The RWI Climate-Mobility Panel
-
Mark A. Andor
,
Nils Christian Hoenow
,
Eva Hümmecke
und
Eva H. Yang
Abstract
This data report describes the first and second wave of the RWI Climate-Mobility Panel, a recurring household survey that aims in particular to investigate individual mobility behavior and preferences with regard to mobility-related policies. It further includes information on attitudes towards environmental protection and climate change of household members in Germany as well as on socio-economic individual and household characteristics. These first two waves, collected via forsa in 2018 and 2019, each comprise responses from over 6,000 households. These waves enable longitudinal analyses of changes in mobility behavior, policy preferences, and environmental attitudes over time, while also offering rich cross-sectional data on additional transportation-related topics. Further, both waves include an assessment of perceived car use costs and selected psychological scales. This uniquely comprehensive data set serves as a basis for novel research and evidence-based policy decisions in the context of the mobility and transport transition. The panel will be extended with additional survey waves conducted in 2022 and 2024, as well as with planned future data collections.
1 Introduction
This report introduces the RWI Climate-Mobility Panel, which aims to advance our understanding of the interplay between individual mobility behavior and public attitudes towards transportation and environmental policies. This panel establishes a systematic approach to gathering data on how these behaviors and attitudes interact within the German population and fills critical gaps left by existing studies.
Conducted by the RWI – Leibniz Institute for Economic Research in Essen, the survey waves collected comprehensive data from German household members on mobility behaviors, preferences regarding transport policies, and attitudes towards environmental protection and climate change. To date, four waves have been conducted (2018, 2019, 2022, and 2024), and data from the first two waves are now publicly available along with this report. Data from the first survey was gathered between April and June 2018, and the second survey was conducted in June 2019 through the forsa.omninet panel, which contains 100,000 members and is representative of the German population aged 14 and above with access to the internet.[1]
Previous mobility studies, such as Mobility in Germany (MiD) and the German Mobility Panel (MOP), have provided valuable insights into individual mobility behaviors (see infas and DLR 2025 or KIT 2025). The MiD offers extensive cross-sectional data from a large sample through travel diaries, while the MOP supplies longitudinal data by having household members document their travel behavior over consecutive days. However, both data sets do not study, for example, the connections between mobility behaviors and attitudes towards transportation policies.
The RWI Climate-Mobility Panel aims to address this gap by providing a nuanced understanding of how mobility decisions and preferences for mobility-related policies are made at the individual and household levels. This is crucial for informing effective mobility and environmental policies that resonate with citizens’ needs and preferences.
This report focuses on the first and second waves of the RWI Climate-Mobility Panel. Further, each survey wave captures unique cross-sectional data that will not be repeated in later waves, including an assessment of the costs of private car use, as well as selected psychological measures. Data from the additional waves conducted in 2022 and 2024 will be made available in due course, ensuring the continued development of the panel for comprehensive longitudinal analysis. In addition, a continuation of the panel beyond 2024 is planned.
The Forschungsdatenzentrum (FDZ) Ruhr–the research data center at RWI – Leibniz Institute for Economic Research–offers the data sets free of charge to researchers through user agreements available at www.rwi-essen.de/fdz/. The QR code will direct users to a comprehensive overview of all data sets provided by the FDZ, including those from the RWI Climate-Mobility Panel.
The next section outlines the survey and the data collection process. We then provide guidance on utilizing the RWI Climate-Mobility Panel. Additionally, we review existing publications based on the first and second wave of the panel, alongside details on data access and supplementary materials available on the FDZ Ruhr website. In the appendix, we present the socio-demographic characteristics of the initial survey sample from 2018 and compare them with official German statistics from the Mikrozensus and Zensus.
2 Survey and Data Collection
The survey questions were designed by the researchers of the RWI – Leibniz Institute for Economic Research. The survey was conducted on the individual level by interviewing randomly selected individuals aged 18 and older from the forsa household panel. The panel is representative of the German-speaking population aged 14 and above with access to the internet. Panel members are recruited via telephone, and there is no option to actively apply for participation in the panel. This approach minimizes, among others, the risk that the sample primarily consists of individuals particularly interested in a topic or survey bots.
The sample used for the survey was randomly drawn from the representative household panel. Unlike a quota sample, where individuals from various socio-demographic groups are invited to participate until predefined target quotas are met, a random sample allows for a direct comparison with the population to determine which groups participated in the survey with higher or lower probabilities. This provides insights into which aspects of the population are well-represented by the sample and where there are differences.
Our data sets allow weighting for three different factors: one for German households (household level), one for the entire German population (individual level) and one for the German population of internet users (individual level). The weighting factors consider the distribution across federal states, age and gender for the individual level and federal states and household size on the household level.
The first wave was collected from April 23 to June 12, 2018. In total, 10,587 forsa.omninet panelists were invited to the survey in the first wave, out of which 7,813 participants started it. Hence, the response rate was 73.8 %. Out of those who started the survey, 6,812 participants completed it, yielding a completion rate of 87.2 %, while 1,001 participants did not finish the survey. The second wave was conducted between June 7 and June 29, 2019. A total of 9,363 forsa.omninet panelists were invited to participate. Of these, 6,838 started the survey, yielding a response rate of 73.0 %. Among those who started, 6,089 completed the survey, resulting in a completion rate of 89.0 %. On average, it took 33 min to complete the survey in 2018. In 2019, participants had the option to pause the survey and continue later. 83.9 % completed the survey without any interruption, with an average time of 33 min. For those who paused, the recorded completion time was often significantly longer, as the duration was measured based on the interval between initial start and end time.
Generally, the data does not contain any sensitive information on individuals, such as real names or contact details. The data was already pseudonymized by forsa such that all participants are assigned an individual identification number. To further ensure the anonymity of the participants, we replace this variable with a new unique key as the identifying variable for each participant. This variable remains the same over all waves and can hence be used for merging different waves of the RWI Climate-Mobility Panel. Further, we censored the last three digits of the zip codes of the respondents’ region of residence. Open text answers, i.e. if respondents encountered problems while filling out the survey or comments on the experiments, are excluded from this data set. The original surveys as well as the data sets are in German. For the publication of these data sets, relevant files such as the survey, the data set and the codebook were translated into English. The translated survey and the codebook for both waves are available as supplementary material available on the FDZ Ruhr Website.
Within the variables, missing answers from participants are generally coded with a value of −2 in the data set. This is the case if questions were not asked to respondents because, for example, they were excluded from the follow-up question due to a previous answer or were not (randomly) assigned to certain parts of the survey.
Content-wise, each survey began with a section on individual mobility behavior. This included questions about the number of cars owned by the respondent’s household, the modes of transport used for commuting to work, school, or private activities, and the frequency of their use. Participants were also asked about their attitudes toward various modes of transportation and perceived barriers to using public transport. Additional sections captured self-assessments and attitudes related to environmental protection and climate change, as well as psychological constructs such as trust, reciprocity, altruism, and prosocial behavior. In both waves, participants were also asked to estimate their mobility behavior in the next 12 months and to evaluate policies for the transportation sector and mobility concepts.
The 2018 wave further included an assessment of the perceived monthly costs of car usage, where car users were asked to guess their monthly costs and estimated the pollutants emitted by their cars. The assessment was repeated in 2019 on a subset of questions. In addition, the willingness to pay for a monthly public transport ticket was collected in the first wave, hypothetically for one part of the sample and through eliciting revealed preferences for another part. For the elicitation of their willingness to pay, the participants were separated into experimental groups. Each participant received two information screens in random order: one presents either their personal costs of running cars, their emitted carbon emissions from car use or a neutral information; the other presents either positively framed or neutral information. The positively framed information emphasizes the perceived advantages of car usage within the population. The control group (with the neutral message) received information either about the average age of all registered vehicles in Germany or that overall transport volume in Germany – covering both public transport and private motorized travel – has remained nearly constant over time (between 2000 and 2016). Respondents were then asked to state their maximum willingness to pay for a monthly public transport ticket. The main difference between the revealed preferences experiment and the hypothetical experiment is that in the former, respondents participated in an auction and could win a monthly public transportation pass within their region, whereas this option was not included in the hypothetical experiment.
In addition to the abovementioned components, the 2019 survey included a psychological scale measuring proneness to guilt, based on responses to everyday scenarios, capturing tendencies toward moral self-evaluation. It also included several questions to measure locus of control, which captures the extent to which individuals believe their life outcomes are determined by their own actions versus by external forces. Further, a carbon offsetting experiment was conducted which investigates how a different framing of information influences donation behavior related to carbon emission reduction. It consists of two consecutive stages. In the first stage, participants were randomly assigned to one of two groups, each receiving a different calculation example illustrating the financial impact of offsetting a specific amount of carbon emissions on the value of a provided voucher. In the second stage, participants were again randomly divided into one of five different treatment groups. The key variation between treatments lies in the framing of emissions either as personally caused or as generally caused by people worldwide. In addition, some treatments were personalized, based on participants’ previously reported emission behavior. Participants were then asked to allocate a freely chosen amount of a fictitious budget of 100 euros to donate to an organization dedicated to carbon emission reduction. This part aimed to assess the effect of different information types on the willingness to donate.
Further, an experiment was included to assess how different types of information affect public perceptions of and support for a city toll. Participants were first divided into groups that either received information about existing city toll systems in other countries or no additional information (control group). They were then exposed to different information highlighting various potential benefits of such a city toll (e.g. environmental impact, social fairness, investment in public transport and cycling infrastructure). Afterward, they were asked to indicate their general level of support for or opposition to a city toll.
At the end of each survey, participants provided socio-economic information. Table 1 outlines the structure of the 2018 wave, while Table 2 provides an overview of the 2019 wave.
Structure of the first survey in 2018.
| Section | Content |
|---|---|
| M. | Mobility and modes of transport (M1 – M22) |
| B. | Barriers to public transport use (B1 – B2) |
| V. | Attitudes towards environmental protection, climate change and general attitudes (V1 – V10) |
| C. | Costs of car usage (C1 – C3) |
| (E-WTP.) | (Experiment - willingness to pay for public transport (C4_1_R – C4_4_H)) |
| C (cont.) | Mobility behavior (additional questions), governmental spending and support for transport policies (C5 – C13) |
| S. | Socio-economic variables (S0 – S16) |
| OP. | Open questions about problems with survey and experiment (OP1 – OP2) |
Structure of the second survey in 2019.
| Section | Content |
|---|---|
| M. | Mobility and mode of transport (M1 – M20a_2) |
| B. | Barriers to public transport use (B1 – B2) |
| G. | Proneness to guilt (G1_2 – G5_2) |
| V. | Attitudes towards environmental protection, climate change and general attitudes (V1 – V9a_2) |
| (O.) | (Offsetting experiment: cold prickle vs. warm glow (O1 – O5)) |
| C. | Costs of car usage (C1 – C4) |
| C (cont.) | Mobility behavior (additional questions) and support for transport policies (C5 – C14b_2) |
| (CM.) | (City toll experiment (CM1 – CM5)) |
| LOC. | Locus of control (LOC1_2 – LOC7_2) |
| S. | Socio-economic variables (S0 – S16) |
| OP. | Question about problems with survey and experiment (OP1) |
The data sets of the RWI Climate-Mobility Panel published with this report do not yet include data from the willingness to pay experiment (from the 2018 wave), the offsetting experiment (from the 2019 wave) and the city toll experiment (from the 2019 wave) because publications are still in process. Nevertheless, the experiments are included in the codebooks and in the surveys and the data will be made available as soon as possible.
3 Panel Applications
The 2018 data is the first wave of the RWI Climate-Mobility Panel collected by the RWI – Leibniz Institute for Economic Research. Together with the survey wave 2019, it was part of the project named “Mobilitätsdaten für die Verkehrswende” (Eng.: Data for the Mobility Transition). Additional waves were collected in the years 2022 and 2024 within the project “Die Mobilitätswende in Deutschland gemeinsam gestalten – Lehren aus dem Ruhrgebiet” (Eng.: Creating the Mobility Transition in Germany together). Both projects were funded by Stiftung Mercator.
Key elements and questions of the 2018 and 2019 wave are repeated in the following years to allow for the creation of a panel data set. The parts of the survey that are repeated across all waves focus primarily on individual mobility behavior and preferences for mobility-related policies. This allows to monitor changes over time. There are also some parts that are unique to the respective year of data collection in each wave (see, for example, Andor et al. 2024 or Andor et al. 2025).
Information on which questions are repeated in other waves of the panel can be found in the codebooks for the respective waves. In a few instances, questions that are repeated in other years were slightly modified, for example through different conditions set to filter questions or through changes in reply items. In such cases, the presence of deviations is marked with an asterisk (*) in the codebook. The codebooks as well as the survey for the waves 2018 and 2019 can be found on the FDZ Ruhr website (see QR code above).
4 Existing Publications Based on the Data
So far, eight publications based on the RWI Climate-Mobility Panel have been published, which are listed in Table 3.
Existing publications based on the RWI Climate-Mobility Panel (2018 and 2019 wave).
| Overview |
|---|
| Andor, M. A., Fink, L., Frondel, M., Gerster, A., & Horvath, M. (2021). Kostenloser ÖPNV: Akzeptanz in der Bevölkerung und mögliche Auswirkungen auf das Mobilitätsverhalten. List Forum für Wirtschafts- und Finanzpolitik 46: 299–325. DOI: 10.1007/s41025-020-00207-y |
| Andor, M. A., Frondel, M., Horvath, M., Larysch, T., & Ruhrort, L. (2020a). Präferenzen und Einstellungen zu vieldiskutierten verkehrspolitischen Maßnahmen: Ergebnisse einer Erhebung aus dem Jahr 2018. List Forum für Wirtschafts- und Finanzpolitik 45: 255–280. DOI: 10.1007/s41025-019-00184-x |
| Andor, M. A., Gerster, A., Gillingham, K. T., & Horvath, M. (2020b). Running a Car Costs Much More Than People Think – Stalling the Uptake of Green Travel. Nature 580: 453–455. DOI: 10.1038/d41586-020-01118-w |
| Andor, M. A., Helmers, V., Hoenow, N. C., Hümmecke, E., & Memmen, M. (2024). Stimmungsbild Verkehrspolitik: Wie steht die deutsche Bevölkerung zu den meistdiskutierten verkehrspolitischen Maßnahmen? – Ein bundesweiter Vergleich der Zustimmung in der Bevölkerung. RWI Materialien. 164. RWI. https://www.rwi-essen.de/fileadmin/user_upload/RWI/Publikationen/RWI_Materialien/rwi-materialien_164.pdf |
| Andor M. A., Hümmecke, E., & Memmen, M. (2025). Präferenzen und Einstellungen zu vieldiskutierten verkehrspolitischen Maßnahmen: Ergebnisse der dritten Welle des RWI Klima-Mobilitäts-Panels aus dem Jahr 2022. Zeitschrift für Wirtschaftspolitik 74 (2): 188–210. DOI: 10.1515/zfwp-2025-2006 |
| Frondel, M. (2019a). Verkehrswende: Busstreifen okay, höhere Parkkosten nicht. RWI Impact Notes 08/2019. https://hdl.handle.net/10419/216892 |
| Frondel, M. (2019b). Straßennutzungsgebühren: Eine Lösung zur Vermeidung von Staus? Perspektiven der Wirtschaftspolitik 20 (3): 218–225. DOI: 10.1515/pwp-2019-0039 |
| RWI – Leibniz-Institut für Wirtschaftsforschung (Ed.) & Stiftung Mercator (Ed.) (2019). Weniger Staus, Staub und Gestank per sozial ausgewogener Städte-Maut (Gemeinsame Handlungsempfehlungen von RWI und WZB). RWI Positionen. 74. RWI. https://www.rwi-essen.de/publikationen/politikberatend/rwi-positionen/detail/weniger-staus-staub-und-gestank-per-sozial-2552 |
