COVID-19 has induced stress motivated by individuals’ fears of infection and death, while mitigation efforts have also exacerbated stress by reducing individuals’ income, creating feelings of social isolation, and imposing barriers to obtaining basic resources. In this study, we use a representative sample of 693 adults in Michigan to investigate the association between COVID-19 stress and life satisfaction. We find that COVID-19 stress is significantly negatively related to life satisfaction, and that this association is strongest among those living alone and those living in households with many other adults. Exploratory analyses suggest that shopping challenges are most strongly associated directly with stress and indirectly with life satisfaction, while income and boredom challenges are less important. We conclude by discussing the implications of declines in life satisfaction due to an ongoing and potentially worsening stressful event, and consider some strategies for mitigating this non-trivial form of COVID-19 harm.

Since the initial detection of COVID-19 in the United States in January 2020, millions of Americans have been infected and/or died. The magnitude of this public health crisis has induced significant stress motivated by individuals’ fears of infection and death. Moreover, attempts to mitigate transmission through social distancing and the closure of workplaces may have exacerbated COVID-19 stress by reducing individuals’ income, creating feelings of social isolation, and imposing barriers to obtaining basic resources such as groceries or toilet paper. Although these stressors are specifically related to the COVID-19 pandemic, they have the potential to impact Americans’ life satisfaction more broadly. Moreover, the way that these stressors may impact Americans’ life satisfaction may be related to the number of others in their household because these additional household members may be sources of social support, but also be potential sources of exposure and consumers of hard-to-get resources. Therefore, this study responds to the recent calls by Horesh & Brown (2020) and Holmes et al. (2020) for research on the stressors and mental health impacts associated with COVID-19, with a particular focus on individuals in small and large households as a uniquely vulnerable group.

In this study, we use a representative sample of 693 adults in Michigan to investigate two pre-registered hypotheses: that COVID-19 stress is negatively associated with life satisfaction, and that this association is quadratically moderated by household size. We find support for the direct effect, and partial support for the moderated effect. Specifically, we find that while total household size does not moderate the stress-satisfaction relationship, total number of adults in the household does. COVID-19 stress has the strongest negative association with life satisfaction among those living alone and those living in households with many other adults. We also use these data to explore the types of daily life challenges that may be responsible for COVID-19 stress and reductions in life satisfaction. We find that shopping-related challenges are most strongly associated directly with stress and indirectly with life satisfaction, while income-related challenges are more weakly associated, and boredom-related challenges are not significantly associated.

The remainder of the paper is organized in four sections. In the first section, we introduce life satisfaction and its links with stress and household size, then situate these phenomena in the COVID-19 pandemic context. In the second section, we describe the Michigan State of the State survey data and our pre-registered analytic plan. In the third section, we first describe our sample and use stratified sampling weights to estimate population means for Michigan adults’ on each variable, including COVID-19 stress and life satisfaction. We then present the results of our pre-registered hypothesis tests examining the association between COVID-19 stress and life satisfaction moderated by household size, followed by the results of a series of exploratory mediation models examining potential sources of COVID-19 stress. We conclude by discussing the implications of declines in life satisfaction due to an ongoing and potentially worsening stressful event, and consider some strategies for mitigating this non-trivial form of COVID-19 harm.

## Background

Life satisfaction is the cognitive component of subjective well-being and refers to a person’s global judgment of their lives based on their own standards (Diener et al., 1985; Pavot & Diener, 2008). Since promoting life satisfaction is an important individual and societal goal, research has continued to examine correlates of life satisfaction. For instance, several demographic characteristics have been found to predict life satisfaction: income and education (Fernández-Ballesteros et al., 2001) are positively linked with life satisfaction while age is often found to have a non-linear association with life satisfaction across the lifespan—life satisfaction declines in the teens and early twenties, is stable until early fifties before it increases and declines again at late life (Qu & de Vaus, 2015). Previous research has also examined situational factors. For example, life satisfaction is lower after experiencing life events such as divorce and unemployment and especially so after experiencing multiple negative life events (Luhmann & Eid, 2009). This is not surprising because those events are likely stressful and stress consistently predicts lower life satisfaction (e.g., Csikos et al., 2020; Hamarat et al., 2001).

