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 in the publication.
Created by Thomas James Gaut (January 2021). These files merge the dataset for Kazakhstan with Belarus and, using the syntax written by Vytenis Juozas Deimantas (2019), create a combined GGS Wave 1 and GGS2020 Belarus and Kazakhstan dataset. This enables the use of the Kazakhstan dataset in comparative studies. The syntax produced by Vytenis Juozas Deimantas (2019) will need to be downloaded alongside this syntax and cited as well. The GGS1_Kazakh_Belarus_readme.txt file contains instructions on how to use the syntax, as well as a brief description of the differences between the GGS2020 Belarus and Kazakhstan datasets. For variables that can be used in analyses, please see the spreadsheet GGS1-GGP2020_BY.xlsx that is downloaded as part of Vytenis Juozas Deimantas’ syntax.
Cite as: Gaut, T.J. (2020). User-written Stata syntax to add the GGS2020 dataset for Kazakhstan to Belarus and the GGS Wave 1. Retreived from: https://www.ggp-i.org/data/methodology/
Created by Vytenis Juozas Deimantas (December 2019). These files allow to put together a combined GGS Wave 1 and GGS2020 Belarus dataset. This makes it convenient to perform comparative studies between countries in GGS Wave 1 and GGS2020 Belarus. Before running the syntax, you will need to read GGS1-GGS2020_BY_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_BY.xlsx file which has a list of variables that can be used for meaningful demographic analyses with the combined GGS Wave 1 and GGS2020 Belarus dataset. NOTE: the syntax does not harmonise the Life Histories, users interested in analysing fertility and partnership histories are encourage to use the Harmonized Histories dataset.
Cite as: Deimantas, V. J. (2019). User-written stata syntax to combine GGS Wave 1 and GGS2020 Belarus Datasets. Retrieved from: https://www.ggp-i.org/data/methodology/
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).
Cite as: Koops, J. C., & Emery, T. (2019). User-written stata syntax to converts dates in GGS Wave 1 into century months. Retrieved from: https://www.ggp-i.org/data/methodology/
Created by Jorik Vergauwen, Jonas Wood, David de Wachter 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.).
Cite as: Vergauwen, J., Wood, J., De Wachter, D., & Neels, K. (2015). Quality of demographic data in GGS Wave 1. Demographic Research, 32, 723-774.
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:
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.