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Full text: Ageing in Germany: Content, Quality and Accessibility of Relevant Data Sources / Nowossadeck, Sonja

Ageing in Germany:
Content, Quality and Accessibility of Relevant
Data Sources
Results of the Data Mapping Project of the Joint
Programming Initiative “More Years, Better Lives”
Sonja Nowossadeck, Maja Wiest, Josefine Lühe &
Andreas Motel-Klingebiel

German Centre of Gerontology – Deutsches Zentrum
für Altersfragen (DZA)
Manfred-von-Richthofen-Straße 2
12101 Berlin
Phone +49 (0)30 – 26 07 40-0
Fax
+49 (0)30 – 78 54 350
Email
dza-berlin@dza.de

discussion papers

This report was compiled on behalf of the Federal Ministry of Education and
Research (BMBF). The scope of work was defined by the BMBF. Results of the
report were not influenced by the BMBF; the contractor is solely responsible.
Our thanks for their assistance in language and proofreading go to Katharina Lux
and Stefanie Hartmann.

Dieser Bericht wurde im Auftrag des BMBF erstellt. Die Aufgabenstellung wurde
vom BMBF vorgegeben. Das BMBF hat das Ergebnis dieses Berichts nicht
beeinflusst; der Auftragnehmer trägt allein die Verantwortung.
Wir danken Katharina Lux und Stefanie Hartmann für ihre sprachliche und
lektorierende Unterstützung.

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CONTENT

1

Introduction

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2

National Report – Germany

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2.1
Demographic context
2.2
Demographic change and policy concerns
2.3
Data Sources
2.3.1 General issues
2.3.2 Health
2.3.3 Social systems and welfare
2.3.4 Work and productivity
2.3.5 Education and learning
2.3.6 Housing, environment and mobility
2.3.7 Attitudes to old age
2.3.8 Social, civic and cultural engagement
2.3.9 Uses of technology
2.3.10 Well-being
2.3.11 Intergenerational relationships
2.4
Data and the policy agenda: Gaps and challenges

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9
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10
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12
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3

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Selected German Datasets on Demographic Change

3.1
Data Mapping: Topics and Datasets
3.1.1 Adult Education Survey (AES) / Weiterbildungsverhalten in
Deutschland
3.1.2 Biographical Data of Selected Social Insurance Agencies in
Germany / Biografiedaten ausgewählter
Sozialversicherungsträger in Deutschland 2007 (BASiD 2007)
3.1.3 Employment after Retirement / Weiterbeschäftigung im
Rentenalter
3.1.4 German Ageing Survey / Deutscher Alterssurvey (DEAS)
3.1.5 German Family Panel / Beziehungs- und Familienpanel (PAIRFAM)
3.1.6 German Health Interview and Examination Survey for Adults /
Studie zur Gesundheit Erwachsener in Deutschland (DEGS)
3.1.7 German Socio-Economic Panel (SOEP) – Sozio-oekonomisches
Panel
3.1.8 German Survey on Volunteering / Deutscher Freiwilligensurvey
(FWS)
3.1.9 IAB Establishment Panel / IAB Betriebs-Panel
3.1.10 Labour Market and Social Security / Arbeitsmarkt und soziale
Sicherung (PASS)
3.1.11 Microcensus / Mikrozensus

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23

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29
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37
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3.1.12 Mobility Panel Germany / Deutsches Mobilitätspanel (MOP)
3.1.13 National Education Panel Study (NEPS) / Nationales
Bildungspanel
3.1.14 Possibilities and Limits of an Independent Living and Health/
Möglichkeiten und Grenzen selbständiger Lebensführung (MUG IIV)
3.1.15 Sample of Insured Persons and their Insurance Accounts /
Versicherungskontenstichprobe der DRV (VKSt)
3.1.16 Sample Survey of Income and Expenditure/ Einkommens- und
Verbrauchsstichprobe (EVS)
3.1.17 Study of Health in Pomerania / Leben und Gesundheit in
Pommern (SHIP)
3.1.18 Survey on Private Usage of Information and Communication
Technologies (ICT) / Erhebung über die private Nutzung von
Informations- und Kommunikationstechnologien (IKT)
3.1.19 Telephone Health Survey - German Health Update / Telefonische
Gesundheitssurveys – Gesundheit in Deutschland aktuell (GEDA)
3.1.20 Time Use Survey / Erhebung zur Zeitverwendung

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References

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1

INTRODUCTION

This report outlines the German results of the Data Mapping Project of the Joint
Programming Initiative “More Years, Better Lives - The Potential and Challenges of
Demographic Change” (JPI MYBL – http://www.jpi-dataproject.eu).
Joint Programming as a European Union research policy activity is member-states
driven and basically aims at fostering collaboration and coordination in research and
development in Europe and at putting the European Research Area (ERA) – an
agenda to strengthen European scientific capacities by transnational co-operation –
into practice. The Joint Programming Initiatives (JPI) are focused on joint research
programs that address major European challenges such as climate change,
ensuring energy and food supply or healthy ageing of citizens (European Research
Area, 2013). The JPI “More Years, better Lives – The Potential and Challenges of
Demographic Change” was launched at the beginning of this decade. It seeks to
enhance coordination and collaboration between European and national research
programs related to demographic change. It aims to identify future research needs
and topics to provide solutions for the upcoming demographic challenges in Europe
(JPI MYBL, 2013).
As a first research activity, the Fast Track Project “Data Mapping” was started in
March 2013. The aim of this project was to create a common basis for JPI MYBL’s
upcoming Strategic Research Agenda (SRA) by describing and evaluating the
available data infrastructure on ageing in Europe in ten interrelated fields of
interest: (1) health, (2) social systems and welfare, (3) work and productivity, (4)
education and learning, (5) housing, urban development and mobility, (6 ) attitudes
towards old age, (7) social, civic and cultural engagement, (8) uses of technology,
(9) well-being and (10) intergenerational relationships.
The project “Data Mapping” comprises the most important data sources on ageing
at the national as well as at the European comparative level, which provide
information on the living situation/conditions of people aged 50 and older. For each
field of interest, the available data infrastructures were evaluated in regard to
relevance, availability, quality and more. It was examined whether there are major
gaps in order to improve future data assessments. The Data Mapping Project
provides both scientists and policy-makers with a comprehensive overview of
where data for cross-disciplinary approaches and evidence-based decision-making
with regard to ageing is available. Information gathered by the project was used to
support the development of the JPI MYBL’s Strategic Research Agenda.
The German Centre of Gerontology was responsible for describing the national
data infrastructure on the living conditions of people aged 50 and over in Germany.
The authors of this report realised the project. The Federal Ministry of Education
and Research (BMBF) financed the German project part and assigned the Max

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Planck Institute for Demographic Research with the European coordination of the
Data Mapping Project.
The selection of data sets described in the following was driven by the fields of
interest identified by the JPI MYBL’s working groups and the scientific advisory
board. The authors acknowledge that the German data infrastructure is more
diverse than depicted in the extract given in this report. To create a feasible report
on data sources on ageing at the European and nation levels, the project partners
agreed to limit the selection to about five data sets at the most for each field of
interest. Furthermore, the data sources had to fulfill the following requirements in
order to be listed: (1) to be current and of high quality, (2) to provide quantitative
data with large sample sizes, and (3) if possible, to be longitudinal as well as policyrelevant. Based on these criteria and given the limitations in space and time only
the most relevant data sources of each topic were included.
Chapter 2 contains the National Report, which evaluates the current data situation
and its gaps with regard to the specific demographic challenges in Germany. The
National Report was written based on the data infrastructure described in
Chapter 3.
In Chapter 3, selected German data sets on demographic change are introduced.
The provided information is an extract from the Data Mapping Project. More
detailed information can be found for each German data set as well as for selected
data sets of all participating states on http://www.jpi-dataproject.eu. All given
information are as of July 2013.

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2

2.1

NATIONAL REPORT – GERMANY

Demographic context

The population of Germany is currently 81.8 million (2011), rising slightly by about
1.5 million over the last two decades as a result of increasing life expectancy and
modest inward migration. The trend differs between West and East Germany with
the West experiencing a growth from 64.5 million (1991) to 69 million (2011) and
the East absorbing a loss of population from 15.8 million (1991) to 12.8 million
(2011). This is mainly due to internal migration and different fertility rates. At the
same time East German mortality patterns assimilated with West Germany.
3.5 million people live in the capital Berlin. Other centers are the Ruhr/Rhein region,
Hamburg, Munich, Frankfurt, Stuttgart and Dresden/Leipzig, most of them located
in the western states of Germany. Several regions have a low population density
that – together with poor economic development – creates particular challenges for
social policy. The German population as a whole is one of the oldest in the world
and has aged more rapidly than the European average during the recent decades.
Fertility rates fell rapidly during the late 1960s and the 1970s. This development
was preceded by the German baby boom, which appeared later than in most other
countries after World War II. It was much more significant in West than in East
Germany in terms of numbers of births – also because of refugee movements from
East to West. Birth figures in East Germany recovered during the late 1970s due to
efforts to increase the compatibility of family and work especially for women while
the decrease continued during this period in West Germany. There was a sharp fall
in fertility rates in East Germany after unification in 1990 but rates converged in the
last decades at the low West German level of about 1.4 children per woman which
is far below replacement level.
In contrast, life expectancy has been constantly rising with diminishing differences
between East and West and severe disparities between social classes. Life
expectancy at 50/65 is 29.7/17.5 years for men and 33.4/20.1 years for women
(2011). It has increased by 3.9/3.2 years for men and 3.0/2.7 years for women in
the last 20 years. Mortality from most of the major health conditions of old age are
comparable with the Western European average.
The percentage of the population aged 50 and older is 41.2 per cent in 2011 (1991:
34.4) and is expected to reach 50 per cent around the year 2030. Due to this,
efforts have been made to increase both the statutory retirement age on the one
hand and the real age of retirement as well as the labour participation of people
aged 50 and older on the other hand. At the moment the statutory retirement age
for both sexes is 65 in Germany and will increase to 67 in the birth cohort 1964,
which will retire at this age in 2031.
Germany basically has a pay-as-you-go pension system as the first and main pillar
of old age security, which is based on previous contributions or analogous

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achievements like child-raising. It includes a means-tested basic component,
including the acknowledgment of achievements outside the labour market, but is
mainly income-related. The net replacement rate of the pension system was
around 64 per cent in 2008, which is quite a high standard. But significant declines
in replacement rates are expected in the future due to adjustments in the
calculation formula that now links the development of the pension-point value to
changes in ratio of pensioners to contributors. As a result, its adjustment is
expected to be 18 per cent lower than the increase of earnings.
The overall system also features a second pillar of occupational pensions and a
third pillar of private provision for old age based on private investments subsidised
by the state. This part was extended as part of the recent reforms after the turn of
the millennium. Participation in these private pension schemes is moderate and
highly selective. The third pillar remains relatively small in Germany despite political
attempts to strengthen it. Besides, there is also a distinct system for civil servants
(Beamte) that is quite generous.
The number of people in need of care is 2.5 million (2011), with women (65 per
cent) and the elderly being in a majority: people of age 60 (65/70/80/85) and older
make up about 87 (83/79/56/36) per cent of people in need of care. Only a minority
of 13 per cent of all people in need of care is younger than 60 and about two thirds
are under 85 years of age.
In Germany, families make a major contribution to caring for dependent people.
Out of these 2.5 million dependent people, 1.2 million live at home supported
exclusively by partners, families and friends. Further, 0.6 million live at home and
receive help from partners, families and friends as well as from formal services.
Besides that about 0.7 million people receive help and care in institutions. After the
age of 80 it is only a minority of people (32 per cent) that lives in residential
institutions, but the number rises to 58 per cent at the age of 90 and older.
The health of older people is a big issue in Germany also due to the cost problems
related to it. Nevertheless, it is documented that each cohort of older people has
better health than its predecessors. In 2008, 21 per cent of older people aged 70 to
85 report no or just one major disorder (1996: 18 per cent). As a consequence, it
remains unclear whether or to what extent demographic processes will lead to an
expansion or compression of morbidity in later life with the corresponding cost
effects.
The good health situation is a contrast to the increase of poverty risks in later life
due to vagrant employment histories and structural changes in social security
systems in Germany. There is no doubt that they will mainly concern the next
generations of retirees but also have an effect on the subsequent birth cohorts.

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2.2

Demographic change and policy concerns

The German Government has recognised the implications of demographic change
during the last decade. The central plank of Government policy is to encourage
people to stay longer in paid employment, in order to reduce the costs of pensions
and welfare and to increase the economic contribution of older people. Measures
include the postponement of the standard retirement age, the introduction of a
sustainability factor into the public pension scheme that reduces individual pension
according to relative cohort size, the implementation of a care insurance system,
efforts to improve images of ageing in society and to establish new age norms like
active and productive ageing as guidelines for individual planning for later life.
There is growing concern about intergenerational justice that as an effect may lead
to reluctance against the challenges of intragenerational balance of risks and
resources, hence, selectively creating new risks in the course of ageing. This may
threaten the legitimacy of the welfare system.
In the Demographic Strategy of the Federal Government, published in 2012 under
the headline “Every age counts” the following aims were highlighted:







2.3

Strengthening of families as communities
Working with motivation and in good health, doing qualified work
Ensuring autonomous life in old age
Fostering quality of life in rural areas and integrative urban policy
Securing the preconditions for sustainable growth and welfare
Sustaining the state's ability to act

Data Sources

2.3.1 General issues
Data is collected in Germany by various agencies and institutions, which are
(mostly) state funded but independent of the government. The German Institute for
Economic Research (DIW Berlin), for example, conducts the German SocioEconomic Panel Study (SOEP), the Robert Koch Institute (RKI Berlin) conducts the
German Health Interview and Examination Survey for Adults (DEGS) and the
Telephone Health Survey – German Health Update (GEDA). The German Centre of
Gerontology (DZA Berlin) conducts the German Ageing Survey (DEAS) and the
upcoming wave of the German Survey on Volunteering (FWS). The Institute for
Employment Research (IAB Nuremberg) is responsible for the Labour Market and
Social Security Survey (PASS) and the IAB Establishment Panel. One of the largest
educational studies – the National Education Panel Study – is conducted by a
network of 200 scientists at several institutes.
There is a variety of data sets that are provided by the official statistics. For
example, the microcensus, which is conducted every year with a one-per-cent-

