Digitizing Land and Property Tax Payment Mechanisms to Improve Compliance: Evidence from Blantyre City, Malawi

Last registered on August 04, 2026

Pre-Trial

Trial Information

General Information

Title
Digitizing Land and Property Tax Payment Mechanisms to Improve Compliance: Evidence from Blantyre City, Malawi
RCT ID
AEARCTR-0019261
Initial registration date
July 28, 2026

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 04, 2026, 9:22 AM EDT

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

Locations

Region

Primary Investigator

Affiliation
George Washington University

Other Primary Investigator(s)

PI Affiliation
World Bank Group
PI Affiliation
World Bank Group

Additional Trial Information

Status
Completed
Start date
2026-02-02
End date
2026-07-22
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Digitalization of tax systems has been recognized as key to reducing the challenges that come with collecting property taxes, but experimentally isolating its effects on compliance can be challenging. In this study, we examine the impact of digitizing property tax billing and payment mechanisms on tax compliance in Malawi. We focus on non-institutionally owned, residential properties Blantyre city. Our intervention has 3 treatment arms: (i) a digital billing group receiving digital bills without an integrated payment option (ii) a digital billing plus payment group receiving digital bills with an integrated digital payment mechanism and (iii) a control arm, with business-as-usual paper-billing and payment methods. The main effects we want to measure are: (i) the overall average treatment effect of the two treatment arms on property tax compliance; (ii) the marginal effect of the digital payment feature beyond billing digitization alone and (iii) heterogeneous impacts across property types and neighborhood formality types through a pre-specified within-stratum analysis. We randomize at the property-owner-level, and due to meaningful differences in baseline compliance across property types (residential or Traditional Housing Authority) and neighborhood formality types (formal or informal), we also stratify our randomization. This yields in four strata, ensuring balance across our 3 treatment arms. Since we randomize at the owner level, multiple properties owned by the same owner are assigned the same treatment status, based on a pre-specified rule for assignment. This ensures non-violation of SUTVA. To be eligible for the study, (i) properties must be matched to the 2021 tax roll with available locational information, and (ii) property owners must be charged land and improvement taxes in 2024 and (iii) their phone numbers should have been collected, through a pilot phone number collecting exercise by the BCC in 2026. This results in a sample of 7,436 properties and 6,659 owners.
External Link(s)

Registration Citation

Citation
Ayalew Ali, Daniel, Klaus Deininger and Kavya Ravindranath. 2026. "Digitizing Land and Property Tax Payment Mechanisms to Improve Compliance: Evidence from Blantyre City, Malawi." AEA RCT Registry. August 04. https://doi.org/10.1257/rct.19261-1.0
Experimental Details

Interventions

Intervention(s)
With this intervention, we aim to estimate if tax compliance and the revenue collected, improves with an integrated digital billing and payment system relative to the baseline option of receiving paper bills and paying via cash or cheques. We also estimate if on receiving this integrated billing and payment option leads to more compliance relative to receiving only the digital billing option. We randomly assign eligible property owners to three treatment arms with equal allocation across arms within each stratum. We then deliver arm-specific interventions, the control group continues to receive paper bills, the first treatment group receives a digital bill and the second payment group receives a digital bill with an integrated payment option, along with instructions and optional reminders.
Intervention (Hidden)
Intervention Start Date
2026-06-15
Intervention End Date
2026-07-22

Primary Outcomes

Primary Outcomes (end points)
Our primary outcome is a binary indicator for tax compliance (land and improvement tax payment) over at least one full tax payment cycle.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In this design we randomize at the property-owner level with 3 treatment arms (a control group with business-as-usual paper bills, a digital billing group (D) and a digital billing plus payment group (DP)). Randomizing at the property-owner level minimizes SUTVA violations and ensures that the same property owner does not receive two different treatments. Randomization is stratified at the property-type and formality-level, ensuring balanced representation of taxpayers across key predictors of compliance and supports precise estimation of heterogeneous effects. Property-owners who hold multiple properties across these 4 strata types will need a pre-specified rule or assignment. The rule is as follows: (i) if the owner holds at least one Residential/Formal property, they are assigned to the Res/Formal stratum; (ii) if not, but they hold at least one Residential/Informal property, they are assigned to the Res/Informal stratum; (iii) if not, but they hold at least one THA/Formal property, they are assigned to the THA/Formal stratum; and (iv) otherwise, they are assigned to the THA/Informal stratum. This rule reflects the policy priority of residential properties over THA properties, and formal neighborhoods over informal ones within each property type, and ensures a consistent and fully pre-specified assignment for all cross-stratum owners without requiring case-by-case judgment. If multiple properties fall within the same stratum, the property with the highest tax liability will be selected.

