Informal Taxation in Rural Sierra Leone

Last registered on October 04, 2023


Trial Information

General Information

Informal Taxation in Rural Sierra Leone
Initial registration date
December 05, 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
December 13, 2022, 10:51 PM EST

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

Last updated
October 04, 2023, 3:49 PM EDT

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



Primary Investigator

Stanford University

Other Primary Investigator(s)

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
This paper investigates the relative efficiency of traditional leaders when informally taxing citizens in low-income states and whether this comes at the expense of relatively poor households. I design a field experiment to measure whether citizens engage in costly actions to avoid contributing their labor to a public good. I randomize communities across different methods to select contributors to compare the status quo of chiefs selecting contributors to two alternatives: random lotteries and progressive selection based on household surveys. I use the random selection arm as a benchmark and estimate whether selection by chiefs or progressive selecting are relatively efficient by generating more or less costly behavior from citizens. I also study the heterogenous effects of these treatment arms by household wealth. This allows me to asses if chiefs appear regressive by mostly burdening poor households and whether this can be corrected by a simple policy instrument.
External Link(s)

Registration Citation

Rodriguez Martinez, Andres Felipe. 2023. "Informal Taxation in Rural Sierra Leone." AEA RCT Registry. October 04.
Experimental Details


The intervention happens within an experimental environment where participants will be offered a one day job in rural Sierra Leone. Some participants will be selected to work for themselves and others for their community. Community workers do the same task as others, but their earnings are donated to their local clinic.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Costly decisions by participants to avoid being selected.
Primary Outcomes (explanation)
The experiment is done in two days and selection of community workers only takes place on the second day. I then measure costly behavior by participants to work on the first day. I do this by eliciting the largest wage-cut they are willing to accept in order to work earlier.

Secondary Outcomes

Secondary Outcomes (end points)
Labor supply decisions within the experiment.
Secondary Outcomes (explanation)
This is based on the experimental job people are offered which is a simple classification task. The outcome is the number of pages participants complete.

Experimental Design

Experimental Design
I implement in multiple communities different methods to select community workers. In some places the local chief selects, in others it is decided by lottery, and in others it is decided by a progressive selection rule inspired by proxy-means-testing. These random variation across communities allows me to study the effects of each selection method on participant's behavior.
Experimental Design Details
Randomization Method
The randomization will be done by the research team in a computer before sending the field team. Field teams will simply have instructions to implement them. Other randomizations within surveys are done via public lotteries with participants.
Randomization Unit
Communities in rural Sierra Leone and participants within these communities.
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
Between 28 and 32.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Based on a clustered design, I have around 73% power to detect a 15% increase in my main outcome. The outcome is measured in monetary units (Leones) and captures wage-cuts people are willing to accept. A 15% increase in wage-cut implies accepting a daily salary 3% lower.

Institutional Review Boards (IRBs)

IRB Name
Administrative Panel on Human Subjects in Non-Medical Research - Stanford University
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials