1. Nonresponse and Measurement Error in Employment Research
Gerrit Müller (IAB)
joint with: Frauke Kreuter (JPSM – U Maryland), Mark Trappmann (IAB)
PASS

2. Research Questions
Do survey respondents recruited with extra effort, provide answers of lower quality?
Are cooperators more motivated to provide accurate data?
Or, are late respondents hampered by recall deficits?
How does extra effort affect total bias?
PASS

3. Survey Data Panel Study “Labor Market and Social Security” (PASS)
Dual frame survey (benefit recipients / residential population)
Wave1: 12,000 HH
20,000 P
RR1: 30.5% (within HH: 85%)
Mixed mode survey (sequential CATI -> CAPI)
PASS

4. Record Linkage I
Individual survey data linked with individual administrative data (80% of all Rs agreed; 72% successfully matched)
Administrative records on: employment, earnings, unemployment, labor market programs ,
Contact data on HH-level only

5. Record Linkage II
Administrative data linked with paradata for the gross sample of recipients (from unemployment register)
Contact data on HH-level only
Indicator for Respondents / Nonrespondents on HH-level only ,

6. Hypotheses about Measurement Error (response process model, Tourangeau ’84)
Unemployment benefit (UBII)
July 2006
Nov 2006
at time of interview
Income in month prior to interview
Occupation
Educational degree
Relationship between ME and response propensity (number of contact attempts)
PASS

7. Contact Quintiles and Follow-Up Efforts
# of contacts:
_min
_max
Q1 (high contactability) 1 2 Q2 3 4 Q3 5 7 Q4 8 14
Q5 (low contactability)
≥15
Transfer CATI to CAPI
CATI NR follow-up of “soft refusals”

8. Measurement Error (in percent) by Contact Quintiles and Follow-up Efforts
UB II July 2006
UB II Interview Date
Q1 (high contactability) 12 11 Q2 13 Q3 14 Q4 16 17 Q5 20 15
To CAPI 18
CATI NR follow-up 21
PASS

9. Measurement Error by Contact Quintiles and Follow-up Efforts
Income (abs. dev.)
Years of Edu. (% mismatch)
Q1 (high contactability)
311 25 Q2
343 28 Q3
370 27 Q4
335 26 Q5
380 24
To CAPI
451
CATI NR follow-up
407
PASS

11. 5. NR-ME Bias Decomposition
for Recipient sample only
HH-level variables only (!)
UBII in Jul06 not feasible for bias decomposition
UBII in Nov
UBII at date of interview
PASS

14. “Pick your brains” ME-Model for UBII in July06 (handout)
Puzzle: high ME for the young? HH-interview by target head?
Administrative data not always „Gold Standard“ (error-free)
Assumption: ME in register data unrelated to ME in survey reports and response propensity

15. “Pick your brains” Decomposition findings statistic-specific
Extend analyses to P-level variables (e.g. employment, income)
Problem: unknown on individual level
How to go ahead?