From Recognition to Adaptation: How does Forecasting relate to International Aid Funding in Food Security?

Abstract: The importance of early adaptation to reduce the impact of recognized risks has been underlined in recent years as featured aspect of the Sustainable Development Goals and the Sendai Framework for Action. The aim of this study is to analyze the relationship between forecasted food insecurity levels and allocated funding directed at food security. The dataset was built by combining quantitative data on food insecurity forecasts (FEWS NET), international aid funding addressing food insecurity (UN OCHA) and population distribution (NASA) as well as by the use of GIS analyzing tools. The statistical analysis of the dataset shows that there is a strong positive correlation between forecast and funding streams in the 27 analyzed countries over the analyzed period from 2011 and 2018. There has been an increase in the strength of this relationship from the year 2012, indicating a greater response to forecasts and learning to reduce the risk of food insecurity. Further, the analysis indicates that the country characteristics: population size (negative) and density (negative), the Human Development Index (negative), the year of independence (positive) as well as whether a country has an UNISDR National Platform (negative) weakly correlate to the funding per person per forecasted food insecurity level. The discussion reflects on the high complexity of the system and the potential for strengthening the relationship between recognition and adaptation for improving early warning systems, forecast-based early action and disaster risk reduction.