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sample of the household population (about 830,000 respondents), brings
population, labour market and the living situation of households into focus.
Furthermore official statistics publish monothematic data sets on income and
expenditures (EVS), the usages of Information and Communications Technology
(ICT; IKT), time use (ZVE) and other single topics in fields like population, education,
health and social issues. Institutions like the German Pension Insurance Fund
provide data on different issues of the social system and welfare in Germany, such
as the Biographical Data of Selected Social Insurance Agencies in Germany (BASID
2007) and the Sample of Insured Persons and Their Insurance Accounts (VKST).
This data is process-produced, contains very large samples and allows
differentiated analyses for a variety of social groups.
The policy themes of the JPI Strategic Agenda are covered by different surveys.
There are only a few data sources which cover more than one topic
comprehensively.
Within Germany’s strict data protection legislation, the majority of data sources are
available for a non-profit scientific use. Data access is often granted by research
data centers. These research data centers facilitate simple and affordable access to
wide-ranging data resources based on uniform and reliable standards. They
respond to the individual needs of data users and data producers alike (RatSWD,
2010).
The following material summarises the major German data sources for the ten
policy themes identified by the JPI "More Years: Better Lives". More detailed
information on these is available online in the JPI data source database at
http://www.jpi-dataproject.eu.
2.3.2 Health
There are multiple data sources on health in Germany, which gather data on
different health and health-related aspects. The health status and differences in
health status between cohorts can be described and analysed with the available
data sources.
The German Health Interview and Examination Survey for Adults (DEGS), the
Telephone Health Survey - German Health Update (GEDA), and the German Ageing
Survey (DEAS) are the major population representative surveys on health in
Germany. The DEGS is a longitudinal survey examining health over the life course
(population 18+), including health questionnaires and examination providing a
detailed picture of health and health changes in adulthood. The GEDA is a regular
cross-sectional survey, which includes a regular set of questions on health and
changing modules on up-to-date health topics. The DEAS is a multidisciplinary
longitudinal survey including in-depth information on related topics such as work,
social relations, and housing etc. Another good data source for health in Germany
is the Study of Health in Pomerania (SHIP), although it is only representative for
one region. The SHIP study provides extensive data on the health and living

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situation of the participants including a wide range of biomarkers and different
psycho-social resources in a longitudinal design.
The strengths of accessible health data in Germany are that a variety of health
aspects and related topics are covered (e. g. subjective health, physical health,
health behaviour, use of health care system, functioning, well-being), the surveys
have large sample sizes, and the assessment modes vary. In sum, reliable and valid
health information for the household population is available for in-depth analysis.
But there are several weaknesses that have to be addressed. Data is missing on
very old age (85 years and older), on people in need for help and care, on people
living in residential institutions and on non-German speaking people. The only
representative data on people in need for help and care in households and
residential homes has been collected in the cross-sectional MuG studies
(Possibilities and Limits of an Independent Living and Health), but data is not
available.
Although longitudinal health data is available, there is still more needed to
understand short- and long-term health changes. In general, only a few studies
provide objective health data and biomarkers, therefore biological processes of
ageing cannot be analysed using the mentioned population representative data
sets. Additionally, new and upcoming topics such as technological innovations and
e-health have not been sufficiently covered yet.
2.3.3 Social systems and welfare
There is a variety of data sets that provide data on different issues of the social
system and welfare in Germany. On the one hand there is data by the official
statistics, such as the Microcensus and the Sample Survey of Income and
Expenditure providing very large samples. The Microcensus contains a much
smaller bias by non-response than other surveys as taking part in this census is
compulsory by law. The German Pension Insurance Fund provides very detailed
data on employment and old-age security.
The Biographical Data of Selected Social Insurance Agencies in Germany (BASID
2007) and the Sample of Insured Persons and their Insurance Accounts (VKST) are
longitudinal data that have a high potential for analyses of employment biographies
and pension claims in old age. This data is process-produced, contains very large
samples and allows differentiated analyses for a variety of social groups. They
enable scientific analyses in the fields of pensions, demography and employment
biographies.
Longitudinal data on the life course, employment biographies, old-age security and
living standard are also provided by longitudinal surveys such as the SocioEconomic Panel (SOEP) and the German Ageing Survey (DEAS). These surveys
offer a greater variety of context variables than the official statistics. They contain
information on health, households, family structures, intergenerational relationships
and on attitudes, which is missing in the process-produced data mentioned above.

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A series of valuable cross-sectional data sets in Germany is available about the
issue of long-term care – the MUG-studies (Possibilities and Limits of an
Independent Living and Health) record the living situation of people in need for care
that are cared for in institutions or at home. Unfortunately, they are not available as
scientific use files.
The field of social systems and welfare is connected to many other issues, like
work and productivity, education and learning or health and care issues. There are
many reliable and valid data sources in Germany in this and the corresponding
fields. All major topics of social systems and welfare are covered. This includes lifecourse changes and institutional fit, old age security and care provision,
employment and economic and distributive performance as well as gender and
other basic issues of social inequality in the heading of governance, sustainable
welfare, economic and social productivity, quality of life and well-being as well as
information and communication. Most major data sets also provide longitudinal
data in these topics. Therefore in-depth analyses are possible. However, certain
relevant sub-populations that are difficult to include into a survey, such as very old
people, people not living in private households, people living at the margins of
society, have not been covered so far. There is a need for more empirical
knowledge on the social situation of older people living in precarious economic
situations as the risk of poverty in old age is increasing. At the moment this social
group is underrepresented in surveys thus efforts must be made to include a larger
sample of them into surveys. In addition, data is insufficient for older people on the
top of the income distribution as they are also underrepresented in surveys. It is
essential to gain more knowledge about them in order to understand the
mechanisms of societal change and social inequality.
2.3.4 Work and productivity
There are multiple data sources on work and productivity in Germany, many of
which are longitudinal surveys. They provide detailed information on different
aspects of the topic.
On the one hand, there are surveys which are based on the individual level. The
German Socio-Economic Panel Study (SOEP) as well as the German Ageing Survey
(DEAS) – although the latter does not focus on retrospective data – allow for a
detailed analysis of employment histories as well as a linkage to other topics such
as health, housing or transition to retirement. The longitudinal survey Labour
Market and Social Security (PASS) enables researchers to analyse the non-intended
side-effects of labour market reforms as well as work and (un)employment
histories and pathways into and out of welfare dependency. All in all, these data
sources provide information on the question of how work is distributed across the
life course. The cross-sectional study Employment after Retirement, however, is
designed to identify factors which have an impact on the willingness to continue to
work beyond retirement age as well as the desired working conditions for this
continuation.

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On the other hand, there is the IAB Establishment Panel, which focuses on the
establishment level. It is the central basis for the analysis of labour demand in
Germany. It can answer several questions on how far ageing populations are
changing the behaviour of employers since it provides information on the amount
of older employees and the establishments’ individual personnel measures for
older workers in establishments of all sectors and sizes.
All in all, there are many reliable, valid and longitudinal data sources in Germany in
the field of work and productivity, which allow for in-depth analyses. Moreover, all
major topics of work and productivity, such as the distribution of work across the
life-course and work beyond retirement, work and health, the organisation of work
and transitions to retirement, are covered.
However, there are also some weaknesses. Each data set focuses on some
specific aspects, therefore the possibilities for analyses of the complex interplay of
several aspects might be limited. Moreover, there is a lack of data providing
information on the exact work content. This, however, is a useful indicator of
integration and task adequacy. Furthermore, most data sets are not suited to
deliver detailed and exact information on the transitions to retirement. In general,
there is little data available on very old people, people not living in private
households and people living at the margins of society.
2.3.5 Education and learning
There are two important data sets on education in adult age in Germany – the Adult
Education Survey (AES) and the National Education Panel Study (NEPS). The AES
records very detailed information on different educational activities in adult age.
The data set is appropriate for trend analyses as it is a repeated cross-sectional
survey. It is also suitable for comparing analyses within the EU-member states. The
NEPS has a cohort-sequential design and is suitable for elaborated longitudinal
analyses. It contains items on several dimensions of education – development of
competencies in life course, education in life course specific learning
environments, social inequality and educational decisions, educational processes of
people with a migration background and returns of education. Both surveys, AES
and NEPS, are limited to age groups up to 65 years, lifelong learning of people
beyond employment age is not in the focus of this data. The ICT-Survey conducted
by the official statistics records data on the dissemination of information and
communication technologies (ICT) and on the use of the internet in private
households. Among others it provides information on the usage of ICT for
educational activities and on attending computer training courses.
The data situation in regard to education and learning is mixed. The available data
covers a broad spectrum of topics but is mostly limited to persons aged 65 years
and younger. Lifelong learning trajectories and education participation of older
persons, in particular of people in their retirement, is not covered. There is one
study – Competencies in Later Life (CILL) by the German Institute of Adult

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Education – that will fill this gap providing data for people between 66 and 80
years. The data will be published in 2014.
2.3.6 Housing, environment and mobility
The German Ageing Survey (DEAS) and the German Socio-Economic Panel Study
(SOEP) include information on housing and attitudes to the home and
neighbourhood, but neither survey includes questions on spatial mobility and urban
development. They allow, however, linkage of the aggregated microdata with
official statistics on housing, regional development, population etc. on at least the
NUTS3 level. Further, changes in housing, attitudes towards the home and
neighbourhood can be linked to health, well-being and other life domains in the
DEAS and SOEP as both surveys focus on a variety of life domains and have a
longitudinal design.
There is one longitudinal data set addressing mobility (Mobility Panel Germany
(MOP)), the MOP is a regular population-representative survey which assesses
how people travel, for what reason and what transportation they are using.
However, the MOP includes very limited information on health and living situations
and does not have a specific focus on people aged 50 years and older. Despite
having the potential of describing the mobility of older people, questions with
regard to what community environments foster well-being and mobility cannot be
analysed.
In general, data on housing needs and mobility of people in need for help, care or
living in residential institutions is rarely available. Although the MUG (Possibilities
and Limits of an Independent Living and Health) studies address this population
and its needs and expectations, data is not available for scientific use.
Data on housing, environment and mobility is limited. Although housing is covered
as a topic among interdisciplinary surveys, they do not include data on urban
development and mobility. But analyses of urban development and housing needs
and expectations are possible if the microdata is linked to official statistics on
housing, regional development, population etc. Data on mobility in later life is
particularly limited and in-depth analyses are not possible with the available data
sources. Future trends in housing such as alternative forms of housing,
technological innovations, or support systems are not yet covered sufficiently. The
available data touches the identified questions within the domain of housing,
environment and mobility but does not allow for in-depth analyses.
2.3.7 Attitudes to old age
The German Ageing Survey (DEAS) is the major source for describing and analysing
attitudes towards old age and ageing. The DEAS also provides information about
experiences of ageism in different life domains. Due to the cohort-sequential
design and interdisciplinary approach, the DEAS allows depicting social and
individual change in attitudes towards ageing. Moreover, it is possible to analyse

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effects of life transitions (e. g. retirement, health events) on images of ageing and
factors that determine different attitudes towards ageing. However, the questions
focus on the individual images of ageing. Societal views of ageing are assessed
broadly without focusing on attitudes towards retirement or working longer etc.
In sum, although the DEAS provides reliable and valid data, more data on attitudes
towards old age and ageing is needed as it is the only data source in Germany so
far. It does not represent very old people, people in need of help and care and
people living in residential institutions. Although the DEAS includes questions on
employment and retirement, it is not possible to answer questions concerning
attitudes to retirement and working longer. The identified questions within the
topic of attitudes towards old age can be examined within Germany, but more indepth data on attitudes towards old age is needed.
2.3.8 Social, civic and cultural engagement
Volunteering has been an issue of growing interest for empirical research in the
recent years. Meanwhile there are several data sets that record data on this issue.
The most important single-topic data set on volunteering is the German Survey on
Volunteering (FWS). The FWS reports several aspects of volunteering in detail,
such as activities and motives or expectations of volunteers, and consists of a large
sample. It is a cross-sectional survey and the fourth wave will be run in 2014. The
German Socio-Economic Panel (SOEP) provides only a few items on volunteering,
but these can be analysed longitudinally. The SOEP includes additional migrant
samples, so up to now longitudinal analyses on volunteering of the migrant
population in Germany can only be conducted with SOEP data.
Volunteering is also included in the DEAS, a longitudinal survey with respondents
aged 40 and above, focusing on voluntary activities in clubs and organisations. A
special kind of data is provided by the Time Budget Survey: In this data set
volunteering is recorded in the personal interview, but the detailed expenditures of
time for different kinds of volunteering can also be analysed with time use data
collected in diaries.
The main one-topic survey with the focus on social engagement is the FWS. It
provides valuable information on the voluntary activity of different social groups and
data is easily available for the scientific community. With the revision of the survey
instrument in wave 2014, more context variables will be included, such as health
and marital status. At the moment the main problem for analyses of volunteering is
the inconsistencies in the terminology. That is why different surveys report
different results on this issue due to divergent definitions and question wording.
2.3.9 Uses of technology
Various data sets provide information on the usage of new technologies by older
people, but there are few which are very detailed: The ICT-Survey on Information
and Communications Technology by the official statistics records data on the

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dissemination of information and communication technologies and on the use of
the internet in private households. This survey provides a large sample and it is
conducted yearly, but there is only little context information for empirical research.
The Adult Education Survey (AES) records data on computer usage as part of
vocational and non-vocational continuing education.
In sum, data on uses of technology in Germany is limited. Although the existing
data sources provide reliable and valid data, their potential is restricted. Now that
the baby-boomers are growing older, the relationship between old age and new
media is changing. Computer usage of the elderly is rising, so knowledge on their
specific needs and preferences in this field will become even more important in
future. There is great potential of ICT in providing solutions for challenges in health
care, autonomous living in old age, networking and others. This requires more
detailed empirical knowledge on ICT in old age and data that is assigned more
weight in such surveys.
2.3.10 Well-being
The German Ageing Survey (DEAS) and the German Socio-Economic Panel (SOEP)
are good data sources for analysing well-being. The DEAS assesses different
facets of well-being and includes information on a variety of life domains.
Therefore, the DEAS allows detailed analyses of what is associated with
differences in well-being within age groups and over time (trend analysis). The
SOEP is an important data set on well-being as life satisfaction has been assessed
annually for over 25 years. Additionally, the SOEP has included more questions on
well-being in the recent years.
In general, well-being is assessed at least with one very basic question in most
German data sets. However, for certain sub-populations, which are unlikely to be
included or are not assessed at all (e. g. very old people, people living in residential
homes, non-German speaking people), only little data is available on well-being.
The German data allows examining how well-being can be promoted in old age. As
well-being is included in most data sets, it is possible to relate aspects such as
social systems, work and employment, housing and environment, social relations,
and health to individual differences in well-being. For sub-populations, especially at
risk for decreasing well-being (e. g. people in need for care, people living in
institutions), the available data provides less information.
2.3.11 Intergenerational relationships
With the German Ageing Survey (DEAS), intergenerational relationships can be
analysed in the context of psychological, health, economic and sociological
aspects. Due to the composition of the sample, the focus of intergenerational
relationships is placed on intergenerational relationships in very old age including
frequency of contacts, spatial distance, emotional closeness, financial transfers or
the grandparent-role. The German Socio-Economic Panel (SOEP) also allows for the

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analysis of contextual factors and provides information on household and family
composition as well as financial transfers. Family networks outside the own
household (spatial distance, self-evaluation of the relationship) are assessed on an
irregular basis only. The German Family Panel (PAIRFAM) depicts intergenerational
relationships in a dyadic perspective and pays particular attention to their
multifaceted portrayal. Biological parent-child relationships, adoptive constellations,
stepparents- and parents-in-law relationships and grandchild-grandparent
relationships are covered. However, due to the composition of the sample, the
data set is not yet suited for the analysis of intergenerational relationships in very
old age.
In sum, the data quality with regard to intergenerational relationships is high and
there are longitudinal data sources which allow for in depth-analyses. All major
topics of intergenerational relationships, such as mutual support relationships,
emotional closeness and frequency of contacts are covered. However, there is not
much data providing information on stepparents- and parents-in-law relationships
as well as dyadic information on relationships. Furthermore, it is difficult to analyse
changes in intergenerational relationships over time in detail. Moreover, the data
sources on intergenerational relationships are not suited to analyse the
intergenerational relationships of sub-populations that are difficult to include in
survey research such as very old people, people in need for help and care or people
who no longer live in private households.

