Recent Data

The Study

The Socio-Economic Panel (SOEP) study is a wide-ranging, nationally representative longitudinal study of private households across Germany that was launched in 1984. It is based at DIW Berlin. Every year, nearly 15,000 households and more than 25,000 individuals are surveyed for the SOEP-Core study by the fieldwork organization Kantar Public (TNS Infratest up to 2017).

Since its inception, the SOEP’s goal has been to collect and provide representative microdata that allow researchers to study stability and change in living conditions. Its approach is micro-econometric, with added variables from sociology and political science (influenced by the social indicator movement).The data provide information on all members of households in both the former East and West, including foreigners, and recent immigrants to Germany.

The study was launched in 1984. Some of the many topics include household composition, occupational biographies, employment, earnings, health and satisfaction indicators. As early as June 1990—even before the Economic, Social and Monetary Union—SOEP-Core expanded to include the states of the former German Democratic Republic (GDR), thus seizing the rare opportunity to observe the transformation of an entire society. Immigrant and refugee samples were added to account for the changes that took place in German society in 1994/95, 2013, 2015, and 2016. Further new samples were added in 1998, 2000, 2002, 2006, 2009, 2011, and 2012.

Since Version 31 (10.5684/soep.v31), the SOEP has included the complete data from “Familien in Deutschland” (Families in Germany, FiD), which has been integrated retrospectively into the SOEP and made available for analysis in user-friendly form. The FiD survey was carried out in parallel to the SOEP from 2010 to 2013 as a "SOEP-related study". The most recent version of the SOEP-Core data contains data from the migration and refugee samples, which have been integrated into SOEP-Core. The SOEP survey is constantly being adapted and developed in response to current social developments. The international version of the SOEP-Core data contains 95% of all cases surveyed.

DOI: 10.5684/soep.v34
Collection period: 1984-2017
Publication date: 2019-03-04
Principal investigators: Stefan Liebig, Jan Goebel, Martin Kroh, Carsten Schröder, Jürgen Schupp, Charlotte Bartels, Alexandra Fedorets, Andreas Franken, Marco Giesselmann, Markus Grabka, Jannes Jacobsen, Selin Kara, Peter Krause, Hannes Kröger, Maria Metzing, Janine Napieraj, Jana Nebelin, David Richter, Diana Schacht, Paul Schmelzer, Christian Schmitt, Daniel Schnitzlein, Rainer Siegers, Knut Wenzig, Stefan Zimmermann

Data collector: Kantar Deutschland GmbH
Population: Persons living in private households in Germany
Dataset informationen:
Dataformat: STATA, SPSS, SAS, CSV

Selection method: All samples of SOEP are multi-stage random samples which are regionally clustered. The respondents (households) are selected by random-walk.

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 16 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 16 years of age, mainly concerning attendance at institutions (kindergarten, elementary school, etc.)

Users outside the European Economic Area (EEA) are only permitted to use a reduced version of the dataset (Scientific Use File) due to data protection regulations.
For complete information, see DOI https://doi.org/10.5684/soep.v34i

Known Bugs/Fixes for wave BH (1984-2017):

Please note that the update was not carried out in the current data transfer. It must be carried out manually by you!

Overview (May 2019):

  1. Dataset: pl / variables: plb0186_v2, plb0186_h

    Values for the variables plb0186_v2 and plb0186_h for the East sample in 1990 are too small by a factor of 10.

  2. Dataset: bhh / variables: bhh_37_01, bhh_37_02

    The names assigned to the raw variables bhh_37_01 “electricity included in rent” and bhh_37_02 “assessed burden of housing expenses (rent and additional expenses)” do not correspond to the standard SOEP concept for naming variables. Both variables will be renamed in the new version.

  3. Dataset: migspell

    The previous version from the migspell dataset was delivered. 

  4. Datasets: biobirth, bioimmig, biojob, bioparen, bioresid, biosib, biosoc, biotwin, pflege / variables: pid, hid, cid

    The new identifiers were not filled in and have to be filled in from the old identifiers.

Details:

1. Dataset: pl
Variables: plb0186_v2, plb0186_h

Values for the variables plb0186_v2 “Actual working time with overtime (1990-2017)” and plb0186_h “Actual working time with overtime (harmonized)” have the wrong values for the East sample in 1990.

The variable plb0186_h is made up of the variables plb0186_v1 (1984-1989) and plb0186_v2 (1990-2017). We included all of the values for plb0186_v1 as they were, and divided all of the valid values for plb0186_v2 by 10. The process of harmonization is necessary due to the fact that the two raw variables for 1990 were provided in different formats:

gpost: gp3601e (two-digit, no comma)
gp: gp39 (three-digit, no comma)

The raw variable gp3601e from gpost was assigned to the variable plb0186_v2 although it does not have to be divided by 10. As a result, all values for the East German population for the year 1990 were mistakenly divided by 10. The simplest way of solving this problem is to multiply the valid values for the East German population by 10. 

cd "Datenpfad"
use "pl.dta"
tabstat plb0186_*,by(syear)
clonevar rep_plb0186_h=plb0186_h
replace rep_plb0186_h = rep_plb0186_h*10 if inputdataset == "gpost" & rep_plb0186_h > 0

Detailed information on the general process used to harmonize variables can be found here:
Versioning and harmonization of variables
Working with harmonized Variables

2. Dataset: bhh
Variables: bhh_37_01, bhh_37_02

The names assigned to the raw variables bhh_37_01 “electricity included in rent” and bhh_37_02 “assessed burden of housing expenses (rent and additional expenses)” do not correspond to the standard SOEP concept for naming variables. Both variables had to be renamed:

bhh_37_01 “Electricity included in rent” → bhh_33
bhh_37_02 “Assessed burden of housing expenses (rent and additional costs)” → bhh_37

To find out more about how raw variables are named in the SOEP, see the SOEPcompanion:
Naming conventions of Variables and Datasets

3. Dataset: migspell

Unfortunately the previous version of the migspell dataset was delivered. For the current version, please contact the SOEPhotline or write an email to soepmail.

4. Dataset: biobirth, bioimmig, biojob, bioparen, bioresid, biosib, biosoc, biotwin, pflege
Variables: pid, cid, hid

In the process of “merging” SOEP-Long and SOEP-Core, all of the SOEP-Long ID variables (pid, hid, cid) were also included in the raw datasets to make merging easier for users. In some datasets, only the ID variables were created but not filled in with the corresponding IDs.

Empty pid: biobirth, bioimmig, biojob, bioparen, bioresid, biosib, biosoc, biotwin, pflege
Empty hid: bioimmig, bioresid, biosoc
Empty cid: biobirth, bioimmig, biojob, bioparen, bioresid, biosib, biosoc, biotwin, pflege

With these datasets, please continue to use persnr, hhnrakt, hhnr, or copy the content into the corresponding new ID variable.

clonevar pid = persnr
clonevar hid = hhnrakt
clonevar cid = hhnr

Further information on SOEP identifiers can be found here:
Dataset Identifier

The following changes have been made to the current data distribution :

1. New, user-friendly integrated data format

The new wave of the SOEP-Core study incorporates our “wide” and “long” data formats, which used to be provided to users separately. Our aim is to eliminate any confusion about what is available in which format and to make data use easier overall. After several years of testing SOEPlong as an additional service designed to facilitate analysis for both experienced and new users, we will now be providing all datasets in the “long” format as a standard part of our SOEP data release. This means that you will find the different SOEP data formats listed below in your data file, some of which will be contained in separate subdirectories.

Please make sure that you unpack the entire directory structure when unpacking your data.

1.1. SOEP in “long” format on the top level

In the top-level (or root) directory, you will find all of the datasets provided up to now with SOEPlong (pl, ppfadl, etc.) as well as all of the additional datasets formerly provided only in our classic “wide” format (biographical or spell data such as bioparen, artkalen, etc.). All of the data in the main SOEP-Core study are therefore contained in the datasets in the top-level directory.

Feedback from experienced and beginning users over the past several years shows that the “long” data offer significant advantages in ease of use, particularly for beginners. We have therefore decided to use this as our primary data format in future data releases.

All available individual year-specific datasets are pooled into a single dataset (e.g., all $P datasets are integrated into the PL dataset). In some cases, this means that we have to harmonize variables in order to be able to define them consistently over time. For instance, income information is given in euros up to 2001 and not in deutschmarks, and in cases where questionnaires have changed, the categories are modified over time. All changes are presented to users in a clear and understandable way, and if harmonization is necessary, all input variables are provided in their original form (see below _v*-variables). SOEPlong thus significantly reduces the number of datasets and the number of variables.

A more detailed description of the format of our SOEP-Core data release can be found in our new SOEPcompanion.

1.1.1. Most important changes to v33 in the long format

  • The following new files have been added:
    • HBRUTT: long file of the HBRUTT$$ files
    • PLUECKEL: long file of the $PLUECKE files
    • VPL: long files of the $VP files
  • Data sets PL and PL2 are being provided again in one combined file (PL).
  • The variable scheme with c-variables (cross-sectional) and l-variables (longitudinal) has been modified as follows:
    • If the variables on which a variable in the long format is based changed in the cross-section, then corresponding _v*-variables will be created for each version. A harmonized _h-variable is provided as well. Further information can be found in the SOEPcompanion (general description, examples)
  • All of the long datasets generated from the various cross-sectional datasets contain the new variable: INPUTDATASET.
  • Due to adjustments to the new joint data release format, some files with “long”-specific names are no longer included in the data release: CDESIGN, CSAMP, CSAMPFID, KIDL, PBREXIT.
  • The following datasets have been renamed to avoid conflicts with the data names in the raw directory:
    • PPATH replaces PPFAD
    • PPATHL replaces PPFADL
    • HPATH replaces HPFAD
    • HPATHL replaces HPFADL

1.2. Classic format in the subdirectory raw

Since we know that many users have existing scripts that are based on the original data format, and to enable users to understand the process of generating the “long” data, we provide all of the datasets in their original SOEP format in the directory raw.

Users who want to continue using the old format simply need to switch into subdirectory rawand use the datasets there.

The only change is that there are now additional identifiers in all of the datasets in the raw directory with the name in the long format (PID and PERSNR or HID and $HHRNAKT) and a survey year variable (SYEAR) so that users can easily merge variables from the two data formats.

1.3. New EU-SILC clone in the subdirectory eu-silc-clone

Many users are undoubtedly aware that the SOEP supports cross-national analysis with CNEF through the dataset PEQUIV. We have now produced a data product that allows you to use the SOEP data in comparative analyses with the EU-SILC (European Union Statistics on Income and Living Conditions) data. EU-SILC, which is provided by Eurostat upon request, offers cross-sectional and longitudinal information for many European countries. Up to now, only cross-sectional information has been available for Germany. The EU-SILC clone offers longitudinal information on private households in Germany based on the SOEP data. All of the information contained in it can be directly compared with the EU-SILC longitudinal information on other European countries.

The EU-SILC clone is integrated into the standard SOEP data release (in subdirectory eu-silc-clone).

Documentation on the 2005-2016 EU-SILC clone can be found here (PDF, 3.01 MB).

2. New samples in the main SOEP study

The new SOEP data release (v34) will be the first to contain data from the IAB-BAMF-SOEP Survey of Refugees in Germany as Sample M5, as well as the continuation of the PIAAC-L Survey, as Sample N.

2.1. IAB-BAMF-SOEP Survey of Refugees (M5)

The SOEP, in cooperation with the Institute for Employment Research (IAB) and the Federal Office for Migration and Refugees (BAMF), has succeeded in integrating a third sample of refugee households (M5) into the SOEP study. The survey was launched in 2017. The population of M5 covers adult refugees who have applied for asylum in Germany since January 1, 2013, and are currently living in Germany. M5 added another 1,519 households of refugees who have migrated to Germany since 2013 to the SOEP framework.

2.2. Integration of respondents from PIAAC-L as Subsample N

Sample N integrated 2,314 households of former participants of the Program for the International Assessment of Adult Competencies (PIAAC and PIAAC-L) in 2017. This is the most recent addition to the SOEP-Core samples. Fieldwork in sample N was conducted between mid-March and mid-August and thus slightly later than the majority of samples A–L1. More information on the PIAAC-L project can be found on the project homepage.

3. Translation errors in some questionnaire languages

In the IAB-BAMF-SOEP Survey of Refugees (M3-M5), there were translation errors in some some of the questions on income components in translated versions of the household questionnaire. Answers for these variables are therefore not comparable with other answers. The corresponding variables were set to -3.

