# **Table 2: Logit Regression Estimates for Food Stress and Food Crisis**

The table presents logistic regression estimates for food insecurity under IPC ≥ 2 (food stress) and IPC ≥ 3 (food crisis). The coefficients represent the cumulative impact over 12 months of a one-unit change in each predictor variable, with the marginal effects indicating the corresponding change in probability. Note that flood anomalies are negative values, and combined with a negative coefficient, they result in an increased probability proportional to the severity of the flood indicator. To simplify interpretation, the negative anomalies are entered into the model as absolute values, ensuring that positive coefficients indicate an increase in risk.

||**IPC ≥ 2 (Food Stres**<br>|**s)**<br>|**IPC ≥ 3 (Food Crisi**<br>|**s)**<br>|
|---|---|---|---|---|
||**Coefficient**|**Marginal Effect**|**Coefficient**|**Marginal Effect**|
|**Flood anomalies**|1.194*** (0.026)|0.234*** (0.005)|0.628*** (0.036)|0.059*** (0.003)|
|**Drought anomalies**|0.227*** (0.039)|0.045*** (0.008)|-0.100* (0.052)|-0.009* (0.005)|
|**Positive NDVI anomalies**|0.918*** (0.021)|0.180*** (0.004)|0.158*** (0.028)|0.015*** (0.003)|
|**Negative NDVI anomalies**|0.449*** (0.024)|0.088*** (0.005)|0.399*** (0.032)|0.037*** (0.003)|
|**Food prices**|-0.006*** (0.001)|-0.001*** (0.0002)|0.001 (0.002)|0.000 (0.0002)|
|**Agricultural wages**|-0.005*** (0.001)|-0.001*** (0.0002)|-0.037*** (0.002)|-0.003*** (0.0002)|
|**Fuel prices**|0.050*** (0.001)|0.010*** (0.0001)|0.028*** (0.001)|0.003*** (0.0001)|
|**Global food prices**|0.034*** (0.001)|0.007*** (0.0001)|-0.002*** (0.001)|-0.0002*** (0.0001)|
|**Exchange rate**|-0.029*** (0.001)|-0.006*** (0.0003)|0.024*** (0.002)|0.002*** (0.0002)|
|**Log Cropland**|-0.129*** (0.004)|-0.025*** (0.001)|-0.463*** (0.007)|-0.043*** (0.001)|
|**Log Ruggedness Terrain**|0.465*** (0.011)|0.091*** (0.002)|1.958*** (0.031)|0.183*** (0.002)|
|**Log Population Density**|0.258*** (0.006)|0.051*** (0.001)|0.799*** (0.014)|0.075*** (0.001)|
|**Log Pastureland**|-0.108*** (0.016)|-0.021*** (0.003)|0.707*** (0.047)|0.066*** (0.004)|
|**Winter Dummy**|0.114*** (0.018)|0.022*** (0.004)|0.372*** (0.027)|0.035*** (0.003)|
|**Summer Dummy**|-0.163*** (0.019)|-0.032*** (0.004)|-0.197*** (0.028)|-0.018*** (0.003)|
|**Spring Dummy**|0.500*** (0.019)|0.098*** (0.004)|0.916*** (0.026)|0.086*** (0.002)|
|**Constant (Fall)**|-12.190*** (0.224)||-48.956*** (0.778)||
|**N**|112,711||112,711||
|**Pseudo R2**|0.147||0.293||
|**Correctly predicted**|69.14%||87.89%||

Note: *p < 0.1, **p < 0.05, ***p < 0.01 The seasonal dummies reveal important variations in food insecurity risks throughout the year. Food insecurity is highest during the winter and spring months, with food stress increasing by 2.2 percentage points in winter and 9.8 percentage points in spring. Similarly, food crisis risks rise by 3.5 percentage points in winter and 8.6 percentage points in spring. These seasonal effects likely reflect reduced agricultural activity in winter and the pre-harvest lean season in spring when food stocks are low. They also coincide with the flooding season, which is important for interpreting the marginal effects of flood anomalies. In contrast, summer is associated with a decrease in food insecurity, with food stress and food crisis risks falling by 3.2 and 1.8 percentage points, respectively, as harvests replenish food supplies. This seasonal baseline is important when analyzing the marginal effects of the drought variables.

# **Climatic Shocks: Flood and Drought Anomalies, NDVI Anomalies**

The results underscore the impact of climatic shocks on food insecurity outcomes. Both rainfall anomalies, characterized by flood and drought events, and NDVI anomalies, reflecting vegetation 16