Data Resource Profile
Data Resource Profile: Panel Study Labour
Market and Social Security (PASS)
and Claudia Wenzig
Panel Study Labour Market and Social Security, Institute for Employment Research, Nuremberg,
Faculty for Social Sciences, Economics, and Business Administration, University of
Bamberg, Bamberg, Germany and
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.
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.
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_
ferent samples will be described in the next section.
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.
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
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Data Resource Profile
that covered 98.4% of the buildings with private house-
holds in Germany.
Target households were selected by
choosing at random one household from each selected
for details and
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
for the most recent one). A brief overview
is given in
. 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.
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
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
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.
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 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
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-
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
xxxxxxxxx x x
Health restrictions and disabilities xxxxxxxxx x x
Subjective assessment of physical and mental health
xxxxxxxxx x x
Health insurance xxxxxxxxx x x
Health – focal topics
Short Form Health Survey (SF-12)
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
Sporting activities (e.g. types of sport practised, frequency and duration of practise, social networks)
Memory power & concentration ability
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
xxxxxxxxx x x
Social networks – focal topics
Characteristics of friends
xxxx xxx x
Life satisfaction (e.g. general, health status, standard of living)
xxxxxxxxx x x
Gender-role attitudes xxx x
Awareness of stigma and prejudices
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,
and the subjective assessment of
physical and mental health.
In every third wave, additional
focal questions are part of the interview. Besides the 12-item
Short Form Health Survey (SF-12, GSOEP version)
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
). A test of memory power and concentration
was implemented in the seventh wave.
In addition, the study includes a variety of questions on
personality traits (e.g. Big Five,
) and work-
related as well as general attitudes (e.g. life satisfaction,
awareness of stigma
). 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
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.
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
Acknowledging that welfare-benefit recipients who
have below-average education and are less integrated into
society are a hard to survey population,
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-
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.
This adaptive survey
design is based on detailed paradata
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
has shown that unconditional cash incentives are superior
to a promised lottery ticket, increasing response rates and
reducing attrition bias in several sociodemographic
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.
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
For example, Kreuter et al.
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
Sakshaug and Kreuter
find only small non-re-
sponse, measurement and linkage consent bias of means
and proportions for most variables they investigate.
Trappmann et al.
showed that the weighting scheme ef-
fectively reduces attrition bias of means and proportions
due to events between waves. Josten and Trappmann
vestigated interviewer effects on a looping question. West
and Sinibaldi et al.
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
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-
Unger et al.
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
effect on health of becoming unemployed and on the chan-
ces of finding a new job. Hajek and Ko¨ nig
moderating effect of personality traits in the relation be-
tween informal caregiving and well-being. Eggs
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.
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-
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.
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
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
can also be accessed
via the research data center (RDC) website at https://fdz.
Data are supplied in the format of the statistical soft-
ware Stata. The doi of the current wave release is 10.5164/
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.
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.
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.
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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
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for onsite use https://fdz.iab.de/en/FDZ_Data_Access/
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