Ljubica Nedelkoska, Shreyas Gadgin Matha, James McNerney, Andre Assumpcao, Dario Diodato, Frank Neffke (2021), Unpublished draft – Druid 2023 Best Paper Award
We build a new longitudinal dataset of job tasks and technologies by transforming the US Dictionary of Occupational Titles (DOT, 1939-1991) and four books documenting occupational use of tools and technologies in the 1940s, into a database akin to, and comparable with its digital successor, the O* NET (1998-today). After creating a single occupational classification stretching between 1939 and 2019, we connect all DOT waves and the decennial O* NET databases into a single dataset, and we connect these with the US Decennial Census data at the level of 585 occupational groups. We use the new dataset to study how technology changed the gender pay gap in the United States since the 1940s. We find that computerization had two counteracting effects on the pay gap-it simultaneously reduced it by attracting more women into better-paying occupations, and increased it through higher returns to computer use among men. The first effect closed the pay gap by 3.3 pp, but the second increased it by 5.8 pp, leading to a net widening of the pay gap.