Ride-hailing platforms face the two important challenges: (i) there are spatial demand imbalances that require some repositioning (empty routing) of drivers; (ii) the control of supply is partially decentralized in that drivers strategically decide whether to join the network, and if so, whether, and where, to reposition when not serving riders. We study the following question for such ride-hailing networks: Under decentralized repositioning, how effective are driver wage policies in achieving the optimal centralized performance benchmark? 

We consider a stationary fluid model of a ride-hailing network in a game-theoretic framework with riders, drivers, and the platform. We characterize the steady-state system equilibrium under decentralized repositioning for various driver wage policies. 
We show how the effectiveness of driver wage policies depends on the interplay of demand imbalances, wage flexibility, and the congestion-sensitivity and spatial relations of travel times. We find that in networks with constant travel times driver wage policies achieve the centralized performance benchmark. In particular, origin-dependent wage rates are sufficient to achieve the benchmark, whereas more limited wage flexibility yields driver idling. In networks with congestion-sensitive travel times, the platform generally cannot achieve the centralized benchmark, even with full wage flexibility. 

Overall, the work highlights how key network operational and financial characteristics affect the efficiency loss of decentralized repositioning in ride-hailing networks.