世界银行-人力资本形成的即时中断：来自马拉维农村高频家庭和地理空间数据的证据（英）

Policy Research Working Paper11202Nowcasting Disruptions to Human Capital FormationEvidence from High-Frequency Household and Geospatial Data in Rural MalawiElizabeth J. TennantAleksandr MichudaJoanna B. Upton Andres ChamorroRyan EngstromMichael L. MannDavid NewhouseMichael WeberChristopher B. BarrettHuman Capital Project &Development Data GroupSeptember 2025 Public Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedProduced by the Research Support TeamAbstractThe Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.Policy Research Working Paper 11202Exposure 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 ad- verse shocks to food security, health, and schooling is critical to facilitating timely and well-targeted humanitar-ian 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 Monitor-ing 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, ill-nesses 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.This paper is a product of the Human Capital Project and the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dnewhouse@worldbank.org. Nowcasting Disruptions to Human Capital Formation:Evidence from High-Frequency Household andGeospatial Data in Rural Malawi∗Elizabeth J. Tennant† Ale