Driving the Drivers: Algorithmic Wage-Setting in Ride-Hailing

Chen, Yanyou; Luo, Yao; Yuan, Zhe

doi:10.2139/ssrn.4299499

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Because such platform-empowered wage discrimination can harm some workers while others may benefit (Adams and Yellen 1976;Thisse and Vives 1988;Chen et al 2019Chen et al , 2022, enriched platform capabilities and the monopsony power of labor platforms has evoked recent policy interest (OECD 2019(OECD , 2022Naidu et al 2018;Naidu and Posner 2022). The purpose of this paper is to inform this debate by looking into the future of platform work.…”

Section: Introductionmentioning

confidence: 98%

Prediction algorithms in matching platforms

Hämäläinen,

Petrikaitė

2024

Econ Theory

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We follow the future trajectory of more targeted wage formation in labor matching platforms, such as freelancing, crowd-sourcing, home-delivery, and ride-hailing, where local job search is coordinated by improving prediction algorithms. A labor matching platform is modelled as a directed search and matching market. We observe that targeted wage setting promotes efficient matching and longer employment spells. However, because a higher employment rate accentuates any disparities between available workers and vacancies, the effects of targeted wage setting on firm competition depend on prevailing market tightness. The impact of targeted wage formation on workers is positive when the vacancy-to-worker ratio is intermediate but turns negative at both extremes. Our results suggest that targeted wage setting may benefit occasional workers while potentially posing drawbacks for full-time platform workers.

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