Public Economics / Public Finance (Hintermann)Head of Research Unit Prof. Dr.Beat HintermannOverviewMembersPublicationsProjects & CollaborationsProjects & Collaborations OverviewMembersPublicationsProjects & Collaborations Projects & Collaborations 51 foundShow per page10 10 20 50 FV-118 | Economic hardship and the negativity of political campaigns Research Project | 1 Project MembersImported from Grants Tool 4708866 Air pollution: adverse effects, adaptation behavior, and environmental policy Research Project | 1 Project MembersImported from Grants Tool 4700202 Coral Bleaching in East Africa: Consequences and Adaptations Research Project | 1 Project MembersImported from Grants Tool 4701411 FV-110 | Mode Choice in Intra-Continental Holiday Travel Research Project | 2 Project MembersImported from Grants Tool 4700774 Sustainable Mobility at Roche Research Project | 1 Project MembersNo Description available FV-101 | Persuading to Investing into Sustainability Research Project | 2 Project MembersCommon wisdom has it that optimists see the glass as half full, whereas pessimists see it as half-empty. Now consider that companies or politicians could describe either version of the glass, and maybe turn their audience into optimists or pessimists. We can generalize from this metaphor to message framing , a technique invented by Nobel-prize winner Daniel Kahneman and the late Amos Tversky, where a message describes the identical event either in reference to losses or in reference to gains. As much prior research we asked how effective a message would be at changing a person's attitude toward an issue when framed either in gain terms or in loss terms . We studied attitudes toward policies that force companies to remove CO2 from the atmosphere. Principally four types of message frames exist, such as, removing CO2 from the atmosphere reduces risks to the environment (non-loss frame), not removing CO2 increases risks (loss frame), regaining CO2 is an opportunity for the environment (gain frame), not regaining CO2 misses out on an opportunity (non-gain frame). Note that any persuasive message of the type "doing X will result in Y" has to use one of the four types of frames, or a mix of them. We address two problems. First, in practice it is often unclear whether a complex persuasive message expresses a gain, non-gain, loss, or non-loss, impeding choice of the most persuasive frame. We have developed a measure that allows such classifications. Second, which frame is most persuasive depends on the situation. However, for some situations two existing theories, one of them by us, make contradictory predictions. We have conducted one successful experiment as part of a master's thesis 2 . We seek to conduct a second experiment that would address a weakness of the first one and test if the first one is replicable. We need to succeed with both to be able to publish the research in a competitive academic journal. Based on the evidence gathered in the proposed project we would be in a strong position to apply for public funding to further investigate this important issue. We summarize our prior result, and expect to observe it again: We showed consumers two different approaches to get society to remove CO2. One approach involved voluntary consumer payments, the other forcing companies to invest. After reading a message consumers rated their attitudes toward these approaches. A new type of measure we developed showed that respondents perceived the consumer voluntary approach as non-loss, and the company mandatory one as gain. For each approach we developed two persuasive messages that communicated that not doing anything about CO2 is bad, thus urging to do something. One message expressed this badness as damage to the environment (loss), and the other as lost opportunity for the environment (non-gain). When we considered all respondents, neither of these two message frames persuaded them more than the other. However, we then divided respondents into two groups depending on how much they cared about the environment, that is, how involved they were with the topic at hand. For those highly involved, one of the two theories predicted well which frame is most persuasive, whereas for those with low involvement, the other theory predicted well. In retrospect, this result is theoretically meaningful, but we did not expect it. Involvement may be a missing piece in the puzzle as to which theory makes more accurate predictions. FV-103 | Gesellschaftlicher Nutzen von Mobility Pricing Research Project | 2 Project MembersIn einem Verkehrsexperiment konnten wir zeigen, dass Mobility-Pricing in der Schweiz zu einer signifikanten Reduktion der externen Kosten führt (Hintermann et al., 2021). Da diese Studie jedoch nur eine Stichprobe der Verkehrsteilnehmer umfasste war der Effekt auf das gesamte Verkehrssystem minimal. Dies war von Vorteil für das Experiment, aber es bedeutet auch, dass es für die Berechnung der Wohlfahrtskonsequenzen von einem allgemeinen Pricing zusätzliche Schritte braucht. Im anvisierten Projekt werden wir den volkswirtschaftlichen Netto-Nutzen einer Einführung von Mobility-Pricing in der Schweiz berechnen, basierend auf einer Methodologie von kürzlich erschienenen Studien. Während sich diese auf den Stau im motorisierten Individualverkehr konzentrierten, werden wir alle wichtigen externen Kosten (Stau, Klima und Gesundheit) und alle Verkehrsträger berücksichtigen (Auto, öffentlicher Verkehr und Langsamverkehr) und somit eine wichtige Lücke in der Literatur schliessen. Dabei werden GPS-Tracking- und Befragungsdaten aus unserem Verkehrsexperiment sowie aus dessen Fortsetzung während der COVID-Pandemie verwendet. Durch die umfangreichen Mobilitätsdaten auf Etappenebene und die ausführlichen Umfragedaten der Probanden, kann auch die Verteilung der Wohlfahrtseffekte untersucht werden, z.B. nach Alter, Geschlecht, Einkommen oder dem Grad der Urbanisierung des Wohnorts. Problemstellung Die wichtigsten externen Kosten im Verkehr entstehen durch Zeitverluste im Stau, Klimaschäden, Gesundheitsschäden durch lokale Emissionen und Unfälle. Diese Kosten werden nicht von den einzelnen Verkehrsteilnehmern getragen, sondern von der Allgemeinheit. In unserem Verkehrsexperiment vom Jahr 2019 haben wir diese externen Kosten mit Mobility-Pricing internalisiert und den Effekt auf ca. 1,200 Probanden gemessen, relativ zu einer Kontrollgruppe. Die Probanden haben auf das Pricing reagiert und ihre externen Kosten reduziert. Dies erfolgte hauptsächlich durch ein Umsteigen vom privaten in den öffentlichen Verkehr und eine Veränderung der Abfahrtszeiten durch Autofahrer. Diese Veränderungen stellen jedoch nur partielle Effekte dar, da sie die Auswirkungen von Mobility-Pricing auf das Verkehrsgleichgewicht nicht berücksichtigen. Wenn viele Leute ihre Abfahrtszeiten verändern oder das Verkehrsmittel wechseln, dann verschiebt sich damit auch das Verkehrsaufkommen im Tagesverlauf und über die Verkehrsträger. Dies führt zu einer Veränderung in den externen Kosten des Verkehrs und damit zu einer Adjustierung der optimalen Mobilitätspreise, was wiederum das Gleichgewicht verändert etc. Aus diesem Grund braucht es für die Wohlfahrtsanalyse eine Berechnung im allgemeinen (statt im partiellen) Gleichgewicht. Zielsetzung Die Quantifizierung der Wohlfahrtseffekte erfordert ein umfassendes Modell von Mobilitätsnachfrage und -angebot. Die wichtigsten Präferenzparameter, die in diesem Modell vorkommen, sind (i) der Wert von Zeit unterwegs («Value of Travel Time Savings»), (ii) die Zahlungsbereitschaft, nicht zu früh oder zu spät am Ziel einzutreffen («Scheduling Costs») und (iii) die relative Präferenz für unterschiedliche Verkehrsmittel. Diese können aufgrund der Beobachtungen während des Experiments ökonometrisch geschätzt werden. Für die allgemeinen Gleichgewichtseffekte braucht es eine Übersetzung der veränderten Verkehrsnachfrage auf den Stau, die Auslastung des öffentlichen Verkehrs und die Verkehrsunfälle (die lokalen und globalen Emissionen sind linear und brauchen keine spezielle Modellierung). Dafür verwenden wir die exogene Variation der Mobilität in der Schweiz aufgrund der COVID-19-Pandemie, die wir mit unserem Tracking Panel beobachtet haben. Die Kombination einer exogenen Preisvariation (im Experiment) und einer exogenen Nachfragereduktion (durch COVID-19) ist einzigartig in der Literatur und schafft sehr gute Voraussetzungen, die Wohlfahrtsgewinne von Mobility-Pricing zuverlässig zu schätzen. Ein wichtiger Aspekt bei Einführung von Mobility-Pricing ist ausserdem die Umverteilung (oder «Regressivität»). Abhängig von den Ergebnissen der obigen Wohlfahrtsanalyse wäre es möglich, ein Rückverteilungssystem zu konzipieren, das gezielt Personen entlastet, die überproportional durch ein Mobility-Pricing System belastet wären. Forschungsförderung EBIS (Kantone, diverse) Research Project | 2 Project MembersThis funding adds to the project "Reduction of transport emissions due to E-biking in Switzerland (EBIS)". The cantonal funding allows us to increase the sample size based on regional over-sampling. Reduction of transport emissions due to E-biking in Switzerland (EBIS) Research Project | 3 Project MembersWe investigate the potential for reducing carbon emissions in the transport sector due to E-biking based on a large sample of cyclists in Switzerland and using a combination of GPS tracking and surveys. The first part of the project assesses the current situation and improves on the present state of knowledge about mode shift due to E-biking and the transport choices of E-bikers. The second part consists of a randomized control trial that implements a pricing intervention on a subset of the participants with the aim of substituting car with E-bike travel. We measure the resulting causal effects on carbon emissions and investigate mode substitution, with a special focus on cycling, driving and public transport. The third part conducts a route choice analysis to inform future policies and measures to promote cycling. Last, the potential for carbon reductions due to E-biking in Switzerland is computed, using insights from this research and considering different scenarios, i.e., future mobility pricing and transport policies. Pollution, environmental regulation and firm performance Research Project | 2 Project MembersThis project investigates the impact of the EU Emissions Trading Scheme (EU ETS) on firms using administrative data from German manufacturing for the years 2003-2019. We study the implications of climate policy on firms and, indirectly via changes in emissions intensities, on the environment. At the same time, there is mounting evidence that environmental quality also affects firms. The second part of our project focuses on this reverse causality and thus contributes to our knowledge about the interactions between the economy and the environment. Our work builds on our