2.4

Data and the policy agenda: Gaps and challenges

There are multiple data sources in Germany that cover various aspects of later life
and can describe demographic change and its effects. Besides interdisciplinary
surveys which cover a range of topics, there are several specific surveys focusing
on in-depth information on one topic. During the last decade significant progress in
starting elaborated surveys with high financial expenses (such as NEPS, DEAS,
DEGS) was made in Germany and many data sets of the official statistics are
accessible to scientific research today. Although many data sources are available,
certain aspects (e. g. alternative forms of housing, long-term care) and subpopulations (e. g. very old people, people with low socio-economic status) are not
yet captured. There are some gaps that need to be addressed to enable
researchers and policy makers to evaluate the challenges of demographic change.
This includes:
Most surveys do not include persons that are very old and/or in need for help and
care. These sub-populations require specific sampling and data collections. The
danger of excluding very old and frail persons in surveys is a biased picture of the
health situation in very old age.
The household population is the target population of all major surveys in Germany.
Therefore people who no longer live in private households are systematically

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excluded and not represented. Due to rising life expectancy, the number to people
living in residential homes is growing. This sub-population needs to be considered
even if data collection costs are higher.
In general, interview burdens need to be adapted for those with health limitations.
These persons are less likely to be included in general, but keeping them in
longitudinal studies would increase the data quality of existing surveys.
Persons with low or high socio-economic status (SES) are less likely to be included
in surveys. But as they are specific target groups for interventions (e. g. persons
with low SES are more likely to experiences health problems), data is needed. Data
available today is sufficient to investigate the economic “mainstream” of older
people, but they exclude people living under very poor and very rich material living
conditions. These are social groups that are relevant for scientific policy advice and
empirical knowledge should be enhanced.
Non-German-speaking persons are underrepresented in all major data sources.
Already 19.5 per cent (2011) of the German population have a migration
background. Consequently, needs and expectations of foreigners and people with
non-German ethnic background need to be evaluated and addressed.
Future themes of older generations need to be included. Since the baby-boomer
generation will retire in the next years, their future plans for housing, arrangements
of time and social relations may be different and adaptation of question modules
might be needed to capture the variety of plans and needs of future generations
entering old age.
Demographic change is not only a phenomenon of the country as a whole. It takes
place in regions and communities and the consequences of population ageing are
very different from federal state to federal state, from urban to rural regions and
from community to community. This fact is not reflected in data sets up to now.
There is not enough data for detailed small-scale regional analyses.
In the following, the listed gaps of the German data landscape are evaluated in
accordance to the policy agenda. Germany faces the challenges of an ageing
population. There are rapid demographic changes due to low fertility rates, only
modest immigration and rising life expectancy, which have been recognised by the
German Government. Under the headline “Every Age Counts” several aims for the
future have been highlighted such as to ensure autonomous life in old age or to
secure the preconditions for sustainable growth and welfare.
The data sets identified and described in this report allow to illustrate the needs of
ageing individuals in communities and to some extent to evaluate the effects of
demographic strategies implemented by the government (e. g. change in standard
retirement age). Large samples which are assessed regularly over time are
necessary but not sufficient to monitor policy strategies within the domain of
demographic challenges. Given the gaps of the available data, e. g. the exclusion of
very old people, people at the margin of society and people no longer living at
home, upcoming trends in the ageing population and changing needs and

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expectations might be difficult to describe and predict. Therefore the challenges for
society and politics to provide and ensure, for example, a functional and fair social
and health care system in Germany might be underestimated.
Within the next 20 years, certain demographic trends will accelerate (e. g. rising
number of people in need of help and care, increasing proportion of regions with
low population density). For that reason it is necessary to invest in understanding
the needs and challenges of ageing people as soon as possible.
Long-term care is one of the biggest challenges of population ageing at present
and in the future. Data sets on this issue available as Scientific Use Files are
lacking. Data on the situation of people in long-term care and on specific aspects of
long-term care for special populations, e. g. migrants, homeless and others, is
needed.
There are several German studies investigating health and health attitudes. A lot of
knowledge on these issues could be gained in recent years, still data on mortality
as a specific aspect of health cannot be sufficiently collected by usual empirical
studies. Taking into account the importance of mortality and its counterpart life
expectancy for demographic change, there are initiatives to establish a National
Mortality Register and a National Cohort Study in Germany. This data will be a
valuable source on these issues in the future.
Generational solidarity is essential for social cohesion. However, in Germany data
has only partly begun to incorporate a generational structure in surveys. Most
available data is individual microdata that cannot be linked to household and family
members. Studies such as PAIRFAM and SOEP already included the simultaneous
assessment of partners,family or household members. To understand
intergenerational solidarity and conflicts more data is needed.
Besides these content-related issues, a practical-methodological perspective needs
to be mentioned to evaluate the data situation in Germany. A variety of data by
official statistics and surveys is available, which are connected to the issues of
demographic change in Germany. It was an important step to establish an
institution connecting producers and data users – the German Data Forum (Rat für
Sozial- und Wirtschaftsdaten, RatSWD). This institution makes important
contributions to collect information on all relevant German data sources, to discuss
focal points of empirical research, methodical approaches and issues of data
dissemination. But there are still tasks to be solved: Despite the strengths and
challenges of German data sources, high international usability of German data is
not yet obtained. Documentation and data sets are not always available in English.
The provision of data by research data centers is one important easement for
international users, but the importance of international usability has not been fully
recognised in Germany. Since data available in Germany is diverse, has a high
quality, and includes big samples, it is important to improve international access
and usability.

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In sum, Germany has established an adequate data structure. Apart from its
weaknesses, there is no need to reorganise the basic structure. Data with openaccess is already on a high level of quality and quantity. Based on the given
strengths and existing gaps it is recommended to extend the German data
landscape with regard to the excluded sub-populations and to improve international
usability.

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3

3.1

SELECTED GERMAN DATASETS ON DEMOGRAPHIC CHANGE

Data Mapping: Topics and Datasets

Topic

Selected Datasets

Health



German Ageing Survey / Deutscher Alterssurvey (DEAS)



German Health Interview and Examination Survey for Adults /
Studie zur Gesundheit Erwachsener in Deutschland (DEGS)



Possibilities and Limits of an Independent Living and Health/
Möglichkeiten und Grenzen selbständiger Lebensführung (MUG)



Study of Health in Pomerania / Leben und Gesundheit in Pormmern
(SHIP)



Telephone Health Survey - German Health Update / Telefonische
Gesundheitssurveys (GSTel und GEDA)

Social systems



and welfare

Biographical Data of Selected Social Insurance Agencies in
Germany / Biografiedaten ausgewählter Sozialversicherungsträger
in Deutschland 2007 (BASiD 2007)



German Ageing Survey / Deutscher Alterssurvey (DEAS)



Microcensus / Mikrozensus



Sample of Insured Persons and their Insurance Accounts /
Versicherungskontenstichprobe der DRV



German Socio-Economic Panel / Sozio-oekonomisches Panel
(SOEP)



Sample Survey of Income and Expenditure (IES) /
Einkommens- und Verbrauchsstichprobe (EVS)

Work and
productivity



Employment after Retirement / Weiterbeschäftigung im Rentenalter



German Ageing Survey / Deutscher Alterssurvey (DEAS)



IAB Establishment Panel / IAB Betriebs-Panel



Labour Market and Social Security / Arbeitsmarkt und soziale
Sicherung (PASS)



German Socio-Economic Panel / Sozio-oekonomisches Panel
(SOEP)

Education and



learning

Adult Education Survey (AES) / Weiterbildungsverhalten in
Deutschland



National Education Panel Study / Nationales Bildungspanel (NEPS)

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

Survey on information and communication technologies / Erhebung
über die private Nutzung von Informations- und
Kommunikationstechnologien (IKT)

Housing,
environment and
mobility



German Ageing Survey / Deutscher Alterssurvey (DEAS)



Mobility Panel Germany / Deutsche Mobilitätspanel (MOP)



Possibilities and Limits of an Independent Living and Health/
Möglichkeiten und Grenzen selbständiger Lebensführung (MUG)



German Socio-Economic Panel / Sozio-oekonomisches Panel
(SOEP)

Attitudes to old



German Ageing Survey / Deutscher Alterssurvey (DEAS)



German Ageing Survey / Deutscher Alterssurvey (DEAS)



German Survey on Volunteering / Deutscher Freiwilligensurvey

age
Social, civic and
cultural
engagement

(FWS)


German Socio-Economic Panel / Sozio-oekonomisches Panel
(SOEP)

Uses of



Time Use Survey / Erhebung zur Zeitverwendung



Adult Education Survey (AES)/ Weiterbildungsverhalten in

technology

Deutschland


Survey on information and communication technologies / Erhebung
über die private Nutzung von Informations- und
Kommunikationstechnologien (IKT)

Well-being



Time Use Survey / Erhebung zur Zeitverwendung



German Ageing Survey / Deutscher Alterssurvey (DEAS)



German Socio-Economic Panel / Sozio-oekonomisches Panel
(SOEP)

Intergenerational
relationships



German Ageing Survey / Deutscher Alterssurvey (DEAS)



German Family Panel / Beziehungs- und Familienpanel (PAIRFAM)



German Socio-Economic Panel / Sozio-oekonomisches Panel
(SOEP)

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3.1.1 Adult Education Survey (AES) / Weiterbildungsverhalten in
Deutschland
Institution, URL

TNS Infratest Sozialforschung, München
(http://www.tnsinfratest.com/SoFo/Expertise/Bildungsforschung_AES.asp)

Type of data

Cross-sectional survey

Access to data

Data available via GESIS - Study No. ZA5074: Adult Education Survey
(AES 2010 - Germany). Data can be downloaded after registration at no
cost. Data and documents are free for academic research and teaching
(www.gesis.de).

Sample and age

Multiple stratified random sample (ADM-standard) of the population in

range

private households aged 18 to 64 years

Time, waves and  Wave 1: Data collected in 2007 with a sample size of approximately
sample size

7,000 individuals.
 Wave 2: Data collected in 2010 with a sample size of approximately
7,000 individuals.
Preceding survey – Berichtssystem Weiterbildung (BSW) (partly
comparable): Data was collected in 1979, 1982, 1985, 1988, 1991,
1994, 1997, 2000, 2003, and 2007.

Language issues

Data is available in German only.

Coverage

The survey monitors learning activities of adults as a part of the national
education reporting and as a part of European official statistics. All kinds
of education in adult age are reported: formal education (attending
regular vocational training), non-formal education (attending further
training) and informal learning activities.

Use of
internationally

 NUTS classification (Nomenclature of territorial units for statistics):
NUTS 1

harmonised

 ISCO 1988 (International Standard Classification of Occupation, 1988)

standards

 ISCED, ISCED-Field (International Standard Classification of
Education)
 NACE (Nomenclature statistique des activités économiques dans la
Communauté Européenne, Code of economic sectors)
 CLA (Classification of Learning Activities)

Strengths and

The AES is a monothematic survey and records information on different

weaknesses

educational activities in adult age in a very detailed manner. The dataset
is appropriate for trend analysis because it is a repeated cross-sectional
survey. It is also suitable for comparing analyses within the EU-member
states. Such analyses were carried out with the data of the AES 2007,
in which most of the EU-member states took part. A variety of socio-

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demographic information is recorded, e. g. the educational background
of the respondents and their parents, labour status, sector and size of
company, job and occupational status, income and so on.
Besides educational and occupational items, context information is
recorded only to a limited extent. AES data are cross-sectional, so
longitudinal analyses are not possible.

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3.1.2 Biographical Data of Selected Social Insurance Agencies in Germany
/ Biografiedaten ausgewählter Sozialversicherungsträger in
Deutschland 2007 (BASiD 2007)
Institution, URL

Research Data Centre of the German Pension Insurance (FDZ-RV)/
Forschungsdatenzentrum der Rentenversicherung (FDZ-RV)
(http://forschung.deutscherentenversicherung.de/FdzPortalWeb/?ViewNavi=StartSeite)

Type of data

Dataset produced by matching two registry data sources

Access to data

Datasets are made available by the Research Data Centre; data are
provided to the scientific community as a Scientific Use File (SUF), as
well as a weakly anonymised dataset accessible by on-site use. A SUF
is available after signing a research contract.

Sample and age

 Disproportional stratified random sample, stratified by insurance

range

provider, sex, nationality and cohort
 Persons at the age of 30 to 67 years with a valid contributions
account with the German Pensions Insurance at 31.12.2007

Time, waves and Data was collected in 2007 and had a sample size of 60,809 individuals.
sample size
Language issues

Data is available in German only.

