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The Social Science of COVID-19

Social science will play a crucial role in understanding the crisis of COVID-19.

  • Doctor with laptop and stethoscope

At the Cornell Center for Social Sciences (CCSS) we believe that social science will play a crucial role in understanding the crisis of COVID-19 and future crises. Some critical topics already emerging are: child abuse and reporting under stay-at-home orders, responses to health/risk communication, risk during incarceration and early release, aging and social isolation, hospice design and technologies, policy/ethical issues around being tested (or possessing antibodies) versus not, global/climate impacts, economic effects, and the promise and peril of mobile data. Of course, there are many more.

For additional resources: Public Opinion Data Related to COVID-19 and Roper 2020 Coronavirus Topic Tracker

We are proud to announce the following projects have been selected for rapid COVID-19 funding. See additional details in the Chronicle

Decoding Tacit Knowledge in Apparel Product Development 

Apparel product development is an iterative problem solving process that is heuristic in nature, and involves turning 2D flat patterns into a 3D garment that would fit human anatomy. During the COVID-19 pandemic, apparel companies have started using 2D/3D digital product development rather than conducting in-person fit sessions. This new way of working changed the mechanism of how tacit knowledge is created and disseminated. The digital transformation trend in product development will accelerate in the post-pandemic era, therefore it would be crucial to understand the dynamics as well as the types of information generated during product development meetings to translate this information into digital realm to better support the future of work in the apparel industry.


Exploring and Modeling COVID-19 Vaccination Preferences

Success of immunization programs aiming at controlling the spread of the coronavirus will depend on acceptance rates of vaccination. The main research goal of this project is to better understand and model individual decision-making under threats to health emerging from the COVID-19 outbreak, with special focus on willingness to receive vaccination once an effective and safe vaccine is available. The econometric approach of choice modeling will be adopted as main research method to derive estimates of expected acceptance rates of a COVID-19 vaccine. Choice modeling for statistical inference on behavioral response to immunization will be informed by microdata collected in an online survey across the US with attitudinal questions, stated actual behavioral change in response to the pandemic, and a suite of choice experiments looking at COVID-19 prevention and testing. Using the collected data, a hybrid choice model will be trained and tested to make posterior inference on the willingness to accept a COVID-19 vaccine. 


Moral Values and Perceptions of COVID-19 Impact and Recovery, CCSS 

People's moral values influence their social, practical, and economic judgments in counterintuitive ways. The values that function to bring us together -- the "binding" values of loyalty, purity, respect for authority -- have been shown to facilitate attitudes that drive us apart. including stigmatization and blame of victims. In this research, using online experimental social psychology methods, we measure the effect of "binding" values on stigmatization of COVID-19 victims, as well as willingness to help communities and purchase from businesses strongly affected by COVID-19. This research has implications for determining the most effective public messaging for encouraging prosociality and economic recovery in the context of COVID-19.


Intergenerational Trauma: Flint, COVID-19 and Racial Justice

The 2014 Flint Water Crisis (FWC) was set in motion by the virtually unanimous decision of the city's unelected "emergency manager" to switch Flint, Michigan's municipal water source to the Flint River as an austerity measure. The fateful move ostensibly exposed most of Flint's 95,000 residents, most of whom are Black and lower-income, to elevated levels of lead (Pb), carcinogenic trihalomethanes, and various other contaminants. Like the FWC, the generation-defining COVID-19 virus, and the reanimated nationwide upheaval related to police brutality against Black communities, has cast the nation's historical "racist oeuvre" into sharp relief. This project proposes a series of contextually-grounded surveys and semi-structured interviews with an existing cohort of Flint residents around these intricate themes. The project's goal is to identify and contextualize potential patterns of intergenerational trauma in this population, assess health impacts and behaviors, locate areas to build resistance, resilience and community capacity against structural violence, and develop a new tool to measure intergenerational violence in the context of socially-embedded public health disasters.



