The Midyear CE Data Quality Profile - 

2020

April 29, 2021 

Clayton Knappenberger, Yezzi Lee, Sharon Pham, & Grayson Armstrong 

Consumer Expenditure Surveys Program Report Series 
Contents

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

Highlights.................................................................................................................................................................................. 2

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)........................................................................................................ 15

5. Income imputation rates (Diary and Interview Surveys).................................................................................................... 19

6. Respondent burden (Interview Survey) ............................................................................................................................. 23

7. Survey mode (Interview Survey) ........................................................................................................................................ 25

8. Survey Response Time (Diary and Interview Surveys) ....................................................................................................... 28

Conclusion .............................................................................................................................................................................. 31

References.............................................................................................................................................................................. 32
Consumer Expenditure Surveys - 2020 Data Quality Profile | 1

Overview 

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

the Bureau of Labor Statistics (BLS) is committed to producing data that are of consistently high statistical quality, i.e., 

accurate, objective, relevant, timely, and accessible. The Consumer Expenditure Surveys Program (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 publicuse 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. Prior DQPs are available on our Data Quality and Data Comparisons page. BLS began providing DQPs 

every year beginning with the 2017 data, though prototype DQPs are available for 2013 and 2015, and began providing

midyear DQPs with the 2020 midyear data release. For data users, the DQP metrics are an indication of quality, and cover 

both the Interview Survey and the Diary Survey. For internal stakeholders, they can signal areas for survey improvements. 

Since the quality of survey estimates is affected by errors that can occur throughout the survey lifecycle, we expect that 

the set of DQP metrics will evolve over time as the BLS 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, Pham, and Armstrong, 2021) 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, “2020q1” refers to all surveys collected in the months of January, February, and March 

of 2020. Because the respondents to the Interview Survey are asked to recall their expenditures over the prior three 

months, the data collected in 2020q1 typically refers to expenditures made in 2019q4. Hence, this profile provides metrics 

up to 2020q3 for the Interivew Survey and up to 2020q2 for the Diary Survey. Where annual rates are used to describe 

metric trends in this report, the annual rate was computed as the average of quarterly rates from the same calendar year

weighted by the number of consumer units in that quarter. 
Consumer Expenditure Surveys - 2020 Data Quality Profile | 2

Highlights

In this section, we highlight some of the metric trends from the past three years. This time frame covers the final 

quarters of the 2017 collection period to the first quarters of the 2020 collection period. Because the respondents 

to the Interview Survey are asked to recall their spending over the prior three months, data collected in 2020q1 

refer to expenditures made in 2019q4. Hence the Interview Survey metrics in this profile cover the time period of 

2017q4 through 2020q3. Respondents to the Diary Survey are asked to report their spending as it occurs, so Diary 

Survey metrics in this profile cover the time period of 2017q3 through 2020q2. Subsequent sections describe the 

individual metrics with detailed data tables.

Trends that are encouraging

 Roughly half of respondents used records, and this trend continues to be stable (Section 2).

 Overall expenditure edit rates declined due to lower allocation rates in both the Diary and Interview 

Surveys (Section 4).

Trends that cause concern

 Diary and Interview Survey response rates continued to decline prior to the onset of the COVID 19 

pandemic, but saw their largest single-quarter declines with the beginning of the pandemic (Section 1). 

 Information Book usage saw large declines for both the Diary and Interview Survey following the onset of 

the COVID 19 pandemic. A large portion of Interview Survey cases report not having access to the

Information Book (Section 3).

 Perceived burden (Section 6) increased for the Interview Survey along with median time spent taking the 

survey (Section 8). 
Consumer Expenditure Surveys - 2020 Data Quality Profile | 3

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

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

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

completed interview, nonresponse due to refusal, nonresponse due to noncontact, and nonresponse due to other

reasons. Completed interviews reclassified to a nonresponse by BLS staff are included within the other 

nonresponse category and are presented in the nonresponse reclassification tables (Table 1.2 and 1.4). More 

information on the non-response reclassification edit, along with information on how we calculate response rates 

can be found in the DQP Reference Guide (Knappenberger, Lee, Pham, and Armstrong, 2021). 

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

estimate if the nonresponse is correlated with that expenditure category. A nonresponse study conducted by the 

BLS showed no meaningful bias in survey estimates (King, Edgar, Gonzalez, Chopova, and Tan, 2009), but in a world 

of declining response rates, BLS continues to evaluate this risk. In addition, higher response rates are preferred for 

more precise estimates. We present unweighted response rates in this report.

