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TAX COLLECTION AND BUREAUCRAT PERFORMANCE: EXPERIMENTAL EVIDENCE FROM THE DRC
Last registered on September 16, 2019

Pre-Trial

Trial Information
General Information
Title
TAX COLLECTION AND BUREAUCRAT PERFORMANCE: EXPERIMENTAL EVIDENCE FROM THE DRC
RCT ID
AEARCTR-0004532
Initial registration date
August 10, 2019
Last updated
September 16, 2019 4:14 PM EDT
Location(s)

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Primary Investigator
Affiliation
LSE
Other Primary Investigator(s)
PI Affiliation
MIT
PI Affiliation
Harvard
Additional Trial Information
Status
On going
Start date
2018-05-01
End date
2019-12-15
Secondary IDs
Abstract
This project examines how working as a tax collector affects local bureaucrat performance. We exploit random variation in whether local bureaucrats known as avenue chiefs were responsible for property tax collection (treatment), or whether agents of the tax ministry collected taxes within avenue chiefs’ jurisdictions instead (control). We estimate the performance of avenue chiefs by measuring how they choose to allocate scarce benefits from a government antipoverty program in their community. The resulting distribution of antipoverty benefits enables us to measure the extent to which chiefs targeted the program toward the neediest families in the community as well as diversion, nepotism, ethnic favoritism, and taxpayer reciprocity. We use additional cross-randomized interventions and survey data to shed light on mechanisms.
External Link(s)
Registration Citation
Citation
Bergeron, Augustin, Gabriel Tourek and Jonathan Weigel. 2019. "TAX COLLECTION AND BUREAUCRAT PERFORMANCE: EXPERIMENTAL EVIDENCE FROM THE DRC." AEA RCT Registry. September 16. https://doi.org/10.1257/rct.4532-1.1.
Former Citation
Bergeron, Augustin, Gabriel Tourek and Jonathan Weigel. 2019. "TAX COLLECTION AND BUREAUCRAT PERFORMANCE: EXPERIMENTAL EVIDENCE FROM THE DRC." AEA RCT Registry. September 16. https://www.socialscienceregistry.org/trials/4532/history/53487.
Experimental Details
Interventions
Intervention(s)
We exploit random variation in whether local bureaucrats known as avenue chiefs were responsible for property tax collection (treatment), or whether agents of the tax ministry collected taxes within avenue chiefs’ jurisdictions instead (control). We study the impacts of working as a tax collector on the distribution of scarce benefits from an antipoverty program. Additionally, we cross-randomize two additional interventions. First, in one treatment arm, we provide information to randomly selected households about the antipoverty program as an encouragement for individuals to seek out the chief and ask for a share of the benefits. Second, in randomly chosen neighborhoods, citizens will have a chance to vote for audits of the chief or the provincial government; audits will occur in the neighborhoods with a highest percentage of voters.
Intervention Start Date
2019-07-21
Intervention End Date
2019-10-31
Primary Outcomes
Primary Outcomes (end points)
The economic need of lottery ticket recipients, errors of inclusion and exclusion, receipt of a lottery ticket for the poverty program, diversion and nepotism by the chief, bias of the chief toward coethnics or taxpayers.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The current project builds on experimental variation in tax collection responsibilities to evaluate how collecting taxes affects the performance of local bureaucrats. Because the mode of collection for the 2018 property tax campaign was assigned on the neighborhood level, there is random variation in whether chiefs (whose jurisdictions are roughly coterminous with neighborhoods) have been charged with tax collection in 2018.

In addition to the underlying random variation in whether avenue chiefs collected property taxes in 2018, we cross-randomize three experimental arms specific to this study in bureaucrat performance. The first arm has no additional intervention. This arm will enable us to measure the first possible mechanism through which tax collection could impact chief performance: altering chiefs’ public spiritedness. This is one channel consistent with the classic tax-accountability hypothesis.

In the second arm, we will randomly select 20% of households within a selected neighborhood to receive information about the lottery.3 This treatment will create variation across neighborhoods in whether citizens have received information, but also within neighborhoods among citizens. Through door-to-door visits to households in selected neighborhoods, citizens will receive written and oral information about (a) the name and rank of the chief who will be responsible for distributing lottery tickets on the avenue, (b) the time period during which the chief will be distributing lottery tickets, (c) the total number of winners per neighborhood. The flier also notes that citizens can “see the chief for more information.” The information treatments function as an encouragement for citizens to talk to the chief and, if eligible, ask for lottery tickets.

Finally, in a third arm, we shock the capacity of citizens in the neighborhood to engage in collective action and exert pressure on their chief. We randomly give citizens an opportunity to vote for community audits of the chief who worked on the program as well as DIVAS itself. The audits will be conducted by well-known and respected local civil society organizations: RIAC (the Network for Transparency and Anti-corruption), which specializes in promoting transparency and fighting corruption, and SOCICO (the Civil Society of Congo), which focuses on government accountability in the areas of violence, conflict, and elections.
Experimental Design Details
Not available
Randomization Method
All randomizations will be done in Stata unless stated otherwise in our pre-analysis plan.
Randomization Unit
Tax collection responsibility was randomized on the neighborhood level. Cross-randomized interventions are assigned at the chief level.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
361 neighborhoods
Sample size: planned number of observations
This varies by the outcome variable, as described in detail in the PAP. The estimated sample for the "need" outcome should have a sample size of more than 9000 observations.
Sample size (or number of clusters) by treatment arms
See pre-analysis plan.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
lSE Research Ethics Committee
IRB Approval Date
2019-07-26
IRB Approval Number
000972
IRB Name
Harvard University-Area Committee on the Use of Human Subjects
IRB Approval Date
2017-07-28
IRB Approval Number
IRB17-0724
Analysis Plan
Analysis Plan Documents
Pre Analysis Plan

MD5: 8518e5ac141ec8cd0fa8eb1cd982655f

SHA1: a164407238bca7ae9d5afef738c96e269cd92595

Uploaded At: August 10, 2019

Pre Analysis Plan (updated 9/16/19)

MD5:

SHA1:

Uploaded At: September 16, 2019