A Framework for Evaluating the Impact of Food Security Scenarios

TL;DR

This paper introduces a framework combining scenario definition, VAR modeling, and Monte Carlo simulation to predict food security impacts, supported by a proprietary database, aiding decision-making in food policy.

Contribution

It presents a novel integrated approach for evaluating food security scenarios using statistical modeling and a comprehensive data repository.

Findings

The approach accurately predicts impacts of food security scenarios.

The proprietary database supports robust scenario analysis.

Insights inform policy decisions on food prices and availability.

Abstract

This study proposes an approach for predicting the impacts of scenarios on food security and demonstrates its application in a case study. The approach involves two main steps: (1) scenario definition, in which the end user specifies the assumptions and impacts of the scenario using a scenario template, and (2) scenario evaluation, in which a Vector Autoregression (VAR) model is used in combination with Monte Carlo simulation to generate predictions for the impacts of the scenario based on the defined assumptions and impacts. The case study is based on a proprietary time series food security database created using data from the Food and Agriculture Organization of the United Nations (FAOSTAT), the World Bank, and the United States Department of Agriculture (USDA). The database contains a wide range of data on various indicators of food security, such as production, trade, consumption, prices, availability, access, and nutritional value. The results show that the proposed approach can be used to predict the potential impacts of scenarios on food security and that the proprietary time series food security database can be used to support this approach. The study provides specific insights on how this approach can inform decision-making processes related to food security such as food prices and availability in the case study region.