Decreasing Wages in Gig Economy: A Game Theoretic Explanation Using Mathematical Program Networks

Pravesh Koirala; Forrest Laine

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arXiv · 2404.10929

Decreasing Wages in Gig Economy: A Game Theoretic Explanation Using Mathematical Program Networks

Pravesh Koirala·Forrest Laine

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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.

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Pravesh Koirala, Forrest Laine. 2024-04-16. Decreasing Wages in Gig Economy: A Game Theoretic Explanation Using Mathematical Program Networks. https://arxiv.org/abs/2404.10929

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