Measuring County-Level Deindustrialization in the United States

Existing measures of county-level deindustrialization in the United States are often single numbers, typically capturing manufacturing job losses over a short time. This misses some nuance because job loss trajectories are not always linear and the starting point is consequential. I implement a time series clustering algorithm to categorize counties based on trends in manufacturing jobs over 53 years for the 2,316 counties with available data. Six distinct clusters represent the major deindustrialization trajectories. Declines have been quite sharp in some clusters, giving those counties less time to transition to a post-industrial economy. These cross-cluster differences are consequential to theories and past findings. For example, I find that TAA claims, a traditional measure of local deindustrialization, meaningfully impact individuals’ views on investment only when they live in non-deindustrialized counties. In deindustrialized counties, views are homogeneous across demographics, except across partisanship, where Republicans are more supportive of investment.