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  5. Expectations, Beliefs and Behaviors during the Pandemic
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Expectations, Beliefs and Behaviors during the Pandemic

Paper Session

Friday, Jan. 7, 2022 3:45 PM - 5:45 PM (EST)

Hosted By: American Economic Association
  • Chair: Tao Wang, Johns Hopkins University

Biases in Information Selection and Processing: Survey Evidence from the Pandemic

Basit A. Khan Zafar
,
University of Michigan and NBER
Ester Faia
,
Goethe University-Frankfurt and CEPR
Andreas Fuster
,
EPFL, Swiss Finance Institute, and CEPR
Vincenzo Pezone
,
Goethe University-Frankfurt

Abstract

How people form beliefs is crucial for understanding decision-making under uncertainty. This is particularly true in a situation such as a pandemic, where beliefs will affect behaviors that impact public health as well as the aggregate economy. We conduct two survey experiments to shed light on potential biases in belief formation, focusing in particular on the tone of information people choose to consume and how they incorporate this information into their beliefs. In the first experiment, people express their preferences over pandemic-related articles with optimistic and pessimistic headlines, and are then randomly shown one of the articles. We find that respondents with more pessimistic prior beliefs about the pandemic are substantially more likely to prefer pessimistic articles, which we interpret as evidence of confirmation bias. In line with this, respondents assigned to the less preferred article rate it as less reliable and informative (relative to those who prefer it); they also discount information from the article when it is less preferred. We further find that these motivated beliefs end up impacting incentivized behavior. In a second experiment, we study how partisan views interact with information selection and processing. We find strong evidence of source dependence: revealing the news source further distorts information acquisition and processing, eliminating the role of prior beliefs in article choice.

Social Networks Shape Beliefs and Behavior: Evidence from Social Distancing During the COVID-19 Pandemic

Michael Bailey
,
Facebook
Drew M. Johnston
,
Harvard University
Martin Koenen
,
Harvard University
Theresa Kuchler
,
New York University, NBER, and CEPR
Johannes Stroebel
,
New York University, NBER, and CEPR
Dominic Russel
,
New York University

Abstract

We show that social network exposure to COVID-19 cases shapes individuals’ beliefs and behaviors concerning the coronavirus. We use de-identified data from Facebook to document that individuals with friends in areas with worse COVID-19 outbreaks reduce their mobility more than otherwise similar individuals with friends in less affected areas. The effects are quantitatively large and long-lasting: a one standard deviation increase in friend-exposure to COVID-19 cases in March 2020 results in a 1.2 percentage point increase in the probability of staying home on a given day through at least the end of May 2020. As the pandemic progresses—and the characteristics of individuals with the highest friend-exposure vary— changes in friend-exposure continue to drive changes in social distancing behavior, ruling out many unobserved effects as drivers of our results. We also show that individuals with higher friend-exposure to COVID-19 are more likely to publicly post in support of social distancing measures and less likely to be members of groups advocating to "reopen" the economy. These findings suggest that friends can influence individuals’ beliefs about the risks of the disease and thereby induce them to engage in mitigating public health behavior.

Information Resonance

Ulrike Malmendier
,
University of California-Berkeley
Laura Veldkamp
,
Columbia University

Abstract

People process the same information differently depending on who delivers it. Information resonates with recipients when they identify with the person who communicates it or whose personal experience it reflects. Resonant information imprints itself in our psyche and comes to mind when making relevant decisions. We describe and formalize the phenomenon of resonant information being more heavily weighted in the process of belief updating, and work through its implications for social transitions in response to COVID-type shocks, for the influence of role models on behavior change such as vaccine adoption, and why social media changes the influence of experts.

Learning from Friends in a Pandemic: Social Networks and the Macroeconomic Response of Consumption

Tao Wang
,
Johns Hopkins University
Christos Makridis
,
Arizona State University and Massachusetts Institute of Technology

Abstract

This paper studies how local shocks can have aggregate effects through interpersonal influences on expectations via social networks. We identified these effects empirically by looking into the consumption effect of COVID-19 infections in geographically distant but socially connected regions. Using daily consumption data across U.S. counties and Facebook’s Social Connectedness Index (SCI), we find that a 10% rise in SCI-weighted cases and deaths is associated with a 0.18% and 0.23% decline in consumption expenditures. These effects are concentrated among consumer goods and services that rely more on social contact. Second, we augment a quantitative pandemic-consumption model under an incomplete market with a tractable mechanism of belief formation through social communications. The model is general enough to nest a number of workhorse theories of expectation formations featuring overreaction, rational updating, or stickiness. It shows that on one hand, social communication slows the speed of aggregate belief adjustment in responses to fundamentally relevant shocks. On the other hand, it moderates overreaction to local noises in aggregation. We also show how the dynamic and average size of aggregate responses depend on the location of the initial shocks and the structure of the network.

Discussant(s)
Paola Sapienza
,
Northwestern University
Hunt Allcott
,
Microsoft Research
Thomas Graeber
,
Harvard Business School
Luigi Pistaferri
,
Stanford University
JEL Classifications
  • E7 - Macro-Based Behavioral Economics
  • D8 - Information, Knowledge, and Uncertainty