What We Can Learn from Selected, Unmatched Data: Measuring Internet Inequality in Chicago

21 Pages Posted: 11 May 2022

See all articles by James Saxon

James Saxon

University of Chicago / Center for Data and Computing

Dan Black

University of Chicago - Harris School of Public Policy

Date Written: May 7, 2022

Abstract

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

Saxon, James and Black, Dan, What We Can Learn from Selected, Unmatched Data: Measuring Internet Inequality in Chicago (May 7, 2022). Available at SSRN: https://ssrn.com/abstract=4103426 or http://dx.doi.org/10.2139/ssrn.4103426

James Saxon (Contact Author)

University of Chicago / Center for Data and Computing ( email )

Chicago, IL
United States

HOME PAGE: http://saxon.cdac.uchicago.edu

Dan Black

University of Chicago - Harris School of Public Policy ( email )

1155 East 60th Street
Chicago, IL 60637
United States

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