July 20, 2020
Impacts from automation diffuse locally - a novel approach to estimate jobs risk in US cities
Workers that become automated may transfer productivity gains to their co-workers or make it easier to automate their jobs too. In this paper, I empirically investigate how automatable jobs have diffused impacts to neighbouring jobs in North American cities between 2007 and 2016. Results indicate that jobs that share similarities with neighbouring high-risk jobs grew less, even when controlling for their own technical risk of automation. Conversely, jobs that share complementarities with neighbouring high-risk jobs grew faster, possibly indicating productivity gains from working with recently automated jobs. In addition to the analysis in this paper, I provide an adjusted index of job automation risk that accounts for local diffusion of impacts (negative and positive) in US cities.
April 5, 2019
What drives the geography of jobs in the US? Unpacking relatedness
There is ample evidence of regions diversifying in new occupations that are related to pre-existing activities in the region. However, it is still poorly understood through which mechanisms related diversification operates. To unpack relatedness, we distinguish between three mechanisms: complementarity (interdependent tasks), similarity (sharing similar skills) and local synergy (based on pure co-location). We propose a measure for each of these relatedness dimensions and assess their impact on the evolution of the occupational structure of 389 US Metropolitan Statistical Areas (MSA) for the period 2005–2016. Our findings show that new jobs appearing in MSA’s are related to existing ones, while those more likely to disappear are more unrelated to a city’s jobs’ portfolio. We found that all three relatedness dimensions matter, but local synergy shows the largest impact on entry and exit of jobs in US cities, thus being the strongest force of diversification.