The Editor vs. the Algorithm: Returns to Data and Externalities in Online News

Jörg Claussen, Christian Peukert, Ananya Sen

Research output: Working paperResearch


We run a field experiment to quantify the economic returns to data and informational ex-ternalities associated with algorithmic recommendation relative to human curation in the context of online news. Our results show that personalized recommendation can outperform human curation in terms of user engagement, though this crucially depends on the amount of personal data. Limited individual data or breaking news leads the editor to outperform the algorithm. Additional data helps algorithmic performance but diminishing economic returns set in rapidly. Investigating informational externalities highlights that personalized recommendation reduces consumption diversity. Moreover, users associated with lower levels of digital literacy and more extreme political views engage more with algorithmic recommendations.
Original languageEnglish
Place of PublicationMunich
PublisherIFO Institute, Center for Economic Studies (CES)
Number of pages34
Publication statusPublished - 2019
Externally publishedYes
SeriesCESifo Working Paper


  • Field experiment
  • Economics of AI
  • Returns to data
  • Filter bubbles

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