previous research funded by the SNSF involving the same data. The first work package examines the long-term effects of the EU ETS via its impact on regulated firms' product choices. The introduction of a price on greenhouse gas emissions changes the relative production costs, which will be reflected in product prices. This is the subject of our previous research, in which we show that manufacturing firms in Germany pass on their marginal costs fully to consumers. In that previous work, we took a short-term perspective and examined what happens to the output price if the production costs increase by one Euro. In this project, we focus on the long-term implications in the form of product choice. Over time, consumers can be expected to substitute away from emissions-intensive products, which have become costlier, towards greener products. In order to retain market share and profits in the face of this demand response, firms will need to adjust their product portfolio towards less emission-intensive products over time. In WP1, we investigate whether such a shift can be observed among German manufacturing firms, and whether this shift is stronger for the firms covered by the EU ETS. Our second work package builds on the recent literature about the effect of air quality and tem-perature on firm outcomes. There is an older literature that documents detrimental health effects as a consequence of air pollution and temperature variations. This is a first-order reason to establish clean air standards. Environmental policy is usually interpreted as a tradeoff between more environmental quality (and better health) on the one hand, and a reduction in GDP and growth on the other. However, if workers (and thus firms) become more productive if the air quality improves, as suggested by the recent literature, then this tradeoff may in fact be a win-win-situation, at least within certain pollution bounds. To investigate this question, we will link satellite-based data of ambient air pollution and temperature to the location of individual manufacturing plants in Germany and investigate whether changes in environ-mental quality causally affect firms' productivity. We address the potential endogeneity problem between industrial production/productivity and pollution by using an instrumental variable approach that relies on temperature inversions and prevailing wind directions. As the main measure for productivity, we propose to use the total factor productivity that we have computed for the firms in our sample in the context of our previous project. There is a high degree of synergy between the proposed and the previous research as both projects rely on the same administrative firm-level data. The data are very rich in terms of information content, but the prevailing confidentiality rules render them difficult to work with. Having incurred significant fixed costs in terms of our understanding the data and their limitations, we are now in a position where additional time invested will likely have a high benefit-cost ratio. We propose to use the same data to follow up on our previous project with a new set of research questions. Including both a Postdoc and a Ph.D. in the project will furthermore contribute to capacity building in research. We hope that the proposed work will deepen our understanding of the effects of air pollution and its regulation on firms and thus inform policy makers about the costs and benefits of environmental regulation. 123...6 1...6 OverviewMembersPublicationsProjects & Collaborations
Projects & Collaborations 51 foundShow per page10 10 20 50 FV-118 | Economic hardship and the negativity of political campaigns Research Project | 1 Project MembersImported from Grants Tool 4708866 Air pollution: adverse effects, adaptation behavior, and environmental policy Research Project | 1 Project MembersImported from Grants Tool 4700202 Coral Bleaching in East Africa: Consequences and Adaptations Research Project | 1 Project MembersImported from Grants Tool 4701411 FV-110 | Mode Choice in Intra-Continental Holiday Travel Research Project | 2 Project MembersImported from Grants Tool 4700774 Sustainable Mobility at Roche Research Project | 1 Project MembersNo Description available FV-101 | Persuading to Investing into Sustainability Research Project | 2 Project MembersCommon wisdom has it that optimists see the glass as half full, whereas pessimists see it as half-empty. Now consider that companies or politicians could describe either version of the glass, and maybe turn their audience into optimists or pessimists. We can generalize from this metaphor to message framing , a technique invented by Nobel-prize winner Daniel Kahneman and the late Amos Tversky, where a message describes the identical event either in reference to losses or in reference to gains. As much prior research we asked how effective a message would be at changing a person's attitude toward an issue when framed either in gain terms or in loss terms . We studied attitudes toward policies that force companies to remove CO2 from the atmosphere. Principally four types of message frames exist, such as, removing CO2 from the atmosphere reduces risks to the environment (non-loss frame), not removing CO2 increases risks (loss frame), regaining CO2 is an opportunity for the environment (gain frame), not regaining CO2 misses out on an opportunity (non-gain frame). Note that any persuasive message of the type "doing X