Politicizing mask-wearing: predicting the success of behavioral interventions among republicans and democrats in the U.S

Academics are more specific, and practitioners more sensitive, in forecasting interventions to strengthen democratic attitudes

Concern over democratic erosion has led to a proliferation of proposed interventions to strengthen democratic attitudes in the United States. Resource constraints, however, prevent implementing all proposed interventions. One approach to identify promising interventions entails leveraging domain experts, who have knowledge regarding a given field, to forecast the effectiveness of candidate interventions. We recruit experts who develop general knowledge about a social problem (academics), experts who directly intervene on the problem (practitioners), and nonexperts from the public to forecast the effectiveness of interventions to reduce partisan animosity, support for undemocratic practices, and support for partisan violence. Comparing 14,076 forecasts submitted by 1,181 forecasters against the results of a megaexperiment (n = 32,059) that tested 75 hypothesized effects of interventions, we find that both types of experts outperformed members of the public, though experts differed in how they were accurate. While academics' predictions were more specific (i.e., they identified a larger proportion of ineffective interventions and had fewer false-positive forecasts), practitioners' predictions were more sensitive (i.e., they identified a larger proportion of effective interventions and had fewer false-negative forecasts). Consistent with this, practitioners were better at predicting best-performing interventions, while academics were superior in predicting which interventions performed worst. Our paper highlights the importance of differentiating types of experts and types of accuracy. We conclude by discussing factors that affect whether sensitive or specific forecasters are preferable, such as the relative cost of false positives and negatives and the expected rate of intervention success.

People in Tight Cultures and Tight Situations Wear Masks More: Evidence From Three Large-Scale Studies in China

Studies have found large differences in masks use during the pandemic. We found evidence that cultural tightness explains mask use differences and this association was more robust in tight situations like subways. In Study 1, we observed 23,551 people's actual mask use in public places around China. People wore masks more in tight situations; however, differences did not extend to outdoor streets and public parks, where norms are looser. We replicated this finding using a data from 15,985 people across China. Finally, in a preregistered study we observed mask use with the removal of COVID-19 restrictions, people still wore masks more in tight situations like subways than in loose situations of parks. These findings suggest that norm tightness has a lasting association with people's health-protective behaviors, especially in tight situations. It provides insight into how different cultures might respond with future pandemics and in what situations people adopt health-protective behaviors.

Nudging enforcers: how norm perceptions and motives for lying shape sanctions

We conduct three studies, employing diverse methodologies (a behavioral experiment, a vignette experiment, and a norm elicitation experiment), to investigate when and how norm enforcement patterns can be modified using norm interventions in the context of dishonesty. Our preregistered, three-part data collection effort explores the extent to which norm violations are sanctioned, the impact of norm-nudges on punishment behavior, and the connection to norm perception. Using a representative sample of US participants in Study 1, we present robust evidence that norm enforcement is sensitive not only to the magnitude of the observed transgression (i.e. the size of the lie) but also to its consequences (whether the lie addresses or creates payoff inequalities). We also find that norm enforcers respond to norm-nudges conveying social information about actual lying behavior or its social disapproval. The results of a separate vignette experiment in Study 2 are consistent with the results in our behavioral experiment, thus hinting at the generalizability of our findings. To understand the interplay of norms, information about them, and punishment, we examine norm perceptions across different transgressions in Study 3. We find that norm perceptions are malleable and norm-nudges are most effective when preexisting norms are ambiguous. In sum, we show how norm enforcement can be nudged and which factors matter for doing so across various contexts and discuss their policy implications.

The impact of threshold decision mechanisms of collective behavior on disease spread

Humans are a hyper-social species, which greatly impacts the spread of infectious diseases. How do social dynamics impact epidemiology and what are the implications for public health policy? Here, we develop a model of disease transmission that incorporates social dynamics and a behavior that reduces the spread of disease, a voluntary nonpharmaceutical intervention (NPI). We use a "tipping-point" dynamic, previously used in the sociological literature, where individuals adopt a behavior given a sufficient prevalence of the behavior in the population. The thresholds at which individuals adopt the NPI behavior are modulated by the perceived risk of infection, i.e., the disease prevalence and transmission rate, costs to adopt the NPI behavior, and the behavior of others. Social conformity creates a type of "stickiness" whereby individuals are resistant to changing their behavior due to the population's inertia. In this model, we observe a nonmonotonicity in the attack rate as a function of various biological and social parameters such as the transmission rate, efficacy of the NPI, costs of the NPI, weight of social consequences of shirking the social norm, and the degree of heterogeneity in the population. We also observe that the attack rate can be highly sensitive to these parameters due to abrupt shifts in the collective behavior of the population. These results highlight the complex interplay between the dynamics of epidemics and norm-driven collective behaviors.

Insights into the accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data.

Drivers of COVID-19 booster uptake among nurses

BACKGROUND

Nurses are at the forefront of efforts to contain COVID-19 and are thus at greater risk of infection from the virus than the general population. Unlike the initial vaccination, booster vaccinations are not always required, and some nurses have not received a booster shot. We investigate the predictors of booster uptake among nurses.

METHODS

We developed an original survey to study booster uptake among nurses. Using contact information from the South Dakota Board of Nursing, we contacted nurses in South Dakota in June and July of 2022. We conducted a multivariate logistic regression to analyze the data.

RESULTS

One thousand eighty-four nurses participated in our study. We found booster uptake among nurses was associated with their partisan self-identification (OR 0.40, 95% CI 0.31-0.52), age (OR 1.04, 95% CI 1.02-1.05), flu vaccination last season (OR 5.61, 95% CI 2.6-12.1), and positive COVID-19 test in last 12 months (OR 0.51, 95% CI 0.35-0.74).

DISCUSSION/CONCLUSIONS

Our results show that COVID-19 booster uptake has been politicized even among nurses. As public health officials continue devising interventions to increase booster uptake among healthcare workers, they should be mindful that they would be viewed through the partisan lens.

Masks as a moral symbol: Masks reduce wearers' deviant behavior in China during COVID-19

Since the outbreak of COVID-19, mask wearing has become a global phenomenon. How do masks influence wearers' behavior in everyday life? We examine the effect of masks on wearers' deviant behavior in China, where mask wearing is mostly a public-health issue rather than a political issue. Drawing on behavioral ethics research, we test two competing hypotheses: (a) masks disinhibit wearers' deviant behavior by increasing their sense of anonymity and (b) masks are a moral symbol that reduces wearers' deviant behavior by heightening their moral awareness. The latter hypothesis was consistently supported by 10 studies (including direct replications) using mixed methods (e.g., traffic camera recording analysis, observational field studies, experiments, and natural field experiment) and different measures of deviant behavior (e.g., running a red light, bike parking in no-parking zones, cheating for money, and deviant behavior in the library). Our research (n = 68,243) is among the first to uncover the psychological and behavioral consequences of mask wearing beyond its health benefits.

The i-frame and the s-frame: How focusing on individual-level solutions has led behavioral public policy astray

An influential line of thinking in behavioral science, to which the two authors have long subscribed, is that many of society's most pressing problems can be addressed cheaply and effectively at the level of the individual, without modifying the system in which the individual operates. We now believe this was a mistake, along with, we suspect, many colleagues in both the academic and policy communities. Results from such interventions have been disappointingly modest. But more importantly, they have guided many (though by no means all) behavioral scientists to frame policy problems in individual, not systemic, terms: To adopt what we call the "i-frame," rather than the "s-frame." The difference may be more consequential than i-frame advocates have realized, by deflecting attention and support away from s-frame policies. Indeed, highlighting the i-frame is a long-established objective of corporate opponents of concerted systemic action such as regulation and taxation. We illustrate our argument briefly for six policy problems, and in depth with the examples of climate change, obesity, retirement savings, and pollution from plastic waste. We argue that the most important way in which behavioral scientists can contribute to public policy is by employing their skills to develop and implement value-creating system-level change.