Diary Survey

Pre-COVID 19 trends (2017q3 – 2019q4)

 Response rates declined 10.1 percentage points from 59.1 to 48.9 percent.

 Refusal rates rose 6.6 percentage points from 23.3 to 29.9 percent and accounted for the largest share of 

the decline in response rates.

 Noncontact rates rose from 5.1 to 7.6 percent and accounted for 2.5 percentage points of the decline in 

response rates.

COVID 19 impacts (2020q1 and 2020q2)

 In mid-March 2020, all in-person interviews were suspended, causing response rates to drop 22.8 

percentage points from 2019q4 to 2020q2.

 Refusal and noncontact rates also declined in the first two quarters of 2020 but this was offset by large 

increases in other nonresponse rates.

 The other nonresponse rate rose 45.6 percentage points and nonresponse reclassifications increased by 

3,205 cases as the BLS reclassified a large number of interviews from ineligible to eligible 

nonrespondents.

1

 

1 Many respondents could not be reached by telephone because interviewers did not have a working telephone 

number for the respondent. Interviewers were instructed to classify these cases as ineligible nonrespondents and 

BLS elected to reclassify the majority as eligible other nonrespondents. For more information on this nonresponse 

reclassification please see the DQP Reference Guide (Knappenberger, Lee, Pham, and Armstrong, 2021). 
Consumer Expenditure Surveys - 2020 Data Quality Profile | 4

Table 1.1 Diary Survey: distribution of final dispositions for eligible sample units

(unweighted)

Row percentage

Quarter Number of

eligible 

sample units

Interview Refusal Noncontact Other 

Nonresponse

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

2020q1 7,474 44.0 22.5 7.3 26.3

2020q2 7,409 26.1 12.1 2.7 59.1
Consumer Expenditure Surveys - 2020 Data Quality Profile | 5

Table 1.2 Diary Survey: prevalence of nonresponse reclassifications

Number of nonresponse reclassifications

Quarter Number of

eligible sample 

units

Total 

reclassifications

COVID 19 

reclassifications

Other

reclassifications

2017q3 4,916 283 0 283

2017q4 5,168 227 0 227

2018q1 5,032 227 0 227

2018q2 5,015 241 0 241

2018q3 5,014 247 0 247

2018q4 5,072 205 0 205

2019q1 4,926 232 0 232

2019q2 5,082 243 0 243

2019q3 5,020 229 0 229

2019q4 5,216 188 0 188

2020q1 7,474 855 562 293

2020q2 7,411 3,393 3,202 191

Interview Survey

Pre-COVID 19 trends (2017q4 – 2019q4)

 Response rates declined 7.6 percentage points from 59.2 to 51.6 percent.

 Refusal rates rose 6.1 percentage points from 30.7 to 36.8 percent and accounted for the largest share of 

the decline in response rates.

 Other nonresponse rates rose from 4.4 to 5.5 percent and accounted for 1.1 percentage points of the 

decline in response rates.

COVID 19 impacts (2020q1 – 2020q2)

 In mid-March 2020, all in-person interviews were suspended, causing response rates to drop 5.7 

percentage points from 2019q4 to 2020q2.

 Refusal and noncontact rates also declined in the first two quarters of 2020 but this was offset by large 

increases in other nonresponse rates.

 The other nonresponse rate rose 32.4 percentage points from 2019q4 to 2020q2 and nonresponse 

reclassifications increased by 2,944 cases through 2020q2.

2

 These impacts were largest for Wave 1 interviews because interviewers were less likely to have a working 

telephone number for these cases.

 

2 Many respondents could not be reached by telephone because interviewers did not have a working telephone 

number for the respondent. Interviewers were instructed to classify these cases as ineligible nonrespondents and 

BLS elected to reclassify the majority as eligible other nonrespondents. For more information on this nonresponse

reclassification please see the DQP Reference Guide (Knappenberger, Lee, Pham, and Armstrong, 2021). 
Consumer Expenditure Surveys - 2020 Data Quality Profile | 6

COVID 19 impacts (2020q3)

 Beginning in July 2020, interviewers were allowed to resume in-person interviews in certain locations as a 

result, other nonresponse rates decreased 10.5 percentage points while refusal rates increased 8.8 

percentage points, and noncontact rates increased 3.2 percentage points.

 As in-person interviews began to resume, the number of reclassifications declined by 2,867 cases.

Table 1.3 Interview Survey: distribution of final dispositions for eligible sample units

(unweighted) 

Row percentage

Quarter Number of 

eligible 

sample units

Interview Refusal Noncontact Other 

nonresponse

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

2020q1 9,956 52.2 33.8 4.7 9.3

2020q2 10,581 45.9 15.4 0.8 37.9

2020q3 11,189 44.5 24.2 4.0 27.4
Consumer Expenditure Surveys - 2020 Data Quality Profile | 7

Table 1.4 Interview Survey: prevalence of nonresponse reclassifications

Number of nonresponse reclassifications

Quarter Number of 

eligible 

sample 

units

Total 

reclassifications

COVID 19 

reclassifications

Other 

reclassifications

2017q4 10,138 15 0 15

2018q1 10,077 1 0 1

2018q2 10,075 1 0 1

2018q3 10,053 8 0 8

2018q4 10,161 5 0 5

2019q1 10,108 8 0 8

2019q2 10,075 2 0 2

2019q3 10,037 9 0 9

2019q4 10,170 14 0 14

2020q1 9,956 197 186 11

2020q2 10,581 2,955 2,944 11

2020q3 11,190 88 74 14

 
Consumer Expenditure Surveys - 2020 Data Quality Profile | 8

2. Records Use (Interview Survey)

This metric measures the proportion of respondents who used records while answering the Interview 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.

Interview Survey

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

conducted during this period that gave a subset of respondents monetary incentives to use records.

 Since 2017, records use has been stable across interview waves.
Consumer Expenditure Surveys - 2020 Data Quality Profile | 9

Table 2.1 Interview Survey: prevalence of records use among respondents

Row percentage

Quarter Wave Number of

respondents

Used Did not use Missing

response

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

2020q1 Wave 1 1,239 53.6 45.2 1.2

2020q1 Waves 2 & 3 2,601 50.7 48.9 0.4

2020q1 Wave 4 1,362 53.4 46.2 0.4

2020q2 Wave 1 965 51.9 47.3 0.8

2020q2 Waves 2 & 3 2,559 50 49.7 0.3

2020q2 Wave 4 1,334 52.4 47.1 0.5

2020q3 Wave 1 1,143 49.3 49.3 1.4

2020q3 Waves 2 & 3 2,444 49.4 50.3 0.3

2020q3 Wave 4 1,393 51 48.7 0.4
Consumer Expenditure Surveys - 2020 Data Quality Profile | 10

3. Information Book use (Diary and Interview Surveys)

The Information Book is a recall aide the interviewer provides for respondents. There are separate Information 

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

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

expenditures that each section/item code is intended to collect. The Information Book use metric 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 it is available on the BLS 

website), 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 can improve reporting quality by clarifying concepts with concrete 

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

Diary Survey

 The prevalence of Information Book use among Diary Survey respondents declined 39.5 percentage points 

from 43.6 percent in 2017q3 to 4.1 percent in 2020q2.

 In mid-March 2020, CE suspended all in-person interviews and Information Book use declined by 29 

percentage points from 2020q1 to 2020q2.
Consumer Expenditure Surveys - 2020 Data Quality Profile | 11

Table 3.1 Diary Survey: prevalence of Information Book use among respondents

Row percentage

Quarter Number of 

respondents

Used Did not use Missing

response

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

2020q1 3,285 33.1 64.0 3.0

2020q2 1,936 4.1 94.0 1.9
Consumer Expenditure Surveys - 2020 Data Quality Profile | 12

Interview Survey

Pre-COVID 19 trends (2017q4 – 2019q4)

 Information Book use in Wave 1 declined 3.4 percentage points from 50.1 percent in 2017q4 to 46.7

percent in 2019q4.

 The rate of Wave 1 respondents who did not have access to the Information Book increased by 3.1 

percentage points from 32.8 percent in 2017q4 to 35.9 percent in 2019q4.

 In subsequent waves, the rate of Information Book use was at least 10 percentage points lower than in 

Wave 1, and about half of respondents did not have access to the Information Booklet.

COVID 19 impacts (2020q1 – 2020q2)

 In mid-March 2020, CE temporarily discontinued the use of physical copies of the Information Book and

referred respondents to the online version. As a result, Information Book use rate declined 44.1 

percentage points for Wave 1 respondents from 2019q4 to 2020q2.

 Declines in Information Book use were similar for subsequent waves and about 95 percent of all 

respondents in 2020q2 did not have access to the Information Book.

COVID 19 impacts (2020q3)

 Beginning in July 2020, disposable copies of the Information Books were provided to respondents and

Information Book use rose to an average of 5.3 percent for all waves in 2020q3.
Consumer Expenditure Surveys - 2020 Data Quality Profile | 13
Consumer Expenditure Surveys - 2020 Data Quality Profile | 14

Table 3.2 Prevalence of Infobook use among Interview Survey respondents

Row percentage

Quarter Wave Number of 

respondents

Used Did not use No Infobook 

available

Missing 

response

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

2020q1 Wave 1 1,239 37.8 15.7 45.4 1.2

2020q1 Wave 2&3 2,601 28.1 13.9 57.6 0.4

2020q1 Wave 4 1,362 28.8 13.7 57.0 0.4

2020q2 Wave 1 965 2.6 1.8 94.8 0.8

2020q2 Wave 2&3 2,559 2.9 1.8 95.0 0.3

2020q2 Wave 4 1,334 3.4 0.8 95.2 0.5

2020q3 Wave 1 1,143 6.7 2.4 89.5 1.4

2020q3 Wave 2&3 2,444 4.8 2.7 92.2 0.3

2020q3 Wave 4 1,393 5.2 2.1 92.4 0.4
Consumer Expenditure Surveys - 2020 Data Quality Profile | 15

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 of reported 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 Interview Survey are broken down into three 

categories: Imputation, allocation, and manual 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 whenever responses are directly edited by CE economists based on their analysis and 

expert judgment. 

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

survey are allocations. The “other edits” category encompasses all other expenditure edits including imputation 

and manual edits, though table 4.1 below shows that these are rare. 

Imputation in CE data results from expenditure amount nonresponse. Allocation is a consequence of 

responses lacking the required details for items asked by the survey. Lower edit rates are preferred in general since 

that lowers the risk of processing error. 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, Pham, and Armstrong, 2021). 
Consumer Expenditure Surveys - 2020 Data Quality Profile | 16

Diary Survey

 In the beginning of January 20202, an increase in CE’s sample size saw the number of reported 

expenditures rise by over 22,000 expenditures. However, as response rates dropped in 2020q2, so did the 

number of expenditures.

3

 The total rate of unedited expenditure amounts increased 0.7 percentage points from 88.9 percent in 

2017q3 to 89.6 percent in 2020q2.

 Decreasing edit rates were driven by an 0.8 percentage point decline in allocation rates from 2017q3 to 

2020q2.

 

3 This increase in sample size was made possible by increased funding to accommodate collection of outlet 

information needed for calculating the Consumer Price Index
Consumer Expenditure Surveys - 2020 Data Quality Profile | 17

Interview Survey

 The total rate of unedited expenditure amounts increased 0.6 percentage points from 83 percent in 

2017q4 to 83.6 percent in 2020q3.

 This was primarily driven by allocation rates declining 0.8 percentage points from 12.4 percent in 2017q4 

to 11.6 percent in 2020q3.

4

 Declines in allocation rates were partially offset by increases in the manual edit rate from 0.1 percent in 

2017q4 to 0.3 percent in 2020q3.

 Imputation rates remained close to 4.3 percent, and the rate of expenditures that were both imputed and 

allocated was steady at 0.2 from 2017q4 through 2020q3.

 

4 The 2017q2 collection quarter saw a large increase in allocation rates accompanied by an almost equal decrease 

in imputation rates. Both were the result of a change in how BLS processes cable, internet, and telephone utility 

expenditures. These had previously been imputed, but are now allocated to preserve more of the data provided by 

respondents.

Table 4.1 Diary Survey: reported expenditure records 

Row percentage

Quarter Number of 

expenditures

Allocated Other edit Unedited

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

2020q1 102,693 9.2 0.0 90.7

2020q2 41,257 10.2 0.1 89.6
Consumer Expenditure Surveys - 2020 Data Quality Profile | 18

Table 4.2 Interview Survey: reported expenditure records

Row percentage

Quarter Number of 

expenditures

Allocated Imputed Imputed 

& 

allocated

Maual 

Edit

Unedited

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

2020q1 246,488 11.6 3.9 0.2 0.2 84.1

2020q2 217,785 11.9 4.1 0.2 0.1 83.6

2020q3 224,639 11.6 4.3 0.2 0.3 83.6
Consumer Expenditure Surveys - 2020 Data Quality Profile | 19

5. Income imputation rates (Diary and Interview Surveys)

This metric describes the 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 Interview and Diary 

Surveys:

1. Model-based imputation: when the respondent mentions receipt of an income source but fails to report 

the amount.

2. Bracket response imputation: when the respondent mentions receipt of an income source, but only 

reports that income as falling within a specified range.

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.

After imputation, income from each component source is summed to compute total income before taxes. In the 

text that follows, income before taxes is defined as “unimputed” if no source of total income required imputation 

for one of the three reasons identified above. Again, this applies to both the Diary and Interview Surveys.

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, Pham, and Armstrong, 2021) and the Users Guide to Income 

Imputation in the CE (Paulin, Reyes-Morales, and Fisher, 2018).

Diary Survey

 The rate of unimputed total income before taxes remained stable at 55.5 percent from 2017q3 to 2020q2.

 Model-based imputation rates declined 2.3 percentage points from 18.8 percent in 2017q3 to 16.5

percent in 2020q2.

 These declines in the rate of model-based imputation were partially offset by the 1.6 percentage point 

rise in bracket response imputation rates from 19.2 percent in 2017q3 to of 20.8 percent in 2020q2.

 These declines were even further offset by the 1.4 percentage point rise in respondents requiring both 

model-based and bracket response imputation from 4.8 percent in 2017q3 to 6.2 percent in 2020q2.
Consumer Expenditure Surveys - 2020 Data Quality Profile | 20

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

Row percentage

Quarter Number of 

respondents

Valid blanks 

converted 

(AVB)

Bracket 

imputation

Model 

imputation

Model & 

bracket 

imputation

Unedited

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

2020q1 3,285 1.9 20.0 17.5 5.1 55.5

2020q2 1,936 1.5 20.8 16.5 6.2 55.5
Consumer Expenditure Surveys - 2020 Data Quality Profile | 21

Interview Survey

 The rate of unimputed total income before taxes declined 0.8 percentage points from 57.4 in 2017q3 to 

56.6 percent in 2020q2.

 Model-based imputation rates rose 0.6 percentage points from 17.6 percent in 2017q3 to 18.2 percent in 

2020q2 and accounted for the largest share of the decline in unimputed income.

 The proportion of respondents requiring both model-based and bracket response imputation rose a 

further 0.5 percentage points from 4.6 percent in 2017q3 to 5.1 percent in 2020q2.

 Rising imputation rates were partially offset by a 0.6 percentage point decline in the rate of respondents 

undergoing all valid blank conversion from 1.7 percent in 2017q3 to 1.1 percent in 2020q2.
Consumer Expenditure Surveys - 2020 Data Quality Profile | 22

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

Row percentage

Quarter Number of 

respondents

Valid blanks 

converted 

(AVB)

Bracket 

imputation

Model 

imputation

Model & 

bracket

Unedited

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

2020q1 5,202 1.3 18.6 17.6 4.5 58.1

2020q2 4,858 1.2 18.1 18.7 4.9 57.1

2020q3 4,980 1.1 18.2 19.0 5.1 56.6
Consumer Expenditure Surveys - 2020 Data Quality Profile | 23

6. Respondent burden (Interview Survey)

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

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

of responses. Beginning in April 2017, the Interview Survey introduced a response burden question with response 

options describing five different levels of burden at the end of the Wave 4 interview. The respondent burden 

metric is based on this question and maps the five burden categories to three metric values: not burdensome, 

some burden, and very burdensome. Please see the Reference Guide (Knappenberger, Lee, Pham, and Armstrong, 

2021) for more details on the question wording and the burden categories.

A caveat to the interpretation of this metric is that since the burden question is only asked at the end of Wave 4,

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 Interview Survey at any point prior to completing the final survey wave. It is also 

possible that the respondent answering this question did not participate in prior interview waves. For example, the 

respondent who participated in the first three survey waves might move out of the sampled address prior to the 

final interview. If someone else moves into the sampled address in time for the final wave, then they would be 

asked these questions. 

Interview Survey

 The rate of respondents who report perceiving no burden declined 3.1 percentage points from 33.6

percent in 2017q4 to 30.5 percent in 2020q3. 

 Rising rates of respondents who felt that the survey was very burdensome accounted for 1.8 percentage 

points of this change, rising from 11.0 percent in 2017q4 to 12.8 percent in 2020q3.

 Respondents perceiving some burden also increased 1.4 percentage points from 52.7 percent in 2017q4 

to 54.1 percent in 2020q3.
Consumer Expenditure Surveys - 2020 Data Quality Profile | 24

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

Row percentage

Quarter Number of 

respondents

Not 

burdensome

Some burden Very 

burdensome

Missing 

response

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

2020q1 1,362 30.8 54.0 12.0 3.2

2020q2 1,334 30.7 54.3 12.5 2.5

2020q3 1,393 30.5 54.1 12.8 2.7
Consumer Expenditure Surveys - 2020 Data Quality Profile | 25

7. Survey mode (Interview Survey)

This metric measures the prevalence of the mode of data collection. The Interview Survey was designed to be an 

in-person interview. However, the interviewer can also collect data for the Interview Survey 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 interviews in-person is important because this is typically the 

respondent’s first experience with the survey. Additionally, BLS has agreements with the Census Bureau that no 

more than 24 percent of first interviews or 48 percent of subsequent interviews will be collected over the phone. 

This agreement is still in effect, but the COVID-19 pandemic has made collecting in-person interviews unsafe for 

respondents and interviewers. BLS expects to return to the agreed upon rates as it becomes safer for in-person 

interviews to resume. 

Interview Survey

Pre-COVID 19 trends (2017q4 – 2019q4)

 The rate of Wave 1 respondents whose interviews were conducted in-person declined 1.1 percentage 

points from 75.3 percent in 2017q4 to 74.2 percent in 2019q4.

 In all but two quarters (2019q1 and 2019q4) the rate of Wave 1 telephone interviews remained below the 

24 percent threshold.

 The rate of subsequent interviews conducted in-person declined 2.8 percentage points from an average 

of 59.7 percent in 2017q4 to an average of 56.9 percent in 2019q4.

 In every quarter the rate of Wave 2 through 4 telephone interviews remained below the 48 percent 

threshold.

COVID 19 impacts (2020q1 – 2020q3)

 In mid-March 2020, the Census Bureau suspended all in-person interviews and by April close to 98 

percent of all interviews were conducted over the phone regardless of wave.

 Beginning in July 2020, interviewers were allowed to resume in-person interviews in some locations and 

the rate of telephone interviews declined to 90 percent across all waves.
Consumer Expenditure Surveys - 2020 Data Quality Profile | 26
Consumer Expenditure Surveys - 2020 Data Quality Profile | 27

Table 7.1 Interview Survey: survey mode

Row percentage

Quarter Wave Number of 

respondents

In-person Telephone Missing

2017q4 Wave 1 1,592 75.3 22.8 1.1

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

2017q4 Wave 4 1,477 58.8 40.3 0.6

2018q1 Wave 1 1,501 75.0 22.6 1.0

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

2018q1 Wave 4 1,464 56.1 42.7 0.7

2018q2 Wave 1 1,529 76.3 22.3 0.7

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

2018q2 Wave 4 1,486 57.2 41.9 0.3

2018q3 Wave 1 1,494 77.6 20.1 0.7

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

2018q3 Wave 4 1,464 56.8 42.3 0.5

2018q4 Wave 1 1,399 76.1 21.9 0.7

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

2018q4 Wave 4 1,390 57.3 42.0 0.3

2019q1 Wave 1 1,465 71.9 25.4 1.0

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

2019q1 Wave 4 1,428 56.7 42.6 0.3

2019q2 Wave 1 1,443 75.6 22.7 0.5

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

2019q2 Wave 4 1,397 58.3 40.9 0.2

2019q3 Wave 1 1,401 77.3 21.1 0.6

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

2019q3 Wave 4 1,285 57.7 41.6 0.5

2019q4 Wave 1 1,318 74.2 24.6 0.4

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

2019q4 Wave 4 1,293 55.0 43.9 0.4

2020q1 Wave 1 1,239 64.2 34.7 1.0

2020q1 Wave 4 1,362 48.8 51.1 0.1

2020q1 Waves 2 & 3 2,601 50.1 49.7 0.2

2020q2 Wave 1 965 1.5 98.2 0.3

2020q2 Wave 4 1,334 1.9 97.9 0.2

2020q2 Waves 2 & 3 2,559 1.8 97.9 0.3

2020q3 Wave 1 1,143 13.0 86.4 0.6

2020q3 Wave 4 1,393 7.4 92.4 0.2

2020q3 Waves 2 & 3 2,444 8.6 91.3 0.2
Consumer Expenditure Surveys - 2020 Data Quality Profile | 28

