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Exploring the Characteristics of 

Partial Interviews in the 

Consumer Expenditure Survey

Laura Erhard, Supervisory Economist

Division of Consumer Expenditure Surveys

CE Survey Methods Symposium

July 16, 2019
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Outline

 Brief overview of the CE

 Response Rates and Partial Interviews

 Research Questions

 Findings
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Consumer Expenditure Survey

 Collects spending data on the U.S. 

Population

 Sponsored by the Bureau of Labor 

Statistics, collected by the Census Bureau

 Survey participants report dollar amounts 

for all non-investment purchases. 

Business expenses and reimbursements 

are excluded.

 Provide expenditure weights for the U.S. 

Consumer Price Index (CPI)

 Quarterly Interview and Diary Survey
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Data

 2017 CE Quarterly Interview (CEQ)

Personal interview

Rotating panel, collected quarterly, each 

household interviewed 4 times.

3-month recall

Designed to collect larger, recurring 

expenses that are easy to recall
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Response Rates and Partials

2017 CEQ n Rate (% eligible)

Eligible 40,193 100

Complete Interviews 24,479 60.9

Type A Non-interview 15,714 39.1

Insufficient Partials ?? ??

 In CE, interviews are counted as complete if they cover all expenditure 

sections (through section 20). 

 “Sufficient Partials” are interviews that complete everything, but the 

income, assets, and liability questions (section 21 and 22).

 “Insufficient Partials” are classified as Type A non-interviews and are 

indistinguishable based on our final interview classification codes.
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Research Questions

 Who are those that drop out?

 Can they help provide insight on other non-responders?

 Can their break-off point inform us about survey design?

 Could their data somehow be used in our processing?
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Defining a Partial: Data

 Audit trail data: paradata created during a Blaise programmed 

Computer Assisted Personal Interview (CAPI) instrument that 

records a detailed history of the sequence, timing, and flow of 

an interview

Summarized Audit Trail timing data in survey files

Detailed, accessible audit trail tables
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Defining a Partial

 Type A (Noninterview) cases with time > 0 for expenditure 

sections

 Eliminate cases where cumulative expenditure time < 65 

seconds or where demographic questions were never asked.

2017 CEQ n Rate (% eligible)

Eligible 40,193 100

Complete Interviews 24,479 60.9

Type A Non-interview 15,714 39.1

Insufficient Partials 294 0.7
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Who are these partials?
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10.7%

22.6%

31.5%

35.2%

12.0%

26.1% 27.1%

34.7%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

Less than h.s. High school Some college Bachelors or higher

COMPLETE (N=24,477) TYPEA_PARTIAL (n=291)

Respondent Education Level

c

2(3) = 3.8, p = 0.28

Cramer's V = 0.01
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80.7%

11.5%

5.2%

2.6%

74.9%

16.2%

5.8%

3.1%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

White Black Asian Other

COMPLETE (N=24,479) TYPEA_PARTIAL (n=291)

Respondent Race

c

2(3) = 7.07, p=0.07

Cramer's V = 0.02
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6.5%

15.9% 15.8%

18.0%

19.6%

14.7%

9.4%

10.3%

19.6%

17.2%

18.9%

16.8%

10.3%

6.9%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

25 and under 26-35 36-45 46-55 56-65 66-75 75 and older

COMPLETE (N=24,479) TYPEA_PARTIAL (n=291)

Respondent Age

c

2(6) = 16.19, p = 0.01 

Cramer's V = 0.03
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63.6%

36.4%

59.9%

40.1%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Owner Renter

COMPLETE (N=24,479) TYPEA_PARTIAL (n=294)

Household Tenure

c

2(1) = 1.76, p = 0.18

Cramer's V = 0.01
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29.7%

34.4%

14.4%

21.5%

29.6%

26.9%

18.0%

25.5%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

1 CU member 2 CU members 3 or 4 CU members 5 or more CU members

COMPLETE (N=24,479) TYPEA_PARTIAL (n=294)

Household Size

c

2(3) = 9.60, p = 0.02 

Cramer's V = 0.02
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Region

17.8%

21.9%

35.8%

24.5% 23.8%

21.1%

29.6%

25.5%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

Northeast Midwest South West

COMPLETE (N=24,479) TYPEA_PARTIAL (n=294)

c

2(3) = 9.27, p = 0.03 

Cramer's V = 0.02
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Participation - Wave

 Wave of data

Count of

complete 

interviews

Percent of

Completes 

(n=24,773)

Percent of

Partials 

(n=294)

1 25.8 28.6

2 24.7 25.5

3 24.2 28.2

4 25.4 17.7
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Participation – other waves

 With 4 waves of data collections, what happens in the other 

waves? 

Count of

complete 

interviews

Percent of

Completes 

(n=24,773)

Percent of

Partials 

(n=294)

0 - 36.1

1 8.25 30.3

2 10.7 20.1

3 17.25 13.6 

4 63.81 -
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Where do respondents break-off?
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Where Respondents Break-Off

0

10

20

30

40

50

60

70

80

Count of respondents
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0

10

20

30

40

50

60

70

80

Count of Respondents

Where Respondents Break-Off

44.9 %
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Time with Respondent Interviewing (minutes)

Mean Mean

Completes (n=24,479) 76.5 60.3

Partials (n=294) 37.7 20.7
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How do the expenditure data compare?
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How do their expenses compare? (Rented Property)

N Mean

Average monthly rent (1st property)

Completes 8,225 $ 900

Partials 62 $ 1,100
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How do their expenses compare? (Owned Property)

N Mean

Annual property tax (1st property)

Completes 13,169 $ 3,200

Partials 98 $ 5,700

Original mortgage amount (1st loan)

Completes 8,315 $ 186,000

Partials 73 $ 349,000

Amount of last monthly payment (Fixed rate 

mortgage) (1st loan)

Completes 8,343 $ 1,500

Partials 75 $ 1,600
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How do their expenses compare? (Utilities)

N Mean

Monthly telephone/internet/cable bills 

Completes 22,571 $ 204

Partials 120 $ 216

Monthly electric/gas/water/etc

Completes 22,406 $ 203

Partials 98 214
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Research Questions

 Who are those that drop out?

Slightly younger, larger, non-white households, in the Northeast

 Can they help provide insight on other non-responders?

 Reported expenditures are higher for those that break-off

 Can their break-off point inform us about survey design?

Around 15-30 minutes seems to be where the majority of partials lose 

interest...or is it content of the sections? More research is needed.

 Could their data somehow be used in our processing?

Data for partials were very sparse, so not likely. 
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Next Steps

 Closer look at the definition of a partial (presence of 

respondent)

 Investigate respondents that do stick around for >1 hour and 

then drop out

 Look at data quality of sections (e.g. item nonresponse) for 

partials

 Investigate interviewer reported reasons that the respondents 

didn’t complete the interview (“doorstep concerns”)
Contact Information

28 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Laura Erhard

Branch Chief, BRPD

Division of Consumer Expenditure Surveys

www.bls.gov/cex

202-691-5115

Erhard.Laura@bls.gov