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6.1 What is the purpose of generated variables and which generated variables are provided?

To assure an easy and fast entry into cross-national data and high convenience while working with the data, it is necessary that certain variables are readily provided for the SHARE users, especially those that allow a valid comparison between countries, like the International Standard Classification of Education (ISCED). Besides internationally standardized variables, there are further generated variables that ease or enhance working with the SHARE data. Table 4 gives an overview of all generated variable modules.

Table 4: Generated variable module

Generated-Variable-Modules

Content

W1

W2

W3

W4

W5

W6

W7

gv_allwaves_cv_r

Coverscreen information across waves

Cross-wave module

gv_longitudinal_weights

Longitudinal weights

Cross-wave module 

gv_weights

Cross-sectional sampling design and calibrated weights

X

X

 

X

X

X

X

gv_imputations

Multiple Imputations

X

X

 

X

X

X

X

gv_isced

International Standard Classification of Education (ISCED-97/since wave 5 additionally ISCED-11)

X

X

 

X

X

X

X

gv_health

Physical and mental health variables and indices like BMI, EURO-D depression scale, etc.

X

X

 

X

X

X

X

gv_housing

Housing and NUTS codes

X

X

 

X

X

X

X

gv_networks

Information on social networks

 

 

 

X

 

X

 

gv_exrates

Exchange rates for all waves, incl. nominal and ppp-adjusted exchange rates 

Cross-wave module 

gv_job_episodes_panel

Labour market status of each SHARELIFE respondent throughout her/his life

Cross-wave module 

gv_grossnet

Net income measures derived from reported gross incomes

X

 

 

 

 

 

 

gv_isco

Classification of occupations via ISCO and of industries via NACE codes

X

 

 

 

 

 

 

gv_ssw

Social security wealth

 

 

 

X

 

 

 

gv_deprivation

Indices for material and social deprivation

 

 

 

 

X

 

 

gv_children

Combined children information

 

 

 

 

 

X

X

gv_linkage

Linkage to Statutory German Pension Insurance data

Cross-wave module 

gv_dbs

Dried Blood Spots

 

 

 

 

 

X

 

gv_big5

Big Five personality traits

 

 

 

 

 

 

X

 

6.2 What is the allwaves-coverscreen good for?

This module is a dataset with merged and enriched information from all waves. In a straightforward way, gv_allwaves_cv_r allows to monitor household composition, changes of status (Is a respondent part of a couple or not? Is he or she dead or alive? etc.) and the type of interviews conducted.

6.3 Can SHARE data be linked to administrative data?

Projects to link SHARE data to administrative data have been set up in several SHARE countries.
Germany: Upon respondents’ written consent, administrative data of the German Pension Fund can be linked to the survey data of the German subsample of SHARE. Beginning in Wave 3, all respondents of the German subsample are asked for consent to link their survey data with administrative data of the German Pension Fund. This longitudinal dataset includes very detailed information on respondents´ employment histories. The module gv_linkage provides first information about who gave consent to link their data with the pension fund. To get access to the administrative data, researchers have to submit an additional form, directly to the data center of the German Pension Fund. Further information on access conditions as well as user guide and codebook for SHARE-RV is available here

REGLINK-SHAREDK is the second successful linkage project in SHARE and relates to the Danish subsample of SHARE. Further information is available here.

6.4 What is the content of gv_exrates?

This module contains currencies (also pre-Euro) and exchange rates for non-Euro countries. Additionally, the module stores nominal exchange rates as well as exchange rates that adjust for purchasing power parity (ppp-adjusted).

6.5 What is the Job Episodes Panel (JEP)?

The JEP is a generated dataset that rearranges information taken from Waves 1 to 3 of SHARE in order to create a ready-to-use “long panel”. It contains the labour market status of each SHARELIFE respondent throughout her/his life. A detailed description of the methodology and assumptions underlying the construction of the dataset is available in the SHARE working paper 11-2013: “Working life histories from SHARELIFE: a retrospective panel”, by Agar Brugiavini, Danilo Cavapozzi, Giacomo Pasini, and Elisabetta Trevisan. When publishing with the SHARE job episodes panel data please use an additional disclaimer as described in the corresponding documentation file (PDF) which is available when downloading the data.

6.6 Which generated health variables are provided?

The gv_health module contains a broad range of generated health variables and health related indices regarding the respondents´ physical and mental health status. The majority of the variables is comparable to the US Health and Retirement Study (HRS). Variables on physical health module are e.g. the US version of self-perceived health (sphus), the body mass index (bmi), the number of chronic diseases (chronic), an index on mobility (mobility) and limitations with instrumental activities of daily living (iadl). Variables on mental health are e.g. the EURO-D depression scale (eurod), a measurement of orientation to date (orienti) and a numeracy score for mathematical performance (numeracy).

6.7 How is education measured in SHARE?

Education is one of the most diverse international variables. Therefore a standard coding is required for international comparisons. The gv_isced module contains the 1997 International Standard Classification of Education (ISCED-97). It is not only provided for respondents´ educational level but also for respondents´ children and former spouses´ as well as interviewers´ level of education (latter only in Wave 1). In Waves 1 and 2 the education of up to four selected children was asked. Wave 4 contains the ISCED-97 values for all children. In 2011, a revision of ISCED was adopted by UNESCO Member States. It takes into account significant changes in education systems worldwide since the last ISCED revision in 1997. From Wave 5 onwards both ISCED versions are provided in the SHARE data. Furthermore also the educational level of the respondents´ parents is included in Waves 5 and 6.

6.8 What information does the gv_isco module contain?

Respondents are asked for their own, their former partner’s and their parents’ occupation. For Wave 1 this information is coded based on the International Standard Classification of Occupations (ISCO-88) provided by the International Labour Organization (ILO). To classify the corresponding industries the gv_isco module additionally contains a version of the Statistical Classification of Economic Activities in the European Community (NACE, version 4 rev. 1 1993) which is slightly modified.

6.9 What kind of geographical information is available in the gv_housing module?

SHARE provides the "Nomenclature of Territorial Units for Statistics" (NUTS) which is a hierarchical classification system for dividing up the economic territory of the EU. It is used to indicate in which territorial unit the SHARE households are located at the moment of sampling and is available in different levels:

  • NUTS 1: major socio-economic regions
  • NUTS 2: basic regions for the application of regional policies
  • NUTS 3: small regions for specific diagnoses.

Due to privacy legislation reasons not every NUTS level is available for every country. E.g. for Germany only NUTS 1 is provided.

6.10 How are social networks captured in SHARE?

The CAPI module on social network (SN) was implemented in the fourth wave of SHARE as an innovative means to measure the personal social environment. The module was again part of Wave 6 and is based on an approach that goes beyond the more common role-relational method of measuring social networks mostly based on socio-demographic proxies. The SN module contains a detailed description of respondents´ personal social networks. Each respondent can name a maximum of seven persons who she/he considers to be her/his confidants. The module records the role relationship of each social network member and obtains information regarding each named person's gender, residential proximity to the respondent, frequency of contact and level of emotional closeness. Information of the SN module can be linked to the social support (SP) and the financial transfers (FT) module.

The generated variables module “gv_networks” stores variables that summarize information on the different attributes of the network. In Wave 6, the variables additionally summarize panel information and provide full information on each social network member.

6.11 How is deprivation measured?

This module is available in Wave 5 and contains three variables on material and social deprivation: depmat, depsoc and depsev. depmat is an aggregate measure of material conditions of older individuals in Europe using a set of 11 items that refer to two broad domains: the failure in the affordability of basic needs and financial difficulties. depsoc is an index for measuring social deprivation based on 15 items. depsev is a single two-dimensional indicator that identifies those with high levels of deprivation in each dimension. The threshold is the 75th percentile of the total distribution of each deprivation index. Individuals with deprivation measures placing them above the threshold in both dimensions are classified as being “severely deprived”.

6.12 Does SHARE contain a measure for social security wealth?

Since release 5.0.0, SHARE wave 4 includes a new generated module containing two measures of individual accrued social security wealth (SSW). The two variables are SSW_nw and SSW_gw respectively. The former is based on net wages earned by individuals during their working career. The latter is based on their approximately grossed-up wages, and additionally takes into account minimum pension benefits whenever the individual is entitled to that benefit. Note that since no information from the JEP was required to compute the SSW for retirees, the two variables SSW_nw and SSW_gw are equal for this group.

 

6.13 When do I need gv_grossnet?

In SHARE wave 1 income variables have been collected before taxes and social insurance contributions. In the following waves income variables have been gathered after taxes and social contributions, to capture the notion of take-home pay. To make the different income measures comparable across waves and to facilitate longitudinal analyses, the module gv_gross_net contains net income measures from reported gross incomes for SHARE wave 1. The instrument chosen to carry out this task is EUROMOD, the EU tax-benefit micro-simulation model.

A detailed description of the dataset and the method used is available in the SHARE Working Paper 25-2016.

6.14 What information does gv_children contain?

Information on the respondents’ children is collected in various parts of the SHARE questionnaire. The variables in the gv_children module were generated in an attempt to make this information more easily accessible to SHARE users. The module combines information from the Wave 6 CAPI modules CH, SN, SP and FT. Please be aware that the gv_children variables are an aggregate of information from within wave 6 but not of information from previous waves.

6.15 Which generated variables are stored in gv_dbs?

In addition to the CAPI variables included in the BS Module, some generated variables are already provided in gv_dbs. The most important one is dbs_values_exp (“Expected availability of laboratory results”). Results will only be available if (a) there is proof of written consent by the respondent, (b) the DBS sample is linkable to the CAPI interview via its barcode number, and (c) the DBS filter card contains enough blood material for at least one analysis. Given all these conditions are met, dbs_values_exp= 1. Further variables in gv_dbs are spots_nr (“Number of blood spots collected”), which ranges from 0 to 5, and spots_co (“Number of blood spots filling pre_printed circle”). The latter indicates how many of the blood spots contain the amount of blood covering the size of the pre-printed circle (1 cm in diameter) on the blood collection card.

6.16 What does gv_big5 contain?

In Wave 7, the 10-item Big-Five inventory (BFI-10) was introduced for the first time, an established personality inventory measuring the “Big Five” personality dimensions with two items each. Introduced by Rammstedt and John (2007) the BFI-10 is an ultra-short measure of personality suitable especially for multi-theme surveys in which assessment time and questionnaire space are limited. For further information on the “Big Five” measurement, please see the corresponding chapter in the Wave 7 First Results Book.