Using Intelligence from International Tax Cooperation to Improve Voluntary Tax Compliance: Evidence from a Swedish Field Study

Last registered on May 04, 2020

Pre-Trial

Trial Information

General Information

Title
Using Intelligence from International Tax Cooperation to Improve Voluntary Tax Compliance: Evidence from a Swedish Field Study
RCT ID
AEARCTR-0005800
Initial registration date
May 04, 2020
Last updated
May 04, 2020, 1:58 PM EDT

Locations

Region

Primary Investigator

Affiliation

Other Primary Investigator(s)

PI Affiliation
Department of Statistics, Uppsala University

Additional Trial Information

Status
On going
Start date
2020-03-25
End date
2020-12-31
Secondary IDs
Abstract
The purpose of the study is to estimate the effect of a low-cost intervention (a digitally disseminated message) on the subsequent compliance of taxpayers with respect to declaration of foreign income. We use a statistical design involving rerandomization.
External Link(s)

Registration Citation

Citation
Angelov, Nikolay and Per Johansson. 2020. "Using Intelligence from International Tax Cooperation to Improve Voluntary Tax Compliance: Evidence from a Swedish Field Study." AEA RCT Registry. May 04. https://doi.org/10.1257/rct.5800-1.0
Experimental Details

Interventions

Intervention(s)
The intervention consists of sending a message in digital form to the treated individuals and leaving the controls untreated.
Intervention Start Date
2020-03-25
Intervention End Date
2020-05-31

Primary Outcomes

Primary Outcomes (end points)
Two variables from the income tax declaration for income year 2019: total capital income and total tax paid.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We use a balanced design, i.e., a design where the number of treated is the same as the number of controls. The experimental design is stratified on two binary variables: gender and a measure of previous tax compliance. Within each stratum, we use a rerandomization stategy where the idea is to remove from consideration allocations with imbalance in observed covariates between treated and control units and then randomize within the set of allocations with balance on these covariates. We randomly select an allocation with a Mahalanobis distance between treated and controls means of six covariates below a threshold. In practice, this is achieved by randomly drawing new allocations until the criterion is fulfilled.
Experimental Design Details
Randomization Method
Rerandomization done by a computer.
Randomization Unit
Individual taxpayers within four strata. The strata were defined by a combination of gender and a measure of previous compliance.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1,000 taxpayers
Sample size: planned number of observations
1,000 taxpayers
Sample size (or number of clusters) by treatment arms
500 treated and 500 controls
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Pre-Analysis Plan

MD5: 40c3791cfb6b23691ad0777955847e0c

SHA1: 7bd9ddcb8ee3b0ce06c4ba1da510ede70c30e2b6

Uploaded At: May 04, 2020

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials