How to Improve Voluntary Income Tax Compliance in Tanzania

Last registered on August 24, 2023


Trial Information

General Information

How to Improve Voluntary Income Tax Compliance in Tanzania
Initial registration date
August 21, 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
August 24, 2023, 6:14 AM EDT

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


Primary Investigator

VATT Institute for Economic Research

Other Primary Investigator(s)

PI Affiliation
VATT Institute for Economic Research and University of Helsinki
PI Affiliation
PI Affiliation
Finnish Tax Administration
PI Affiliation
Tanzania Revenue Authority
PI Affiliation
Tanzania Revenue Authority

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
SSA countries suffer from limited capacity to enforce tax payments combined with various obstacles that reduce tax compliance. Domestic revenues need to be mobilized to finance growing needs demanded by population growth, debt levels and declining development aid, amidst rampant tax evasion, corruption and weak institutions. This paper examines the scope of various types of behavioural messages to improve tax compliance and raise tax revenue in a large and diverse East African economy, Tanzania. The external validity of the results can be expected to extend beyond its borders.
External Link(s)

Registration Citation

Berghäll, Elina et al. 2023. "How to Improve Voluntary Income Tax Compliance in Tanzania." AEA RCT Registry. August 24.
Experimental Details


Behavioral messages sent to income taxpayers
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The change in tax compliance and tax revenue collection in terms of amounts of tax revenue generated, as well as Increases in the number of active tax-payers.
Primary Outcomes (explanation)
Amounts declared and collected relative to the previous period/year
Number of taxpayers/ registered taxpayers relative to the previous period/year

Secondary Outcomes

Secondary Outcomes (end points)
Spill-over effects to tax filing and revenue from VAT, PAYE, customs, and other potential tax revenue for which data is available.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The data will be pooled and the RCT will randomly assign eligible taxpayers to the 6 different treatment groups receiving distinct types of text messages and the 2 control groups. Stratification will be carried out based on type of taxpayer and background variables such as location, industry, firm age and size. Actual categories will be defined based on the data. Randomization can also be carried out separately by tax type.
Experimental Design Details
Not available
Randomization Method
The assignment of the treatment status is done with STATA. A randomization seed is set for replicability.
Randomization Unit
The randomization unit is the tax identification number (TIN). Taxpayers included in the baseline are those that had registered by latest year for which full returns have been submitted, being hence liable for income tax filing in the trial period, having an active registration status, and being uniquely identified. The taxpayer must also have a valid SMS number located in Tanzania.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
8 treatment arms each of 6000 observations: 6 different treatment and 2 control groups. If necessary, these 8 treatment arms can be collapsed to 4 treatment arms, including 1 control group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
To be specified by the data. By design the eight treatment arms can be reduced to four if response rates are low.

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number