The COVID-19 pandemic in the U.S. saw a remarkable behavioral response through physical mobility. There was a sharp contraction in non-residential mobility in the first half of 2020—over 40-50% declines relative to the pre-pandemic levels—which then dissipated over time as the pre-pandemic patterns started to be restored, though with smaller contractions in each of the two winters. Residential mobility had returned to something close to pre-pandemic levels by mid-2022.
Whose mobility are we talking about here? An important role has been played by individual choices related to physical mobility, especially in the early stages when pharmaceutical interventions were not yet available. The individual choices comprised both personal actions (such as choosing to shop online rather than in person, and compliance with local policies) as well as efforts to influence the actions of others (advocating local “stay-at-home” mandates, for example). Those choices were undoubtedly influenced by the socioeconomic characteristics of people and communities. We can thus ask about the socioeconomic incidence of the mobility response. Depending on that incidence, the pandemic may come to reflect, and possibly reinforce, antecedent socioeconomic inequalities.
In a new paper, “Inequality and Social Distancing during the Pandemic,” Caitlin Brown and I study the socioeconomic incidence of the dramatic behavioral responses through physical mobility over the course of the pandemic. We merge Google Mobility Reports across the 3,000 U.S. counties with socioeconomic characteristics as well as (more standard) covariates suggested by the epidemiological literature. We use these data to try to understand the joint epidemiological and socioeconomic covariates of the mobility responses across counties, and how these changed over the course of the pandemic.
Our paper points to theoretical ambiguities in the socioeconomic incidence of the mobility responses to the threat of infection. Among the socially excluded and largely non-working poor (such as the elderly or disabled poor), social distancing may not be much of a burden. Yet the cost could be high among the working poor, since such families cannot easily maintain their consumption in isolation. The pre-pandemic levels of social and economic interaction are likely to be higher for wealthier people, and they face costs of adjusting quickly to a lower level of physical mobility. The marginal effects on social distancing of income differences may also vary with income across counties, though here too we argue that the direction of this effect of between-county inequality on social distancing could go either way.
For the US we find that counties with a higher median income tended to experience greater reductions in mobility outside the home in the initial phase, though re-bounding substantially by mid-2022. This holds when we control for the poverty rate, suggesting that the effect is coming from the attenuated mobility of the non-poor in the early phase of the pandemic, alongside enhanced mobility in the subsequent re-adjustment. While pre-existing inequalities were reflected in social distancing in the pre-pharmaceutical phase, this was partly reversed later.
Counties with a higher poverty rate and higher income inequality tended to see larger declines in non-residential mobility. (Since we are controlling for average income, these are relative distributional effects, rather than absolute.) Behavioral responses through physical mobility in the pre-pharmaceutical phase were more protective of those living in more affluent and unequal areas. The near-linearity of the median income effect across counties implies little or no trade-off between reducing geographic inequality and the aggregate mobility response to the pandemic. The distributional effects are mainly through within-county channels, with only a small reduction in non-residential mobility attributable to between-county inequality.
Our results are not consistent with the view that higher inequality undermines social distancing, such as by eroding local prosocial norms relevant to the chances of infection. The results are more suggestive of greater scope in high-inequality areas for enforcing such norms during the pandemic, possibly backed-up by local policies. By interpretation, the same desire among the rich (buttressed by their ability-to-pay) to protect themselves from infection by personal effort helped promote local public efforts for social distancing in high-inequality areas.
We point to two broader implications of our study. First, efforts to understand social distancing, and to respond through policy, cannot ignore the distribution of income. While voluntary social distancing can be a strongly protective response, it is a response that is firmly grounded in antecedent socio-economic inequalities. The behavioral response to the threat of infection can be highly heterogeneous across income strata and over time during the course of the pandemic. However, the specific pattern of such dependence is hard to predict on a priori grounds, and it is not (as we have shown) simply a situation in which inequality impedes collective social distancing—indeed, our results suggest the opposite.
Second, there may be implications for social policies. Our findings suggest that, in the absence of enforced policies to support social distancing, it will be the poorer and yet relatively equal areas that are more vulnerable to the spread of infection. Self-protection is easier for those in relatively well-off and unequal areas. Our interpretation is that poorer families are less able to afford to protect themselves, which leads them to make different social-distancing choices. This suggests that there may be a role for antipoverty policies as a complement to more direct health-policy measures in combating infectious disease, especially in the initial pre-pharmaceutical phase. Such policy implications beg for exploration in greater evaluative depth than we have been able to provide.