Tax Evasion Experiment

Last registered on November 05, 2021

Pre-Trial

Trial Information

General Information

Title
Tax Evasion Experiment
RCT ID
AEARCTR-0008468
Initial registration date
November 02, 2021
Last updated
November 05, 2021, 6:18 PM EDT

Locations

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Primary Investigator

Affiliation
Alzahra University

Other Primary Investigator(s)

PI Affiliation
University of Leicester
PI Affiliation
Centre for Social and Behaviour Change, Ashoka University

Additional Trial Information

Status
On going
Start date
2021-10-18
End date
2022-04-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This experiment measures loss and risk aversion in the context of entitled and unentitled income in order to understand tax evasion with relation to mental accounting. Participants will be asked to make decisions regarding paying taxes while taking into account various factors like tax, audit, and fine rates. Subject-specific loss aversions and risk aversions are elicited through binary lotteries.
External Link(s)

Registration Citation

Citation
Dhami, Sanjit, Narges Hajimoladarvish and Pavan Mamidi. 2021. "Tax Evasion Experiment." AEA RCT Registry. November 05. https://doi.org/10.1257/rct.8468-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2021-11-10
Intervention End Date
2021-12-31

Primary Outcomes

Primary Outcomes (end points)
Tax evasion, Loss aversion, Risk aversion
Primary Outcomes (explanation)
Tax evasion is calculated as income minus declared income in the experiment (we have tax evasion for varying policy parameters. However our primary variable of interest is when the probability of audit is decreasing in declared income).
Risk aversion is calculated through a 6 bisection procedure to elicit certainty equivalent of a lottery. This indifference helps us to estimate the parameter of utility function which is required for the calculation of loss aversion.
Loss aversion is calculated through an indifference between subjects' income (y) and a lottery that pays y-z with 50 per cent chance and y + x otherwise.

Secondary Outcomes

Secondary Outcomes (end points)
Our control variables include Age, Gender, Marital Status, Education, Expenses, Employment, Religion, Number of earning members in the family, Household annual income and time spent in the experiment.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We are interested to investigate if tax evasion is related to the source of income (Labour and non-labour income) and behavioural parameters such as loss aversion as predicted by our model. Subjects will either earn some income (labour) in the experiment or they are randomly given some income and have to declare it for tax purposes. We also elicit subject-specific loss aversion and risk aversion. We test if the source of income, loss aversion and risk aversion affects tax evasion. We also randomly prime subjects to tax evasion data in India and see if has any effect on subjects tax evasion.
Experimental Design Details
Not available
Randomization Method
Randomization is through a random draw by a computer built within the experiment online.
Randomization Unit
Our experiment design has multiple layers of randomization that take into account the various effects we are interested in exploring. We first randomly assign subjects into the primed group and the unprimed group, wherein the primed group will be exposed to data on tax evasion in India and the unprimed group will not be exposed to that data. We are also interested in distinguishing between the effects of the type of income source, hence subjects are further assigned between the entitled/earned income arm and the unentitled/random income arm. Finally, we take into account task order randomization between the lottery (loss aversion) task and the tax payment task to control for any possible order effects.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
We will have 8 groups of people.
Sample size: planned number of observations
We will have a maximum of 60 individuals in each group.
Sample size (or number of clusters) by treatment arms
60 primed entitled income (lottery-tax), 60 primed entitled income (tax-lottery), 60 primed unentitled income (lottery-tax), 60 primed unentitled income (tax-lottery), 60 unprimed entitled income (lottery-tax), 60 unprimed entitled income (tax-lottery), 60 unprimed unentitled income (lottery-tax), 60 unprimed unentitled income (tax-lottery). Hence, in total we will have 480 individuals.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
The Ashoka Institutional Review Board
IRB Approval Date
2021-10-28
IRB Approval Number
21_21_Mamidi
Analysis Plan

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