price impacts must be interpreted alongside nominal wage effects and vice versa. For instance, floods raise food prices by about 3.4% annually and agricultural wages by 1.8%, but the greater rise in food prices erodes purchasing power, making food less accessible—an effect not fully captured by wage data alone.

# 6.2 Food Security Monitoring

The interplay between environmental shocks and economic factors can create complex, counterintuitive outcomes—for example, rising food prices may slightly reduce food stress in agricultural regions due to increased income for producers, but when combined with inflationary pressures, they exacerbate overall food insecurity. Effective early warning systems must therefore target both direct and market-mediated food insecurity drivers.

Despite these complexities, the risks are well captured within a highly interpretable modeling framework. Our final estimates demonstrate that significant spatial and temporal variations in food insecurity can be tracked using readily observable data, making the model practical for real-time monitoring and early intervention. For example, the model captures widespread food stress across Afghanistan, with 28 out of 34 provinces exhibiting average probabilities of over 50% of being classified as IPC phase 2 or higher. In contrast, food crisis risks are more localized, with provinces like Ghor and Badakhshan showing probabilities exceeding 50%. These areas are likely more vulnerable due to structural factors such as geographical isolation, limited infrastructure, and greater exposure to climatic shocks.

The model's ability to reflect historical trends in food insecurity, such as through the global food price crisis of 2010–2011 and periods of increased conflict and climatic shocks, reinforces its practical proposition. Its capacity to detect early warning signals of escalating risks highlights the utility of this approach for continuous monitoring. By leveraging such models, policy makers and humanitarian organizations can anticipate food security crises, allocate resources efficiently, and implement timely interventions that address the compounded risks of climatic and economic factors. This ongoing monitoring is essential for managing the dynamic nature of food insecurity and improving resilience in vulnerable regions.

# 6.3 Limitations and Future Research

While this study provides insights into the impacts of climatic and economic shocks on food security, several limitations exist related to data availability, model specifications, and variable interactions.

The analysis relies on available datasets, limiting the granularity of certain indicators. Gaps in data, such as from FEWS NET, highlight the need for higher-frequency monitoring and underscore challenges in model validation when target data are missing. This introduces potential uncertainties about precision during critical periods and may bias training. For example, data gaps during conflicts or restricted access to regions may lead to underrepresentation of actual food insecurity, resulting in lower risk estimates.

Although the models capture joint effects of climatic and economic shocks, disentangling causal pathways remains challenging. The interactions between environmental conditions, market dynamics, and food security are complex, even with linear models. Lagged effects in climatic and 29