Measuring and Predicting 'New Work' in the United States: The Role of Local Factors and Global Shocks

Abstract
The evolution of work is of emerging importance to advanced economies' growth. In this study, we develop a new semantic-distance-based algorithm to identify “new work,” namely the new types of jobs introduced in the US. We characterize how “new work” relates to task content of jobs and skill characteristics of workers and document its geographic distribution and association with employment growth. Then, we analyze whether local factors associated in the previous literature with agglomeration economies and productivity growth as well as local exposures to global shocks—technology, trade, immigration, and population aging—predict the creation of “new work.” We find local supply of college educated in 1980 as the strongest predictor of “new work.” Using the historical location of 4-year colleges, a strong instrument for local college share, we find a positive and significant causal effect of local supply of human capital on “new work.” Joint work with Gueyon Kim abnd Cassandra Merritt.