ArticlePDF Available

Data Resource Profile: Panel Study Labour Market and Social Security (PASS)

Authors:

Figures

Data Resource Profile
Data Resource Profile: Panel Study Labour
Market and Social Security (PASS)
Mark Trappmann,
1,2
*Sebastian Ba¨hr,
1
Jonas Beste,
1
Andreas Eberl,
1,3
Corinna Frodermann,
1
Stefanie Gundert,
1
Stefan Schwarz,
1
Nils Teichler,
1
Stefanie Unger
1
and Claudia Wenzig
1
1
Panel Study Labour Market and Social Security, Institute for Employment Research, Nuremberg,
Germany,
2
Faculty for Social Sciences, Economics, and Business Administration, University of
Bamberg, Bamberg, Germany and
3
University of Erlangen-Nuremberg, Institute of Labor Market and
Socioeconomics, Nuremberg, Germany
*Corresponding author. Institute for Employment Research, Regensburger Str. 104, D-90478 Nuremberg, Germany.
E-mail: mark.trappmann@iab.de
Editorial decision 21 February 2019; Accepted 11 March 2019
Data resource basics
The Panel Study Labour Market and Social Security (PASS),
is a household panel survey of the German residential popu-
lation oversampling households receiving welfare benefits.
1
Those benefits are paid to all households with insufficient
income in which at least one person is of working age (15–
65 years) and able to work. PASS is primarily designed as a
data source for research into the labour market, poverty and
the welfare state. However, there is a focus on the social
consequences of poverty and unemployment including so-
cial exclusion and health outcomes.
Within each sampled household the head of the household
is requested to complete a household questionnaire.
Subsequently all household members aged 15 years or older
are targeted with a person questionnaire. A household is
counted as a respondent household if the household question-
naire and at least one person questionnaire have been com-
pleted. Data have been collected every year since 2006/07.
Currently eleven waves of data are available to researchers.
Figure 1 gives an overview of the number of households
in each wave. The numbers in the bars denote the propor-
tion of all respondent households from the initial wave of a
sample that are still responding in wave n. Households that
moved abroad (altogether n¼115) or in which all members
died (altogether n¼378) are subtracted from the original
sample size. New samples in PASS have response rates rang-
ing from 25 to 35% calculated as the number of interviewed
households divided by the number of households in the sam-
ple. In all waves and samples refusals followed by inability
to contact the household are the main reasons for non-re-
sponse. Detailed information on fieldwork and response
rates is documented in the methods and field reports for
each wave available at https://fdz.iab.de/en/FDZ_
Individual_Data/PASS/PASS-SUF0617v2.aspx.Thedif-
ferent samples will be described in the next section.
Data collected
PASS uses a dual-frame sampling design, combining a sam-
ple of the residential population of Germany with a sample
of welfare-benefit recipients. When combined and
weighted appropriately the complete sample can be pro-
jected to the German residential population.
2
Due to the
resulting disproportionate stratification of the sample, sta-
tistical power for analyses concerning the bottom part of
the income distribution is vastly increased.
Approximately half of the original wave 1 sample was
drawn from an address database of a commercial supplier
V
CThe Author(s) 2019. Published by Oxford University Press on behalf of the International Epidemiological Association. 1411
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
journals.permissions@oup.com
International Journal of Epidemiology, 2019, 1411–1411g
doi: 10.1093/ije/dyz041
Advance Access Publication Date: 11 April 2019
Data Resource Profile
that covered 98.4% of the buildings with private house-
holds in Germany.
3
Target households were selected by
choosing at random one household from each selected
building (see
3
for details and
4
for a short summary). The
other half of the sample was drawn from complete registers
of recipients of welfare benefits. Both samples were drawn
in a two-stage design with probability proportionate to
size (PPS) in the same 300 postcode areas that served as
primary sampling units.
The welfare-benefit recipient sample is refreshed annu-
ally by a sample of new entries to welfare benefits, who
would otherwise not be represented in the welfare-benefit
recipient sample. The population sample was refreshed be-
fore waves 5 and 11 to compensate for loss of statistical
power due to panel attrition. These refreshments were
drawn from official population registers.
The weighting scheme consists of three steps: design
weights correspond to inclusion probabilities in the gross
sample, propensity weights are estimated to compensate
for non-response/attrition, and finally weights are cali-
brated to known population totals from official statistics.
Details of the weighting scheme can be found in each year’s
data report (see
5
for the most recent one). A brief overview
is given in
2,6
. PASS provides three different cross-sectional
weighting factors on each level (household and person)
corresponding to different populations of interest: one for
the combination of all welfare-benefit samples, one for the
combination of all population samples and one for the
overall sample. In addition, staying probabilities are sup-
plied that can be used to construct longitudinal weights.
2
The PASS data are collected in a mix of computer assisted
personal interviews (CAPI) and computer assisted telephone
interviews (CATI). In waves 1–4 CATI was the default mode
for households entering the panel whenever a telephone
number was available. Since wave 5, the initial default mode
for new samples has been changed to CAPI. For each house-
hold, the previous wave mode then becomes the default
mode for the subsequent wave. Mode switches are possible
— even within households — whenever a household cannot
be located or contacted or wishes to switch mode. Details of
the fieldwork can be found in the yearly field and methods
reports (see
7
for the most recent one). In wave 11 about two
thirds of the interviews were conducted in CAPI.
Conceptually, panel members, once recruited, remain in
the panel until they die or move abroad. In practice the ma-
jority of dropout occurs due to unsuccessful follow-up.
German data-protection laws determine that refusals — un-
less they are situational, i.e. they do not generally refuse, but
state reasons that can be considered temporary (e.g. being
busy) — may not be re-approached. Temporary dropouts
due to non-contact or situational refusals are re-approached
in one more wave before they become permanent dropouts.
PASS uses an infinite degree contagion model in which
persons moving into a participating household become
panel members, and remain panel members even after leav-
ing the household. When new household members move in
with them, those become PASS members as well.
The PASS study provides data on the socio-economic
situation of individuals and households in Germany. The
data can be used to investigate how changes in people’s
employment status affect their living conditions and health
status, and vice versa.
Household-level information is collected in the house-
hold questionnaire (see top of Table 1). The latter includes
detailed questions about household composition, house-
hold income and material deprivation. Information on the
duration and amount of welfare benefit receipt
(Unemployment Benefit II) is collected retrospectively and
covers the whole period between two consecutive inter-
views. It is stored as spell data in the scientific use file. For
Figure 1. Number of households by sample in each wave.
1411a International Journal of Epidemiology, 2019, Vol. 48, No. 5
families with children under 15 years of age, additional
questions address various aspects of children’s social
participation.
The personal questionnaire covers a large range of
individual-level information (see bottom of Table 1), includ-
ing basic socio-demographic characteristics. To map indi-
viduals’ employment and unemployment histories, there are
retrospective questions about periods of employment, unem-
ployment and other activities (e.g. education). Monthly in-
formation on each activity (including the beginning and end
dates) is provided as spell data. Questions with regard to
employment refer to formal job characteristics (e.g. wages
and working hours) as well as individuals’ subjective assess-
ment of job quality (e.g. job satisfaction and psychosocial
stress). The latter is measured by a short version of the ef-
fort–reward imbalance (ERI) scale.
8
For periods of unemployment respondents report the
duration and amount of unemployment benefits. Those
who receive means-tested welfare benefits are asked about
their interactions with welfare agencies. In addition, there
Table 1. Overview of the PASS questionnaire modules
Questionnaire modules of waves 1–11 1234567891011
Household level
Household composition and housing situation xxxxxxxxx x x
Net household income, savings and debts, material deprivation xxxxxxxxx x x
Receipt of “Unemployment Benefit II”: e.g. date of beginning & end, amount, cut-backs xxxxxxxxx x x
Information on children in the household (child care, education, social participation) xxxxxxxxx x x
Individual level
Demographic information (e.g. marital status, migration, education and training, social origin) xxxxxxxxx x x
Employment and unemployment
Employment history: e.g. occupation, wages, job characteristics, receipt of “Unemployment Benefit I” xxxxxxxxx x x
Job quality: e.g. intrinsic job quality, job satisfaction, job security, work–life-balance, Effort-Reward-
Imbalance-Scale
8
xxx x x
Contact to welfare agencies, participation in active labour market programmes (e.g. One-Euro-Jobs) xxxxxxxxx x x
Health – basic module
Frequency of visits to the doctor or hospital
9
xxxxxxxxx x x
Health restrictions and disabilities xxxxxxxxx x x
Subjective assessment of physical and mental health
10
xxxxxxxxx x x
Health insurance xxxxxxxxx x x
Health – focal topics
Short Form Health Survey (SF-12)
11
xxx
Subjective assessment of employability x x x x x x x
Health-related behaviour (smoking) x x x
Body height and weight xxx
Participation in health-promotion courses xx x
Presentism xx
Sporting activities (e.g. types of sport practised, frequency and duration of practise, social networks)
12
xxx
Memory power & concentration ability
13
x
Social networks and participation – basic module
No. of close friends xxxxxxxxx x x
Participation in organizations/clubs xxxxxxxxx x x
Subjective assessment of social integration
17
xxxxxxxxx x x
Social networks – focal topics
Characteristics of friends
19
xx x
Social support
19
xx x
Personality traits
‘Big Five’
14
x
Self-efficacy
15
xxxx xxx x
Impulsiveness/risk aversion
20,21
x
Life satisfaction (e.g. general, health status, standard of living)
16
xxxxxxxxx x x
Attitudes
Work orientations
17
x xxxxxx
Gender-role attitudes xxx x
Awareness of stigma and prejudices
18
x
International Journal of Epidemiology, 2019, Vol. 48, No. 5 1411b
are questions on participation in so-called One-Euro-Jobs,
an active labour market programme (ALMP) for long-term
unemployed welfare recipients with particularly poor la-
bour market prospects.
Over the years, a growing part of the survey has been de-
voted to respondents’ health. All waves of the PASS study in-
clude a basic set of questions referring to severe health
restrictions and disabilities, the frequency of hospital stays
and visits to the doctor,
9
and the subjective assessment of
physical and mental health.
10
In every third wave, additional
focal questions are part of the interview. Besides the 12-item
Short Form Health Survey (SF-12, GSOEP version)
11
these
questions address health-related behaviour (e.g. current and
past smoking behaviour, participation in health-promotion
courses) as well as body weight and height. Apart from that,
questions focusing on particular aspects of health have been
included in single waves of the study. For instance,
whether and to what extent respondents were exercising was
collected in a module on sporting activities from wave 6–8
(based on
12
). A test of memory power and concentration
ability
13
was implemented in the seventh wave.
In addition, the study includes a variety of questions on
personality traits (e.g. Big Five,
14
self-efficacy
15
) and work-
related as well as general attitudes (e.g. life satisfaction,
16
work orientations,
17
awareness of stigma
18
). These ques-
tions were derived (and sometimes slightly adapted) from
well-tested instruments of other studies or newly developed
and tested multiple times before entering the panel using
techniques like cognitive interviewing and field pre-tests
with interviewer debriefings (see Table 1).
PASS interviews are conducted in German as well as in
Russian, Arabic (since wave 10) and Turkish (until wave
9). The vast majority of foreign-language interviews is con-
ducted by telephone by interviewers who are native speak-
ers. All original questionnaires as well as English
translations can be accessed on the website of our research
data center (http://doku.iab.de/fdz/pass/Questionnaires_
PASS_EN.zip for English versions).
PASS asks respondents aged 15–64 for consent to link
their survey data to rich administrative data of the Federal
Employment Agency. These include full employment biog-
raphies containing exact information on wages, occupa-
tions, employers, times in unemployment and benefits
received as well as participation in active labour market
programmes.
22
Consent rate for linkage to these adminis-
trative data is 94% for wave 10 participants aged 15–64.
The combined dataset is available to external scientific
users as PASS-ADIAB.
23
The most recent version, PASS-
ADIAB7515, includes PASS data up to wave 9 and admin-
istrative data from 1975 to 2014.
Data collection is funded by the Federal Ministry of
Work and Social Affairs as part of the general funding of
research by the Institute for Employment Research (IAB)
according to §55 of Social Code II. IAB is an independent
research institute within the German Federal Employment
Agency.
Acknowledging that welfare-benefit recipients who
have below-average education and are less integrated into
society are a hard to survey population,
24
PASS uses a
range of methods to increase data quality.
In each year, the fieldwork is preceded by an extended
in-person interviewer training of 8 hours for each inter-
viewer who is new to the survey and 6 hours for each inter-
viewer with prior wave experience in the study. The
training focuses on standardized interviewing, navigating
through the instrument as well as on refusal conversion.
During the fieldwork itself an adaptive fieldwork de-
sign
25
is used to optimize the outcome of the fieldwork by
increasing response rates or by balancing response rates be-
tween subgroups, increasing the effort for groups under-
represented in the survey so far.
2628
This adaptive survey
design is based on detailed paradata
29,30
including timing
and detailed outcomes of each contact attempt.
Incentives are paid in cash (ten euros per wave) to in-
crease cooperation. These incentives are prepaid uncondi-
tionally for panel respondents and paid conditional on
participation to first-time respondents (an experiment
31
has shown that unconditional cash incentives are superior
to a promised lottery ticket, increasing response rates and
reducing attrition bias in several sociodemographic
variables).
