Insight
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Publications in Labor by NOMIS researchers

NOMIS Researcher(s)

March 15, 2024

We answer three core questions about the hypothesized role of newly emerging job categories (“new work”) in counterbalancing the erosive effect of task-displacing automation on labor demand: what is the substantive content of new work, where does it come from, and what effect does it have on labor demand? We construct a novel database spanning eight decades of new job titles linked to U.S. Census microdata and to patent-based measures of occupations’ exposure to labor-augmenting and labor-automating innovations. The majority of current employment is in new job specialties introduced since 1940, but the locus of new-work creation has shifted from middle-paid production and clerical occupations over 1940–1980 to high-paid professional occupations and secondarily to low-paid services since 1980. New work emerges in response to technological innovations that complement the outputs of occupations and demand shocks that raise occupational demand. Innovations that automate tasks or reduce occupational demand slow new-work emergence. Although the flow of augmentation and automation innovations is positively correlated across occupations, the former boosts occupational labor demand while the latter depresses it. The demand-eroding effects of automation innovations have intensified in the past four decades while the demand-increasing effects of augmentation innovations have not.

Research field(s)
Information & Communication Technologies, Economics

Jobs increasingly require good decision-making. Workers are valued not only for how much they can do, but also for their ability to decide what to do. In this paper we develop a theory and measurement paradigm for assessing individual variation in the ability to make good decisions about resource allocation, which we call allocative skill. We begin with a model where agents strategically acquire information about factor productivity under time and effort constraints. Conditional on such constraints, agents’ allocative skill can be defined as the marginal product of their attention. We test our model in a field survey where participants act as managers assigning fictional workers with heterogeneous productivity schedules to job tasks and are paid in proportion to output. Allocative skill strongly predicts full-time labor earnings, even conditional on IQ, numeracy, and education, and the return to allocative skill is greater in decision-intensive occupations.

Research field(s)
Economics