  Search engines play a central role in routing political information to
citizens. The algorithmic personalization of search results by large search
engines like Google implies that different users may be offered systematically
different information. However, measuring the causal effect of user
characteristics and behavior on search results in a politically relevant
context is challenging. We set up a population of 150 synthetic internet users
("bots") who are randomly located across 25 US cities and are active for
several months during the 2020 US Elections and their aftermath. These users
differ in their browsing preferences and political ideology, and they build up
realistic browsing and search histories. We run daily experiments in which all
users enter the same election-related queries. Search results to these queries
differ substantially across users. Google prioritizes previously visited
websites and local news sites. Yet, it does not generally prioritize websites
featuring the user's ideology.