4. Deletion of interviews not conducted in line with the standards of the IAB-BAMF-SOEP group in the IAB-BAMF-SOEP Survey of Refugees (M3/M4)

In the process of data preparation, three interviewers were identified who had not conducted interviews in line with the standards of the IAB-BAMF-SOEP group (more information here). The interviewers in question were responsible for 88 households in 2016 and 112 households in 2017. The households affected in the first wave of the survey (2016) were completely removed from the dataset. The households affected in 2017, who were supposed to be interviewed for the second time, were deleted for 2017 but left in the dataset for 2016. There are no indications that the first interviews (by a different interviewer) were not conducted in line with IAB-BAMF-SOEP standards. The interviews and cases deleted from the data release may be accessed upon request from a guest work station at the SOEP-RDC for survey methodological analysis. After these lines were deleted from all datasets, the following adjustments were made:

  • The deletion of the household and individual interviews required an update of the weights (dataset HHRF and PHRF), which now take account of the slightly reduced case numbers in survey years 2016 and 2017.
  • Update / inclusion of the new weights in the datasets BGPEQUIV and BHPEQUIV.

5. Extended variable naming convention

The extended variable naming convention is applied only to data sets from wave BH onwards and only applicable for the datasets $P, $H, $KIND. We added underscores between unit of analysis, question identifier, and item identifier to clearly separate the analysis unit, question, and item visually. In addition, a questionnaire identifier was introduced, which is also separated by an underscore from the item. This new version of naming variables is only used if the survey instrument differs from the “original” SOEP-Core instrument.

Due to our different samples in the SOEP, there are some respondents that receive sample-specific questions, such as the refugee sample that started in 2016. For that specific group, we created an extended individual questionnaire with some specific questions along with the standard SOEP questions that are asked every year. For the specific questions, you can use the instrument variable to see the source of the variables.

Examples and more detailed descriptions can be found in the chapter on this subject in the SOEP Companion.

6. Changes in specific variables

  • New variables for interview year: HIYEAR in HGEN, HPATHL, and PIYEAR in PGEN, PPATHL. These new variables indicate, for all survey years, the household and individual interviews that were finalized after (or before) the survey year (variable syear), which is the reference year for the questionnaires and for data collection.


6.1. Dataset PPATH / PPATHL (in raw: PPFAD)

6.1.1. SEXOR

  • The previous data release was the first to include the variables SEXOR (sexual orientation) and SEXORINFO (source of information on sexual orientation). The value -1 “insufficient information” has been changed to 2 “insufficient information”.

6.1.2 PARINFO

  • The value -1 “unclear” has been changed to 5 “unclear”.

6.1.3 Migration information

  • The coding of GERMBORN, CORIGIN, IMMIYEAR, and MIGBACK was changed for inconsistent cases (for more information, see the PPATH/PPFAD documentation).

6.1.4. Asylum-Seekers and Refugees

  • The variables for asylum-seekers and refugees [AREBACK, AREFINFO] have been renamed (in v33: REFBACK, REFINFO) and revised. The variable AREFINFO now also allows identification of specific subgroups (more information is available in the documentation).

6.2. Dataset PGEN

6.2.1 Partner pointer

  • For the variable PGPARTZ (PARTZ$), the value -1 (“no answer”) has been replaced by the correct value 5 (“unclear”).
  • Starting with wave BH, the new quality control processes implemented in generating the partner indicator have improved the quality of data from previous waves:
    • Contradictory answers between partners regarding their relationship have been identified and corrected.
    • Partnerships with differing partner indicators (1 “spouse” or 2 “life partner”) within a relationship have been identified and corrected.
    • Errors in the assignment of PARTZ values (1 “spouse” and 2 “life partner”) due to different filter routing in the different survey instruments have been corrected. Marriages were asked differently in the individual biography questionnaire for Sample J+K and in the individual questionnaire for Samples A-I. Separating out samples J and K played a key role in this correction, since these two led to errors due to their different filter routing.
    • Partnerships with recently deceased individuals were identified and deleted.
    • Respondents’ data on divorce, separation, or the death of a life partner within the past year have been taken into account for the first time in the process of generating the data.
    • For the first time, family status, civil status, partner’s first name (permanent partner number) and place of residence of the partner in the case of refugees’ partnerships (Samples M3-M5) have been taken into account in addition to interviewer given relationships between the different houshold members.