Mode switches between CATI and CAPI are used to op-
timize response rates (under budgetary restrictions). Non-
contacts in one mode are switched to another mode. A re-
fusal conversion is implemented in CATI mode and admin-
istered by specially trained and successful interviewers.
The mixed-mode design has been shown to reduce non-re-
sponse bias of means and proportions to near zero whereas
measurement error was unaffected.
32
The data are factually anonymized. The main steps in-
volved are deletion of all regional information below state
level and categorizing nationalities and countries of origin
as well as family structures.
PASS has implemented an extensive panel maintenance
and respondent tracking. Proactive tracking measures in-
clude advance letters, thank you letters, and season’s greet-
ing postcards that include free online and mail options to
notify the survey agency of address changes. In addition,
several registers are searched for new addresses.
Methodological research into the data quality of PASS
is regularly published in peer-reviewed journals. This re-
search benefits from the unique opportunity to link the sur-
vey data to administrative data (given informed consent)
and to link survey data and administrative data to the
1411c International Journal of Epidemiology, 2019, Vol. 48, No. 5
paradata of the survey. While the first allows research into
measurement error, the latter also allows research into
non-response error.
33
For example, Kreuter et al.
33
have shown that initial
non-response bias of means and proportions vanishes over
the course of the fieldwork and that at the same time mea-
surement error bias of these means and proportions does
not increase. For welfare benefit receipt there is initially a
substantial measurement error bias that decreases across
time.
3437
Sakshaug and Kreuter
38
find only small non-re-
sponse, measurement and linkage consent bias of means
and proportions for most variables they investigate.
Trappmann et al.
39
showed that the weighting scheme ef-
fectively reduces attrition bias of means and proportions
due to events between waves. Josten and Trappmann
40
in-
vestigated interviewer effects on a looping question. West
et al.
41
and Sinibaldi et al.
42
investigated the potential of
interviewer observations and of commercial micro-
geographical data for non-response adjustment.
Data resource use
As a multiple-topic survey open to users from different
countries and academic fields, PASS has attracted a large
number of users. We are aware of almost 300 publications
based on PASS over the past 11 years (A full publication
list can be viewed at http://www.iab.de/580/section.aspx/
Projekt/k060821f35). Thus, the focus here must be on
health-related publications based on the PASS data.
Krug and Eberl
43
used the data to investigate the nega-
tive effect of unemployment on health. Their analysis is
mainly based on a self-assessed scale (0 to 10) on health
satisfaction. By using the 11 point scale variable in combi-
nation with the long-running panel data the authors were
able to perform a dynamic panel model (system generalized
methods of moments (GMM)) and thus account for unob-
served confounders and reversed causality. Due to the vari-
ety of health variables in the PASS the authors were able to
run some robustness checks with mental health and self-
rated health and thus could further strengthen their find-
ings. The findings support the causality thesis that unem-
ployment leads to bad health. Further, the authors showed
that the negative effect of unemployment on health is par-
tially explained by the loss of self-perceived social status
and not through the loss of income or social status by ob-
jective measures.
Unger et al.
44
used the data for an article analysing the
effect of labour-market transitions on physical and mental
health using the SF-12 scale. This scale covers 12 questions
assessing health-related quality of life, addressing mental
and physical health functioning in 6 questions each. Using
wave 3 and 6 of PASS and a combination of the differences
in difference approach with Propensity Score Matching
they focused on within-person changes in health after
changes in employment status (job loss and re-employment
separately) using a control group with similar characteris-
tics and a similar probability of the respective transition
who were continually (un-)employed. They made use of
the possibility to merge PASS with administrative employ-
ment records, thus utilizing more precise information on
changes in employment status that even include short inter-
ruptions that respondents tend to underreport in surveys.
They hypothesized and found that age is an important fac-
tor in how re-employment and job loss affect health and
that women and men are affected differently. Older men
were affected most severely by job loss, whereas re-
employment was found to improve mental health only in
women aged 31–44 years.
Other publications in subject areas relevant to readers
of the International Journal of Epidemiology shall briefly
be mentioned. Hollederer and Voigtla¨ nder
45
estimate the
effect on health of becoming unemployed and on the chan-
ces of finding a new job. Hajek and Ko¨ nig
46
investigate the
moderating effect of personality traits in the relation be-
tween informal caregiving and well-being. Eggs
47
examines
the interrelation of employment, benefit receipt and self-
rated health using fixed-effects models. Further publica-
tions describe the health (satisfaction) of welfare recipients
compared with the general population.
4850
Strengths and weaknesses
The main strength of the PASS data are the large number
of cases (10 000 household / 15 000 persons per wave),
specifically the large number of unemployed and welfare
recipients in a sample that can be projected to the general
population of Germany. This makes PASS ideally suited to
investigate the interdependence of labour-market partici-
pation, poverty and health. The panel structure of the data
and the long observation history make PASS attractive for
the estimation of causal effects and individual health tra-
jectories. The rich set of variables from the survey can fur-
ther be augmented by linking PASS to administrative data
about the labour market.
On the downside all health measures in PASS are self-
rated measures. No diagnoses or physical samples can be
accessed in the dataset. Thus, there might be a threat of de-
pendent measurement error between exposure and out-
come measures.
51
Certainly, the potential of the PASS data for epidemio-
logical research could be increased by linkage to objective
health data, which would also allow an assessment of the
validity of the self-reported health measures in the survey.
While this is clearly an option for the future, it is
International Journal of Epidemiology, 2019, Vol. 48, No. 5 1411d
complicated by the decentralized German health insurance
system. Currently 43 private and 110 public health insur-
ance providers exist in Germany.
52,53
The terms of linkage
have to be negotiated with each insurance separately in
compliance with regulations on data protection according
to §75, Social Code X.
Data resource access
The PASS data are available to non-profit research as a sci-
entific use file at the research data center of the Federal
Employment Agency at the Institute for Employment
Research. The form to order the data can be accessed at
http://fdz.iab.de/en/FDZ_Data_Access/FDZ_Scientific_Use_
Files.aspx
The data are organized as a user friendly long file. This
means that an interview with one person (household) in
one year is a row in the person (household) dataset.
Identical questions asked in different years are coded in the
same variable. Apart from the person and household data-
sets, there are weight datasets, register datasets and spell
datasets for biographical data collected in spell format.
Rich documentation including all questionnaires, the
field and methods reports and the data reports for all
waves of the panel and a user guide
54
can also be accessed
via the research data center (RDC) website at https://fdz.
iab.de/en/FDZ_Individual_Data/PASS/PASS-SUF0617v2.
aspx
Data are supplied in the format of the statistical soft-
ware Stata. The doi of the current wave release is 10.5164/
IAB.PASS-SUF0617.de.en.v2.
