A Stitch in Time: 

Evaluating Seam Bias in the Redesigned SIPP

Federal Committee on Statistical Methodology

October 2022

Neil Bennett

Mark Klee

Robert Munk

The U.S. Census Bureau reviewed this data product for unauthorized 

disclosure of confidential information and approved the disclosure 

avoidance practices applied to this release. CBDRB-FY23-POP001-0002.
Motivation

• Declining labor market fluidity in the United States (Molloy et al, 

2016)

• Labor force turnover has been explored in the SIPP (Gottschalck, 

2004; Beckhusen, 2014; Ham et al., 2016)

• Seam bias has been documented in longitudinal surveys, Survey of 

Income and Program Participation (SIPP) and Panel Study of Income 

Dynamics. Authors have attempted to uncover the source of this bias. 

(Callegaro, 2007; Callegaro, 2008; Ham et al, 2009)
Research Question

• What do job transitions look like in the redesigned 2014 SIPP? 

• We find that seam bias is present among job transitions in the first week of 

the calendar year
Research Question

• What do job transitions look like in the redesigned 2014 SIPP? 

• We find that seam bias is present among job transitions in the first week of 

the calendar year

• How do job transitions in the SIPP compare to job transitions from 

other data sources?

• What could be driving this bias?

• Imputation, survey feedback, proxy interviews
Snapshot of Findings

• Seam bias among job transitions in the 2014 SIPP

• The average turnover rate in the first week of the year is 20 

percent, relative to an average of 1 percent among all other weeks 

of the year

• Job Openings and Labor Turnover Survey (JOLTS) shows some 

indication of job cyclicality in January, but not of the same magnitude 

as in SIPP

• Little evidence that bias is linked to imputation, survey feedback, or 

proxy interviews
Data

• 2014 SIPP

• Calendar years 2013-2016

• Use an employment recode (RWKESR) to generate a person-week panel for 

labor force participation

• SIPP collects information on seven jobs for any given respondent, we use 

information about individual jobs to develop a measure of job transitions
Definitions

• Employment Transition: A change in employment status between two 

consecutive weeks

• Move to employment: A person goes from being not employed (either not in 

the labor force or unemployed) to employed

• Job separation: A person goes from being employed to being not employed 

(either not in the labor force or unemployed)

• Job transition: A person moves from one job to another over the course of 

two consecutive weeks

• Primary employer: The employer for which the person worked the most hours 

during the week

• Turnover Rate: The total number of employment transitions divided by the total 

number of employed people within a week
Weekly Turnover Rate: January 2013 to December 2016

Turnover rate = 

Number of Events / 

Total Employment

= Standard Errors

Monthly Transitions by Type of Transition

• Plot shows the overall 

number of transitions as 

opposed to the relative 

change between types of 

job transitions

• Largest jumps in job 

transitions and transitions 

to employment

• Smallest jump in job 

separations
How Does the SIPP Compare to JOLTS?

• The Job Openings and Labor Turnover Survey (JOLTS) 

• Monthly data on job openings, hires, and separations

• Produced by the BLS and draws its frame from the QCEW

• New establishments are added in January of each year, and followed for three 

years

• Hires are measured as total nonfarm hires, not seasonally adjusted

• Separations are the total number of quits, layoffs, and discharges 

from a job
JOLTS: Number of Hires and Separations Over Time

• There does appear to be a ‘spike’ 

in the number of turnover 

events in January

• Not of the same magnitude as 

what we see in the SIPP

• Additional indication of 

cyclicality in transitions over the 

course of the year
Comparing Job 

Transitions Between 

SIPP and JOLTS

• In January, there are 18 million more 

transitions in the SIPP on average, relative 

to December

• JOLTS estimates about 1.3 million more 

transitions in January, relative to 

December

TTTTTTTTTTTTTTTTssmmmm = αα + γγmm + δδ + εεmmmm

 (1) (2) 

 JOLTS SIPP

January 1,350*** 18,000***

 (152) (6540) 

February -627*** -788 

 (178) (871) 

March 208 -115 

 (162) (828) 

April 1,510*** 232 

 (130) (914) 

May 1,840*** 982 

 (141) (1030) 

June 2,150*** 2230** 

 (139) (912) 

July 2,100*** 773 

 (146) (1034) 

August 2,830*** 2,150* 

 (170) (1110) 

September 1,950*** 1,430 

 (196) (968) 

October 1,850*** 359 

 (190) (1250) 

November 417*** 215 

 (133) (1100) 

Average Depend 10,000 6,630

Year Fixed Effec X X

Observations 48 48

Dependent variable is number of people who either moved 

into a new job or separated from a job, in thousands. 

Standards errors are robust. Significance is given at the * 

p< 0.1, ** p<0.05, and *** p< 0.01 levels.
Visualizing this 

Comparison • Standard errors are robust

TTTTTTTTTTTTTTTTssmmmm = αα + γγmm + δδ + εεmmmm

The average annual transitions in SIPP and JOLTS are not statistically different for 2014-2016
What Could be Driving the Seam Bias?

• It is possible that several methods for data collection in the SIPP could 

result in a greater amount of seam bias

• Type of Interview- May observe more bias in proxy interviews if people have 

trouble recalling employment changes for others

• Survey Feedback- May observe more bias if people do not agree to have their 

information recalled to them in later waves

• Imputation- Differences in employment turnover based on how responses 

are imputed
Seam Bias and Type of Interview

• In all years, seam bias 

appears to be present 

for each different 

interview type

• While proxy reporting 

appears to be larger in 

later years, it is not 

significantly different 

from self-reported interviews

• Type Z interviews do 

appear to have less 

seam bias (and are 

generally noisier in 

other weeks), but 

there is still significant 

seam bias
Seam Bias and Survey Feedback

• When people have 

interviews fed back 

to them in later 

survey years, there 

is still evidence of 

seam bias

• Opposite of what 

we might expect

• There does not 

appear to be seam 

bias in instances of 

no feedback
Seam Bias and Imputation

• When the beginning 

month of a spell is 

imputed, relative to 

reported cases and the 

start week being 

imputed, the seam bias 

appears to be higher • Though, there is still 

seam bias in reported 

cases and cases where 

start week is imputed
Discussion and Conclusion

• Seam bias is present in job turnovers in the 2014 SIPP

• More prevalent in SIPP than in other surveys

• Differences in magnitude of the seam bias based on the type of employment 

transition

• Not related to imputation or proxy interviews
Next Steps

• Finding potential solutions

• LFSB is considering changes to instrument to improve survey data

• In the project

• Oaxaca-Blinder decomposition

• Comparing job transitions with administrative data
Thank you!

• Neil Bennett

• Neil.bennett@census.gov

• Mark Klee

• Mark.a.klee@census.gov

• Robert Munk 

• Robert.o.munk@census.gov