The Consumer Expenditure Survey 

Data Quality Profile

(Prototype Iteration 2)

August 16, 2017

Evan Hubener, Clayton Knappenberger, Julie Sullivan, and Lucilla Tan 

CE Surveys Program Report Series
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Overview 

The Consumer Expenditure Survey (CE) has historically 

provided some limited metrics for data users to evaluate the 

overall quality of output provided in its products. Published 

tables provide standard errors; the public-use microdata user 

guide provides response rates, and the datasets contained in 

the public-use microdata provide all the variables and flags

necessary for users to create his or her own quality 

measures. There has long been a recognition for the need for 

more comprehensive data quality metrics that are timely,

routinely updated, and accessible to data users from a single 

source, a Data Quality Profile (DQP). However, there is also 

recognition of the high cost in terms of resources and 

commitment to identifying appropriate metrics and 

establishing the information base necessary to routinely 

produce reports on survey data quality. In order for this 

effort to be sustainable, the benefits from it must be relevant 

and useful to survey operations and data users.

This report, the CE Data Quality Profile Prototype 

version 2 (DQP2), is the second in a series of iterations 

towards developing a single reference on a comprehensive 

set of CE data quality metrics that are timely and routinely 

updated for the Consumer Expenditure Interview Survey 

(CEQ) and the Consumer Expenditure Diary Survey (CED).

Recognizing the benefits of “learning-by-doing” – using 

cumulative experience to provide CE with a practical understanding of what resources are needed and how best to deploy 

them to routinely produce a DQP - the first iteration DQP1 produced a small set of metrics with limited resources. The

goals of this second iteration were to refine and expand the set of metrics reported in DQP2, and begin to identify the 

resources needed to build the infrastructure to produce these metrics routinely.

Visual Summary

Metrics:

 Response rates: official published 

tables

 Use of Records in the CEQ

 Expenditures edit rate: processed 

data

 Income Imputation Rates

Appendix: detailed definitions

 Response and nonresponse rates

 Records use comparison groups

 Survey mode comparison groups

 Processed expenditures, Reported 

item counts

 Expenditure edit type

 Income edit type

Content links
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The metrics described in this report are for the years 2010 through 2015. The associated data quality dimension(s) of each 

metric is summarized in the table below:

Metric

Total Survey Error dimensions

associated with the metric

Measurement Nonresponse Processing

Final Disposition Rates of eligible units, CED & CEQ (official 

tables/SMD)

 unweighted and base-weighted 

Records Use by CEQ respondents

 conditioned on mode

 reported number of items and total expenditures



CEQ Processed Expenditure Data Edit Rate

 conditioned on survey mode   

CED & CEQ Processed Income Data Edit Rate   

In the next section, we present visualizations that highlight findings about the metrics. Detailed metric tables and 

descriptions then follow, and finally, definitions used to construct the metrics appear in the Appendix. 

Visual Summary

In this section, metric trends for the reporting period (2010 through 2015) are highlighted in a panel of graphs (Figure 1).

Further details about the individual metrics and detailed data tables are in the sections that follow the visual summary. 

Metrics with declining trends

 Unweighted response rates for the universe of CUs whose data are used to produce CE’s official tables continued to 

decline for both CE surveys in 2015 (Figure 1 , top of left panel). Base-weighted rates were similar to unweighted 

rates. The trend of declining response rates call for understanding if and how differences between respondents and 

nonrespondents affect nonresponse bias of key survey estimates.

 The prevalence of overall record use for the CEQ declined across the reporting period. Record use was higher among 

personal visit than phone survey modes, but declined for both modes (Figure 1 , top of right panel). This declining 

trend has an adverse impact on measurement error; among respondents who use records, the average number of 

reported items per CU member is higher (Figure 1 , middle left panel), as are total expenditures. Also, for these 

respondents, the overall edit rate of processed expenditure reports is lower (Figure 1 , bottom of right panel).
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Metrics with rising trends

 Survey refusal and noncontact rates were higher in the second half of the reporting period than the first for both the 

CEQ and the CED (Figure 1 , top of left panel). Other nonresponse rates (reasons for non-response other than refusal 

and noncontact) are higher in the CED than in the CEQ throughout the reporting period; this difference can be 

partially explained by diaries classified as nonresponse due to “Diaries Placed Too Late “ – a nonresponse code that 

does not exist in CEQ. Beginning in 2017, interviewers will have the full month instead of just 7 days to place a Week 

1 diary. We therefore expect the difference between CED and CEQ other nonresponse rates to be reduced in 2017. 

 Overall edit rates of processed expenditure reports are higher in the CED than the CEQ (Figure 1 , top right panel). 

The gradual increase in the overall edit rate of these reports was driven by the rise in the imputation rate for the CEQ

in 2015. For the CED, allocation is the dominant expenditure edit method, and the allocation rate also increased in 

2015. Since higher imputation rates imply higher rates of item nonresponse, and higher allocation rates imply 

incomplete responses (insufficient detail), an increase in these rates adversely affects measurement error. Increasing 

edit rates also increase the risk of processing error. If this trend continues, an attempt to understand if there are 

sections of the survey that are driving the higher edit rates would inform possible design interventions.

 The trend for the prevalence of no imputed income sources in “Total CU income before tax” continued to rise for 

both the CEQ and the CED in 2015, due to declining model-based imputation rates. Model-based imputation rates 

have been the dominant income imputation method (Figure 1 , bottom of right panel). While this is a desirable trend 

for measurement error, more than a third of CUs continue to have some income component of total income that is 

imputed. The CE will continue to investigate and monitor how imputation rates vary among the income components. 
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Figure 1. Select metric trends from 2010 to 2015
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1. Final Disposition Rates

The unit of observation for the CE is the Consumer Unit (CU), so response and nonresponse rates are computed at 

the CU level. The CE adopts the Census Bureau’s categorization of eligible CUs who do not respond to the survey as 

a “Type A nonresponse”. Type A nonresponse is differentiated into subcategories of reasons for nonresponse: 

“Noncontact” when the interviewer is unable to contact an eligible member of the CU; “Refusal” when the 

contacted CU member refuses; and “Other nonresponse” for miscellaneous other reasons. Among the “Type A 

Other nonresponse” reasons is a minimal expenditure edit check performed at BLS (CE) that could change an 

interviewer-coded “completed interview” for a CU to “nonrespondent”; this type of edit is referred to as the 

nonresponse reclassification. A mapping of the CE to AAPOR final disposition codes for in-person, household survey 

is presented in the Appendix. 

Response and nonresponse rates are measures of cooperation levels in a survey. Since not all eligible 

sample units will be available or agree to participate in the survey, there will be some nonresponse to the survey 

request. Characteristics of nonrespondents may differ from respondents, and if these characteristics correlate with

their expenditures, their omission from the survey may result in bias in the estimates produced from the survey. 

