Algorithms in the Marketplace: An Empirical Analysis of Automated Pricing in E-Commerce
58 Pages Posted: 8 Nov 2021
Date Written: September 30, 2021
We analyze algorithmic pricing on Bol.com, the largest online marketplace in the Netherlands and Belgium. Based on more than two months of pricing data for around 2,800 popular products, we find that algorithmic sellers can both increase and reduce the price of the Buy Box (the most prominently displayed offer for a product). Consistently with collusion, algorithms benefit from each other’s presence: Prices are particularly high if two algorithms bid against each other and there is a medium number of sellers in the market. We identify several algorithmic pricing patterns that are often associated with collusion. Algorithmic sellers are more likely to win the Buy Box, implying that consumers may face inflated prices more often. We also document efficiencies due to algorithmic pricing. With a sufficient number of competitors, algorithmic sellers reduce the Buy Box price and compete particularly fiercely. Algorithms furthermore reduce prices in monopoly markets. We explain this by the inability of traditional product managers to manually adjust prices product-by-product for a large number of items, which automated agents may correct. Overall, our findings call for careful policy with respect to pricing algorithms, that considers both the risk of collusion and the need to preserve potential efficiencies.
Keywords: Algorithmic pricing, Artificial intelligence, Collusion, Forensic economics
JEL Classification: D42, D82, L42
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