Quantifying COVID-19 policy impacts on subjective well-being during the early phase of the pandemic: A cross-sectional analysis of United States survey data from March to August 2020

Abstract

To stop the spread of COVID-19, a number of public health policies and restrictions were implemented during the pre-vaccination phase of the pandemic. This study provides a quantitative assessment of how these policies impacted subjective well-being (SWB) in the United States over a 6-month period spanning March to August 2020. We study two specific research objectives. First, we aim to quantify the impacts of COVID-19 public health policies at different levels of stringency on SWB. Second, we train and implement a conditional inference tree model for predicting individual SWB based both on socio-demographic characteristics and policies then in place. Our results indicate that policies such as enforcing strict stay-at-home requirements and closing workplaces were negatively associated with SWB, and that an individual's socio-demographic characteristics, including income status, job, and gender, conditionally interact with policies such as workplace closure in a predictive model of SWB. Therefore, although such policies may have positive health implications, they also have secondary environmental and social implications that need to be taken into account in any cost-benefit analysis of such policies for future pandemic preparedness. Our proposed methodology suggests a way to quantify such impacts through the lens of SWB, and to further advance the science of pandemic preparedness from a public health perspective.

Fig 1. The partial correlation (y-axis) between SWB-ladder variable and each COVID-19 policy measure (x-axis) after controlling for respondent’s income status (a), party affiliation (b), and job areas (c).

No correlation was observed between the school closing policy and the life satisfaction of respondents with job areas highly impacted by COVID-19 as the policy remained constant at level 3 across all corresponding data points. Error bars represent the standard errors of the correlation coefficients. Policy acronyms were enumerated earlier in Table 1.

Fig 2. A visualization of the conditional inference tree (CIT) model for predicting life satisfaction (SWB-ladder) specifically for individuals in the high-income (a), and low-income groups (b).

The tree shows only variables significant at the 90% level or higher. The total sample size (N) for the life satisfaction responses is 9,285, with half used for training, and half for testing / validation.

Fig 3. A visualization of the conditional inference tree (CIT) model for predicting life satisfaction (SWB-ladder) specifically for individuals in the middle-income group.

The tree shows only variables significant at the 90% level or higher. The total sample size (N) for the life satisfaction responses is 9,285, with half used for training, and half for testing / validation.