Real-time Nowcasting of Food Insecurity with Google Trends Data

This research explores the potential of Google Trends (GT) as a tool for generating a daily index of food insecurity at the national level, focusing on countries monitored by the Famine Early Warning Systems Network (FEWS NET) and the U.S. Global Fragility Act (GFA). Drawing inspiration from previous studies on GT’s predictive capabilities, the authors employ Natural Language Processing (NLP) to analyze FEWS NET’s food security reporting and identify key predictors of food insecurity using a LASSO regression approach. The predictors are then queried on GT to construct a daily sentiment index for each country. Unlike other approaches, the study considers multiple languages and weighs search terms based on LASSO coefficients. The resulting Synthetic Search Interest (SSI) Index for food insecurity demonstrates a statistically significant correlation with the UN’s Food and Agriculture Organization’s (FAO) share of the population in severe food insecurity, affirming GT’s potential as a monitoring tool. This research introduces a novel methodology and valuable insights for leveraging real-time data to enhance early warning systems in food security. JEL Classification: C53, C82, Q18.