The CE 2017 Data Quality Profile

September 7, 2018 

Evan Hubener, Clayton Knappenberger, Yezzi Lee, & Lucilla Tan 

Consumer Expenditure Surveys Program Report Series 
 

Dedication 

We dedicate this report in honor of the memory of our colleague, Scott S. Fricker, Office of Survey 

Methods Research. Scott had significantly contributed to the development of the data quality 

framework and infrastructure to support and sustain the routine production of data quality profiles for 

the CE Program. 
Contents 

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

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

1. Final disposition rates of eligible sample units..................................................................................................................... 5

2. Records use in the CEQ........................................................................................................................................................ 9

3. Expenditure data edit rate................................................................................................................................................... 11

4. Income imputation rates..................................................................................................................................................... 14

7. Conclusion.......................................................................................................................................................................... 18

APPENDIX: Definitions........................................................................................................................................................ 19

Response and nonresponse rates ........................................................................................................................................ 19

Summary of changes to data collection in 2015................................................................................................................. 23

Records use comparison groups......................................................................................................................................... 24

Reported expenditures........................................................................................................................................................ 24

Expenditure edits................................................................................................................................................................ 25

Income imputation.............................................................................................................................................................. 27
Consumer Expenditure Surveys 2017 Data Quality Profile | 1

Overview 

The Consumer Expenditure Surveys Program (CE) has historically provided a variety of metrics for data users to evaluate 

the overall quality of its products. Official tables provide standard errors, the public-use microdata user documentation

provides response rates, and the datasets contained in the public-use microdata provide all the variables and flags necessary 

for users to create their own quality measures. One of the goals of the CE is the routine production of an annual Data 

Quality Profile (DQP) that reports on a comprehensive set of metrics tracked over time for the CE Interview Survey (CEQ) 

and the CE Diary Survey (CED). For external users, these metrics are an indication of data quality; for internal users, the 

metrics are actionable and provide a basis for survey improvements. Since the quality of survey estimates is affected by 

errors that occur anywhere in the survey lifecycle, it is expected that the set of DQP metrics will evolve over time as the CE 

continually researches methods to monitor and improve data quality. 

 The 2017 CE Data Quality Profile is the third in a series of iterations towards an annual CE DQP. The metrics in 

this report include indicators for Measurement, Nonresponse, and Processing error. The metrics are based on data collected 

in 2010 through 2017. Future metrics will measure Coverage error and Sampling error. The data quality dimension that 

each metric is an indicator for is denoted by a “” in the following table: 

Metrics reported in the CE 2017 Data Quality Profile 

Metric

Total Survey Error dimensions

associated with the metric

Measurement Nonresponse Processing

Final disposition rates of eligible units in the 

CEQ and CED 

Records use by CEQ respondents 

Edit rate of reported expenditures in the CEQ 

& CED

  

Edit rate of income in the CEQ and CED   

 

In the next section, we present visualizations to highlight findings about the metrics. The subsequent sections provides 

detailed metric tables and interpretations. Definitions used to construct the metrics appear in the Appendix. 
Consumer Expenditure Surveys 2017 Data Quality Profile | 2

Highlights 

In this section, we highlight metric trends for the reporting period (2010 through 2017) with the description below and a 

panel of graphs in Figure 1. Further details about the individual metrics and detailed data tables are provided in the sections 

that follow the visual summary. 

Trends for concern 

The trend of declining response rates continued for both the CEQ and CED through 2017 (see Section 1). The CE 

Program currently has a nonresponse study underway to better understand if and how differences between 

respondents and nonrespondents affect nonresponse bias of key survey estimates. Survey refusal rates continued 

to rise in 2017 for both the CEQ and CED. For the CEQ, the issue of time was a dominant reason for refusals. For 

the CED, changes in diary placement procedures in 2017 largely account for the shift in nonresponse because of 

“other reasons” to nonresponse because of refusals. Nonresponse reclassification rates as a proportion of “Other 

reasons for nonresponse” also rose in 2017 for both CEQ and CED, although it consistently remains much lower in 

the CEQ.

Trends that are encouraging 

While the overall edited reported expenditures for the CEQ remained relatively constant between 2015 and 

2017, there was an increase in allocation rate offset by a decrease in imputation rate in 2017. This is largely 

attributable to modifications made to the processing procedure for Section 4 Part A (Telephone, Internet, and 

Cable/Satellite TV Expenses) that sought to preserve reported expenditure totals and component proportions in the 

imputation process (see Section 3). The overall edited reported expenditures for the CED has also remained 

relatively constant and has been consistently lower than the CEQ. 

