We incorporated up to 12 lag periods to capture the delayed effects of climate shocks, consistent with studies such as Tang et al. (2021) and Prather et al. (2023). Economic variables like agricultural wages, fuel prices, domestic food prices, exchange rates, and global food prices often respond to climatic shocks with time lags, as indicated by Schmidhuber and Tubiello (2007).

The system includes many parameters, and to assess the cumulative impact of various factors, we sum the coefficients across all lags. This approach provides insights into the combined influence of these factors over time. For the logit equations, we convert the parameter estimates into marginal effects to aid interpretation, calculated over the full-year effects. Robust standard errors are used for all parameters to address potential heteroscedasticity. Finally, the estimated parameters allow us to predict the probabilities of food stress and food crisis, generating averages for each administrative area. This enables mapping variations in food insecurity risks between 2009 and 2023.

# 5. Results

This section presents the results of the logit regressions estimating the impact of climatic and economic shocks on food security. The results are presented in stages. First, we examine the direct effects of climatic shocks, particularly rainfall and NDVI anomalies, on food stress and food crisis. Next, we evaluate the role of economic factors, such as changes in food prices and agricultural wages, in influencing food insecurity, with a focus on how these variables react to climatic shocks.

## 5.1 Logit Regression Estimates for Food Stress and Food Crisis Drivers

Table 2 reports the results of the logit regressions for two thresholds of food insecurity: IPC phase 2 or greater (food stress) and IPC phase 3 or greater (food crisis). The model includes climatic variables (rainfall and NDVI anomalies), economic factors (food prices, agricultural wages), and a set of control variables to capture structural determinants of food security. The model is estimated using monthly data with twelve lags. To summarize, the coefficients are combined to represent the cumulative impact over 12 months, with marginal effects indicating the corresponding change in probability.

### **Seasonal Effects**

Before analyzing the impacts of the climatic variables, it is useful to first consider the average seasonal dynamics captured by the seasonal controls. In Afghanistan, floods most often occur during the spring, particularly from March to June, due to snowmelt from mountainous regions and seasonal rainfall. As temperatures rise, snow from higher altitudes melts, and combined with heavy rainfall, this leads to flooding, especially in river valleys and lower-lying areas. Floods can also occur during the summer, particularly during the monsoon season from July to September, though this impact is less widespread than in the spring.

Droughts in Afghanistan typically occur during the summer and autumn, especially from June to November, due to limited rainfall. Areas that rely on seasonal rain or snowmelt for water are particularly affected. The arid and semi-arid climate, combined with climate variability, makes the country highly susceptible to droughts. Droughts are often worsened by insufficient winter snowpack or reduced spring rains, leading to water shortages, reduced agricultural output, and declining groundwater levels.

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