Green-SÖP: The Socio-ecological Panel Survey: 2012-2016 - De Gruyter
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Journal of Economics and Statistics 2020; aop Data Observer Larissa Klick, Gerhard Kussel and Stephan Sommer* Green-SÖP: The Socio-ecological Panel Survey: 2012–2016 https://doi.org/10.1515/jbnst-2020-0065 Received December 9, 2020; accepted December 15, 2020 Abstract: Evaluating environmental questions is a crucial issue in today’s eco- nomic research and policy making. The Green-SÖP offers a comprehensive data base to enrich an empirically led scientific discourse as a survey data set on environmental and energy-related topics in Germany. The data set on more than 6000 households was collected by RWI – Leibniz Institute for Economic Research and partners between 2012 and 2016. The questions are very diverse and range from personal attitudes to environmental policy issues with a special focus on the consequences of climate change and individual behaviors as well as opinions on ecologically related matters. Keywords: household panel, climate change, adaption, electricity consumption JEL Classification: Q3, Q4 1 Introduction With a share of about 18% of final energy consumption (AGEB 2020) and 12% of direct CO2 emissions (UBA 2020) in 2018, private households have a significant impact on the environment in Germany. At the same time, private households are a key target group for political interventions to combat climate change. Against this backdrop, policy has stipulated numerous actions to reduce energy consumption and to promote renewable energy technologies, such as CO2-based automobile taxes and financial incentives for energy-related renovation measures (e.g. KfW support programmes). These political actions require careful evaluation of their *Corresponding author: Stephan Sommer, RWI – Leibniz Institute for Econmic Research, Essen, Germany, E-mail: stephan.sommer@rwi-essen.de Larissa Klick and Gerhard Kussel, RWI – Leibniz Institute for Econmic Research, Essen, Germany. https://orcid.org/0000-0003-3681-1860 (L. Klick)
2 L. Klick et al.
effectiveness and cost-efficiency to avoid costly redundancies due to overlapping
instruments.
Such an evaluation of environmental and energy policy measures requires a
reliable database. Even though there are a few surveys that address environmental
and energy-related attitudes, such data has not been available for private house-
holds in Germany. For instance, the German Environmental Survey, GerES V,
which spans from 2014 to 2017 and is conducted by the Federal Environmental
Office (Umweltbundesamt) and the Robert-Koch-Institute, focused on childrens’
health outcomes caused by environmental influences in current waves (cp. Schulz
et al. 2017). In another survey, the Federal Environmental Office has elicited
ecological awareness. It consists of repeated cross-sectional surveys and addresses
various issues around consciousness regarding climate and environmental pro-
tection, consumption behavior and mobility (cp. Rubik et al. 2019). Furthermore,
the RWI-German Residential Energy Consumption Survey (GRECS) is a nation-wide
panel survey on the energy consumption, car use and living conditions of private
households between 2005 and 2013 (cp. RWI/forsa 2015).
Owed to the lack of a data set that allows for an evaluation of environmental
and energy policy measures, the Socio-Ecological Panel (Green-SÖP) was estab-
lished in 2012. While it also collects some information on living conditions and
energy use, its emphasis lies in the evaluation of behavior and opinions, the
willingness to pay for energy goods and the perception of climate change. It is
freely available to the scientific community. Based on this data, researchers can,
for instance, conduct econometric analyses of various preference indicators and
analyze the adaptation behavior of private households to climate change.
In the following section, we describe the data collection and structure of the
Green-SÖP. Subsequently, we describe the socio-demographic characteristics of
the survey sample and discuss its representativeness. Section 4 provides some
examples of publications using the Green-SÖP data as well as further research
possibilities to utilize the data set. Finally, Section 5 provides information on data
access.
2 Data Collection
RWI – Leibniz Institute for Economic Research designed the surveys for wave 1–4
together with ZEW – Leibniz Centre for European Economic Research as part of the
project “Evaluating Climate Mitigation and Adaptation Policies (Eval-MAP)” and
for wave 5 jointly with Technical University Clausthal, University of Bremen and
Helmut-Schmidt University Hamburg in the project “The social acceptance of the
energy transition (AKZEPTANZ)”.The Socio-ecological Panel Survey 3
The first wave was collected in October and November 2012, the second in May
and June 2013, the third in June 2014, the fourth in March and April 2015 and the
current final wave was collected between December 2015 and February 2016. The
Green-SÖP focuses on the risk assessment and perception of climate change, the
adaptive behavior among German households and their willingness to pay for
climate protection. Additionally, all survey waves contain information on socio-
economic household characteristics, such as household size, place of residence,
net household income as well as gender and age of the head of household.
