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Policy Promises

Last registered on April 16, 2020


Trial Information

General Information

Policy Promises
Initial registration date
May 09, 2018

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
May 10, 2018, 4:37 PM EDT

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

Last updated
April 16, 2020, 5:23 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

Department of Economics, University of Salzburg and VCEE, University of Vienna

Other Primary Investigator(s)

PI Affiliation
Department of Economics, University of Vienna
PI Affiliation
Department of Economics, University of Auckland

Additional Trial Information

Start date
End date
Secondary IDs
In a controlled laboratory experiment, we investigate in a standard voting game whether politicians choose ambiguous platforms and whether voters vote for these platforms. In particular, we focus on how the correlation structure in the population affects behavior of politicians and voters and how competition between politicians affects platform choices.
External Link(s)

Registration Citation

Tolvanen, Juha, James Tremewan and Alexander K. Wagner. 2020. "Policy Promises." AEA RCT Registry. April 16.
Former Citation
Tolvanen, Juha, James Tremewan and Alexander K. Wagner. 2020. "Policy Promises." AEA RCT Registry. April 16.
Sponsors & Partners

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Experimental Details


The main experimental treatments vary exogenously the strategic incentives for candidates to choose ambiguous policies and voters to support ambiguous platforms.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The primary outcome variables of interest are individual-level behavior of candidates and voters in the voting game, i.e. choice of an ambiguous platform (for candidates) and voting for a proposed ambiguous platform (for voters).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Secondary outcomes of interest are aggregate-level voting outcomes and individual-level variables such as risk aversion, ambiguity aversion, and correlation neglect. These individual-level characteristics are predicted theoretically to influence voters’ propensity to vote for ambiguous platforms proposed by candidates, and to influence candidates’ propensity to choose ambiguous platforms.

Experimental Design

Experimental Design
Between-subject design.
Experimental Design Details
We run two treatments. In one treatment one of the two candidates always commits to a centrist policy. In the second treatment, both candidates are free to select a platform.

We hypothesize that ambiguous platforms are more common and more likely to win the election when an unknown challenger is running against a known centrist incumbent than when two unknown candidates are competing against each other. Our main treatment tests this hypothesis.

Outcome variables of interest will be tested across treatments in the first game, on average, and over time in the repeated-one shot games. In addition, we also test whether risk preferences, ambiguity preferences, as well as the individual degree of correlation neglect affects the choice of ambiguous platforms and voting for ambiguous platforms.
Randomization Method
At the start of each session, participants are randomly assigned to a treatment.
Randomization Unit
The randomization unit is an experimental session.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
2 (between-subject treatments) x 8 (sessions). There will be 15 voters and 10 candidates in each session, each of whom makes 20 decisions. Decisions are clustered at the session level.
Sample size: planned number of observations
Sample size for voters is 4800 (15 voter * 20 periods * 16 sessions). Sample size for candidates is 3200 (10 candidates * 20 periods * 16 sessions).
Sample size (or number of clusters) by treatment arms
In each of the two treatment arms there is an equal number of observations for voters (2400) and for candidates (1600).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Collection Completion Date
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials