security shocks are sporadic or reflect deeper structural vulnerabilities, and how building agricultural resilience could provide a solution to chronic food insecurity.

This study addresses these gaps by investigating the impacts of climatic shocks on food security over a thirteen-year period in Afghanistan. Using a vector-autoregressive (VAR) model, we analyze the direct and indirect effects of weather anomalies on food insecurity, food prices, and agricultural wages. By understanding these dynamics, we provide evidence on how climatic shocks have impacted food security. Specifically, the model uses a logistic regression with two specifications to measure different levels of food insecurity: IPC levels 2 or greater (food stress) and 3 or greater (food crisis). The model evaluates the probability of these outcomes using drought and flood anomalies as primary variables, along with control variables for time-invariant vulnerabilities and economic factors including food prices and agricultural wages. In addition to the logit, food prices and agricultural wages are modeled using linear specifications. All three regressions have the same covariate structure and incorporate up to 12 time lags to account for delayed climate effects on food insecurity. The VAR setup allows analysis of both direct and indirect effects, showing how droughts impact food security directly and through price and wage effects. The model can predict at-risk populations using weighted probabilities.

Our analysis contributes to existing literature in two main ways. First, the results highlight the significant impact of climatic shocks, particularly floods, on food insecurity. A one-in-six-year flood event increases the likelihood of food stress by 23.4 percentage points and food crisis by 5.9 percentage points over 12 months. When combined with positive NDVI (Normalized Difference Vegetation Index) anomalies during the spring planting season, indicating lush natural vegetation from excessive rainfall, the impact is even greater—raising the probability of food stress by 51.2 percentage points and food crisis by 16.0 percentage points. These findings underscore the importance of resilience-building measures in flood-prone regions to mitigate food security risks.

Climatic shocks also influence food prices and wages, affecting food security. For example, floods increase food prices by 3.40% annually, while agricultural wages rise by 1.80%. These wage increases are insufficient to offset higher food prices, resulting in a net negative purchasing power shock and worsening food insecurity. Droughts similarly induce purchasing power shocks. In our model, a one-in-six-year drought leads to a 5.17% annualized increase in agricultural wages but a 6.36% increase in food prices. The model shows that climatic shocks impact food security both directly, through agricultural losses and reduced food availability, and indirectly, through price and wage dynamics. This interconnectedness highlights the need for comprehensive climate adaptation strategies that address food production and market stability to improve food security outcomes.

This paper also contributes to the literature on food security modeling. Our model's predictive accuracy—correctly classifying 69.14% of food stress cases and 87.89% of food crisis cases— provides a foundation for developing early warning systems. These systems are crucial for anticipatory humanitarian actions, allowing policy makers to monitor risks in real time and take preemptive measures before crises unfold. This work builds on research incorporating climate simulations and satellite data to account for weather impacts on crop yields, food supply chains, and food security (Lobell et al., 2008; Cartuyvels et al., 2023) and connects to studies using stochastic modeling (Wang et al., 2020; 2022), threshold modeling (Penson et al., 2024), and machine learning methods such as Random Forest (Andrée et al., 2020) and Bayesian networks (Semakula et al., 2024) for predicting food security outcomes. Our model's ability to produce accurate, population-weighted 3