Detecting Resale Price Maintenance for Competition Law Purposes: Proof-of-Concept Study Using Web Scraped Data

Abstract

Computational antitrust tools can help competition authorities in the detection of antitrust infringements. However, these tools require the availability of suitable data sets in order to produce reliable results. The present proof-of-concept study focuses on the so far understudied area of resale price maintenance, that is, the fixing of retail prices between manufacturers and retailers. By applying web scraping to price data for washing machines in Austria from a publicly accessible price comparison website, we compiled a comprehensive data set for a period of nearly three months. Visualised with the help of interactive dashboards, this data could then be analysed using various benchmarks in order to determine whether individual washing machine manufacturers and their retailers may be engaging in resale price maintenance. We conclude that the availability of data is a strong driver for research into and the application of computational antitrust tools. If market data were publicly accessible and provided in a more structured format, researchers and competition enforcers could develop ever-more refined computational antitrust applications that would, ultimately, safeguard competition in markets.

Keywords: antitrust violations, artificial intelligence, computational antitrust, descriptive statistics, machine learning, price comparison, proof-of-concept, public enforcement, resale price maintenance, washing machine market, web scraping