5 Data Access
The data sets are available as a Scientific Use File at the FDZ Ruhr, the research data center at RWI – Leibniz Institute for Economic Research. The data access is only granted for scientific, noncommercial studies. Potential users include researchers affiliated with scientific institutions, universities, and government agencies. Access requires a signed data usage agreement, which can be applied for on the FDZ Ruhr website. The data can be obtained as a Stata® data set (.dta) or a csv file. Users are requested to cite the source correctly and to inform FDZ Ruhr about publications with the data. When using the data set, please cite each wave separately as:
Andor M. A., Frondel M., Gerster A., Hoenow N. C., Horvath M., Hümmecke E., and Yang E. H. (2025). RWI Climate-Mobility Panel - First Survey Wave in 2018 [Data set]. In RWI-Micro (Version 1). RWI – Leibniz Institute for Economic Research. https://doi.org/10.7807/RWI:CLIMATE:MOBILITY:2018:V1.
Andor M. A., Frondel M., Gerster A., Hoenow N. C., Horvath M., Hümmecke E., and Yang E. H. (2025). RWI Climate-Mobility Panel - Second Survey Wave in 2019 [Data set]. In RWI-Micro (Version 1). RWI – Leibniz Institute for Economic Research. https://doi.org/10.7807/RWI:CLIMATE:MOBILITY:2019:V1.
In addition to the data sets, we would be very pleased if you would cite this data report.
Funding source: Stiftung Mercator
Acknowledgments
We gratefully acknowledge financial support by the Stiftung Mercator, without which this project would not have been possible. We are furthermore grateful for the cooperation with our colleagues from the Social Science Research Centre Berlin (WZB) and the InnoZ, in particular Lisa Ruhrort, Weert Canzler and Andreas Knie, as well as the RWI colleagues who have participated in the design of the surveys, in particular Manuel Frondel, Andreas Gerster and Marco Horvath. Furthermore, we thank Tobias Larysch and Leonie Sander for excellent research assistance.
Sample Description
In this section, we provide an overview of the socio-economic characteristics of the individuals in our survey sample, as well as information about the households in which they live. To assess the representativeness of our sample, we compare the distributions of these characteristics with data for the general population in Germany. For this purpose, we primarily use official data from the 2018 Mikrozensus, published by the German Federal Statistical Office (Statistisches Bundesamt 2019, 2025b, 2025c). For age, we use data from the Zensus 2011 (Statistisches Bundesamt 2025a). As it was conducted in 2011, it provides extrapolations for the following decade.
Both the Mikrozensus and Zensus collect information at the household level and therefore, include information for all household members. In contrast, our survey was conducted with individual participants. Some variables in our dataset we assessed refer to the entire household (provided by the individual participant), such as the federal state of residence or net household income. Others, such as age and the level of education, refer solely to the individual participant.
To ensure clarity and readability, we decided to compare the first survey wave (2018) with data from the Zensus and Mikrozensus.[2] In the following, the descriptive statistics are shown for those participants who completed the survey and gave an answer to the respective question other than “(don’t know)/no answer”. Therefore, the number of respondents can differ by question.
The distribution of the households in the sample across federal states (Table A1) is generally consistent with the regional distribution in the Mikrozensus 2018 (Statistisches Bundesamt 2019). The shares of participants are slightly overrepresented for our sample in Berlin, Brandenburg and North Rhine-Westphalia, while the share of respondents from Baden-Wuerttemberg is slightly underrepresented.
