Association for Public Policy Analysis & Management

On-demand peer-to-peer services (‘gigs’) coordinated by platforms like Uber, allow workers to decide for themselves when and how much to work. This flexible work arrangement offers workers granular control over their earnings. For example, an Uber driver who wants to earn more money one week can decide to spend more time driving for Uber. However, there are limits to gig-workers' control. Platforms pay workers per service instead of per hour, so the amount a worker earns depends on the number of consumers seeking service. For example, an extra hour spent driving for Uber may be a lucrative use of time on a busy night but may not even cover expenses on a slow afternoon. The resulting lack of worker protections (e.g. minimum wage) has provoked a string of lawsuits aimed at Uber (Lien, 2006) and skepticism that gig-work improves worker welfare (McCabe and Devaney, 2015). In this paper, we analyze the net effect of gigs on the welfare of workers.

We study the effect of the launch of Uber's UberX service in a location on the financial outcomes of that location's population. Uber is one of the largest (valuated at $72 billion) and oldest (founded in 2009) gig-economy platforms. Its UberX service was Uber's first service that allowed ordinary car-owners (as opposed to licensed livery drivers) to drive for hire, so the launch of this service in a market represents a significant increase in access to gig-economy work for the residents of that market. Taking advantage of the geographic and temporal staggering of the launch of UberX across markets, we estimate the causal effect of this launch on financial outcomes via a difference-in-difference design, which controls for time-invariant geographic heterogeneity as well as macroeconomic shocks experienced simultaneously across markets. This approach has been used to estimate the effect of creating access to gig-economy work on entrepreneurship (Burtch, et al., 2018), DUI citations (Greenwood and Wattal, 2017), and incumbent industry market share (Kroft and Pope, 2014; Zervas et al., 2017).

To study financial outcomes, we use tax records and survey responses spanning 2012-2017 from a large sample of households that use a free online tax preparation service whose use is restricted to eligible low-income filers. We focus on two financial outcomes: net income (i.e. gross income less taxes) and financial hardship (i.e. failure to pay bills on time). Analysis of the former illustrates the net effect of removing barriers to entry to the taxi service marketplace. Analysis of the latter determines whether introducing gigs effects the likelihood that a worker reaches his budget constraint. The pay-per-service nature of gigs introduces income uncertainty, which typically exacerbates financial hardship among low-income workers (Gunderson and Gruber, 2011; Bania and Leete, 2007). However, gigs afford workers a degree of control not considered in the extant literature. Understanding the effect of gigs on worker income and financial hardship will elucidate the role of gig-style work arrangements in our economy.