While weighting adjustments may reduce bias, the effectiveness of this approach depends on the availability and 

quality of variables used in the weighting and so concerns about bias persist. A single, survey-level measure, such 

as a survey response rate, in itself is an inadequate measure of nonresponse error. Nevertheless, higher response 

rates are preferred in the absence of other indicators of nonresponse bias.

The nonresponse reclassification is conducted in both the Interview and Diary surveys. The nonresponse 

reclassification rates can serve as an indicator of the potential for nonresponse bias because the minimal 

expenditure edit (which triggers reclassification) converts these ‘respondents’ to nonrespondents. If those 

reclassified as nonrespondents are systematically different from respondents, nonresponse bias will result. Thus, 

ceteris paribus, lower reclassification rates are desired.

Response rates are reported unweighted and weighted. Unweighted response rates provide an indication 

of the proportion of the sample that resulted in useable information to produce estimates, They also serve as a 

useful means of monitoring the progress of fieldwork and for identifying problems with nonresponse that can be 

addressed during fieldwork operations. Weighted response rates provide an indication of the proportion of the 

survey population for which useable information is available, since the weights allow for inference of the sample to 

the population. The weights used are base weights (the inverse probability of selecting the sample units). Both 

rates are usually similar unless the probabilities of selection and the unit response rates in the categories with 

different selection probabilities vary considerably.

Each survey wave is treated independently (that is, as an independent CU) in the computation of the CE 

Interview Survey response rates, and each diary week is treated independently for the CE Diary Survey rates. So 

the rates reported in this section are not longitudinal response rates.
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Official published tables 

CE Interview Survey (CEQ)

Unweighted rates: official CE tables. CEQ response rates steadily declined from 73.4 percent to 64.2

percent between in 2010 and 2015 (Table 1.1). The drop of 3 percentage points in 2013 could be partially 

accounted for by the Federal Government shut down that affected data collection in October, but another dip of 2 

percentage points occurred in 2015. This may be partly attributable to a change in data collection staff arising from 

a sample redesign - there is a loss of some experienced CE data collectors in areas dropped, and the addition of 

new, less experienced CE data collectors in the areas added.

Refusal rates rose from 18.6 percent to 21.2 percent between 2010 and 2015, with a smaller increase in 

unweighted noncontact rates from 4.3 percent to 6.8 percent over the same period. The nonresponse 

reclassification rate declined from 2.1 percent to 0.8 percent of “Other nonresponse” cases between 2010 and 

2015 (Table 1.3).

Base-weighted rates: official CE tables. The magnitudes and trends of the CEQ base-weighted response 

rates, refusal rates, and noncontact rates were similar to their respective unweighted rates (Table 1.2). 

Table 1.1 CEQ Distribution of Final Dispositions for Eligible CUs (official tables, unweighted)

Year* No. Eligible 

CUs

Interview Refusal Noncontact Other 

Nonresponse

Row Percent Distribution

2010 38,718 73.4 18.6 4.3 3.7

2011 38,348 70.4 20.8 4.6 4.2

2012 38,835 69.5 20.8 5.0 4.7

2013 39,142 66.7 22.1 5.4 5.8

2014 39,003 66.4 23.3 5.2 5.0

2015 36,692 64.2 24.8 6.8 4.2

* Excludes bounding interviews prior to 2015, and excludes Jan 2015 data due to sample redesign. 

Table 1.2 CEQ Distribution of Final Dispositions for Eligible CUs (official tables, base-weighted)

Year* Interview Refusal Noncontact Other Nonresponse

Row Percent Distribution

2010 73.8 18.2 4.3 3.7

2011 70.4 20.7 4.6 4.3

2012 69.7 20.8 4.8 4.8

2013 66.8 22.1 5.3 5.8

2014 66.6 23.2 5.1 5.1

2015 63.8 25.1 6.9 4.2

* Excludes bounding interviews prior to 2015, and excludes Jan 2015 data due to sample redesign. 
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Table 1.3. CEQ: Prevalence of nonresponse reclassifications (official tables, unweighted)

Nonresponse Reclassifications

Year* No. Eligible 

CUs

No. Other 

Nonresponse

No. CUs Other 

Nonresponse

(%)

Eligible CUs

(%)

2010 38,718 1,427 30 2.1 0.077

2011 38,348 1,606 24 1.5 0.063

2012 38,835 1,816 13 0.7 0.033

2013 39,142 2,258 18 0.8 0.046

2014 39,003 1,960 10 0.5 0.026

2015 36,692 1,537 13 0.8 0.035

* Excludes bounding interviews prior to 2015, and excludes Jan 2015 data due to sample redesign. 

CE Diary Survey (CED)

Unweighted rates: official CE tables. While CED response rates were consistently lower than the CEQ 

between 2010 and 2015, they mirrored the same declining trend of the CEQ. The CED response rate fell from 71.5 

percent in 2010 to 57.7 percent in 2015 (Table 1.4), with the largest annual percentage points drop of 7 percent in 

2013 and 2015. The drop off in the response rate in 2013 can partially be explained by the shutdown of the Federal 

Government, and partially in 2015 by an increase of 3 percentage points in the CED refusal rate. The CED refusal 

rate has trended upwards but were consistently lower than those for the CEQ, rising from 10.8 percent in 2010 to 

16.7 percent in 2015. The CED noncontact rate increased from 3.6 percent to 5.4 percent during the same period. 

The nonresponse reclassification rate as a proportion of other-nonresponse is higher in the CED than the CEQ, 

but it had declined from 43.0 percent to 25.2 percent of other non-response cases between 2010 and 2015 (Table 

1.6). Other nonresponse rates (reasons for non-response other than refusal and noncontact) are higher in the CED 

than in the CEQ throughout the reporting period; this difference can be partially explained by diaries classified as 

nonresponse due to “Diaries Placed Too Late “ – a nonresponse code that does not exist in CEQ. Beginning in 2017, 

interviewers will have the full month instead of just 7 days to place a Week 1 diary. We therefore expect the 

difference between CED and CEQ other nonresponse rates to be reduced in 2017. 

Base-weighted rates: official CE tables. The magnitudes and trends of the CED base-weighted response 

rates, refusal rates, and noncontact rates were similar to their respective unweighted rates (Table 1.5). 

