"Opening the Black Box of Self-Employment: Identifying Alternative Work Arrangements in the United States"
While 18.4% of workers report engaging in self-employment, there exists a dearth of data identifying heterogeneity in the nature of these work arrangements. To address this gap, this paper uses novel data using machine learning leveraging internal data collected in the 2003-2019 waves of the Panel Study of Income Dynamics on respondents' narrative descriptions of their industry and type of work along with their employer names. The paper uses these data to examine trends in the prevalence and nature of self-employment work arrangements, transitions across these arrangements, and who works in these arrangements. Findings show disparate trends in the share of workers engaging in different types of self-employment work arrangements that would otherwise be masked. Further results suggest that the informally self-employed tend to be less educated, are less likely to be male and non-Hispanic White, have less labor income, and have worse measures of wellbeing.