will result in Y" has to use one of the four types of frames, or a mix of them. We address two problems. First, in practice it is often unclear whether a complex persuasive message expresses a gain, non-gain, loss, or non-loss, impeding choice of the most persuasive frame. We have developed a measure that allows such classifications. Second, which frame is most persuasive depends on the situation. However, for some situations two existing theories, one of them by us, make contradictory predictions. We have conducted one successful experiment as part of a master's thesis 2 . We seek to conduct a second experiment that would address a weakness of the first one and test if the first one is replicable. We need to succeed with both to be able to publish the research in a competitive academic journal. Based on the evidence gathered in the proposed project we would be in a strong position to apply for public funding to further investigate this important issue. We summarize our prior result, and expect to observe it again: We showed consumers two different approaches to get society to remove CO2. One approach involved voluntary consumer payments, the other forcing companies to invest. After reading a message consumers rated their attitudes toward these approaches. A new type of measure we developed showed that respondents perceived the consumer voluntary approach as non-loss, and the company mandatory one as gain. For each approach we developed two persuasive messages that communicated that not doing anything about CO2 is bad, thus urging to do something. One message expressed this badness as damage to the environment (loss), and the other as lost opportunity for the environment (non-gain). When we considered all respondents, neither of these two message frames persuaded them more than the other. However, we then divided respondents into two groups depending on how much they cared about the environment, that is, how involved they were with the topic at hand. For those highly involved, one of the two theories predicted well which frame is most persuasive, whereas for those with low involvement, the other theory predicted well. In retrospect, this result is theoretically meaningful, but we did not expect it. Involvement may be a missing piece in the puzzle as to which theory makes more accurate predictions. FV-103 | Gesellschaftlicher Nutzen von Mobility Pricing Research Project | 2 Project MembersIn einem Verkehrsexperiment konnten wir zeigen, dass Mobility-Pricing in der Schweiz zu einer signifikanten Reduktion der externen Kosten führt (Hintermann et al., 2021). Da diese Studie jedoch nur eine Stichprobe der Verkehrsteilnehmer umfasste war der Effekt auf das gesamte Verkehrssystem minimal. Dies war von Vorteil für das Experiment, aber es bedeutet auch, dass es für die Berechnung der Wohlfahrtskonsequenzen von einem allgemeinen Pricing zusätzliche Schritte braucht. Im anvisierten Projekt werden wir den volkswirtschaftlichen Netto-Nutzen einer Einführung von Mobility-Pricing in der Schweiz berechnen, basierend auf einer Methodologie von kürzlich erschienenen Studien. Während sich diese auf den Stau im motorisierten Individualverkehr konzentrierten, werden wir alle wichtigen externen Kosten (Stau, Klima und Gesundheit) und alle Verkehrsträger berücksichtigen (Auto, öffentlicher Verkehr und Langsamverkehr) und somit eine wichtige Lücke in der Literatur schliessen. Dabei werden GPS-Tracking- und Befragungsdaten aus unserem Verkehrsexperiment sowie aus dessen Fortsetzung während der COVID-Pandemie verwendet. Durch die umfangreichen Mobilitätsdaten auf Etappenebene und die ausführlichen Umfragedaten der Probanden, kann auch die Verteilung der Wohlfahrtseffekte untersucht werden, z.B. nach Alter, Geschlecht, Einkommen oder dem Grad der Urbanisierung des Wohnorts. Problemstellung Die wichtigsten externen Kosten im Verkehr entstehen durch Zeitverluste im Stau, Klimaschäden, Gesundheitsschäden durch lokale Emissionen und Unfälle. Diese Kosten werden nicht von den einzelnen Verkehrsteilnehmern getragen, sondern von der Allgemeinheit. In unserem Verkehrsexperiment vom Jahr 2019 haben wir diese externen Kosten mit Mobility-Pricing internalisiert und den Effekt auf ca. 1,200 Probanden gemessen, relativ zu einer Kontrollgruppe. Die Probanden haben auf das Pricing reagiert und ihre externen Kosten reduziert. Dies erfolgte hauptsächlich durch ein Umsteigen vom privaten in den öffentlichen Verkehr und eine Veränderung der Abfahrtszeiten durch Autofahrer. Diese Veränderungen stellen jedoch nur partielle Effekte dar, da sie die Auswirkungen von Mobility-Pricing auf das Verkehrsgleichgewicht nicht berücksichtigen. Wenn viele Leute ihre Abfahrtszeiten verändern oder das Verkehrsmittel wechseln, dann verschiebt sich damit auch das Verkehrsaufkommen im Tagesverlauf und über die Verkehrsträger. Dies führt zu einer Veränderung in den externen Kosten des Verkehrs und damit zu einer Adjustierung der optimalen Mobilitätspreise, was wiederum das Gleichgewicht verändert etc. Aus diesem Grund braucht es für die Wohlfahrtsanalyse eine Berechnung im allgemeinen (statt im partiellen) Gleichgewicht. Zielsetzung Die Quantifizierung der Wohlfahrtseffekte erfordert ein umfassendes Modell von Mobilitätsnachfrage und -angebot. Die wichtigsten Präferenzparameter, die in diesem Modell vorkommen, sind (i) der Wert von Zeit unterwegs («Value of Travel Time Savings»), (ii) die Zahlungsbereitschaft, nicht zu früh oder zu spät am Ziel einzutreffen («Scheduling Costs») und (iii) die relative Präferenz für unterschiedliche Verkehrsmittel. Diese können aufgrund der Beobachtungen während des Experiments ökonometrisch geschätzt werden. Für die allgemeinen Gleichgewichtseffekte braucht es eine Übersetzung der veränderten Verkehrsnachfrage auf den Stau, die Auslastung des öffentlichen Verkehrs und die Verkehrsunfälle (die lokalen und globalen Emissionen sind linear und brauchen keine spezielle Modellierung). Dafür verwenden wir die exogene Variation der Mobilität in der Schweiz aufgrund der COVID-19-Pandemie, die wir mit unserem Tracking Panel beobachtet haben. Die Kombination einer exogenen Preisvariation (im Experiment) und einer exogenen Nachfragereduktion (durch COVID-19) ist einzigartig in der Literatur und schafft sehr gute Voraussetzungen, die Wohlfahrtsgewinne von Mobility-Pricing zuverlässig zu schätzen. Ein wichtiger Aspekt bei Einführung von Mobility-Pricing ist ausserdem die Umverteilung (oder «Regressivität»). Abhängig von den Ergebnissen der obigen Wohlfahrtsanalyse wäre es möglich, ein Rückverteilungssystem zu konzipieren, das gezielt Personen entlastet, die überproportional durch ein Mobility-Pricing System belastet wären. Forschungsförderung EBIS (Kantone, diverse) Research Project | 2 Project MembersThis funding adds to the project "Reduction of transport emissions due to E-biking in Switzerland (EBIS)". The cantonal funding allows us to increase the sample size based on regional over-sampling. Reduction of transport emissions due to E-biking in Switzerland (EBIS) Research Project | 3 Project MembersWe investigate the potential for reducing carbon emissions in the transport sector due to E-biking based on a large sample of cyclists in Switzerland and using a combination of GPS tracking and surveys. The first part of the project assesses the current situation and improves on the present state of knowledge about mode shift due to E-biking and the transport choices of E-bikers. The second part consists of a randomized control trial that implements a pricing intervention on a subset of the participants with the aim of substituting car with E-bike travel. We measure the resulting causal effects on carbon emissions and investigate mode substitution, with a special focus on cycling, driving and public transport. The third part conducts a route choice analysis to inform future policies and measures to promote cycling. Last, the potential for carbon reductions due to E-biking in Switzerland is computed, using insights from this research and considering different scenarios, i.e., future mobility pricing and transport policies. Pollution, environmental regulation and firm performance Research Project | 2 Project MembersThis project investigates the impact of the EU Emissions Trading Scheme (EU ETS) on firms using administrative data from German manufacturing for the years 2003-2019. We study the implications of climate policy on firms and, indirectly via changes in emissions intensities, on the environment. At the same time, there is mounting evidence that environmental quality also affects firms. The second part of our project focuses on this reverse causality and thus contributes to our knowledge about the interactions between the economy and the environment. Our work builds on our previous research funded by the SNSF involving the same data. The first work package examines the long-term effects of the EU ETS via its impact on regulated firms' product choices. The introduction of a price on greenhouse gas emissions changes the relative production costs, which will be reflected in product prices. This is the subject of our previous research, in which we show that manufacturing firms in Germany pass on their marginal costs fully to consumers. In that previous work, we took a short-term perspective and examined what happens to the output price if the production costs increase by one Euro. In this project, we focus on the long-term implications in the form of product choice. Over time, consumers can be expected to substitute away from emissions-intensive products, which have become costlier, towards greener products. In order to retain market share and profits in the face of this demand response, firms will need to adjust their product portfolio towards less emission-intensive products over time. In WP1, we investigate whether such a shift can be observed among German manufacturing firms, and whether this shift is stronger for the firms covered by the EU ETS. Our second work package builds on the recent literature about the effect of air quality and tem-perature on firm outcomes. There is an older literature that documents detrimental health effects as a consequence of air pollution and temperature variations. This is a first-order reason to establish clean air standards. Environmental policy is usually interpreted as a tradeoff between more environmental quality (and better health) on the one hand, and a reduction in GDP and growth on the other. However, if workers (and thus firms) become more productive if the air quality improves, as suggested by the recent literature, then this tradeoff may in fact be a win-win-situation, at least within certain pollution bounds. To investigate this question, we will link satellite-based data of ambient air pollution and temperature to the location of individual manufacturing plants in Germany and investigate whether changes in environ-mental