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Bottom-Up Transparency Initiatives to Reduce Corruption
Last registered on August 23, 2018

Pre-Trial

Trial Information
General Information
Title
Bottom-Up Transparency Initiatives to Reduce Corruption
RCT ID
AEARCTR-0003108
Initial registration date
August 22, 2018
Last updated
August 23, 2018 7:10 PM EDT
Location(s)
Region
Primary Investigator
Affiliation
Paris School of Economics
Other Primary Investigator(s)
PI Affiliation
Paris School of Economics
Additional Trial Information
Status
In development
Start date
2018-08-23
End date
2019-04-30
Secondary IDs
Abstract
Transparency interventions frequently rely on a top-down approach in which a policymaker or external entity demands information about activity in a particular sector. Bottom-up transparency interventions differ by offering citizens the opportunity to directly initiate efforts to improve public goods or services. However, to effectively implement these initiatives, citizens need information about where and how to best target their efforts.

Questions:
● Where are the best entry points for citizen to target their efforts?
● What approaches are most effective in seeking to improve public services through
greater transparency
External Link(s)
Registration Citation
Citation
Eynde, Oliver and Liam Wren-Lewis. 2018. "Bottom-Up Transparency Initiatives to Reduce Corruption." AEA RCT Registry. August 23. https://doi.org/10.1257/rct.3108-1.0.
Former Citation
Eynde, Oliver and Liam Wren-Lewis. 2018. "Bottom-Up Transparency Initiatives to Reduce Corruption." AEA RCT Registry. August 23. https://www.socialscienceregistry.org/trials/3108/history/33450.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2018-08-23
Intervention End Date
2018-08-30
Primary Outcomes
Primary Outcomes (end points)
(Described in hidden part)
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We will randomnly carry out our intervention across the state.
Experimental Design Details
Randomization Method
We randomized using Stata, stratifying on district and distance to nearest town (from the 2011 census). Treatment and control villages were those villages that were marked as having a PHC in the 2011 census and we could match to our list of PHCs taken from administrative data.
Randomization Unit
Public Information Officers
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
306 Public Information Officers
Sample size: planned number of observations
For survey data: 96 Public Information Officers and 96 units linked to the public information officers For administrative data: 306 Public Information Officers and all units linked to the public information officers (approximately 2000)
Sample size (or number of clusters) by treatment arms
158 Public Information Officers treated, 148 Public Information Officers control
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
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers