Title: Non-Robustness of Diffusion Estimates On Networks With Measurement Error Abstract: Network diffusion models are used to study disease transmission, information spread, technology adoption, and other socio-economic processes. We show that estimates of these diffusions are highly non-robust to mismeasurement. First, even when the network is measured perfectly, small and local mismeasurement in the initial seed generates a large shift in the locations of the expected diffusion. Second, if the initial seed is known, small measurement error in links with the share of missed links close to zero causes diffusion forecasts to be significant under-estimates. Such failures exist even when the basic reproductive number is consistently estimable. We explore strategies for estimating the volume of measurement error in the network. Finally, we examine the empirical content of this non-robustness in the context of mitigating the spread of COVID-19 and in seeding of valuable information to maximize diffusion. Our results imply measurement error necessitates stronger disease mitigation efforts to achieve the same disease outcomes.

Title: Interacting Policies in Containing a Disease Abstract: Regional quarantine policies, in which a portion of a population surrounding infections is locked down, are an important tool to contain disease. However, jurisdictional governments-such as cities, counties, states, and countries-act with minimal coordination across borders. We show that a regional quarantine policy’s effectiveness depends on whether 1) the network of interactions satisfies a growth balance condition, 2) infections have a short delay in detection, and 3) the government has control over and knowledge of the necessary parts of the network (no leakage of behaviors). As these conditions generally fail to be satisfied, especially when interactions cross borders, we show that substantial improvements are possible if governments are outward looking and proactive: triggering quarantines in reaction to neighbors’ infection rates, in some cases even before infections are detected internally. We also show that even a few lax governments-those that wait for nontrivial internal infection rates before quarantining-impose substantial costs on the whole system. Our results illustrate the importance of understanding contagion across policy borders and offer a starting point in designing proactive policies for decentralized jurisdictions.

Title: The Political Content of College Courses Abstract: We measure two dimensions of politics in college courses — the amount of political content and its partisan orientation — using course descriptions from nearly 1,000 U.S. colleges and universities over the past 25 years. Using a novel text-as-data method, we separate the amount of “political content,” the extent to which a course engages with topics salient to politics and public policy, from “slant,” the partisan direction of that content. Since 2000, the average course has 0.15 SD more political content and 0.13 SD more liberal slant. Changes are most pronounced in the tails of the distribution, particularly for slant, and are modest relative to persistent cross-field differences. Following instructors who move between institutions, we find that instructors account for 60-65% of cross-sectional differences in course political content and slant. Using enrollment data, we find students show no preference for liberal content before the 2010s; demand for liberal content rises over the following decade, peaks in 2020, and falls slightly thereafter.