Structuring Gig Work: An Investigation of the Relationship between Racial Residential Segregation and Labor Participation in the Gig Economy within U.S. Counties between 2010-2018

In the United States, the number of individuals relying on gig work for their main source of income is dramatically increasing (Brookings 2016). The term gig economy refers to income-earning activities outside of traditional, long-term employer-employee relationships. Gig work is typically low skill service and care work, and includes, but is not limited to income earned through platform applications such as Uber, Lyft, and Care.com. What macro and historical structures predict changes in labor participation in the gig economy over time? To investigate this phenomenon, I ask the following question: Does variation in income inequality, unemployment rates, and racial segregation predict variation in the growth of labor participation in the gig economy across U.S. counties from 2010 to 2018. In other words, this paper investigates whether growth in the gig economy is dependent upon widening structural inequality. Through a fixed effect panel model, I find that racial residential segregation and income inequality do predict growth in labor participation in the gig economy, but I do not find evidence that unemployment rates predict growth in labor participation in the gig economy.