Indeed, stress is frequently associated with lower life satisfaction for people in diverse contexts, including injured athletes (e.g., Malinauskas, 2010), students in college (e.g., Mahmoud et al., 2012), and people who have experienced natural disasters (e.g., Calvo et al., 2015; Galatzer-Levy et al., 2018; Hamama-Raz et al., 2017; A. L. D. Lau et al., 2008; Murakami et al., 2018). Conversely, the presence of others is associated with higher life satisfaction. For instance, positive relationships (Gustavson et al., 2016) and greater social support are linked with higher life satisfaction, leading older people living with family to be more satisfied with their lives than the older people living alone (Shin & Sok, 2012).

The higher life satisfaction observed among those living with others may occur because the social support provided by others can help people manage and cope with stress (Malinauskas, 2010), suggesting that household size may mitigate the negative association between stress and life satisfaction. For example, in a stressful situation such as parenting a child with ASD, social support moderated the link between stress and life satisfaction (Lu et al., 2018). However, other work suggests it is also possible to be surrounded by too many others. For example, although there is a positive association between social contact and health at low to moderate levels of contact, high levels of social contact have null or negative associations with health (Stavrova & Ren, 2020). This may be why older people living with three generations also were less satisfied with their lives (Fengler et al., 1983).

Together, this past work on stress, life satisfaction, and household size suggests that stress is associated with lower life satisfaction. However, that association can be moderated by household size, such that it is weaker (i.e. less negative) when an individual is living with some (but not too many) others. Because the COVID-19 pandemic was a key source of stress in 2020, and the associated quarantine measures restricted individuals’ activities to their own households, it presents an ideal context for examining how stress and household size work are jointly associated with life satisfaction.

### Life satisfaction, household size and stress during COVID-19

In 2020, the COVID-19 pandemic presented a new context that induced stress motivated by individuals’ fears of infection and death, while mitigation efforts have also exacerbated stress by reducing individuals’ income, creating feelings of social isolation, and imposing barriers to obtaining basic resources. Thus, Horesh & Brown (2020) and Holmes et al. (2020) called for research on the stressors and mental health impacts associated with COVID-19. There has been some evidence that COVID-19 related stress is negatively linked with life satisfaction and that related constructs such as loneliness (Groarke et al., 2020) and post-traumatic stress disorder (PTSD; Hyland, Shevlin, Karatzias, et al., 2020; Hyland, Shevlin, Murphy, et al., 2020; B. H. P. Lau et al., 2020; Murphy et al., 2020; Park et al., 2020) were high during the initial phase of the pandemic lockdown orders. In the United States, worry or stress about COVID-19 has been associated with anger (Charles, 2020) and depression (Kujawa et al., 2020) among adults, and emotional problems among adolescents (Miller et al., 2020). Outside the United States, stress surrounding COVID-19 has been associated with reduced life satisfaction in Hungary (Csikos et al., 2020), Japan (Sugawara et al., 2020), Poland (Trzebiński et al., 2020), and Turkey (Satici et al., 2020), while in China the severity of the outbreak in one’s hometown was associated with reduced life satisfaction (Zhang et al., 2020). Accordingly, the negative association between aspects of COVID-19 stress and aspects of psychological well-being exhibits remarkable cross-cultural stability.

One key pandemic mitigation strategy was the ‘lockdown,’ during which individuals’ activities were severely restricted. For example, in May 2020 when the state of Michigan was experiencing among the largest number of confirmed COVID-19 cases and deaths (57,601 and 5,479, respectively, by May 25), the governor placed the state’s residents under a stay-at-home order. Because this order restricted individuals’ interactions to members of their own household, households became a key context within which the association between COVID-19 stress and life satisfaction emerged. It therefore offers an opportunity to more closely examine the potential contextual role of household size in moderating a stress-satisfaction association that has been hinted at in prior work (Fengler et al., 1983; Lu et al., 2018; Malinauskas, 2010; Stavrova & Ren, 2020). Drawing on this earlier work, we anticipated that the negative association between COVID-19 stress and life satisfaction would be non-linearly moderated by household size, with the strongest association occurring among those living alone or living in large households. However, the reasons for a strong stress-satisfaction association in small households likely differ from the reasons for a strong association in large households.

The negative association between COVID-19 stress and life satisfaction may be particularly strong for those in small households, and especially for those living alone, due to isolation and lack of social support. Loneliness has repeatedly been found as a negative predictor of life satisfaction (Salimi, 2011), and loneliness levels have been high during the pandemic (Groarke et al., 2020). Moreover, previous research suggests that social support is a protective factor for life satisfaction for people experiencing consequences of natural disasters (Calvo et al., 2015; Glass et al., 2009). Following Hurricane Katrina, most people’s happiness recovered from the drop following Hurricane Katrina; however, those who were single or living alone after the hurricane continued to show lower happiness (Calvo et al., 2015). This work suggests that living alone may reduce access to social support, thereby strengthening negative association between stress and life satisfaction.

The negative association between COVID-19 stress and life satisfaction may also be particularly strong for those in large households for two reasons. First, there may be diminishing marginal returns to social support derived from cohabitants, such that while the first or second cohabitants are a source of social support, third and subsequent cohabitants are not. Second, additional cohabitants may induce a multiplier effect on the satisfaction-reducing effects of stress. That is, while stress may always reduce life satisfaction ceteris paribus, stress may reduce life satisfaction even more when surrounded by many others (who are likely also dealing with stress) in the house. In the context of COVID-19, there are several ways that additional cohabitants might lead the satisfaction-reducing effects of stress to be multiplied. First, each additional cohabitant represents an additional COVID-19 exposure risk. Second, because individuals could not leave their homes during lockdown, additional cohabitants reduced opportunities for alone time and privacy, while also creating challenges for working from home (Adams et al., 1996; Ernst Kossek & Ozeki, 1998). Third, the presence of additional cohabitants, and especially children or older family members, may mean additional care-giving responsibilities during a period when even basic activities (e.g., grocery shopping) was challenging (Abramson et al., 2008).

In sum, we expect that COVID-19 stress and life satisfaction have a negative association because stressors often reduce satisfaction with life. However, because restricting individuals’ activities to their own households was a common pandemic mitigation strategy, we expect that the household is a particularly important context whose features may alter this negative association between stress and satisfaction. Individuals living alone lack sources of social support and therefore may experience a stronger negative association between COVID-19 stress and life satisfaction. Individuals in households with a few other cohabitants do have sources of social support, which can serve to mitigate the deleterious effects of stress, and therefore may experience a weaker negative association between COVID-19 stress and life satisfaction. Finally, individuals living in households with many other cohabitants, whose presence may not offer any additional social support and indeed may multiply the effects of already existing stress, may experience a stronger negative association between COVID-19 stress and life satisfaction. Accordingly, there may be an optimal household size that ensures social support while also limiting the risks and challenges of a large household, and thereby minimizes the negative association between COVID-19 stress and life satisfaction. However, because household size has not previously been investigated as a moderator for the link between stress and life satisfaction in the context of COVID-19, this remains an open question.

### Pre-registered hypotheses

Based on prior work concerning the associations among stress, life satisfaction, and household size during the COVID-19 pandemic, we pre-registered two hypotheses (see https://osf.io/683bp/). First, we hypothesized that greater COVID-19 stress is associated with lower life satisfaction (H1). This hypothesis has previously been observed only in non-representative convenience samples (e.g., Charles, 2020; Csikos et al., 2020; Kujawa et al., 2020; Miller et al., 2020; Satici et al., 2020; Sugawara et al., 2020; Trzebiński et al., 2020), however here we investigate it in a representative sample of Michigan adults. Second, we hypothesized that the association between COVID-19 stress and life satisfaction is quadratically moderated by household size, such that the effect of COVID-19 stress will be stronger for individuals living alone or with many others (H2). This hypothesis is informed by the few COVID-19 studies that have found associations between COVID-19 stress and household structure (e.g., Flesia et al., 2020; Shevlin et al., 2020), but also by a broader literature on the impacts of household size on life satisfaction (e.g., Adams et al., 1996; Ernst Kossek & Ozeki, 1998; Salimi, 2011).

## Method

### Data

We use data from the State of the State Survey (SOSS), which was collected by YouGov for the Institute For Public Policy and Social Research (IPPSR) at Michigan State University between May 8 and 25, 2020. During this period, data was collected from 1086 respondents, whom YouGov matched on gender, age, race, and education to a sampling frame constructed from the 2016 American Community Survey, to yield a final representative sample of 1000 Michigan adults. The timing of the data collection is significant for two reasons. First, it occurred while Michigan residents were under a stay-at-home order, and at a time that Michigan had among the largest number of confirmed COVID-19 cases and deaths (57,601 and 5,479, respectively, by May 25). Second, YouGov completed their data collection on the day that George Floyd was murdered by then-officer Derek Chauvin in Minneapolis, which sparked widespread protests against police brutality, particularly toward Blacks. Thus, these data offer insight into Michigan residents’ stress and life satisfaction during the initial COVID-19 outbreak in Michigan, but before these could these variables were impacted by Mr. Floyd’s murder or the subsequent protests (c.f., Anderson-Carpenter & Neal, 2021; Neal & Neal, 2021).

### Key variables

Our pre-registered analyses focus on three key variables: life satisfaction (DV), COVID-19 stress (IV), and household size (moderator).

Life satisfaction was measured using the satisfaction with life scale (SWLS), which includes 5 items, such as “In most ways, my life is close to my ideal,” each measured on a 7-point scale ranging from strongly disagree to strongly agree (Diener et al., 1985). The scale was constructed by computing the mean across all five items. In this sample, the SWLS exhibits an acceptable reliability of $α=0.91$.

COVID-19 stress was measured using responses to the question “How has the COVID-19 impacted how stressed or anxious you are overall?” which ranged from 1 for “much less stressed/anxious” to 5 for “much more stressed/anxious.” This question was asked after the SWLS items, and the two were separated by more than 100 unrelated questions covering diverse topics including community involvement, reproduction, personality, and media consumption.

Household size was measured as the total number of additional adults and children living in the respondent’s household. The pre-registered hypothesis referred to total household size, however because adults and children were measured separately, we also constructed disaggregated household size variables that measured (a) the total number of additional adults in the respondent’s household, not including the respondent, and (b) the total number of children in the respondent’s household.

### Exploratory variables

Our exploratory analyses also examined three kinds of challenges experienced as a result of COVID-19. Income challenges were measured using responses to the question “How has the COVID-19 crisis impacted your household income?” which ranged from 1 for “increased a lot” to 6 for “eliminated all income.” Shopping challenges were measured using 5 items that asked respondents how much difficulty they had getting the following items due to COVID-19: cleaning supplies and hand sanitizer, groceries, prescription medications, toilet paper, and fruits and vegetables. The items were measured on a 4-point scale that ranged from 1 for “I haven’t had any difficulty” to 4 for “I haven’t been able to get it at all” and were averaged to yield a shopping challenges scale. The shopping challenges scale exhibits an acceptable reliability of $α=0.69$. Finally, boredom challenges were measured as the number of the following selected outside-the-home activities permitted under Michigan’s stay-at-home order that the respondent nonetheless had refrained from participating in: work, shopping, picking up prescriptions, walking around the neighborhood, and visiting a local park or trail. This variable is coded such that larger values indicate refraining from more permitted activities, and thus greater risk of boredom.

### Covariates

All models controlled for several individual characteristics previously shown to be associated with life satisfaction: age (Qu & de Vaus, 2015), gender, relationship status (Joshanloo & Jovanović, 2019), education, household income (Fernández-Ballesteros et al., 2001), race (Barger et al., 2009), and political ideology (Schlenker et al., 2012). This set of covariates was included in the pre-registration; their inclusion in the models as statistical controls is essential to correctly estimate the independent effect of the variables of interest.

Sex was measured using a binary indicator variable coded 0 for males and 1 for females. Due to small numbers of respondents identifying with specific non-White racial groups, race was measured using a binary indicator variable coded 0 for white alone, and 1 for not white alone. Relationship status was measured using a categorical variable with categories for married (omitted), formerly married (i.e., divorced, separated, or widowed), partnered, and single. Education was measured on a 7-point scale that ranged from 0 for no high school, to 6 for graduate degree.1 Household income was measured on a 10-point scale with $10,000 intervals from$10,000 to $100,000, plus intervals for$100,000 - $149,000, and$150,000 or more.2 Finally, political ideology was measured on a 7 point scale that ranged from 1 for very conservative, to 7 for very liberal.

### Analytic Plan

We estimate population means for all variables using the complete N = 1000 sample, incorporating sampling weights that ensure representiveness by sex, age, race, education, and 2016 presidential vote choice using the survey package for R (Lumley, 2010). For all other analyses, we use listwise deletion to obtain an analytic sample of N = 693, and do not use sampling weights because they are a function of covariates already included in the models (Winship & Radbill, 1994). All continuous independent variables are mean-centered prior to analysis, and prior to the construction of squared and interaction terms.

We use OLS regression to test both our pre-registered hypotheses (H1 and H2), and a modified version of our pre-registered hypothesis using a disaggregated measure of household size (H2a). These models controlled for demographic variables that were pre-registered because of their links with life satisfaction found in previous research (as described in the Introduction)–age, age2, race/ethnicity, gender, education, income, political ideology and relationship status. For the sake of completeness, and following recommendations from the editor and a peer reviewer, we also report estimates from reduced models that exclude these covariates.

In our exploratory analyses, which were not pre-registered, we examine whether the association of several types of COVID-19 challenges with life satisfaction is mediated by COVID-19 stress. In these exploratory mediation models, the indirect effect is estimated nonparametrically as an Average Causal Mediation Effect (ACME) with 1000 bootstraps following recommendations from Imai et al. (2010) as implemented in the R mediate package (Tingley et al., 2014). In all analyses, we use an alpha level of 0.05 to assess statistical significance.

The replication code and data for these analyses are available at https://osf.io/683bp/.

## Results

### Sample and population characteristics

Table 1 presents sample and estimated population characteristics, while Table 2 presents the bivariate correlations among all variables in the sample. All characteristics of the sample closely match the estimated characteristics of the adult population of Michigan, indicating that it is a representative sample of the population.

Table 1. Sample descriptives (N = 693) and estimated population characteristics.
 Sample (N=693) Populationa Variable Mean/Proportion SD Min Max Mean SE COVID Stress 3.71 0.89 1 5 3.69 0.04 Life Satisfaction (α = 0.91) 4.34 1.55 1 7 4.25 0.08 Additional Residents 2.59 1.5 1 12 2.72 0.08 Additional Adultsb 1.19 1 0 6 1.21 0.05 Childrenc 0.4 0.89 0 6 0.5 0.05 Age 54.34 16.9 19 95 52.1 0.95 Race: White 0.83 0.37 0 1 0.82 0.02 Race: Non-white 0.17 0.37 0 1 0.18 0.02 Sex: Male 0.44 0.5 0 1 0.48 0.02 Sex: Female 0.56 0.5 0 1 0.52 0.02 Education 2.77 1.79 0 6 2.47 0.09 Income 5.89 2.76 1 11 5.77 0.15 Political Ideology 4.15 2.06 1 7 3.97 0.1 Relationship Status: Formerly Married 0.17 0.37 0 1 0.16 0.02 Relationship Status: Married 0.54 0.5 0 1 0.48 0.02 Relationship Status: Partnered 0.07 0.26 0 1 0.07 0.01 Relationship Status: Single 0.22 0.42 0 1 0.29 0.03 COVID Challenges: Income 3.49 1.01 1 6 3.57 0.06 COVID Challenges: Shopping (α = 0.69) 1.71 0.52 1 4 1.74 0.03 COVID Challenges: Boredom 2.42 1.22 0 5 2.44 0.05
 Sample (N=693) Populationa Variable Mean/Proportion SD Min Max Mean SE COVID Stress 3.71 0.89 1 5 3.69 0.04 Life Satisfaction (α = 0.91) 4.34 1.55 1 7 4.25 0.08 Additional Residents 2.59 1.5 1 12 2.72 0.08 Additional Adultsb 1.19 1 0 6 1.21 0.05 Childrenc 0.4 0.89 0 6 0.5 0.05 Age 54.34 16.9 19 95 52.1 0.95 Race: White 0.83 0.37 0 1 0.82 0.02 Race: Non-white 0.17 0.37 0 1 0.18 0.02 Sex: Male 0.44 0.5 0 1 0.48 0.02 Sex: Female 0.56 0.5 0 1 0.52 0.02 Education 2.77 1.79 0 6 2.47 0.09 Income 5.89 2.76 1 11 5.77 0.15 Political Ideology 4.15 2.06 1 7 3.97 0.1 Relationship Status: Formerly Married 0.17 0.37 0 1 0.16 0.02 Relationship Status: Married 0.54 0.5 0 1 0.48 0.02 Relationship Status: Partnered 0.07 0.26 0 1 0.07 0.01 Relationship Status: Single 0.22 0.42 0 1 0.29 0.03 COVID Challenges: Income 3.49 1.01 1 6 3.57 0.06 COVID Challenges: Shopping (α = 0.69) 1.71 0.52 1 4 1.74 0.03 COVID Challenges: Boredom 2.42 1.22 0 5 2.44 0.05

a Estimated population parameters from a stratified N = 1000 sample with sampling weights. b This includes 143 respondents living with no other adults, 378 living with 1 adult, 104 living with 2 adults, 48 living with 3 adults, 12 living with 4 adults, and 8 living with 5+ adults. c This includes 533 respondents with no children at home, 85 living with 1 child, 47 living with 2 children, 19 living with 3 children, 4 living with 4 children, and 5 living with 5+ children.

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