Occupational classifications: A machine learning approach

Abstract

Characterizing people's occupations is important for both policy and research. However, as large scale administrative records are increasingly being used to describe labor market activity, it will become important to find new automated approaches to describing occupations. We apply new machine learning techniques to new sources of data and investigate the potential of using algorithms to classify occupations. We find that job titles are both inherently noisy and inconsistent across organizations, but a subset of them can be assigned algorithmically, with little impact on accuracy.