 
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Table 1.4 CED Distribution of Final Dispositions for Eligible CUs (official tables, 

unweighted)

Year No. Eligible 

CUs

Interview Refusal Noncontact Other 

Nonresponse

Row Percent Distribution

2010 19,988 71.5 10.8 3.6 14.1

2011 19,823 70.2 11.4 3.3 15.1

2012 20,298 67.8 12.1 3.5 16.6

2013 20,296 60.8 12.8 4.7 21.7

2014 20,476 65.0 13.9 4.7 16.4

2015 20,517 57.7 16.7 5.4 20.2

Table 1.5 CED Distribution of Final Dispositions for Eligible CUs 

(official tables, base-weighted)

Year Interview Refusal Noncontact Other 

Nonresponse

Row Percent Distribution

2010 71.9 10.7 3.4 14.0

2011 70.3 11.4 3.1 15.2

2012 67.7 12.2 3.3 16.7

2013 60.7 12.8 4.6 22.0

2014 64.8 14.0 4.7 16.5

2015 57.7 16.9 5.4 19.9

Table 1.6. CED: Prevalence of nonresponse reclassifications (official tables, unweighted): 2010-2015

Nonresponse Reclassifications

Year* No. Eligible 

CUs

No. Other 

Nonresponse

No. CUs Proportion of 

Other 

Nonresponse (%)

Proportion of 

Eligible CUs (%)

2010 19,988 2,811 1,209 43.0 6.0

2011 19,823 3,000 1,129 37.6 5.7

2012 20,298 3,370 1,109 32.9 5.5

2013 20,296 4,411 1,112 25.2 5.5

2014 20,476 3,357 1,141 34.0 5.6

2015 20,517 4,141 1,045 25.2 5.1
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2. Use of Records in the CEQ

Responses based on expenditure records to survey questions about spending result in higher reporting accuracy 

and lower measurement error. Thus, a higher prevalence of record use is desirable. 

Use of records during the interview 

The creation of the comparison groups for records usage analysis is based on the overall records used

question asked of the interviewer at the end of the CEQ survey (for details, see Appendix ). It is optional for 

respondents to use records, so it is likely that respondents who do choose to use any records and refer to them on 

an as needed basis are more engaged than those respondents who choose not to consult records. In addition, it is 

plausible that “no or very few records were used” would be more salient in the interviewer’s recollection of the 

interview than the varying extent of records used in the other response options. For these reasons, and for 

simplicity of interpretation, two comparison groups were created for analysis: “Records” vs “None”, where the 

None group consisted of CUs whom the interviewer reported as using records “never or almost never (less than 

10% of the time)”. These two groups comprised roughly half the respondents in 2010. (Table 2.1). However, the 

prevalence of no records used during an interview increased by 6.8 percent between 2010 and 2015.

Survey mode 

Three comparison groups for survey mode were created for analysis: the “Personal Visit”, “Phone”, and 

“Mixed” mode groups (for details, see Appendix). Most interviews were completed using a single mode, with less 

than 1.5 percent by Mixed mode between 2010 through 2015 (Table 2.1). The dominant survey mode is by 

Personal Visit, comprising about two-thirds of the completed interviews. After about a 3 percentage points decline 

in Personal Visit interviews in 2013 and 2014 from 2012, there was an upturn in 2015 (66.7 percent Personal Visit 

interviews). This may likely be due to the CEQ survey panel changing from five to four waves. Prior to 2015, 1st

interviews were 20% of the sample, thereafter, they account for 25% of the sample. Sample units are more likely 

to be interviewed in person on their first survey wave.

Records use by survey mode 

Consistent with expectations that face-to-face interactions provide more opportunities to encourage 

record use, the prevalence of record use was higher among Visit than Phone interviews between 2010 and 2015 

(Table 2.2). However, the trend of records use among Visit interviews had steadily declined from 54.2 percent in 

2010 to 48.5 percent in 2015, a fall of 5.7 percentage points. A steeper rate of decline was observed among Phone 

interviews over the same period, a fall of 9.2 percentage points. It is important to point out that while the ‘Mixed’ 

mode has the lowest prevalence of respondents that used no records, these respondents comprised only about 1 

percent of all households in the years 2010-2015 (Table 2.1). 
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CEQ Reported Number of items and Expenditures by Records Use

CE’s ultimate goal in encouraging in person interviews and respondent record use is higher quality data. Our 

hope is that record use would first, help a respondent remember to report items they otherwise would not have 

reported, and second to remember the correct cost of a certain item purchased. Therefore we relate records use

to the number of unique expenditures reported and to the overall amount reported. To control for the size of the 

CU, both measurements are divided by the number of people that are in the CU. The number of expenditure items 

reported by the CU was computed from the reported expenditure records file (see Appendix for details about how 

this file was created).

The average and median number of item reports per CU member was about 4 additional reports among the 

Records group compared with the None (no records) group (Table 2.3). The similarity in the difference between 

the averages and the medians between the Records and the None groups suggests that this result was not skewed 

by outliers. This finding confirms our belief that encouraging records use prompts the recollection of items that 

would otherwise have gone unreported. 

Mean expenditures per person for the Records group were consistently higher than the None (no records) 

group between 2010 and 2015; in 2015, the difference in means between the two groups was $1,498 (Table 2.4). 

However, the difference in the medians was $1,275 suggesting that one of the means was more skewed by a heavy 

right tail distribution. Our investigation into CE’s expenditure edit rates showed that the imputation rate among

respondents who never used records was 5 percentage points higher than for respondents who used records

(Table 3.3). Therefore, the disparity in the above means is reduced because of the higher imputation rate for 

respondents that did not use records.

Table 2.1: Prevalence of records use and survey mode among CEQ respondents

Survey Mode Record Use Group

Missing Mixed Phone Personal Visit Missing None Records

Year No. CUs Row Percent Distribution Row Percent Distribution

2010 28,429 0.3 0.9 34.0 64.8 0.7 48.0 51.3

2011 26,990 0.3 1.0 34.7 64.0 0.9 49.1 50.0

2012 26,993 0.3 0.9 33.6 65.2 0.8 48.5 50.7

2013 26,108 0.3 1.1 35.9 62.8 0.8 50.4 48.8

2014 25,908 0.3 0.9 36.5 62.2 0.7 54.0 45.3

2015 23,574 0.4 1.2 31.8 66.7 0.7 54.8 44.5
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Table 2.2: Prevalence of records use by survey mode in the CE Interview 

Survey 

Mode

Mixed Phone Personal Visit

Records 

Use

Missing None Records Missing None Records Missing None Records

Row Percent Distribution Row Percent Distribution Row Percent Distribution

2010 2.2 39.3 58.5 0.7 53.4 45.9 0.3 45.5 54.2

2011 1.8 37.5 60.7 0.7 55.5 43.8 0.4 46.1 53.5

2012 3.6 44.6 51.8 0.6 54.4 45.1 0.4 45.8 53.9

2013 1.1 46.5 52.5 0.6 55.9 43.4 0.4 47.6 52.1

2014 1.7 51.9 46.4 0.4 60.3 39.3 0.3 50.6 49.1

2015 0.7 55.3 44.0 0.3 62.9 36.7 0.2 51.3 48.5

Table 2.3: CEQ respondents: distribution of counts of reported items per CU member by records use

Records Use None Records

Year P25 Mean Median P75 P25 Mean Median P75

2010 5.0 10.2 8.5 13.5 7.5 14.3 12.3 19.0

2011 5.0 10.4 8.7 13.7 7.7 14.6 12.5 19.0

2012 5.0 10.2 8.5 13.3 7.5 14.3 12.0 19.0

2013 5.0 9.9 8.0 13.0 7.3 14.3 12.0 19.0

2014 5.0 9.9 8.0 13.0 7.5 14.4 12.0 19.0

2015 5.0 10.0 8.0 13.0 7.5 14.1 12.0 18.0

Table 2.4: CEQ respondents: distribution of reported expenditures ($) per CU member by records use

Records Use None Records

Year P25 Mean Median P75 P25 Mean Median P75

2010 2,220 4,716 3,519 5,674 3,011 6,269 4,781 7,643

2011 2,264 4,872 3,655 5,954 3,043 6,335 4,868 7,693

2012 2,347 5,118 3,763 6,099 3,150 6,534 5,019 7,892

2013 2,427 5,080 3,833 6,143 3,227 6,719 5,095 8,120

2014 2,486 5,318 3,960 6,438 3,367 6,870 5,306 8,284

2015 2,579 5,646 4,134 6,804 3,448 7,144 5,409 8,581
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3. Expenditure data edit rate

At the completion of an interview, data from the interviewer’s laptop are transmitted to the Census Master 

Control System. The Census Bureau’s Demographics Surveys Division preforms some preliminary processing and 

reformatting of the data before transmitting the data to BLS on a monthly basis. At BLS, a series of automated and 

manual edits are applied to the data in order to ensure consistency, fill in missing information, and to correct 

errors in the collected data. (For more description about the data collection and processing for the CE surveys, see 

Handbook of Methods: Consumer Expenditure Survey ).

Edits are defined as any changes in the data made during processing that require judgment or assumptions 

(whether model based or manually adjusted by an economist). Imputation and allocation are two major types of 

data edits to improve estimates derived from the Interview and Diary Surveys (see Appendix for definitions of 

edits):

 Data imputation edits account for missing or invalid entries and currently apply to most expenditure and 

income fields, excluding assets. 

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

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

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

respondent (such as natural gas and electricity). 

In addition to allocation and imputation, data are reviewed and manually edited as needed by BLS economists 

based on their research and expert judgment. 

The need for data imputation results from missing data (item or price nonresponse). Thus, lower 

imputation rates are desirable. The need for data allocation is a consequence of responses that did not contain the 

required details of the item asked by the survey. Likewise, lower allocation rates are also preferred, and in general, 

lower data editing rates are preferred since that lowers the risk of processing error. However, imputation based on 

sound methodology can improve the completeness of the data and improve overall survey estimates.

Processed expenditure reports 

In this section, we describe the edit rates of processed expenditure data. Processed expenditure data refer to the 

final set of expenditure records at the end of data editing, which includes new records that are the result of an edit 

(for example, an original single report of “groceries” may result in 4 additional records after allocation). Processed 

expenditure data are used in the CE microdata files used by external data users, as well as the data used to 

produce estimates that appear in the official CE tables. For the CEQ, we also describe the edit rates conditioned on 

the respondents’ use of records during the interview. The definition of the records use comparison groups, 

“Records” and “None, is described in the Appendix.
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CE Interview Survey (CEQ) 

The increase in the overall proportion of edited expenditure reports between 2010 and 2015 was small 

(from 16.6 percent to 17.9 percent, respectively), with a larger increase in imputation rates from 7.1 percent to 9.4 

percent over the same period (Table 3.1). 

Use of records during the interview resulted in a higher number of expenditure reports (Table 3.2; and 

also seen in Table 2.3). In addition, the prevalence of unedited expenditure reports is higher, with imputation rates

and allocation rates lower among record users each year between 2010 and 2015, but the difference in allocation 

rates between the 2 groups was less than 1 percentage point (Table 3.3). The Records group had an average rate of 

unedited expenditure reports of 85.1 percent compared to 80.7 percent for respondents who did not use records 

(average of unedited rates over the 6 years for each group in Table 3.3). Average imputation rates for the six years 

was 6.0 percent among respondents who used records compared to 10.0 percent among respondents who did not

use records.

CE Diary Survey (CED) 

The CED has a higher edit rate for expenditure reports compared to the CEQ, but like the CEQ, the 

increase in the overall proportion of edited CED expenditure reports between 2010 and 2015 was small (from 25.6 

percent to 27.4 percent, respectively; Table 3.4). Due to the nature of the diary survey, where it is not possible to 

comprehensively prompt the diary keeper for item detail or categorization required, allocation is the primary edit 

and generally occurs where multiple items are purchased but one total cost is reported for those items. Between 

2010 and 2015, the average rate of unedited expenditure records was 74.0 percent, and 25.8 percent of records 

are allocated (average of the 6 years of rates in Table 3.4 ).

Table 3.1 CEQ processed expenditure reports: edit rates 

Type of Edit

Allocated* Imputed & 

Allocated

Imputed Other Edit Unedited

Year No. Expn Reports Row Percent Distribution

2010 2,272,431 8.7 0.1 7.1 0.7 83.4

2011 2,178,837 8.5 0.1 7.3 0.6 83.4

2012 2,192,794 8.8 0.1 7.5 0.6 83.0

2013 1,968,739 8.2 0.1 8.1 0.6 83.0

2014 1,901,766 8.0 0.1 8.0 0.6 83.2

2015 1,752,344 7.7 0.2 9.4 0.6 82.1

* One .G record included in "Allocated" count.
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Table 3.2. CEQ: No. of processed expenditure reports by CU’s records use status

Year Records Use Group

Missing None Records Used

No. of Reports Total

2010 16,327 946,431 1,309,673 2,272,431

2011 16,908 932,798 1,229,131 2,178,837

2012 15,478 928,292 1,249,024 2,192,794

2013 12,744 863,899 1,092,096 1,968,739

2014 11,679 909,562 980,525 1,901,766

2015 11,578 862,003 878,763 1,752,344

Table 3.3 CEQ processed expenditure records: edit type rate by Records use 

Year No. Expn 

Reports

Other Allocated* Imputed & 

Allocated

Imputed Other Edit

Row Percent Distribution

Records Used

2010 1,309,673 0.7 8.6 0.0 5.5 85.2

2011 1,229,131 0.6 8.4 0.1 5.6 85.3

2012 1,249,024 0.7 8.5 0.1 5.9 84.9

2013 1,092,096 0.7 8.0 0.0 6.2 85.0

2014 980,525 0.7 7.7 0.1 6.1 85.3

2015 878,763 0.6 7.4 0.1 7.1 84.8

None

2010 946,431 0.6 8.8 0.2 9.2 81.2

2011 932,798 0.6 8.7 0.2 9.4 81.1

2012 928,292 0.6 9.1 0.2 9.5 80.6

2013 863,899 0.5 8.4 0.2 10.3 80.5

2014 909,562 0.6 8.3 0.2 10.0 81.0

2015 862,003 0.5 8.0 0.2 11.7 79.5

Missing

2010 16,327 0.8 8.8 0.8 17.6 72.0

2011 16,908 0.8 8.6 0.6 16.3 73.8

2012 15,478 0.8 8.5 0.8 14.7 75.1

2013 12,744 0.5 8.3 0.5 16.9 73.8

2014 11,679 0.8 8.4 0.4 16.8 73.6

2015 11,578 0.8 5.8 0.8 15.0 77.6

* One .G record included in "Allocated" count.
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Table 3.4 CED processed expenditure records: edit rate

Type of Edit

Allocated * Other Edit Unedited

Year No. Expn Reports Row Percent Distribution

2010 499,712 25.4 0.2 74.4

2011 494,069 24.6 0.2 75.2

2012 487,101 25.3 0.2 74.5

2013 432,184 26.8 0.2 73.0

2014 458,421 25.9 0.1 74.0

2015 412,635 27.4 0.1 72.6

*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. 
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4. CE Income Imputation Rates

CE performs three edits relating to the imputation of income for both the Diary and Interview Surveys. The first is 

referred to as “Model-based” imputation and occurs when a CU indicates an income source but fails to report the 

amount of income received. The second is referred to as “Bracket response” imputation, and it occurs when a CU

indicates the receipt of an income source and fails to report the amount of income, but does provide a bracket 

range estimate of the amount of income received. The third income edit is referred to as “All valid blank” 

conversion, and it occurs when a CU reports that they received no income from any source, but the BLS 

determines that some income was received from at least one source. For income imputation flag values, see 

Appendix. Since the need for imputation reflects item nonresponse or 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.

CE Interview Survey (CEQ)

The proportion of CUs whose total income before tax included no imputed income sources followed an increasing 

trend from 52.5 percent in 2010 to 56.4 percent in 2015 (Table 4.1). This increasing trend appears to be driven by 

declining rates of model-based and bracket response imputation which are down by 2.1 and 0.9 percentage points 

from 2010 to 2015. The proportion of CUs whose total income before tax included both model-based and bracket 

response imputations has also decreased over this period by 0.8 percentage points. Overall, the proportion of CUs

converted from zero income to a positive income amount remained stable from 2010 to 2015 despite slight 

increases to 2.0 percent in 2013 and 2.1 percent in 2014.

CE Diary Survey (CED)

As with the CEQ, the proportion of CUs whose total income before tax included no imputed income sources 

continued an increasing trend from 46.3 percent in 2010 to 50.1 percent in 2015 (Table 4.2). This increasing trend 

appears to be driven by declining rates of model-based and bracket response imputation which are down by 1.9 

and 1.7 percentage points from 2010 to 2015. The proportion of CUs whose total income before tax included both 

model-based and bracket response imputations has also decreased over this period by 1.0 percentage points. 

However, the proportion of CUs converted from zero income to a positive income amount has increased from 3.3 

percent to 4.1 percent.

NOTE: Drop offs in income imputation rates occurred for both surveys. In 2013, substantial revisions were 

made to the income section: some questions were merged together or split into new questions. The change in 

questionnaire is likely the cause of the decrease in income imputation rates for both CED and CEQ in 2013. 

However, the continuing decline in income imputation rates observed in 2014 and 2015 are likely not attributable 
- 17 -

to this questionnaire change. If this trend continues, additional research will be necessary to determine what is 

driving this decline in income imputation rates, especially if expenditure edit rates continue to rise. From a data 

quality perspective, a decline in income imputation rates at first seems to be an unequivicably positive trend. A 

declining imputation rate suggests that respondents are providing higher quality income reports, removing the 

need to perform edits. However, declining income imputation rates could also indicate an overall decline in income 

reports if respondents are simply not reporting income sources. The rise in valid blank conversion imputation 

coupled with the increasing rates of expenditure imputation suggest that this question deserves additional 

investigation.

Table 4.1: CEQ income imputation rates

Year No. 

CUs

Not 

Imputed

Model 

Imputation Only

Bracket 

Imputation Only

Model and Bracket 

Imputation

Valid Blank 

Converted

Row percent distribution

2010 28,429 52.5 20.8 19.5 5.6 1.6

2011 26,990 52.5 20.4 19.8 5.7 1.6

2012 26,993 52.3 21.1 19.6 5.4 1.5

2013 26,108 53.8 21.4 17.6 5.2 2.0

2014 25,908 54.4 21.1 17.7 4.7 2.1

2015 23,574 56.4 18.7 18.6 4.8 1.6

Table 4.2: CED income imputation rates

Year No. 

CUs

Not 

Imputed

Model 

Imputation Only

Bracket 

Imputation Only

Model and Bracket 

Imputation

Valid Blank 

Converted

Row percent distribution

2010 14,296 46.3 25.2 19.2 6.0 3.3

2011 13,925 46.6 24.9 19.1 5.7 3.6

2012 13,761 47.9 24.8 18.8 5.0 3.5

2013 12,335 50.2 24.5 16.3 5.3 3.7

2014 13,305 50.0 23.2 18.1 5.3 3.4

2015 11,841 50.1 23.3 17.5 5.0 4.1
- 18 -

APPENDIX

Response and nonresponse rates 

Appendix Table A shows the mapping of the CE final disposition codes to the American Association for 

Public Opinion Research (AAPOR) final disposition codes for in-person household survey:

Appendix Table A. Mapping of CE final disposition codes with AAPOR codes***

AAPOR(2015, p.67) Table 2: 

Final disposition codes for in-person, household survey 

CEQ Final Disposition Codes

(OUTCOME)

CED Final Disposition Codes

(PICKCODE) 

1. Interview 1.0

Complete (I) 1.1 201 Completed interview 201 Completed interview

Partial (P) 1.2 203 Sufficient partial 

(through Section 20, no 

further follow-up )

*217 Interview- Temporarily 

Absent 

2. Eligible, Non-Interview 2.0

Refusal and break-offs (R) 2.10

 Refusals 2.11 321 Refused, hostile(A)

322 Refused, time(A)

323 Refused, language (A)

324 Refused, other (A)

321 Refused, hostile(A)

322 Refused, time(A)

323 Refused, language (A)

324 Refused, other - specify 

(A)

 Household-level refusal 2.111 na na

 Known respondent refusal 2.112 na na

Break-off 2.12 215 Insufficient partial (A)

Non-contact (NC) 2.20

 Unable to enter 

 building/reach housing unit 

2.23 219 Other (A) 219 Other (A)

