With the onset of the COVID-19 pandemic and resulting nation-state policies, economies on a global scale have experienced an atypical recession, where sectors\u2019 susceptibility to shocks and the shape of their recoveries depend on their reliance on in-person contact, as opposed to sensitivity to real business cycles or income elasticities. On a state level in the United States, the substitution effect for aggregate demand becomes prominent, while any income losses become compensated. With the construction of a probabilistic event accounting for such substitution away from in-person services and towards remote services, a parsimonious linear probability model (LPM) empirically establishes the association between US consumers\u2019 COVID-related health concerns and the probability of such a substitution event occurring. Further principal component analysis and random forest classification findings corroborate the structural validity of the LPM specification, lending support to the central hypothesis.<\/p>\n\n\n\n