The Good Council: Deliberating inequality in a field experiment

Last registered on June 25, 2024


Trial Information

General Information

The Good Council: Deliberating inequality in a field experiment
Initial registration date
June 24, 2024

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
June 25, 2024, 2:10 PM EDT

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


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Primary Investigator

University of Oxford

Other Primary Investigator(s)

PI Affiliation
Vienna University of Economics and Business
PI Affiliation
FORESIGHT/University of Vienna
PI Affiliation
Vienna University of Economics and Business
PI Affiliation
University of Vienna

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
This document describes the research design and analysis strategy of our field experiment, designed around a citizens’ assembly to evaluate the impact of deliberative democracy on preferences for redistribution. The citizens’ assembly, Good Council (“Guter Rat”) takes place in 2024 in Austria. It consists of 50 residents of Austria who are selected to convene and develop ideas for addressing economic inequality, deciding on allocating an endowment of EUR 25 million to purposes conducive to this goal. The Good Council is an example of deliberative democracy, where participants “redistribute” a significant sum donated by a high-net-worth individual. We study whether participation in a citizens’ assembly affects (redistributive) policy preferences and related outcomes of participants. In particular, we are interested in preferences for redistribution and democracy using standard measures. Additionally, our rich questionnaire includes a battery of questions on people’s specific policy views and priorities.
The pre-analysis plan starts with an outline of the treatment and its timeline. We, then, provide a detailed discussion of our study design including data, sample selection, treatment assignment, variables used, and outcomes of interest. Finally, we conclude by specifying our statistical approach to inference.
External Link(s)

Registration Citation

Disslbacher, Franziska et al. 2024. "The Good Council: Deliberating inequality in a field experiment." AEA RCT Registry. June 25.
Sponsors & Partners

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Experimental Details


From March 2024 to December 2024, we conduct a field experiment designed in cooperation and implemented by the Good Council (“Guter Rat” in German). 50 people are randomly selected for the Good Council to reflect the population of Austria as accurately as possible. The 50 people selected meet over six weekends to deliberate and receive input from experts on the distribution of wealth in Austria and its impact on politics and society. The Good Council develops ideas for dealing with the distribution of wealth and eventually decides how the EUR 25 million endowment should be spent. The funding is provided by million heiress Marlene Engelhorn, who donates EUR 25 million.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Political efficacy, Political participation, Knowledge of wealth distribution, Support for an egalitarian wealth distribution, Support for wealth taxation
Primary Outcomes (explanation)
Political efficacy: Normalized index of a four-item scale, Political participation: Normalized index of a seven-item scale, Knowledge of wealth distribution: Normalized index of a one-item scale, Support for an egalitarian wealth distribution: Normalized index of a one-item scale, Support for wealth taxation: Binary indicator of a two-item scale

Secondary Outcomes

Secondary Outcomes (end points)
detailed in the pre-analysis plan.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
To identify the causal effects of participation in deliberative democracy, we rely on random treatment assignment. The treatment group consists of 50 people from the pool of registered individuals who serve on the Good Council. Registered individuals are selected into the treatment group based on a random process that is specifically designed to ensure representativeness of the population when selecting members for citizens’ assemblies (Flanigan et al., 2021). The control group comprises a subgroup of other registered individuals. Since individuals opt in for our study’s data collection, the control group sample size is smaller than the complete set of registered individuals. The balance table (Table 2) details the means in the characteristics used for stratification between treatment and control groups. Since the treatment group is selected to correspond to the characteristics of the total population but the entire pool of those interested in participating does not, the control group differs by definition in some observable characteristics from the treatment group. Yet, the detailed stratification yields balanced groups for most covariates. Statistically significant imbalances emerge by age (for those 60 years and older 2), educational attainment (specifically, compulsory schooling, apprenticeship, and university degrees), employment type (dependent employment, retirement), household income (first income quartile), and views on wealth equality. The differences reflect the disproportionate self-selection of individuals with 30-44 years, higher educational attainment, dependent employment, with incomes in the upper half of the distribution, and more pessimistic views about the fairness of the perceived wealth distribution. Further, Table 3 compares the characteristics of participants with replacement group members, and Table 4 with control group members and replacement group members. There are no significant differences, though in part due to the small sample size of 14 individuals in the replacement group. In addition, Table 5 compares the characteristics of participants with those of the general population. The comparison confirms that the treatment group is highly representative of the general population.

We use three contrasts to adjust for imbalances between our treatment and control groups: First, we compare individuals in the treatment with the control group. The groups are balanced in most, but not all, characteristics. They should further be identical in unobserved characteristics conditional on the imbalanced observable characteristics. Second, we compare our treatment group with a random sample of the Austrian population that is freshly drawn in June 2024. Both groups should balanced in terms of observed characteristics since the treatment group is representative in observable characteristics. However, the treatment group may differ in unobserved characteristics as treatment is based on opting-in by registering for the Good Council citizens’ assembly. Third, we compare our control group with the random sample of the general population. By reweighting respondents in the control group to correspond to the general population in terms of observable characteristics, any differences in outcomes can be inferred to stem from unobservable differences due to self-selection. This serves as a test to quantify the magnitude of the potential selection bias from registering for the Good Council. It allows us to assess how clean or biased the comparison between the treatment group and the random sample of the Austrian population is.
Experimental Design Details
Not available
Randomization Method
The treatment is randomly assigned in the office by a computer.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
50 participants in the treatment group, 292 participants in the control group, and around 1000 individuals in the general population group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
WU Ethics board
IRB Approval Date
IRB Approval Number
Analysis Plan

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