 No one at residence 2.24 216 No one home 216 No one home 

 Respondent away/unavailable 2.25 217 Temporarily absent

Other (O) 2.30 324 Refused, other 324 Refused, other

 Dead 2.31 219 Other (A) 219 Other (A)

 Physically or mentally

 unable/incompetent 

2.32 219 Other (A) 219 Other (A)

 Language (did not refuse) 2.33 219 Other (A) 219 Other (A)

 Household-level language

 problem 

2.331 219 Other (A) 219 Other (A)

 Respondent language

 problem 

2.332 219 Other (A) 219 Other (A)

 No interviewer available

 for needed language 

2.333 219 Other (A) 219 Other (A)

 Miscellaneous 2.36 219 Other (A) 219 Other (A)

325 Diary placed too late (A)
- 19 -

Appendix Table A. Mapping of CE final disposition codes with AAPOR codes***

AAPOR(2015, p.67) Table 2: 

Final disposition codes for in-person, household survey 

CEQ Final Disposition Codes

(OUTCOME)

CED Final Disposition Codes

(PICKCODE) 

326 Blank diary, majority of 

items recalled w/o receipts 

(A)

3. Unknown eligibility, non-interview ** 3.0

Unknown if housing unit occupied (UH) 3.10 na na

 Not attempted or worked 3.11

 Unable to reach/unsafe area 3.17

 Unable to locate address 3.18 258 Unlocated sample 

address (C): Treated as 

ineligible for CE

258 Unlocated sample 

address (C): Treated as 

ineligible for CE

Housing unit/Unknown if eligible 

respondent (UO) 

3.20 na na

 No screener completed 3.21

 Other 3.90

4. Not Eligible 4.0

Out of sample 4.10

Not a housing unit 4.50 228 Unfit, to be demolished 

(B)

229 Under construction, not 

ready (B)

231 Unoccupied tent/trailer 

site (B)

232 Permit granted, 

construction not started (B)

240 Demolished (C)

241 House/trailer moved (C)

243 Converted to permanent 

nonresidential (C)

228 Unfit, to be demolished 

(B)

229 Under construction, not 

ready (B)

231 Unoccupied tent/trailer 

site (B)

232 Permit granted, 

construction not started (B)

240 Demolished (C)

241 House/trailer moved (C)

243 Converted to permanent 

nonresidential (C)

Business, government office, other 

organization 

4.51 243 Converted to permanent 

nonresidential (C)

243 Converted to permanent 

nonresidential (C)

Institution 4.52 Na Na

Group quarters 4.53 252 Located on military base 

or post (C) 

252 Located on military base 

or post (C)

Vacant housing unit 4.60 226 Vacant for rent (B)

331 Vacant for sale (B)

332 Vacant other (B)

341 CU moved (C)

342 CU merged with another 

CE CU within the same 

address (C)

226 Vacant for rent (B)

331 Vacant for sale (B)

332 Vacant other (B)

341 CU moved (C)

342 CU merged with another 

CE CU within the same 

address (C)

Regular, Vacant residences 4.61

Seasonal/Vacation/Temporary residence 4.62 332 Vacant other (B)

225 Occupied by persons 

with URE (B)

332 Vacant other (B)

225 Occupied by persons 

with URE (B)

Other 4.63 233 Other (B) 233 Other (B)
- 20 -

Appendix Table A. Mapping of CE final disposition codes with AAPOR codes***

AAPOR(2015, p.67) Table 2: 

Final disposition codes for in-person, household survey 

CEQ Final Disposition Codes

(OUTCOME)

CED Final Disposition Codes

(PICKCODE) 

244 Merged units within 

same structure (C) 

245 Condemned (C)

247 Unused serial number or 

listing sheet (C)

248 Other (C)

259 Unit does not exist or is 

out of scope 

290 Spawned in error (C)

244 Merged units within 

same structure (C) 

245 Condemned (C)

247 Unused serial number or 

listing sheet (C)

248 Other (C)

259 Unit does not exist or is 

out of scope 

No eligible respondent 4.70 224 All persons under 16 (B) 224 All persons under 16 (B)

Quota filled 4.80 Na Na

NOTES: 

Census Bureau non-interview categories: (A)=Type A (B)=Type B (C)=Type C

* CED: Type A code “217 – temporarily absent” is treated as “completed interview” by CE-SMD. The Diary survey is designed 

to collect data for respondents when they are at home, and the Interview survey is designed to collect data for respondents when 

they are both at home and away on trips. When everyone is away on a trip in a Diary household for the entire week, they are 

counted as completed interviews with $0 of expenditures at home. Instead, expenditures for those away on trips comes from the 

Interview survey. Since Diary and Interview data are merged or “integrated” during estimation, this practice is designed to 

capture the right amount of expenditures.

** CE does not have an “Unknown eligibility” classification because Census trains interviewers to treat any case of unknown 

eligibility as Type A. This is consistent with AAPOR recommendation: if the definitive situation for a case cannot be determined, 

one should take the conservative approach of assuming the case is eligible or possibly eligible rather than not eligible.

***Reference: The American Association for Public Opinion Research (2015). Standard Definitions: Final dispositions of case 

codes and outcome rates for surveys. 8th edition.

In the following definitions for eligible sample, response rate, refusal rate, noncontact rate, and other nonresponse rate, the formula contain the alphabets I, P, R, NC, O, which refer to groupings of final disposition codes 

that are defined in Appendix Table A above.

Eligible Sample (denominator for response, refusal, noncontact, and other nonresponse rates)

= I + P + R + NC + O

The total number of eligible units - those who completed interviews (I, P), plus non-response due to refusals, noncontact, or other reasons (R, NC, O). This excludes any address that was sampled and ineligible (for example, an 

abolished household at a sampled address or a commercial business at a sampled address). 

Response Rate (AAPOR definition RR2) 

= (I + P) / (I + P + R + NC + O)

Defined as total number of good and partial interviews (interviews that provide data for use in the production 

tables), divided by the eligible sample. For the CE, unknown eligible housing units are coded as “Eligible noninterview” (i.e. Type A).

Refusal Rate (AAPOR definition REF3)

= R / (I + P + R + NC + O)

Defined as total number of eligible non-responses that were refused or started, but not completed, divided by the 

eligible sample. Refused interviews includes refusals due to time, language problems, and other types of refusals.

Noncontact Rate (1 - AAPOR definition CON3)

= NC / (I + P + R + NC + O)
- 21 -

Defined as total number of eligible non-responses due to inability to make contact with an eligible sample unit 

member. 

Other Nonresponse Rate 

= O / (I + P + R + NC + O)

Defined as total number of eligible non-responses due to reasons other than refusal and noncontact with an 

eligible sample unit member. 

The sum of Response Rate, Refusal Rate, Noncontact Rate, and Other Nonresponse Rate comprise 100 percent of 

the universe of eligible sample units. In addition to these four rates, we also report on the Nonresponse 

Reclassification rate, which is a subset of Other Nonresponse cases.

Nonresponse reclassification rate

Defined as the total number of interviews that were changed from completed to a Type A non-interview based on 

a review of total expenditures (CE’s Minimal Expenditure Edit routine) and other information about the CU, divided 

by the eligible sample.

For the CEQ

OUTCOME = 219 Other Type A Noninterview, & TYPEASP = “Minexpn”

For the CED

INTRVIEW = 

5 Diaries with zero items reported in both weeks of the survey OR Diaries with zero items reported and the 

diary from the other diary week is a Type A, B, or C non-interview

6 Diaries with zero items reported and the diary from the other diary week has > 10 items reported in FDB with 

the total cost of these items being <= $50 OR Diaries with zero items reported and the diary from the other 

diary week has <= 10 items reported in FDB with the total cost of these items being <= $50 and the CU does 

not live in a rural area or a college dormitory and no members of the CU were away during the reference 

period

7 Diaries where there is one person in the CU and the total amount spent on food (at home and away from 

home) is <= $5 in the current week and <= $15 in the other diary week, and the number of items reported 

for non-food items in the current week is < 4 or the total cost of items reported for non-food items in the 

current week is < $30

8 Diaries where there are 2 or 3 members in the CU the total amount spent on food (at home and away from 

home) is <= $10 in the current week and <= $20 in the other diary week and the number of items reported of 

non-food items in the current week is < 4 or the total cost of non-food items reported in n the current week is 

< $30

9 Diaries where there are four or more CU members and CU the total amount spent on food (at home and 

away from home) is <= $20 in the current week and <= $30 in the other diary week and the number of items 

reported of non-food items in the current week is < 4 or the total cost of non-food items reported in n the 

current week is < $30

Summary of changes to data collection in 2015 

In this section, we provide a brief overview of the changes in data collection in 2015 that impact the universe of 

eligible sample units included in the production of CE’s official published tables, and response rate computations.
- 22 -

1. CE Sample Redesign

The CE sample is updated after every Decennial Census to ensure it reflects the population. The 2010 Decennial 

Census geographic boundaries were implemented for the CE in 2015, and are henceforth referenced as the 2010 

Sample Redesign. The first month of expenditures in 2015 that are eligible under the 2010 Sample Redesign is 

January 2015.

 CEQ: The CEQ has a three-month retrospective reference period prior to the month of data collection 

(sample month). Thus, February 2015 is the first sample month for CEQ cases under the 2010 Sample 

Redesign for producing the official published tables for 2015 (since the February 2015 sample month 

has a reference period of Nov 2014, Dec 2014, and Jan 2015).

 CED: Unlike the CEQ, the CED has a prospective 1-week reference period after the diary is placed. 

Thus, January 2015 is the first sample month for CED cases under the Sample Redesign for producing 

the official published tables for 2015.

2. Bounding Interview dropped in the CEQ 

The CEQ bounding interview in Wave 1 of the five-wave survey panel was dropped starting with the 2010 Sample 

Redesign. The bounding interview had a 1-month recall and its data had not been previously used to produce 

estimates for the CE official published tables. Thus with the dropping of the bounding interview, cases in the 2015 

CEQ survey panel under the 2010 Sample Redesign, and moving forward, will comprise of four waves of interview. 

However, due to the rotating panel design of the CEQ, there were still Wave 5 cases from CEQ survey panels that 

started in 2014 but did not complete until 2015.

In summary, the CE data used to compute final disposition rates to match the data used in the production 

of CE’s official published tables are as follows:

CEQ Prior to 2015 2015 

Calendar months of data used Jan through Dec Feb through Dec

Waves in survey panel 2 through 5 

(Wave 1 was bounding interview)

All waves

(No bounding interview)

CED Prior to 2015 2015 

Calendar months of data used Jan through Dec Jan through Dec

Diary week 1 and 2 1 and 2

 

 
- 23 -

Records use comparison groups 

Respondent use of records for reporting expenditures is an indicator of accurate reporting of the amount of 

the expense and the details about the expense. At the end of the CEQ, there is a “Post Interview for Field 

Representatives” section. One of the questions in that section asks the interviewer, “How often did the respondent 

consult records?” in the interview, and provides four response options: 

1. always or almost always (90% of the time or more)

2. most of the time (50 to 89%)

3. occasionally (10 to 49% of the time)

4. never or almost never (less that 10% of the time). 

Two comparison groups for records usage was created for analysis, “Records” vs “None”:

 Records group consisted of interviews in which the interviewer reported records were used 

occasionally, most of the time, or always;

 None group comprised of interviews in which the interviewer reported records were used never or 

almost never. 

Survey mode comparison groups 

Although the CEQ is designed to be a personal visit interview, phone interviews may be conducted where 

it would be costly in time and resources to travel to a CU. However, a personal visit is the preferred method of data 

collection because of the opportunity for the interviewer to build a rapport with the respondent, and thereby 

hopefully increase the likelihood of encouraging respondents to use records. Since use of records is highly 

desirable for meeting the objective of accurate reporting, it is important to know if records use differs by survey 

mode. In the Post Interview for Field Representatives section, the interviewer is also asked about the mode used 

to collect data for the interview, and 6 response options are provided: 

i. Personal visit for all sections, 

ii. Personal visit for all sections, but telephone follow-up for some questions

iii. Personal visit for more than half of the sections, the rest by telephone

iv. Equally split between personal visit and telephone

v. Telephone for more than half of the sections, the rest by personal visit

vi. Telephone for all sections

Three comparison groups for survey mode was created for analysis, “Personal Visit”, “Phone”, and “Mixed”:

 Personal Visit group consisted of interviews reported to be conducted by modes i. and ii

 Phone group consisted of interviews by mode vi

 Mixed group consisted of interviews by modes iii., iv, and v. 
- 24 -

Processed expenditures, Reported item counts 

Processed expenditure records

For the CEQ. The processed expenditure records file is sourced from the CE Production database Post 

Phase 3 MTAB data table for the CEQ. 

For the CED. The data source is the CE Production database Post Phase 3 EXPN data tables (EFDB, EMLS, 

ECLO, EOTH), excluding records with invalid cost values

Reported item counts.

Ideally, the reported expenditure records files would comprise the expenditure variables that correspond 

directly to the survey questions about the expenditures. However, because there are hundreds of expenditure 

variables spread across more than 40 data tables, for convenience, modifications were made to the MTAB data to 

create the reported expenditures file. The following paragraphs describe the creation of the reported expenditures 

file for the CEQ and the CED. 

For the CEQ. We made modifications to the MTAB file to attempt to get as accurate a count of the 

number of uniquely reported items for each CU as possible while still taking advantage of the convenience of the 

MTAB data file. We subset the MTAB data file to records that were unique by the combination of three variables 

on the MTAB file: CU identifier (FAMID or NEWID), SEQNO, and EXPNAME. Then, the COST_ flag associated with 

each record was used to determine the type of data edit for each record. We acknowledge these modifications are 

not comprehensive enough to capture all post-data collection edits (for example edits made to a source variable 

may not carry forward to the mapped variable and edits made to non-cost fields are not captured), but these 

modifications make some strides towards that goal.

For the CED. We made modifications to the Post Phase 3 EXPN files by extracting records that were 

unique by the combination of two variables on the EXPN files: CU identifier (FAMID or NEWID) –SEQNO. Then, the 

flag variable COST_ was used to determine the type of edit. Again, this modification does not capture the universe 

of all edits made in processing, but it does improve the accuracy of our computed edit rates relative to what has 

been reported previously.

Count of reported (expenditure) items per CU. The number of reported expenditure items by the CU was 

computed by counting the number of records in the reported expenditures file for a CU. 
- 25 -

Expenditure edit type 

CE Interview Survey (CEQ)

Interview expenditure edits are calculated using the interview monthly tabulation file (MTAB). The flag variable 

COST_ is used to identify if an expenditure was edited and what type of edit was done (imputation, allocation, 

combination, other). In addition, the “allocation number” is used to determine whether the resulting estimate has 

been allocated. The different types of edits (or non-edits) was identified by the following flag values for the CEQ:

CEQ 

MTAB 

Flag value

Flag Description Edit group Edit Subgroup

0 All of the source fields were flagged either as 0 (No Census 

adjustment) or -300 output from screens selected for 

microfilm review/no change or -400 output from screens; but 

not selected for microfilm review (no change)

Unedited NA

1 One of the source fields was flagged by Census (source flag >0) Unedited NA

2 Manually updated (expenditure flag = -100) Changed in 

superfix (not a valid data adjustment source record field [-

500]) Changed in superfix (is a valid data adjustment source 

record field [-600]) (Note: All of the following flags (3-9 & Q-S) 

indicate the source field was data adjusted by BLS. The two 

digit numbers in the parenthesis are the trailing digits of the 

source field flag, and indicate the method(s) of adjustment 

named after the parenthesis.)

Edited Other

3 (-01 through -10) IMPUTATION Edited Imputed

4 (-12 through -19) ALLOCATION Edited Allocated

5 (-20 through -27) IMPUTATION and ALLOCATION Edited Combination

6 (-30 through -32) COMPUTATION only Unedited NA

7 (-35 through -43) COMPUTATION and IMPUTATION Edited Imputed

8 (-45 through -52) COMPUTATION and ALLOCATION Edited Allocated

9 (-53 through -68) COMPUTATION, IMPUTATION and 

ALLOCATION

Edited Combination

Q (-70 through -74,-75,-76) MANUAL IMPUTATION Edited Imputed

R (-78 through -85,-86,-87,-88) MANUAL ALLOCATION Edited Allocated

S (-90) SECTION 18 SPECIAL PROCESSING Edited Other

CE Diary Survey (CED)

The diary expenditure edit rate is calculated using the expenditure files from diary. The flag variable COST_ is used 

to identify if an expenditure was edited. In addition, the “allocation number” is used to determine whether the 

resulting estimate had been allocated. An expenditure record will be considered unedited if it has one of the 

following flags:
- 26 -

CED EES 

Flag 

Value

Description Explanation

‘0’ Default - no change to data No adjustments were made during processing.

‘-3’ Reviewed, no update; default adjustment status The value was reviewed during processing, but 

no adjustments were made.

‘11’ Sales Tax, Preliminary edits, or Minimal 

expenditure reclassification edit

Sales tax is a calculation applied to the data and 

will be treated as unedited for these rates. 

‘15’ Phase 1 Confirmed. Operator/Error Resolution

Overrides (confirms value)

This flag is carried from the CAPI instrument 

and is present when a Field Representative 

suppresses a prompt to check the value 

(confirming the reported value). No changes 

are made to the data.

‘16’ Phase 1 Changed. Error Resolution Changes value This flag is carried from the CAPI instrument 

and is present when a Field Representative 

updates a value after prompted to check the 

value. Though the data is changed, it is 

assumed that it is edited based on the 

respondent’s input and not considered as 

edited during processing. 

All other flags indicate some type of adjustment during processing and are considered edited. An allocation rate is 

also produced using the allocation number of a given item (ALCNO). Any allocation number not equal to ‘000’ is an 

allocated value. It is important to note that the values that are allocated are included in the editing rate; however, 

these values may also have been edited in some other way during the processing. It is not possible to delineate 

other edits from the current data available. 

Note: for both CEQ and CED, the number of targets selected for an allocation will affect the adjustment 

rates - the total number of items that are allocated will add to both the numerator and the denominator for 

analysis using processed expenditure records (but not reported expenditure records).

Income edit type 

The CE implemented multiple imputations of income data, starting with the publication of 2004 data. Prior to that, 

only income data collected from complete income reporters were published. However, even complete income 

reporters may not have provided information on all sources of income for which they reported receipt. With the 

collection of bracketed income data starting in 2001, this problem was reduced but not eliminated. One limitation 

was that bracketed data only provided a range in which income falls, rather than a precise value for that income. In 

contrast, imputation allows income values to be estimated when they are not reported. In multiple imputations, 

several estimates are made for the same CU, and the average of these estimates is published.

Income data from the Diary Survey are processed in the same way as in the Interview Survey.
- 27 -

Imputation rates for income are calculated based on the processed CE data (EES Post Phase 3 data that 

are used to produce the published tables) for each collection period. Following the model of the production tables, 

each wave of data will be treated independently for the CE quarterly interview survey (CEQ) and each weekly diary 

are treated independently for the Diary survey (CED). Imputation rates are calculated for final income before 

taxes. The income is counted as imputed if any of its summed components were imputed during processing. This 

will be identified using the imputation indicator flag. Any value of the flag not equal to ‘100’ is considered 

imputed. 

Imputation 

Flag Value

Description

100 No imputation. This would be the case only if NONE of the variables that are summed to get 

the summary variables is imputed.

2nn Imputation due to invalid blanks only. This would be the case if there are no bracketed

responses, and at least one value is imputed because of invalid blanks.

3nn Imputation due to brackets only. This would be the case if there are no invalid blanks, and 

there is at least 1 bracketed response

4nn Imputation due to invalid blanks AND bracketing 

5nn Imputation due to conversion of valid blanks to invalid blanks. (Occurs only when initial values 

for all sources of income for the consumer unit and each member are valid blanks.)

NOTE: an all valid blank conversion rate is calculated indicating the percent of instances that were converted from 

all valid non-responses (i.e., the respondent replied that the CU did not receive income from any source) to invalid 

non-responses that were subsequently imputed during processing. This will be based on the indicator flag with a 

value of ‘500’ or above.