Framing urban tolls

Last registered on January 23, 2023

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Pre-Trial

Trial Information

General Information

Title

Framing urban tolls

RCT ID

AEARCTR-0010783

Initial registration date

January 18, 2023

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

January 23, 2023, 6:44 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

Region

Country

Region

Primary Investigator

Name

Johanna Arlinghaus

Affiliation

Mercator Research Institute on Global Commons and Climate Change

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Théo Konc

PI Affiliation

Berlin Technical University, Potsdam Institute for Climate Impact Research (PIK)

Contact Investigator

PI Name

Linus Mattauch

PI Affiliation

Berlin Technical University, Potsdam Institute for Climate Impact Research (PIK)

Contact Investigator

PI Name

Stephan Sommer

PI Affiliation

Bochum University of Applied Sciencs, RWI-Leibniz Institute for Economic Research

Contact Investigator

Additional Trial Information

Status

In development

Start date

2023-01-30

End date

2023-03-31

Keywords

Environment & Energy

Additional Keywords

JEL code(s)

D30, H23, H41, H71, I14, J22, O18, Q53

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

In this project, we analyse how different policy framings affect citizens' acceptance of urban tolls in two major European metropolitan areas: Paris-Ile de France and the Berlin-Brandenburg agglomeration. Furthermore, we investigate heterogeneity in views based on urban vs. suburban residence, trust in institutions and political views. To this end, we implement a large-scale survey of a total of 4000 urban and suburban households, representative for gender, education and age across the two metropolitan areas with the survey company respondi/bilendi. Within the survey, we randomize in-built video treatments to inform respondents of the tolls’ expected effects on (i) air pollution, (ii) time savings or (iii) greenhouse gas emissions. The effects of the treatment videos on support for an urban toll are compared to a control group, which receives a video with largely uninformative content. The research design allows testing a range of hypotheses with regards to the effects of the different treatments on policy support, including across different population groups (e.g. urban vs suburban, Berlin-Brandenburg vs Paris Ile de France) as well as hypotheses relating to interactions of these main hypothesized effects with several individual characteristics, such as political attitudes and mobility behavior.

External Link(s)

Registration Citation

Citation

Arlinghaus, Johanna et al. 2023. "Framing urban tolls." AEA RCT Registry. January 23. https://doi.org/10.1257/rct.10783-1.0

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Experimental Details

Interventions

Intervention(s)

We use a questionnaire with in-built video treatments to analyse the effect of different policy framings on the support of urban tolls. After screening for the respective quotas within the questionnaire, we ask respondents a suite of questions on their personal background as well as on their personal and political attitudes and their mobility behavior. After these questions we introduce the topic of congestion prices. Specifically, we provide information on the functioning of urban tolls by displaying short video messages within the survey. After the video messages, we tell respondents to consider the introduction of an urban toll in their city and ask whether or not they would support this policy. We use the magnitude of the London congestion charge upon its introduction in 2003 as an anchoring price. We then ask a range of questions on revenue use, predicted behavior change as well as the potential winners and losers of the policy, including in the urban and suburban context.

Intervention Start Date

2023-01-31

Intervention End Date

2023-02-13

Primary Outcomes

Primary Outcomes (end points)

Our primary outcome variable is support for an urban toll.

Primary Outcomes (explanation)

The support for an urban toll is a question in our survey, placed just after the display of the video treatments.

Secondary Outcomes

Secondary Outcomes (end points)

We quantify heterogeneous treatment effects by including interactions between socioeconomic characteristics and treatment status. Two interesting candidates for interactions are whether the respondents reside within the city center or in suburban areas and to what extent they use the car for commuting.

Secondary Outcomes (explanation)

Experimental Design

In each city, the sample is divided in four randomised groups, three of which are shown a treatment video on the potential effects of urban tolls. The control group likewise views a city-specific video, but the contents bear no relation to urban tolls. The content of the video messages is delivered in French and in German, respectively. We then ask a range of questions on revenue use, predicted behavior change as well as the potential winners and losers of the policy, including in the urban and suburban context.

Experimental Design Details

Randomization Method

Random assignement to treatment group is done by the survey company.

Randomization Unit

The display of the videos is randomized at individual level.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

4000 households, with 2000 each in the Berlin-Brandenburg and the Paris-Ile-de-France agglomeration.

Sample size: planned number of observations

4000 households, with 2000 each in the Berlin-Brandenburg and the Paris-Ile-de-France agglomeration.

Sample size (or number of clusters) by treatment arms

We include 4000 households in our survey who are randomly split into four groups, i.e. 1000 per group.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

We include 4000 households in our survey who are randomly split into four groups (T = 0.5), i.e. 1000 per group. Based on Hamilton (2014), we assume that a third of respondents would support an urban toll in the absence of any treatment (P). Using the standard level of significance alpha=0.05, the standard power level of beta=0.8 and a two-sided test, the minimum detectable treatment effect amounts to delta=5.7 percentage points.

Supporting Documents and Materials

IRB