Abstract: Contact tracing has been a central pillar of the public health response to the COVID-19 pandemic. Yet, contact tracing measures face substantive challenges in practice and well-identified evidence about their effectiveness remains scarce. This paper exploits quasi-random variation in COVID-19 contact tracing. Between September 25 and October 2, 2020, a total of 15,841 COVID-19 cases in England (around 15 to 20% of all cases) were not immediately referred to the contact tracing system due to a data processing error. Case information was truncated from an Excel spreadsheet after the row limit had been reached, which was discovered on October 3. There is substantial variation in the degree to which different parts of England areas were exposed – by chance – to delayed referrals of COVID-19 cases to to the contact tracing system. We show that more affected areas subsequently experienced a drastic rise in new COVID-19 infections and deaths alongside an increase in the positivity rate and the number of test performed, as well as a decline in the performance of the contact tracing system. Conservative estimates suggest that the failure of timely contact tracing due to the data glitch is associated with more than 125,000 additional infections and over 1,500 additional COVID-19- related deaths. Our findings provide strong quasi-experimental evidence for the effectiveness of contact tracing.

Thiemo Fetzer is an Associate Professor in the Department of Economics at the University of Warwick and a Visiting Fellow at the London School of Economics. He is also affiliated with Pearson Institute at University of Chicago, the Centre for Competitive Advantage in the Global Economy (CAGE) at Warwick and the Centre for Economic Policy Research (CEPR). He holds a PhD in Economics from the London School of Economics, has held previous appointments at the University of Chicago and worked as consultant for the World Bank, the International Monetary Fund and the United Nations. His research focuses primarily on applied microeconomics studying natural experiments across a broad range of subjects.