Expenditure edit rates (Diary and Interview Surveys)

This metric measures the proportion of reported expenditure data that are edited. Expenditure data edits are 

changes made to the reported expenditure data during CE data processing excluding changes due to calculations 

(e.g. conversion of weekly value to quarterly value) and top-coding or suppression ofreported values. Top-coding 

and suppression are done to protect respondent confidentiality in the public-use microdata and more information

is available on the CE Website. Expenditure edit rates for the CEQ are broken down into three categories: 

Imputation, allocation, andmanual edits:

 Imputation replaces missing or invalid responses with a valid value

 Allocation edits are applied when respondents provide insufficient detail to meet tabulation 

requirements. For example, if a respondent provides a non-itemized total expenditure report for the 

category of fuels and utilities, that total amount will be allocated to the target items mentioned by the 

respondent (such as natural gas and electricity). 

 Manual edits occur wheneverresponses are directly edited by CE economists based on their analysis and 

expert judgment. 

Expenditure edit rates for the CED are only broken down into two categories. Almost all edits in CED are 

allocations. The “other edits” categoryencompasses all other expenditure edits including imputation and manual 

edits, though as you can see below, these are rare. 

Lower edit rates are preferred in general since that lowers the risk of processing error. Imputation in CE results 

fromexpenditure amount nonresponse, and lower imputation rates are desirable. Allocation is a consequence of 

responses lacking the required details for items asked by the survey, and lower allocation rates are also preferred. 

However, edits based on sound methodology can improve the completeness of the data, and thereby reduce the 

risk of measurement error and non-response bias in survey estimates. Additional information on expenditure edits 

are available in the DQP Reference Guide (Knappenberger, Lee, and Tan, 2020). 

CED

 The rate of edited reported expenditure records declined about 1 percentage point from an annual rateof 

10.7 percent in 2017 to 9.9 percent in 2019.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 15

Table 4.1 CED: reported expenditure records 

Row percentage

Quarter Number of 

expenditures

Allocated Other edit Unedited

2017q1 88,654 10.6 0.1 89.4

2017q2 92,985 10.9 0.1 89.0

2017q3 89,370 11.0 0.1 88.9

2017q4 92,031 10.3 0.0 89.7

2018q1 86,798 9.8 0.1 90.1

2018q2 87,649 10.0 0.1 89.9

2018q3 88,342 10.0 0.3 89.7

2018q4 80,129 10.3 0.2 89.5

2019q1 79,626 10.2 0.0 89.7

2019q2 85,329 9.1 0.1 90.8

2019q3 83,639 10.5 0.0 89.5

2019q4 80,510 9.5 0.0 90.4

* It is possible for a record to have been split into multiple records by allocation 

and the allocated records manually corrected to a single record without the 

allocation variable being reset to 0. 
Consumer Expenditure Surveys - 2019 Data Quality Profile | 16

CEQ 

 The total rate of unedited expenditure amountsincreased about 1 percentage point from an annual rate

of 83 percent in 2017 to 84 percent in 2019. 

 Imputation rates declined 6.7 percentage points from 2017q1 to 2017q2 while allocation rates increased 

by 6.4 percentage points in the same quarter.3

 Allocation rates declined about 0.6 percentage points from a three quarter average of 12.3 percent in 

2017q2 –2017q4 to an annual rateof 11.7 percent in 2019.4

 Imputation rates also declined about 0.6 percentage points from a three quarter average of 4.3 per cent in 

2017q2 –2017q4 to an annual rateof 3.7 in 2019.4

 

3 Beginning in 2017q2, CE started allocating cable, internet, and telephone utility expenditures that had previously 

been imputed. This preserves more of the respondent provided data.

4 A three quarter average is used here instead of the full year to evaluate the trend in expenditure edit rates after 

the 2017q2 processing change to cable, internet, and telephone utility expenditures.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 17

Table 4.2 CEQ: reported expenditure records

Row percentage

Quarter Number of 

expenditures

Allocated Imputed Imputed 

& 

allocated

Maual 

Edit

Unedited

2017q1 272,929 5.7 11.0 0.1 0.1 83.1

2017q2 276,568 12.1 4.3 0.2 0.1 83.4

2017q3 281,533 12.5 4.3 0.2 0.1 82.9

2017q4 277,032 12.4 4.3 0.2 0.1 83.0

2018q1 275,949 11.7 4.3 0.2 0.1 83.7

2018q2 270,726 12.0 3.9 0.2 0.1 83.9

2018q3 269,909 12.1 3.8 0.2 0.1 83.8

2018q4 259,508 12.0 3.8 0.2 0.1 84.0

2019q1 264,424 11.8 3.6 0.2 0.1 84.3

2019q2 255,037 11.7 3.7 0.2 0.1 84.2

2019q3 251,370 11.6 3.7 0.2 0.2 84.3

2019q4 244,834 11.6 3.8 0.2 0.2 84.2
Consumer Expenditure Surveys - 2019 Data Quality Profile | 18

5. Income imputation rates (Diary and Interview Surveys)

This metric describesthe rate of editing performed on a consumer unit’s nonresponse to at least one source of 

income. This edit is based on three types of imputation methods, applicable to both the CEQ and CED:

1. Model-basedimputation: when the respondent mentionsreceipt of an income source but fails to report 

the amount.

2. Bracket response imputation: when the respondent mentionsreceipt of an income sourcebut only 

reports that income as falling within a specifiedrange.

3. All valid blank conversion:when the respondent reports no receipt of income from any source, but the CE 

imputes receipt from at least one source.

Since the need for imputation reflects either item nonresponse or that insufficient item detail was provided, lower 

imputation rates are desirable for lowering measurement error. However, imputation based on sound 

methodology can improve the completeness of the data and reduce the risk of nonresponse bias due to dropping

incomplete cases from the dataset. Further details on the income imputation methodology can be found in the 

Reference Guide (Knappenberger, Lee, and Tan, 2020).

CED

 Between 2017 and 2019, model-based imputation rates declined from 19.0 percent to 17.3 percent and 

this drove the rising rate of unimputedtotal income before tax.

 The rate of bracket response imputation slightly increased from an annual rate of 19.6 percent in 2017to 

an annualrateof 20.1 percent in 2019.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 19

Table 5.1 CED: income imputation rates for total amount of family income before taxes

Row percentage

Quarter Number of 

eligible CUs

Valid blanks 

converted 

(AVB)

Bracket 

imputation

Model 

imputation

Model & 

bracket 

imputation

Unedited

2017q1 2,841 1.8 19.4 19.7 5.9 53.2

2017q2 3,003 2.5 20.2 18.2 5.8 53.3

2017q3 2,904 1.8 19.2 18.8 4.8 55.4

2017q4 2,910 1.8 19.7 19.4 4.7 54.5

2018q1 2,791 1.9 18.9 18.7 4.1 56.5

2018q2 2,781 1.9 17.4 19.6 4.5 56.7

2018q3 2,896 1.5 18.4 21.3 5.1 53.8

2018q4 2,611 2.4 19.1 18.3 6.0 54.3

2019q1 2,671 1.8 18.7 17.8 4.9 56.8

2019q2 2,713 2.9 20.2 17.6 5.0 54.3

2019q3 2,745 2.1 22.1 18.5 4.9 52.4

2019q4 2,553 2.6 19.2 15.2 6.5 56.4
Consumer Expenditure Surveys - 2019 Data Quality Profile | 20

CEQ 

 Between 2017 and 2019, model-based imputation rates declined from an annual rate of 18.1 percent to 

17.3 percent and this drove the rising rate of unimputed total income before tax.

 The rate of bracket imputation for the CEQ was steadyfrom 2017to 2019.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 21

Table 5.2 CEQ: income imputation rates for total amount of family income before taxes

Row percentage

Quarter Number of 

eligible CUs

Valid blanks 

converted 

(AVB)

Bracket 

imputation

Model 

imputation

Model & 

bracket

Unedited

2017q1 6,208 1.9 19.6 17.2 4.3 57.0

2017q2 6,177 1.1 18.4 17.7 4.0 58.8

2017q3 6,090 1.3 17.4 18.6 4.3 58.4

2017q4 6,004 1.7 17.6 18.8 4.6 57.4

2018q1 5,916 1.5 17.5 18.0 4.6 58.4

2018q2 5,899 1.2 16.8 17.1 5.2 59.8

2018q3 5,773 1.4 17.9 16.6 4.7 59.4

2018q4 5,571 1.4 18.2 17.3 4.5 58.5

2019q1 5,623 1.9 18.0 17.0 4.3 58.8

2019q2 5,493 1.4 18.3 17.5 4.4 58.4

2019q3 5,337 1.2 17.8 17.7 4.6 58.7

2019q4 5,248 1.4 18.9 17.2 5.0 57.5
Consumer Expenditure Surveys - 2019 Data Quality Profile | 22

6. Respondent burden (Interview Survey)

Response burden relates to the respondent’s perceived level of effort exerted to answer survey questions. Survey 

designers are concernedabout response burden because it could negatively impact response rates and the quality 

of responses. Beginning in April 2017, the CEQ introduced a response burden questionwith response options 

describing different levels of burden at the end of the Wave 4 interview. The respondent burden metric is based 

on this question.

5

A caveat to the interpretation of this metric is that since the burden question is only asked at the end of Wave4,

the metric likely underestimates survey burden due to self-selection bias. That is, respondents who have agreed to 

participate through the final wave of the survey are likely to find the survey less burdensome than sample units 

who had dropped out of the CEQ at any point prior to completing the final survey wave.

CEQ 

 Since the tracking of this metric began in 2017q2, the rate of respondents who report perceiving no 

burden has declined from an annual rateof 33.5percentin 2017 to 30.9percent in 2019. 

 

5 Previously,the CEQ had intermittently collected information on respondent burden for research purposes, but 

these data are not available to the public. 
Consumer Expenditure Surveys - 2019 Data Quality Profile | 23

Table 6.1 CEQ: respondents’ perceived burden in the final survey wave 

Row percentage

Quarter Number of 

eligible CUs

Not 

burdensome

Some burden Very 

burdensome

Missing 

response

2017q2 1,601 34.2 52.5 10.0 3.2

2017q3 1,576 32.7 51.8 12.1 3.5

2017q4 1,477 33.6 52.7 11.0 2.6

2018q1 1,464 31.7 52.7 12.4 3.2

2018q2 1,486 32.4 52.8 12.2 2.6

2018q3 1,464 33.7 51.4 13.0 1.9

2018q4 1,390 34.2 50.8 12.1 2.9

2019q1 1,428 30.5 55.1 12.7 1.6

2019q2 1,397 30.9 52.4 13.7 2.9

2019q3 1,285 29.4 54.3 13.4 2.9

2019q4 1,293 32.9 53.8 11.3 2.0
Consumer Expenditure Surveys - 2019 Data Quality Profile | 24

7. Survey mode (Interview Survey)

This metric measures the prevalence of themodeof data collection. TheCEQ was designed to be an in-person 

interview.However, the interviewer can also collect data for the CEQ over the phone, or by a combination of the 

two modes. Higher prevalence of in-person data collection is preferred since the interviewer can actively prompt 

the respondent, as well as encourage the use of recall aids, thereby reducing the risk of measurement error. 

Conducting first wave interviewsin-personisimportant because this is typically the respondent’s first interaction 

with the survey. 

CEQ 

 The rate of in-person data collection in Wave 1 has gradually declined from an annual rateof 76.9 percent 

in 2017 to 75.6percent in 2019, reflecting anincrease in Wave 1 phone interviews.

 In subsequent waves, the rate of in-person interviews declinesfurther, to an annual rateof 58.4percent 

in 2019.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 25

Table 7.1 CEQ:survey mode

Row percentage

Quarter Wave Number of 

eligible CUs

In-person Telephone Mixed Missing

2017q1 Wave 1 1,557 75.1 22.9 1.0 1.0

2017q1 Waves 2 & 3 3,078 59.0 40.0 0.5 0.5

2017q1 Wave 4 1,573 55.4 44.1 0.2 0.3

2017q2 Wave 1 1,573 79.1 18.6 1.5 0.8

2017q2 Waves 2 & 3 3,003 60.9 37.9 0.8 0.4

2017q2 Wave 4 1,601 58.7 40.5 0.6 0.2

2017q3 Wave 1 1,581 78.1 20.1 1.2 0.6

2017q3 Waves 2 & 3 2,933 61.8 37.2 0.4 0.6

2017q3 Wave 4 1,576 57.7 41.5 0.4 0.4

2017q4 Wave 1 1,592 75.3 22.8 0.8 1.1

2017q4 Waves 2 & 3 2,935 60.2 38.9 0.5 0.4

2017q4 Wave 4 1,477 58.8 40.3 0.3 0.6

2018q1 Wave 1 1,501 75.0 22.6 1.5 1.0

2018q1 Waves 2 & 3 2,951 58.8 40.3 0.4 0.5

2018q1 Wave 4 1,464 56.1 42.7 0.5 0.7

2018q2 Wave 1 1,529 76.3 22.3 0.8 0.7

2018q2 Waves 2 & 3 2,884 59.4 39.8 0.3 0.5

2018q2 Wave 4 1,486 57.2 41.9 0.6 0.3

2018q3 Wave 1 1,494 77.6 20.1 1.5 0.7

2018q3 Waves 2 & 3 2,815 60.8 38.5 0.3 0.3

2018q3 Wave 4 1,464 56.8 42.3 0.3 0.5

2018q4 Wave 1 1,399 76.1 21.9 1.2 0.7

2018q4 Waves 2 & 3 2,782 60.1 38.9 0.6 0.4

2018q4 Wave 4 1,390 57.3 42.0 0.4 0.3

2019q1 Wave 1 1,465 71.9 25.4 1.7 1.0

2019q1 Waves 2 & 3 2,730 59.0 40.3 0.3 0.3

2019q1 Wave 4 1,428 56.7 42.6 0.5 0.3

2019q2 Wave 1 1,443 75.6 22.7 1.2 0.5

2019q2 Waves 2 & 3 2,653 60.0 39.2 0.5 0.2

2019q2 Wave 4 1,397 58.3 40.9 0.6 0.2

2019q3 Wave 1 1,401 77.3 21.1 1.0 0.6

2019q3 Waves 2 & 3 2,651 59.7 39.5 0.5 0.4

2019q3 Wave 4 1,285 57.7 41.6 0.3 0.5

2019q4 Wave 1 1,318 74.2 24.6 0.8 0.4

2019q4 Waves 2 & 3 2,637 57.9 41.5 0.5 0.2

2019q4 Wave 4 1,293 55.0 43.9 0.8 0.4
Consumer Expenditure Surveys - 2019 Data Quality Profile | 26

8. Survey Response Time (Diary and Interview Surveys)

For the CEQ, survey response time isthe median length oftime to complete the interview. For the CED, survey 

response time is the median length of time to complete thepersonal interview component that collects 

informationabout income and demographics. Survey responsetimehas been used as anobjective indicator for 

respondent burden:the longer the time needed to complete the survey, the more burdensome the survey. Fricker, 

Gonzalez, and Tan (2011) find that higher respondent burden negatively affects both response rates and data 

quality. However, survey response time could also reflect the respondent’s degree of engagement. Engaged and 

conscientious respondentsmighttake longer to complete the survey because they report more thoroughly or use 

records more extensively. Finally,tracking the median survey response time can be useful for assessing the effect 

of changes in the survey design. 

CED

 The time to complete the personal interview component for income and demographics in the CED 

remained just over one-half hour from an annual average of 32.3 minutes in 2017 to an annual average of 

34.4 minutes in 2019.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 27

Table 8.1 CED: median length of time to 

complete the interview components (income and 

demographics)

Quarter Number of

eligible CUs

Minutes

2017q1 2,841 32.5

2017q2 3,003 32.7

2017q3 2,904 32.0

2017q4 2,910 32.0

2018q1 2,791 31.2

2018q2 2,781 32.2

2018q3 2,896 34.2

2018q4 2,611 33.2

2019q1 2,671 35.0

2019q2 2,713 33.8

2019q3 2,745 34.3

2019q4 2,553 34.4

CEQ

 Annual averages of median time to complete the CEQ rose each yearbetween 2017 and 2019 in every 

wave – from 71.2 minutes to 75.8 minutes in Wave 1, 50.3 minutes to 54.1minutes in Waves 2 and 3, and 

55.9 minutes to 60.7 minutes in Wave 4.

 The increase in survey responsetime canin part be explained by theaddition of point of purchase and 

veteran status questionsin 2018q3.
Consumer Expenditure Surveys - 2019 Data Quality Profile | 28

Table 8.2 CEQ: median length of time to complete survey

Minutes

Quarter Number of 

eligible CUs

Wave 1 Waves 2 & 3 Wave 4

2017q1 6,202 71.3 50.2 52.9

2017q2 6,163 71.5 50.5 57.2

2017q3 6,081 71.5 50.4 57.1

2017q4 6,003 70.5 50.0 56.4

2018q1 5,910 73.4 51.5 56.2

2018q2 5,894 70.5 49.6 56.4

2018q3 5,771 77.0 53.8 60.3

2018q4 5,570 76.7 52.0 58.6

2019q1 5,618 75.8 52.8 58.8

2019q2 5,486 75.9 56.4 60.2

2019q3 5,332 74.1 54.0 62.8

2019q4 5,239 77.4 53.3 60.8
Consumer Expenditure Surveys - 2019 Data Quality Profile | 29

Conclusion

CE is committed to producing data that are consistently of high statistical quality. As part of that commitment, CE 

publishes the DQP and its accompanying Reference Guide to assist data users as they evaluate CE data quality as 

they judge whether CE data fit their needs. DQP metrics therefore cover both surveys (CED and CEQ), multiple 

dimensions of data quality, and several stages of the survey lifecycle. Additionally, CE makes use of these metrics 

internally to identify areas for potential survey improvement, evaluate the affects of survey changes, and to 

monitor the health of the surveys. 

Some trends are encouraging. From 2017 to 2019, income imputation rates and expenditure edit rates 

declined for both CED and CEQ. On the other hand, some trends warrant concern. Over the three year period of 

2017 to 2019, response rates continued to decline, information book use declined, and perceived respondent 

burden increased for the CEQ. Finally a fewmetrics either showed little change over the 2017 to 2019 period, or 

the trend has an uncertain impact on data quality. Records use in CEQ was stable over the three year period 

despite a one-time boost around the time CE incentivized using records in a field test. Survey time for CED 

remained stable around just over half-an-hour. Survey time did increase for CEQ, but CE believes that this is 

related to the addition of new survey questions requested by our customers to improve CE’s fitness for their use. 

In-person interviews also declined in each wave of CEQ, but conducting an interview by phone is preferable to 

losing the case entirely.

CE will continue to monitor these trends, and the next issue of the CE Data Qualtiy Profile will be be 

released in 2021, with metrics incorporating data through 2020. 

References

Abdirizak, S., Erhard, L., Lee, Y., & McBride, B. (2017). Enhancing Data Quality Using Expenditure Records. Paper 

Presented at the Annual Conference of the American Association for Public Opinion Research, New Orleans 

LA.

Fricker, S., Gonzalez, J., & Tan, L. (2011). Are you burdened? Let’s find out. Paper Presented at the Annual 

Conference of the American Association for Public Opinion Research, Phoenix AZ. 

Knappenberger, C., Lee, Y., & Tan, L. (2020). Reference Guide CE Data Quality Profile. Program Report Series, the 

Consumer Expenditure Surveys. Bureau of Labor Statistics.