Distribution of households across federal states in the sample (n = 6,812) and in Germany according to Mikrozensus 2018.
| Federal state | Number of households in sample | Share of households in sample | Share of households according to Mikrozensus |
|---|---|---|---|
| Baden-Wuerttemberg | 676 | 9.9 % | 12.8 % |
| Bavaria | 1,118 | 16.4 % | 15.6 % |
| Berlin | 379 | 5.6 % | 4.9 % |
| Brandenburg | 328 | 4.8 % | 3.0 % |
| Bremen | 75 | 1.1 % | 0.9 % |
| Hamburg | 122 | 1.8 % | 2.4 % |
| Hesse | 464 | 6.8 % | 7.5 % |
| Lower Saxony | 619 | 9.1 % | 9.6 % |
| Mecklenburg-Western Pomerania | 126 | 1.9 % | 2.0 % |
| North Rhine-Westphalia | 1,551 | 22.8 % | 21.2 % |
| Rhineland-Palatinate | 280 | 4.1 % | 4.7 % |
| Saarland | 71 | 1.0 % | 1.2 % |
| Saxony | 368 | 5.4 % | 5.2 % |
| Saxony-Anhalt | 185 | 2.7 % | 2.8 % |
| Schleswig-Holstein | 262 | 3.9 % | 3.6 % |
| Thuringia | 188 | 2.8 % | 2.7 % |
The respondents are between 18 and 92 years old and the mean as well as the median age is 52 years. For a comparison, age is grouped in eight classes (Figure A1). The largest age group is from 45 to 54 years (22.5 %), followed by 55–64 years (19.5 %) and 65–74 years (18.2 %). The comparison of the age distribution with that of the Zensus[3] (Statistisches Bundesamt 2025a) shows that individuals aged 18 to 24 and 75 and older seem to be underrepresented in the survey sample, while individuals between 45 and 74 tend to be overrepresented.
Age distribution in the sample (n = 6,812) and according to Zensus 2011. Source: Statistisches Bundesamt (2025a).
53.7 % of the sample is male, while 46.3 % is female. Individuals with a higher general school education seem to be overrepresented in our sample (Table A2). 32.6 % of the population have the highest school-leaving qualification according to the Mikrozensus, whereas 44 % of the sample have this qualification.
Distribution of highest school-leaving certificate in the sample (n = 6,751) and according to Mikrozensus 2018.
| Highest school-leaving qualification | Share in the sample | Share according to Mikrozensus |
|---|---|---|
| No school-leaving qualification yet | 0.3 % | 3.6 % |
| No school-leaving qualification | 0.6 % | 4.0 % |
| Secondary general schoola | 19.2 % | 29.7 % |
| Intermediate secondary schoolb | 35.9 % | 30.0 % |
| Upper secondary schoolc (higher education entrance qualification) | 44.0 % | 32.6 % |
-
aGerman, Haupt-/Volksschule; bGerman, Mittlere Reife/Realschule; cGerman, (Fach-)Hochschulreife. Statistisches Bundesamt (2025b).
In addition, the share of survey participants with an academic degree, i.e. a degree from a university or a doctorate, of 28.5 % exceeds the percentage in the Mikrozensus of 17.9 % (Statistisches Bundesamt 2025c). One obvious reason for these differences is that the Mikrozensus includes individuals who are 15 years or older, whereas in our sample only individuals who are 18 or older are included.
The monthly net household income is measured on a scale of 500-euros intervals ranging from less than 700 euros to 5,700 euros and above, resulting in 12 categories (Figure A2). The median income is in the income group from 3,200 to 3,700 euros. The largest share of the sample has a monthly household net income between 2,200 and 2,700 euros (13.9 %). Since the income groups are defined differently for the Mikrozensus, the income groups in Table A3 are summarized in five similar classes to allow comparisons.
