Estimating Local Agricultural GDP across the World

Abstract

The 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.

Machine Learning Guided Outlook of Global Food Insecurity Consistent with Macroeconomic Forecasts

“…However, the average rainfall level measured over the entire area of countries like Brazil, the Russian Federation or China would hide any and all local climatic shocks. Recent work has focused on mapping subnational agricultural GDP (Blankespoor et al, 2022) and such approaches could in theory be combined with spatially explicit data sets on hazards and climate shocks to estimate the fraction of agricultural GDP that is produced in areas vulnerable to climate. Alternatively, Koomen et al (2022) develop spatial explicit simulation methods that may project populations vulnerable to hazards and climatic shocks under alternative Shared Socioeconomic Pathways (SSP) (scenario definitions commonly used in climate scenario analysis).…”