The Editor vs. the Algorithm

In the paper presented in this ZEW Research Seminar a field experiment is carried out with a major news outlet to quantify the economic returns to data and informational externalities associated with algorithmic recommendation. Automated recommendation can outperform a human editor in terms of user engagement, though this crucially depends on the amount of training 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.