A few months ago, it may have seemed unimaginable that from one day to the next, you would not be allowed to go to work, travel, exercise, or see your friends and relatives anymore. Flash forward to today, and these are restrictions that many individuals around the globe have recently experienced. We are currently experiencing a new and unusual situation, that lends itself for reflection and scientific investigation.

Side-effects of stay-at-home orders

The consequences of the coronavirus COVID-19 on our society have not been limited to public health, as the outbreak has also affected several other aspects of people’s daily lives. Many governments have issued lockdown or “stay-at-home” orders that restrict people’s movements, in order to reduce the spread of the virus. At the time of writing, stay-at-home orders have successfully reduced the transmission of the virus, and have reduced the pressure of the virus outbreak on healthcare systems. However, these measures may have negative, and often less visible side-effects that should be understood and, if possible, alleviated. For example, the lack of contact with friends of colleagues is likely to impact social wellbeing. In a research project1, we set out to study how governmental stay-at-home measures impact individuals’ social, mental, functional, physical, and financial wellbeing, and whether resilience can buffer effect of these measures on wellbeing.

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Our key concepts: Resilience and wellbeing

In this research project, we intended to answer two research questions. The first question concerned how stay-at-home orders affect distinct components of wellbeing. Instead of focusing on overall life satisfaction, we distinguished between five components of wellbeing²: physical, mental, psychosocial, financial, and functional. By looking at these distinct components, we created a more nuanced view of which components of wellbeing were affected.

Our second research question focused on the role of resilience. Resilience is about to what extent individuals are able to recover after facing challenges. People’s resilience can be compared to that of an elastic band: stretching a band puts it under stress, comparable to how challenges in our daily lives may put people under stress. When stretched, some elastic bands will tear, while others will bounce back to their original shape. Some bands can stretch a lot further than others, before reaching a breaking point. When we apply this to people the way that people cope with adversities, we see that some individuals have more capabilities to bounce back than others.

A natural experiment in the United States

A unique possibility to study the effect of stay-at-home orders on wellbeing emerged in the United States. Since the United States are a federal country, every state was responsible for determining its own policy for issuing stay-at-home orders or not. The first state to issue such an order was California (March 19), after which a large majority of states followed. Seven states did not follow this development, and did not implement stay-at-home orders at all. These circumstances were the setting for our natural experiment, in which we studied the effects of these orders on wellbeing, and the role of resilience. We surveyed over 450 participants from states that had a “stay at home order” in place at that time (California, Kansas and Mississippi), and from states that had not (Arkansas, Iowa, North Dakota, Nebraska, South Carolina and South Dakota).

The direct and indirect impact of stay-at-home orders on wellbeing

Our results showed the following:

1. Stay-at-home orders are associated with lower social wellbeing.

Even though digital interaction was possible (and frequently used) during these orders, it seems that these possibilities have not been able to compensate for the loss of physical social interaction.

2. Stay-at-home orders are associated with lower current financial wellbeing

Stay-at-home orders have led to financial shocks such as the loss of income or employment. In addition, people may be more uncertain about their financial situations. These objective and subjective factors may have reduced people’s assessment of their current financial situation. We find not effect on expectations of future financial wellbeing.

3. In states with stay-at-home orders, functional wellbeing is lower, than in states without these orders.

In states with stay-at-home orders, people report that they are less able to carry out their daily activities as normal, and report to feel more restricted.

4. Resilience is associated with higher levels of all components of wellbeing

This finding indicates that resilience is a core predictor of wellbeing

5. When people’s daily lives are negatively affected by stay-at-home orders (reduced functional wellbeing), this is associated with lower resilience.

This indirect effect shows that there is a specific indirect pathway in which stay-at-home orders may reduce functional wellbeing, and that lower functional wellbeing is associated with lower resilience. This is of large importance, as resilience can alleviate potential negative effects of those orders on well-being components.

What’s next? Improving wellbeing through fostering resilience

Our results show that resilience is a key predictor for social, mental, financial, and physical wellbeing, and that it can dampen the negative side-effects of stay-at-home orders on wellbeing. This implies that governments should increase their efforts into improving people’s resilience. There are several strategies to increase resilience, for example previous interventions have focused on coping skills, changing people’s mindset, and social support.


¹ Barrett, A.M., Hogreve, J., & Brüggen, E. C. (2020). Coping with governmental restrictions: The relationship between stay-at-home orders, resilience, and functional, social, mental, physical and financial wellbeing, [under review].

² Halleröd, B., & Seldén, D. (2013). The multi-dimensional characteristics of wellbeing: How different aspects of wellbeing interact and do not interact with each other. Social Indicators Research, 113, 807–825

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