Prediction algorithms in matching platforms

Saara Hämäläinen and Vaiva Petrikaitė ()
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Vaiva Petrikaitė: SOE State Registers

Economic Theory, 2024, vol. 78, issue 3, No 10, 979-1020

Abstract: Abstract 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.

Keywords: Prediction algorithms; Matching platforms; Efficiency; Targeted wages; Employment; Competitive search equilibrium (search for similar items in EconPapers)
JEL-codes: D82 D83 E24 J64 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00199-024-01566-9

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