Downscaling occupational employment data from the state to the Census tract level.

The lack of detailed occupational employment data at more granular geographic levels presents significant challenges in forecasting and analyzing local and regional employment changes in the era of the new technological revolution. This study aims to develop detailed occupational employment data by downscaling state-level employment information to the Census tract level. We introduce two downscaling algorithms that leverage employment, population, and sociodemographic composition data sourced from the American Community Survey, the Current Population Survey, and the Occupational Employment and Wage Statistics. This approach allows us to create a tract-level employment dataset covering 808 occupations. Such data are crucial for examining the effects of expected technological and demographic shifts on employment at this scale, which is critical for understanding tax base implications and job mobility opportunities. We demonstrate the value of these datasets by examining employment projections for two occupations anticipated to decline due to technological advancements in the near future. • We downscaled state-level employment data and created a tract-level employment dataset covering 808 occupations. • Our downscaling method leverages employment, population, and sociodemographic composition data. • The accuracy of downscaling has been evaluated. • The downscaling method is flexible and suitable for downscaling other datasets. • Examples of using the downscaled data are provided. [ABSTRACT FROM AUTHOR]