6.2.2. Volunteer work and side jobs

  • The PGEN (raw: $$PGEN) files contain nine new variables. In 2017, the SOEP fundamentally revised how respondents were surveyed about side jobs. Now, for the first time, respondents can provide answers on three different side jobs. They can also now differentiate the type of side job, whether volunteer work (variables HONOR1, HONOR2, HONOR3) and whether they are working for an employer or working freelance (SNDTYP1, SNDTYP2, SNDTYP3). The amount of gross additional income from side jobs is provided as imputed information (SNDJOB1, SNDJOB2, SNDJOB3).
    • SNDTYP117 : First side job occupational status
    • SNDTYP217 : Second side job occupational status
    • SNDTYP317 : Third side job occupational status
    • SNDJOB117 : Current gross additional income from side job 1 (gen.) in euros
    • SNDJOB217 : Current gross additional income from side job 2 (gen.) in euros
    • SNDJOB317 : Current gross additional income from side job 3 (gen.) in euros
    • HONOR117 : Volunteer work 1
    • HONOR217 : Volunteer work 2
    • HONOR317 : Volunteer work 3


6.2.3. Educational degrees

  • In v34, CASMIN and ISCED are based on additional information on educational degrees obtained abroad. Hence, some individuals with degrees from abroad display higher ranks in v33 than in v34.
  • The error in the CASMIN variable in v33 is fixed: In v33, individuals with 2c_voc (vocational maturity certificate) were mistakenly categorized as 2c_gen (general maturity certificate).

6.2.4. AUTONO

  • The generation of autono was discontinued in 2017 due to the difficulty in comparing this variable with the usual models of autonomy. Work is currently underway to introduce comparable definitions of autonomy.


6.3 Dataset PEQUIV

  • The PEQUIV (raw: $$PEQUIV) files contain six new variables. These are:
    • IAUS117 : Pensions from another country
    • AUS217 : Widows / orphans pension from another country
    • ASYL17 : Asylum-seeker benefit
    • FASYL17 : Imputation flag: Asylum-seeker benefit
    • EDUPAC17 : Benefits from the educational package
    • FEDUPAC17 : Imputation flag: Benefits from the educational package
    For more details, see the SOEP Survey Paper: Codebook for the $PEQUIV File 1984-2017.

6.4. Dataset BIOAGEL and BIOPUPIL

  • Variables from questionnaires given to 12-year-olds and 14-year-olds are now provided in BIOPUPIL dataset to reflect the differences in survey mode (parents being asked questions about their children vs. children being surveyed directly).
  • Variables from additional questions in refugee samples are integrated in BIOAGEL and BIOPUPIL datasets.


6.5. Dataset HGEN

A number of changes have taken place in recent years in questions on home rental. The first change took place in the hosehold questionnaire of wave BF (2014). The question asked about the costs of utilities in such detail that respondents were not able to provide correct answers. This led to underestimation of both base rent and utilities.

It emerged that this led to a slight break in the time series. Rent has increased continuously over the years since 1984. In 2014 and 2015, however, rental costs fell and have been increasing again sharply since 2016. This break can be explained by the change in the questionnaire.

Starting with wave BH, respondents are being asked about rent in the same way as in wave BG (2016) and in wave BD (2013) in order to maintain long-term comparability. In addition, with wave BH, the new migration sample M5 and the new refresher sample N are part of the SOEP. Since Sample M5 was not surveyed on utility costs in a comparable way and since many of these respondents probably live in group housing or receive subsidies to cover living costs, no rent variable was generated for them.

v33 - rent

v34 - rent

2010: 486.25

2010: 486.21

2011: 484.93

2011: 485.64

2012: 491.01

2012: 490.75

2013: 505.00

2013: 505.59

2014: 470.95

2014: 473.74

2015: 507.06

2015: 508.57

2016: 545.53

2016: 541.90

 

2017: 550.67

6.6. Dataset BIOIMMIG

  • The population of BIOIMMIG shrunk due to a change of coding of BIIMGRP (for more information, see the BIOIMMIG documentation)
     

6.7. Dataset HHRF/PHRF

  • New variables in HHRF: BHHHRF, BHHBLEIB, BHHHRFAM4, BHHHRFM5, BHHHRFN
  • New variables in PHRF (and ENUMHRF, available on request): BHPHRF, BHPBLEIB, BHPHRFAM4, BHPHRFM5, BHPHRFN
  • Please note that with our new integrated data format, you’ll find all weighting variables now directly in PPATHL or HPATHL.
  • On request, we provide stand-alone weighting variables (BHPHRFM35, BHHHRFM35) for the refugee samples M3, M4, and M5.

6.7.1. Revisions and Bugfixes

  • Due to confusion in the country codes for Iran and Russia in the sampling frame (Central Register of Foreign Nationals, AZR), design weights for Samples M3 and M4 as well as their cross-sectional weights for wave BG had to be updated.
    In Wave BG, we interpreted the population of samples M3 and M4 as refugees who immigrated to Germany between January 2013 and January 2016. In fact, only those refugees whose registration at the Central Register of Foreign Nationals (AZR) took place until April 2016 were included in those samples. In Sample M5, among others, those refugees were interviewed who, although they had immigrated in the same period, were registered later. For this reason, the total for the post-stratification of the second wave of M3 and M4 has been reduced by the number of refugees with a later registration date.

1. Citing the data source

To improve our documentation on data use, we ask that you include a reference to the dataset used and institution providing the data in all future publications (ideally in a footnote at the beginning of the paper or in the foreword to books). This should read as follows:

The Socio-Economic Panel (SOEP) data used in this [publication, paper, book, etc.] were provided by DIW Berlin..

2. Citing the data version

Precise citation of data sources is becoming ever more important in the research context. The SOEP group recommends that you cite the data according to the following (fictitious) example:

Example:

Englisch:
Socio-Economic Panel (SOEP), data for years 1984-2019, Version 36, SOEP, 2020, doi:10.5684/soep.v36.

Deutsch:
Sozio-oekonomisches Panel (SOEP), Daten für die Jahre 1984-2019, Version 36, SOEP, 2020, doi:10.5684/soep.v36.

Short version:
SOEP v36.

3. References

In your references, please cite one of the following publications:   

If you need a publication that describes the SOEP Public Use File, the 95% version distributed to international users, please cite:

When using PanelWhiz, a collection of Stata/SE® add-on programs that automatically extract data from large panel datasets including the SOEP, please cite:

For more information on citation of the various versions of the SOEP data, please see Digital Objekt Identifier.

Hier erstmal Erklärtext. Was sind MD5-Fingerprints und wie kann man Sie nutzen.

MD5 fingerprints

 

Weitergabeformat zip-Datei Einzeldatensätze
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Stata deutsch 9cbe419645ee17bdb5265df5a5662802   (TXT, 19.29 KB)
Stata englisch 3a195c128e21b732d8b1f0ff64316b35   (TXT, 19.29 KB)
SPSS deutsch 33763b1f68c54f790d9826b4923ac276   (TXT, 19.29 KB)
SPSS englisch 0454017269b9f5601d3fe30ace13211f   (TXT, 19.29 KB)
SAS deutsch 84c5124b696a552340b1d7bca79c8c15   (TXT, 21.53 KB)
SAS englisch e6cb205a9d2abec3a37872f1dbf2a6e8   (TXT, 21.53 KB)
CSV df524ba26e46b42ff77dd6991046485d   (TXT, 19.29 KB)
GGKBOU 1fd60d2f3f1a405d508cf472ff916cc9   (TXT, 140 Byte)
GGKBOU englisch 67c43e2e72aab736e6c6dafb75da57f5   (TXT, 140 Byte)
Lehrversionen
Stata deutsch 3ecf547c653dfac561cb618c306972c8
Stata englisch 598ba143e4d7115fcc183dd1517af0d1
SPSS deutsch 0f6ffcfcbdf0982afe48582603e20f97
SPSS englisch 96adf7fef897ddb346253598a9e93242
SAS deutsch 16a66eacf4032b2ba8fe55f5e242bc3f
SAS englisch d921f61ee31459a4b54ea74d0dda9d10

The SOEP allows you to link data from a wide variety of other studies to SOEP-Core data. For instance, you can analyze data at the regional level or match micro-marketing indicators with survey data. For more a detailed description and further data linkage possibilities, see here.