Innovations and Economic Output Scale with Social Interactions in the Workforce

Abstract

The COVID-19 pandemic of 2020 fundamentally changed the way we interact with and engage in commerce. Social distancing and stay-at-home orders leave businesses and cities wondering how future economic activity moves forward. The reduction in face-to-face interactions creates an impetus to understand how social interactivity influences economic efficiency and rates of innovation. Here, we create a measure of the degree to which a workforce engages in social interactions, analyzing its relationships to economic innovation and efficiency. We do this by decomposing U.S. occupations into individual work activities, determining which of those activities are associated with face-to-face interactions. We then re-aggregate the labor forces of U.S. metropolitan statistical areas (MSA) into a metric of urban social interactiveness. Using a novel measure of urbanized area, we then calculate each MSA’s density of social work activities. We find that our metric of urban socialness is positively correlated with a city’s per worker patent production. Furthermore, we use our set of social work activities to reaggregate the workforces of U.S. industries into a metric of industry social interactivness, finding that this measure scales superlinearly with an industry’s per worker GDP. Together, the results suggest that social interaction among workers is an important driver of both a city’s rate of invention and an industry’s economic efficiency. Finally, we briefly highlight analogies between cities and stars and discuss their potential to guide further research, vis-à-vis the density of social interactions “igniting” a city or industry.

Modelling long-term COVID-19 impacts on the U.S. workforce of 2029

“…Social IWAs are defined as those likely to require face-to-face interaction between employees. Painter et al [ 27 ] recently demonstrated that the spatial density of social ONET elements is positively correlated with patenting output of cities and that the number of social IWAs per worker in an industry is superlinearly related to the industry’s per worker GDP. Thus, socialness is positively correlated with both innovation and productivity.…”

“…As with elements, IWAs must be mapped to BLS occupations codes. Since there is no value to collapse by averaging, we adopt the method used in [ 27 ] to assign a present/absent indicator for each BLS occupation. If an activity is present in any one of the ONET occupations that map to a single BLS occupation, it is designated as present in the BLS occupation.…”

“…This measure of industry socialness as the number of social IWAs per worker. I adopt the determination of which IWAs are social from Painter et al [ 27 ] and apply these to each occupation in the industry’s employment projections. This allows comparison of industry socialness between the baseline projection and the two COVID impact scenarios.…”