Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/215014 
Year of Publication: 
2019
Series/Report no.: 
CESifo Working Paper No. 8012
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
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.
Subjects: 
field experiment
economics of AI
returns to data
filter bubbles
JEL: 
L82
L51
J24
Document Type: 
Working Paper
Appears in Collections:

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.