Document Type

Paper

Publication Date

9-2025

Published In

Policy Research Working Papers

Series Title

Policy Research Working Papers

Abstract

Exposure to extreme weather events and other adverse shocks has led to an increasing number of humanitarian crises in developing countries in recent years. These events cause acute suffering and compromise future welfare by adversely impacting human capital formation among vulnerable populations. Early and accurate detection of adverse shocks to food security, health, and schooling is critical to facilitating timely and well-targeted humanitarian interventions to minimize these detrimental effects. Yet monitoring data are rarely available with the frequency and spatial granularity needed. This paper uses high-frequency household survey data from the Rapid Feedback Monitoring System, collected in 2020–23 in southern Malawi, to explore whether combining monthly data with publicly available remote-sensing features improves the accuracy of machine learning extrapolations across time and space, thereby enhancing monitoring efforts. In the sample, illnesses and schooling disruptions are not reliably predicted. However, when both lagged outcome data and geospatial features are available, intertemporal and spatiotemporal prediction of food insecurity indicators is promising.

Published By

World Bank Group

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Comments

This work is freely available under a Creative Commons license.

Download

Included in

Economics Commons

Share

COinS