The trend for unimputed income sources in “Total CU income before tax” continued to rise for both the CEQ and 

the CED in 2017, due primarily to the decline in the rate of model-based imputation (see Section 4). For two 

dominant income sources examined – Salary/wage, and Social Security/Retirement benefits – the prevalence of 

model-based imputation declined for both these sources in the CEQ and for Salary/wage in the CED. 

Athough still not more than half the CEQ respondents are reported to use records, the prevalence of declining 

overall record use among CEQ respondents between 2010 and 2015 appears to have halted with a slight upturn in 

2016 and 2017(see Section 2).
Consumer Expenditure Surveys 2017 Data Quality Profile | 3

Figure 1. Select metric trends from 2010 to 2017
Consumer Expenditure Surveys 2017 Data Quality Profile | 4

Figure 1. Select metric trends from 2010 to 2017
Consumer Expenditure Surveys 2017 Data Quality Profile | 5

1. Final disposition rates of eligible sample units 

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 interviewercoded “completed interview” for a CU to “nonrespondent” if too few purchases are reported; this type of edit is 

referred to as the nonresponse reclassification. A mapping of the CE to AAPOR final disposition codes for inperson, 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, 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 CEQ and CED. 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, all else being equal, lower 

reclassification rates are desired. 

Response rates can be reported unweighted or 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 typically used are base weights (the inverse probability of selecting the sample units). Since 

past analyses found weighted and unweighted rates to be similar in both the CEQ and CED, only unweighted rates 

are presented in this report. 
Consumer Expenditure Surveys 2017 Data Quality Profile | 6

 Each survey wave is treated as independent from another wave in computing official CEQ survey 

estimates, so each survey wave is treated as independent from another wave for computing CEQ response rates. 

Similarly for the official CED survey estimates and response rates, each diary week is treated as independent from 

another diary week. In the tables on final disposition rates shown in this section, the number of eligble CUs is the 

number of CU addresses eligible for the interview in the collection period, and thus are not counts of unique CUs. 

 

CEQ disposition rates 

CEQ response rates declined from 73.4 percent to 60.9 percent between 2010 and 2017 (Table 1.1). This 

decline is primarily attributable to a rise in refusal rates, from 18.6 percent to 29.1 percent between 2010 and 2017. 

Interviewers have several pre-coded options for describing a refusal, which are: “Refusal: Hostile respondent”, 

“Refusal: Time related excuses”, “Refusal: Language problems”, and “Refusal: Other”. There was an increase in 

rates of “Refusal: Time related excuses” and “Refusal: Other” in 2017. Interviewer notes for “Refusal: Other” 

showed a high prevalence of these keywords: “silence”, “avoidance”, “interest”, and “privacy.”. Nonresponse 

reclassification rates as a proportion of “Type A Other nonresponse” increased from 0.6 percent to 3.1 percent 

between 2016 and 2017 (Table 1.2).

Table 1.1 CEQ distribution of final dispositions for eligible CUs (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

2016 40,375 63.0 25.8 6.5 4.7

2017 40,193 60.9 29.1 5.4 4.5

* [1] For years prior to 2015, bounding interviews in Wave 1 were excluded and only Waves 2 

through 5 were used in computing final disposition rates. Starting in 2015, the bounding 

interview was dropped from the CEQ so all four waves (1 through 4) were used in computing 

final disposition rates. [2] Data from January 2015 were excluded due to sample redesign. 
Consumer Expenditure Surveys 2017 Data Quality Profile | 7

Table 1.2 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

2016 40,375 1,884 12 0.6 0.030

2017 40,193 1,861 58 3.1 0.144

* [1] For years prior to 2015, bounding interviews in Wave 1 were excluded and only Waves 2 

through 5 were used in computing final disposition rates. Starting in 2015, the bounding interview 

was dropped from the CEQ so all four waves (1 through 4) were used in computing final 

disposition rates. [2] Data from January 2015 were excluded data due to sample redesign. 

CED disposition rates

CED response rates have been consistently lower than the CEQ, but moved closer to the CEQ in 2017. The 

CED response rate fell from 71.5 percent in 2010 to 56.7 percent in 2016, but rose to 58.0 percent in 2017 (Table 

1.3). A diary placement procedural change implemented in 2017 likely explains this increase in response rates: the 

“Early Placement Date” was eliminated so interviewers had the full month to place Week 1 and Week 2 diaries. This 

change reduced the incidence of nonresponse from a diary being placed too late). A second procedural change in 

2017 was the elimination of “double placement” of diaries (i.e. the two one-week diaries were placed 

simultaneously instead of sequentially) with the sample unit. However, these procedural changes may have 

decreased one type of nonresponse but increased another type of nonresponse – while there was a sharp decline in 

Other nonresponse rate from 20.2 percent to 11.9 percent in 2017, there was a sharp rise in refusal rates from 16.7 

percent in 2015 to 24.2 percent in 2017. The longer window for diary placement reduced nonresponse due to diaries 

being placed too late, but the refusal rate also increased since more eligible CUs who were contacted refused 

participation in the survey. 

The CED noncontact rate rose from 3.6 percent to 5.8 percent from 2010 to 2017 (Table 1.3). The 

nonresponse reclassification rate among eligible CUs declined from 6.0 to 4.9 percent from 2010 to 2017 (Table 

1.4). The nonresponse reclassification rate as a proportion of Other nonresponse declined from 43.0 percent to 24.3 

percent between 2010 and 2016, but rose sharply to 41.1 percent in 2017. 
Consumer Expenditure Surveys 2017 Data Quality Profile | 8

Table 1.3 CED distribution of final dispositions for eligible CUs (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

2016 20,391 56.7 17.4 5.7 20.2

2017 20,110 58.0 24.2 5.8 11.9

Table 1.4 CED: prevalence of nonresponse reclassifications (unweighted)

Nonresponse reclassifications

Year No. eligible 

CUs

No. other 

nonresponse

No. CUs Other

nonresponse (%)

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

2016 20,391 4,124 1,001 24.3 4.9

2017 20,110 2,397 985 41.1 4.9
Consumer Expenditure Surveys 2017 Data Quality Profile | 9

2. Records use in the CEQ 

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

lower measurement error. Thus, a higher prevalence of records use is desirable. Respondents to interviews 

conducted by personal visit and by telephone, and whose data were used to produce CE’s official tables, were 

included for this analysis. 

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.) Records usage is 

optional for respondents, so it is likely that respondents who do choose to use any records at all – even if only for 

occasional reference 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 

“No records”, where the latter group consisted of CUs whom the interviewer reported as using records “never or 

almost never (less than 10% of the time).” It is important to note that respondents’ use of records is reported by 

interviewers based on their subjective judgement at the end of the interview. In addition, interviewers need not 

respond to the question to close out the case, hence the high incidence of item nonresponse for the records use 

question. 

Overall, the prevalence of records use declined slightly from 51.3 percent to 50.8 percent between 2010 and 

2017 (see Table 2.1, last column). This overall trend obscures the 6.8 percentage point decline in records use from 

2010 to 2015 and the subsequent 6.3 percentage point rise in records use from 2015 to 2017. From 2010 to 2014, 

records use by wave tended to follow a similar pattern of declining from waves 2 to 4 and then rising in wave 5. 

Beginning in 2015, this pattern mostly disappeared with records use declining in every wave except for the final 

wave of 2017 where records use increase by about 2 percentage points.

Table 2.1 Prevalence of records use among CEQ respondents by survey wave

Year Wave* No. of 

respondents

Missing No record Record use Overall 

record use

2010 2 7,040 1.1 46.9 52.0 51.3

2010 3 7,028 0.7 49.0 50.3

2010 4 7,073 0.7 49.2 50.1

2010 5 7,288 0.5 46.9 52.6

2011 2 6,898 1.2 47.6 51.2 50.0

2011 3 6,664 0.7 50.5 48.8

2011 4 6,605 0.6 50.5 48.9
Consumer Expenditure Surveys 2017 Data Quality Profile | 10

