Ultra Poor Graduation Pilot in Philippines

Last registered on November 09, 2021


Trial Information

General Information

Ultra Poor Graduation Pilot in Philippines
Initial registration date
September 12, 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
September 17, 2019, 9:45 AM EDT

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

Last updated
November 09, 2021, 7:27 PM EST

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


Primary Investigator

Northwestern University

Other Primary Investigator(s)

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

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
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

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


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
Intervention End Date

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
Random assignment was used to avoid selection and program placement bias. In each of the 30 barangays, about 80 eligible households surveyed by IPA were 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. This was done to simplify the logistics of implementation. Rural barangays often cover extensive areas. Had such barangays’ households been assigned to treatment groups completely at random, some participants would have had to travel far for trainings, meetings and coaching sessions, which would likely have reduced attendance. The geographical separation of different treatments and the control group also reduces the likelihood of spillovers. Households were randomized in two stages:

1. Each barangay was divided into four clusters based on GPS coordinates collected during surveying. Households were assigned points based on their distance to the nearest cluster's center minus the distance to the farthest and ranked accordingly. Subsequently, each of the four clusters was filled with the highest ranked households until 20 households were placed, at which point that cluster was taken out of the equation and the next cluster filled with the highest-ranking households.

This process was repeated until all 120 clusters of 20 households across the 30 sample barangays were filled.

2. Once all households had been assigned to clusters, randomized assignment to treatment clusters and control clusters began. For treatment groups to be comparable and the control group to be a valid counterfactual, all groups need to have, on average, statistically identical characteristics. The four clusters in all barangays were repeatedly assigned to different treatment groups, or control. Statistical tests were run on each configuration, comparing every treatment to control and the treatments to one another based on four key covariates. Finally, the statistically most balanced configuration was chosen.

Households assigned to treatment groups were eligible to receive treatment as soon as the randomization was complete. There is unlikely to be differential attrition: households are unlikely to drop out of the treatment group, and controls are unlikely to opt in to treatment. Further, IPA and BRAC will mitigate differential attrition by minimizing the link between the research and program implementation. This is done through carefully monitoring implementation to ensure assignment to treatment and control groups is maintained. If there are any crossovers, the original assignment will be used for analysis.
Randomization Method
Randomization Unit
Cluster level.
Was the treatment clustered?

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 .

Institutional Review Boards (IRBs)

IRB Name
Innovations for Poverty Action Institutional Review Board
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials