최신자료
Nowcasting Disruptions to Human Capital Formation : Evidence from High-Frequency Household and Geospatial Data in Rural Malawi
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
ad- verse 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.
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 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.
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