For the PASS dataset combined with the administrative
data (PASS-ADIAB), data access is restricted to onsite data
access at one of the many locations worldwide [outside
Germany in Ann Arbor (USA), Cornell (USA), Berkeley
(USA), Harvard (USA), Los Angeles (USA), Princeton
(USA), Essex (UK), London (UK)] of the RDC. https://fdz.
iab.de/en/FDZ_Data_Access/FDZ_On-Site_Use.aspx
Data users are requested to cite the doi and all docu-
mentation and sources they consulted in order to be able to
use the PASS data. The peer reviewed data set descriptions
found here may be ideally suited as short reference.
Funding
Data Collection is Funded by the Federal Ministery of Work and
Social Affairs as part of the general funding of research by the
Institute for Employment Research (IAB).
Conflict of interest: None declared.
References
1. Trappmann M, Beste J, Bethmann A, Mu¨ ller G. The PASS panel
survey after six waves. J Labour Mark Res 2013;46:275–81.
2. M Trappmann. Weighting. In: Bethmann A, Fuchs B, Wurdack
A (eds). User Guide “Panel Study Labour Market and Social
Security” (PASS).Nu¨rnberg: Wave 6, FDZ-Datenreport, 2013,
pp. 56–66.
3. Rudolph H, Trappmann M. Design und Stichprobe des Panels
“Arbeitsmarkt und Soziale Sicherung” (PASS). In: Promberger
M (ed). Neue Daten Fu¨ r Die Sozialstaatsforschung. Zur
Konzeption Der IAB-Panelerhebung “Arbeitsmarkt Und Soziale
Sicherung”.Nu¨ rnberg: IAB-Forschungsbericht, 2007, pp.
60–101.
4. Trappmann M, Mu¨ ller G, Bethmann A. Design of the study. In:
Bethmann A, Fuchs B, Wurdack A (eds). User Guide “Panel
Study Labour Market and Social Security” (PASS).Nu¨ rnberg,
Germany: Wave 6, FDZ-Datenreport, 2013, pp. 13–22.
5. Berg M, Cramer R, Dickmann C et al.Codebook and
Documentation of the Panel Study ‘Labour Market and Social
Security’ (PASS).Nu¨rnberg, Germany: Datenreport Wave 11,
FDZ-Datenreport, 06/2018 (en).
6. Trappmann M. Weights. In: Bethmann A, Fuchs B, Wurdack A
(eds). User Guide “Panel Study Labour Market and Social
Profile in a nutshell
PASS was set up as a population-based panel study
for the investigation of welfare-benefit dynamics and
the material and social consequences of benefit
recipiency in Germany. Benefit recipient households
are oversampled and new entries to benefit receipt
are added to the sample each year. This makes
PASS a unique database for the evaluation of the
consequences of unemployment and benefit receipt.
PASS was initiated in 2006/07 and has collected
yearly data on about 15 000 respondents in 10 000
households since then.
Participants report detailed information about their
labour-market participation and history, income and
deprivation, social inclusion and self-rated health.
Thus, PASS is well suited for the analysis of the in-
terrelation between unemployment and health and
its moderating and mediating effects.
PASS data have been linked to rich administrative
data on individual labour-market and programme-
participation histories of the respondents.
The PASS data are available as a scientific use file at
the research data center of the Federal Employment
Agency at the Institute for Employment Research
(10.5164/IAB.PASS-SUF0617.de.en.v2) https://fdz.iab.
de/en/FDZ_Data_Access/FDZ_Scientific_Use_Files.aspx.
Linked survey and administrative data are available
for onsite use https://fdz.iab.de/en/FDZ_Data_Access/
FDZ_On-Site_Use.aspx.
1411e International Journal of Epidemiology, 2019, Vol. 48, No. 5
Security” (PASS).Nu¨rnberg: Wave 6, FDZ-Datenreport, 2013,
pp. 81–99.
7. Jesske B, Schulz S. Methodenbericht Panel Arbeitsmarkt und
Soziale Sicherung PASS 11. Erhebungswelle 2017.Nu¨ rnberg:
FDZ-Methodenreport, 13/2018.
8. Siegrist J, Wege N, Pu¨ hlhofer F, Wahrendorf M. A short generic
measure of work stress in an era of globalization—effort-reward
imbalance. Int Arch Occup Environ Health 2009;82:1005–13.
9. Ziebarth NR. Measurement of health, the sensitivity of the con-
centration index, and reporting heterogeneity. Soc Sci Med
2010;71:116–24.
10. Ellert U, Lampert T, Ravens-Sieberer U. Messung der gesund-
heitsbezogenen Lebensqualita¨ t mit dem SF-8. Eine
Normstichprobe fu¨ r Deutschland. Bundesgesundheitsbl -
Gesundheitsforsch - Gesundheitsschutz 2005;48:1330–37.
11. Andersen HH, Mu¨ hlbacher A, Nu¨ bling M, Schupp J, Wagner
GG. Computation of standard values for physical and mental
health scale scores using the SOEP version of SF-12v2. J Appl
Soc Sci Stud 2007;127:171–82.
12. Lechner M. Long-run labour market and health effects of indi-
vidual sports activities. J Health Econ 2009;28:839–54.
13. Mehrbrot T, Gruber S, Wagner M. Scales and Multi-Item
Indicators – SHARE (Survey of Health, Ageing and Retirement
in Europe). 2017. http://www.share-project.org/fileadmin/pdf_
documentation/SHARE_Scales_and_Multi-Item_Indicators.pdf
(28 December 2018, date last accessed).
14. Rammstedt B, John OP. Kurzversion des Big Five Inventory
(BFI-K). Diagnostica 2005;51:195–206.
15. Schwarzer R. Optimistische Kompetenzerwartung: Zur
Erfassung einer personellen Bewa¨ltigungsressource. Diagnostica
1994;40:105–23.
16. Richter D, Metzing M, Weinhardt M, Schupp J. SOEP Scales
Manual. SOEP Survey Papers. Berlin: DIW/SOEP, 2013, p. 138.
17. Meßmann S, Bender S, Rudolph H et al.Lebenssituation Und
Soziale Sicherung 2005 (LSS 2005). IAB-Querschnittsbefragung
SGB II. Handbuch-Version 1.0.0.Nu¨rnberg: FDZ-Datenreport,
2008.
18. Gurr T, Jungbauer-Gans M. Stigma consciousness among the
unemployed and prejudices against them: development of two
scales for the 7th wave of the panel study “Labour Market and
Social Security (PASS)”. J Labour Market Res 2013;46:335–51.
19. Wolf C. Netzwerke Und Soziale Unterstu¨ tzung. Mannheim:
GESIS-Working Papers, 2009.
20. Keye D, Wilhelm O, Oberauer K. Structure and correlates of the
German version of the brief UPPS impulsive behavior scales. Eur
J Psychol Assess 2009;25:175–85.
