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. Government intervention may be necessary to maintain a desirable level of transparency. 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.

This paper studies the growth of online peer-to-peer markets. Using data from TaskRabbit, a rapidly expanding marketplace for domestic tasks, we show that growth is highly heterogeneous across cities, which is a common feature of peer-to-peer markets for local services. To disentangle the potential drivers of growth, we present a general model of a frictional market for services, and estimate it using variation in the number of buyers and sellers present on the marketplace over time and across cities. First, we find that 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. The first result implies that in markets where supply can accommodate wide fluctuations in demand, growth relies on attracting buyers at a faster rate than sellers. Second, we show that cities where the market fundamentals promote efficient matching of buyers and sellers are also the cities that grow fast in the number of buyers. 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). The second result implies that when network effects are limited by the local and time-sensitive nature of the services exchanged, growth of peer-to-peer markets largely depends on strategic geographic expansion.