The Editor vs. the Algorithm: Returns to Data and Externalities in Online News
We run a eld 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 recommen-
dations.