Prediction algorithms in matching platforms
研究了自由职业、众包、外卖和网约车等劳动匹配平台中，预测算法如何影响工资设定、匹配效率和就业时长，发现对临时工有利但可能损害全职平台工人。
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.