Volume 22, Issue 2 - Special Issue: Causes and Consequences of Retail Food Price Inflation

The issue of volatile food prices is a consistent problem for American consumers, as rising prices make it challenging to afford nutritious food that meets dietary standards. Various complex factors influence this price volatility, including economic conditions, weather patterns, global trade, energy prices, and more. Notably, the impact of food price increases is not equal for everyone. Low-income individuals and those in rural areas are disproportionately affected. A comprehensive understanding of the driving factors is essential to tackle this issue effectively. We employ advanced time-series techniques such as Vector Error Correction Models (VECM) and modern causal inference methods such as probabilistic graphical models implemented via machine learning and artificial intelligence approaches on monthly data from 2000 to 2021 to investigate the U.S. food price inflation issue. These methods help unravel the intricate dynamics among key variables driving food price inflation. The study aims to achieve several objectives. It intends to (1) clarify how factors influencing food price inflation in the U.S. change over time using VECM models, (2) establish causal relationships among interconnected variables to develop probabilistic graphical models using innovative search algorithms, and (3) create and validate forecasts related to U.S. food price inflation. The end goal is to provide actionable insights for policy design. Results show that food price inflation is heavily tied to commodity pricing and pricing for medical services. Additionally, historical decompositions for COVID-19 show ties between food price inflation and energy inflation.

This paper shows that food price inflation varies across rural, urban, and metro counties in the United States. To quantify this disparity, we calculate food price indices using retail scanner data. Our indices suggest that between 2006 and 2020, rural areas had an overall higher food price inflation compared to urban and metro regions, with food price inflation averaging 0.7 percent lower in urban areas and 1.6 percent lower in metro areas. Additionally, we examine shifts in these disparities during key periods. We find that during the Great Recession, these differences were negligible, but in the post-Great Recession years, urban and metro areas consistently had lower food price inflation. However, the onset of the COVID-19 pandemic in 2020 led to a shift, with both urban and metro areas experiencing higher food price inflation, surpassing rural regions.

As worldwide food prices rise, there is a growing interest in understanding the effect of these increases on households. Building on previous work, while applying recent methodological advances, we focus our attention on México during the COVID-19 pandemic. We document these price escalations for a basket of foods representative of Mexican households’ diets. The price increases are substantial across the basket, ranging from 20 percent in meat to 40 percent in tortilla. Using these estimates, we calculate the welfare effect from the recent food price escalation to cost households $17.07 billion annually, close to 1.5 percent of Mexico’s Gross Domestic Product in 2020. We estimate households would need to increase their food expenditure budgets by 28.66 percent, the compensating variation, to achieve pre-price increase utility levels.