Earning Sans Learning: Noisy Decision-Making and Labor Supply on Gig Economy Platforms

68 Pages Posted: 20 Oct 2021

See all articles by Daniel Freund

Daniel Freund

MIT Sloan School of Management

Chamsi Hssaine

Cornell University - School of Operations Research and Information Engineering

Date Written: October 1, 2021

Abstract

We study a gig economy platform's problem of finding optimal compensation schemes when faced with workers who myopically base their participation decisions on limited information with respect to their earnings. The stylized model we consider captures two key, related features absent from prior work on the operations of on-demand service platforms: (i) workers' lack of information regarding the distribution from which their earnings are drawn and (ii) worker decisions that are sensitive to variability in earnings. Despite its stylized nature, our model induces a complex stochastic optimization problem whose natural fluid relaxation is also a priori intractable. Nevertheless, we uncover a surprising structural property of the relaxation that allows us to design a tractable, fast-converging heuristic policy that is asymptotically optimal amongst the space of all policies that fulfill a fairness property. In doing so, via both theory and extensive simulations, we uncover phenomena that may arise when earnings are volatile and hard to predict, as both the empirical literature and our own data-driven observations suggest may be prevalent on gig economy platforms.

Keywords: Sharing economy, online labor platforms, service operations, stochastic modeling

Suggested Citation

Freund, Daniel and Hssaine, Chamsi, Earning Sans Learning: Noisy Decision-Making and Labor Supply on Gig Economy Platforms (October 1, 2021). Available at SSRN: https://ssrn.com/abstract=3934640 or http://dx.doi.org/10.2139/ssrn.3934640

Daniel Freund

MIT Sloan School of Management ( email )

100 Main Street
E62-584
Cambridge, MA 02142
United States

Chamsi Hssaine (Contact Author)

Cornell University - School of Operations Research and Information Engineering ( email )

Ithaca, NY 14853
United States

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