Matthew Zahn

Bio: Matthew Zahn is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Social distance. The author has an hindex of 2, co-authored 3 publications receiving 94 citations.

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

Abstract: 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.

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

Abstract: 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.

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

Abstract: 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.

A literature review of the economics of COVID-19

Abstract: The goal of this piece is to survey the developing and rapidly growing literature on the economic consequences of COVID-19 and the governmental responses, and to synthetize the insights emerging from a very large number of studies. This survey: (i) provides an overview of the data sets and the techniques employed to measure social distancing and COVID-19 cases and deaths; (ii) reviews the literature on the determinants of compliance with and the effectiveness of social distancing; (iii) mentions the macroeconomic and financial impacts including the modelling of plausible mechanisms; (iv) summarizes the literature on the socioeconomic consequences of COVID-19, focusing on those aspects related to labor, health, gender, discrimination, and the environment; and (v) summarizes the literature on public policy responses.

The Cost of the COVID-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending

Abstract: We study how the differential timing of local lockdowns due to COVID-19 causally affects households’ spending and macroeconomic expectations at the local level using several waves of a customized survey with more than 10,000 respondents. About 50% of survey participants report income and wealth losses due to the corona virus, with the average losses being $5,293 and $33,482 respectively. Aggregate consumer spending dropped by 31 log percentage points with the largest drops in travel and clothing. We find that households living in counties that went into lockdown earlier expect the unemployment rate over the next twelve months to be 13 percentage points higher and continue to expect higher unemployment at horizons of three to five years. They also expect lower future inflation, report higher uncertainty, expect lower mortgage rates for up to 10 years, and have moved out of foreign stocks into liquid forms of savings. The imposition of lockdowns can account for much of the decline in employment in recent months as well as declines in consumer spending. While lockdowns have pronounced effects on local economic conditions and households’ expectations, they have little impact on approval ratings of Congress, the Fed, or the Treasury but lead to declines in the approval of the President.

Urban Density and Covid-19

Abstract: This paper estimates the link between population density and COVID-19 spread and severity in the contiguous United States. To overcome confounding factors, we use two Instrumental Variable (IV) strategies that exploit geological features and historical populations to induce exogenous variation in population density without affecting COVID-19 cases and deaths directly. We find that density has affected the timing of the outbreak, with denser locations more likely to have an early outbreak. However, we find no evidence that population density is positively associated with time-adjusted COVID-19 cases and deaths. Using data from Google, Facebook, the US Census and The County Health Rankings and Roadmaps program, we also investigate several possible mechanisms for our findings. We show that population density can affect the timing of outbreaks through higher connectedness of denser locations. Furthermore, we find that population density is positively associated with proxies for social distancing measures, access to healthcare and income, highlighting the importance of these mediating factors in containing the outbreak.

Expectations, reference points, and compliance with COVID-19 social distancing measures

Abstract: We surveyed representative samples of Italian residents at three critical points in the COVID-19 pandemic, to test whether and how intentions to comply with social-isolation restrictions respond to the duration of their possible extension. Individuals reported being more likely to reduce, and less likely to increase, their self-isolation effort if negatively surprised by a given hypothetical extension (i.e., if the extension is longer than what they expected), whereas positive surprises had no impact. These results are consistent with reference-dependent preferences, with individual expectations serving as a reference point, and loss aversion. Our findings indicate that public authorities should carefully manage expectations about policy measures and account for behavioral reactions to deviations from previous announcements.

Understanding How Race, Ethnicity, and Gender Shape Mask-Wearing Adherence During the COVID-19 Pandemic: Evidence from the COVID Impact Survey.

Abstract: Mask wearing has been shown to be an effective strategy for slowing the spread of COVID-19. While early studies have uncovered some evidence of racial and ethnic differences in mask-wearing behavior, critical gaps remain. We begin to address these gaps by (1) more comprehensively investigating the role of race and ethnicity on mask wearing during the COVID-19 pandemic and (2) examining whether gender intersects with race and ethnicity to differently influence mask-wearing patterns. Data were drawn from the COVID-19 Impact Survey, a cross-sectional, nationally representative survey of adults living in the U.S. Data were pooled from three time points that ranged from late April 2020 to early June 2020. The final analytic sample consisted of 4688 non-institutionalized adults living in the U.S. A series of logistic regression models with robust standard errors were used to estimate differences in mask-wearing patterns. Compared with White respondents, results revealed Black, Latina/o, and Asian respondents were more likely to report wearing a mask in response to the coronavirus. Moreover, results show White men were least likely to wear a mask from late April 2020 to early June 2020. Overall, findings demonstrate mask-wearing patterns during the COVID-19 pandemic are differently shaped by racial and ethnic background and gender. Findings from this study can inform targeted strategies designed to increase mask-wearing adherence among U.S. adults.