Although big data technologies such as digital contact tracing and health certification apps have been widely used to combat the COVID-19 pandemic, little empirical evidence regarding their effectiveness is available. This paper studies the economic and public health effects of the "Health Code" app in China. By exploiting the staggered implementation of this technology across 322 Chinese cities, I find that this big data technology significantly reduced virus transmission and facilitated economic recovery during the pandemic. A macroeconomic Susceptible-Infectious-Recovered (SIR) model calibrated to the micro-level estimates shows that the technology reduced the economic loss by 0.5% of GDP and saved more than 200,000 lives by alleviating informational frictions during the COVID-19 outbreak.

In this paper, we provide an overview of the subprime mortgage securitization process and the seven key informational frictions that arise. We discuss the ways that market participants work to minimize these frictions and speculate on how this process broke down. We continue with a complete picture of the subprime borrower and the subprime loan, discussing both predatory borrowing and predatory lending. We present the key structural features of a typical subprime securitization, document how rating agencies assign credit ratings to mortgage-backed securities, and outline how these agencies monitor the performance of mortgage pools over time. Throughout the paper, we draw upon the example of a mortgage pool securitized by New Century Financial during 2006.