Speaker: Kasey Vangelov, SPP
Abstract: The process of deindustrialization (offshoring, automation, job dislocation) has fundamentally changed regional labor markets across the United States, with potentially devastating consequences for community health and wellbeing. While existing research has documented connections between macroeconomic change and mortality outcomes, the spatial and temporal dynamics through which automation exposure translates into community-level "deaths of despair" remain incompletely understood. This study addresses this gap by examining how industrial robot adoption affects commuting-zone-level mortality from drug overdoses, alcohol-related causes, and suicide, with particular attention to how these effects diffuse across space and time. Rooted in predominant theories – particularly Case and Deaton's (2020) deaths of despair framework, Polanyi's (1944) concept of market-driven social dislocation, and Wilson’s analysis (1996) of concentrated poverty — this research investigates whether automation functions as a market expansion that systematically undermines wellbeing. My work reframes diseases of despair not as individual failures but as community-level crises resulting from the erosion of place-based social and economic institutions.
This study extends the empirical framework applied by O'Brien et al. (2022) by employing a spatial econometric approach that integrates data on industrial automation from 1990 to 2014 with mortality outcomes at the commuting zone level. Automation exposure is measured using a Bartik-style shift-share approach that weights national industry-level robot adoption from the International Federation of Robotics by local baseline employment shares. To address endogeneity concerns, I implement an instrumental variables strategy using European robot adoption patterns (from Denmark, Finland, France, Italy, and Sweden) as an exogenous predictor of U.S. automation exposure. This approach leverages the fact that technological diffusion patterns in Europe are driven by factors independent of local U.S. labor market conditions and mortality trends. In addition to automation exposure, I explore the impact of Chinese trade shocks on mortality outcomes. The spatial econometric models incorporate both spatial and temporal lags to capture how automation shocks propagate across neighboring commuting zones and persist over time. Mortality data encompass deaths from drug overdoses (including opioids, heroin, synthetic opioids, and stimulants), alcohol-related causes, and suicide from 1999 to 2023. The analysis controls for baseline demographic characteristics, economic conditions, and time-varying confounders at the commuting zone level.
I evaluate spatial autocorrelation and spillover effects, assessing how automation shocks in one commuting zone affect mortality in neighboring regions. By modelling temporal lags, I explore whether the health consequences of automation unfold over multiple years, suggesting cumulative processes of social deterioration rather than immediate responses to economic displacement. These findings will have important implications for understanding how market-driven transformations interact with spatial inequalities and for designing policies to support communities through economic transitions.