The primary focus of wave 1 and 3 lies on the attitudes of private households
toward climate change and their adaption behavior associated with leisure time
activities, travelling as well as housing and insurance decisions. The question-
naires also include detailed questions on the perception of climate change, related
expected losses and on experiences with natural disasters. Against this back-
ground, the third round in 2014 explicitly addresses the perception of flooding as a
reflection of the experience of a severe flood in South and East Germany in the
previous year.
The questions in wave 2 and 4 address the determinants of energy demand, the
willingness to pay for different electricity mixes, precisely for pure green elec-
tricity, and the reaction of the energy demand on information and price signals.
Wave 2 also contains questions on the influence of the nuclear disaster of
Fukushima in 2011 on environmental attitudes. Wave 4 is supplemented with a
discrete choice experiment on the role of energy label design in the purchase
decision of a refrigerator (this part of the survey is available on special request at
the FDZ Ruhr). The data of wave 2 and 4 allow the estimation of crucial de-
terminants of private household energy demand, such as price and income elas-
ticities through the connection to the energy consumption data (RWI-GRECS).
Wave 5, stemming from a different research project but with similar topics of
interests, includes more questions regarding the willingness to pay for the tran-
sition toward cleaner energy, such as accepting additional costs for the promotion
of renewable energy.
An overview of the topics included in the Green-SÖP data set is given in Table 1.
A full codebook is provided by Kussel and Larysch (2017) and Cordes et al. (2020)
and the questionnaires are available on the FDZ Ruhr website. The surveys were
conducted within the forsa household panel that nowadays consists of approxi-
mately 75,000 individuals in Germany, who are representative for the German
population aged at least 14 years. The questions were asked to the household
heads defined as the person who is responsible for the financial decisions within
the household. The participants are usually familiar with surveys. Most house-
holds participated via an online questionnaire, while the households without an
Internet connection received a device by forsa to take part offline. The participants4 L. Klick et al.
Table : Overview of topics in survey waves.
Wave in Wave in Wave in Wave in Wave in
A. Personal A. General: Power A. Personal A. General: Power A. General: Power
attitude and supply and change attitude and supply and change of supply and
experience of power supplier experience power supplier change of power
supplier
B. Leisure B. Willingness to B. Leisure B. Willingness to pay V. Willingness to
behavior pay for different en- behavior for different pay for security in
ergy sources (and energy sources power supplya
willingness to (and willingness
switch) to switch)
C. House and C. Attitudes C. House and C. Attitudes towards N. Experiment on
apartment towards political apartment political issues, network
issues, energy energy sources and expansiona
sources and electricity in general
electricity in
general
D. Finance D. Influence of D. Climate L. Labelling of energy G. Assessment of
and Fukushimaa Change efficient fridges (only justice
insurance available on special preferencesa
request to FDZ Ruhr)a
E. Climate E. Knowledge about E. Investments S. Socio-economic Z. Willingness to
Change state support of and insurance data pay for different
renewable energy sources
energiesa
F. Socio- F. Cost burden on F. Socio- K. Cost burden of
economic private households economic data private
data (electricity) households (elec-
tricity and gas)
G. Renewed query of DCE: Insur- S. Socio-
willingness to paya ance demanda economic data
S. Socio-economic DCE: Choice of
data air conditioning
measures for
rental housinga
a
Only introduced in a single wave, no available time variation in chapter. Green-SÖP (a, b, c,
a, b).
gained bonus points which can be traded in for premiums. Table 2 reports the
sample size in the five survey waves.
The Green-SÖP data set is connectable to other RWI data sets that rely on the
forsa panel, particularly the RWI-GRECS (cp. RWI/forsa 2011, 2013, 2015).The Socio-ecological Panel Survey 5
Table : Sample size of the survey waves.
Wave , Wave , Wave , Wave , Wave ,
Surveys completed
Surveys quitted before
completing
Participating households ,
Green-SÖP (a, b, c, a, b).
