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Decomposing Trust
Last registered on December 11, 2019


Trial Information
General Information
Decomposing Trust
Initial registration date
December 10, 2019
Last updated
December 11, 2019 11:43 AM EST

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Primary Investigator
Lund University
Other Primary Investigator(s)
PI Affiliation
HU Berlin
PI Affiliation
DIW and HU Berlin
PI Affiliation
HU Berlin
Additional Trial Information
In development
Start date
End date
Secondary IDs
Trust is thought to be an important driver of economic growth and other economic outcomes. Previous studies suggest that trust may be a combination of risk attitudes, social preferences, betrayal aversion, and beliefs about the probability of being reciprocated. We compare the results of a binary trust game to the results of a series of control treatments that remove the effects of one of or more of these components of trust by design. This allows us to decompose variation in trust behavior into its underlying factors. We will compare our results to previous studies that use different methods to decompose trust, and also decompose the drivers of a potential gender difference in trust, should one emerge.
External Link(s)
Registration Citation
Engelmann, Dirk et al. 2019. "Decomposing Trust." AEA RCT Registry. December 11. https://doi.org/10.1257/rct.5146-1.0.
Experimental Details
We compare the results of a binary trust game to the results of a series of within-subject control treatments that remove the effects of individual explanatory factors of trust (or combinations thereof) by design.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Trust and trust-equivalent decisions
Primary Outcomes (explanation)
Trust-equivalent decisions are the binary choices in the control treatments that are similar to the binary trust game but iteratively remove the effect of one of the explanatory factors of trust behavior by design.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We investigate choices in a laboratory experiment in a binary trust game. We compare the choices in a standard game with choice lists that condition on the number of reciprocating players (or a corresponding lottery). These choice lists systematically vary whether social preferences, risk preferences and betrayal aversion should have an impact. We also elicit beliefs about the number of reciprocating participants in the session and independent measures of risk and social preferences.
Experimental Design Details
Not available
Randomization Method
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
200 Individuals
Sample size: planned number of observations
200 Individuals
Sample size (or number of clusters) by treatment arms
200 individuals per treatment arm (this is a within-subject design).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See the pre-analyis plan.
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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