The Socio-Economic Panel (SOEP) is a representative, multi-cohort survey that has been running since 1984. Every year, individuals in households throughout Germany are surveyed by our survey institute on behalf of DIW Berlin. These respondents provide information on topics such as their income, employment history, education, and health. Because the same people are surveyed every year, it is possible to track long-term psychological, economic, societal, and social developments. To keep pace with changes in society, random samples are added regularly and the survey is adapted accordingly.
Title: Socio-Economic Panel (SOEP), data from 1984-2019, EU Edition
DOI : 10.5684/soep.core.v36eu
Collection period: 1984-2019
Publication date: 2021-03-31
Principal investigators: Stefan Liebig, Jan Goebel, Markus Grabka, Carsten Schröder, Sabine Zinn, Charlotte Bartels, Alexandra Fedorets, Andreas Franken, Martin Gerike, Florian Griese, Jannes Jacobsen, Selin Kara, Johannes König, Peter Krause, Hannes Kröger, Elisabeth Liebau, Maria Metzing, Jana Nebelin, Marvin Petrenz, David Richter, Paul Schmelzer, Christian Schmitt, Jürgen Schupp, Daniel Schnitzlein, Rainer Siegers, Hans Walter Steinhauer, Knut Wenzig, Stefan Zimmermann
Contributor: Kantar Deutschland GmbH (Data Collector)
Population: Persons living in private households in Germany
Amount of households: 19.032
Amount of individuals: 32.050 + 3476 Children
Special samples: Citizens of the GDR (1990), Immigration/Migration (1994/95, 2013, 2015), Refugees (since 2016). See the chapter SOEP-Samples in Detail on the SOEPcompanion for a description of all our samples.
Selection method: All samples of SOEP are multi-stage random samples which are regionally clustered. The respondents (households) are selected by random-walk or register sample.
Collection Mode:The interview methodology of the SOEP is based on a set of pre-tested questionnaires for households and individuals. Principally an interviewer tries to obtain face-to-face interviews with all members of a given survey household aged 12 years and over. Additionally one person (head of household) is asked to answer a household related questionnaire covering information on housing, housing costs, and different sources of income. This covers also some questions on children in the household up to 12 years of age, mainly concerning attendance at institutions (kindergarten, elementary school, etc.)
Citation of the Data Set: Socio-Economic Panel (SOEP), data for years 1984-2019, SOEP-Core v36, EU Edition, 2021, doi:10.5684/soep.core.v36eu
If you don‘t exclude observations from the Migration Samples in your analysis, please also cite as follows:
IAB-SOEP Migration Samples (M1, M2), data of the years 2013-2019, DOI: 10.5684/soep.iab-soep-mig.2019
If you don‘t exclude observations from the Refugee Samples in your analysis, please also cite as follows:
IAB-BAMF-SOEP Survey of Refugees (M3-M5), data of the years 2016-2019, DOI: 10.5684/soep.iab-bamf-soep-mig.2019
Publications using this file should refer to the above DOI Find an explanation on the usage of DOI here.and cite one of the following references
For the SOEP-Core data 1984-2019 (v36) - waves A to BJ - we provide the following editions:
soep.core.v36eu (EU Edition, 100%)
soep.core.v36i (International Scientific Use Version, 95%)
soep.core.v36t (Teaching Edition, 50%)
soep.core.v36at (Add-on: Area types)
soep.core.v36pr (Add-on: Planning regions)
soep,core.v36r (Remote Edition)
soep.core.v36o (Onsite Edition)
Find detailed information on the SOEPcompanion.
These datasets are included in SOEP v36, but is also available as individual data sets upon request:
soep.iab-soep-mig.2019 (Migration samples)
soep.iab-bamf-soep-mig.2019 (Refugee samples)
New Sample P
“Top Shareholder Sample”: Sample P was conceptualized as a sample of highly affluent households in Germany. Against the backdrop of the increasing income and wealth inequality in Germany over recent decades, despite economic growth, there has been a growing need for data on wealthy populations in the social sciences. Sample P was created to improve the empirical base for the German government’s poverty and wealth report and to lay the foundation for medium- and long-term cross-sectional and longitudinal analysis. The gross sample consisted of 23,259 households.
New Sample Q
“LGB* Sample”: Sample Q is a boost sample of a hard-to-survey population: lesbians, gays, bisexuals, transgender people, and those who identify as non-binary. While the actual percentage of LGBTQ+ people in the general population is unknown, this group was too scarcely represented in the SOEP to allow for meaningful analysis. 835 households were recruited through an approximately 9-month-long telephone screening process. Of these households, 477 participated in the survey between April and November.
Dataset BIOL - Variables on recognition of occupational qualifications in samples M3-M5 and the CAMCES module (identifiable in the variable label by the abbreviation AA/AAC) were corrected. The slightly different biographical questionnaire for samples M1-M2 is no longer used, and variables on migration history have been added to the SOEP-Core biographical questionnaire, which is now used for all samples. The variables have been integrated, versioned, and harmonized in biol accordingly. The religious affiliation of the father and mother has been reversioned and harmonized to include the response option “konfessionslos” (no religious affiliation). Additional variables with occupational codes have been added. Some variables at the federal state level were included as East-West variables with the suffix _ew. Since bioresid and biosoc will no longer be part of the data distribution, some data processing steps for the variables in these datasets have been included in the versioning and harmonization routines for biol.
Dataset PL - Variables on balance of assets (identifiable in variable label by abbreviation VB) have been corrected, re-sorted, and labeled. Inheritance variables have been re-versioned. Religious background has been re-versioned and harmonized to include the response option “konfessionslos” (no religious affiliation).
Dataset HBRUTTO - Some regional variables at the federal state level have been included as East-West variables with the suffix _ew. New variables on incentive type, incentive model, and variables describing screening process for the LGB sample have been added. Residential environment variables (wum) will no longer be included in the survey starting in 2019.
Dataset PBRUTTO - Some regional variables at the federal state level have been included as East-West variables with the suffix _ew. New variables on DRV record linkage and IAB record linkage have been added. Variables have been added indicating which questionnaire was used.
Dataset JUGENDL - Since bioage17 will no longer be part of the data distribution, some of the data processing steps for bioage17 variables have been included in the versioning and harmonization routines for jugendl.
Dataset KIDLONG - k_nrkid has been corrected to count only 16-year-old children in the household. Households with children without a stated birth year have been assigned a missing value of -1. - bgk93_r/kd_cty_r included incorrect values and has been corrected.
Dataset PLUECKEL - lpid has been removed.
Dataset HBRUTT - Some regional variables at the federal state level have been included as East-West variables with the suffix _ew.
Due to changes in data protection and privacy law, variables containing information on Germany’s federal states (Bundesländer) may not be transmitted to recipients outside the European Union. We have developed a new concept with different editions for the different data access procedures resulting from the change in law (listed in ascending order by the amount of information contained in each edition):
The default edition that we transmit to European users by sending them a personalized download link is the EU Edition. Some datasets may not be available in more restricted editions. If variables are not available in a more restricted edition, they are recoded to -7, a new missing value labeled “only available in less restricted edition”.
Originally, BIOIMMIG was generated by appending each new wave of data to the data from the previous years. This practice bears potential for errors since the SOEP includes a large number of variables that need to be comparable over time. In order to minimize this potential for error, v36 of BIOIMMIG is the first version of the dataset that has been generated using longitudinal data.
Variable bireason “Main reason for moving to Germany”
Variable biscger “Attended school in Germany”
There has been a significant increase in cases due to the addition of further variables and the new migration questions from the biographical questionnaire. The variable “country of last school attendance” was only included as an indicator from wave bd (2013) onwards to generate the variable biscger. The corresponding long variable, lb0186_v1, also has values for the years 2001-2012.
Variable bicamp “Refugee residence Y, N”
Due to two newly added variables (lr3440, plm0679), there are significantly more cases with a value of "2 No".
Variables birelh[p|gp|c|sb|sh|dr|fr] “Family in Country of Origin”
Previously, in the generation process, it was not defined whether these variables should represent country of origin or country of residence or both. In the previous versions, the two were arbitrarily merged. The decision to include only the country of origin leads to a significant reduction of cases.
Variable birelhc2 "Underage children not in Germany”
Due to two newly added years (1997, 1999), there are significantly more cases with the value "2 No".
Variable biwfam "Already had family in country”
The variable biwfam was changed from a category (y/n) to a binary variable, since otherwise there would be a distortion of the content. The data generated previously were too imprecise, since the recoding to value “2 No” does not unambiguously exclude cases in which respondents have no family members in Germany.
For a closer look at the changes and variables used, see the bioimmig documentation.
Variable hgtyp2hh in hgen
Datasets BIOSOC, BIORESID, BIOAGE17
The datasets biosoc, bioage17, and bioresid will no longer be provided. Most of the information from biosoc and bioresid will be maintained in the biol dataset with different variable names. In jugendl, the variables from bioage17 are retained. In order to reduce the number of datasets and to avoid redundant information, we decided to include the variables from biosoc and bioresid in biol and bioage17 in jugendl. The generated data from biosoc, bioage17, and bioresid are reproduced in the best possible way in biol and jugendl by applying versioning and harmonization. Users who have used biosoc, bioage17, or bioresid should use this table to facilitate transition.
The datasets with the suffixes mig and refugees—for instance, bep_mig and bgp_refugees—are no longer available. This information from the migration and refugee samples is fully integrated into the associated “raw” and “long” files.
Individual (PAPI) 2019: Field-de,en Field-en
Individual (CAPI) 2019: Var-de
Household (PAPI) 2019: Field-de,en Field-en
Household (CAPI) 2019: Var-de
Biography (PAPI) 2019: Field-de,en Field-en
Biography (CAPI) 2019: Var-de
Catch-up Individual (PAPI) 2019: Field-de
Catch-up Individual (CAPI) 2019: Var-de
Youth (16-17-year-olds, PAPI) 2019: Field-de
Youth (16-17-year-olds, CAPI) 2019: Var-de
Early Youth (13-14-year-olds, PAPI) 2019: Field-de
Early Youth (13-14-year-olds, CAPI) 2019: Var-de
Pre-teen (11-12-year-olds, PAPI) 2019: Field-de
Pre-teen (11-12-year-olds, CAPI) 2019: Var-de
Mother and Child (Newborns, PAPI) 2019: Field-de
Mother and Child (Newborns, CAPI) 2019: Var-de
Mother and Child (2-3-year-olds, PAPI) 2019: Field-de
Mother and Child (2-3-year-olds, CAPI) 2019: Var-de
Mother and Child (5-6-year-olds, PAPI) 2019: Field-de
Mother and Child (5-6-year-olds, CAPI) 2019: Var-de
Parents and Child (7-8-year-olds, PAPI) 2019: Field-de
Parents and Child (7-8-year-olds, CAPI) 2019: Var-de
Mother and Child (9-10-year-olds, PAPI) 2019: Field-de
Mother and Child (9-10-year-olds, CAPI) 2019: Var-de
Deceased Individual (PAPI) 2019: Field-de
Deceased Individual (CAPI) 2019: Var-de
Please find all sample specific questionnaires of this year and all questionnaires of previous years on this site
1) Documentation of ISCED generation based on the CAMCES Tool in the IAB-SOEP Migration Samples M1/M2 and IAB-BAMF-SOEP Survey of Refugees M3/M4/M5 until 2019
2) SOEP-Core v36 – Documentation of Sample Sizes and Panel Attrition in the German Socio-Economic Panel (SOEP) (1984 until 2019)
3) SOEP-Core – 2019: Design, Nonresponse, and Weighting in the Sample Q (Queer)
5) SOEP-Core – 2019: Sampling, Nonresponse, and Weighting in the Sample P
6) SOEP-Core v36 – PPATHL: Person-Related Meta-Dataset
7) SOEP-Core v36 – Biographical Information in the Meta File PPFAD (Month of Birth, Immigration Variables, Living in East or West Germany in 1989)
8) SOEP-Core v36 – HPATHL: Household-Related Meta-Dataset
9) SOEP-Core v36 – INTERVIEWER
10) SOEP-Core v36 – PBRUTTO: Person-Related Gross File
11) SOEP-Core v36 – HBRUTTO: Household-Related Gross File
12) SOEP-Core v36 – PGEN: Person-Related Status and Generated Variables
13) SOEP-Core v36 – HGEN: Household-Related Status and Generated Variables
14) SOEP-Core v36 – Codebook for the $PEQUIV File 1984-2019: CNEF Variables with Extended Income Information for the SOEP
17) SOEP-Core v36 – BIOPAREN: Biography Information for the Parents of SOEP-Respondents
18) SOEP-Core v36 – BIOAGEL & BIOPUPIL: Generated Variables from the "Mother & Child", "Parent", "Pre-Teen", and "Early Youth" Questionnaires
19) SOEP-Core v36 – BIOSIB: Information on Siblings in the SOEP
20) SOEP-Core v36 – The Couple History Files BIOCOUPLM and BIOCOUPLY, and Marital History Files BIOMARSM and BIOMARSY
21) SOEP-Core v36 – PFLEGE: Documentation of Generated Person-level Long-term Care Variables
22) SOEP-Core v36 – LIFESPELL: Information on the Pre- and Post-Survey History of SOEP-Respondents
1) Handgreifkraftmessung im Sozio-oekonomischen Panel (SOEP) 2006 und 2008
2) The new IAB-SOEP Migration Sample: an introduction into the methodology and the contents
3) The Request for Record Linkage in the IAB-SOEP Migration Sample
4) Flowcharts for the Integrated Individual-Biography Questionnaire of the IAB-SOEP Migration Sample 2013
5) The Measurement of Labor Market Entries with SOEP Data: Introduction to the Variable EINSTIEG_ARTK
6) Job submission instructions for the SOEPremote System at DIW Berlin – Update 2014
7) SOEP 2015 – Informationen zu den SOEP-Geocodes in SOEP v32
8) Editing and Multiple Imputation of Item Non-response in the Wealth Module of the German Socio-Economic Panel
9) Die Vercodung der offenen Angaben zu den Ausbildungsberufen im Sozio-Oekonomischen Panel
10) Das Studiendesign der IAB-BAMF-SOEP Befragung von Geflüchteten
11) Scales Manual IAB-BAMF-SOEP Survey of Refugees in Germany – revised version
12) SOEP 2010 – Preparation of data from the new SOEP consumption module: Editing, imputation, and smoothing
13) SOEP Scales Manual (updated for SOEP-Core v32.1)
14) Kognitionspotenziale Jugendlicher - Ergänzung zum Jugendfragebogen der Längsschnittstudie Sozio-oekonomisches Panel (SOEP)
15) Die Vercodung der offenen Angaben zur beruflichen Tätigkeit nach der International Standard Classification of Occupations 2008 (ISCO08) - Direktvercodung - Vorgehensweise und Entscheidungsregeln bei nicht eindeutigen Angaben
16) Die Vercodung der offenen Angaben zur beruflichen Tätigkeit nach der Klassifikation der Berufe 2010 (KldB 2010): Vorgehensweise und Entscheidungsregeln bei nicht eindeutigen Angaben
17) Multi-Itemskalen im SOEP Jugendfragebogen
18) Zur Erhebung des adaptiven Verhaltens von zwei- und dreijährigen Kindern im Sozio-oekonomischen Panel (SOEP)
19) Documentation of ISCED Generation Based on the CAMCES Tool in the IAB-SOEP Migration Samples M1/M2 and IAB-BAMF-SOEP Survey of Refugees M3/M4 until 2017
20) Dokumentation zum Entwicklungsprozess des Moduls „Einstellungen zu sozialer Ungleichheit“ im SOEP (v38)
21) SOEP-CoV: Project and Data Documentation
22) Missing Income Data in the German SOEP: Incidence, Imputation and its Impact on the Income Distribution
23) SOEP 2006 – TIMEPREF: Dataset on the Economic Behavior Experiment on Time Preferences in the 2006 SOEP Survey
24) Assessing the distributional impact of "imputed rent" and "non-cash employee income" in microdata : Case studies based on EU-SILC (2004) and SOEP (2002)
25) SOEP-Core v36: Codebook for the EU-SILC-like panel for Germany based on the SOEP
All documentation for filtering can be found on this page