We document the role of intangible capital in manufacturing firms' substantial contribution to non-manufacturing employment growth from 1977-2019. Exploiting data on firms' "auxiliary" establishments, we develop a novel measure of proprietary in-house knowledge and show that it is associated with increased growth and industry switching. We rationalize this reallocation in a model where firms combine physical and knowledge inputs as complements, and where producing the latter in-house confers a sector-neutral productivity advantage facilitating within-firm structural transformation. Consistent with the model, manufacturing firms with auxiliary employment pivot towards services in response to a plausibly exogenous decline in their physical input prices.
Manufacturers perform the majority of US patenting and R&D. The decades-long decline of US manufacturing employment raises concerns that US innovation will falter. We investigate the relationship between between physical production and innovation by constructing a new dataset linking all US firms and their establishments to location geocodes and innovative activities. Pre- liminary results indicate that while firms with both manufacturing and innovation establishments exhibit higher patenting when these facilities are more spatially proximate, manufacturing firms’ overall contribution to US innovation declines steadily and substantially over time. Moreover, cohorts of firms permanently exiting manufacturing in the 1990s and 2000s continue to patent at their prior pace.
We propose a method for identifying exposure to changes in trade policy based on asset prices that has several advantages over standard measures: it encompasses all avenues of exposure, it is natively firm-level, it yields estimates for both goods and service producers, and it can be used to study reductions in difficult-to-quantify non-tariff barriers in a way that controls naturally for broader macroeconomic shocks. Applying our method to two well-studied US trade liberalizations provides new insight into service sector responses to trade liberalizations as well as dramatically different responses among small versus large firms, even within narrow industries.
We show that unexpected changes in the trajectory of COVID-19 infections predict US stock returns, in real time. Parameter estimates indicate that an unanticipated doubling (halving) of projected infections forecasts next-day decreases (increases) in aggregate US market value of 4 to 11 percent, indicating that equity markets may begin to rebound even as infections continue to rise, if the trajectory of the disease becomes less severe than initially anticipated. Using the same variation in unanticipated projected cases, we find that COVID-19-related losses in market value at the firm level rise with capital intensity and leverage, and are deeper in industries more conducive to disease transmission. These relationships provide important insight into current record job losses. Measuring US states' drops in market value as the employment weighted average declines of the industries they produce, we find that states with milder drops in market value exhibit larger initial jobless claims per worker. This initially counter-intuitive result suggests that investors value the relative ease with which labor versus capital costs can be shed as revenues decline.