The preferred design is structured in two parts: (i) a pooled analysis with a Bonferroni correction and (ii) a pre-specified within-stratum analysis with no correction (single analysis). The first part estimates the overall average treatment effect using a pooled regression with stratum fixed effects, specified as:

Y= α+β1D+β2DP+si+ε
Y
=

𝛼
+
𝛽
1
D
+
𝛽
2
D
P
+
s
i
+
𝜀



where Y is a binary indicator equal to 1 if the property owner complied with the land and improvement tax in the study period and 0 otherwise, D and DP are indicators for the Digital Bill and Digital Bill + Payment treatment arms respectively, and stratum fixed effects (sᵢ) absorb baseline compliance differences across strata, improving precision beyond what a simple two-sample calculation assumes. The pooled baseline compliance is computed as a weighted average across the four strata, with weights reflecting each stratum’s share of total properties in the sub-sample of the Blantyre tax registry for which phone numbers have been collected.



This specification from the pooled data allows us to test two hypotheses:

H1 — the full intervention increases compliance relative to control (DP vs. C, corresponding to
β2
𝛽
2

), tested at α* = 0.025; and

(ii) H2 — the payment feature adds incremental value beyond digital billing alone (DP vs. D, corresponding to
β2−β1
𝛽
2

𝛽
1

), also tested at α* = 0.025. There is a Bonferroni correction applied to control the family-wise error rate (FWER) at α = 0.05 Treating both comparisons as co-primary reflects the study’s dual policy objectives: establishing the overall effectiveness of the integrated intervention and isolating the specific contribution of the payment mechanism. The comparison of digital billing against control (D vs. C, corresponding to
β1
) is retained as an informative decomposition estimate and does not constitute a pre-specified hypothesis; it is tested at the full α = 0.05 and reported for interpretive purposes only.

For the second, within-stratum analysis, we primarily test DP vs C. The secondary comparison (DP vs. D) and informative comparison (D vs. C) are reported within each stratum as exploratory. The final required sample size based on power calculations, is the larger of the pooled co-primary analysis and the pre-specified subgroup, single hypothesis per stratum grand, since both constraints must be satisfied. The pooled co-primary analysis is the binding constraint, requiring 1,028 per arm and 3,084 in total, compared to 2,127 for the pre-specified subgroup design alone.

The final sample size used for the randomization is however, based on properties that satisfy these criteria: (i) properties must be matched to the 2021 tax roll with available locational information, and (ii) property owners must be charged land and improvement taxes in 2024 and (iii) their phone numbers should have been collected, through a pilot phone number collecting exercise by the BCC in 2026. This leaves us with a sample of 6,659 owners. We then randomly sample 1/3rd observations from the four strata, Residential/Formal, Residential/Informal, THA/Formal, THA/Informal, into each treatment arm.

To ensure that those receiving the digital bill view it as authentic, we design the digital bill to closely resemble physical bills. The digital bill will therefore include the charges and valuation amounts for all three tax categories: land, improvements, and sewerage. It will also include a surcharge notice, the Blantyre City Council header, and the Council’s motto.
Experimental Design Details
Randomization Method
Randomization will be done in an office using a computer
Randomization Unit
Property Owner
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
6,659 property-owners, 7,436 properties
Sample size: planned number of observations
6,659 property-owners, 7,436 properties
Sample size (or number of clusters) by treatment arms
2,469 properties in Control, 2,487 properties in Treatment 1 (Digital Bill only) and 2,480 properties in Treatment 2 (Digital Pill + Payment)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials