SURGE PRICING AND SHORT-TERM WAGE ELASTICITY OF LABOR SUPPLY INREAL-TIME RIDESHARING MARKETS br

The prominence of real-time ridesharing services, such as Uber and Lyft, has dramatically changed the lands-cape of traditional industries. This study provides acomprehensive analysis ofthe short-term wage elasticityof labor supply inreal-time ridesharingmarkets usingdatafrom amajor ridesharingplatform inChina. Byexploitinganexogenousshock from unevendrivingrestrictions as aninstrumental variable, we findanegativelabor supply elasticity forridesharingdrivers, suggestingthat drivers tendtodrive less duringdays withahigher average hourly wage. Specifically, apercent increase inhourlywagewill leadtoa0.931percentdecrease in daily working hours. This surprisingfinding is consistent with the behavioral income-targetingmodel basedonthe theory of reference-dependent preferences: Drivers have heuristic daily targets for totalearnings andare more motivatedtosupply labor whenthey are below their income target than whenthey areabove it. Therefore, they work less on days when earnings per hour are high and quit the market once theirincome target is reached. Inaddition, we findthattaxi drivers are more rationalandhave positive laborsupply elasticity, whichimplies that drivers are more rationalwhenthey have repeatedopportunities forlearning. Estimating labor supply elasticity is criticaltounderstandingthe economic efficiency of varioussurge pricingalgorithms anddriver subsidizationprograms for ridesharingplatforms andpolicymakers. Ourresearchsuggests that a uniform price surging or driver subsidizationapproach for all ridesharing drivers may not incentivize the labor supply of drivers effectively