Equilibrium Effects of Pay Transparency

The public conversation about increasing pay transparency largely ignores equilibrium effects, namely how it leads firms to change hiring and wage-setting policies and workers to adjust bargaining strategies. In this paper, we study these effects with a methodologically diverse approach. Our analysis combines longitudinal study of thousands of workers and employers facing different levels of pay transparency on TaskRabbit, an online labor market, with a parsimonious equilibrium model of dynamic wage setting and negotiation. We find, theoretically and empirically, that increasing pay transparency can increase employment, decrease inequality in earnings, and shift surplus away from workers and toward their employer. Intermediate levels of pay transparency, achieved through a permissive environment to discuss relative pay, can exacerbate the gender pay gap by virtue of network effects. There may be a direct need for government intervention in order to maintain a desirable level of transparency. Any scheme in which employers vary transparency based on private characteristics is unsustainable, as the signal sent to prospective workers is sufficiently strong to cause unraveling toward full transparency. We observe this unraveling on TaskRabbit. We also conduct a field experiment on internet workers to investigate an alternative model in which wage compression is driven by social aversion to observed wage inequality. Our findings are consistent with our bargaining model but not with this alternative. Read

Outsourcing Tasks Online: Matching Supply and Demand on Peer-to-Peer Internet Platforms

We study a central economic problem for peer-to-peer online marketplaces: how to create successful matches when demand and supply are highly variable. To do this, we develop a parsimonious model of a frictional matching market for services, which lets us derive the elasticity of labor demand and supply, the split of surplus between buyers and sellers, and the efficiency with which requests and offers for services are successfully matched. We estimate the model using data from TaskRabbit, a rapidly expanding platform for domestic tasks, and report three main findings. First, supply is highly elastic: in periods when demand doubles, sellers work almost twice as hard, prices hardly increase and the probability of requested tasks being matched only slightly falls. Second, we estimate average gains from each trade to be $37. Because of the matching frictions and search costs needed to find potential matches, the ex-ante gains are more modest, but are maximized by the elastic labor supply: if the number of hours worked were held constant, there would be 15 percent fewer matches in equilibrium. Third, we find that platform success varies greatly across cities. The cities which grow fast in the number of users are also those where the market fundamentals promote efficient matching of buyers and sellers. This heterogeneity in matching efficiency is not attributable to scale economies, but is instead related to two measures of market thickness: geographic density (buyers and sellers living close together), and level of task standardization (buyers requesting homogeneous tasks). Read