Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance
The COVID-19 pandemic has devastated many low- and middle-income countries (LMICs), causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have mobilized targeted social assistance programs. Targeting is a central challenge in the administration of these programs: given available data, how does one rapidly identify the individuals and families with the greatest need? This challenge is particularly acute in the large number of LMICs that lack recent and comprehensive data on household income and wealth. Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our approach uses traditional survey-based measures of consumption and wealth to train machine learning algorithms that recognize patterns of poverty in non-traditional data; the trained algorithms are then used to prioritize aid to the poorest regions and mobile subscribers. We evaluate this approach by studying Novissi, Togo’s flagship emergency cash transfer program, which used these algorithms to determine eligibility for a rural assistance program that disbursed millions of dollars in COVID-19 relief aid. Our analysis compares outcomes – including exclusion errors, total social welfare, and measures of fairness – under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo at the time, the machine learning approach reduces errors of exclusion by 4-21%. Relative to methods that require a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine learning approach increases exclusion errors by 9-35%. These results highlight the potential for new data sources to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date.
Isabel Onate Falomir, Shikhar Mehra, Suraj Nair, Adrian Dar Serapio, Nathaniel Ver Steeg, and Rachel Warren provided invaluable research assistance on this project. This project would not have been possible without the dedication of our project partners in Togo, especially Minister Cina Lawson, Shegun Bakari, Stanislas Telou, Leslie Mills, Kafui Ekouhoho, Morlé Koudeka, and Attia Byll. The team at GiveDirectly was instrumental in implementing the Novissi expansion studied in this paper (especially Han Sheng Chia, Michael Cooke, Kristen Lee, Alex Nawar, and Daniel Quinn). We thank Esther Duflo, Luis Encinas, Tina George, Rema Hanna, Ethan Ligon, and Ben Olken for helpful feedback. We are grateful for financial support from Google.org, data.org, the Center for Effective Global Action, the Jameel Poverty Action Lab, the Global Poverty Research Lab at Northwestern University, and the World Bank, which financed the phone surveys and data collection under the WURI program. Blumenstock is supported by NSF award IIS – 1942702. Authors retained full intellectual freedom in conducting this research, and as such all opinions and errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.