Inequity During COVID-19

As we investigate the short- and long-term impacts of COVID-19 on people’s lives, it is important to recognize that health and well-being outcomes from COVID-19 vary considerably among different groups. Financial insecurity in low-income areas, health concerns among elderly and immunocompromised individuals, racism against Asian Americans, and heightened virus exposure risk among frontline workers have all been cited as major equity concerns during the pandemic. Here, we use a representative sample to consider three questions related to equity in the United States during the pandemic. First, what populations are placed in situations where there is a high risk of contracting COVID-19? Second, what populations have lost the ability to work or study because of the pandemic? Third, what populations have experienced large life disruptions because of the pandemic?

Avoiding COVID-19

One of the easiest ways to view inequity is to see what groups of respondents were more or less able to avoid the virus itself. There were several important discrepancies that appeared in our data when analyzing the ability of people to stay safe.

One of the questions we looked at was whether or not respondents had the option to work from home. Naturally, if one could work from home instead of having to return to the workplace several days a week, they could avoid contracting the virus from co-workers or on their commute. Looking at the breakdown of respondents’ annual incomes, it became clear that higher income groups were more able to work from home. For instance, three-fourths of individuals with annual earnings of $15,000-24,999 were not to be able to work from home while less than one-fourth of those making over $200,000 were prevented from working from home. Another set of differences arose when looking at the professions of respondents. Two-thirds of individuals working in retail, food services, and healthcare services were left with no choice but to go into the workplace. Lastly, when observing people’s education levels, it is evident that those with college educations were significantly more able to work from home. Notably, over three-quarters of those who have completed graduate school were able to work safely from home, while two-thirds of those with a high school diploma or less could not work from home.

Another indicator of one’s ability to stay safe in the pandemic is the ownership of a personal vehicle, like a car or bike. If somebody could travel via a personal vehicle, rather than having to use public transit or travel with others, they could avoid exposure to COVID-19. Respondents in lower income brackets tend to be without a personal vehicle. For those earning less than $25,000 per year, 18% had neither a car nor a bike, compared to only 5.7% for those earning more than $25,000. Higher education levels also correlated positively with vehicle ownership. Taking car ownership as an example, 65% of respondents who owned both a car and bike had graduated from college, while 80% of respondents without a private vehicle were not college graduates.

The last means of measuring the ability to avoid the virus is how often respondents were able to order groceries for delivery. Given that grocery stores were open throughout the duration of the pandemic, there is a significant chance that one could be exposed to the virus by going to the store to buy groceries. Staying at home and ordering them through a service, on the other hand, reduces that likelihood. Our results indicate that higher-income groups could afford more grocery deliveries per week. Of respondents indicating that they ordered groceries four or more time per week, over over three-fifths were from the 46% of our sample making over $75,000 per year.

Working and Studying

Even if one could avoid the virus, it is possible that the pandemic itself created several obstacles to continue working or studying.

One student-oriented question that we asked on this topic was how respondents in school felt their learning had changed after classes were moved online to adapt to school in a pandemic. Around 30% of respondents indicated that their learning had gotten somewhat worse, and 20% actually felt it had gotten much worse. A little over 30% were neutral on the issue, and only 17% felt that their learning had improved in some way.

Those trends are for the individuals who remained in school during these changes, but another important set of statistics comes from those who withdrew from their classes altogether after the pandemic hit. The majority of students did not withdraw from their courses. Notably though, 60% of the lowest income bracket measured in our survey – those making under $10,000 per year – were forced to withdraw from their courses. That income bracket, making up only 7% of our sample, contained 10% of the respondents who withdrew from their courses.

For those in the workplace, a good way to measure if work was disrupted during the pandemic is to see how people felt their productivity changed. Breaking this down by income, we see that higher income groups were able to maintain or actually improve productivity. Those who made over $50,000 per year made up over 80% of those who reported an increase in productivity, and 75% of those who maintained similar productivity as before the pandemic, but less than half of those whose productivity fell. Another lens through which we looked at productivity is home-ownership. We found that only 30% of homeowners said that they had a decrease in productivity, while we found the opposite for renters of either homes or apartments- only 30% indicated an increase in productivity. On important contributor to this may be that apartment renters are likely to have smaller spaces in which to make a functional home office, and most share walls with neighbors, making the potential for a distracting environment much higher for this group.

Another question we used to measure the stability of work is simply employment. Did respondents stay employed during the pandemic? Those in lower-income groups were much less likely to keep their jobs, with those making under $25,000 per year constituting nearly one-quarter of those left unemployed (despite making up less than 15% of the sample). Looking at education levels, we also can see that those with more education were more likely to stay employed. For people with some college education or less, nearly 60% lost their jobs. On the other hand, nearly two-thirds of college graduates were able to maintain employment.

The Stability of Daily Life

The last realm of inequity that we examined was the ability of our respondents to maintain stability in their daily lives and routines. This was measured in several ways.

The first question we looked into for life stability was the most often used means of travel before the pandemic hit. Those who mostly traveled by driving a personal car, biking, or walking were likely much more able to continue their daily habits and lifestyles than those who used ride-hailing services or public transit, modes that became unpopular during the pandemic because of COVID-19 exposure risk. Low-income groups likely felt this disruption more, as they were heavier public transit users. Nearly 30% of transit riders made less than $35,000 per year. We also observed this through profession, where we saw that first responders made up the second-highest percentage of those using public transportation at 14.9% of transit riders (behind only grocery retail workers who made up 17%).

The next measure of life stability we looked at is an individual’s level of concern about the virus itself. It is likely that people who perceived the virus as less threatening continued living very similarly during the pandemic as they did before it. Disruptions to their lives likely came from externally imposed restrictions such as stay-at-home orders rather than personally enforced attempts to isolate, resulting in a less drastic lifestyle change. Looking at income, the only groups that were more concerned about the virus than not were middle and middle-high income respondents (making between $35,000 and $150,000). Low- and high-income respondents expressed less concern. Around 60% of first responders and 53% of other healthcare workers expressed concern about the virus, which is a higher level of concern than was expressed by workers in other industries.

Another way to look at life stability is the social or emotional response of our respondents. In our survey, we asked who (if anyone) people would turn to if they needed assistance in a variety of tasks including home maintenance, using technology, and more. Here, we consider a question that asked respondents who they would turn to if they were feeling “a bit down or depressed”. Lower-income groups were much less likely to report not having anyone for support, as those earning under $35,000 per year made up over 40% of individuals who said they had nobody to turn to (despite making up less than 9% of the sample). Similarly, those with lower education levels (only some college or less) were almost 60% of the group who said they did not have anyone to turn to (even though they comprised less than half of the sample). One important implication of the pandemic has been its effect on mental health, and these statistics show that there is a severe disparity in mental health-related resources within our sample.

The final trend we saw in life stability was about access to social interaction technologies. From our convenience sample, we found that lower-income groups had less access to these technologies. Take video calling for example, where we see that those making under $35,000 made up almost 25% of those who do not frequently utilize video calling, despite making up less than 9% of the sample. In contrast, of those making over $100,000 per year, 96% used video calling technology somewhat frequently.

Our comprehensive survey has brought to light many different inequities that people across the United States are facing in the midst of this pandemic. There are three major strains of inequity considered here: the ability to avoid COVID-19, the ability to stay working and studying, and the ability to maintain stability in daily life. We found that those with less income and lower levels of education were more at risk of contracting the virus, less able to continue working or studying in the same way or with the same productivity, and less likely to have stability in their daily routines. These trends have extremely important implications for future pandemics, where governments and service providers can better ensure that the response to future viruses does not adversely impact already struggling peoples in society.

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