quality causally affect firms' productivity. We address the potential endogeneity problem between industrial production/productivity and pollution by using an instrumental variable approach that relies on temperature inversions and prevailing wind directions. As the main measure for productivity, we propose to use the total factor productivity that we have computed for the firms in our sample in the context of our previous project. There is a high degree of synergy between the proposed and the previous research as both projects rely on the same administrative firm-level data. The data are very rich in terms of information content, but the prevailing confidentiality rules render them difficult to work with. Having incurred significant fixed costs in terms of our understanding the data and their limitations, we are now in a position where additional time invested will likely have a high benefit-cost ratio. We propose to use the same data to follow up on our previous project with a new set of research questions. Including both a Postdoc and a Ph.D. in the project will furthermore contribute to capacity building in research. We hope that the proposed work will deepen our understanding of the effects of air pollution and its regulation on firms and thus inform policy makers about the costs and benefits of environmental regulation. 123...6 1...6
FV-118 | Economic hardship and the negativity of political campaigns Research Project | 1 Project MembersImported from Grants Tool 4708866
Air pollution: adverse effects, adaptation behavior, and environmental policy Research Project | 1 Project MembersImported from Grants Tool 4700202
Coral Bleaching in East Africa: Consequences and Adaptations Research Project | 1 Project MembersImported from Grants Tool 4701411
FV-110 | Mode Choice in Intra-Continental Holiday Travel Research Project | 2 Project MembersImported from Grants Tool 4700774
FV-101 | Persuading to Investing into Sustainability Research Project | 2 Project MembersCommon wisdom has it that optimists see the glass as half full, whereas pessimists see it as half-empty. Now consider that companies or politicians could describe either version of the glass, and maybe turn their audience into optimists or pessimists. We can generalize from this metaphor to message framing , a technique invented by Nobel-prize winner Daniel Kahneman and the late Amos Tversky, where a message describes the identical event either in reference to losses or in reference to gains. As much prior research we asked how effective a message would be at changing a person's attitude toward an issue when framed either in gain terms or in loss terms . We studied attitudes toward policies that force companies to remove CO2 from the atmosphere. Principally four types of message frames exist, such as, removing CO2 from the atmosphere reduces risks to the environment (non-loss frame), not removing CO2 increases risks (loss frame), regaining CO2 is an opportunity for the environment (gain frame), not regaining CO2 misses out on an opportunity (non-gain frame). Note that any persuasive message of the type "doing X will result in Y" has to use one of the four types of frames, or a mix of them. We address two problems. First, in practice it is often unclear whether a complex persuasive message expresses a gain, non-gain, loss, or non-loss, impeding choice of the most persuasive frame. We have developed a measure that allows such classifications. Second, which frame is most persuasive depends on the situation. However, for some situations two existing theories, one of them by us, make contradictory predictions. We have conducted one successful experiment as part of a master's thesis 2 . We seek to conduct a second experiment that would address a weakness of the first one and test if the first one is replicable. We need to succeed with both to be able to publish the research in a competitive academic journal. Based on the evidence gathered in the proposed project we would be in a strong position to apply for public funding to further investigate this important issue. We summarize our prior result, and expect to observe it again: We showed consumers two different approaches to get society to remove CO2. One approach involved voluntary consumer payments, the other forcing companies to invest. After reading a message consumers rated their attitudes toward these approaches. A new type of measure we developed showed that respondents perceived the consumer voluntary approach as non-loss, and the company mandatory one as gain. For each approach we developed two persuasive messages that communicated that not doing anything about CO2 is bad, thus urging to do something. One message expressed this badness as damage to the environment (loss), and the other as lost opportunity for the environment (non-gain). When we considered all respondents, neither of these two message frames persuaded them more than the other. However, we then divided respondents into two groups depending on how much they cared about the environment, that is, how involved they were with the topic at hand. For those highly involved, one of the two theories predicted well which frame is most persuasive, whereas for those with low involvement, the other theory predicted well. In retrospect, this result is theoretically meaningful, but we did not expect it. Involvement may be a missing piece in the puzzle as to which theory makes more accurate predictions.
FV-103 | Gesellschaftlicher Nutzen von Mobility Pricing Research Project | 2 Project MembersIn einem Verkehrsexperiment konnten wir zeigen, dass Mobility-Pricing in der Schweiz zu einer signifikanten Reduktion der externen Kosten führt (Hintermann et al., 2021). Da diese Studie jedoch nur eine Stichprobe der Verkehrsteilnehmer umfasste war der Effekt auf das gesamte Verkehrssystem minimal. Dies war von Vorteil für das Experiment, aber es bedeutet auch, dass es für die Berechnung der Wohlfahrtskonsequenzen von einem allgemeinen Pricing zusätzliche Schritte braucht. Im anvisierten Projekt werden wir den volkswirtschaftlichen Netto-Nutzen einer Einführung von Mobility-Pricing in der Schweiz berechnen, basierend auf einer Methodologie von kürzlich erschienenen Studien. Während sich diese auf den Stau im motorisierten Individualverkehr konzentrierten, werden wir alle wichtigen externen Kosten (Stau, Klima und Gesundheit) und alle Verkehrsträger berücksichtigen (Auto, öffentlicher Verkehr und Langsamverkehr) und somit eine wichtige Lücke in der Literatur schliessen. Dabei werden GPS-Tracking- und Befragungsdaten aus unserem Verkehrsexperiment sowie aus dessen Fortsetzung während der COVID-Pandemie verwendet. Durch die umfangreichen Mobilitätsdaten auf Etappenebene und die ausführlichen Umfragedaten der Probanden, kann auch die Verteilung der Wohlfahrtseffekte untersucht werden, z.B. nach Alter, Geschlecht, Einkommen oder dem Grad der Urbanisierung des Wohnorts. Problemstellung Die wichtigsten externen Kosten im Verkehr entstehen durch Zeitverluste im Stau, Klimaschäden, Gesundheitsschäden durch lokale Emissionen und Unfälle. Diese Kosten werden nicht von den einzelnen Verkehrsteilnehmern getragen, sondern von der Allgemeinheit. In unserem Verkehrsexperiment vom Jahr 2019 haben wir diese externen Kosten mit Mobility-Pricing internalisiert und den Effekt auf ca. 1,200 Probanden gemessen, relativ zu einer Kontrollgruppe. Die Probanden haben auf das Pricing reagiert und ihre externen Kosten reduziert. Dies erfolgte hauptsächlich durch ein Umsteigen vom privaten in den öffentlichen Verkehr und eine Veränderung der Abfahrtszeiten durch Autofahrer. Diese Veränderungen stellen jedoch nur partielle Effekte dar, da sie die Auswirkungen von Mobility-Pricing auf das Verkehrsgleichgewicht nicht berücksichtigen. Wenn viele Leute ihre Abfahrtszeiten verändern oder das Verkehrsmittel wechseln, dann verschiebt sich damit auch das Verkehrsaufkommen im Tagesverlauf und über die Verkehrsträger. Dies führt zu einer Veränderung in den externen Kosten des Verkehrs und damit zu einer Adjustierung der optimalen Mobilitätspreise, was wiederum das Gleichgewicht verändert etc. Aus diesem Grund braucht es für die Wohlfahrtsanalyse eine Berechnung im allgemeinen (statt im partiellen) Gleichgewicht. Zielsetzung Die Quantifizierung der Wohlfahrtseffekte erfordert ein umfassendes Modell von Mobilitätsnachfrage und -angebot. Die wichtigsten Präferenzparameter, die in diesem Modell vorkommen, sind (i) der Wert von Zeit unterwegs («Value of Travel Time Savings»), (ii) die Zahlungsbereitschaft, nicht zu früh oder zu spät am Ziel einzutreffen («Scheduling Costs») und (iii) die relative Präferenz für unterschiedliche Verkehrsmittel. Diese können aufgrund der Beobachtungen während des Experiments ökonometrisch geschätzt werden. Für die allgemeinen Gleichgewichtseffekte braucht es eine Übersetzung der veränderten Verkehrsnachfrage auf den Stau, die Auslastung des öffentlichen Verkehrs und die Verkehrsunfälle (die lokalen und globalen Emissionen sind linear und brauchen keine spezielle Modellierung). Dafür verwenden wir die exogene Variation der Mobilität in der Schweiz aufgrund der COVID-19-Pandemie, die wir mit unserem Tracking Panel beobachtet haben. Die Kombination einer exogenen Preisvariation (im Experiment) und einer exogenen Nachfragereduktion (durch COVID-19) ist einzigartig in der Literatur und schafft sehr gute Voraussetzungen, die Wohlfahrtsgewinne von Mobility-Pricing zuverlässig zu schätzen. Ein wichtiger Aspekt bei Einführung von Mobility-Pricing ist ausserdem die Umverteilung (oder «Regressivität»). Abhängig von den Ergebnissen der obigen Wohlfahrtsanalyse wäre es möglich, ein Rückverteilungssystem zu konzipieren, das gezielt Personen entlastet, die überproportional durch ein Mobility-Pricing System belastet wären.
Forschungsförderung EBIS (Kantone, diverse) Research Project | 2 Project MembersThis funding adds to the project "Reduction of transport emissions due to E-biking in Switzerland (EBIS)". The cantonal funding allows us to increase the sample size based on regional over-sampling.
Reduction of transport emissions due to E-biking in Switzerland (EBIS) Research Project | 3 Project MembersWe investigate the potential for reducing carbon emissions in the transport sector due to E-biking based on a large sample of cyclists in Switzerland and using a combination of GPS tracking and surveys. The first part of the project assesses the current situation and improves on the present state of knowledge about mode shift due to E-biking and the transport choices of E-bikers. The second part consists of a randomized control trial that implements a pricing intervention on a subset of the participants with the aim of substituting car with E-bike travel. We measure the resulting causal effects on carbon emissions and investigate mode substitution, with a special focus on cycling, driving and public transport. The third part conducts a route choice analysis to inform future policies and measures to promote cycling. Last, the potential for carbon reductions due to E-biking in Switzerland is computed, using insights from this research and considering different scenarios, i.e., future mobility pricing and transport policies.
Pollution, environmental regulation and firm performance Research Project | 2 Project MembersThis project investigates the impact of the EU Emissions Trading Scheme (EU ETS) on firms using administrative data from German manufacturing for the years 2003-2019. We study the implications of climate policy on firms and, indirectly via changes in emissions intensities, on the environment. At the same time, there is mounting evidence that environmental quality also affects firms. The second part of our project focuses on this reverse causality and thus contributes to our knowledge about the interactions between the economy and the environment. Our work builds on our previous research funded by the SNSF involving the same data. The first work package examines the long-term effects of the EU ETS via its impact on regulated firms' product choices. The introduction of a price on greenhouse gas emissions changes the relative production costs, which will be reflected in product prices. This is the subject of our previous research, in which we show that manufacturing firms in Germany pass on their marginal costs fully to consumers. In that previous work, we took a short-term perspective and examined what happens to the output price if the production costs increase by one Euro. In this project, we focus on the long-term implications in the form of product choice. Over time, consumers can be expected to substitute away from emissions-intensive products, which have become costlier, towards greener products. In order to retain market share and profits in the face of this demand response, firms will need to adjust their product portfolio towards less emission-intensive products over time. In WP1, we investigate whether such a shift can be observed among German manufacturing firms, and whether this shift is stronger for the firms covered by the EU ETS. Our second work package builds on the recent literature about the effect of air quality and tem-perature on firm outcomes. There is an older literature that documents detrimental health effects as a consequence of air pollution and temperature variations. This is a first-order reason to establish clean air standards. Environmental policy is usually interpreted as a tradeoff between more environmental quality (and better health) on the one hand, and a reduction in GDP and growth on the other. However, if workers (and thus firms) become more productive if the air quality improves, as suggested by the recent literature, then this tradeoff may in fact be a win-win-situation, at least within certain pollution bounds. To investigate this question, we will link satellite-based data of ambient air pollution and temperature to the location of individual manufacturing plants in Germany and investigate whether changes in environ-mental quality causally affect firms' productivity. We address the potential endogeneity problem between industrial production/productivity and pollution by using an instrumental variable approach that relies on temperature inversions and prevailing wind directions. As the main measure for productivity, we propose to use the total factor productivity that we have computed for the firms in our sample in the context of our previous project. There is a high degree of synergy between the proposed and the previous research as both projects rely on the same administrative firm-level data. The data are very rich in terms of information content, but the prevailing confidentiality rules render them difficult to work with. Having incurred significant fixed costs in terms of our understanding the data and their limitations, we are now in a position where additional time invested will likely have a high benefit-cost ratio. We propose to use the same data to follow up on our previous project with a new set of research questions. Including both a Postdoc and a Ph.D. in the project will furthermore contribute to capacity building in research. We hope that the proposed work will deepen our understanding of the effects of air pollution and its regulation on firms and thus inform policy makers about the costs and benefits of environmental regulation.
