The GGP is currently preparing for a new round of data collection which will commence at the end of 2019. As part of this, the methodology of the GGP is being updated and renewed to ensure that social scientists and policy makers have access to the highest quality data possible. The survey will be implemented from the GGP Central Coordination Office’s at NIDI in the Hague to ensure that data is collected efficiently and effectively across a wide range of countries. The survey uses a new questionnaire which ensures comparability with the the FFS (1990’s) and the previous GGS (2000’s). The GGP hopes to collect data from more than 20 countries in 2020 and further enrich the GGP research infrastructure. Countries interested in fielding the GGS can either read the guidelines below or read our FAQ section here.
For detailed information on the data preparation procedures and a guide how to use the files below please visit the Data Preparations section. Detailed information on variable coding and country specificities can also be found in our online data analysis suite.
For detailed information on the data preparation procedures and a guide how to use the files below please visit the Data Preparations section.
Country specific documentation
In July 2019 the GGP survey data has been included in the DANS archive (wave 1, and wave 2). From then on, the GGP requests that research using the GGS Wave 1, and GGS wave 2 data include references to the GGS data in the following form:
This paper uses data from the GGS Waves 1, and 2 (DOIs: 10.17026/dans-z5z-xn8g, 10.17026/dans-xm6-a262), see Gauthier, A. H. et al. (2018) or visit the GGP website (https://www.ggp-i.org/) for methodological details.
If you have syntax that you would like to share with the rest of the user community we would be very happy to make it available via the website and can work with you to make sure its user friendly. If you have any questions about user synatx files or would like to make your own contribution, email@example.com.
If you use syntax for work included in a publication we ask that you acknowledge the authors of the syntax (stated at the top of each file) in the publication.
Created by Vytenis Juozas Deimantas (December 2019). These files allow to put together a combined GGS Wave 1 and GGS2020 dataset. This makes it convenient to perform comparative studies between countries in GGS Wave 1 and GGS2020. Before running the syntax, you will need to read GGS1-GGS2020_readme.txt file that contains guidelines on how to successfully produce a joint dataset. After running the syntax, you will have to consult GGS1-GGP2020.xlsx file which has a list of variables that can be used for meaningful demographic analyses with the combined GGS Wave 1 and GGS2020 dataset.
Created by Judith Koops and Tom Emery (August 2015). This file converts all dates in wave 1 of the GGS into century months. Century Months express the date of an event in terms of the number of months past since January 1st 1900. This makes the file easy to use for analysing the life course as you can simply subtract one date from another in order to get the number of months between the two events. Before running the syntax you will need to open a 4.2 version of a wave 1 file and run then just run the syntax. The variables are then converted from month/year format to century month format (i.e. ahg6m_1 ahg6y_1 -> anhg6_CM).
Created by Jorik Vergauwen, Jonas Wood, David Wechter and Karel Neels and reported in the following article of Demographic Research (Link). Please cite the article if used. The syntax files allow the user/reader to replicate all different nuptiality indicators (ASFFMR, Period TFFMR, Cohort TFFMR, Period MAFFM and Cohort MAFFM) and fertility indicators (ASFR, Period TFR, Cohort TFR, Period MAC and Cohort MAC) for GGS Belgium. The code can be modified and applied to the microdata for other countries by using the information in Table 1 of the article. This table provides the user/reader with more information on the selections used for the different countries (for rates up to age 49). Note that the time periods considered for validation in Table 1 are based on rates calculated up to the age of 49 (while syntax selections are for the max. range, i.e. rates up to age 39). Also note that variable names differ between GGS countries (e.g. the max. number of partnerships, max. number of resident biological children, etc.).
There are a number of papers which cover the technical aspects of the GGP and the implications for substantive research. A selection of these is provided below:
Please acknowledge the Harmonized Histories by including the following either within the acknowledgements or footnotes of any publications using the data:
“The Harmonized Histories data file was created by the Non-Marital Childbearing Network (www.nonmarital.org) (see Perelli-Harris, B.; Kreyenfeld, M.; Kubisch, K.: Harmonized histories: manual for the preparation of comparative fertility and union histories (2011) Rostock, MPIDR Working Paper WP-2010-011).
It harmonizes childbearing and marital histories from 14 countries in the Generations and Gender Programme (GGP) with data from Spain (Spanish Fertility Survey), United Kingdom (British Household Panel Study) and United States (National Survey for Family Growth). Thank you to everyone who helped collect, clean, and harmonize the Harmonized Histories data, especially Karolin Kubisch at MPIDR.”
If you need to refer to procedures for harmonizing the Histories, please cite: Perelli-Harris, Brienna, Michaela Kreyenfeld, and Karolin Kubisch. 2010. “Technical Manual for the Harmonized Histories Database.” Rostock, MPIDR Working paper 2010-011.
The Harmonized Histories data file was created by the Non-Marital Childbearing Network (see Perelli-Harris, B.; Kreyenfeld, M.; Kubisch, K.: Harmonized histories: manual for the preparation of comparative fertility and union histories (2011) Rostock, MPIDR Working Paper WP-2010-011). It harmonizes childbearing and marital histories from 13 GGP countries with data from Spain, United Kingdom and United States. The data file builds on the public release files of the GGS by further harmonizing and standardizing the histories within the GGP and ensuring the data is ready for use in event history analysis. The GGP is indebted to the Non-Marital Childbearing Network for its work on the standardisation of partnership and fertility histories and making this data file publicly available to the GGP Research Community. Access to the harmonised histories file is provided through the GGP User Space. Users with existing access to the GGP are granted access automatically and will need to log out and then log in again to access the data.
We are very grateful to the members of the Nonmarital Childbearing Network www.nonmarital.org from various research institutions who in 2009 initiated the idea of a comparative database of union and fertility histories. Brienna Perelli-Harris, Michaela Kreyenfeld, Karolin Kubisch, Wendy Sigle-Rushton, Renske Keizer, Paola DiGiulio, and other members of the Nonmarital Childbearing Network created a manual to facilitate the harmonization (see Perelli-Harris, B.; Kreyenfeld, M.; Kubisch, K.: Harmonized histories: manual for the preparation of comparative fertility and union histories (2010) Rostock, MPIDR Working Paper WP-2010-011).
We acknowledge the important contribution of the project head Brienna Perelli-Harris (Max Planck Institute for Demographic Research and later University of Southampton) and of the project leader at the Max Planck Institute for Demographic Research Michaela Kreyenfeld.
We acknowledge the important contribution of Karolin Kubisch at the Max Planck Institute who managed the standardization and cleaning process of the surveys. She also drew up detailed documentation, updated the surveys, and responded to users.
We acknowledge the important contribution of Sigrid Gellers-Barkmann at the Max Planck Institute who managed data distribution from Rostock and was responsible for data protection and contacts with users.
This project would not have been possible without the encouragement and financial support of the Max Planck Institute for Demographic Research (MPIDR) in Rostock and the support from the Datalab of the MPI and especially its head Vladimir Shkolnikov, who supported the enormous amount of work of Karolin Kubisch on this project. We are also grateful to the former director of MPIDR Joshua Goldstein for his support.
We also thank the IT department of the MPIDR for technical support especially in the development of secure data transfer methods.
We thankfully acknowledge the contributions of the following researchers and institutions. In alphabetical listing by country/area, they include:
AUT: Caroline Berghammer (Vienna Institute of Demographie)
ESP: Alicia Adsera (Princeton University)
ITA: Paolo di Giulio (Vienna Institute of Demography)
NLD: Renske Keizer (Rotterdam University)
NOR: Trude Lappegård (Statistics Norway)
UK: Wendy Sigle-Rushton (London School of Economics)
USA: Katherine Michelmoore and Kelly Musick (Cornell University)
These researchers provided us with country specific prepared files according the specifications of the Harmonized Histories.
Many thanks to our users for helping to find errors in the database, including Aija Duntava, Gerda Neyer, Sunnee Billingsley and Gunnar Andersson from Stockholm University, and Paulina Galezewska and Julia Mikolai from the University of Southampton.
GGS data were obtained from the GGP archive and were created by the organizations and individuals listed at About GGP Page.
UK: The BHPS study is funded by the Economic and Social Research Council (ESRC). The data was originally collected by the ESRC Research Centre on Micro-Social Change at the University of Essex. Over time, additional funding for the British Household Panel Survey (BHPS) has been provided by the Health Education Authority (HEA), Office for National Statistics (ONS) and Eurostat. The HEA helped to carry out the survey of 11-15 year old members of the BHPS sample included from Wave 4 onwards. The Northern Ireland sample, included from Wave 11, is jointly funded by the Economic and Social Research Council (ESRC) and various Northern Ireland government departments. Chiara Daniela Pronzato cleaned the fertility and partnership histories and her data set (available from the UK data archive) was used in this analysis. (see: www.nonmarital.org/HarmHist-GB.htm)
Netherlands: Data about the Netherlands were derived from the The Family and Fertility Survey 2003 (Onderzoek Gezinsvorming). The FFS contains detailed information about relationships and family (formation). Data were collected by Statistics Netherlands. (see: www.nonmarital.org/HarmHist-Netherlands.htm)
For Spain, data comes from the Spanish Fertility Survey. (see: www.nonmarital.org/HarmHist-Spain.htm)
Please note: “The Survey of Fertility and Values was collected by the Centro de Investigaciones Sociológicas, but it is still undergoing processing. Therefore, the CIS holds no responsibility for any inaccuracies found in the data.”
USA: The National Survey of Family Growth was conducted by the Division of Vital Statistics at the U.S. Centers for Disease Control. (see: www.nonmarital.org/HarmHist-US.htm)
Germany Pairfam: For Germany, we include data from the German Family Panel (pairfam) into the Harmonized Histories. We include data from the German Family Panel, because of potential data limitations of the German GGS (Kreyenfeld, Hornung, & Kubisch, 2012).Caution should be used when using the German Family Panel as it follows a cohort approach and includes only respondents of the birth cohorts 1971-73, 1981-83, 1991-93. Also note that the German Family Panel includes an oversample of East Germans, so that descriptive analysis need to be weighted.
Information on how the Harmonized Histories were created and further information about the Non-Marital Childbearing Network can be found via their website: www.nonmarital.org
As part of the GGP – ‘Evaluate, Plan, Initiate’ Project, an experiment was conducted which aimed to examine the feasibility of online data collection in the GGP. Collecting data from individuals via an online survey has the advantage of being cheaper and quicker than face to face data collection through interviews. However, scientists and policy makers are often concerned that it leads to lower quality data as fewer people respond to the survey, and those who do are more likely to provide inaccurate information.
The GGP Push to Web Experiment looked to explore this by conducting face to face and web surveys in parallel in three countries: Germany, Portugal and Croatia. These countries were specifically picked as they represent challenging environments for online data collection. Details about the design of the experiment can be downloaded here. Results and publications stemming from the study will appear here in due course.