Moreover, we aim to connect additional surveys to the Green-SÖP data set, e.g.
data collected in further projects on environmental topics within RWI.
3 Representativeness
In this chapter, we address the representativeness of the participants of the Green-
SÖP sample in terms of regional and socio-economic characteristics with respect to
the overall German population of household heads.1 The data for the number of
German households stems from the official population projection based on the last
census in 2011 (Federal Statistical Office 2020a). Table 3 contrasts the regional
coverage of sample households with administrative data. The number of surveyed
households is strongly balanced on the federal state (NUTS 2) level. This compo-
sition only slightly varies between the survey waves.
To characterize the distribution of the socio-economic characteristics, we use
the data from the first and fourth wave (2012 and 2015) to contrast the figures over
time. We compare their distribution with the figures from the 2011 census and its
population projection (Federal Statistical Office 2020a, 2020b, 2020c, 2020d).
The surveyed households are represented by the household heads. The gender
of the household head is predominantly male by two thirds of the responses. This is
in line with the findings from census projection, as displayed in Table 4. Regarding
household size, with 40% two-person households are most prevalent in the Green-
SÖP. This group is, therefore, over-represented compared to the census projection,
where single households represent the largest category (Figure 1).
This finding might be partly driven by the age structure of the survey house-
holds. The age of the interviewed household head varies from 18 to 91 years (in
wave 4). The largest age group comprises household heads between 45 and
1 The Federal Statistical Office asks the main income earner in their census, while we ask the
person making the main financial decisions in the household as household heads.6 L. Klick et al.
Table : Distribution of surveyed households in German Federal States, in percentage share.
Wave , Wave , Wave , Wave , Wave ,
Federal state GS cns GS cns GS cns GS cns GS cns
Baden-Württemberg . . . . . . . . .
Bavaria . . . . . . . . .
Berlin . . . . . . . .
Brandenburg . . . . . . . . . .
Bremen . . . . . . . . . .
Hamburg . . . . . . . . . .
Hesse . . . . . . . . .
Lower Saxony . . . . . . . . . .
Mecklenburg-Vorpommern . . . . . . . . . .
North Rhine-Westphalia . . . . . . . . .
Rhineland-Palatinate . . . . . . . . .
Saarland . . . . . . . . . .
Saxony . . . . . . . . . .
Saxony-Anhalt . . . . . . . . . .
Schleswig-Holstein . . . . . . . . .
Thuringia . . . . . . . . . .
Total
Federal Statistical Office (a) (cns), Green-SÖP (a, b, c, a, b), own calculation.
65 years, the so called “mature consumers” in market research (Figure 2).
Compared to the German population of household heads from the census data,
these groups are overrepresented.
4 Possibilities of the Dataset and Selected
Publications
The survey has a special focus on the personal perception of and attitudes towards
climate change. It captures the development of economic, environmental, and
political opinions in Germany between 2012 and 2016. It also offers the possibility
to characterize the households not only according to their socio-economic status
but also to their risk and utility patterns in terms of environmental decisions and
goods. Three publications, presented in the following, illustrate the variety of
possibilities working with the Green-SÖP data set and how they enhance the po-
litical and scientific dialogue on the energy transition process in Germany. First,The Socio-ecological Panel Survey 7
Table : Gender of household heads.
Wave , Wave ,
a
Gender of household head Green-SÖP Census Green-SÖP Census
Male .% .% .% .%
Female .% .% .% .%
Total
Source: Federal Statistical Office (b), Green-SÖP (a, b), own calculation and depcition. Note: In
Green-SÖP, the main financial decision maker is asked, while Federal Statistical office (b) asks the main
income earner.
Figure 1: Share of people in the household.
Source: Federal Statistical Office (2020c), Green-SÖP (2016a, 2020b), own calculation and
depiction.
Andor et al. (2020) analyze information on private flood precaution strategies of
homeowners and contrast respondents from flood exposed and non-exposed re-
gions. They detect a charity hazard, a variant of moral hazard, as homeowners tend
to rely on charity and governmental aid rather than installing precaution and
buying insurances.
Second, Kussel (2018) exploits information on adaptive measures in the survey
waves 2012 and 2014 and analyzes the adaptive behavior of households (e.g. air-
conditioning, or green roofs) with respect to increasing temperatures. He finds that
individuals who experience higher average summer temperatures are more likely
to install cooling technologies.8 L. Klick et al.
Figure 2: Share of age group.
Note: Household head in census = main income earner; in Green-SÖP = main financial decisions
maker. Source: Federal Statistical Office (2020d), Green-SÖP (2016a, 2020b), own calculation
and depiction.
Last, Andor et al. (2017) analyze the willingness-to-pay for renewable energy
sources and its costs. They utilize questions from the second and fourth wave of the
Green-SÖP. They detect a paradox that while the support for green energy de-
velopments increased between the two surveys, the willingness to pay for this type
of energy decreased over time.
5 Data Access
The Green-SÖP data set is available as Scientific Use File at the research data centre
Ruhr (FDZ Ruhr) at RWI – Leibniz-Institute for Economic Research. The data access
is only given for scientific, non-commercial studies and granted to affiliated re-
searchers of scientific institutions. It requires a signed data use agreement that can
be applied for via the FDZ Ruhr website. The data can be obtained as a Stata®
dataset (.dta) file. The users are requested to cite the source correctly and to inform
FDZ Ruhr about publications with the data.
When using the dataset Green-SÖP, please cite each wave separately as:
Wave 1: Frondel, M., C. Vance, M. Andor, G. Kussel, C.M. Schmidt et al. (2016),
Socio-Ecological Panel. First Survey Wave. Green-SÖP. Version: 1. RWI – Leibniz-Institut
für Wirtschaftsforschung. Datensatz. https://doi.org/10.7807/greensoep:en:v1
Wave 2: Frondel, M., C. Vance, M. Andor, G. Kussel, C.M. Schmidt et al. (2016),
Socio-Ecological Panel. Second Survey Wave. Green-SÖP. Version: 1. RWI –The Socio-ecological Panel Survey 9
Leibniz-Institut für Wirtschaftsforschung. Datensatz. https://doi.org/10.7807/
greensoep:en:v2
Wave 3: Frondel, M., C. Vance, M. Andor, G. Kussel, C.M. Schmidt et al.
(2016), Socio-Ecological Panel. Third Survey Wave. Green-SÖP. Version: 1.
RWI – Leibniz-Institut für Wirtschaftsforschung. Datensatz. https://doi.org/10.
7807/greensoep:en:v3
Wave 4: Frondel, M., C. Vance, M. Andor, C.M. Schmidt, G. Kussel, et al. (2020),
Sozial-Ökologisches Panel, 4. Befragungswelle. Green-SÖP. Version: 1. RWI –
Leibniz-Institut für Wirtschaftsforschung. Dataset. https://doi.org/10.7807/
greensoep:en:v4
Wave 5: Frondel, M., S. Sommer, M. Andor,C. Vance, Technische Universität
Clausthal et al. (2020), Socio-Ecological Panel, fifth Survey Wave. Green-SÖP.
Version: 1. RWI – Leibniz Institute for Economic Research. Dataset. https://doi.
org/10.7807/greensoep:en:v5
Furthermore, we recommend citing this data description.
References
AGEB (2020). Auswertungstabellen zur Energiebilanz Deutschland Daten für die Jahre von 1990 bis
2018, Table 2.2. Berlin: Arbeitsgemeinschaft Energiebilanzen, Available at: https://ag-
energiebilanzen.de/index.php?article_id=29&fileName=awt_2018_d.pdf (Accessed 08 May
2020).
Andor, M., Frondel, M., and Vance, C. (2017). Germany’s energiewende: a tale of increasing costs
and decreasing willingness-to-pay. Energy J. 38: 211–228.
Andor, M., Osberghaus, D., and Simora, M. (2020). Natural disasters and governmental aid: is
there a charity hazard? Ecol. Econ. 169, https://doi.org/10.1016/j.ecolecon.2019.106534.
Cordes, O., Klick, L., Krieg, M., and Sommer, S. (2020). FDZ Data Description Socio-Ecological
Panel – wave 5 (Green-SÖP). Essen: RWI Projektbericht.
Federal Statistical Office (2020a). Genesis-online, 12211-0113: Privathaushalte: Bundesländer,
Jahre (Accessed 11 May 2020), Data licence by-2-0.
Federal Statistical Office (2020b). Genesis-online, 12211-0105: Privathaushalte: Deutschland,
Jahre, Geschlecht der Bezugsperson, Haushaltsnettoeinkommensklassen (Accessed 11 May
2020), Data licence by-2-0.
Federal Statistical Office (2020c). Genesis-online, 12211-0102: Privathaushalte: Deutschland,
Jahre, Haushaltsgröße (Accessed 11 May 2020), Data licence by-2-0.
Federal Statistical Office (2020d). Genesis-online, 12211-0003: Bevölkerung, Erwerbstätige,
Erwerbslose: Deutschland, Jahre, Geschlecht, Altersgruppen (Accessed 11 May 2020), Data
licence by-2-0.
Frondel, M., Sommer, S., Andor, M., Vance, C., Technische Universität Clausthal, et al. (2020a).
Socio-ecological Panel, 5th survey wave. Green-SÖP. Version: 1. Essen: RWI – Leibniz
Institute for Economic Research, Dataset, https://doi.org/10.7807/greensoep:en:v5.10 L. Klick et al.
Frondel, M., Vance, C., Andor, M., Kussel, G., Schmidt, C.M., Osberghaus, D. (2016a). Socio-
ecological Panel. 1st survey wave. Green-SÖP. Version: 1. Essen: RWI – Leibniz Institute for
Economic Research, Data Set, https://doi.org/10.7807/greensoep:en:v1.
Frondel, M., Vance, C., Andor, M., Kussel, G., Schmidt, C.M., Osberghaus, D. (2016b). Socio-
ecological Panel. 2nd survey wave. Green-SÖP. Version: 1. Essen: RWI – Leibniz Institute for
Economic Research, Data Set, https://doi.org/10.7807/greensoep:en:v2.
Frondel, M., Vance, C., Andor, M., Kussel, G., Schmidt, C.M., Osberghaus, D. (2016c). Socio-
ecological Panel. 3rd survey wave. Green-SÖP. Version: 1. Essen: RWI – Leibniz Institute for
Economic Research, Data Set, https://doi.org/10.7807/greensoep:en:v3.
Frondel, M., Vance, C., Andor, M., Schmidt, C.M., Kussel, G., Osberghaus, D. (2020b). Socio-
ecological Panel, 4th survey wave. Green-SÖP. Version: 1. Essen: RWI – Leibniz Institute for
Economic Research, Data Set, https://doi.org/10.7807/greensoep:en:v4.
Kussel, G. (2018). Adaptation to climate variability: evidence from German households. Ecol. Econ.
143: 1–9.
Kussel, G., and Larysch, T. (2017). Sozial-ökologisches panel: datenbeschreibung der
Haushaltsbefragung. RWI Materialien 110.
Rubik, F., Müller, R., Harnisch, R., Holzhauer, B., Schipperges, M., and Geiger, S. (2019). Federal
Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU), Federal
Environment Agency (UBA) (2020), Environmental Awareness in Germany 2018: Results of a
representative survey, Available at: https://www.umweltbundesamt.de/sites/default/files/
medien/5750/publikationen/ioew-umweltbewusstseinsstudie_2018_eng_0.pdf (Accessed
05 January 2021).
RWI and forsa (2011). The German residential energy consumption survey 2006–2008. RWI
Projektberichte.
RWI and forsa (2013). The German residential energy consumption survey 2009–2010. RWI
Projektberichte.
RWI and forsa (2015). The German residential energy consumption survey 2011–2013. RWI
Projektberichte.
Schulz, C., Kolossa-Gehring, M., and Gies, A. (2017). German environmental survey for children
and adolescents 2014–2017 (GerES V) – the environmental module of KiGGS wave 2. Berlin,
https://doi.org/10.17886/RKI-GBE-2017-108.
UBA (2020). National trend Tables for the German Atmospheric emission reporting. 1990–2018,
table CO2. Berlin: Umweltbundesamt, Available at: https://www.umweltbundesamt.de/
sites/default/files/medien/361/dokumente/2019_01_15_em_entwicklung_in_d_
trendtabelle_thg_v0.6.1_f-gase.xlsx (Accessed 08 May 2020).You can also read
