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.
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We are grateful to the editor and an anonymous reviewer whose outstanding comments played a pivotal role in helping our paper to reach its full potential. S. Hämäläinen acknowledges the financial support of the OP Group research foundation (Grants Nos. 201600053, 201500138, 20180119, and 20170107). V. Petrikaitė acknowledges the financial support of the Ministry of Economy and Competitiveness of Spain (Grants Nos. ECO2014-59959-P and ECO2015-74328-JIN (AEI/FEDER/UE)) and 2016 FBBVA grant “Innovación e información en la Economía Digital".
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Hämäläinen, S., Petrikaitė, V. Prediction algorithms in matching platforms. Econ Theory 78, 979–1020 (2024). https://doi.org/10.1007/s00199-024-01566-9
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DOI: https://doi.org/10.1007/s00199-024-01566-9
Keywords
- Prediction algorithms
- Matching platforms
- Efficiency
- Targeted wages
- Employment
- Competitive search equilibrium