  We study the matching of jobs to workers in a queue, e.g. a ridesharing
platform dispatching drivers to pick up riders at an airport. Under FIFO
dispatching, the heterogeneity in trip earnings incentivizes drivers to
cherry-pick, increasing riders' waiting time for a match and resulting in a
loss of efficiency and reliability. We first present the direct FIFO mechanism,
which offers lower-earning trips to drivers further down the queue. The option
to skip the rest of the line incentivizes drivers to accept all dispatches, but
the mechanism would be considered unfair since drivers closer to the head of
the queue may have lower priority for trips to certain destinations. To avoid
the use of unfair dispatch rules, we introduce a family of randomized FIFO
mechanisms, which send declined trips gradually down the queue in a randomized
manner. We prove that a randomized FIFO mechanism achieves the first best
throughput and the second best revenue in equilibrium. Extensive counterfactual
simulations using data from the City of Chicago demonstrate substantial
improvements of revenue and throughput, highlighting the effectiveness of using
waiting times to align incentives and reduce the variability in driver
earnings.
