Social Preferences and Tax Compliance
Last registered on April 29, 2019


Trial Information
General Information
Social Preferences and Tax Compliance
Initial registration date
April 27, 2019
Last updated
April 29, 2019 10:59 PM EDT

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Primary Investigator
Other Primary Investigator(s)
PI Affiliation
Universidad de la Republica
PI Affiliation
Universidad de la Republica
PI Affiliation
Universidad de la Republica
Additional Trial Information
On going
Start date
End date
Secondary IDs
We study the role of social preferences and social norms in shaping tax compliance.
External Link(s)
Registration Citation
Bergolo, Marcelo et al. 2019. "Social Preferences and Tax Compliance." AEA RCT Registry. April 29.
Experimental Details
We designed and conducted an experiment to understand the role of social preferences and social norms in shaping tax compliance.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
We plan to use the tax records to construct proxies for tax evasion.
Primary Outcomes (explanation)
We will not know which of the proxies will have meaningful variation until after we gain access to the administrative records. For example, one proxy for evasion that we are planning to use is based on employed individuals, whose wages are reported to the tax agency by the employee as well as by the employer. One proxy of tax evasion is then given by the gap between the amount reported by the employee and the amount reported by the employer. We are planning to construct other proxies too. For example, we can compare the number of dependents claimed by the subject in their tax forms to the actual number of dependents that they have as reported by third-parties. And, if we can find other sources of third-party reporting, we can construct other proxies in a similar spirit. If multiple proxies are viable, we will analyze them separately. Last, we will try to exploit the fact that self-employed individuals have more wiggle room than employed individuals when it comes to under-reporting. For example, if a particular piece of information increases the income reported by the self-employed but does not change the income reported by the salaried workers, that would suggest that the piece of information reduced tax evasion.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
There are two types of analysis: non-experimental and experimental. They are both intended to shed light on similar questions, but using complementary approaches.
Experimental Design Details
Not available
Randomization Method
Real-time randomization programmed into the Qualtrics platform.
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
The final number of respondents will depend on the response rate to the survey. We are sending survey invitations to about 80,000 individuals. Based on a small pilot, our best guess is that we will collect around 5,000 responses.
Sample size: planned number of observations
We expect a minimum of 2,000 individuals
Sample size (or number of clusters) by treatment arms
The four groups in the information-provision experiment have equal likelihood of being chosen, resulting in four treatment groups with equal number of observations.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
IRB Approval Date
IRB Approval Number