_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _26_ There is also a benefit to relying on CPI inflation alone. Macro-economic frameworks and monetary policies are centered around general consumer prices. For instance, central banks often focus on long-term inflation metrics less impacted by short-term price volatility, and consider a “core CPI” for this that excludes items such as food, shelter, energy, and used cars and trucks. In other cases, an index of Personal Consumption Expenditures (PCE) is preferred which similarly excludes volatile commodities. This suggests also that monetary policies aimed at stabilizing core CPI or PCE can in essence be reached without normalizing food prices and so it is an important question whether restoring the CPI inflation rate is sufficient to improve global food insecurity conditions. The simple specification of the model made investigating that through counterfactual predictions much simpler. The counterfactual analysis revealed that stabilizing domestic inflation in the ECA region would be sufficient to revert hungry populations back to prepandemic levels there, but not in other regions. This is a potentially important finding that would be worth investigating further, particularly given the result that international prices need to come down to keep development assistance needs to manageable levels in these regions.

A second possible shortcoming is that the percentage of forest land cover provides only a meager description of climate factors. The first challenge to improving this is that most climate data sets describe phenomena that are inherently spatial and not at all straightforward to summarize at a national level, particularly for larger countries. For instance, rainfall and NDVI (Normalized Difference Vegetation Index) data easily capture area-specific droughts and floods, and have shown to be highly predictive of local severe food insecurity conditions in some of the work that has been cited earlier. 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). Efforts to summarize such vulnerability trends to a national level likely deserve a full paper on their own, particularly if the estimates were to be projected several years into the future. Finally, Andr ́ee et al. (2019a) provide projections through 2030 for deforestation, human exposure to air pollution, and carbon footprints for countries that account for a combined 85% of the world’s population. This could capture some climate and environmental risk components, but their machine learning model relies on economic variables that have overlap with the covariates used here, showing that much of the information on these environmental aspects may already be contained in poverty, income and variables describing the structural