Targeting Aid Better: Introducing our First Round of Funded Studies

This post was written by CEGA Program Associate Anya Marchenko.

In fall 2020, CEGA’s Targeting initiative launched an RFP designed to help governments and NGOs rapidly target emergency services and aid in response to COVID-19. Our aim was to fund projects that are deploying and testing innovative approaches to targeting aid and social protection in low- and middle-income countries (LMICs), with a particular focus on addressing COVID-19. We prioritized projects that leveraged new types of data (e.g., mobile phone or satellite data); used existing data (e.g., administrative records) in novel ways; or developed non-traditional targeting approaches (e.g., machine learning).

Below are some descriptive statistics about the RFP:

Photo: The Targeting RFP pipeline, from the number of Expressions of Interest to projects funded.

Photo: CEGA prides itself on supporting inclusive research. Two of the three projects we funded had an LMIC co-investigator, and around 50% of projects at all other stages had at least one LMIC investigator.

We now know that COVID-19 has dramatically impacted living standards and food security in LMICs, underscoring the importance of these projects which seek to better direct aid and expand social protections. Read on to learn about the three projects selected by the review committee, whose funded work began in December 2020 and will continue through November 2021.

Combining Satellite Imagery and Machine Learning to Target Social Protection in Pakistan

Photo: Flooding in Sindh province. (Credit: Rashid Memon)

Researchers: Sean Fox (Univ. of Bristol), Rashid Memon (Lahore University of Management Sciences), Levi Wolf (Univ. of Bristol), Felix Agyemang (Univ. of Bristol)

Partner: Sindh Social Protection Strategy Unit (SPSU), Government of Pakistan

In Pakistan, the newly established Sindh Social Protection Strategy Unit (SPSU) is in charge of implementing three cash transfer programs. This project aims to help the government design a dynamic, shock responsive system for targeting these interventions. To do this, the team will use machine learning to create small area estimates of socioeconomic conditions across Sindh province. This will highlight the villages or ‘clusters’ of greatest (or least) need, allowing them to be targeted for aid from SPSU. CEGA’s funding is enabling the team to conduct a survey to provide ground-truth data to validate their algorithm. For more information about this project, see the description on the CEGA website.

Quantifying the benefits of better targeting in employment programmes

Photo: View of Amman, Jordan. (Credit: Simon Quinn)

Researchers: Simon Quinn (Oxford), Stefano Caria (Univ. of Warwick), Eva Kaplan, Maximilian Kasy (Oxford), Soha Shami (Danish Refugee Council), Alexander Teytelboym (Oxford)

Partner: The International Rescue Committee

Refugees must often combat the dual hardships of displacement and job uncertainty in their new country of residence. The research team is implementing a new targeting method in a field experiment designed to help Syrian refugees and local job seekers in Jordan find work. This new targeting method is an algorithm that flexibly chooses who receives interventions in response to past results, and balances the goals of 1) maximizing the precision of treatment effects with 2) maximizing the welfare of participants in the experiment. CEGA will fund the team to run a two-year follow-up survey after the intervention. For more information about this project, see the description on the CEGA website.

A new index of poverty to target government cash transfers in Ghana

Photo: Woman being enrolled in LEAP. (Credit: LEAP Program, Ministry of Gender, Children and Social Protection, Ghana)

Researchers: Ethan Ligon (UC Berkeley), Angela Owusu Ansah(Ashesi Univ.), Sena Agbodjah Agyepong (Ashesi Univ.), Carly Trachtman (UC Berkeley)

Partner: Government of Ghana

The Government of Ghana’s flagship social protection program, Livelihood Empowerment Against Poverty (LEAP), provides cash transfers and free health insurance to nearly a quarter million extremely poor Ghanaian households. But the LEAP program lacks resources to immediately reach all extremely poor households, and their targeting methodology faces problems such as excluding eligible households and including ineligible ones.

The research team will construct an index to predict both household poverty, as well as which households will benefit most from the cash transfer (an essential estimate for targeting). By using the new index for targeting, the Ghanaian government should be able to identify households for whom cash transfers will have the largest benefit. For more information, see the description on the CEGA website.