Research
Job Market Paper:
Studying the Supply Behavior in the Ride-Sharing Market
(with Qiyuan Wang, Tat Chan, Dennis Zhang)
Abstract: The sharing economy allows suppliers full autonomy over when and how long they work, creating challenges for managing the supply in response to demand changes. In this study, we investigate key economic and behavioral factors that determine suppliers’ daily work decisions within the context of a ride-sharing company. Using a rich dataset from a ride-sharing platform, we develop a structural model that accounts for the full heterogeneity of work costs across individual drivers. We combine a field experiment with observational data to identify the income sensitivity from work costs of drivers. We further introduce a novel nested iteration method in the model estimation to address the computational challenge due to the high dimensionality of the parameter space. Using the estimation results, we conduct a counterfactual analysis to explore how the platform can provide cost-effective subsidies to drivers in response to a temporal demand increase. We show that subsidies based on the time drivers work are costly to the platform because drivers are not very income-sensitive; however, a targeting subsidy schedule based on the cost estimates of individual drivers can help the platform save nearly 50% more cost. Our findings highlight the importance of understanding and leveraging driver heterogeneity to improve the profitability of platforms.
Publications:
The Impact of Government Interventions on Covid-19 Spread and Consumer Spending
Management Science, 70(5):3302-3318. (with Nan Zhao, Song Yao, Raphael Thomadsen)
Abstract: We examine the impact of government interventions on the spread of COVID-19 and consumer spending. We do this by first estimating models of COVID-19 spread, consumer spending, and social distancing in the United States during the early stages of the COVID-19 pandemic. Social distancing has a large effect on reducing COVID-19 spread, and is responsive to national and local case numbers. Non-mask government interventions reduce COVID-19 spread, while the effectiveness of mask mandates is much smaller and statistically insignificant. Mask mandates tend to increase social distancing, as do non-mask governmental restrictions as a whole. Social distancing hurts spending in the absence of a mask mandate, but has a negligible effect on spending if there is a mask mandate. Mask mandates have a direct effect of increasing spending in counties with high levels of social distancing, while reducing spending in counties with low levels of social distancing. We use these three estimated models to calculate the effect of mask mandates and other governmental interventions on COVID-19 cases, deaths and consumer spending. Implemented mask mandates decreased COVID-19 cases by a statistically insignificant 774,000 cases, saving 28,000 lives, over a 4-month period, but led to $76B – $155B of additional consumer spending. Other non-mask governmental interventions that were implemented reduced the number of COVID-19 cases by 34M, saving 1,230,000 lives, while reducing consumer spending by approximately $470B – $703B over our 4-month period of the study. Thus, these restrictions were cost effective as long as one values each saved life at $387,000 – $608,000 or more.
Working Papers:
Using Field Experiment to Infer Cross-Side Network Effects in the Ride-Sharing Market: How Does the Driver Supply Impact Rider Orders, Cancellations, and Customer Lifetime Value?
Reject and Resubmit at Marketing Science (with Tat Y. Chan, Qiyuan Wang, Song Yao)
Abstract: In the sharing economy, how do changes in supply impact demand? We study this question by collaborating with a ride-sharing platform to conduct a natural field experiment. Our experimental design employs an instrumental variable strategy: by exogenously manipulating the driver subsidy schedule, we use the subsidy as an instrumental variable to identify the cross-side network effects of driver supply on orders, cancellations, and the aggregate customer lifetime value (CLV) of users. The results show that increasing the number of drivers at work by 1% will increase the number of customer orders by 2.01% and, conditional on the orders, further reduce the cancellation rate by 0.48%. The results also imply a long-term impact on the future revenue and profitability of the platform. We find that, for a 1% increase in the number of drivers working in the afternoon or at night, the aggregate CLV of users will increase by 1.62% or 0.50%, respectively. Our findings can help platforms improve operations, adjust incentives for suppliers, and strengthen the user experience.
The Effect of External Goal Switch on Performance
Awaiting Decision, Journal of Consumer Psychology (with Yanyi Leng, Stephen Nowlis, Song Yao)
Abstract: Companies often provide incentives to influence employee performance or customer engagement. In this research, we investigate how companies structure their incentives so that they increase the performance and engagement of their employees and customers without increasing the rewards. In particular, we examine situations where companies change the way they frame and order their goals to incentivize their employees and customers. Across a lab and a field experiment, when rewards do not change, we find that performance increases when goals are perceived as becoming less challenging over time. In the lab experiment, consumers play a video game, and we track the influence of altering the way customers’ goals are framed and ordered over time on their scores. In the field experiment, we partnered with a ride-sharing company to examine the influence of the way that employees’ goals are structured on the number of rides taken. Subsequently, our research finds that performance can be enhanced when goals are structured such that they are perceived as getting less challenging over time, without needing to spend more money on additional rewards.
Income Prediction Bias in the Gig Economy
Working Paper (with Chuck Howard, David Hardisty, Dale Griffin)
Abstract: It is widely believed that consumers who work in the gig economy are financially vulnerable because gig income is unpredictable. However, no research has examined whether gig workers can accurately forecast their earnings or not. Here, the authors test the hypothesis that gig workers display an income prediction bias in which they over-predict their future earnings. This hypothesis is supported in five longitudinal studies conducted with participants from three paradigmatic gigs: rideshare driving, online human intelligence tasks, and food delivery. The authors also show that (a) people overpredict how many hours they will work at their gig, but not the amount they will earn per hour, (b) the bias is not associated with individual differences such as how long a person has worked at their gig, and (c) the magnitude of the bias is reduced by prompting people to consider relevant past experience when predicting their future income, but not by prompting them to consider atypical outcomes. In addition to documenting and debiasing a previously unidentified prediction error with broad implications for consumer financial decision making, these findings contribute to the debate regarding the costs and benefits of gig economy employment, and how to make gig work more equitable.