Algorithmic Pricing and Transparency in the Gig Economy

Funded Research Proposal

Algorithms control pricing and match customers and workers in the gig economy. However, algorithms face several critiques: they lack transparency, can be biased, and can be inefficient. We empirically analyze these issues and show that algorithms lose efficiency from two sources: competition between platforms and misaligned worker incentives. We model workers’ strategic responses to variation in pricing and estimate counterfactuals on the effects of minimum wage and transparent pricing policies.Read More

Measuring Strategic Behavior by Gig Economy Workers: Multihoming and Repositioning

Funded Research Proposal

Using a structural model, we show that workers are highly heterogenous in their preferences for both multihoming and repositioning. We provide counterfactual estimates on the effects of proposed firm and regulatory policies aimed at multihoming and repositioning.Read More