8. Survey Response Time (Diary and Interview Surveys)

For both Interview and Diary Surveys, survey response time is the amount of time needed to complete an 

interview. For the Interview Survey, the survey response time metric is the median length of time to complete the 

interview. For the Diary Survey, the survey response time metric is the median length of time to complete the

personal interview component that collects information about income and demographics. Survey response time

has been used as an objective 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 respondents might take longer to complete the 

survey because they report more thoroughly, or use records more extensively. Tracking the median survey 

response time can be useful for assessing the effect of changes in the survey design. 

Diary Survey

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

Surcey remained just over one-half hour from 32.0 minutes in 2017q3 to 34.9 minutes in 2020q2.
Consumer Expenditure Surveys - 2020 Data Quality Profile | 29

Table 8.1 Diary Survey: median length of time to 

complete the interview components (income and 

demographics)

Quarter Number of

respondents

Minutes

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

2020q1 3,281 33.3

2020q2 1,936 34.9

Interview Survey

 Median time to complete Wave 1 interviews rose 6.3 minutes from 70.5 minutesin 2017q4 to 76.8

minutes in 2020q3.

 Median time to complete Waves 2 and 3 interviews rose 6.7 minutes from 50.0 minutes in 2017q4 to 56.7

minutes in 2020q3.

 Wave 4 interviews similarly took 5.8 minutes longer, rising from 56.4 minutes in 2017q4 to 62.2 minutes 

in 2020q3.

 These increases in survey response time can in part be explained by the addition of a new extended recall 

section in 2019q2.
Consumer Expenditure Surveys - 2020 Data Quality Profile | 30

Table 8.2 Interview Survey: median length of time to complete survey

Minutes

Quarter Number of 

respondents

Wave 1 Waves 2 & 3 Wave 4

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

2020q1 5,199 78.8 56.0 59.9

2020q2 4,855 76.4 54.6 62.2

2020q3 4,980 76.8 56.7 62.2
Consumer Expenditure Surveys - 2020 Data Quality Profile | 31

Conclusion

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

BLS publishes the DQP and its accompanying Reference Guide (Knappenberger, Lee, Pham, and Armstrong, 2021) 

to assist data users as they evaluate CE data quality as they judge whether CE data fit their needs. DQP metrics 

therefore cover both the Interview and Diary Surveys, multiple dimensions of data quality, and several stages of 

the survey lifecycle. Additionally, BLS uses 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 the final quarters of 2017 to the first quarters of 2020, records use 

rates remained stable for the Interview Survey and expenditure edit rates declined for both the Interview and 

diary Surveys. On the other hand, some trends warrant concern. Over the same time period, response rates 

continued to decline, Information Book use declined, and perceived respondent burden increased for the 

Interview Survey. The COVID 19 pandemic in particular led to severe declines in response rates, Information Book 

use, and almost every interview was conducted over the phone rather than in-person. A few metrics either 

showed little change over the this time period, or had trends with an uncertain impact on data quality. Income 

imputation rates for the Diary Survey remained stable while rates for the Interview Survey went up after a period 

of decline. Survey time for the Diary Survey remained stable around just over half-an-hour. Survey time did 

increase for the Interview Survey, and BLS believes that this is related to the addition of new survey questions 

requested by our customers to improve CE’s fitness for their use. 

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

released in September 2021 with BLS’s annual release of CE data and will report on the remainder of the 2020 

data. 
Consumer Expenditure Surveys - 2020 Data Quality Profile | 32

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. 

King, S., Edgard, J., Gonzalez, J., Chopova, B., and Tan, L. (2010). Nonresponse Bias in the Consumer Expenditure

Survey: A Case Study. Paper Presented at the Annual Conference of the Joint Statistical Meetings, 

Vancouver, BC.

Knappenberger, C., Lee, Y., Pham, S. & Armstrong, G. (2021). Reference Guide CE Data Quality Profile. Program 

Report Series, the Consumer Expenditure Surveys. U.S. Bureau of Labor Statistics. 

Paulin, G., Reyes-Morales, S., & Fisher, J (2018). User's Guide to Income Imputation in the CE. U.S. Bureau of Labor 

Statistics.