Correcting COVID-19 Risk Misperceptions via Information Provision

Provide information about COVID-19 infection and fatality risks.

2023-04-25

2023-04-27

Respondents' perceptions on COVID-19 infection and fataility risks

We divide participants into four groups. We do not provide Group 1---the control group---with any information. Meanwhile, we provide Group 2 with the recent total infected cases and deaths (referred to as ``level'' information). We provide Group 3 with the recent actual probabilities of getting infected and dying from COVID-19 once infected (``percentage'' information). Finally, we provide Group 4 with qualitative information about a possible future path of new infection (``qualitative'' information). Below is the exact wording for these three types of information:

Level information: From mid-March 2023 to mid-April 2023 in Japan, the total infected cases is 226,007. From April 2022 to March 2023 in Japan, the total deaths were 45,727.

Percentage information: From mid-March 2023 to mid-April 2023 in Japan, the actual infection rate is 0.18\%. From April 2022 to March 2023 in Japan, the actual fatality rate is 0.17\%.

Qualitative information: The number of new cases has been gradually increasing, and there is concern about the spread of infection after the holidays in May. On April 19, the expert group mentioned the possibility of a 9th wave, which would be larger than the 8th wave. Compared to the 6th and 7th waves (January-April 2022 and July-September 2022), the 8th wave (November 2022-February 2023) showed an increase in fatality rate.

After providing information about COVID-19, we ask participants about their perceptions of infection or fatality risks, namely, the subjective probability of being infected with COVID-19 within the next month and the probability of fatality if infected with COVID-19. For both infection and fatality risks, we present participants with the following response options: (1) less than 0.001%, (2) 0.001% to less than 0.01%, (3) 0.01% to less than 0.1%, (4) 0.1% to less than 1%, (5) 1% to less than 5%, (6) 5% to less than 10%, (7) 10% to less than 20%, (8) 20% to less than 50%, (9) 50% or higher.

We ask participants about their background demographic and socio-economic characteristics, including age, gender, place of residence, education level, marital status, health condition (whether they have chronic diseases), and the primary source of media (television, newspaper, internet, SNS, or others). We also ask about their COVID-19-related experiences, including vaccination status, the number of past infections, and whether they have any acquaintances who died from the virus.

By computer

10,000

No

10,000 individuals

10,000 individuals

2,500 for each treatment group (one control group and three treatment groups)

Given this sample size, we perform a power analysis to compute the minimum detectable effect using a two-sample t-test and assuming equal and unknown standard deviations. At a significance level of alpha = 0.05 and a power (1-beta) = 0.8, the minimum detectable effect is 0.0793 standard deviations. This means that with 2,500 respondents per group, there is an 80\% power to detect an effect size as small as 0.0793 standard deviations between a pair of groups. When increasing the power to 0.9, the minimum detectable effect is 0.0917 standard deviations. Because a minimum detectable effect of less than 0.2 standard deviations is widely accepted in the literature, the power analysis suggests that our sample size is sufficiently large to produce reliable treatment-effect estimates.