What We Can Learn from Selected, Unmatched Data: Measuring Internet Inequality in Chicago
21 Pages Posted: 11 May 2022
Date Written: May 7, 2022
By integrating a “big” dataset of Internet Speedtest® measurements from Ookla® with data on household incomes from the American Community Survey (ACS), we attempt to measure Internet speeds across income tiers. In the Ookla data, each measurement is technically rigorous but the sample frame is unknown. The ACS provides necessary information on income and Internet access from a known sample frame. Our likelihood combines these data and endogenizes selection effects to identify Internet speed distributions by income tier. We credibly identify the speed distribution for middle and high-income households. However, because the participation rate of low- income households in the Speedtest data is so limited, the speed estimates for these households are not identified.
Keywords: selection effects, Internet, big data, geographic data
JEL Classification: C5, I3, R3
Suggested Citation: Suggested Citation