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From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising

Journal: American Economic Review

Date: 20211001

Author: Decarolis, Francesco; Rovigatti, Gabriele

Abstract:
This paper analyzes the impact of intermediary concentration on the allocation of revenue in online platforms. We study sponsored search documenting how advertisers increasingly bid through a handful of specialized intermediaries. This enhances automated bidding and data pooling, but lessens competition whenever the intermediary represents competing advertisers. Using data on nearly 40 million Google keyword auctions, we first apply machine learning algorithms to cluster keywords into thematic groups serving as relevant markets. Using an instrumental variable strategy, we estimate a decline in the platform's revenue of approximately 11 percent due to the average rise in concentration associated with intermediary merger and acquisition activity.

Link: Google Scholar

Key Findings

Impact on Search Engine Revenue

Intermediary concentration leads to an 11.3% decline in Google's revenue, based on average HHI increase of 245 points following merger events

Market Concentration

The "Big Four" networks control 74% of search volume in 2017, with increasing concentration over time

Price Effects

Intermediary concentration primarily impacts revenue through lower cost-per-click (CPC), rather than changes in search volume or keywords

Network Market Share Evolution

  • WPP and Publicis show increasing market share from 2014-2017
  • Omnicom and Havas experience declining market share
  • Independent agencies' share decreases from 13% to 6%

Revenue Components Analysis

  • CPC shows significant negative correlation with concentration (-1.271)
  • Search volume has weaker negative correlation (-0.669)
  • Number of keywords shows moderate negative correlation (-0.842)

Market Structure Changes

  • Average market has 37 keywords and 4 competing advertisers
  • 40% of markets are highly concentrated (HHI > 2,500)
  • Concentration increases from 37% to 47% between 2014-2017

Contribution and Implications

  • First systematic analysis of countervailing power in digital advertising markets
  • Novel approach to market definition using machine learning for keyword clustering
  • Evidence that intermediary concentration can partially offset platform market power
  • Insights for competition policy in digital platform markets

Data Sources

  • Network market share evolution based on Table 1, Panel B market share data
  • Revenue components analysis derived from Table 5 IV estimates
  • Market structure metrics from Table 2 competitive clusters statistics