Table 2.1 Prevalence of records use among CEQ respondents by survey wave

Year Wave* No. of 

respondents

Missing No record Record use Overall 

record use

2011 5 6,823 0.9 48.0 51.1

2012 2 6,727 1.1 46.5 52.3 50.7

2012 3 6,654 0.7 49.0 50.3

2012 4 6,709 0.6 50.1 49.4

2012 5 6,903 0.8 48.4 50.8

2013 2 6,565 0.9 48.9 50.2 48.8

2013 3 6,444 0.5 52.3 47.3

2013 4 6,430 0.7 51.5 47.8

2013 5 6,669 0.9 49.0 50.0

2014 2 6,599 0.7 53.3 46.0 45.4

2014 3 6,429 0.5 54.3 45.2

2014 4 6,364 0.5 54.8 44.7

2014 5 6,516 0.9 53.6 45.5

2015 1 5,432 1.1 53.9 46.0 44.5

2015 2 4,825 0.4 55.0 44.6

2015 3 4,789 0.4 55.5 44.1

2015 4 4,779 0.6 54.8 44.5

2015 5 3,749 0.7 56.5 42.7

2016 1 6,612 1.0 42.5 56.5 48.8

2016 2 6,293 0.5 52.2 47.3

2016 3 6,176 0.5 54.5 45.0

2016 4 6,360 0.8 53.3 45.9

2017 1 6,303 1.0 48.6 50.3 50.8

2017 2 6,037 0.5 49.2 50.3

2017 3 5,912 0.6 49.1 50.3

2017 4 6,227 0.7 47.0 52.3

* For years prior to 2015, bounding interviews in Wave 1 were excluded and only Waves 2 through 5 

were used in computing official CE tables. Starting in 2015, the bounding interview was dropped from 

the CEQ so all four waves (1 through 4) were used in computing official CE tables.
Consumer Expenditure Surveys 2017 Data Quality Profile | 11

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 performs 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 with the exception of calculations (e.g.. 

conversion of weekly value to quarterly value). 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 replaces missing or invalid entries 

 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. 

 Ideally, the computation of edit rates are based on the edit flag values of the expenditure variables that 

correspond directly to the survey questions about the expenditures. Since there are hundreds of expenditure variables 

spread across more than 40 data tables that currently cannot be easily identified, we modified the processed 

expenditure interview monthly tabulation file (MTAB) data files to create a file of reported expenditure records 

before data editing. The modifications made to MTAB data are described in the Appendix. We acknowledge that the 

current modifications made are not comprehensive enough to remove all post-data collection computed or derived 

expenditure variables, but these modifications do make some strides towards that goal. 
Consumer Expenditure Surveys 2017 Data Quality Profile | 12

CEQ edit rates 

Between 2010 and 2017 the overall edit rate of reported expenditures increased from 14.5 percent to 16.9, 

respectively (Table 3.1). Imputation rates rose from 8.3 percent to 11.3 percent between 2010 and 2016. In 2017, 

imputation rates decreased to 6.0 percent. This decline in imputation rates is offset by a similar increase in allocation 

rates and is attributable to a change in how expenditures are processed in Section 4 Part A (Telephone, Internet, and 

Cable/Satellite TV Expenses). Combined bills of some combination of telephone, internet, and television that were 

previously imputed are now allocated. The processing change was motivated by a desire to keep as much reported 

data as possible. For example, suppose a respondent reports having spent a combined $100 on their cable and 

internet bill but did not break down the expenditure into cable and internet components. This expenditure might 

previously have been overwritten via a hot deck imputation using a complete donor record of $120 in total with $60 

on cable and $60 on internet. Beginning in 2017, these expenditures are allocated based on calculated proportions 

derived from the pool of potential donors to allocate the $100 total into cable and internet components – say $45 and 

$55 if the proportions are 45% for cable and 55% for internet. This change allows us to provide complete 

expenditures for microdata users, while preserving reported expenditure totals and component proportions. 

Summary statistics (e.g.. means and variances) will reflect a more complete use of reported data. 

CED edit rates 

In general, edit rates for CED are lower than edit rates for CEQ. Between 2010 and 2017, the overall edit 

rate for reported expenditure records increased from 9.4 percent to 10.8 percent (Table 3.2). Almost all edits in CED 

are allocations. The allocation rate increased from 9.1 percent to 10.7 percent from 2010 to 2017. The other edits 

category encompasses all other expenditure edits including manual edits. 

Table 3.1 CEQ reported expenditure records: edit type rate

Type of Edit

Allocated* Imputed & 

allocated

Imputed Other edit Unedited

Year No. expn reports Row percent distribution

2010 1,269,117 5.1 0.1 8.3 1.0 85.5

2011 1,218,306 5.1 0.1 8.8 0.9 85.1

2012 1,211,102 5.3 0.1 9.2 1.0 84.4

2013 1,122,318 4.9 0.1 9.7 1.0 84.4

2014 1,095,782 4.7 0.1 9.7 0.9 84.6

2015 1,003,449 4.5 0.1 12.0 0.9 82.5

2016 1,097,966 4.8 0.1 11.3 0.8 83.0

2017 1,108,023 10.1 0.2 6.0 0.6 83.1

*One invalid blank expenditure record included in the “Allocated” count.
Consumer Expenditure Surveys 2017 Data Quality Profile | 13

Table 3.2 CED reported expenditure records 

Type of edit

Allocated * Other edit Unedited

Year No. expn reports Row percent distribution

2010 410,064 9.1 0.3 90.6

2011 407,758 8.6 0.3 91.1

2012 400,708 9.2 0.2 90.5

2013 349,749 9.5 0.3 90.3

2014 375,720 9.6 0.1 90.2

2015 336,514 10.9 0.1 89.0

2016 334,443 11.1 0.1 88.8

2017 363,040 10.7 0.1 89.2

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

allocated records manually corrected to a single record without the allocation variable being 

reset to 0. 
Consumer Expenditure Surveys 2017 Data Quality Profile | 14

4. Income imputation rates 

 

The CE Program performs three types of imputations for income in the CEQ and CED. The first is “Model-based” 

imputation for when the respondent indicates an income source but fails to report an amount of income received. 

The second is “Bracket response” imputation for when the respondent indicates the receipt of an income source, 

fails to report the exact amount of income but does provide a bracket range estimate of the amount of income 

received. The third type of income imputation is referred to as “All valid blank” (AVB) conversion for when the 

respondent reports no receipt of income from any source, but the CE Program imputes receipt from at least one 

source. Flag indicators for income imputation are described in the Appendix. Since the need for imputation reflects 

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. 

CEQ imputation rates 

The prevalence of unimputed total family income before tax exhibited an increasing trend from 52.5 

percent in 2010 to 57.9 percent in 2017 (Table 4.1). This increasing trend appears to be driven by declining rates of 

model-based imputation and bracket response imputation, which decreased by 2.7 and 1.2 percentage points from 

2010 to 2017, respectively. The prevalence of combined model-based and bracket response imputation also 

decreased over this period by 1.3 percentage points. Overall, prevalence of all valid blank conversions from zero 

income to a positive income amount remained stable around 1.5 percent between 2010 to 2017, despite a slight 

increase to 2.1 percent in 2013 and 2014. 

 New for the 2017 DQP are metrics for income imputation rates of two major components of total family 

income before tax: total amount of income received from salary or wages and total amount of Social Security 

benefits and Railroad Retirement benefits. For salary and wages, the share of unimputed income increased from 68.6 

percent in 2010 to 71.5 percent in 2017 (Table 4.2). Model-based imputation decreased from 15.2 percent in 2010 to 

12.5 percent in 2017. Bracket-only imputation has remained constant from 14.6 percent in 2010 to 14.5 percent in 

2017 with some fluctuation in between. Model and bracket imputation has also remained constant, fluctuating 

between 1.5 and 1.8 percent during the period. Income imputation rates for Social Security benefits remained fairly 

constant between 2010 and 2017 at around 93 percent (Table 4.3).

CED imputation rates 

 Similar to the CEQ, the prevalence of unimputed total income before tax exhibited an increasing trend, 

from 46.3 percent in 2010 to 54.1 percent in 2017 (Table 4.4). This increasing trend appears to be driven by 

declining rates of model-based imputation which declined by 6.2 percentage points from 2010 to 2017. Bracket 

Imputation increased from 16.3 percent in 2013 to 19.6 percent in 2017. Other imputation rates declined slightly

between 2010 and 2017. 

 
Consumer Expenditure Surveys 2017 Data Quality Profile | 15

 Imputation rates for CED reports of total salary and wages and total amount of Social Security and 

Railroad Retirement benefits are also reported. For salary and wages (Table 4.5), the share of unimputed income 

increased from 65.6 percent in 2010 to 69.6 percent in 2017. Model based imputation fell from 19.3 percent in 2010 

to 14.2 percent in 2017. Bracket imputation increased from 13.5 percent to 14.6 percent. Model and bracket 

imputation remained fairly constant over the period. Social Security and Railroad Retirement benefits are presented 

in Table 4.6. All five imputation methods changed by less than one percentage point over the 2010 to 2017 period. 

Unimputed and bracket imputation rates were up slightly, and model-only and valid blank converted imputation 

rates are down slightly. 
Consumer Expenditure Surveys 2017 Data Quality Profile | 16

Table 4.1 CEQ income imputation rates for total amount of family income before taxes

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 2.1

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

2016 25,441 56.9 18.1 18.8 4.5 1.6

2017 24,479 57.9 18.1 18.3 4.3 1.5

Table 4.2 CEQ income imputation rates for total amount of income received from salary or wages

Year No. 

CUs

Not 

imputed

Model 

imputation only

Bracket 

imputation only

Model and bracket 

imputation

Valid blank 

conversion*

Row percent distribution

2010 28,429 68.6 15.2 14.6 1.6 na

2011 26,990 68.6 15.0 14.7 1.7 na

2012 26,993 68.2 15.1 14.9 1.8 na

2013 26,108 69.6 14.7 14.0 1.6 na

2014 25,908 69.7 14.5 14.0 1.7 na

2015 23,574 70.8 12.9 14.7 1.7 na

2016 25,441 70.7 12.5 15.1 1.7 na

2017 24,479 71.5 12.5 14.5 1.5 na

* Receipt of salary or wages is not imputed through this process.

Table 4.3 CEQ income imputation rates for total amount of Social Security and Railroad Retirement

benefits

Year No. 

CUs

Not 

imputed

Model 

Iimputation only

Bracket 

imputation only

Model and bracket 

imputation

Valid 

blank conversion

Row percent distribution

2010 28,429 93.4 3.9 2.0 0.1 0.7

2011 26,990 93.5 3.5 2.2 0.1 0.7

2012 26,993 93.0 4.0 2.2 0.1 0.7

2013 26,108 92.8 4.1 2.2 0.0 0.9

2014 25,908 92.9 4.0 2.2 0.1 0.8

2015 23,574 92.9 3.5 2.8 0.1 0.7

2016 25,441 93.2 3.5 2.3 0.1 0.8

2017 24,479 93.3 3.4 2.4 0.1 0.7
Consumer Expenditure Surveys 2017 Data Quality Profile | 17

Table 4.4 CED income imputation rates total amount of family income before taxes

Year No. 

CUs

Not 

imputed

Model 

imputation only

Bracket 

imputation only

Model and bracket 

imputation

Valid 

blank conversion

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

2016 11,552 49.1 23.5 18.7 5.4 3.3

2017 11,658 54.1 19.0 19.6 5.3 2.0

Table 4.5 CED income imputation rates total amount of income received from salary or wages

Year No. 

CUs

Not 

imputed

Model 

imputation only

Bracket 

imputation only

Model and bracket 

imputation

Valid 

blank conversion*

Row percent distribution

2010 14,296 65.6 19.3 13.5 1.6 na

2011 13,925 65.8 19.3 13.6 1.3 na

2012 13,761 66.2 19.1 13.3 1.3 na

2013 12,335 68.0 18.0 12.5 1.5 na

2014 13,305 68.6 16.5 13.6 1.4 na

2015 11,841 68.7 16.7 13.1 1.5 na

2016 11,552 66.3 16.9 15.1 1.6 na

2017 11,658 69.6 14.2 14.6 1.6 na

* Receipt of salary or wages is not imputed through this process

Table 4.6 CED income imputation rates for total amount of Social Security and Railroad Retirement

benefits

Year No. 

CUs

Not 

imputed

Model 

imputation only

Bracket 

imputation only

Model and bracket 

Imputation

Valid 

blank conversion

Row percent distribution

2010 14,296 91.5 3.9 2.3 0.1 2.2

2011 13,925 91.4 3.8 2.2 0.1 2.5

2012 13,761 91.7 3.7 2.0 0.1 2.4

2013 12,335 90.6 3.8 2.8 0.1 2.7

2014 13,305 91.8 3.4 2.7 0.1 2.0

2015 11,841 91.7 2.8 2.7 0.1 2.7

2016 11,552 91.8 3.0 2.8 0.1 2.3

2017 11,658 92.3 3.3 3.1 0.2 1.2
Consumer Expenditure Surveys 2017 Data Quality Profile | 18

7. Conclusion 

This concludes the Data Quality Profile for 2017. While some trends are encouraging (e.g., edited reported 

expenditures remained relatively constant between 2015 and 2017), other trends (declining response rates) warrant 

concern, or have an uncertain significance (e.g., the trend in record use). For a brief summary of these findings, see 

the Highlights section at the beginning of this report. The next issue of the CE Data Qualtiy Profile will be be 

released in 2019, with metrics incorporating data through 2018. 
Consumer Expenditure Surveys 2017 Data Quality Profile | 19

APPENDIX: Definitions 

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: [AAPOR_StandardDefinitions 

(2016)]. 

