Dataset: Generations and Gender Survey Wave 2 - Consolidated

Abstract

The Generations and Gender Survey (GGS) provides micro-level data with the aim of significantly improving the knowledge base for social science and policymaking in Europe and developed countries elsewhere.  
In Europe 2020, the European Union develops a strategy "to help us come out stronger from the crisis and turn the EU into a smart, sustainable and inclusive economy delivering high levels of employment, productivity and social cohesion". The economic crisis affects not only day-to-day decisions, but also fundamental choices at all stages of people's lives:  marriage and childbearing, the combination of employment and caring responsibilities for the young and the old, retirement, housing, and ageing well. The GGS has been developed to provide scientists with high-quality data to contribute scientifically grounded answers to these key policy questions. Survey content focuses on intergenerational and gender relations between people, expressed in care arrangements and the organization of paid and unpaid work. Key feature of the survey are:  
- Cross-national comparability. In each country data is collected on the basis of a common international questionnaire and guidelines about the methodology. Data processing includes central harmonization of national datasets.  
- A broad age range. It includes respondents between the ages of 18 and 80.
- A longitudinal design. It has a panel design, collecting information on the same persons at three-year intervals.  
- A large sample size. It has an average of 9,000 respondents per country at Wave 1.
- A theory-driven and multidisciplinary questionnaire. It provides data for policy relevant research by demographers, economists, sociologists, social policy researchers, social psychologists and epidemiologists. The questionnaire is inspired by the theory of planned behavior.
- Possibility to combine the survey data with macro data provided by the GGP Contextual Database. This combination enables analyses of individuals and families in their cultural, economic, political, social and policy contexts.

Variable Groups

Document Description

Full Title

Generations and Gender Survey Wave 2 - Consolidated

Alternative Title

GGS Wave 2 - Cons.

Identification Number

GGSW2.Cons.

Date of Distribution

2015-04-17

Version

Working Version: GGS Wave 2 Version 1.3. - Consolidated

Release of Wave 2 for Poland.
Please refer to "File Description" for information on Wave 2 Version 1.3.

Date: 2018-02-26

Guide To Codebook

In the field “Study Description”, users can find information applicable to any GGS Wave 1 harmonized datasets. This includes the distributors, keywords, abstract, and guidelines on the bibliographic citation.  
Users can find country specific metadata about surveys in the field “Study Description” of each country data file available in the GGS Online Data Analysis web platform. Country specific metadata include information on survey producers, methodology and processing. Unless otherwise specified, this information was provided by GGP-country teams, based on a metadata grid with pre-structured questions. Links to relevant references (e.g., working papers and questionnaires) are also provided.  

The field “Data Files Description” provides metadata about the data file, such as file contents, missing values, etc.  
Users can find information about changes across different GGS versions in the field “Data Files Description” of each country data file available in the GGS Online Data Analysis web platform.

The field "Variable Description" provides information on each variable, such as question text, descriptions of country specific categories and variables, universe (i.e., subset of respondents to whom the question was asked), country specific deviations to GGS routing, descriptions of the ways in which consolidated and derived variables are calculated. Variables are ordered according to the sections of the GGS codebook.

PLEASE NOTICE THAT WE DOCUMENT ONLY VARIABLES HAVING VALID CASES.  
VARIABLES HAVING ALL SYSTEM MISSING CASES ARE NOT DOCUMENTED.  
This is the reason why the total no. of variables in the documentation is smaller than the total number of variables in the SPSS and STATA files.

Full Title

GGS_Wave2_V.1.3

Producer

Name Affiliation Abbreviation Role
Arianna Caporali Institut national d'études démographiques (INED) AC

Producer

Name Affiliation Abbreviation Role
Tom Emery Netherlands Interdisciplinary Demographic Institute (NIDI) TE

Study Description

Full Title

Generations and Gender Survey Wave 2 - Consolidated

Alternative Title

GGS Wave 2 - Cons.

Identification Number

GGSW2.Cons.

Data Distributor

Name Affiliation Abbreviation
Institut national des études démographiques - 133 boulevard Davout 75980 Paris Cedex 20, France. INED
Netherlands Interdisciplinary Demographic Institute - Lange Houtstraat 19, NL-2511 CV The Hague, The Netherlands NIDI

Bibliographic Citation

United Nations 2005. Generations & Gender Programme: Survey Instruments. New York and Geneva: UN, 2005.

List of Keywords

Restrictions

In order to access micro data files, users have to sign and submit a Statement of affiliation, confidentiality and acceptable usage. They also have to submit a title and abstract of their research project. They can use the data for all their research projects, except for datasets from Australia and Norway. Users of these datasets need to submit a new application form if they want to use the data in a different research project. The access rights from Wave 1 data are transferred to the Wave 2 data.

Access Authority

Name Affiliation E-mail address Universal Resource Identifier
UNECE Population Unit - Palais des Nations - CH-1211 Geneva 10 - Switzerland. Tel: +41 22 917 24 77 - fax: +41 22 917 01 07 ggp@unece.org http://www.unece.org/pau/

Citation Requirement

In any work emanating from research based on the Generations and Gender Survey micro-data, I will acknowledge that these data were obtained from the GGP Data Archive and refer to the publication that describes the model survey instruments: United Nations 2005. Generations & Gender Programme: SurveyInstruments. New York and Geneva: UN, 2005

Deposit Requirement

Users of GGS micro-data are required to send any research papers based on the Generations and Gender Survey micro-data or aggregate tabulations to the Population Activities Unit of the UN Economic Commission for Europe, for inclusion in the GGP publications archive.

Conditions

In order to access, it is necessary to subscribe to the GGP Data User Space, and to follow the instructions available on the GGP data access webpage.

Disclaimer

The authors and producers bear no responsibility for the uses of the GGS data, or for interpretations or inferences based on these uses. The producers accept no liability for indirect, consequential or incidental damages or losses arising from use of the data collection, or from the unavailability of, or break in access to the service for whatever reason.

Data Files Description

File Name

GGS_Wave2_V.1.3..NSDstat

Contents of Files

This study includes the consolidated GGS Wave 2 datasets. It contains all the GGS Wave 2 released datasets, except for Germany Turkish-subsample. All the available variables are included (also the country specific variables).

VARIABLES HAVING ALL SYSTEM MISSING CASES ARE DROPPED BEFORE PUBLICATION IN NESSTAR.
This is the reason why the total no. of variables in the Nesstar data file is smaller than the total number of variables in the SPSS and STATA files.

Variables are ordered according to the sections of the GGS codebook: Household, Children, Partnerships, Household Organisation and Partnership Quality, Parents and Parental Home, Fertility, Health and Well-Being, Respondent's Activity and Income, Activity and Education History, Partner's Activity and Income, Household Possessions, Income and Transfers, Value Orientations and Attitudes, Interviewers' report.
The variables begin with a letter designating the wave of data collection ("a" for the first wave likewise "b" for the second wave). We have attempted to keep the names of variables the same across the waves, and all the new variables would be identified as follows ["wave letter"]n e.g.  bn301.  
Although we encourage the countries to strictly follow the GGS Questionnaire, countries might implement a question that differs to a considerable extent from the GGS Questionnaire. In this case either we add country specific response values, or we introduce a country specific variable.  
Country specific values are added when the question follows the model questionnaire, but the answers are not at all or partly compatible. They are at least 4 digits long (F4 format) and begin with the country code: e.g., Australia 2401. Hence, the country code, as an example, for Australia is 24.  
A country specific variable is introduced when the question differs from the model questionnaire albeit measuring the same concept. This kind of variables is identified with a suffix given by the country code plus a number, e.g., Australia a119_2401.
In order to have an overview of GGS country code, please refer to the variable "acountry".

Overall Case Count

86175

Overall Variable Count

2144

Type of File

Nesstar 200801

Place of File Production

The file is produced centrally by the Netherlands Interdisciplinary Demographic Institute (NIDI, The Netherlands), in collaboration with the Survey Department of the "Institut national d'études démographiques" (INED, France).

Extent of Processing Checks

DATA HARMONISATION
The data is submitted in an already pre-harmonised form. It is prepared and organised according to the GGS standards.  
Harmonisation aims at achieving a clear and comparable format of the GGS micro-data files that would be adequate for cross-country comparison.  The harmonisation procedure basically is composed of:
1. Label checks  
This step makes sure that all the variables are named the same across the countries and refer to a particular question in the GGS Questionnaire. Also the value labels are checked. They should be the same across GGS datasets.  
2. Dealing with grids
The GGS Questionnaire holds several grids of either event history information or members of the household. Such data needs to be harmonized with specific attention to order and logical consistency of grid-rows (be either household members or events such as births). In data sense each row of the grid is represented by variable name followed by a subscripted number ("_#"). Each subscript thus represents one household member or one event. Part of the grid harmonization is grid sorting. Grid rows are sorted according to pre-defined key. For example in the household grid, the household members are sorted according to their relationship to the respondent i.e. the relation to respondent variable (ahg3_# or bhg3_# ). Respondents would appear, first, followed by their partners and children if any and then followed by other household members. As there may be more then one child (or other relative) living in the household they also would need to be sorted. In the case of the household grid, age is used as the secondary sorting key (starting with the oldest person to the youngest).
3. Routing
Routing check ensures that the structure of underlying data set matches the structure of the GGS questionnaire. Its main goal is to code any given variable in the dataset to either a valid response, nonresponse or skip as indicated in the questionnaire. Consequently, the indicated skip in the quetionnaire is represented with a system missing code (. in STATA, sysmis in SPSS), while the missing information for other reasons is coded into non-applicable/no response (i.e. codes 7, 8, 9 in SPSS or .a, .b, .c in STATA).  
4. Consolidation  
The process consolidates the information scattered over several variables into a single one. The consolidation procedure is carried out in the Children Section, the Partnership Section and the Parents and Parental Home Section.
5. Imputation  
Due to its sensitive nature, the respondents are reluctant to share income information with the interviewer. In order to be able to use income information in a cross country comparative study and not to loose too many observations in the process it is necessary to impute the approximately correct distribution of the income variable in each country.  
6. Calculation of derived variables
We calculate derived variables out of the following variables:
- grid variables (i.e., household grid, children grid, and partnership history grid); the codebook starts with the constructed variables that sum the key socio-demographic characteristics of the respondent.
- month and year variables,  
- hours and minutes variables,
- frequency and unit variables.  
Occupation variables are recoded into ISCO-88 1 digit.
Explanations of the ways in which consolidated and derived variables are obtained, are available under the field "Note" of the "Variable Description" sections.
For a more detailed and technical procedure please refer to the Data Cleaning and Harmonisation Guidelines.

Missing Data

The following missing values have been assigned:
- 6, 96, 996, etc. = Unknown (only for consolidated variables in the group "administrative variables")
- 7, 97, 997, etc. = Don't know
- 8, 98, 998, etc. = Refusal
- 9, 99, 999, etc. = Not-applicable/no response

Version

Harmonized dataset, GGS Wave2, version 1.3.

Release of Wave 2 for Poland.

Notes

IMPROVEMENTS INTRODUCED WITH GGS_Wave2_V.1.3 (August 2016)
Correction of the following variables that were previously  erronous: b343_*, bnnumdissol, bnumdissol, bnnumdivorce, bnumdivorce, bnnummarriage, bnummarriage.

IMPROVEMENTS INTRODUCED WITH GGS_Wave2_V.1.2 (April 2015)
The update from v1.1 to v1.2 does not include corrections of existing variables. The update only includes additional variables which are derived from the pre-existing datasets.  

- Variables derived from grid variables and variables which concern the respondents and his/her partner: bnumdissol, bnnumdissol, bnumdivorce, bnnumdivorce, bnnummarriage, bnummarriage, bnumpartnerships, bnnumpartnerships, bnrespartafterw1, blivingwithpartner, bchildprevp, bnchildprevp, bfemage, bmaleage, bfemeduc , , bmaleeduc, bfertintent, bnumbiol, bnumnonres, bnumres, bnumstep, bnumallchild, bageoldest, bageyoungest, bcoreschild, bcoresgrandp, bcoresparen, bcoressibl , bhhtype.
- Variables derived from month and year variable: b121AgeR, b150AgeR , bn152AgeR, b239aAgeR, b239bAgeR, b240AgeR, bn304Agb303cAgeP, b311AgeR, b315AgeP, b316cAgeP, b371AgeR, b372bAgeR, b372bTdiff, b374cAgeP, b5116AgeR, b5117bAgeR, b603AgeR, b608AgeP, b608AgeR, b610AgeP, b610AgeR, b621AgeP, b621AgeR, b871AgeR, b907Dur, b911Dur, b914AgeP, b914Dur, b941AgeP.
- Variables derived from hours and minutes variables: b324hour, b520hour, b540hour, b221hour_x.
- Variables derived from frequency and unit variables: b203c_xw, b204c_xw, b205mnth, b241mnth, b325mnth, b521mnth, b1008mnth.
- Occupation variables recoded into ISCO-88 1 digit: b828_1dig, b832_1dig, b861_1dig, b917_1dig, b921_1dig, b933_1dig.
- Three groups of variables derived from section no. 8 "Activity and Education History": 1) variables counting the total number of different activity and education situations Rs has had since age 16 (i.e., bnnumworkstatuses, bnnumstudentstatuses, bnnumemplstatuses, bnnumselfemplstatuses, bnnumhelpfamstatuses, bnnumunemplstatuses, bnnumretiredstatuses, bnnummilitarystatuses, bnnumhomestatuses, bnnummatleavestatuses, bnnumparleavestatuses, bnnumdisabilitystatuses, bnnumotherstatuses, bnnum1401, bnnum1501, bnnum1801, bnnum1301, bnnumparttime, bnnumfulltime, bnnumboth, bnnumparttime_1801, bnnumparttime_1802, bnnumpartfulltime_1803, bnnumfulltime_1804, 2) the total duration in month of each of the different situation (i.e., bndurstudentstatuses, bnduremplstatuses, bndurselfemplstatuses, bndurhelpfamstatuses, bndurunemplstatuses, bndurretiredstatuses, bndurmilitarystatuses, bndurhomestatuses, bndurmatleavestatuses, bndurparleavestatuses, bndurilldisabledstatuses, bndurotherstatusstatuses, bndur1501, bndur1401, bndur1301, bndurparttime, bndurlastparttime, bndurstudwhilework), 3) the age of R at the beginning and end of part-time employments (i.e., bn876_xAgeR, bn877_xAgeR, bn878xAgeR, bn879_xAgeR).

The availability of these variables in each different country-specific file depends on the availability of variables used for their calculation.

IMPROVEMENTS INTRODUCED WITH V.1.2. in Nesstar GGS micro data files (April 2015):
Publication of variables that were previously deleted before dataset release in Nesstar. The following variables are concerned: grid variables, month and year variables, hours and minutes variables, frequency and unit variables, and occupation variables.

Notes

Before publication in Nesstar GGS micro data files are further processed so as to ease online data browsing and analysing.
We delete variables having all system missings.  

WAVE 2 DATASETS - Main differences compared to WAVE 1 datasets
Wave 2 datasets include an additional new section that had not been implemented in the Wave 1 data collection. It is the section no. 8 "Activity and Education History". Respondents report comprehensively on their activity and education history since age 16. Two additional sections are also present at the end of wave 2 dataset: "Interviewer observations" and "Interviewer report" (respectively sections no. 13 and 14).
A set of constructed variables at the top of the data file increase the usability of the GGS data by summarizing key socio-demographic characteristics of the respondent (age, birth year, sex, level of educational attainment, activity status, partnership status, number of co-resident partners, number of children, household size, household type). An additional set of variables consolidates information on the current activity of the respondent and his/her partner that is otherwise spread over the questionnaire. Another set of consolidated variables concern respondents' parents and parental home.

WAVE 2 DATASETS - Variables names
Variables in the Wave 2 data sets that are consistent with variables implemented in the Wave 1 questionnaire are named identically. Wave 2 variable names start with the letter "b" compared to letter "a" in Wave 1. Variables that have not been implemented in Wave 1 but collected in Wave 2 begin with "bn".  
In Wave 2 datasets published in Nesstar, the variable "brid - R identification number" has been renamed into "arid" (same variable name than Wave 2). This allows the user to merge Wave 1 and Wave 2 datasets in Nesstar.
In Wave 2 datasets published in Nesstar, variable labels have the indication "(W2)". This allows the user to distinguish Wave 2 variables from Wave 1 variables, on the basis of the variable labels.

Download

Metadata Index

This is the Metadata Index for a Nesstar Server.
Nesstar is a tool used for analysing, visualising and downloading datasets.

Click the "Explore Dataset" button to open the dataset.