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Research Roundup for April 2020

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Welcome back to TPI’s Research Roundup, our semi-regular compilation of recent outside research of interest to tech policy nerds. With everybody stuck inside, what better to do than read some economics papers? If you’ve read a paper you think might be interesting to include in the next roundup, feel free to send it to nlovin@techpolicyinstitute.org

DISCLAIMER: The papers and authors are not affiliated with TPI. We do not necessarily agree with everything, or even anything, in these papers, but find them interesting and informative. 

Prime and Punishment by Mehmet Canayaz

What it studies: The effects of removing counterfeit goods from online stores.

What they find: Although Amazon does not catch all counterfeit goods, when it removes a counterfeit product, the number of unnoticed counterfeit goods sold by the same seller decreases, and the market value of the counterfeit goods declines for other sellers.

Why it matters: Even removing a small share of counterfeit goods from online stores can have a large impact on the market for counterfeits.

Digitalization and Pre-purchase Information by Imke C. Reimers and Joel Waldfogel

What it studies: The effects of online, crowdsourced book reviews on platforms such as Amazon versus the effects of professional reviews in newspapers and other sources on sales and consumer welfare.

What they find: Professional book reviews increase book sales regardless of whether the review itself is positive. Amateur reviews can increase or decrease book sales depending on whether the reviews are positive or negative. The different effects mean that reader book reviews on Amazon generate about 15 times more consumer surplus than do professional book reviews in the New York Times. 

Why it matters: Online reviews by normal people matter. Amateur and crowdsourced reviews have far larger effects on book sellers and increases in consumer surplus than do professional reviews.

The Geographic Spread of COVID-19 Correlates with Structure of Social Networks as Measured by Facebook by Theresa Kuchler, Dominic Russel, and Johannes Stroebel

What it studies: How Facebook connections can predict the spread of COVID-19

What they find: Areas that have more social connections to a cluster of cases also have more infections, even when controlling for other factors that might affect infection rates. Areas with stronger social media connections to Westchester county in NY—the center of one of the earliest outbreaks in the U.S.—had more COVID cases per capita people on March 30rd, even when further away from Westchester. A similar relationship existed between the hotbed of Lodi, Italy and coronavirus cases in that country.

Why it matters: The results provide evidence that social media connections can be useful for studying the spread of diseases, including predicting where a disease like COVID-19 will spread next.