Computational Social Science Forum — The Impact of Behavioral and Economic Drivers on Gig Economy Workers

CSS Training Program

November 16, 2021
4:00pm to 5:00pm
Virtual Participation

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Computational Social Science Forum
Date: Tuesday, November 16, 2021
Time: 4:00-5:00 PM Pacific Time
Location: Virtual Participation – Register to attend via Zoom

The Impact of Behavioral and Economic Drivers on Gig Economy Workers

Speaker: Park Sinchaisri, Assistant Professor of Operations & IT Management, Haas School of Business, UC Berkeley

Abstract: Gig economy companies benefit from labor flexibility by hiring independent workers in response to real-time demand. However, workers' flexibility in their work schedule poses a great challenge in terms of planning and committing to a service capacity. Understanding what motivates gig economy workers is thus of great importance. In collaboration with a ride-hailing platform, we study how on-demand workers make labor decisions; specifically, when to work and for how long. Our model offers a way to reconcile competing theories of labor supply regarding the impact of financial incentives and behavioral motives on labor decisions. We are interested in both improving how to predict the behavior of gig economy workers and understanding how to design better incentives. Using a large comprehensive dataset, we develop an econometric model to analyze workers' labor decisions and responses to incentives while accounting for sample selection and endogeneity. We find that financial incentives have a significant positive influence on the decision to work and on the work duration-confirming the positive income elasticity posited by the standard income effect. We also find support for a behavioral theory as workers exhibit income-targeting behavior (working less when reaching an income goal) and inertia (working more after working for a longer period). We demonstrate via numerical experiments that incentive optimization based on our insights can increase service capacity by 22% without incurring additional cost, or maintain the same capacity at a 30% lower cost. Ignoring behavioral factors could lead to understaffing by 10-17% below the optimal capacity level. Lastly, inertia could be a potential sign of workers' loyalty to the platform.

The Computational Social Science Forum is an informal setting for the interdisciplinary exchange of ideas and scholarship at the intersection of social science and data science. Participants engage in a variety of activities such as presentations of work in progress, discussions and critiques of recent papers, introductions to new tools and methods, discussions around ethics, fairness, inequality, and responsible conduct of research, as well as professional development. This Forum is organized as part of the Computational Social Science Training Program, and weekly meetings are hosted by researchers from BIDS and D-Lab. The group welcomes social scientists and researchers with interests in data science methods and tools, and data scientists with applications or interests in public policy, social, behavioral, and health sciences. Participants include graduate students, postdocs, staff, and faculty, and members are encouraged to attend regularly in order to foster community around improving computational social science research, supporting the development and research of group members, and fostering new collaborations. Interested UC Berkeley community members are invited to use this registration form to receive the schedule and access links. Please contact css-t32@berkeley.edu for more information or if you are interested in presenting current research for an upcoming session.

Speaker(s)

Park Sinchaisri

Assistant Professor of Operations & IT Management, Haas School of Business, UC Berkeley

Park Sinchaisri is an Assistant Professor of Operations & IT Management at UC Berkeley Haas School of Business. His primary research interests center around combining tools from operations, economics, machine learning, and behavioral sciences to study how to manage the future of work and the human-AI interface. He received a PhD in Operations, Information & Decisions and an MA in Statistics from Wharton, an SM in Computational Science & Engineering from MIT, and an ScB in Computer Engineering & Applied Mathematics-Economics from Brown. Growing up in Bangkok, Park hopes to expand his research to make a positive social impact, from solving urban problems to helping the marginalized work populations.