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Effective Targeting of Anti-Poverty Programs II
Last registered on November 20, 2013

Pre-Trial

Trial Information
General Information
Title
Effective Targeting of Anti-Poverty Programs II
RCT ID
AEARCTR-0000099
Initial registration date
Not yet registered
Last updated
November 20, 2013 3:53 PM EST
Location(s)
Primary Investigator
Affiliation
Harvard University
Other Primary Investigator(s)
PI Affiliation
World Bank
PI Affiliation
World Bank
PI Affiliation
MIT
PI Affiliation
MIT
PI Affiliation
World Bank
Additional Trial Information
Status
On going
Start date
2010-12-01
End date
2014-12-31
Secondary IDs
Abstract
Targeting social transfers to the poor has become a means to improve poverty reduction programs. In Indonesia, the government has launched several large-scale, targeted programs, such as the Direct Cash Transfers (BLT), Health Insurance for the People (Jamkesmas) and Rice for the Poor (Raskin). Ensuring that limited program resources reach the poor instead of non-poor households remains an ongoing challenge. To improve targeting performance, Indonesia’s Ministry of National Development and Planning (BAPPENAS) has asked World Bank and J-PAL to provide technical assistance in devising a unified targeting system that can be used to select the recipients of multiple social programs. As part of this work, This study aims to test the efficacy of different targeting methods in Program Keluarga Harapan (PKH), Indonesia’s Conditional Cash Transfers (CCT) program will be evaluated. Typically, targeting is achieved through Proxy-Means Testing, where the government collects data on asset possession and uses this data to predict who falls below the poverty line. However, this method often results in high error rates (upwards of 40%) and a lack of community satisfaction. In this project, we will focus on two alternative targeting systems that feature greater community involvement: (1) Self-targeting method, wherein the poor apply to be part of the program and are independently verified, and (2) Community inclusion method, wherein the communities modify the government’s existing recipient list.
External Link(s)
Registration Citation
Citation
Alatas, Vivi et al. 2013. "Effective Targeting of Anti-Poverty Programs II." AEA RCT Registry. November 20. https://doi.org/10.1257/rct.99-1.0.
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Experimental Details
Interventions
Intervention(s)
We randomly allocated each village to one of two three targeting methodologies:
• Proxy Means Testing (PMT): the government collects information on assets and demographic characteristics to create a “proxy” for household consumption or income, and this proxy is in turn used for targeting.
• Community Based Targeting: the government allows the community or some part of it (e.g. local leaders) to select the beneficiaries through a pre-specified process.
• Self-targeting: In the economic literature, this is called an ordeal mechanism. It imposes requirements on the program that are differentially costly for poor and rich people, dissuading the rich but not the poor from participating.
Intervention Start Date
2011-01-17
Intervention End Date
2011-03-28
Primary Outcomes
Primary Outcomes (end points)
Mistargeting, exclusion error, inclusion error
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The project was carried out during the 2011 expansion of PKH (Indonesia’s conditional cash transfer Program) to new areas. We chose 6 districts and within these districts, we randomly selected a total of 600 villages. Each of the these villages was randomly allocated to one of three methods for determining which households would be beneficiaries of the program—self-targeting, community targeting, and the status quo, where households are automatically enrolled in PKH based on their PMT score.
Experimental Design Details
Randomization Method
Computer random number generator
Randomization Unit
Village
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
600 villages
Sample size: planned number of observations
9 households per village
Sample size (or number of clusters) by treatment arms
200 villages got self-targeting treatment, 200 villages got community targeting and and 200 villages got status quo.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
MIT COUHES
IRB Approval Date
2010-01-21
IRB Approval Number
0912003624
IRB Name
Harvard
IRB Approval Date
2010-04-02
IRB Approval Number
F18720-101
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
March 28, 2011, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
April 03, 2011, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
600 villages
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
9 households per village
Final Sample Size (or Number of Clusters) by Treatment Arms
200 villages got self-targeting treatment, 200 villages got community targeting and 200 villages got status quo.
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS