Why are some diseases more stigmatized than others? And, has disease stigma declined over time? Answers to these questions have been hampered by a lack of comparable, longitudinal data. Using word embedding methods, we analyze 4.7 million news articles to create new measures of stigma for 106 health conditions from 1980 to 2018. Using mixed-effects regressions, we find that behavioral health conditions and preventable diseases attract the strongest connotations of immorality and negative personality traits, and infectious diseases are most marked by disgust. These results lend new empirical support to theories that norm enforcement and contagion avoidance drive disease stigma. Challenging existing theories, we find no evidence for a link between medicalization and stigma, and inconclusive evidence on the relationship between advocacy and stigma. Finally, we find that stigma has declined dramatically over time, but only for chronic physical illnesses. In the past four decades, disease stigma has transformed from a sea of negative connotations surrounding most diseases into two primary conduits of meaning: infectious diseases spark disgust, and behavioral health conditions cue negative stereotypes. These results show that cultural meanings are especially durable when they are anchored by interests, and that cultural changes intertwine in ways that only become visible through large-scale research.

I set up a potential outcomes framework to analyze spillover effects using instrumental variables. I characterize the population compliance types in a setting in which spillovers can occur on both treatment take-up and outcomes, and provide conditions for identification of the marginal distribution of compliance types. I show that intention-to-treat (ITT) parameters aggregate multiple direct and spillover effects for different compliance types, and hence do not have a clear link to causally interpretable parameters. Moreover, rescaling ITT parameters by first-stage estimands generally recovers a weighted combination of average effects where the sum of weights is larger than one. I then analyze identification of causal direct and spillover effects under one-sided noncompliance, and show that causal effects can be estimated by 2SLS in this case. I illustrate the proposed methods using data from an experiment on social interactions and voting behavior. I also introduce an alternative assumption, independence of the peers’ types, that identifies parameters of interest under two-sided noncompliance by restricting the amount of heterogeneity in average potential outcomes. Supplementary material of this article will be available in online.