Occupational segregation in the digital economy? A Natural Language Processing approach using UK Web Data

This paper investigates whether and how occupational segregation affects the digital economy. Despite the continuous growth of entrepreneurial activity in the digital, little is known about the demographic characteristics of people actively engaging with it and bene ting from it. Further, while popular discourse portrays the digital as a \level playing eld" for economic engagement, the literature has yet to empirically test these claims. Gaining a better understanding of whether occupational segregation is replicated in the digital can assist us in bridging new types of digital inequalities and demystify meritocratic narratives around success in the digital economy. To address this question, we use textual data extracted from UK commercial websites and model digital economic activities through Natural Language Processing techniques. We compare our findings across different gender and ethnicity groups, adopting a research framework informed by intersectionality theory. Our results indicate that occupational segregation persists in the digital economy, as male and female entrepreneurs tend to engage with economic activities stereotypically associating with their gender. However, we do not find the same results when comparing entrepreneurial outputs of female and male entrepreneurs of colour. Our results pave the way for more research in entrepreneurship using Natural Language Processing, textual data and analyses at the intersectional level.