Distribution of monthly net household income in the sample (n=5,984).
Distribution of monthly net household income in the sample (n = 5,984) and according to Mikrozensus 2018.
| Share in the sample | Share according to Mikrozensus | ||
|---|---|---|---|
| Under 700 euros | 1.8 % | Under 900 euros | 8.4 % |
| 700 to under 1,200 euros | 5.3 % | 900 to under 1,300 euros | 11.5 % |
| 1,200 to under 2,700 euros | 36.2 % | 1,300 to under 2,600 euros | 38.3 % |
| 2,700 to under 4,700 euros | 40.9 % | 2,600 to under 4,500 euros | 29.0 % |
| More than 4,700 euros | 15.8 % | More than 4,500 euros | 15.3 % |
It stands out that households with an income of 2,700 to 4,700 euros seem to be overrepresented in our sample. The overrepresentation of households with higher income groups is likely associated with the overrepresentation of higher educated individuals participating in the survey.
Further, single-person households seem to be underrepresented in the sample with 25 % compared to 41.9 % according to the Mikrozensus (Table A4). Meanwhile, larger households are overrepresented, in particular two-person households with 44.4 %. Persons living in households with three or four members and above account for about 15 % and 16 %, respectively.
Distribution of household size in the sample (n = 6,746) and according to Mikrozensus 2018.
| Share in the sample | Share according to Mikrozensus | |
|---|---|---|
| Single household | 25.0 % | 41.9 % |
| Two-person household | 44.4 % | 33.8 % |
| Three-person household | 14.7 % | 11.9 % |
| Four and more persons in household | 15.9 % | 12.5 % |
In addition to socio-economic characteristics, respondents were also asked about their preferences for a political party. 4,351 respondents (63,8 %) indicated that they tend to favor a specific party (Figure A3). Among those, 32.9 % indicated that they lean towards the Christian Democratic Union of Germany (Christlich Demokratische Union Deutschlands, short CDU) or its sister party, Christian Social Union in Bavaria (Christlich-Soziale Union in Bayern, short CSU), followed by the Social Democratic Party (Sozialdemokratische Partei Deutschlands, short SPD) with 26.7 % and the Greens (Bündnis 90/Die Grünen) with 16.0 %. So, these were the three most favored parties in the sample. As an indicator, in the 2017 German federal election, the CDU/CSU received 33.0 %, the SPD 20.5 %, the Alternative for Germany (Alternative für Deutschland, short AfD) 12.6 %, the Free Democratic Party (Freie Demokratische Partei, short FDP) 10.7 %, Left party (Die Linke) 9.2 %, and the Greens 8.9 % (Der Bundeswahlleiter 2017). It is important to note, however, that the question in the survey referred to long-term party preference, not actual voting behavior in a specific election.
![Figure A3:
Answer to the question in the survey: “Many people lean towards a specific political party for a long time […]. Do you generally favor a specific party? [If yes] and which party is that?” (n = 4,351).](/document/doi/10.1515/jbnst-2025-0022/asset/graphic/j_jbnst-2025-0022_fig_003.jpg)
Answer to the question in the survey: “Many people lean towards a specific political party for a long time […]. Do you generally favor a specific party? [If yes] and which party is that?” (n = 4,351).
References
Andor, M. A., L. Fink, M. Frondel, A. Gerster, and M. Horvath. 2021. “Kostenloser ÖPNV: Akzeptanz in der Bevölkerung und mögliche Auswirkungen auf das Mobilitätsverhalten.” List Forum für Wirtschafts- und Finanzpolitik 46: 299–325. https://doi.org/10.1007/s41025-020-00207-y.Suche in Google Scholar
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Andor, M. A., A. Gerster, K. T. Gillingham, and M. Horvath. 2020b. “Running a Car Costs Much More Than People Think – Stalling the Uptake of Green Travel.” Nature 580: 453–5. https://doi.org/10.1038/d41586-020-01118-w.Suche in Google Scholar
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