21. Kovaleva A, Beierlein C, Kemper CJ, Rammstedt B. Eine
Kurzskala zur Messung von Impulsivita¨t nach dem UPPS-
Ansatz: Die Skala Impulsives-Verhalten-8 (I-8). Mannheim:
GESIS-Working Papers, 2012.
22. Antoni M, Dummert S, Trenkle S. PASS-Befragungsdaten ver-
knu¨ pft mit administrativen Daten des IAB (PASS-ADIAB)
1975–2015.Nu¨ rnberg: FDZ-Datenreport, 2017.
23. Antoni M, Bethmann A. PASS-ADIAB - linked survey and ad-
ministrative data for research on unemployment and poverty.
Jahrb Natl Okon Stat. doi:10.1515/jbnst-2018-0002.
24. Tourangeau R, Edwards B, Johnson TP (eds). Hard-to-Survey
Populations. Cambridge: Cambridge University Press, 2014.
25. Wagner JR. Adaptive Survey Design to reduce nonresponse bias.
Doctoral dissertation, University of Michigan, 2008.
26. Trappmann M, Mu¨ ller G. Introducing adaptive design elements
in the Panel Study “Labour Market and Social Security” (PASS).
In: Canada Statistics (ed). Beyond Traditional Survey Taking:
Adapting to a Changing World. Quebec: Proceedings of
Statistics Canada Symposium, 2014.
27. Kreuter F, Mu¨ ller G. A note on improving process efficiency in
panel surveys with paradata. Field Methods 2015;27:55–65.
28. West BT, Elliott MR, Mneimneh Z, Peytchev A, Wagner J,
Trappmann M. An examination of an interviewer-respondent
matching protocol in a longitudinal CATI study. J Surv Stat
Methodol 2018.
29. Couper MP. Measuring survey quality in a CASIC environment.
Proceedings of the Section on Survey Research Methods of the
American Statistical Association, 1998, pp. 41–49.
30. Kreuter F (ed). Improving Surveys with Paradata: Analytic Uses
of Process Information, Vol. 581. Hoboken, NJ: John Wiley &
Sons, 2013.
31. Felderer B, Mu¨ ller G, Kreuter F, Winter J. The effect of differen-
tial incentives on attrition bias. Evidence from the PASS Wave 3
incentive experiment. Field Methods 2018;30:56–69.
32. Levenstein R. Nonresponse and Measurement Error in Mixed-
Mode Designs. Ph.D. Dissertation, Michigan, 2010. http://deep
blue.lib.umich.edu/bitstream/2027.42/78764/1/rmlev_1.pdf (24
May 2018, date last accessed).
33. Kreuter F, Mu¨ ller G, Trappmann M. Nonresponse and measure-
ment error in employment research. Making use of administra-
tive data. Public Opin Q 2010;74:880–906.
34. Kreuter F, Mu¨ ller G, Trappmann M. A note on mechanism lead-
ing to lower data quality of late or reluctant respondents. Sociol
Methods Res 2014;43:452–64.
35. Bruckmeier K, Mu¨ ller G, Riphahn RT. Who misreports welfare
receipt in surveys? Appl Econ Lett 2014;21:812–16.
36. Bruckmeier K, Mu¨ ller G, Riphahn RT. Survey misreporting of
welfare receipt—respondent, interviewer, and interview charac-
teristics. Econ Lett 2015;129:103–7.
37. Bruckmeier K, Hohmeyer K, Schwarz S. Welfare receipt misre-
porting in survey data and its consequences for state dependence
estimates: new insights from linked administrative and survey
data. J Labour Mark Res 2018; doi:10.1186/s12651-
018-0250-z.
38. Sakshaug J, Kreuter F. Assessing the magnitude of non-consent
biases in linked survey and administrative data. Surv Res
Methods 2012;6:113–22.
39. Trappmann M, Gramlich T, Mosthaf A. The effect of events be-
tween waves on panel attrition. Surv Res Methods 2015;9:31–43.
40. Josten M, Trappmann M. Interviewer effects on a network size
filter question. J Off Stat 2016;32:349–73.
41. West BT, Kreuter F, Trappmann M. Is the collection of inter-
viewer observations worthwhile in an economic panel survey?
New evidence from the German Labor Market and
Social Security (PASS) Study. J Surv Stat Methodol 2014;2:
159–81.
42. Sinibaldi J, Trappmann M, Kreuter F. Which is the better invest-
ment for nonresponse adjustment. Purchasing commercial auxil-
iary data or collecting interviewer observations?. Public Opin Q
2014;78:440–73.
International Journal of Epidemiology, 2019, Vol. 48, No. 5 1411f
43. Krug G, Eberl A. What explains the negative effect of unemploy-
ment on health? An analysis accounting for reverse causality.
Res Soc Stratif Mobil 2018;55:25–39.
44. Unger S, Tisch A, Tophoven S. Age and gender differences in the
impact of labour-market transitions on subjective health in
Germany. Scand J Public Health 2018;46:49–64.
45. Hollederer A, Voigtla¨ nder S. Die Gesundheit von Arbeitslosen und
die Effekte auf die Arbeitsmarktintegration. Ergebnisse im Panel
Arbeitsmarkt und soziale Sicherung (PASS), Erhebungswellen 3 bis
7 (2008/09–2013). Bundesgesundheitsbl 2016;59:652–61.
46. Hajek A, Ko¨ nig HH. The relation between personality, informal
caregiving, life satisfaction and health-related quality of life: evi-
dence of a longitudinal study. Qual Life Res 2018;27:1249–56.
47. Eggs J. Unemployment Benefit II, Unemployment and Health.
IAB-Discussion Paper, Nu¨ rnberg, Germany, 2013.
48. Eggs J, Trappmann M, Unger S. Grundsicherungsempfa¨ nger
Und Erwerbsta¨tige im Vergleich: ALG-II-Bezieher scha¨ tzen ihre
Gesundheit schlechter ein.Nu¨ rnberg, Germany: IAB-
Kurzbericht, Nr, 2014.
49. Hollederer A, Voigtla¨ nder S. Gesundheit und Gesundheitsverhalten
von Arbeitslosen. WSI 2016;69:381–5.
50. Unger S, Trappmann M, Eggs J. Arbeitslosigkeit und
Gesundheit. In: Mo¨ ller J, Walwei U (eds). Arbeitsmarkt
Kompakt. Analysen, Daten, Fakten. (IAB-Bibliothek, 363).
Bielefeld: Bertelsmann, 2017, pp. 57–59.
51. VanderWeele TJ, Herna´ n MA. Results on differential and depen-
dent measurement error of the exposure and the outcome using
signed directed acyclic graphs. Am J Epidemiol 2012;175:
1303–10.
