# **7 Conclusion**

In this paper we presented a fully stochastic framework to model and predict food crisis dynamics. We build on the recent advancements in food security analysis and measurement, most notably the IPC framework. A key novelty in our approach is to leverage food insecurity information of an entire population distribution, rather than calculating a single food insecurity metric. Consequently, the model provides a holistic picture of food crisis risk to decision-makers. It also lends itself to construct stochastic catalogues and exceedance probability curves that can serve as hazard modules for insurance and risk management products.

In order to model entire food insecurity distributions with conventional statistical methods, we introduce a convenient transformation, namely the compositional transformation. With it we can fit our model to the data, identify drivers of food insecurity, simulate alternative scenarios, and make forecasts into the near future. Due to the brevity of recorded food insecurity’s historic data, it is difficult to estimate a separate model for each country. We therefore employ a panel approach where we pool the data of 15 countries and estimate a joint multi-country model.

Food insecurity research has shown that agronomic, weather, conflict and economic variables are major drivers for food crises. However, which exact variables or transformations thereof should be included to predict either high or low states of food insecurity is not immediately clear. We therefore construct a total set of 1,670 candidate variables for each of the above categories. Using a statistical variable selection method, we identify the 30 most important drivers. We find that food insecurity states are indeed past-dependent and that states of low food insecurity are more likely to transition to high states than vice versa. Furthermore, conflict variables in the recent past are more predictive for high food insecurity levels while agricultural and weather-related variables are more important for lower levels. Food prices are predictive for both cases.

Finally, our model was designed to complement expert opinion. It leaves ample room to incorporate subjective judgements and tailor the model to specific applications. This is essential for unprecedented situations that are not part of the recorded history, which makes them difficult for the model to anticipate. At the same time, consequences of natural disasters or civil wars are obvious to human observers. Using a Bayesian extension, we demonstrate how prior information can be incorporated into our model and how it can significantly improve model performance. This extension is particularly advantageous in a data scarce environment.

This framework may be a useful addition to the analytical toolkit of policymakers and humanitarian organizations as well as financial institutions. The stochastic nature could be particularly attractive for financial triggering decisions, scenario analyses or other risk assessments. While it is well-suited for short- to medium-term predictions, application-specific adjustments and extensions are necessary for forecasts further into the future. We especially advise to pay careful attention to the variable selection step.

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