- We estimate automation’s expected impact on jobs in cities according to 3.1where Jobs denotes the set of occupations, sharem(j) denotes the employment share (as a percentage) in city m with occupation j according to the US BLS and pauto(j) denotes the probability of computerization for occupation j as estimated by  (see electronic supplementary material, S3 for more details).
- In particular, how does labour diversity, or specialization, mediate the relationship between city size and the expected job impact from automation? As automation typically targets workplace skills , we consider the O*NET skill dataset, which relates occupations to their constituent workplace tasks and skills, in addition to employment data.
- How do different types of occupations change with city size , and how do these changes contribute to the differential impact of automation across cities? While it is tempting to look only for the largest changes in employment share, more subtle differences for very automatable, or very not automatable, occupations can also produce big changes in expected job impact.
- In figure 5c, we quantify each job cluster’s contribution to the differential impact of automation across large and small cities according to 3.4The low automatability and high difference in employment of highly specialized job cluster (represented by purple) in large and small cities indeed explains a significant amount of the difference in expected job impact.
- (b) The actual Em values for each city plotted against the predicted values using model 8 from (a), which captures 66% of the variance in expected job impact from automation across US cities (see electronic supplementary material, S4 for additional analysis).
- On aggregate, differences in employment for occupations that are relatively resilient to automation contribute the most to the differential impact of automation in large and small cities (see figure 4 inset).
- Furthermore, we demonstrate in electronic supplementary material, S3.1 that the observed negative trend relating city size to expected job impact from automation is robust to errors in the probabilities of computerization (i.e. pauto) produced by Frey & Osborne  and robust to random removal of occupations from the analysis.