Price variations in food products: A time series dataset for analysis across regions in Italian supermarkets

TL;DR

This paper introduces a detailed dataset of food prices in Italian supermarkets, collected over two years, to analyze regional and temporal price variations.

Contribution

The novel contribution is a structured, time-series dataset of Italian supermarket food prices with regional and product-level details.

Findings

The dataset captures price trends and regional disparities in meat, fruit, and vegetable products across Italian regions.

Automated web scraping enabled consistent and scalable data collection over a 2.5-year period.

The dataset supports analysis of inflation, market competition, and consumer behavior in the Italian food retail sector.

Abstract

This study presents a comprehensive dataset on retail food prices, specifically covering meat, fruit, and vegetable products, collected through automated web scraping techniques from online supermarket platforms across multiple Italian regions. The dataset spans a period of over two years, from December 2020 to March 2023, and includes structured information on product prices, store locations, and regional variations. Data collection was carried out using Python-based scripts, ensuring automated and consistent extraction of price listings. Supermarkets were geolocated based on their online presence, and products were categorized using the COICOP classification system to facilitate standardized economic analysis.

The dataset enables an in-depth examination of food price dynamics, allowing researchers to investigate regional price disparities, retailer-specific pricing strategies, and temporal price trends across different product categories. By providing granular and time-series data, this resource can support economic studies on inflation, market competition, and consumer purchasing behaviors. Additionally, the dataset can be used for policy-oriented research, aiding in the assessment of food affordability, price volatility, and the impact of external factors such as supply chain disruptions or economic policies. Given its structured nature, the dataset is well-suited for statistical modeling, machine learning applications, and comparative studies on regional price variations within the Italian food retail sector.

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