Improving State Capacity to Target Extreme Poverty: An Evaluation of a Randomized Intervention in Bangladesh

Last registered on October 09, 2024
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Pre-Trial

Trial Information

General Information

Title
Improving State Capacity to Target Extreme Poverty: An Evaluation of a Randomized Intervention in Bangladesh
RCT ID
AEARCTR-0004510
Initial registration date
December 27, 2019

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
January 04, 2020, 11:35 PM EST

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

Last updated
October 09, 2024, 4:29 AM EDT

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

Locations

Country
Bangladesh
Region
Rangpur Division and Rajshahi Division

Primary Investigator

Name
Viola Asri
Affiliation
University of Konstanz
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Other Primary Investigator(s)

PI Name
Kumar Biswas
PI Affiliation
University of Konstanz
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PI Name
Sebastian Fehrler
PI Affiliation
University of Konstanz
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PI Name
Urs Fischbacher
PI Affiliation
University of Konstanz
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PI Name
Katharina Michaelowa
PI Affiliation
University of Zurich
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PI Name
Atonu Rabbani
PI Affiliation
University of Dhaka
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Additional Trial Information

Status
Completed
Start date
2019-09-15
End date
2021-12-31
Keywords
Governance, Welfare
Additional Keywords
targeting, social programs, local government, poverty
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This research project aims to contribute to the understanding of how the selection of beneficiaries of social transfers can be improved. While most of the existing impact evaluations have focused on monitoring, incentives and accountablility, they have largely ignored that local government representatives responsible for these services may simply lack the ability to respond to public demands. Our project intends to contribute to the understanding of the role of state capacity in improving the allocation and targeting performance of old-age allowances in Bangladesh. Our formative primary data collection documents that local representatives lack the knowledge and tools to target the benefits according to the guidelines by the national government. We therefore aim to evaluate a state-capacity-building intervention that addresses the existing capacity constraints in two ways: First, by training local representatives on the national government guidelines for the selection of new beneficiaries and second, by providing them with required data on individuals in the target group to enable them to follow the government guidelines. The impact evaluation will focus on assessing whether in treatment unions newly selected beneficiaries are on average more eligible than newly selected beneficiaries in control unions using baseline and endline data from local government representatives as well as endline data from newly selected beneficiaries. The evaluation design will allow us to assess the causal impact of this capacity building intervention and thereby has the potential to generate evidence that can help to make anti-poverty policies in Bangladesh more effective.
External Link(s)

Registration Citation

Citation
Asri, Viola et al. 2024. "Improving State Capacity to Target Extreme Poverty: An Evaluation of a Randomized Intervention in Bangladesh ." AEA RCT Registry. October 09. https://doi.org/10.1257/rct.4510-5.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
The intervention to be evaluated consists of two components:
- Training of local government representatives regarding old age allowance eligibility criteria, priority criteria to select most eligible beneficiaries among the eligible elderly, and exclusion criteria according to the government guidelines. The training module will be in an audio-video format embedded in a training module presented on a tablet to ensure that local representatives with low literacy could comprehend the training. It also allows us to provide a homogenous training to all local government representatives which does not depend much on the skills of the trainer.
- Collection and transfer of data on the target group: We will use miking announcements to encourage elderly in the target group to come to a designated place in their ward of residence on one day. During this day so-called listing officers will fill eligibility information cards (EIC) including all relevant information for the selection of beneficiaries. With consent from the elderly, one copy of the EIC will be given to the elderly, one copy will remain with the listing officers and one copy will be transferred to the local government. The elderly will be informed that their EIC will be provided to the local government representatives to facilitate the selection of beneficiaries and that during the selection period they can use their EIC to provide all relevant information to the local government representatives.
Intervention Start Date
2020-01-04
Intervention End Date
2020-02-29

Primary Outcomes

Primary Outcomes (end points)
Targeting performance
Primary Outcomes (explanation)
We will assess targeting performance by comparing eligibility of newly selected beneficiaries in treatment and control union using one eligibility measure reflecting the rules as formulated in the government guidelines and one commonly used poverty measure such as probability of poverty index.
Apart from using these aggregated indices, we will also be able to compare whether newly selected beneficiaries in treatment areas are more eligible than newly selected beneficiaries in control areas in terms of individual variables such as land ownership, income, assets and physical weakness and living arrangements with other household members.

Secondary Outcomes

Secondary Outcomes (end points)
Local government representatives' knowledge of eligibility and priority rules
Secondary Outcomes (explanation)
Our questionnaire includes questions on the most relevant targeting criteria such as income threshold, land threshold or age cutoff. We will use these variables to generate a knowledge index indicating how familiar local government representatives are with the national guidelines.

Experimental Design

Experimental Design
We randomly assign 80 unions (located in 80 different upazilas) to treatment and control. In the treatment group, we will provide training on the selection guidelines to all 18 selection committee members in the union and the eligibility information card filling to collect data on the elderly in the target group will be carried out by listing officers in 3 out of 9 wards in each union.

Experimental Design Details
Randomization Method
Randomization is done in office by a computer.
Randomization Unit
The unit of randomization is the upazilla.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
80 unions
Sample size: planned number of observations
1440 local government representatives 1800 newly selected Old Age Allowance beneficiaries 1200 newly selected Widow Allowance beneficiaries
Sample size (or number of clusters) by treatment arms
40 treatment unions,
40 control unions
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
0.25 sd of poverty index
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
BRAC James P Grant School of Public Health
IRB Approval Date
2019-07-30
IRB Approval Number
2019-021-IR
Analysis Plan

Analysis Plan Documents

PAP v1

MD5: cf83f2a6d3858f8b47b4e987e33100d7

SHA1: a8701ef532e7a3bc5068688f0648084f3048a20c

Uploaded At: December 27, 2019

Asri_et_al_2020_PAP.pdf

MD5: bbf8d44356b9d7b79ff432701fc7b6ce

SHA1: 48e88fd61422599a8f363588ac88175ad381cd6a

Uploaded At: April 22, 2021

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
February 29, 2020, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials