Inequality-Augmented Regional Resilience: a Methodological Proposal and an Analysis of Its Determinants

Abstract This paper proposes an inequality-augmented measure of regional economic resilience and applies it to 384 US metropolitan statistical areas (MSAs) over the period 2019–2022, covering the COVID-19 shock and the subsequent recovery. Two different components of resilience are analysed: the resistance component, describing the ability to experience lower losses during the COVID-19 shock; and the recovery component, capturing the speed at which regional economies bounce back to their pre-shock state. By integrating real GDP per capita and income inequality into a unified composite indicator, the analysis shows that resilience assessments change significantly once distributional dynamics are considered. In fact, output-based measures often mask important heterogeneity in regional responses, as regions with similar GDP trajectories may experience markedly different inequality dynamics. The results obtained using the proposed methodology demonstrate that inequality-augmented resilience components do not display a clear spatial clustering, suggesting that they were driven primarily by local structural characteristics rather than by spatial spillovers. Regression analysis also reveals that the resistance component is negatively associated with trade intensity and positively associated with information-sector specialization. However, the recovery dynamics differ from the resistance in that the first- and second-year recovery is faster in larger MSAs, but it is negatively associated to the information sector intensity. Overall, the findings suggest that ignoring income distribution leads to an incomplete and potentially misleading evaluation of regional economic resilience. Incorporating inequality into resilience measurement provides a more comprehensive understanding of regional vulnerability and recovery, with direct implications for empirical research and place-based policy design.