Building Tax Capacity at Scale: Evidence from Technology Investments in Ghana

Last registered on July 25, 2018

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Pre-Trial

Trial Information

General Information

Title

Building Tax Capacity at Scale: Evidence from Technology Investments in Ghana

RCT ID

AEARCTR-0003103

Initial registration date

July 17, 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

July 25, 2018, 2:27 PM EDT

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

Locations

Country

Ghana

Region

Primary Investigator

Name

Anders Jensen

Affiliation

Harvard University

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Other Primary Investigator(s)

PI Name

Henry Telli

PI Affiliation

International Growth Centre

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PI Name

David Lagakos

PI Affiliation

UC San Diego

Contact Investigator

PI Name

James Dzansi

PI Affiliation

International Growth Centre

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Additional Trial Information

Status

In development

Start date

2017-11-01

End date

2020-01-01

Keywords

Governance, Welfare

Additional Keywords

Tax capacity

JEL code(s)

Secondary IDs

Abstract

At the heart of any strong tax system lies the ability to accurately observe the activities of economic agents, and the ability to use this information as inputs into an efficient billing, collection, and enforcement process. Constraints on either of these two capacities can have large adverse impacts. In the context of local property taxation, appropriately designed information technologies (IT) have the potential to alleviate these capacity constraints, and the optimistic view surrounding IT solutions has gained momentum from recent technological advances. But despite technology investments holding the promise to significantly improve tax and broader state capacities, there is no well-identified evidence on the returns to such investments. In collaboration with the Ministry of Finance and the Ministry of Local Government, we randomized the order in which 64 local governments in Ghana were offered a locally developed IT system to collect property and businesses taxes.

External Link(s)

Registration Citation

Citation

Dzansi, James et al. 2018. "Building Tax Capacity at Scale: Evidence from Technology Investments in Ghana." AEA RCT Registry. July 25. https://doi.org/10.1257/rct.3103-1.0

Former Citation

Dzansi, James et al. 2018. "Building Tax Capacity at Scale: Evidence from Technology Investments in Ghana." AEA RCT Registry. July 25. https://www.socialscienceregistry.org/trials/3103/history/32163

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

We randomize the order in which local governments will receive the offer to implement a new IT system developed by a local private Ghanaian firm. Prior to the intervention, less than 20 percent of local governments are supported by technology in the collection of local property and business taxes. The new IT system provides a transition from manual to electronic process in three areas: creation of property tax registry; valuation of properties; and, billing, collection, and enforcement of tax payments. In the first area, the technology consists of a geographic information system which significantly reduces the time and cost required to identify newly built properties, and integrate them into pre-existing digital property maps. In the second area, the local company developed a computer assisted mass appraisal system which uses as inputs the property characteristics collected for the property registration, in combination with the formulas which convert these inputs into property valuations. In the third area, the local firm has developed a revenue management software which is tailored to the specific tax structure of each district, and which automates the creation of bills, the recording of tax payments, and the flagging and creation of follow-up notices for non-payers.

Intervention Start Date

2018-07-18

Intervention End Date

2020-01-01

Primary Outcomes

Primary Outcomes (end points)

Key outcomes include:
- Gross tax revenue collected
- Cost of collection
- Citizen level of awareness of the local tax system and engagement with local government
- Local expenditure choices

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

The experimental sample consists of 64 districts that were jointly chosen by the Government and the local firm. The experimental sample excludes the capital districts in each region, and the most rural districts. Once the sample had been decided upon, individual districts were randomized into treatment and control areas, stratified by baseline level of revenue collected and administrative type. The evaluation period is 18 months.

Experimental Design Details

Randomization Method

Randomization done in office by a computer

Randomization Unit

District

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

64 districts

Sample size: planned number of observations

64 districts for the aggregate revenue and expenditure analysis, 3,500 households for the survey-based analysis

Sample size (or number of clusters) by treatment arms

Treatment group: 43 districts
Control group: 21 districts

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

We use Monte Carlo simulations, using the following assumptions: 43 treatment districts and 21 control districts, 18 periods of data both before and after the intervention start date. Under these assumptions, we can detect a 15% effect with 99% probability.

Supporting Documents and Materials

IRB