How to Improve Voluntary Income Tax Compliance in Tanzania

Last registered on August 24, 2023

View Trial History

Pre-Trial

Trial Information

General Information

Title

How to Improve Voluntary Income Tax Compliance in Tanzania

RCT ID

AEARCTR-0011909

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.

Locations

Country

Tanzania, United Republic of

Region

Primary Investigator

Name

Elina Berghäll

Affiliation

VATT Institute for Economic Research

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Jukka Pirttilä

PI Affiliation

VATT Institute for Economic Research and University of Helsinki

Contact Investigator

PI Name

Amina Ebrahim

PI Affiliation

UNU-WIDER

Contact Investigator

PI Name

Jori Grym

PI Affiliation

Finnish Tax Administration

Contact Investigator

PI Name

Ephraim O Mdee

PI Affiliation

Tanzania Revenue Authority

Contact Investigator

PI Name

Massaga Fimbo

PI Affiliation

Tanzania Revenue Authority

Contact Investigator

Additional Trial Information

Status

In development

Start date

2023-03-06

End date

2025-03-31

Keywords

Behavior, Other

Additional Keywords

tax compliance, Tanzania, income tax

JEL code(s)

D22, D83, D9, H2, H3

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

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

Citation

Berghäll, Elina et al. 2023. "How to Improve Voluntary Income Tax Compliance in Tanzania." AEA RCT Registry. August 24. https://doi.org/10.1257/rct.11909-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Behavioral messages sent to income taxpayers

Intervention (Hidden)

Different types of behavioral text messages sent repeatedly to income taxpayers

Intervention Start Date

2023-12-01

Intervention End Date

2024-03-31

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

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

The 8 treatment arms consist of 2 slightly different reminder groups; 2 “nation building” groups: A social norm and a fiscal exchange message; 2 deterrence messages reminding of consequences of not filing (and mentioning third-party information); 2 control groups.
The RCT will randomly assign eligible taxpayers to the several treatment groups receiving distinct types of text messages and two control groups. A balance on taxpayers categorized as active, nil- or non-filers will be sought in the treatment groups based on their prior CIT/PIT returns. Non-filers (non-compliant taxpayers) are those that had not filed by the deadline. Nil-filers are those that reported zero taxable income in the previous year. If there is large variability in the data, to take account of behavioral shifts over time, similarly to Santoro et al. (2022), we may use data from prior years to characterize taxpayers as perpetual taxpayers, if they do not change their filing behavior. In addition to taxpayer type, stratification will be carried out on key characteristics for which data is available, such as region, industry, firm age and size if possible.

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

50,000

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.

Supporting Documents and Materials

IRB