Decreasing Wages in Gig Economy: A Game Theoretic Explanation Using Mathematical Program Networks
Pravesh Koirala, Forrest Laine
TL;DR
This work addresses why drivers in gig-economy ridesharing often receive low wages despite platform competition. It develops a three-tier sequential game among competing platforms, drivers, and passengers within a Mathematical Program Network framework to incorporate both cross-side and same-side externalities and to accommodate single- and multi-homing. The analysis reveals that profitable duopolies in this setting generally require tacit collusion (on prices and/or commissions) between platforms, with single-sided collusion on commissions being particularly plausible and harder to detect. The findings offer managerial and policy insights, notably the potential need for minimum commissions to prevent driver exploitive outcomes in duopolistic ridesharing markets.
Abstract
Gig economy consists of two market groups connected via an intermediary. Popular examples are rideshares where passengers and drivers are mediated via platforms such as Uber and Lyft. In a duopoly market, the platforms must compete to attract not only the passengers by providing a lower rate but also the drivers by providing better wages. While this should indicate better driver payout, as platforms compete to attract the driver pool, real world statistics does not indicate such. This goes completely against the intuition that the worker side of a gig economy, given their importance, should always earn better. We attempt to answer the low wages of drivers in the gig economy by modeling the ridesharing game between duopoly platforms, drivers, and passengers using Mathematical Program Networks. Our model is parsimonious, expressive, models the same-side and cross-side externalities of the economy, and has interpretations under both single-homing and multi-homing regimes. We derive the conditions for the existence of a profitable duopoly and show that it can only happen if the platforms collude together to pay the bare minimum to the drivers. This not only answers why drivers are paid less but also provides strong managerial insights to any interested policy maker.