Driving Low-Income Mothers to Greater Success: The Impact of Ride-hailing on Employment and Income

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

This research project will use new research methods to investigate whether providing subsidized or free transportation to low-income mothers will increase their labor force participation and employment. It does so by providing free or subsidized transportation to a selected group of low income mothers and comparing their labor market efforts and outcomes to low income mothers who do not receive transport subsidy. The research will answer two specific questions: (i) will providing subsidized ride-hailing services to the poor enable better access to employment, training, and social services? (ii) will subsidized transportation raise labor force participation and generate benefits that outweigh the costs? Low-skilled workers in urban America are often concentrated in areas that are far from jobs that match their skills. The high cost of transportation poses special problems for low-income mothers with children, given their child care responsibilities making it difficult and costly for them to find jobs if they rely on public transportation. New technology that makes ride-hailing, such as those provided by UBER and LYFT, possible offers flexible, convenient, and relatively inexpensive transportation that does not require private car ownership. The results of this research will help increase labor market participation and employment of low-income mothers, thus improving the living standards of these mothers. This project will empirically test the hypothesis that reducing transportation cost to low income mothers will increase their labor market participation and employment, using a large-scale randomized control trial (RCT). Leveraging the emergence of ride-hailing as a new transportation option, the PIs will provide free rides to the treatment group of low-income mothers and compare employment and other outcomes to a demographically equivalent control group. The PIs will work in partnership with UBER technologies and will exploit the diffusion of smartphones among the poor to track the impact of lower transportation costs on employment and wages. GPS technology will also be extensively used to track the mobility of participants as they travel to search for, and go to work. In addition to self-reported data collected from participants, the PIs will use data collected by local governments to supplement the research efforts. The results of this research will help increase labor market participation and employment of low-income mothers, thus improving the living standards of these mothers This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.