Information, Fiscal Capacity and Tax Revenues: An Experimental Evaluation

Last registered on July 20, 2023
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Pre-Trial

Trial Information

General Information

Title
Information, Fiscal Capacity and Tax Revenues: An Experimental Evaluation
RCT ID
AEARCTR-0002958
Initial registration date
May 04, 2018

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
May 05, 2018, 6:55 PM EDT

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

Last updated
July 20, 2023, 3:53 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
Uganda
Region

Primary Investigator

Name
Miguel Almunia
Affiliation
CUNEF Universidad
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Other Primary Investigator(s)

PI Name
Jonas Hjort
PI Affiliation
Columbia University
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PI Name
Justine Knebelmann
PI Affiliation
Paris School of Economics
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PI Name
Lin Tian
PI Affiliation
Columbia University
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PI Name
David J. Henning
PI Affiliation
UCLA
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Additional Trial Information

Status
Completed
Start date
2016-09-01
End date
2019-09-01
Keywords
Firms & Productivity, Governance
Additional Keywords
Taxes, fiscal capacity, value-added tax (VAT), spillovers
JEL code(s)
H26, H32, L14, O23
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The value-added tax (VAT) is one of the most important sources of domestic tax revenue in developing countries. In theory, the VAT has two desirable properties: it is more efficient than alternative tax instruments, and it is self-enforcing because the two sides of each transaction (seller and buyer) have incentives to report truthfully. However, when enforcement capacity is limited, these two properties can break down, leading to economic distortions and tax revenue losses. When cross-checking the transactions reported monthly by all VAT-registered firms in Uganda in the period 2015-2017, we estimated substantial discrepancies leading to a large revenue shortfall.
This experiment aims to improve compliance along the VAT chain through the implementation of a tax enforcement intervention that encourages taxpayers to amend their past tax returns and comply with tax laws in the future. Specifically, the treatment will consist of sending notification letters to about 1,000 randomly selected firm-pairs (i.e., buyer-seller pairs) with the following information: (i) their VAT returns will be closely monitored (ii) a list of up to 3 discrepancies that have been found in their past returns (iii) failure to comply could result in additional enforcement measures or prosecution. We will measure the direct effect of the letters on the tax compliance behavior of the treated firms, and also the spillover effects on their trading partners.
External Link(s)

Registration Citation

Citation
Almunia, Miguel et al. 2023. "Information, Fiscal Capacity and Tax Revenues: An Experimental Evaluation." AEA RCT Registry. July 20. https://doi.org/10.1257/rct.2958-1.2
Former Citation
Almunia, Miguel et al. 2023. "Information, Fiscal Capacity and Tax Revenues: An Experimental Evaluation." AEA RCT Registry. July 20. https://www.socialscienceregistry.org/trials/2958/history/187023
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
The intervention consists of sending notification letters to a randomly-selected sample of firm pairs (i.e., a seller-buyer match) for which reporting discrepancies were observed in the year before the intervention. The letters state that the tax authority has developed a new system to monitor VAT compliance and that the VAT returns will be closely monitored. The letters list up to 3 detected discrepancies and ask taxpayers to submit an amendment to correct any existing discrepancies. Finally, the letters point out that a failure to comply can result in additional enforcement measures or prosecution.
Intervention (Hidden)
Intervention Start Date
2018-03-02
Intervention End Date
2019-03-01

Primary Outcomes

Primary Outcomes (end points)
Retrospective outcomes: amendment of past returns (binary), amount amended (in million UGX), change in pre-treatment VAT liability after amendment (in million UGX).

Forward-looking outcomes: actual vs. predicted VAT liability in post-treatment returns, probability that the VAT liability is positive, probability that VAT liability in a given month is higher than the VAT liability in the same month the year before, probability of misreporting, amount misreported, probability of underreporting, amount underreported, continuation of the trading relationship, reported transaction volume for the pair (in million UGX).
Primary Outcomes (explanation)
We define by misreporting a case where the amount reported by the seller (S) is smaller than the amount reported by the buyer (B), i.e. a case where S<B. Similarly, we define underreporting as a case where S>B.

Note: we will measure all outcomes both for firms receiving notification letters directly, as well as for their trading partners, whose behavior may be affected through spillover effects. We will also analyze spillover effects on other trading partners of the treated firms.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The first step of the experimental design selects a "base sample" of firm-pairs. The criteria to be included in this base sample are: both firms in the buyer-seller pair must be registered for VAT, must have filed a VAT return in the last month, must not be part of the URA's annual audit plan for the financial year (2017/18), and must have an accumulated discrepancy above 1 million UGX (approximately 270 USD) over the previous 10 months.

In the second step, we employ an iterative randomization procedure to select firm-pairs to be included in the "study sample". Once the first firm-pair is randomly selected, we remove all the trading partners of the two firms (buyer and seller) from the base sample. Then, we randomly select another firm-pair and remove all their trading partners. Following this iterative procedure, we obtain a set of firm-pairs which are (i) separated by at least two nodes in the network of firm-to-firm transactions and (ii) have an accumulated misreporting discrepancy that is greater than 270 USD over the past year.

In the third step, we apply a stratified randomization procedure to allocate firms from the study sample to three treatment arms and a control group. In the first treatment arm, the letter is sent only to the buyer in the selected pair. In the second treatment arm, the letter is sent to the seller. Finally, in the third arm, letters are sent to both the buyer and seller.
Experimental Design Details
The criteria for the first step were determined in collaboration with the Uganda Revenue Authority. Our base sample consists of 15,983 firm-pairs. In the second step, we obtain a study sample of 1,673 firm-pairs. In the third step, we randomly assign 40% of firms (677) to be part of the control group. The remaining 60% (996) are divided up equally across the three treatment arms. In this randomization process, we stratify by the ratio of sales to final consumers, firm size (measured by total revenue from sales), and by whether or not the firms' headquarters are located in or outside Kampala (the capital city of Uganda).
Randomization Method
The randomization was conducted in office using a computer.
Randomization Unit
Firm-pair, defined as a buyer-seller pairs that trade with each other in the year prior to the intervention.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1,673 firm-pairs.
Sample size: planned number of observations
1,673 firm-pairs (3,346 firms).
Sample size (or number of clusters) by treatment arms
(1) a letter is sent to the buyer – 335 firm-pairs.
(2) a letter is sent to the seller – 332 firm-pairs.
(3) a letter is sent to the buyer and the seller – 329 firm-pairs.
(4) no letter was sent (control group) – 677 firm-pairs.
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's Institutional Review Board
IRB Approval Date
2017-09-06
IRB Approval Number
AAAN1410
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
April 30, 2018, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
December 31, 2018, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
1235 firm pairs.
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
1235 firm pairs.
Final Sample Size (or Number of Clusters) by Treatment Arms
494 Control, 741 treatment (242 buyer only, 238 seller only, 261 buyer & seller).
Data Publication

Data Publication

Is public data available?
No

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Program Files

Program Files
No
Reports, Papers & Other Materials

Relevant Paper(s)

Abstract
We use information on firms’ trading networks from VAT return data to design a randomized tax compliance intervention in Uganda. In treated pairs, either the seller, the buyer, or both receive letters listing discrepancies detected in past tax returns. The amendment rate is 22 percentage points higher in the treatment group, compared to 1.8% in the control group. We find spillover effects within treated firm pairs and in transactions with their untreated trading partners. Overall, there is a small increase in VAT liability for the amended returns. The intervention also leads to fewer discrepancies in subsequent tax declarations.
Citation
Almunia, Miguel, David Henning, Justine Knebelmann, Dorotyh Nakyambadde and Lin Tian (2023), “Leveraging Trading Networks to Improve Tax Compliance: Experimental Evidence from Uganda”, CEPR Discussion Paper No. 18151.
URL
https://cepr.org/publications/dp18151

Reports & Other Materials