Socio-Demographic Factors Associated with Self-Protecting Behavior during the Covid-19 Pandemic

Disease spread is in part a function of individual behavior. We examine the factors predicting individual behavior during the Covid-19 pandemic in the United States using novel data collected by Belot et al. (2020). Among other factors, we show that people with lower income, less flexible work arrangements (e.g., an inability to tele-work) and lack of outside space at home are less likely to engage in behaviors, such as social distancing, that limit the spread of disease. We also find evidence that region, gender and beliefs predict behavior. Broadly, our findings align with typical relationships between health and socio-economic status. Moreover, they suggest that the burden of measures designed to stem the pandemic are unevenly distributed across socio-demographic groups in ways that affect behavior and thus potentially the spread of illness. Policies that assume otherwise are unlikely to be effective or sustainable.

Modeling Behavior during a Pandemic: Using HIV as an Historical Analogy

Many models from epidemiology are not designed to capture tradeoffs between health and economic well-being and few are equipped to predict how these tradeoffs interact with individuals’ preferences to influence behavior. Policies based on such models may not reflect societal preferences or capture how preferences affect behavior and disease spread. Models with epidemiological processes that do incorporate behavior change or health-wealth tradeoffs (mostly from economics) drastically limit individual-level heterogeneity to remain tractable. This makes it difficult to understand variation in behavioral responses and distributional consequences of the pandemic, which should be central to any evaluation of pandemic-related policy. Without the proper tools, we are unable to fully address even basic policy questions, such as whether the long-run effects of social-distancing justify the costs - and if so, for whom and under what circumstances. After reviewing modeling approaches from epidemiology and economics (among other fields), I propose a framework adapted from earlier work on the HIV/AIDS epidemic. Analysis of individual behavior is based on the idea that health is a form of human capital in which individuals invest through their choices. The model integrates individual level variation that other frameworks omit, but does so at the cost of drastically limiting aggregate economic and epidemiological processes. I conclude that what is needed is a multi-disciplinary effort that helps to bridge gaps across disciplines, which would amount to constructing models that capture key features of different approaches. To be useful in evaluating pandemic-related policy, such a model should have rich heterogeneity and capture how individual choices are linked to the broad contours of disease spread and aggregate economic output. Whether such an effort would be feasible and fruitful remains to be seen.

Unequal consequences of Covid 19: representative evidence from six countries.

Covid-19 and the measures taken to contain it have led to unprecedented constraints on work and leisure activities, across the world. This paper uses nationally representative surveys to document how people of different ages and incomes have been affected in the early phase of the pandemic. The data was collected in six countries (China, South Korea, Japan, Italy, UK, and US) in the third week of April 2020. First, we document changes in job circumstances and social activities. Second, we document self-reported negative and positive consequences of the crisis on well-being. We find that young people have experienced more drastic changes to their life and have been most affected economically and psychologically. There is less of a systematic pattern across income groups. While lower income groups have been more affected economically, higher income groups have experienced more changes in their social life and spending. A large fraction of people of low and high income groups report negative effects on well-being.

Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic.

Given the role of human behavior in the spread of disease, it is vital to understand what drives people to engage in or refrain from health-related behaviors during a pandemic. This paper examines factors associated with the adoption of self-protective health behaviors, such as social distancing and mask wearing, at the start of the Covid-19 pandemic in the USA. These behaviors not only reduce an individual's own risk of infection but also limit the spread of disease to others. Despite these dual benefits, universal adoption of these behaviors is not assured. We focus on the role of socioeconomic differences in explaining behavior, relying on data collected in April 2020 during the early stages of the Covid-19 pandemic. The data include information on income, gender and race along with unique variables relevant to the current pandemic, such as work arrangements and housing quality. We find that higher income is associated with larger changes in self-protective behaviors. These gradients are partially explained by the fact that people with less income are more likely to report circumstances that make adopting self-protective behaviors more difficult, such as an inability to tele-work. Both in the USA and elsewhere, policies that assume universal compliance with self-protective measures-or that otherwise do not account for socioeconomic differences in the costs of doing so-are unlikely to be effective or sustainable.

Six-Country Survey on COVID-19

From the Abstract: This paper presents a new data set collected on representative samples across 6 countries: China, South Korea, Japan, Italy, the UK and the four largest states in the US. The information collected relates to work and living situations, income, behavior (such as social-distancing, hand-washing and wearing a face mask), beliefs about the Covid 19 [coronavirus disease 2019] pandemic and exposure to the virus, socio-demographic characteristics and pre-pandemic health characteristics. In each country, the samples are nationally representative along three dimensions: age, gender, and household income, and in the US, it is also representative for race. The data were collected in the third week of April 2020. The data set could be used for multiple purposes, including calibrating certain parameters used in economic and epidemiological models, or for documenting the impact of the crisis on individuals, both in financial and psychological terms, and for understanding the scope for policy intervention by documenting how people have adjusted their behavior as a result of the Covid-19 pandemic and their perceptions regarding the measures implemented in their countries. The data is publicly available.COVID-19 (Disease);Public health;Epidemics