Attacking Tax Evasion in Production Networks: Theory and Evidence from Paraguay

Last registered on October 04, 2023
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Pre-Trial

Trial Information

General Information

Title
Attacking Tax Evasion in Production Networks: Theory and Evidence from Paraguay
RCT ID
AEARCTR-0012208
Initial registration date
September 28, 2023

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
October 04, 2023, 4:10 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country
Paraguay
Region

Primary Investigator

Name
Michael Best
Affiliation
Columbia University
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Other Primary Investigator(s)

PI Name
Gaston Pierri
PI Affiliation
Inter-American Development Bank
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PI Name
Evan Sadler
PI Affiliation
Columbia University
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Additional Trial Information

Status
On going
Start date
2021-01-01
End date
2024-06-30
Keywords
Firms & Productivity, Governance
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The creation of a tax system that raises substantial revenues efficiently and equitably is one
of the central challenges in economic development. In large part, this relies on the creation of
the capacity to enforce taxes effectively—to reduce tax evasion. Doing this requires a detailed
understanding of the drivers of tax evasion and the optimal allocation of extremely scarce tax
enforcement resources. Our project combines new theory and a Randomized Controlled Trial
(RCT) in Paraguay to provide new insights into tax evasion by firms, the strength of enforcement
spillovers through production networks, and how to optimally target enforcement activities.
External Link(s)

Registration Citation

Citation
Best, Michael, Gaston Pierri and Evan Sadler. 2023. "Attacking Tax Evasion in Production Networks: Theory and Evidence from Paraguay." AEA RCT Registry. October 04. https://doi.org/10.1257/rct.12208-1.0
Sponsors & Partners

Sponsors

Partner

Name
Subsecretaría de Estado de Tributación (SET)
Type
government
URL
https://www.set.gov.py/web/portal-institucional/
Experimental Details

Interventions

Intervention(s)
The intervention in the first experiment consists of sending notices to taxpayers suspected of underreporting
their taxable sales, based on the work of Carrillo, Pomeranz, and Singhal (2017). For
each taxpayer-month we calculated the sum of sales made by that taxpayer as reported by their clients in third-party information reports (“libros de compras”) annexed to the clients’ VAT returns. If this amount was larger than the total sales reported by the taxpayer by at least
10,000,000 Guaranies (around USD 7,000), that taxpayer-month was eligible to receive a notice.
Intervention Start Date
2022-08-15
Intervention End Date
2024-01-31

Primary Outcomes

Primary Outcomes (end points)
Do firms that receive the intervention declare more sales? Less inputs? Pay more taxes?
Is the discrepancy between the seller's VAT reports and the sum of the buyers' purchases report reduced by the intervention? Is the discrepancy between the seller's bilateral sales reports and the buyer's bilateral purchases report reduced by the intervention?
Do the clients of firms that received the intervention change their behaviour?
Do the suppliers of firms that received the intervention change their behaviour?
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Do firms update their tax declaration? When do they make these amendments? Do firms pay more taxes on the declarations for which there were discrepancies?
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We randomly assign taxpayers to receive notices. The randomization method is optimized to allow for the detection of spillovers.
Experimental Design Details
Not available
Randomization Method
computer
Randomization Unit
taxpayer
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
5500 taxpayers
Sample size: planned number of observations
5,500 treated taxpayers, circa 100,000 untreated taxpayers. All observed in the administrative data from January 2021-present.
Sample size (or number of clusters) by treatment arms
5,500 treated, remaining 29,000 eligible firms as controls
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Columbia University
IRB Approval Date
2020-02-04
IRB Approval Number
AAAS8400
Analysis Plan

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