52. Liste deutscher privater Krankenversicherer. https://de.wikipe
dia.org/wiki/Liste_deutscher_privater_Krankenversicherer (28
December 2018, date last accessed)
53. GKV Spitzenverband. Krankenkassenliste. https://www.gkv-spit
zenverband.de/krankenkassenliste.pdf (28 December 2018, date
last accessed).
54. Bethmann A, Fuchs B, Wurdack A (eds). User Guide “Panel
Study Labour Market and Social Security” (PASS).Nu¨ rnberg:
Wave 6, FDZ-Datenreport, 2013.
1411g International Journal of Epidemiology, 2019, Vol. 48, No. 5
... Against this background, we investigate non-participation in the IAB-SMART study that combined self-reports with passive data collection on smartphones . We invited German-speaking Android smartphone owners in the Panel Study Labour Market and Social Security (PASS), an annual German mixed-mode study on the labour market and poverty (Trappmann et al., 2019), to download a research app. The app collected data over six months through short surveys administered in the app and passive mobile measurement using different groups of sensors and log files on the smartphone. ...
... PASS is an annual, probability-based household panel survey of the German residential population aged 15 and older conducted by the Institute for Employment Research (IAB) (Trappmann et al., 2019). The primary goal of PASS is to provide a data source for research on the labour market, poverty, and the welfare state in Germany. ...
Article
Full-text available
Research apps allow to administer survey questions and passively collect smartphone data, thus providing rich information on individual and social behaviours. Agreeing to this novel form of data collection requires multiple consent steps, and little is known about the effect of non-participation. We invited 4,293 Android smart-phone owners from the German Panel Study Labour Market and Social Security (PASS) to download the IAB-SMART app. The app collected data over six months through (a) short in-app surveys and (b) five passive mobile data collection functions. The rich information on PASS members from previous survey waves allows us to compare participants and non-participants in the IAB-SMART study at the individual stages of the participation process and across the different types of data collected. We find that 14.5 percent of the invited smartphone users installed the app, between 12.2 and 13.4 percent provided the different types of passively collected data, and 10.8 percent provided all types of data at least once. Likelihood to participate was smaller among women, decreased with age and increased with educational attainment, German citizen-This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
... This is a representative household panel survey of the German residential population oversampling households receiving welfare benefits (UB-II subsample). The PASS panel was established by the Institute for Employment Research (IAB) in 2006 [15]. The PASS population consists of individuals nested in households. ...
... With respect to panel attrition, the sample population was refreshed in 2005 and 2011. Additionally, the welfarereceiving population was refreshed annually to respond to new household entrants into welfare recipiency [15]. Each household member above the age of 15 is addressed annually with an individual questionnaire [16]. ...
Article
Full-text available
Background Given the inconsistent findings regarding associations between obesity and unemployment, our analysis is one of the few that explores bidirectional changes in obesity and unemployment. In our prospective study, we address factors associated with the transition into and transition out of obesity, including unemployment, and transition into and out of unemployment, including obesity. Subjects and methods The Labor Market and Social Security-Panel (PASS) consists of two independent, nationally representative German subsamples: residents receiving unemployment benefits (50%) and a representative sample of residents (50%). The sample contains N = 11 361 observations between two measurement points three years apart of N = 8440 individuals participating in two or three waves between 2009 and 2015. We analyzed potential predictors of the transition in and out of obesity and unemployment, including health-related quality of life (HrQoL) and physical activity, using logistic regression models. Results Transition into obesity: Unemployed participants had a higher probability of exhibiting a body mass index (BMI) ≥ 35 kg/m² three years later (transition into obesity classes II and III; Exp(B) = 1.5). Transition out of obesity: Unemployment did not predict transition out of obesity. Physical activity at least once weekly increased the probability of no longer having a BMI ≥ 35 kg/m² three years later (Exp(B) = 2.0). Transition into unemployment: Obesity was not associated with becoming unemployed three years later. Participants with a lower mental HrQoL were more likely to become unemployed (Exp(B) = 0.98). Transition out of unemployment: Unemployed individuals reporting a BMI of 30–34.9 kg/m² were less likely to leave unemployment (Exp(B) = 0.67). A better physical HrQoL was associated with a higher probability of leaving unemployment (Exp(B) = 1.01). Conclusions Obesity does not predict future unemployment, but unemployed individuals with obesity have a lower probability of labor market re-entry. Unemployment increases obesity risk. Interactions between obesity and possible confounding variables and their effect on unemployment warrants further examination.
... Employment is an important indicator to measure a country's economic situation. In recent years, with the large-scale enrollment of colleges and universities, the number of college students has increased year by year, and the total number of college graduates has increased several times in the past [1,2]. College graduates have become one of the largest employment groups in various countries. ...
Article
Full-text available
The dynamic balance of supply and demand in the labor market is a key issue at present. The change of employment quality is relatively complex. The current dynamic balance model of supply and demand cannot obtain high-precision evaluation results of employment quality and meet the requirements of practical application. Therefore, a dynamic balance model of labor market supply and demand under flexible employment is constructed. Big data network is used to set data acquisition channels, complete data collection, unify data sample format, formulate data processing flow, and obtain processed data samples. The correlation analysis method in big data analysis technology is used to complete the analysis of college graduation employment. This paper analyzes the relevant research work of employment quality evaluation, establishes the employment quality evaluation index system, collects the index data, normalizes the index data, then uses the grey correlation method to determine the weight value of the employment quality evaluation index, and uses the fuzzy c-means algorithm to establish the dynamic balance model of supply and demand in the labor market. The experimental results show that the designed method can better balance the supply-demand relationship in the labor market and has a good effect.
Article
Information on individuals holding managerial or supervisory positions within establishments is important for various aspects of labour market research. However, identifying managers or supervisors in German administrative records is not straightforward. This paper uses survey information from the Panel Study Labour Market and Social Security (PASS) to predict managerial or supervisory tasks in administrative records that can be used to enhance the identification of managers and supervisors in the Sample of Integrated Labour Market Biographies (SIAB). Furthermore, I provide an applied example in which I calculate gender differences in the probability to hold a managerial position.
Article
Full-text available
Applying fixed-effects models using Waves 2 to 13 (2007–19) of the German Labour Market and Social Security panel study, we examine how unpaid caring changes labour supply and if monthly monetary transfers from the care recipient to the carer motivate a reduction in labour supply. We find that for both women and men, starting high-intensity caring increased the likelihood of becoming non-employed. Women were already likely to reduce working hours when starting non-intensive caring, whereas only intensive caring reduced working hours for men. Receiving low monetary transfers was a higher motivation to become non-employed for men, and receiving low monetary transfers only reduced working hours for women.
Article
Full-text available
We investigate the general effect of the COVID-19 pandemic on subjective well-being and determine whether this effect differs between recipients of basic income support (BIS) and the rest of the working-age population in Germany. BIS recipients constitute one of the most disadvantaged groups in Germany and might lack resources for coping with the crisis. Thus, our analysis contributes to investigations of whether the pandemic exacerbates or equalises preexisting social inequality. Our analysis employs data from the panel survey “Labour Market and Social Security” (PASS). These data have the key advantage that the collection in 2020 started prior to implementation of the first COVID-19-related policies. This situation enables us to apply a difference-in-differences approach to investigate the causal change in subjective well-being. Our results suggest that well-being declined during the first phase of the COVID-19 pandemic. However, we find no difference in this decline between BIS recipients and other German residents. Thus, our results suggest that the first phase of the COVID-19 pandemic neither exacerbated nor equalised pre-existing inequalities.
Article
Surveys serve as an important source of information on key anthropometric characteristics such as body height or weight in the population. Such data are often obtained by directly asking respondents to report those values. Numerous studies have examined measurement errors in this context by comparing reported to measured values. However, little is known on the role of interviewers on the prevalence of irregularities in anthropometric survey data. In this study, we explore such interviewer effects in two ways. First, we use data from the US National Health and Nutrition Examination Survey and the UK Household Longitudinal Study to evaluate whether differences between reported and measured values are clustered within interviewers. Second, we investigate changes in adult self-reported height over survey waves in two German large-scale panel surveys. Here, we exploit that height should be constant over time for the majority of adult age groups. In both analyses, we use multilevel location-scale models to identify interviewers who enhance reporting errors and interviewers for whom unlikely height changes over waves occur frequently. Our results reveal that interviewers can play a prominent role in differences between reported and measured height values and changes in reported height over survey waves. We further provide an analysis of the consequences of height misreporting on substantive regression coefficients where we especially focus on the role of interviewers who reinforce reporting errors and unlikely height changes.
Article
Survey measures of the reservation wage may reflect both the consumption-leisure trade-off and job market prospects (the arrival rate of job offers and the wage distribution). We examine what a survey measure of the reservation wage reveals about an individual’s willingness to trade leisure for consumption. To this end, we combine the reservation wage measure from a large labor market survey with the reservation wage for a one-hour job that we elicit in an online experiment. The two measures show a strong positive association. For unemployed individuals, the experimental reservation wage increases on average by around one Euro for every Euro increase in the survey measure. For employed individuals, the association between the two measures is weaker and depends on their occupation-specific risk of unemployment. We show that these results are robust to selection into the experiment, and that demographic variables have a similar influence on both reservation wage measures.
Article
Full-text available
Sanctions are payment cuts that case managers implement in order to discipline welfare recipients. Previous research suggests that immigrants face a particularly high risk to receive such reductions, primarily due to the prevalence of stereotyping in street-level bureaucracy. The study contributes to this literature with help of a triangulation between in-depth interviews, survey data and administrative records for the case of the German social assistance system. Our findings indicate that immigrants tend to be sanctioned at a lower rate than other benefit recipients in this context, especially if they arrived at the country only recently on grounds of international protection. This finding can be explained by the importance of reciprocity and control in the country's ‘Bismarckian’ welfare state. Our qualitative data shows that case managers exert a considerable level of agency over the implementation process. This discretion is, on the one hand, used to discipline benefit recipients who are perceived as having contributed little to the welfare system as a whole through taxes and social insurance contributions. Those who are considered to have limited control over their labour market position, on the other hand, are given a certain degree of leeway. We therefore conclude, against the background of the current street-level bureaucracy literature, that immigration can also act as a deservingness cue in means-tested social assistance, given that the benefit system is embedded into a welfare regime in which labour market participation, work-testing and social insurance contributions are the dominating principles of eligibility.
Article
Full-text available
Abstract In many advanced welfare states, welfare recipients often receive benefits for long periods. This persistence of welfare receipt can be caused by two distinct mechanisms: genuine or spurious state dependence. Knowledge of which of the two mechanisms drives the observed state dependence is important because the policy implications are different. Most of the empirical evidence on state dependence relies on survey data. However, survey data on welfare receipt are subject to substantial measurement error (i.e., misreporting of welfare benefit receipt), which may also bias state dependence estimates. This paper uses rich linked survey and administrative data to measure the effect of misreporting in the survey data on the estimated state dependence in welfare receipt in Germany. We find a rate of underreporting of welfare benefits of 8.6%. Recipients with relatively good labour market chances tend to underreport benefits more frequently. Overreporting benefits is less pronounced with a rate of 1.6%. Within the survey data, we observe more transitions into and out of the welfare system. However, our estimates of state dependence in welfare receipt based on a dynamic random effects model reveal that the effect of misreporting on estimated state dependence is small, even when we distinguish between working and non-working recipients in the model.
Article
Full-text available
Purpose: Personality characteristics of the caregiver might play a role in the relation between informal caregiving and health-related quality of life as well as life satisfaction. However, a limited body of research has examined this relation. This study aimed to examine the role personality characteristics of the caregiver might play in the relation between informal caregiving and well-being outcomes using a longitudinal approach. Methods: Data were derived from the large Panel ‘Labour Market and Social Security.’ This is an annual household survey, which is conducted by order of the Institute for Employment Research covering persons and households registered as residents of Germany. The SF-12 was used to capture health-related quality of life (covering physical and mental health). A short version of the Big Five Inventory (BFI-K) was used to quantify personality factors. Life satisfaction was measured by a single-item measure. Concentrating on these factors, we used data from the third (2008/2009), sixth (2012), and ninth wave (2015). 34,548 observations were used in fixed effects regressions. Results: Adjusting for various potential confounders, linear fixed effects regressions showed that the onset of informal caregiving reduced life satisfaction (β = − .14, p < .01), but not physical and mental health. The relation between informal caregiving and life satisfaction was significantly moderated by agreeableness (p < .01). Conclusions: Findings of the present study emphasized that agreeableness moderates the relationship between informal caregiving and life satisfaction. Measuring personality characteristics of the informal caregiver is important for tailoring interventional strategies in order to increase the benefit of these programs.
Article
Full-text available
There is evidence that survey interviewers may be tempted to manipulate answers to filter questions in a way that minimizes the number of follow-up questions. This becomes relevant when ego-centered network data are collected. The reported network size has a huge impact on interview duration if multiple questions on each alter are triggered. We analyze interviewer effects on a network-size question in the mixed-mode survey “Panel Study ‘Labour Market and Social Security’” (PASS), where interviewers could skip up to 15 follow-up questions by generating small networks. Applying multilevel models, we find almost no interviewer effects in CATI mode, where interviewers are paid by the hour and frequently supervised. In CAPI, however, where interviewers are paid by case and no close supervision is possible, we find strong interviewer effects on network size. As the area-specific network size is known from telephone mode, where allocation to interviewers is random, interviewer and area effects can be separated. Furthermore, a difference-in-difference analysis reveals the negative effect of introducing the follow-up questions in Wave 3 on CAPI network size. Attempting to explain interviewer effects we neither find significant main effects of experience within a wave, nor significantly different slopes between interviewers.
Article
This article presents results from an experimental study in Germany designed to test the effectiveness of a novel protocol for matching participants in a national panel survey with interviewers employing computer-assisted telephone interviewing (CATI) on selected sociodemographic features, including sex, age, and education. We specifically focus on the ability of the protocol to engender close matches between respondents and interviewers in terms of these features, using both theory and empirical evidence to suggest that this type of matching will improve cooperation rates in surveys employing CATI. We also focus on indicators of "success" at first contact (defined as a successful interview or establishment of an appointment for an interview) as a function of whether the matching protocol was in use on a given day and whether specific types of matches generated higher rates of success overall. We find strong evidence of the protocol effectively establishing close matches, and we also observe that matches based on education proved especially effective for rates of "success" in a panel survey that focused primarily on labor market topics. We conclude with thoughts on practical implementation of this approach in other settings and suggested directions for future work in this area.
Article
The unemployed are often in poorer health than their employed counterparts. This cross-sectional correlation is often attributed to a causal effect of unemployment on health. Recent research analyzing longitudinal data often supports alternative explanations, such as spurious correlation and/or selection of unhealthy workers into unemployment (i.e., reverse causality). In this paper, we apply a dynamic panel data estimator (system GMM) to account for both unobserved confounders and reverse causality. Despite some evidence for health selection, we still find strong support for the causality thesis. Furthermore, we show that the adverse health effect is partially explained by the loss of self-perceived social status due to unemployment but not by the loss of household income or social contacts.
Article
Aims: Applying a gender- and age group-sensitive approach, we investigated the effect of labour-market transitions (job loss and re-employment) on subjective physical and mental health. Methods: A combination of the difference-in-differences approach and propensity score matching controls for selectivity and initial health differences. This allowed us to analyse the causal effect of job loss and re-employment on subjective health. We made use of data from the German Panel Study Labour Market and Social Security and combined survey information with administrative records of the Federal Employment Agency for employed and unemployed men and women 31-60 years of age ( n = 2213). We controlled for labour-market experiences before the time period under study and for labour-market transitions between the interviews. Subjective health was assessed using the SF-12 health questionnaire, enabling us to differentiate between subjective mental and physical health functioning. Results: We found that physical health was affected mainly in older persons between 45 and 60 years old. Controlling for covariates using propensity score matching, mental health was affected only when living-wage jobs (i.e. jobs that provide sufficient income to achieve a defined minimum standard of living above the social benefit level) are gained or lost. Younger women showed a significant improvement in mental health after re-employment. In contrast, job loss affected only older individuals' mental health, with a particularly negative effect observed for men. Conclusions: Our results not only showed that women and men are affected differently by job loss and re-employment, but also that age is an important factor. Older men were affected most severely by job loss, whereas re-employment was found to improve mental health only in women aged 31-44 years. It is therefore important to address the health problems of different socio-demographic groups separately, and to apply active labour-market policies with regard to unemployed men and women with health impairments. Based on our results, we suggest the promotion of employment with income levels above the maximum welfare benefit award.
Article
Respondent incentives are widely used to increase response rates, but their effect on nonresponse bias has not been researched as much. To contribute to the research, we analyze an incentive experiment embedded within the third wave of the German household panel survey “Panel Labor Market and Social Security” conducted by the German Institute for Employment Research. Our question is whether attrition bias differs in two incentive plans. In particular, we want to study whether an unconditional €10 cash incentive yields less attrition bias in self-reported labor income and other sociodemographics than a conditional lottery ticket incentive. We find that unconditional cash incentives are more effective than conditional lottery tickets in reducing attrition bias in income and several sociodemographic variables.
Article
Deutsch: Arbeitslosigkeit geht nach dem aktuellen Forschungsstand mit Beeinträchtigungen der Gesundheit einher. Über das Gesundheitsverhalten von Arbeitslosen bestehen Informationsdefizite. Datengrundlage sind die Befragungsdaten des Panels Arbeitsmarkt und soziale Sicherung (PASS) der Welle 6 (2012). Auf Basis des SF12v2 bewerten arbeitslose Männer und Frauen sowohl ihre körperliche als auch psychische Gesundheit im Durchschnitt negativer als Beschäftigte. Es berichten deutlich mehr Arbeitslose als Beschäftige einen Krankenhausaufenthalt im letzten Jahr. Ca. zwei Drittel der arbeitslosen Männer und die Hälfte der arbeitslosen Frauen rauchen. Der Raucheranteil bei Arbeitslosen ist doppelt so hoch wie bei Beschäftigten. Dagegen bekunden Arbeitslose häufiger als Beschäftigte, dass sie nie Alkohol konsumieren. In Relation zu Beschäftigten gibt es einen größeren Anteil an Arbeitslosen, die nie aktiv Sport, Fitness oder Gymnastik treiben. Die Auswertungen zeigen erhebliche Disparitäten zwischen Arbeitslosen und Beschäftigten bei Gesundheit, stationärer Behandlung sowie im Gesundheitsverhalten auf. English: Based on the current state of research, unemployment goes hand in hand with quana number of health impairments. However, there is a lack of knowledge in regard to the health behaviour of the unemployed. Data is drawn from the panel study “Labour Market and Social Security” (PASS), wave 6 (2012). Based on the SF12v2, unemployed men and women rate their mental and physical health levels, on average, more negatively than employed men and women. There are also significantly more unemployed persons than employed who report a hospital stay in the year before the interview. About two thirds of unemployed men and half of unemployed women smoke, with the proportion of smokers among the unemployed being double the proportion among the employed. On the other hand, unemployed persons more often state that they never consume alcohol. In comparison to the employed, there is a higher proportion of unemployed persons who never do sports, fitness training or work out at the gym. The analysis shows substantial disparities between unemployed and employed persons regarding health, hospitalisation periods as well as their health behavior.
Article
Survey methodologists worry about trade-offs between nonresponse and measurement error. Past findings indicate that respondents brought into the survey late provide low-quality data. The diminished data quality is often attributed to lack of motivation. Quality is often measured through internal indicators and rarely through true scores. Using administrative data for validation purposes, this article documents increased measurement error as a function of recruitment effort for a large-scale employment survey in Germany. In this case study, the reduction in measurement quality of an important target variable is largely caused by differential measurement error in subpopulations and respective shifts in sample composition, as well as increased cognitive burden through the increased length of recall periods among later respondents. Only small portions of the relationship could be attributed to a lack of motivation among late or reluctant respondents.