Ultra Poor Graduation Pilot in Philippines

Last registered on November 09, 2021

Pre-Trial

Trial Information

General Information

Title
Ultra Poor Graduation Pilot in Philippines
RCT ID
AEARCTR-0004658
Initial registration date
September 12, 2019
Last updated
November 09, 2021, 7:27 PM EST

Locations

Primary Investigator

Affiliation
Northwestern University

Other Primary Investigator(s)

PI Affiliation
Northwestern University
PI Affiliation
University of Vermont
PI Affiliation
Northwestern University
PI Affiliation
ADB

Additional Trial Information

Status
On going
Start date
2018-06-05
End date
2021-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The Ultra Poor Graduation Pilot in the Philippines is an on-going initiative funded by the Asian Development Bank (ADB) and the Department of Labor and Employment (DOLE) of the Philippine government and implemented by DOLE and BRAC-USA, a non-profit organization. The graduation approach is a combination of social protection, health, life skills and livelihood programs that offers a holistic package of sequenced interventions aimed at helping the poorest “graduate” into sustainable livelihoods. Innovations for Poverty Action (IPA) has completed the baseline survey on 2,400 ultra-poor households in 30 communities across five municipalities in northern Negros Occidental. IPA plans to conduct an endline round of data collection in 2020.
External Link(s)

Registration Citation

Citation
Beam, Emily et al. 2021. "Ultra Poor Graduation Pilot in Philippines." AEA RCT Registry. November 09. https://doi.org/10.1257/rct.4658-3.1
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
The Graduation pilot combines elements of social protection, livelihood development, financial inclusion, and social empowerment to deliver results by combining support for immediate needs with longer-term human capital and asset investments. The objective is to actively support participants in the short run while promoting sustainable and independent livelihoods in the long run. The components complement each other to help push participants out of poverty.
Intervention Start Date
2019-02-04
Intervention End Date
2020-09-01

Primary Outcomes

Primary Outcomes (end points)
Consumption, food security, income, assets, productive time use, mental health, physical health, financial behavior, social capital, women empowerment
Primary Outcomes (explanation)
- Household consumption. The primary measure of living standards used in this study, will be consumption based. This will be measured by the following variables:
a. Per capita consumption
b. Food security

- Economic activity of all household members measured by:
a. Monthly income and revenues
b. Time spent on productive activities
c. Total asset holdings and value (including households’ dwellings, livestock and agriculture)

- Financial inclusion (savings, money borrowed and lent)
- Health, measured by:
a. Self-reported mental health
b. Physical health (number of accidents/illnesses among household members).

- Women’s empowerment measured by whether a woman has major say in decisions regarding food

- Social capital, measured by:
a. Trust and social networks among community members
b. Business relations and entrepreneurial activity among community members

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The sample for the Ultra Poor Graduation Pilot in the Philippines consists of 2,400 ultra-poor households that were drawn from recipient households of the Philippine Department of Social Welfare and Development’s (DSWD) 4Ps conditional cash transfer program. In each of the 30 barangays, about 600 eligible households surveyed by IPA were randomly assigned to one of three treatment groups or the control group by computer. Randomization was conducted by dividing the barangay into 4 smaller geographic regions and assigning a treatment to
each, simplifying the logistics of implementation and reducing the risk of spillovers as rural barangays often cover extensive areas.
Experimental Design Details
Not available
Randomization Method
Computer.
Randomization Unit
Cluster level.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
120 clusters of 20 households each across the 30 sample barangays.
Sample size: planned number of observations
2,400 households
Sample size (or number of clusters) by treatment arms
600 households control, 600 individual livelihood and group coaching, 600 group livelihood and group coaching, 600 individual livelihood and individual coaching
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Using baseline data, IPA calculates an intra-cluster correlation coefficient (ICC) on key variables of interest (total income, durable asset value, 30-day consumption) ranging from less than 0.001 to 0.0015 within the subdivided barangays. Conservatively using 0.0015, we estimate that we have 80% power to detect a minimum detectable effect size (MDE) of 0.16 standard deviations between any treatment group and the control group when we randomize by sub-barangay clusters, assuming full compliance and no attrition. Power improves for pooled comparisons: testing any group coaching (n = 1,200) vs. any individual coaching (n = 600) or any group livelihood (n = 600) vs. any individual livelihood (n = 1,200) yields estimated MDEs of 0.14 standard deviations. In Banerjee et al. (2016), treatment assignment, covariates, and stratification FE explain between 5% and 60% of total variation, depending on the outcome of interest. In the case of consumption, for example, the previously estimated R2 of 0.46 would provide 80% power to detect an MDE of 0.12 standard deviations between treatment and control, and an MDE of 0.10 standard deviations for the pooled comparisons described above .
IRB

Institutional Review Boards (IRBs)

IRB Name
Innovations for Poverty Action Institutional Review Board
IRB Approval Date
2018-05-31
IRB Approval Number
14304
Analysis Plan

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