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Initial registration date
December 10, 2019
December 11, 2019 11:43 AM EST
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Other Primary Investigator(s)
DIW and HU Berlin
Additional Trial Information
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.
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 (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 (end points)
Secondary Outcomes (explanation)
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
Was the treatment clustered?
Sample size: planned number of clusters
Sample size: planned number of observations
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.
INSTITUTIONAL REVIEW BOARDS (IRBs)