Closing the Gap between COVID-19 Information and Beliefs

We investigate the associations between beliefs about covid-19 infection case counts, risk perceptions, official information about covid-19 cases, and self-reported protective health behavior of individuals. We collected data between March and August 2020 using a daily online survey on MTurk (ð = 12,274) covering all 50 states + D.C., and found an inconsistency between beliefs about future infection cases and perception of infection chances, reflected by both inconsistent levels and weak correlations. Furthermore, in our data, perceptions about cases are closely related to official information while perceptions about chances risk perceptions are not; but risk perceptions are better predictors of reported behavior. Can we close this gap between information-based case perceptions and behavior-relevant risk perceptions? Our proposed research will (a) investigate whether these findings generalize to more covid-related outcomes, including economic outcomes (e.g., unemployment risk), whose baseline probabilities are much higher; and (b) attempt to close the gap between people's inconsistent sets of perceptions using different randomly assigned debiasing treatments.


A Comparative Study of Expertise for Policy in the COVID-19 Pandemic, CCSS

As policy makers across the globe work to avert catastrophic health and economic outcomes, they are struggling with a difficult question: what makes expert knowledge credible, legitimate, and reliable for use in public policy? That question becomes especially urgent when national and regional authorities face scientific uncertainty and fast-moving events that cross geopolitical borders. Using the method of cross-national comparison, this project will analyze the challenges of translating knowledge into policy during a fast-moving, global crisis. Prior research in science & technology studies (STS) has shown that a nation’s institutions, traditions, and cultural commitments influence its ways of gathering knowledge. A research team of established STS scholars in 10 countries will compare the sources of expertise policymakers used. Results may contribute to cross-national learning and identification of best practices. 


Training Data for Encoding Social and Political Texts

This seed grant proposal requests funding to search for and catalogue open-ended surveys in the Roper Center archives (estimated to exceed 500 open-ended surveys since the 1990s) and hand label open-ended survey responses and social media posts that are partially linked to closed-ended surveys. This data will lay the foundation for the development and validation of semi-automated tools that can representatively encode and classify public opinion, hate speech and bias, and descriptions of everyday life in text. These tools will measure very broad orientations in text (e.g. sociopolitical biases and heuristics, approaches to life and living), and, in the long run, will assist in estimating that a hand coding scheme has explained a large fraction of the relevant variation in a text corpus. Relevant refers to features of text that often predict offline behaviors, opinions and beliefs when measured in closed-ended survey responses, and outcomes related to health and well-being.


Equity in Group Work between In-person and Remote Labs

The rapid shift to remote and hybrid instruction due to the COVID-19 pandemic has radically changed students' learning experiences. At Cornell, all introductory physics laboratories (labs) have shifted from in-person group work using standard lab equipment to remote labs where students use household materials and collaborate over Zoom. This project aims to understand how this shift changes students' interactions through a direct comparison of recorded Zoom lab sessions this fall with existing video of lab groups from Fall 2019. We will analyze the video for measures of equity in group work in physics labs to compare the students' interactions in the two formats. This work will contribute to national work seeking to understand representation issues in physics and probe potential challenges and opportunities with remote instruction.


Revising Anti-Vaccination Beliefs During the COVID-19 Global Pandemic

Medical research has indisputably shown that vaccines are both safe and effective, yet anti-vaccination sentiment is unusually high with regard to the COVID-19 vaccine. This increased skepticism presents a novel and dire problem for those working to counter antivaccination
attitudes in the United States. Because the COVID-19 vaccine is still in production, previously successful interventions countering anti-vaccination attitudes that cite the history of vaccine effectiveness may not be successful. Drawing on previous literature on belief revision, the proposed research aims to survey the beliefs that drive skepticism about the COVID-19 vaccine (Study I) and employ experimental methods to revise anti-vaccination beliefs (Study II).


Measuring the Economic and Environmental Consequences of COVID-19, CCSS and Cornell Atkinson

Real-time, high-frequency, and high-resolution data will be used to measure the economic and environmental impacts of COVID-19. When the availability and reliability of official statistics are in question, we put big data, machine learning, and economic modeling to work to deliver fact-based analysis and in-time policy recommendations. The primary data sources come from China and US and the results have global implications. 


Social Isolation, Loneliness, and Prosociality During COVID-19, CCSS

A national sample across the adult age spectrum will be used to examine how prosocial orientations and behaviors are linked to well-being and behavior over the next 4 months of the pandemic and examine whether a brief prosocial intervention can alter perceptions of loneliness, as well as psychological perceptions of the pandemic threat essential for adaptive coping. Specifically, our aims are:

1. To recruit a national sample of 2,000 adults stratified across age groups (18-34, 35-49, 50-64, 65+) for a 4-month, 3-wave longitudinal study of how prosocial orientations and behaviors are linked to mental and physical well-being, social isolation and loneliness, and health behavior over time.

2. To test whether a brief prosocial orientation intervention can reduce perceptions of loneliness and enhance perceptions of control, optimism, self-efficacy regarding optimal pandemic coping behaviors, social connectedness, and less distress.


Coronavirus, Health Behavior, and Public Policy, CCSS

This award supports Waves 3 and 4 of a panel survey of 3000 Americans on the political foundations of health behavior in response to COVID-19. Waves 1 and 2 (both completed) were funded through an NSF Rapid (Award # 2026737). Embedded in the survey are a range of experimental manipulations to probe the links from partisanship, xenophobia/anti-immigrant bias, economic hardship, emotions, and pro-social behaviors and policy preferences. In addition to supporting Waves 3 and 4, funding from CCSS will seed a future application to the Russell Sage Foundation to support anticipated Waves 5-8.


CoRUS: Coronavirus in Russia and Ukraine Survey, CCSS

The COVID-19 pandemic has been seen widely as a boon to autocrats and a threat to democracy. This study examines citizens' willingness to trade-off rights and liberties for perceived security, as well as how citizens assess their government's response to the crisis, using panel surveys of public opinion in Russia and Ukraine.


Understanding Covid-19 Vaccine Hesitancy and Resistance

Misinformation about Covid-19 is dangerous. Surveys suggest a worrisome proportion of Americans already report they will not seek a vaccine once available. The researchers seek to better understand and characterize vaccine hesitancy, using Covid-19 and influenza vaccines as cases. Vaccine hesitancy literature reveals multiple reasons why some oppose a vaccine for themselves or a loved one, including fear of side effects, concerns about government mandates, religious reasons, or uncertainty about the vaccine. These different reasons for hesitancy lead to different vaccine seeking or rejecting behaviors. Due to Covid-19, the researchers are pivoting to online bulletin boards focusing on Chicago, IL, Newark, NJ and McCulloch County, TX, seeking to understand and characterize three things: first, where people obtain information and develop opinions about a vaccine; second, to explore and categorize participants' reasons for vaccine hesitancy; and, third, to test example threads using our motivational-interviewing-based protocol designed to counteract anti-vaccination sentiment.


Understanding increased social bias during the COVID-19 crisis in the United States, CCSS*

Award returned due to NSF funding, congrats!

The COVID-19 crisis has generated major social and economic strife in the U.S., including increased reports of social bias directed toward immigrants and people of Asian descent. By partnering with the world’s largest survey organization (SurveyMonkey), this project will draw on more than 150,000 survey interviews to provide an unprecedented view of the spread and scope of COVID-related social bias, including how bias tracks the spread of the disease itself, over time and across regions. In addition to documenting social bias, this project will test novel theoretical predictions about the links between health and economic threats, risk perception, and intergroup prejudice, at the national as well as local levels.


Implications of Course Enrollment Structure for the Potential of Epidemic Spread on a College Campus, CCSS

This project will use complete student transcript data to map the bipartite (two-mode) network that connects students to each other via their enrollment in college courses, thereby creating social structural conditions for the spread of COVID-19 on college campuses. We will evaluate how clusters of course offerings, the timing of courses throughout the week, and mode of class instruction affect the structure of this network. We will also assess how students with different majors, level in school, gender, and race occupy different positions within the network.


Flood Risk in COVID-19 Context, CCSS and Cornell Atkinson

This project will measure comparative and interactive risk perceptions and responses for COVID-19 and flood risk in flood-vulnerable municipalities. A 750-household questionnaire on flood risk will be redesigned to incorporate parallel measures of COVID-19 risk perception and behavior. We will measure predictors of concern and response behavior derived from relevant literature. Flood and COVID-19 risks differ across key psychometric and sociological constructions in their risk profiles. We will test hypotheses, with special attention to risk interactions, on how self-reported perceptions and responses vary between these two risks.