Working Paper: NBER ID: w29452
Authors: Ori Heffetz; Guy Ishai
Abstract: We investigate the relationship between (a) official information on COVID-19 infection and death case counts; (b) beliefs about such case counts, at present and in the future; (c) beliefs about average infection chance—in principle, directly calculable from (b); and (d) self-reported health-protective behavior. We elicit (b), (c), and (d) with a daily online survey in the US from March to August 2020 (N ≈ 13,900).\nWe have three main findings: (1) beliefs elicited as infection case counts are closely related to present and future official case-count information; however (2) beliefs elicited as risk perceptions—i.e., the chance to get infected—are inconsistent with those case-count beliefs, even when mathematically, they should be identical; notably, (3) it is the latter—the risk perceptions—that are significantly better predictors of reported behavior than the former.\nTogether, these findings suggest that researchers and policymakers, who increasingly engage in direct elicitation and communication of numeric measures of uncertainty, may get very different outcomes, depending on which measures they use. We discuss potential implications for public communication of health-risk information.
Keywords: COVID-19; beliefs; behavior; risk perception; public health
JEL Codes: D83; D84; D91; I12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| individuals' beliefs about infection case counts (D83) | official COVID-19 case counts (Y10) |
| risk perceptions (D81) | self-reported health-protective behaviors (I12) |
| case count beliefs (C25) | self-reported health-protective behaviors (I12) |
| risk perceptions (D81) | case count beliefs (C25) |
| official COVID-19 case counts (Y10) | individuals' beliefs about infection case counts (D83) |