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

AAPOR(2016, p.76) 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

 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)

320 Week 2 Diary pickup too 

early

325 Diary placed too late (A)
Consumer Expenditure Surveys 2017 Data Quality Profile | 20

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

AAPOR(2016, p.76) 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)

244 Merged units within 

same structure (C) 

233 Other (B)

244 Merged units within 

same structure (C) 
Consumer Expenditure Surveys 2017 Data Quality Profile | 21

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

AAPOR(2016, p.76) Table 2: 

Final disposition codes for in-person, household 

survey 

CEQ Final Disposition 

Codes

(OUTCOME)

CED Final Disposition 

Codes

(PICKCODE) 

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)

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: 

* 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. 

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

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 non-response 

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) 

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

member. 
Consumer Expenditure Surveys 2017 Data Quality Profile | 22

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 nonfood 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
Consumer Expenditure Surveys 2017 Data Quality Profile | 23

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. 

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
Consumer Expenditure Surveys 2017 Data Quality Profile | 24

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). 

The above response options were reworded from July 2016 on: 

1. always or almost always 

2. most of the time 

3. occasionally, or used at least one record 

4. never, no records used 

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. 

Reported expenditures 

For analysis of expenditure edit rates in this report, we made a distinction between the set of expenditure records 

based on respondent reports (“reported expenditure records”) from post-processed final set of expenditure records 

used to produce official tables and released as public-use microdata (“processed expenditure records”). The set of 

reported expenditure records is smaller than the set of processed expenditure records because during BLS data 

editing, additional expenditure records are generated (for example, due to time adjustment of a quarterly record 

value allocated to 3 monthly records values). The focus is on reported expenditures to understand how much of the 

collected data were edited. 

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. 
Consumer Expenditure Surveys 2017 Data Quality Profile | 25

Processed expenditure records. 

For the CEQ, the processed expenditure records file is sourced from the CE Production database Post Edit 

and Estimation Subsystem (EES) MTAB data table for the CEQ. For the CED, the data source is the CE Production 

database Post EES EXPN data tables (EFDB, EMLS, ECLO, EOTH). TARGRTYP=”c” is a result of a blank or 

invalid cost in ECOM. 

Reported expenditure records. 

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

for the number of uniquely reported items for each CU, 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 EES 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.

Expenditure edits 

CEQ 

 Interview expenditure edits are calculated using the MTAB data. 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
Consumer Expenditure Surveys 2017 Data Quality Profile | 26

CEQ 

MTAB 

Flag 

value

Flag Description Edit group Edit 

Subgroup

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

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: 

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. 
Consumer Expenditure Surveys 2017 Data Quality Profile | 27

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 imputation 

 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. 

Imputation rates for income are calculated based on the processed CE data (Post EES 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 is 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.)
Consumer Expenditure Surveys 2017 Data Quality Profile | 28

 “All Valid Blank” (AVB) conversion rate 

This measure quantifies the instances when all valid non-responses (i.e., the respondent replied that the CU did not 

receive income from any source) are converted to invalid non-responses, which were subsequently imputed during 

processing. This will be based on the indicator flag with a value of ‘500’ or above. 

NOTE: AVB conversion is not applicable to wage or salary income (SALARYX in the CEQ, or WAGEX in the 

CED). The reason is that AVB consumer units are by definition "non-earners." That is, if the respondent answered, 

"no" to every income receipt question, including SALARYX or other labor income (NONFARMX and FFRMINCX 

before 2013Q1, SEMPFRMX after), then the "earner" variables such as INCWEEKQ, INC_HRSQ, and 

NO_EARNR should equal 0. However, if INCWEEKQ, INC_HRSQ or NO_EARNER is positive (or perhaps even 

if invalidly blank), someone must have earned something, and therefore, the consumer unit is not a true AVB case.