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

Jörg Claussen, Christian Peukert, Ananya Sen

Research output: Contribution to conferencePaperResearch


We run a field experiment to quantify the economic returns to data and informational externalities associated with algorithmic recommendation 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 decreasing 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
Publication date2019
Number of pages32
Publication statusPublished - 2019
EventCESifo Area Conference on Global Economy. CESifo Conference Centre - München, Germany
Duration: 30 Jan 200431 Jan 2004


ConferenceCESifo Area Conference on Global Economy. CESifo Conference Centre

Cite this