In the absence of medical treatment and vaccination, non-pharmaceutical interventions and spontaneous behavioral changes are essential to control the spread of COVID-19. Non-pharmaceutical interventions aim to reduce sustained transmission and infection in the population by reducing person-to-person contacts and so far, they have varied considerably across countries, ranging from moderate (e.g., school closures) to drastic (e.g., nationwide lockdowns). At the same time, individual behaviors, rather than governmental actions, might be crucial in the long run, as individuals may spontaneously modify their behavior and adopt preventive measures, thereby reducing the likelihood of transmission and infection. However, a key problem is the lack of data to assess people’s behavior and reactions to epidemics. Public health decision-making requires specific, reliable, and timely data not only about infections but also about human behavior in order to develop optimal control policies to contain the spread of COVID-19.
In this project, we seek to narrow this data gap. We developed a rapid response monitoring system to continuously collect key information on people’s response to the COVID-19 pandemic, through the COVID-19 Health Behavior Survey (CHBS). The CHBS is an online survey that continuously runs in seven European countries (Belgium, France, Germany, Italy, the Netherlands, Spain, and the United Kingdom) and in the United States. The questionnaire was designed to collect key information on people’s health status, behaviors, social contacts, and attitudes in response to the COVID-19 pandemic. We developed an innovative approach to recruit participants via targeted Facebook advertisement campaigns and generate balanced national samples to which post-stratification weights can be applied to render our results approximately representative of the general population. Participation in the study is voluntary and anonymous and open to Facebook users who are at least 18 years old. By the end of May 2020, we had collected complete questionnaires from more than 100,000 participants.
First results show that people’s reactions to the pandemic differ across demographic groups and that the effect of non-pharmaceutical interventions on contact patterns varies across countries. Specifically, women and older individuals generally perceive COVID-19 as a larger threat than do men and younger individuals, and women are also more likely to adopt a wider range of preventive behaviors than men. Furthermore, our results show that the numbers of social contact mainly decreased after governments issued physical distancing guidelines rather than after announcing national lockdown measures. Compared to pre-COVID levels, these numbers decreased by 48%-85% across countries.
For first results based on the survey data see the following preprints:
A. Grow, D. Perrotta, E. Del Fava, J. Cimentada, F. Rampazzo, S. Gil-Clavel & E. Zagheni. 2020. Addressing Public Health Emergencies via Facebook Surveys: Advantages, Challenges, and Practical Considerations. SocArXiv, doi: https://doi.org/10.31235/osf.io/ez9pb.
D. Perrotta, A. Grow, F. Rampazzo, J. Cimentada, E. Del Fava, S. Gil-Clavel & E. Zagheni. 2020. Behaviors and Attitudes in Response to the COVID-19 Pandemic: Insights from a Cross-National Facebook Survey. medRxiv, doi: https://doi.org/10.1101/2020.05.09.20096388.
E. Del Fava, J. Cimentada, D. Perrotta, A. Grow, F. Rampazzo, S. Gil-Clavel & E. Zagheni. 2020. The Differential Impact of Physical Distancing Strategies on Social Contacts Relevant for the Spread of COVID-19. medRxiv, doi: https://doi.org/10.1101/2020.05.15.20102657.
Understanding how status differentiation comes about has preoccupied social scientists in many disciples, such as anthropology, political science, economics and sociology. One reason is that status differentiation is a nearly universal phenomenon, occurring at many levels (ranging from the small group level to the state level) and in many settings (e.g., in the family realm as well as in formal organizations). A second reason is that the status people hold can have consequences for important life outcomes, such as economic success, marriage, fertility, health, and life expectancy. Thus, understanding how status differentiation comes about is a central piece in the puzzle of social inequality.
One difficulty often faced by scholars who study status differentiation is that the social processes generating status hierarchies tend to be dynamic and complex. More specifically, status hierarchies often emerge from decentralized actions and interactions among a large number of people. The underlying behavioral and cognitive principles have the potential to generate self-reinforcing dynamics that are difficult to predict and that can have consequences that may be unintended (and may be even undesired) by the individual actors. Studying such dynamic and complex processes is often difficult with traditional methods in the social science tool box, and the mechanisms that create status differentiation therefore are still poorly understood.
This project seeks to addresses this lacuna by studying processes of status differentiation by means of agent-based computational (ABC) modelling. In ABC modelling, a given population of individuals is represented as agents in a computer model, in which they act and interact with each other based on theoretically and empirically informed rules for behavior. By that, it becomes possible to assess (1) how status hierarchies emerge from the interactions of multiple individuals and (2) how such emergence can depend on the assumptions that are made about people’s behavior and the social structures in which it occurs.
The main focus of this project is on unraveling the behavioral and cognitive processes that underlie the emergence of status differentiation between social groups, such as between men and women, whites and non-whites, and ethnic majorities and minorities.
Women’s labor market opportunities have improved drastically since the mid-20th century. Starting in the 1960s, women have entered higher education in ever greater numbers, and today they often outperform men in terms of enrolment and success in tertiary education. This is paralleled by an increase in female-labor force participation and an influx of women into previously male-dominated occupations of high status. Hence, the economic roles of men and women in society have become more similar. Many scholars have argued that this “gender revolution” is likely to have important implications for family formation and stability. Indeed, earlier research has shown that traditional family patterns have started to change already. For example, women in marriage used to be either similarly or less educated than their male partners, whereas today they tend to have either a similar or even a higher level of education. At the same time, patterns of divorce also changed. In the past, marriages used to have a higher risk of being divorced than other marriages when the woman surpassed the man in education attainment. But today, they are not more or less likely to be dissolved.
The foregoing examples illustrate the far-reaching consequences that the gender revolution may have for family life. But the mechanisms that link changes in men’s and women’s relative economic position with changes in family behavior are still poorly understood. This project seeks to address this lacuna, by studying dynamics of family formation and dissolution – and changes therein over time – in the European context. The focus is on Europe, motivated by the fact that the onset and permeation of the gender revolution has greatly varied across countries. For example, Scandinavian countries are commonly considered to be among the vanguard of the gender revolution, they show the highest level of gender egalitarianism. In German-speaking countries, by contrast, the gender revolution started later and is still lagging behind. This variation across time and space provides statistical leverage to study the processes that are in the focus of this project.
Methodologically, two approaches are used: first, traditional empirical analysis of existing large-scale survey data (e.g., multi-level regression analysis) to study and describe patterns of family formation and dissolution; second agent-based computational modelling, a complement to the first approach. The combination of the two enables us to explore the social mechanisms that may have brought changes in patterns of family formation and dissolution.