Timing and the Effectiveness of Reminders: Applying Behavioural Insights to Improve SME Tax Compliance

Last registered on November 24, 2022
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Pre-Trial

Trial Information

General Information

Title
Timing and the Effectiveness of Reminders: Applying Behavioural Insights to Improve SME Tax Compliance
RCT ID
AEARCTR-0010387
Initial registration date
November 10, 2022

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
November 15, 2022, 3:53 PM EST

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

Last updated
November 24, 2022, 10:39 AM EST

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

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Primary Investigator

Name
Eko Arief Yogama
Affiliation
The University of Sheffield
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Other Primary Investigator(s)

PI Name
Matthew D. Rablen
PI Affiliation
The University of Sheffield
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PI Name
Daniel J. Gray
PI Affiliation
The University of Sheffield
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Additional Trial Information

Status
In development
Start date
2022-11-01
End date
2023-04-30
Keywords
Behavior
Additional Keywords
Small-Medium Enterprises, Tax Compliance, Randomised Controlled Trial, Reminders
JEL code(s)
C93, D91, H26
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Boosting small-medium enterprises (SMEs) tax compliance is essential as SMEs contribute to a significant portion of GDP. SMEs are often characterised by low tax compliance due to low tax literacy. Reminders combined with deterrence messages will be used to induce timeliness in SMEs' monthly income tax payments. This study seeks to answer how the variations of timing and frequency affect a taxpayer's decision to pay or not their monthly income tax. Four message timing and frequency delivery variations will be tested, namely Early, Close, Combined, and Post. The message will be sent through a digital channel, WhatsApp, Indonesia's most popular messaging application. The design of the
message consists of a picture reflecting the monthly income taxes payment and filing deadlines and some text stating the financial and legal consequences of no or late payment and filing. A particular interest of our study is how long taxpayers continue to pay the tax after receiving the message. To analyse this, we employ an inverse survival approach. A survival function, in our context, represents tax non-compliance. The treatment effect, which indicates the differences between treatment groups, will be estimated using a Logit model. It is expected that the results of this study will serve as resourceful guidance for policymakers, primarily the tax authority, to increase tax compliance among SMEs in a
cost-efficient channel. In addition, it also provides a reference regarding the most effective timing of sending a message to taxpayers and how often the message should be delivered.
External Link(s)

Registration Citation

Citation
Yogama, Eko Arief, Daniel J. Gray and Matthew D. Rablen. 2022. "Timing and the Effectiveness of Reminders: Applying Behavioural Insights to Improve SME Tax Compliance." AEA RCT Registry. November 24. https://doi.org/10.1257/rct.10387-1.1
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Experimental Details

Interventions

Intervention(s)
We designed a message containing graphic information on SME monthly tax payment deadline and a text expressing penalties for not paying and reporting taxes on time. The message will be delivered to individual and small firm taxpayers who satisfy the sampling criteria through a WhatsApp digital channel. Indonesia is one of the fifth largest users of WhatsApp. Therefore, the usage of WhatsApp as a delivery channel is considered more preferred and justifiable compared to other digital channels such as email and short message services (SMS).

The sample will be divided into five groups: Control (no message), Early (13 days before payment deadline), Close (2 days before payment deadline), Combined (13 days and 2 days before payment deadline), and Post (3 days after payment deadline). The messages will be dispatched following the period below:
The monthly income tax payment deadline for SMEs is day 15th of the following month
The monthly income tax payment reporting deadline for SMEs is day 20th of the following month
Control: No message will be sent
Early: The message will be sent 13 days before the payment deadline
Close: The message will be sent 2 days before the payment deadline
Combined: The message will be sent twice, within 13 days and 2 days before the payment deadline
Post: The message will be sent 3 days before the reporting deadline

Intervention Start Date
2022-12-01
Intervention End Date
2023-01-31

Primary Outcomes

Primary Outcomes (end points)
1. On-time monthly income tax payment.
2. Tax payment quality.
Primary Outcomes (explanation)
1. Monthly income tax paid not exceeding the payment deadline.
2. Amount of income tax paid at least the same as last year's monthly income tax.

Secondary Outcomes

Secondary Outcomes (end points)
1. Monthly income tax payment before the annual income tax return filing period.
2. On-time annual income tax payment.
3. On-time annual income tax reporting.
Secondary Outcomes (explanation)
1. Monthly income tax paid before day 60 after the payment deadline.
2. Annual income tax payment date.
3. annual income tax return filing date.

Experimental Design

Experimental Design
Message design:
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--- picture ---

"Please pay your monthly income taxes as early as possible. A late filing penalty of Rp 100,000 will be imposed for late tax return filing, and an additional late payment penalty will also be imposed if you have late payments."

"According to Law No. 9/2017, Banks and Other Financial Institutions are required to provide their customers' account information periodically. Therefore, all recorded financial transactions could be detected by the DGT."

"For further information, you may access our website at https://pajak.go.id or call us through the national call centre 1500200."

"Your registered tax office: <Tax Office> can be reached directly by WhatsApp number listed in https://pajak.go.id/id/unit-kerja."
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Experimental Design Details
Not available
Randomization Method
A stratified randomisation strategy will be performed at the regional tax office level (which might consist of more than one local tax office). The random allocation will be stratified based on the taxpayers' characteristics that will be collected during the early phase of the sample selection. The characteristics that will be collected can be seen in Appendix 2. The randomisation will be performed using a computer command to ensure no selection by the DGT or the taxpayers. The seed number will be generated randomly from random.org so that it can be replicated in the future the randomisation procedure will be looped repeatedly to ensure no significant differences in characteristics between groups. The best-balanced results for all pre-experimental covariates controlling for the random block dummies will be selected. Further, a formal procedure to ensure balanced characteristics across trial arms will be conducted according to baseline characteristics.
Randomization Unit
Individual randomisation at tax office level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clustering
Sample size: planned number of observations
In total, 14,500 taxpayers are targeted to be recruited as the sample of this study. The composition of the total samples will be 20 % from small firms and 80 % from individuals. The samples will be divided into three regional tax offices. It is estimated that 12,500 (around 86 %) taxpayers will be collected as samples from regional tax offices in Java Island (including Jakarta) and 2,000 (around 14 %) from the regional tax office located out of Java Island. The sample will be randomly allocated into one control and four treatment groups. Each treatment group within the regional tax offices in Jakarta and Java Island will consist of 1,250 taxpayers. In comparison, each treatment group within the regional tax office out of Java Island will consist of 400 taxpayers.
Sample size (or number of clusters) by treatment arms
Each group will be allocated 2,900 taxpayers (580 small firms and 2,320 individuals)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
The University of Sheffield Ethics Committee
IRB Approval Date
2022-11-06
IRB Approval Number
049945
Analysis Plan