Spatial Dependence of Prices for Vegetables and Fruits

This article presents the results of the analysis of spatial autocorrelation of daily prices for 8 types of fruit and vegetables (potatoes, cabbage, carrots, tomatoes, grapes, tangerines, apples, persimmons) in 2096 municipal districts of Russia for 2021–2024. The study included the calculation of global and local Moran’s indices with dynamically recalculated spatial weights matrices based on data detailed at the level of day, municipality, type of fruit/vegetable. We consider the choice of methods for building spatial weight matrices and estimating weights, as well as the impact of data aggregation on general conclusions about the spatial autocorrelation of prices. The study confirmed a stable positive spatial autocorrelation of prices for fruits and vegetables in Russia, regardless of the level of data aggregation (day/month and municipality/region) and the type of spatial weights matrix. The highest degree of spatial autocorrelation was found for carrots and tangerines, and its highest seasonal fluctuation was revealed for persimmon. It was found that most municipalities consistently belong to one predominant type of clusters, with high prices primarily registered in Siberia/Far East, whereas lower prices were registered in Central and Southern Russia. We also identified areas that demonstrate distinct spatial relationships. For example, municipalities in the Moscow region exhibit negative spatial autocorrelation, while areas in the Urals exhibit weak spatial dependencies. These results are of interest to the researchers and economists involved in pricing issues, as well as problems of integrating regional markets and studying regional aspects of inflation