Coverage

The dataset connects longitudinal information from the pension
insurance accounts with biographical data from the Federal
Employment Agency (BA).
Information by the Sample of Pension Insurance Accounts includes
employment, unemployment, education, military/civil service, periods of
sickness, child-raising periods, self-employment subject to social
insurance, non-professional long-term caring (since 1995), minor
employment (since 1999), pension and place of residence.
Information by the datasets of the Federal Employment Agency (BA)
includes profession, current job, receipt of unemployment benefits,
times of job seeking, measures during job seeking, information on the
enterprise, places of residence and occupation, occupational status and
working time.

Use of



internationally

Systematisches und alphabetisches Verzeichnis der

harmonised
standards

Classification of occupations / Klassifizierung der Berufe.
Berufsbenennungen, Eds.: Bundesagentur für Arbeit



Classification for Industrial Branches WZ73, WZ93, WZ03, WZ08
referring to NACE



BLK (Blossfeld’s Occupational Classification)



EGP – Classes (Erikson, Goldthorpe and Portocarero)

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

MPS ( Magnitude Prestige Scale)

Strengths and

The Research Data Centre of the Federal Employment Agency in the

weaknesses

Institute for Employment Research (FDZ BA/IAB) and the Research
Data Centre of the German Pension Insurance (FDZ-RV) offer
longitudinal individual-level datasets. These datasets contain, on the one
hand, information on the social security notifications and, on the other
hand, characteristics of the administrative procedures of both
institutions. In each institution, only information about the
accomplishment of their own current tasks is recorded. The ambition of
this project is to compile a common dataset which contains data of the
RV and the BA, respectively the IAB. The richness of information on
individuals will be increased through filling up gaps in the single data
sources with information of the other data source. This will provide new
opportunities for scientific research. The combination of different data
sources also supports the improvement of the quality of administrative
records. (Source: http://doku.iab.de/fdz/reporte/2011/DR_09-11.pdf)
The sample is very large and data have a high validity and correctness.
It is possible to carry out differentiated analyses in small groups.
Data comprise a large time frame – the whole insurance biography.
There are no non-responses or memory failures. A comprehensive
catalogue of items on the employment biography is recorded.
The sample is very complex, descriptive results have to be analysed
with sample weights.
Only biographical information that is connected to pension insurance is
recorded. Information on civil servants, occupationally insured
(berufsständisch Versicherte) and foreigners without permanent
residence status are missing. Information on (married) couples and
households are also missing. There are no data on men and their
children.

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3.1.3 Employment after Retirement / Weiterbeschäftigung im Rentenalter
Institution, URL

Federal Institute for Population Research, Wiesbaden
(http://www.bib-demografie.de/EN/Research/Surveys/EARS/ears.html)

Type of data

Cross-sectional data

Access to data

The dataset is available for scientific, non-profit use. The data set is free
of charge and can be downloaded via GESIS (Study number 5457) after
signing a contract.

Sample and age

Employees (freelancers excluded) born 1944-1953

range
Time, waves and The data were collected in 2008 with a sample size of 1,500 units
sample size
Language issues

Data available in German only.

Coverage

With the objective to identify reasons for the continuation of work
beyond retirement age, the data set includes variables on a variety of
topics related to the field of work and productivity. The data set covers
the following topics within the work domain:


Current employment (contractual number of hours, position, receipt
of pension besides income from employment, short-time work,
seasonal work, position, hours worked within a week, years worked
for the current workplace, number of former employers),



Current workplace (number of employees, sector),



Unemployment (when and for how long),



Retirement (opinion on the increase of the retirement age from 65
to 67 years, own retirement age without deduction, desired
retirement age),



Self-assessment of current working conditions (interest,
identification with workplace, motivation, satisfaction,
concentration, monotony, posing risks to health etc.),



Working atmosphere (relationship to colleagues and management
etc.),



Continuation of work after reaching retirement age (motivation,
requirements and desires).

Use of

No information available.

internationally
harmonised
standards
Strengths and

The data set is designed to identify factors which have an impact on the

weaknesses

willingness to continue to work beyond retirement age, as well as the
desired working conditions for this continuation. The study allows for

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the analysis of the current professional situation, the opinion on the
increase of the retirement age, job satisfaction, working conditions,
work atmosphere, health and information on further education.
Moreover, the correlation of these aspects with others such as gender,
family status, various personality traits or establishment size and sector
can be analysed.
However, the data set does not include freelancers and allows for the
analysis of employees who were born between 1944 and 1953 only.
Moreover, it is a cross-sectional study, which was conducted in 2008.
Therefore, it cannot be surveyed if the intention to work beyond
retirement age has been realised or not. Furthermore, the data are
available in German only.

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3.1.4 German Ageing Survey / Deutscher Alterssurvey (DEAS)
Institution, URL

German Centre of Gerontology (DZA), Berlin
(http://www.dza.de/forschung/deas.html)

Type of data

Combination of regular cross-sectional and longitudinal survey (cohortsequential design).

Access to data

Access via research data centre for scientific, non-profit use after
signing a data distribution contract, data free of charge
http://www.dza.de/forschung/deas.html)

Sample and age

Community-dwelling people aged 40-85 years (base samples)

range
Time, waves and 

Wave 1: Data collected in 1996 (DOI 10.5156/DEAS.1996.M.001)

sample size

with a sample size of 4,838 individuals.


Wave 2: Data collected in 2002 (DOI 10.5156/DEAS.2002.M.001)
with a base sample of 3,084 individuals, a migrant sample of 586
individuals, and a panel sample of 1,524 individuals.



Wave 3: Data collected in 2008 (DOI 10.5156/DEAS.2008.M.001)
with a base sample of 6,205 individuals and a panel sample of
1,995 individuals.



Wave 4: Data collected in 2011 with a panel sample of 4, 855
individuals.



Wave 5: Data will be collected in 2014. A new base sample will be
drawn and the panel sample will be reassessed.

Language issues

Documentation and data is available in German and English

Coverage

The DEAS is a multidisciplinary survey providing national representative
data of the German population aged over 40 years on various topics:


Employment and retirement



Generations, family and social networks



Activities outside the work environment and volunteer work



Housing situation and mobility



Economic situation and economic behaviour



Subjective well-being and quality of life



Health and health behaviour, need of assistance and need of care,



Attitudes, norms, values and



Images of age and ageing

Health:


Physical health, functional health, subjective health, mental health,
health behaviour, need of care, need of assistance, health care
utilization, sentinel health events, pain, sleep, health test and
information on mortality (among others)

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Social systems and welfare:


Employment careers, transition to retirement, incomes, property,
financial and material transfers, saving and dis-saving, subjective
fulfillment of needs, and living standards

Work and productivity:


Education and first employment, the current employment (position,
average number of working hours, number of employees,
workplace sector), breaks in employment and their reasons and
durations, the last employment (position, hours worked, number of
employees, part-time employment prior to retirement); transition to
retirement; work beyond retirement (hours, motivation and
reasons), subjective indicators such as satisfaction with current
working conditions (working hours, income, further education etc.),
stresses and strains; and a self-assessment of the probability of
unemployment and the chances of finding new employment,
education and employment of the current and the last partner

Housing, urban development and mobility:


Housing indicators cover: ownership status, housing cost, moving,
neighbourhood surroundings and residential environment,
residential history, type of dwelling, household facilities, and
satisfaction with housing (current, past and future expectations).
Mobility, besides ownership of a car, is not assessed.

Public attitudes towards older age:


Individual views on ageing, images of ageing in society, subjective
age, attitudes towards retirement and ageism

Social, civic and cultural engagement::


Memberships in associations for older people and in other
associations, the duration of membership, frequency of
volunteering, honorary office, expenditure of time for volunteering,
barriers to volunteering, volunteering in the past, being interested in
(more) volunteering, area of volunteering and informal help for
others.

Well-being:


Cognitive (life satisfaction in general, domain-specific satisfaction)
and emotional well-being (frequency of experiencing positive and
negative emotions); a screening instrument for depression,
loneliness and optimism; in 2014, the well-being module will be
expanded by adding a measure of stress

Intergenerational relationships:

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30



Persons who can be asked for advice and turned to when in need
for comfort and cheering up instrumental support and financial
transfers (given and received); housework done for relatives;
worries, quarrels and joy/happiness related to relatives; paternalism;
spatial distance, frequency of contacts, emotional closeness
(parents, children, grandchildren); importance of the grandparentrole and childcare provided by grandparents; existence and number
of great-grandchildren; evaluation of family relations

Use of



internationally

NUTS classification (Nomenclature of territorial units for
statistics):NUTS 3

harmonised



ISCED-97 (International Standard Classification of Education)

standards



ISCO-88 (International Standard Classification of Occupation)

Strengths and

The DEAS provides a data structure that allows analysing social change

weaknesses

and the consequences of an ageing society as well as individual change
over time or in relation transitions (e.g. retirement). It has a cohortsequential design, so empirical analyses can be conducted in different
ways – with a cross-sectional, a longitudinal or a cohort focus. The
DEAS stands out because of his interdisciplinary approach. It is a multitopic dataset, therefore items can be analysed in the context of many
other variables, e.g. family structures, socio-economic status, health,
attitudes on age, regional variables. Moreover, the German Ageing
Survey includes variables on a wide variety of topics combining
psychological, economic and sociological aspects as well as subjective
indicators. In sum the DEAS is one of the data sources in Germany
which provides the most comprehensive data on the living situation of
people 50 years and older.
As a result multiple topics of the JPI MYBL are covered in the DEAS.
However the extent of coverage varies between topics, with health,
social systems and welfare, work and productivity and intergenerational
relationships covered more broadly than well-being, housing and
mobility, attitudes towards ageing and volunteering.
Some specific coverage strengths are that health is assessed as a
multi-dimensional construct. Further information on the employment
situation of officially unemployed persons are provided as well as the
educational attainment and the occupational position of the current and
the last partner of the respondent, as well as for his/her parents and
children. Therefore, intergenerational occupational mobility can be
analysed. Moreover, the possible linkage of subjective data to registry
information on regional context factors (such as population structure in
the community, gross-domestic product of community etc.) on the
NUTS3 level is a great potential of the DEAS. As an ageing survey with
respondents aged 40 and above, the DEAS gives, in addition, in-detailinformation on specialised clubs and organisations for the elderly.

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31

Some coverage specific weaknesses are on the other hand that the
majority of health indicators are self-assessed, objective health
indicators are limited, bio markers are not available. The DEAS also do
not include information on mobility, which limit the survey to questions
concerning housing.
The DEAS has other weaknesses, too, that need to be mentioned.
Although the DEAS assesses participants over the age of 85 and
participants living in care facilities, the sample is not representative for
very old age (85 years and older) and persons living in institutions,
therefore, the living situation as well as societal and individual change
within these sub-populations cannot be investigated without loss of
data quality. The first wave of the DEAS was limited to participants with
German citizenship, since 2002 the inclusion criteria is ability to speak
and understand German. Despite the effort to include foreigners and
people with migration background, the DEAS is still not representative
for this sub-population.
Nevertheless, it can be pointed out that there is a good documentation
(survey instruments, methodological reports, codebooks,
correspondence of variables) available on the website of the Research
Data Centre (www.dza.de/.../deas-documentation.html) as well as
access to English Versions of the data and documentations.

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3.1.5 German Family Panel / Beziehungs- und Familienpanel (PAIRFAM)
Institution, URL

Chemnitz University of Technology, University of Bremen and the
Ludwig Maximilian University of Munich,
(http://www.pairfam.de/en/study.html)

Type of data

Longitudinal survey, cohort study

Access to data

Data is released for academic research and teaching on receipt of the
data depositor’s written authorisation. Depending on the user’s status,
a standard CD-Rom is available for 20€ for academic users. Online
download, however, is free of charge.

Sample and age

PAIRFAM is based on a multi-actor design. In wave 1, the anchors and

range

their partners were interviewed. Interviews with the anchor’s parents
and / or stepparents as well as his/her child (between the ages of 8 to
15) have been conducted since wave 2.
The anchor individuals are living in private households in Germany and
were born within one of the following time periods: between
01.01.1991 and 31.12.1993 (Cohort 1); between 01.01.1981 and
31.12.1983 (Cohort 2); and between 01.01.1971 and 31.12.1973
(Cohort 3).

Time, waves and Data is collected annually since 2008.
sample size



Wave I: Data collected from 2008 to 2009. Sample size of 16,145
units (12,402 anchor individuals + 3,743 partners)



Wave II: Data collected from 2009 to 2010. Sample size of and
17,643 units (9,069 anchor individuals + 2,688 partners + 5,015
parents + 862 children)



Wave III: Data collected from 2010 to 2011. Sample size of and
15,196 units (7,901 anchor individuals + 2,362 partners + 3,946
parents + 987 children)



Wave IV: Data collected from 2011 to 2012. Sample size of 13,672
units (7,049 anchor individuals + 2187 partners + 3352 parents +
1084 children)

Language Issues

All documents, labels, questionnaires etc. are available in German and
English.

Coverage

PAIRFAM depicts intergenerational relationships at different points in
time and pays particular attention to their exact and multifaceted
portrayal. Biological parent-child relationships, adoptive constellations,
stepparents- and parents-in-law relationships and grandchildgrandparent relationships are covered. Since the focus is on the
anchor’s relationships to their biological, adoptive, and stepparents,
these parent-child relationships are portrayed in more detail. All in all,
the dataset allows for a description of central aspects of

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33

intergenerational relationships and the generations involved (e.g.
intergenerational solidarity, intergenerational ambivalence) (cf. Schmahl
et al. 2012: 85). Aspects covered are, for example, attitudes on
intergenerational support, frequency of contact, relationship quality and
transmission processes between generations; material and immaterial
intergenerational transfers; familial norms and children’s expectations of
their parents (cf. PAIRFAM 2013).


NUTS classification (Nomenclature of territorial units for

harmonised



ISCED-97 (International Standard Classification of Education)

standards



CASMIN (Comparative Analysis of Social Mobility in Industrial

Use of
internationally

statistics):NUTS 1, NUTS 5

Nations)


KldB (Classification of Occupation (Klassifikation der Berufe)
proposed by the German Statistical Office)



ISCO-88 (International Standard Classification of Occupation)



EGP (Class scheme according to Erikson, Goldthorpe and



ISEI (International Socio-Economic Index)



SIOPS/Treiman-Scale (Standard International Occupational Prestige

Portocarrero,

Scale)


MPS (Magnitude-Prestige-Scale)



GCEE (Net equivalence income according to the German Council of
Economic Experts)

Strengths and

PAIRFAM is a multidisciplinary, longitudinal study on partnership and

weaknesses

family dynamics and includes a wide variety of topics. The dataset
offers unique opportunities for the analysis of partner and
intergenerational relationships as they develop over the course of
multiple life phases. The multi-actor design offers preponderant, full
dyadic perspectives on a variety of intergenerational relationships
between children, parents, grandparents, stepparents and parents-inlaw of mainly grown-up anchors. All in all, all central aspects of
intergenerational relationships can be described. The focus, however, is
not on intergenerational relationships in very old age (oldest anchors
born in 1971). Moreover, the dataset offers the opportunity to deal with
the relevance of contextual conditions by linking microdata from the
PAIRFAM survey with a selection of external macrodata. Finally, a very
good documentation of all instruments, as well as the do-files and
syntaxes for basic operations are available on the PAIRFAM homepage.

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3.1.6 German Health Interview and Examination Survey for Adults / Studie
zur Gesundheit Erwachsener in Deutschland (DEGS)
Institution, URL

Robert Koch Institute (RKI), Berlin (http://www.degsstudie.de/english/home.html)

Type of data

Combination of regular cross-sectional and longitudinal survey

Access to data

Available for scientific, non-profit use after signing a data distribution
contract, 90 Euros are charged for each data CD

Sample and age

Persons living in Germany aged 18 years and older

range
Time, waves and 

Wave 1: Data collection for the GNHIES98 (German National Health

sample size

Interview and Examination Survey 1998) sample was carried out
from 1997 to 1999. It included a sample size of 7,124 individuals.


Wave 2: Data collection for the DEGS1 sample was carried out
from 2008 to 2011. It included a sample size of 8,152 individuals
(Panel:
3,959 individuals).



Wave 3: Data collection for the next wave will be from 2014 to
2015.


Language issues

A new sample will be drawn in 2017-2018.

Documentation (in parts) is available in German and English, while
variables and value labels are available in German only. The homepage
also contains all basic information (data access, etc.) in English.

Coverage

The DEGS assesses health and health-related questions covering a
wide range of different, highly important topics.


Self-reported morbidity, morbidity assessed by physician or health
examination, medication use, objective health measures,
symptoms and complaints, mental health, subjective health,
gender-specific health issues, injuries, falls, functional health,
disability, health-related behaviour, living and social conditions,
socio-demographic context variables, health care service utilization
(among others)

Use of

The majority of used instruments are standardised questions and scales

internationally

as the RKI measuring standards comply with European and international

harmonised

recommendations.

standards



NUTS classification (Nomenclature of territorial units for
statistics):NUTS 3

Strengths and

The health data collected in DEGS are highly valuable as the survey

weaknesses

provides extensive, nationally representative data for Germany. The
assessment modes are ideal as the DEGS combines personal

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35

interviews, self-assessed questionnaires, physical examinations, and
tests and analyses of blood and urine samples. The DEGS is the best
dataset to determine disease prevalence in the population. The DEGS
allows for cross-sectional and trend analysis because of the regular
assessment of baseline samples with comparable characteristics over
time. Furthermore, individual changes in health can be analysed using
the longitudinal data of reassessed participants (panel design). The RKI
has been involved in European initiatives to standardise national health
interview and examination surveys, and for that reason, most data is
comparable to other datasets in Germany and Europe.
Minor weaknesses of the DEGS are the long period between first and
re-assessment of participants (10-14 years between GNHIES98 and
DEGS1), the underrepresentation of very old people, people living in
institutions, and people with an immigration background. The focus on
health also limited the potential of the survey to analyse underlying
mechanisms, such as socio-economic status, social relations or healthrelated behaviour, in addition to the assessed well-known risk factors.

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3.1.7 German Socio-Economic Panel (SOEP) – Sozio-oekonomisches Panel
Institution, URL

Deutsches Institut für Wirtschaftsforschung (DIW), Berlin
(http://www.diw.de/en/diw_02.c.222517.en/data.html)

Type of data

Longitudinal survey

Access to data

Available for scientific, non-profit use only. In accordance with the data
protection law, the individual SOEP data sets cannot be downloaded
directly from the homepage. A DVD will be sent via certified mail for a
price of 30€ + 8€ forwarding expenses.

Sample and age

Persons aged at least 17 years. The SOEP consists of a complex

range

system of partial samples which have been integrated in different years
into the panel.


Sample A: Residents in the FRG (1984)



Sample B: Foreigners in the FRG (1984)



Sample C: German Residents in the GDR (1990)



Sample D: Immigrants (1994/95),



Sample E: Refreshment (1998),



Sample F: Innovation (2000),



Sample G: Oversampling of High Income (2002),



Sample H: Refreshment (2006),



Sample J: Incentivation (2009),



Sample K: Increase (2011)

Time, waves and Data is collected annually since 1984 (West Germany) / 1990 (East
sample size

Germany).
Samples sizes of partial samples:


Sample A + B: 12,245 individuals



Sample C: 4,453 individuals



Sample D: 1,078 individuals



Sample E: 1,923 individuals



Sample F: 10,890 individuals



Sample G: 2,671 individuals



Sample H: 2,616 individuals



Sample J: 2,509 individuals



Sample K: 5,161 individuals

Complete sample sizes for selected years:


1984: 12,245 individuals;



1990: 13,971 individuals;



1995: 13,768 individuals;



1998: 14,692 individuals;



2000: 24,582 individuals;



2002: 23,443 individuals;

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37

Language Issues



2006: 22,665 individuals;



2007: 21,7232 individuals,



2008: 19,945 individuals;



2009: 18,602 individuals,



2010: 17,156 individuals,



2011: 21,336 individuals

All documents, labels, questionnaires etc. are available in German and
English.

Coverage

The SOEP is a multidisciplinary survey that provides nationally
representative household data on various topics
Social systems and welfare:


The SOEP provides rich data on the social systems and welfare
such as items on: education, training and qualification, labour
market and occupational dynamics, earnings, income and social
security.

Work and productivity:


Employment-related questions and earnings and income can be
analysed in particular detail. The data set does not only include a
wide variety of questions on current employment (e.g. working
hours, working overtime, correspondence with trained occupation,
number of employees in the company, type of employment
contract, job as part of job-creation measure or ‘1-Euro-Job’,
commuting, individual preferences concerning working hours and
days, partial retirement, industry, occupation, monthly salary,
bonuses and benefits besides salary), but also information on the
very first and the last job (e.g. when and how did it end, existence
of new job prospect etc.), and hence allows observation of
employment histories and occupational dynamics. Moreover, the
survey includes information on job satisfaction, on job-related
expectations (e.g. the estimated probability of losing the job, or
chances on the labour market in case of job loss) and also captures
secondary employment/work beyond retirement.

Housing, urban development and mobility:


The household questionnaire assesses the majority of housingrelevant indicators in the SOEP. Not all indicators are available for all
waves, but since 1999 basic questions have been assessed
annually. The housing indicators cover: ownership status, quality of
dwelling, housing cost, moving, neighbourhood surroundings and
residential environment, residential history, type of dwelling,
household amenities, and satisfaction with housing. With regard to
mobility, the SOEP had a specific module in 1998 and 2003, but

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very basic indicators such as availability of a driver’s licence or
access to cars are assessed more often, but not annually. The
mobility module covers existence, accessibility and use of public
transportation on site; existence and use of cars and bike in the
household; type of transportation used to go to work, go shopping,
during leisure time, for day trips and to transport kids; attitudes
towards driving, public transportation, leisure time at home, and
environment consciousness. Besides the annual assessment of
satisfaction with housing, satisfaction with living area, environment
conditions and goods and services offered on site are assessed
irregularly.
Social, civic and cultural Engagement:


The SOEP contains some items on volunteering and other forms of
participation: volunteer work in clubs or social services, involvement
in a citizens' group/political party/local government, attending
church/religious events.

Well-being:


Life satisfaction using a one-item indicator has been assessed since
1984, while satisfaction with other life domains is assessed
regularly, but is based on a different number of life domains during
the years. Emotional well-being is assessed with four one-item
indicators on frequency of feeling angry, worried, happy, and sad
since 2007. Since 2002, every two years the SF-12 questionnaire,
which assesses mental and physical health, is applied. In 2006 and
2011, questions concerning work stress were assessed.

Intergenerational relationships:


Family networks (spatial distance and self-evaluation of the
relationship with parents, children, grandparents, grandchildren,
siblings, other relatives) are assessed on an irregular basis (1991,
1996, 2001). Financial transfers given to relatives are assessed in
almost all waves, while received financial transfers from relatives,
however, are only assessed in certain waves (e.g. 2011, 2010, and
2009).

Use of

The SOEP is part of the Cross-National Equivalent File (CNEF). The

internationally

CNEF contains equivalently defined variables for eight major surveys in

harmonised

eight countries: the US, Germany, Britain, Australia, Canada,

standards

Switzerland, Korea and Russia. (cf. Cornell University 2013; Frick et al:
2007).


NUTS classification (Nomenclature of territorial units for
statistics):NUTS 1 - 5



ISCO-88 (International Standard Classification of Occupation)



ISCED-97 (International Standard Classification of Education)

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39



NACE (Nomenclature des statistiques des activités économiques
de la Communauté européenne - Statistical Classification of
Economic Activities in the European Community)



KLAS (occupational classification of the German Federal Statistical



ISEI (International Socio-Economic Index of Occupational Status)



EGP (Erikson and Goldthorpe Class Category)



SIOPS (Treimans Standard Int. Occupation Prestige Score),



MPS (Magnitude-Prestige Scala - Wegener)



CASMIN (Comparative Analysis of Social Mobility in Industrial

Office)

Nations)
Strengths and

The SOEP contains a wide variety of topics and a combination of

weaknesses

objective and subjective indicators. Moreover, the SOEP allows for the
analysis of individual life-courses, as well as trends over time.
Furthermore, all aspects cannot only be analysed on an individual, but
also on a household level. Moreover, subjective data can be linked with
registry information on regional context factors (such as population
structure in the community, etc., NUTS1 up to NUTS5/LAU2 and even
zip-code level), which is a great potential. The documentation of the
data and support by the Research Data Centre at the DIW as well as
the availability and usability for English-speaking researchers are
excellent.
Multiple topics of the JPI MYBL are covered in the SOEP. However, the
extent of their coverage varies: Work and productivity, social systems
and welfare as well as well-being are covered in more detail than
housing, civic engagement and intergenerational relationships.
Some specific strengths are that occupational dynamics and
employment biographies can be analysed in detail. Furthermore,
changes in housing needs and expectations can be analysed with
regard to regional characteristics. Moreover, it is possible to analyse
changes in life satisfaction over time and with regard to life events and
the assessment of domain-specific satisfaction allows for more detailed
analyses. Additionally, determinants of well-being, as well as
consequences of well-being differences can be analysed.
However, there are also some specific weaknesses. Apart from a few
questions on partial retirement or work beyond retirement, the SOEP
contains no special questions concerning older workers. Although the
SOEP continues to interview participants after moving into institutional
settings, the data is not representative for this sub-population with
specific housing needs and mobility limitations. While it is positive that
the SOEP assesses emotional well-being since 2007, the measurement
limits analysis to four basic emotions and may not be able to capture
emotional well-being in old age and among the oldest old.

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40

Intergenerational relationships are not assessed extensively. Family
networks and financial transfers given to relatives are assessed in
almost all waves, while received financial transfers from relatives,
however, are only assessed in certain waves. Moreover, relatives
cannot be identified on an individual level which is why relationships
dynamics cannot be analysed. Moreover, the dataset does not include
items on basic dimensions of relationships such as emotional
closeness, frequency of contact or conflicts.

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3.1.8 German Survey on Volunteering / Deutscher Freiwilligensurvey
(FWS)
Institution, URL

German Centre of Gerontology (DZA), Berlin
(http://www.dza.de/en/research/deutscher-freiwilligensurvey-fws.html)

Type of data

Cross-sectional survey data

Access to data

Data 1999, 2004: access via GESIS Data Archive,
http://www.gesis.org/en/home/
Questionnaire and methods report 1999 and 2004 as well as
questionnaire, methods report and data 2009: access via DZA Research
Data Centre. Data and documents are only released for academic
research following the data depositor’s written authorization. For this
purpose, the Data Archive obtains written permission with specification
of the user and the purpose of analysis.

Sample and age

Random sample of the population in private households aged 14 years

range

and older, only respondents that are able to answer a German
questionnaire (up to 2009)

Time, waves and 

Wave 1: Data collected in 1999 with a sample size of 14,922

sample size

individuals.


Wave 2: Data collected in 2004 with a sample size of 15,000
individuals.



Wave 3: Data collected in 2009 with a sample size of 20,005
individuals.



Wave 4: Data collection is planned for 2014.

Language issues

Data is available in German only.

Coverage

Study with a focus on social, civic and cultural engagement and
volunteering. It covers aspects like activities in 14 areas of volunteering,
potential of volunteering, detailed information about the most timeconsuming activity, context indicators of volunteering (organisations,
region), individual background.

Use of
internationally
harmonised
standards



NUTS classification (Nomenclature of territorial units for statistics):
NUTS 1

No other harmonised standards in the waves from 1999 to 2009.

Strengths and

The German Survey on Volunteering (FWS) is a representative survey

weaknesses

on volunteer work, honorary office and civic engagement of the German
population aged 14 and older. The FWS provides a substantial database
for the description of volunteering in Germany and allows detailed
reporting on participation among population groups and across regions.

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It is the basic instrument for social accounting on volunteering in
Germany.
The FWS is focused on a variety of volunteering issues associated with
institutions such as associations, organisations, churches and others.
Some informal volunteering activities are omitted (such as
neighbourhood assistance). There are only a few correlates to analyse
relations to other factors of the living situation (items on e.g. health
status are lacking up to wave 2009).
The dataset is easily available by the Research Data Centre FDZ-DZA
(wave 2009) or by GESIS (1999, 2004). By transferring the datasets to
the Research Data Centre FDZ-DZA, handling of the dataset and
consulting will become even more convenient. So far, the dataset is
only available in German, which is a disadvantage for its dissemination.
At present, it takes about two years after field time to publish the
current dataset. The FWS is the only survey with an exclusive focus on
volunteering and it provides a large number of cases (about 25,000
cases planned for 2014).

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3.1.9 IAB Establishment Panel / IAB Betriebs-Panel
Institution, URL

Research Data Centre of the German Federal Employment Agency (BA)
and the Institute for Employment Research (IAB) , Nuremberg
(http://fdz.iab.de/en/FDZ_Establishment_Data/IAB_Establishment_Panel/
IAB_Establishment_Panel_Data_Access.aspx)

Type of data

Longitudinal survey

Access to data

Available for scientific, non-profit use and for the purpose of
employment research only. Data can be downloaded after signing a
data distribution contract. The data set is free of charge.

Sample and age

Establishments of all sectors and sizes

range
Time, waves and Data is collected annually since 1993 (West Germany) / 1996 (East
sample size

Germany) and includes a sample size of 16,000 establishments of all
sectors and sizes. The reference date of the sample is June 30th.

Language Issues

All documents, labels, questionnaires etc. are available in German and
English.

Coverage

The number of employees older than 50 years is surveyed in some
years (e.g. 2002, 2006). Concerning the question of how far an ageing
population is changing employers’ behaviour, the data set includes (for
certain years: 2002, 2006, 2008, 2011) variables on individual personnel
measures for older workers. These cover partial retirement, special
equipment at the workplace, adaptation of performance requirements,
further education, mixed-age working groups and health promotion (the
latter in 2011 only). These personnel measurements can be analysed on
an establishment level with regard to establishment size and sectors,
etc. Additionally, the 2002 questionnaire puts a focus on elderly
employees and asks employers to assess if certain characteristics such
as creativity, flexibility, loyalty or the willingness to learn are more
commonly found in older or younger employees. Moreover, this wave
allows analysis of circumstances in which employees older than 50
years would be employed (part-time, fixed-term, financial support, only
if there are no younger applicants etc.). Furthermore, the importance of
keeping older skilled employees longer in the establishment was
surveyed in 2011.

Use of
internationally



NUTS classification (Nomenclature of territorial units for statistics):
NUTS 3

harmonised
standards

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Strengths and

The IAB Establishment Panel is the central basis for the analysis of

weaknesses

labour demand in Germany. However, questions on older employees
are not included in all waves, but starting in 2002, they are assessed on
a regular basis (2002, 2006, 2008 and 2011). Therefore, the data set can
answer several questions on how far ageing populations are changing
employers’ behaviours and attitudes since it provides information on
the number of older employees and individual personnel measures for
older workers. Moreover, the wave in 2002 provides information on
how employers assess certain characteristics of older employees, such
as creativity or flexibility.
However, the data set only allows analysis on a workplace level. In
order to obtain information on employee histories and occupational
mobility, the Establishment Panel is available in versions of the Linked
Employer/Employee Data (LIAB).

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3.1.10 Labour Market and Social Security / Arbeitsmarkt und soziale

Sicherung (PASS)
Institution, URL

Institute for Employment Research (IAB) of the German Federal
Employment Agency (BA), Nuremberg
(http://www.iab.de/962/section.aspx/Projektdetails/k060821f35)

Type of data

Longitudinal household survey

Access to data

Available for scientific, non-profit use and for the purpose of
employment research only. Data can be downloaded after signing a
data distribution contract. The data set is free of charge.

Sample and age

Persons aged at least 15 years. The survey consists of two

range

subsamples, each of ca. 6,000 households: Individuals and households
receiving the Unemployment Benefit II (sample I) and individuals and
households residing in Germany, with an overrepresentation of lowincome households (sample II). Samples III, IV and V are refreshment
samples for sample I.

Time, waves and The survey is conducted annually.
sample size

Wave 1
Data collection was carried out from 2006-2007. The sample sizes
were:


Sample I: 9,386 individuals (living in 6,804 households)
Sample II: 9.568 individuals (living in 5,990 households).

Wave 2
Data collection was carried out from 2007-2008. The sample sizes
were:


Sample I: 4,753 individuals (living in 3,491 households)
Sample II: 6,392 individuals (living in 3,897 households)
Sample III: 1,342 individuals (living in 1,041 households).

Wave 3
Data collection was carried out from 2008-2009. The sample sizes
were:


Sample I: 4,913 individuals (living in 3,754 households)
Sample II: 6,207 individuals (living in 3,901 households)
Sample III: 898 individuals (living in 694 households)
Sample IV: 1,421 individuals (living in 1,186 households).

Wave 4
Data collection was carried out in 2010. The sample sizes were:


Sample I: 3,958 individuals (living in 2,815 households)
Sample II: 5,016 individuals (living in 2,977 households)
Sample III: 786 individuals (living in 563 households)

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Sample IV: 983 individuals (living in 745 households)
Sample V: 1,025 individuals (living in 748 households).
Wave 5
Data collection was carried out in 2011. The sample sizes were:


Sample I: 3,394 individuals (living in 2,382 households)
Sample II:4,511 individuals (living in 2,680 households)
Sample III: 653 individuals (living in 464 households)
Sample IV: 822 individuals (living in 608 households)
Sample V: 760 individuals (living in 517 households)
Sample VI: 2,589 individuals (living in 1,510 households)
Sample VII: 1,859 individuals (living in 1,321 households)
Sample VIII: 1,019 individuals (living in 753 households).

Language issues

Documentation and data is available in German and English

Coverage

PASS allows for the analysis of work and employment histories with a
focus on entries into, and exits from drawing benefits and their relation
to individual events or social and labour market policy measures
according to the Social Security Statute Book II (SGB II). Besides sociodemographic, subjective and benefit-related characteristics, a number
of employment-related characteristics are assessed:


Status of (un)employment; mini-job; working hours; occupational
status (detailed); occupation (ISCO-88 und KldB-92); ISCO-based
measures of occupational status and prestige (ISEI, SIOPS, MPS,
EGP, ESeC); income from employment (gross & net); employment
biography with (un)employment episodes and periods of nonemployment; start date of current employment; fixed-term
employment; change of fixed-term status; supervisory function;
other employment; detailed information on the employment search
status and reservation wage as well as employment orientation (cf.
FDZ IAB 2013).

Use of



internationally

NUTS classification (Nomenclature of territorial units for
statistics):NUTS 1

harmonised



ISCO-88 (International Standard Classification of Occupation)

standards



ISCED-97 (International Standard Classification of Education)



CASMIN (Comparative Analysis of Social Mobility in Industrial
Nations)



EGP (Class scheme according to Erikson, Goldthorpe and
Portocarrero)



ESeC (European Socio-economic Classification)



MPS (Magnitude-Prestige-Scale)



SIOPS/Treiman-Scale (Standard International Occupational Prestige
Scale)



ISEI (International Socio-Economic Index)

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

WZ2003 (Classification of Economic Activities 2003).

Strengths and

The panel design of Labour Market and Social Security allows for the

weaknesses

analysis of social processes and the non-intended side-effects of labour
market reforms. It is suited to answer many questions concerning
work, productivity and the impact of institutional, welfare and regulatory
regimes. The panel design enables the user to analyse work and (un)employment histories, as well as pathways into and out of
dependency. In order to ensure that there are no systematically missing
data for migrants, who are an important target group, the interviews are
administered not only in German and English but also in Turkish and
Russian, the mother tongues of two important migrant groups in
Germany. For persons older than 65, however, a shortened version of
the questionnaire is employed. The Research Data Centre of the
German Federal Employment Agency (BA) and the Institute for
Employment Research (IAB) offers a consulting service and workshops
for working with PASS data. Moreover, all questionnaires,
documentations and other working tools are available in German and
English.

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3.1.11 Microcensus / Mikrozensus
Institution, URL

Research Data Centre of the Federal Statistical Office Forschungsdatenzentrum des Statistischen Bundesamtes, Düsseldorf
(http://www.forschungsdatenzentrum.de)

Type of data

Combination of regular cross-sectional and longitudinal survey

Access to data

The dataset is available from the Research Data Centre. Scientific Use
Files (SUF) are only available for scientific research and only for
scientists in Germany. Some detailed data is only available for on-site
workplaces. Access given only for projects limited in content and time
(3 years at the most), SUF available on application and after signing a
data privacy commitment and a research contract, most data with fees.

Sample and age

No age range

range
Time, waves and Ongoing since 1973, References for Microcensus (basic files) 1973 to
sample size

2007: GESIS- MISSY, 2008 to 2011: quality reports (own calculations)


Wave 1: Data was collected in 1973 and had a sample size of



Wave 2: Data was collected in 1976 and had a sample size of

448,366.
442,791.


Wave 3: Data was collected in 1978 and had a sample size of



Wave 4: Data was collected in 1980 and had a sample size of

441,563.
440,824.


Wave 5: Data was collected in 1982 and had a sample size of



Wave 6: Data was collected in 1985 and had a sample size of

443,154.
437,603.


Wave 7: Data was collected in 1987 and had a sample size of



Wave 8: Data was collected in 1989 and had a sample size of

439,015.
385,831


Wave 9: Data was collected in 1991 and had a sample size of



Wave 10: Data was collected in 1993 and had a sample size of

516,038.
513,830.


Wave 11: Data was collected in 1995 and had a sample size of



Wave 12: Data was collected in 1996 and had a sample size of

512,509.
509,243.


Wave 13: Data was collected in 1997 and had a sample size of
509,892.

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

Wave 14: Data was collected in 1998 and had a sample size of
507,861.



Wave 15: Data was collected in 1999 and had a sample size of
506,897.



Wave 16: Data was collected in 2000 and had a sample size of
503,185.



Wave 17: Data was collected in 2001 and had a sample size of
503,961.



Wave 18: Data was collected in 2002 and had a sample size of
503,075.



Wave 19: Data was collected in 2003 and had a sample size of
502,873.



Wave 20: Data was collected in 2004 and had a sample size of
499,849.



Wave 21: Data was collected in 2005 and had a sample size of
477,239.



Wave 22: Data was collected in 2006 and had a sample size of
496,815.



Wave 23: Data was collected in 2007 and had a sample size of
483,595.



Wave 24: Data was collected in 2008 and had a sample size of
approximately 483,000.



Wave 25: Data was collected in 2009 and had a sample size of
approximately 490,000.



Wave 26: Data was collected in 2010 and had a sample size of
approximately 490,000.



Wave 27: Data was collected in 2011 and had a sample size of
approximately 487,000.

Language issues

Data is available in German only, but study documentations are available
in English for the waves 1985 to 2005 (available on
http://idsc.iza.org/metadata/).

Coverage

The Microcensus contains indicators of a variety of living conditions.
There is a yearly basic questionnaire that contains indicators like
sociodemographic items (household and family structure), items on
employment, job and job-seeking, items on qualification and advanced
vocational training, items on income and sources and amount of the
living, old-age provisions, long-term care insurance. Some information is
collected only every 4 years in an additional questionnaire, such as
private and employee pensions, housing conditions, migration,
commuting (for pupils, students and employees), number of born
children (only women aged 15 to 75 years) and health topics.

Use of



internationally

NUTS classification (Nomenclature of territorial units for statistics):
NUTS 1



ISO (ISO country classification)

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harmonised



WZ 2008 (German Classification of Economic Activities, 2008)

standards



ISCO 1988 (International Standard Classification of Occupation,
1988)



KldB 1992 (Classification of Occupations, 1992)



ISCED (International Standard Classification of Education)

Strengths and

The Microcensus is the largest official household survey in the

weaknesses

European Union. Since 1957 – in East Germany (including Berlin-East)
since 1991 – the Microcensus has provided statistical information in a
detailed, subject-related and regional breakdown on the population
structure, the economic and social situation of the population, families,
consensual unions and households, on employment, job search,
education/training and continuing education/training, the housing
situation and health. The Labour Force Survey of the European Union
(EU Labour Force Survey) forms an integral part of the Microcensus.
The Microcensus is the base for adjustment for many official and nonofficial household and individual surveys, such as the Income and
Consumption Survey (EVS) and the Continuous Household Budget
Surveys. Items on housing and health are integrated every four years.
The Microcensus also has close relations to other official statistics, in
particular to other official labour statistics.
The Microcensus contains a very large sample (one per cent of the
population), so data make highly differentiated analyses possible. The
design of the Microcensus as a multi-topic survey enables various
combinations of its specific survey parts and the fulfillment of complex
information requirements. Comparative analyses over long historic
periods are possible, thus the Microcensus is particularly suitable to
analyse social change.
Respondents in the sample are legally obliged to take part in the survey,
so the bias by non-response is much smaller than in other surveys.
Data quality is ensured by a team of well trained and experienced
interviewers and by automatic checks of data plausibility. Changing the
survey mode from a “reporting week mode” to a continuous survey in
2005 has increased the representativeness of the data by taking into
account seasonal fluctuations. (Source: Statistisches Bundesamt,
Qualitätsbericht Mikrozensus)
The Microcensus is a multi-topic survey, so not all topics can be
analysed in great detail and with the whole variety of instruments. For
some topics, important items are missing (e. g. health – items on
subjective health). There are no items on subjective aspects (opinions,
attitudes etc.). Longitudinal data are available, but only for the years
1996 to 1999 and 2001 to 2004

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3.1.12 Mobility Panel Germany / Deutsches Mobilitätspanel (MOP)
Institution, URL

Institute for Transport Studies, Karlsruhe Institute of Technology (KIT),
Karlsruhe
(http://mobilitaetspanel.ifv.uni-karlsruhe.de/en/index.html).

Type of data

Combination of regular cross-sectional and longitudinal survey

Access to data

Available for scientific, non-profit use via Clearing House of Transport
Data at the Institute of Transport Research
(http://www.dlr.de/cs/en/desktopdefault.aspx/1177_read-2160/) after
signing a data distribution contract, 100 Euros are charged for the data

Sample and age

No age range: Every household member independent of age

range

(community dwelling households), e.g. MOP11 age range: 0-92 years
(children under the age of 10 do not fill out a person questionnaire and a
trip diary)

Time, waves and Data collection is ongoing since 1994.
sample size

Language issues



MOP94: Households: 239, persons: 517, trips: 12,380



MOP95: Households: 385, persons: 744, trips: 16,816



MOP96: Households: 748, persons: 1,487, trips: 37,233



MOP97: Households: 764, persons: 1,523, trips: 38,262



MOP98: Households: 746, persons: 1,500, trips: 36,770



MOP99: Households: 1,013, persons: 1,888, trips: 46,387



MOP00: Households: 837, persons: 1,618, trips: 38,273



MOP01: Households: 1,122, persons: 2,015, trips: 49,603



MOP02: Households: 982, persons: 1,774, trips: 43,219



MOP03: Households: 1,103, persons: 1,996, trips: 46,192



MOP04: Households: 1,033, persons: 1,838, trips: 44,384



MOP05: Households: 967, persons: 1,727, trips: 42,177



MOP06: Households: 907, persons: 1,555, trips: 38,246



MOP07: Households: 904, persons: 1,567, trips: 37,520



MOP08: Households: 1,062, persons: 1,783, trips: 43,029



MOP09: Households: 982, persons: 1,630, trips: 39,014



MOP10: Households: 1,042, persons: 1,768, trips: 42,131



MOP11: Households: 1,074, persons: 1,800, trips: 42,594

The website with information is available in German and English, and a
selection of reports and presentations are in English and French. The
code plan of the data is available in English and a translation of the
questionnaires to English is scheduled.

Coverage

The rotating panel survey provides information on household
characteristics, socio-demographics, travel behaviour, trips and types of
transportation, use of transportation, transportation available on site,
reasons for trips, and environmental characteristics on site. Additionally

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in the trip diary, every household member over the age of 10 records
each trip with reason, mode of transportation, distance, duration of trip.
Use of
internationally
harmonised



NUTS classification (Nomenclature of territorial units for
statistics):NUTS 3

Assessment of mobility profile is comparable to international research

standards
Strengths and

The MOP surveys multiday and multi-period travel data for the

weaknesses

household population in Germany. A unique characteristic of the MOP
is the trip diary over the course of a week. The MOP can be used as a
basis for descriptive mobility statistics, such as the general
development of travel demand over time. The data can also be used to
estimate long-term changes in transport demand under changing
general conditions. Analysing the individual daily trip data allows for a
description of the mobility of socio-demographic sub- and age groups.
Since the individual data can be linked to regional data on mobility,
individual and objective data on mobility can be associated. But there
are some weaknesses in the MOP. Older persons, small households
and households without a car are underrepresented in the survey.
Furthermore, very active (mobile) participants are less likely to
participate in the panel due to the high survey burdens of the trip diary.
The potential to analyse the specific behaviour, needs and expectations
of the elderly for travelling and transportation is limited, as the survey
focuses on mobility of persons before retirement age (questions asked
for distance to work, school, kindergarten, etc.). Information on health
and other life domains is very limited, thus the possibility for examining
underlying mechanisms is limited as well.

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3.1.13 National Education Panel Study (NEPS) / Nationales Bildungspanel
Institution, URL

NEPS Data Center, University of Bamberg
(https://www.neps-data.de/en-us/datacenter.aspx)

Type of data

Multi-cohort-sequential survey, longitudinal data

Access to data

Scientific Use Files (SUF), Remote Data Processing (RemoteNEPS), Onsite workplaces are available. Data is only for staff with academic
qualification working at scientific institutions (universities or research
institutes). Access to data is granted after signing a contract.
Preconditions: Certificate to prove affiliation with the scientific
institution, detailed description of the research project, confidentiality
obligation. The use of NEPS-data is basically free of charge. Additional
requirements of the NEPS resulting from the work with these data are
payable by the data user.

Sample and age

Several panel cohorts from newborn children up to adults.

range
Time, waves and 2009 - 2013: six cohorts drawn in the years from 2009 to 2012, more
sample size

than 60,000 individuals. These cohorts are interviewed regularly, from
the first research period up to 2013 at least yearly.

Language issues

Data is available in German and English.

Coverage

The NEPS concentrates on five dimensions of education, so-called
pillars:


Pillar 1: Development of competencies in life-course



Pillar 2: Education in life-course specific learning environments



Pillar 3: Social inequality and educational decisions



Pillar 4: Educational processes of people with a migration
background



Pillar 5: Returns of education

The life-course is subdivided into 8 stages:


Stage 1: New-born and entrance into educational institutions of
early childhood



Stage 2: Kindergarten and enrolment



Stage 3: Primary school and transitions to secondary school I



Stage 4: Learning in secondary school I and transitions to secondary
school II



Stage 5: Upper secondary school und transitions to college,
vocational training or labour market



Stage 6: Start of a vocational training and later on entry into labour
market



Stage 7: Higher education (university or university of applied
sciences)

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

Stage 8: Continuing vocational education and training (VET) and
continuing general education

Use of



internationally

NUTS classification (Nomenclature of territorial units for
statistics):NUTS 1 -3

harmonised



KldB 1988 and KldB 2010 (German Classification of Occupations)

standards



ISCO-88 and ISCO-08 (International Standard Classification of
Occupation)



ISEI (International Socio-Economic Index of Occupational Status)



SIOPS (Standard International Occupational Prestige Scale)



MPS (Magnitude Prestige Scale)



EGP (Erikson, Goldthorpe, and Portocarero’s class categories)



BLK (Blossfeld’s Occupational Classification)



CASMIN (Comparative Analysis of Social Mobility in Industrial
Nations)



ISCED-97 (International Standard Classification of Education)



Years of Completed Education

Strengths and

The NEPS is a promising approach for educational research in adult age.

weaknesses

In comparison to the AES, it provides panel data, which allow for
longitudinal analyses. Further advantages of the NEPS are the recording
of educational participation with regard to life stages and the more
comprehensive recording of context information. The NEPS is designed
as a long-standing study. It has an innovative concept of “pillars”, which
is realised by investigating different cohorts from newborns to adults.
For each of these cohorts, longitudinal information is collected for the
pillars development of competence, learning environments, educational
decisions, ethnic background and educational returns.
The NEPS is limited to adults up to the age of 65. With regard to the
issue of lifelong learning and to the interaction of educational,
occupational and life-courses, the NEPS could be a very valuable data
source if the sample would also include older people. A further
weakness is the recording of informal learning. Only few forms of
informal learning are registered. There is no differentiation for most
types of informal learning whether they were assessed as being useful
for professional or for private purposes.

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3.1.14 Possibilities and Limits of an Independent Living and Health/
Möglichkeiten und Grenzen selbständiger Lebensführung (MUG I-IV)
Institution, URL

TNS Infratest Sozialforschung, München
(http://www.tnsinfratest.com/SoFo/Expertise/Hilfe_und_Pflegebedarf.asp)

Type of data

Occasional cross-sectional survey

Access to data

Data not available for scientific or public use

Sample and age

MuG I + III: community dwelling individuals 18 years and older

range

MuG II + IV: people living in care homes, 60 years and older

Time, waves and 

MuG I: Data collected from 1991-1993; sample size of 25,736

sample size

households and 2,950 individuals in need of care


MuG II: Data collected from 1994-1996; sample size of 535



MuG III: Data collected from 2002-2005; sample size of 25,095

institutions and 4,464 residents
households and 3, 622 individuals in need of care


MuG IV: Data collected from 2005-2007; sample size of 609
institutions and 4,229 residents

Language Issues

No information available

Coverage

The MuG I and the MuG III focus on the living situation of people in
need of care living in private households. The cross-sectional surveys
provide information on the type and degree of care needed, specific
medical needs, support network, arrangements of help and care taking,
professional help, aid supply, residential environment, care-specific
housing facilities, socio-demographics, material resources and region.
The MuG II and MuG IV focus on the living situation of people in need
of care living in institutions. The nursing staff was interviewed about the
living situation and characteristics of the institution. The studies provide
information on the home residents regarding socio-demographics,
mobility limitations, form and degree of care need, care situation,
residential environment, social contacts. In relation to the institutions,
the following information was gathered: type, size and type of
operation, care and procedure concept, range of services, professional
situation in regard to number of staff, inclusion of family members and
volunteers.

Use of

Instruments were in part standardised questions, scales and tests, e.g.

internationally

measure on activities of daily living (ADL)/ instrumental activities of daily

harmonised

living (IADL) or cognitive impairment test (6CIT, Brooke and Bullock,

standards

1999).

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Strengths and

The surveys provide a comprehensive picture of the structure and need

weaknesses

for help and care. The MuG studies I and III focus on people in private
households and the MuG studies II and IV provide representative data
on people living in institutions in Germany. The MuG III also includes a
small subsample of cognitively impaired persons, which provides a
description of the specific situation of people with dementia and their
needs and care arrangements. If participants were not able to take part,
a family member or other proxy was interviewed. To be representative
for people in need of care, people over the age of 70 were oversampled
for MuG I and III.
A wide range of indicators allow an analysis of the potential and
limitation of independent living in private households and institutions,
however the data is not available for scientific use. If there is interest in
further analysis, this is done by the data holder. Therefore, the potential
of this data is not used.
The cross-sectional surveys focusing on private households were more
likely to include people in need of help and care who were relatively
independent and living on their own.

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3.1.15 Sample of Insured Persons and their Insurance Accounts /
Versicherungskontenstichprobe der DRV (VKSt)
Institution, URL

Research Data Centre of the German Pension Insurance (FDZ-RV)/
Forschungsdatenzentrum der Rentenversicherung (FDZ-RV)
(http://forschung.deutscherentenversicherung.de/FdzPortalWeb/?ViewNavi=StartSeite)

Type of data

Longitudinal registry data

Access to data

Datasets are made available by the Research Data Centre. Scientific
Use Files (SUF) are available only for recognised, independent, scientific
institutions. Public Use Files (PUF) are available for teaching purposes.
Remote Data Processing is available for registered data users with
SPSS- and STATA-files (www.fernrechnen.de). On-site workplaces for
guest scientists (more data with less reduced characteristics than
SUFs). SUF available after signing a research contract.

Sample and age



Sample: Insured persons aged 15 to 67 years

range



SUF: only Germans living in Germany and aged 30 to 67 years

Time, waves and 

Wave 2002 has a sample of 57,832 (SUF).

sample size



Wave 2004 has a sample of 58,611 (SUF).



Wave 2005 has a sample of 59,457 (SUF).



Wave 2006 has a sample of 60,304 (SUF).



Wave 2007 has a sample of 60,821 (SUF).



Wave 2008 has a sample of 61,410 (SUF).



Wave 2009 has a sample of 61,894 (SUF).



Wave 2010 has a sample of 62,705 (SUF).

Language issues

Data is available in German only.

Coverage

Information based on pension insurance accounts about:


Socio-demographic items (age, sex, children born, profession and
others)



Insurance history - times of employment, unemployment,
education, military and community service, illness, child-raising
periods, self-employment subject to social insurance, nonprofessional long-term caring (since 1995), minor employment
(since 1999)


Use of

Entitlements from the pension insurance (earning points)

No information provided.

internationally
harmonised
standards

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Strengths and

The Sample of Pension Insurance Accounts deals with pension

weaknesses

entitlements of insured people. Detailed information on times relevant
to pension insurance is available in the dataset. The dataset contains
process-produced longitudinal data, so employment histories can be
analysed without facing the usual problems of longitudinal surveys in
social sciences – such as panel mortality or memory errors. The dataset
contains more validated information on details of the employment
history than respondents normally remember. The sample is very large,
so that detailed analyses are possible. The panel has been built since
1983, so it contains a great deal of information on social change.
Information on topics other than occupation is mostly missing. There is
only some information on education and qualification, but this is
incomplete because these data are reported by employers voluntarily.
Information on children born is only included for women, not for men.
Households and couples cannot be identified. Data in this dataset are
selective – periods of employment as civil servants or independent
entrepreneurs are not recorded. Data for employment periods in minor
jobs are only recorded if they were registered by the minor employed.
There is information missing on occupation periods of people who
immigrated to Germany or who moved abroad from Germany. That
means that nearly all people in Germany have a pension insurance
account, but many of these contain large gaps without information on
the occupation. Unclear accounts are the reason for another type of
missing data.
Data is not easy to handle as there is a lot of information in the dataset
that cannot be analysed and interpreted without knowledge of the
German pension law.

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3.1.16 Sample Survey of Income and Expenditure/ Einkommens- und
Verbrauchsstichprobe (EVS)
Institution, URL

Research Data Centre of the Federal Statistical Office and Research
Data Centres of the Statistical Offices of the Federal States,
(http://www.forschungsdatenzentrum.de)

Type of data

Cross-sectional survey data, collected every 5 years

Access to data

Dataset is available from the Research Data Centre. For scientific use,
microdata of the EVS are available from the Research Data Centre as
Scientific Use files (SUF) for scientists from Germany only for projects
limited in content and time (3 years at the most). For remote data
processing and for on-site workplaces data is also available for guest
scientists. All these data is with costs and available following
completion of an application and research contract.

Sample and age

Survey is a household sample, so it has no age limits.

range
Time, waves and 

Wave 1: Data collected in 1962/63 with a sample size of 34,000

sample size

households. Only a public use file is available.


Wave 2: Data collected in 1978. No information on sample size.



Wave 3: Data collected in 1983. No information on sample size.



Wave 4: Data collected in 1988. No information on sample size.



Wave 5: Data collected in 1993. No information on sample size.



Wave 6: Data collected in 1998 with a sample size of 62,150



Wave 7: Data collected in 2003 with a sample size of 59,713

households.
households.


Wave 8: Data collected in 2008 with a net sample size of 58,984
households.

For waves 2-8, scientific use files and on-site access are available.
Language issues

Data is available in German only. English documentation is available.

Coverage

The EVS provides data on the composition of the household (age, year
of birth, level of education etc.), household members' participation in
professional life, consumer goods consumption, type and level of
income, type and level of assets and debt of private households, wealth
of private households, consumption spending of private households
(equipment existing in household etc.), region and place of living

Use of



internationally
harmonised
standards

NUTS classification (Nomenclature of territorial units for statistics):
NUTS 1



SEA 98 (Systematisches Verzeichnis der Einnahmen und Ausgaben
der privaten Haushalte, Ausgabe 1998), according to COICOP
(Classification of Individual Consumption by Purpose)

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60



ISCED-97 (International Standard Classification of Education)



NUTS-08 (Nomenclature des unités territoriales statistiques)

Strengths and

The Sample Survey of Income and Expenditure is an official statistic

weaknesses

survey describing the economic situation of households in Germany. It
consists of a very large sample and records incomes in a differentiated
way by a quota sample that is stratified by social groups. Accordingly, it
provides representative data that are deeply structured and in high
quality.
Differentiated analyses are possible for nearly all household types, with
the exception of people living in institutions. Households with a monthly
net income of more than 18,000 Euros are not considered because the
data would not be statistically reliable. Foreigners are not sampled
representatively.

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3.1.17 Study of Health in Pomerania / Leben und Gesundheit in Pommern
(SHIP)
Institution, URL

Institut für Community Medicine - SHIP-KEF
(http://www.medizin.uni-greifswald.de/cm/fv/english/ship_en.html)

Type of data

Longitudinal survey of a specific cohort

Access to data

Data is available for scientific, non-profit use; however, data is primarily
available for members and employees of the Community Medicine
Network at the University of Greifswald and for their co-workers. Thus,
obtaining the data is expensive.
Signing a data distribution contract is required prior to obtaining the
data. Furthermore, the planned project using the data has to be
described comprehensively and is evaluated by a board, which holds
meetings every month.

Sample and age

Community dwelling persons aged 20-79 years (base sample)

range
Time, waves and 

Wave 1, SHIP-0: Data was collected from 1997-2001 and had a

sample size

sample size of 4,308 individuals.


Wave 2, SHIP-1: Data was collected from 2002-2006 and had a



Wave 3, SHIP-2: Data was collected from 2008-2012 and had a

sample size of 3,300 individuals.
sample size of approximately 1,700 individuals.


Wave 4, SHIP-Trend-0: Data was collected from 2008-2011 and had
a sample size of approximately 3,200 individuals.

Language issues

Website, documentation, data and questionnaires are available in
German and English

Coverage

The survey aims at investigating the complexity of health, thus
comprehensive health data is assessed including an oral health
examination, a medical examination, a health-related interview, and a
health- and risk factor-related questionnaire.


The oral health examination includes the teeth, periodontium, oral
mucosa, craniomandibular system, and prosthodontics.



The medical examination includes blood pressure measurements,
electrocardiography, echocardiography, carotid, thyroid and liver
ultrasounds, neurological screening, blood and urine sampling.



The computer-aided health-related interview includes cardiovascular
symptoms, utilisation of medical services, health-related
behaviours, and socioeconomic variables.



The self-administered questionnaire comprises housing conditions,
social network, work conditions, subjective well-being and
individual consequences of German reunification.

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62



Health information for the population relevant to diseases under
study is given particular attention (cardiovascular diseases, diabetes
mellitus, liver and biliary tract diseases, neurological diseases,
thyroid diseases, dental diseases, lung diseases, addiction and risk
behaviour).

Use of

Majority of instruments are standardised questions, scales, test and

internationally

measurements.

harmonised
standards
Strengths and

The aim of SHIP is to examine the complex nature of health. Therefore

weaknesses

extensive health data is available, which allows for an investigation of
the prevalence and incidence of diseases and their risk factors. Data
focuses on those diseases which are population relevant (cardiovascular
diseases, diabetes mellitus, liver and biliary tract diseases, neurological
diseases, thyroid diseases, dental diseases, lung diseases, addiction
and risk behaviour). The survey enables researchers to study predictors,
risk factors and underlying mechanisms, as well as complex interaction
between those factors and living situations. Besides extensive
subjective and objective health data (Biomarkers inclusive), a variety of
socio-demographic and psycho-social factors are assessed which
extend the analysis potential of the SHIP data. The follow-up of
participants allows for an investigation of the progress of subclinical
findings. Furthermore, health trends in the region can be compared with
the SHIP-0 and the SHIP-Trend-0 data. Power of the data to carry out
longitudinal analysis may be limited due to sample size in the follow-up
surveys (SHIP-1 & SHIP-2). The potential to investigate health in old age
is limited as SHIP baseline samples (SHIP-0 & SHIP-Trend-0) cover an
age range of 18 to 79 years.

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3.1.18 Survey on Private Usage of Information and Communication
Technologies (ICT) / Erhebung über die private Nutzung von
Informations- und Kommunikationstechnologien (IKT)
Institution, URL

Research Data Centre of the Federal Statistical Office
Research Data Centres of the Statistical Offices of the Federal States
http://www.forschungsdatenzentrum.de

Type of data

Cross-sectional data

Access to data

Only on-site access (workplace in research data centre or remote data
processing). Data use contract with fees.

Sample and age

Quota sample, base: all private households at the main place of

range

residence, persons aged 10 years and older

Time, waves and 

Wave 1: Data collected in 2002 (pilot study) with a sample size of

sample size

4,000 households.


Wave 2: Data collected in 2003 (pilot study) with a sample size of
4,000 households



Wave 3 (first dataset available): Data collected in 2004 (pilot study)
with a sample size of 4,000 households.



Wave 4: Data collected in 2005 (pilot study) with a sample size of
4,000 households.



Wave 5: Data collected in 2006 with a sample size of 12,000
households.



Wave 6: Data collected in 2007 with a sample size of 12,000
households.



Wave 7: Data collected in 2008 with a sample size of 12,000
households.



Wave 8: Data collected in 2009 with a sample size of 12,000
households.



Wave 9: Data collected in 2010 with a sample size of 12,000
households.



Wave 10: Data collected in 2011, with a sample size of 12,000
households.



Wave 11: Data collected in 2012, with a sample size of 12,000
households.

Language issues

Data is available in German only.

Coverage

Study on dissemination of information and communication technologies
(ICT) and on the use of the internet in private households. It also covers
socio-economic and socio-demographic items, items on infrastructure
of communication technology in private households, information on the
way, frequency and purposes of the internet usage (e.g. E-government,
E-commerce, E-learning), information on scruples and barriers to the

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64

use of new technologies (Source: Statistisches Bundesamt 2012.
Quality Report on IKT 2012 and Fachserie 15, Reihe 4,
Wirtschaftsrechnungen)


NUTS classification (Nomenclature of territorial units for statistics):

harmonised



ISO-3166 (Classification of Country Codes)

standards



ISCED-97 (International Standard Classification of Education)



ISCO-08 (International Standard Classification of Occupation)

Use of
internationally

NUTS 1

Strengths and

The ICT-Survey records data on the dissemination of information and

weaknesses

communication technologies (ICT) and on the use of the internet in
private households. Among others, it provides information on the usage
of ICT for educational activities and on attending computer training
courses. The ICT-survey has a large sample. As it is conducted every
year, the ICT-survey allows for trend analyses over a longer period. Data
is harmonised between the EU-member states, so the results are
directly comparable. Documentation and metadata in German are
available via internet.
The survey contains only few context variables, mainly socio-economic
and socio-demographic items. There is no Scientific Use File, data can
only be used by on-site access and remote data processing. Data and
documentation are available in German only.

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3.1.19 Telephone Health Survey - German Health Update / Telefonische
Gesundheitssurveys – Gesundheit in Deutschland aktuell (GEDA)
Institution, URL

Robert Koch Institute (RKI), Berlin
(http://www.rki.de/DE/Content/Gesundheitsmonitoring/Studien/Geda/G
eda_node.html).

Type of data

Regular cross-sectional survey

Access to data

Available for scientific, non-profit use after signing a data distribution
contract, 90 Euros are charged for each data CD

Sample and age



18 years and older

range
Time, waves and 

Wave 1: Data for GesTel03 was collected in 2003 and had a sample

sample size

size of 8,318.


Wave 2: Data for GesTel04 was collected in 2004 and had a sample
size of 7,341.



Wave 3: Data for GesTel05 was collected in 2005 and had a sample
size of 4,401.



Wave 4: Data for GesTel06 was collected in 2006 and had a sample
size of 5,600.



Wave 5: Data for GEDA09 was collected in 2009 and had a sample
size of 21,262.



Wave 6: Data for GEDA10 was collected in 2010 and had a sample
size of 22,050.



Wave 7: Data for GEDA12 was collected in 2012 and had a sample
size of approximately 26,000.

Language issues

Documentation (in parts) is available in German and English, while
variables and value labels are available in German only. The homepage
with all basic information (data access, etc.) is available in English as
well. For GSTel03 and GEDA09 a questionnaire translation is available
via http://www.euhsid.org/database.html

Coverage

In addition to a regular module, each cross-sectional survey addresses
specific policy-relevant topics which change for each assessment.
The surveys regularly cover: subjective health, health-related behaviour,
e.g. physical exercise, diet, alcohol consumption, smoking, chronic
diseases, injuries, health consequences and disabilities, health-related
support and stress, mental health, socio-demographic characteristics
such as age, gender, education, occupational status, migration
background. The selection of the regular health module follows the
European Health Survey (EHS).
As specific topics, gastrointestinal diseases, organ donation and the
extent to which interviewees make use of healthcare services were
assessed in GEDA09. GEDA10 asked questions about injuries and the

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66

use of cancer screening. Measles and measles vaccination, care of
family members and noise pollution were assessed in GEDA12.
Use of

The selection of the regular health module follows the European Health

internationally

Survey (EHS). Majority of instruments are standardied questions,

harmonised

scales, test and measurements. The measuring standards of the Robert

standards

Koch Institute comply with European and international
recommendations.

Strengths and

The GEDA surveys are part of health monitoring by the Robert Koch

weaknesses

Institute. The surveys cover basic health information at each
assessment, which are complemented by specific policy-relevant topics
(such as attitudes towards organ donation) that are changing. Therefore,
one of the strengths of the GEDA surveys is that they enable the
government to respond quickly and flexibly in the field of health policy
because data is collected rapidly. The regular cross-sectional samples
allow health trends to be analysed over time. Furthermore, the basic
health module that is collected in each survey follows the European
Health Survey, which allows for comparison in health trends with other
European countries. Due to the large number of individuals taking part
each year, the GEDA provides reliable and valuable data on health in
Germany. As this survey is supposed to be representative for the
residential population over the age of 18 in Germany, there is no
specific focus on people aged 50 years and older, and the health
questions do not cover all important aspects of health in old age.
Because of the cross-sectional sampling, it is not possible to analyse
individual health changes and underlying mechanisms. Another
weakness of the GEDA survey is that they cannot be used for specific
analysis focusing on foreigners or people with migration backgrounds
as they are underrepresented in the survey.

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3.1.20 Time Use Survey / Erhebung zur Zeitverwendung
Institution, URL

Research Data Center of the Federal Statistical Office
(http://www.forschungsdatenzentrum.de)

Type of data

Cross-sectional survey data

Access to data

Scientific Use Files (SUF) available by Research Data Centre and only
for scientific use, SUF only for scientists in Germany, for some detailed
data only on-site workplaces, CAMPUS-Files for teaching

Sample and age



Wave 1: 12 years and older

range



Wave 2: 10 years and older

Time, waves and 

Wave 1: Data collected in 1991/92 with a sample size of 6,845

sample size

households, 19,708 individuals.


Wave 2: Data collected in 2001/02 with a sample size of 5,160
households, 13,758 individuals.



Wave 3: Data is currently being collected (2012/13).

Language issues

Data is available in German only.

Coverage

Individual and household information from interviews and individual
time use data from diaries filled in over 3 randomly selected days during
field time.
Detailed study for time use. Description of the 24-hour course of the
day in 10 minute intervals (main activities and simultaneous activities,
persons who were present, locations and means of transport), sociodemographic and socio-economic items for households and householdmembers, support given and received, volunteering, learning,
infrastructure of place of residence, subjective assessment of time use
.
Topics in the field of engagement:


Personal interview: Field of engagement/volunteering in the last 12
months, expenditure of time per month for
engagement/volunteering



Activities (diary): Voluntary activities, informal help for other
households

Use of



internationally
harmonised

NUTS classification (Nomenclature of territorial units for statistics):
NUTS 1



ISCO 1988 (International Standard Classification of Occupation,



ISCED(International Standard Classification of Education)

standards

1988)

Strengths and

The Time Use Survey provides unique diary data on the issue of time

weaknesses

use in daily life. Data are collected in a large sample, so analyses for
different social groups and household types can be conducted. Data are

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68

very detailed, many kinds of activities are registered – e. g. housework,
child care, civic engagement, neighbourhood assistance, education,
media use and many others. In the field of volunteering, activities are
registered both in the diary and in the personal interview. The personal
interview registers the fields of volunteering in the last 12 months and
the expenditure of time per month. The diaries show the exact time
expenditure for voluntary activities and informal help for other
households.
Data of the Time Use Survey allow trend analyses between the waves
1991/92 and 2001/02 (and the following waves), as well as European
comparisons. A special strength of the survey is its large sample size.
There is multi-stage quality control of all data.
There are only few context variables besides socio-demographic items
and items on employment. More complex causal analyses on the
influence of various factors on time use are not possible.

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REFERENCES

Cornell University (2013): CNEF. Retrieved from
http://www.human.cornell.edu/pam/research/centers-programs/germanpanel/cnef.cfm on 10 November 2013.
European Research Area, 2013. Joint Programming. Retrieved from
http://ec.europa.eu/research/era/joint-programming_en.html on 18
November 2013.
FDZ IAB (2011): Codebook and Documentation of the Panel Study ‘Labour Market
and Social Security’ (PASS). Datenreport Wave 4. Retrieved from
doku.iab.de/fdz/reporte/2011/DR_08-11_I_EN.pdf. on 10 November 2013.
FDZ IAB (2013): Panel Study 'Labour Market and Social Security' (PASS): Outline.
Retrieved from http://fdz.iab.de/en/FDZ_Individual_Data/PASS/Outline.aspx
on 10 November 2013.
Frick, J. R., Jenkins, S. P, Lillard, D. R., Lipps, O. & Wooden, M. (2007): The CrossNational Equivalent File (CNEF) and its Member Country Household Panel
Studies. Schmollers Jahrbuch, 127, 627-654.
JPI MYBL, 2013. Joint Program Initiative “More years, better lives”. Retrieved
from http://www.jp-demographic.eu on 18 November 2013.
Statistisches Bundesamt (2013): Pflegestatistik 2011. Pflege im Rahmen der
Pflegeversicherung. Ländervergleich – Pflegebedürftige. Wiesbaden:
Statistisches Bundesamt.
Statistisches Bundesamt (2012): Fachserie 15, Reihe 4, Wirtschaftsrechnungen.
Wiesbaden: Statistisches Bundesamt.
Statistisches Bundesamt (2012): Quality Report on IKT 2012. Wiesbaden:
Statistisches Bundesamt.
Motel-Klingebiel, A., Wurm, S., & Tesch-Römer, C. (Eds.). (2010). Altern im
Wandel: Befunde des Deutschen Alterssurveys (DEAS) [Ageing and Social
Change: Findings of the German Ageing Survey (DEAS)]. Stuttgart:
Kohlhammer.
PAIRFAM (2013): Intergenerational relationships and transfers between
generations. Retrieved from http://www.pairfam.de/en/study/studythemes/intergenerational-relationships.html on 10 November 2013.
Rat für Sozial- und Wirtschaftsdaten (RatSWD) (2010). Research Data
Infrastructure. Retrieved from http://ratswd.de/en/data-infrastructure/info on
29 May 2013.
Schmahl, F., Wilhelm, B., Fiedrich, S., Wendt, E.-V., Thönnissen, C. & Walper, S.
(2012): Scales Manual. Wave 1 to 3. Retrieved from

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http://www.pairfam.de/fileadmin/user_upload/redakteur/publis/Dokumentati
on/Manuals/Scales-Manual_en_pairfam_3.1.pdf on 10 November 2013.

DZA – Diskussionspapier Nr. 56, 2014 Ageing in Germany

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