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The Effects of Decentralized Tax Collection on Citizen Engagement
Last registered on September 06, 2019

Pre-Trial

Trial Information
General Information
Title
The Effects of Decentralized Tax Collection on Citizen Engagement
RCT ID
AEARCTR-0004683
Initial registration date
September 06, 2019
Last updated
September 06, 2019 1:32 PM EDT
Location(s)

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Primary Investigator
Affiliation
J-PAL and Massachusetts Institute of Technology
Other Primary Investigator(s)
PI Affiliation
University of Pittsburgh
PI Affiliation
Harvard University
PI Affiliation
London School of Economics
Additional Trial Information
Status
On going
Start date
2019-05-01
End date
2019-12-15
Secondary IDs
Abstract
We plan to analyze the effects of provincial versus local (chief) taxation on citizen engagement. Our study is based on a field experiment implemented in Kananga, DRC where 367 neighborhoods were randomly assigned to have property taxes collected by provincial tax collectors
versus by local chiefs. We propose to examine whether taxation by different levels of government induces greater citizen engagement directed towards the level of government that collected taxes. Moreover, we probe whether decentralized taxation is accountability enhancing in light
of the fact that chiefs might also engage in more ethnic taxation, which could lead to more ethnic-based collective action and deepen ethnic divisions within communities. We measure citizen engagement using a novel behavioral exercise in which citizens have an opportunity to
act collectively to demand a community monitoring meeting in the context of a real-world anti-poverty program. Overall, this inquiry aims to shed light on the engagement and accountability implications of taxation at different levels of government in fragile states.
External Link(s)
Registration Citation
Citation
Bergeron, Augustin et al. 2019. "The Effects of Decentralized Tax Collection on Citizen Engagement." AEA RCT Registry. September 06. https://doi.org/10.1257/rct.4683-1.0.
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 how variation in the level of tax collection affects citizen engagement, collective action, and accountability pressures in the context of an anti-poverty program administered by local chiefs, in which citizens will have a chance to vote for audits of the chief and the provincial government. Additionally, we cross-randomize a tax prime at the individual level, which varies whether citizens are referred to as `citizens' or `taxpayers' in a community audit request form they receive. We will use the tax prime to examine whether the hypothesized effects are stronger in locations where the collective action decision is explicitly framed as an appeal to taxpayers.
Intervention Start Date
2019-05-01
Intervention End Date
2019-12-15
Primary Outcomes
Primary Outcomes (end points)
Participation in collective action opportunity (submission of audit forms); perceptions of government capacity; demands for government accountability; engagement with government actors; expectation of receiving benefits; targeting of coethnics in taxation
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
This project builds on experimental variation in the level of government responsible for tax collection to evaluate affects on citizen engagement. Because the level of collection for the 2018 property tax campaign was assigned on the neighborhood level, there is random variation in whether local chiefs (whose jurisdictions are roughly coterminous with neighborhoods) or officials from the provincial tax ministry were charged with tax collection in 2018. We study how this existing variation impacts citizen engagement, perceptions of government capacity, and demands for accountability, across levels of government, and how these outcomes vary with ethnic characteristics.

We partner with the Division of Social Affairs (DIVAS) for the Province of Kasai Central, DRC, to provide citizens the opportunity to engage in collective action by allowing them to vote for community audits of an anti-poverty program administered at the neighborhood level. We will randomly select 20% of households within neighborhoods to receive audit forms, which will allow citizens to independently request audits of the provincial government body responsible for the program, DIVAS, and the local chief administering the anti-poverty program at the neighborhood level. 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. We will measure how participation in this collective action opportunity varies by the level of government responsible for tax collection, at the neighborhood level.

We also cross-randomize a tax prime at the individual level (within neighborhoods), which varies whether citizens are referred to as `citizens' or `taxpayers' in a community audit request form they receive. We will assess whether explicitly framed appeals to taxpayers influence the collective action decision.
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 intervention assigned at the individual level.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
161 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 collective action outcome should have a sample size of approximately 4000 individual level observations. The estimated sample for survey outcomes is more than 2000 individual level 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
PAP_FINAL_SUBMITTED.pdf

MD5: f5d341bb3c8b100e9f1f21d91170dc3e

SHA1: 46272de1b2344005f980c73042ff1f8d926d5779

Uploaded At: September 06, 2019