We propose a novel methodology to uncover the sorting pattern in labor markets. We identify the strength of sorting solely from a ranking of firms by profits. To discern the sign of sorting, we build a noisy ranking of workers from wage data. Our test for the sign of sorting is consistent even with noisy worker rankings. We apply our approach to a panel dataset that combines social security earnings records with detailed financial data for firms in the Veneto region of Italy. We find robust evidence of positive sorting. The correlation between worker and firm types is about 52 percent. (JEL J24, J31, J41, J62, L25)

Observational learning is typically examined when agents have precise information about their position in the sequence of play. We present a model in which agents are uncertain about their positions. Agents sample the decisions of past individuals and receive a private signal about the state of the world. We show that social learning is robust to position uncertainty. Under any sampling rule satisfying a stationarity assumption, learning is complete if signal strength is unbounded. In cases with bounded signal strength, we provide a lower bound on information aggregation: individuals do at least as well as an agent with the strongest signal realizations would do in isolation. Finally, we show in a simple environment that position uncertainty slows down learning but not to a great extent.

A continuum of homogeneous rational agents choose between two competing technologies. Agents observe a private signal and sample others' previous choices. Signals have an aggregate component of uncertainty, so aggregate behavior does not necessarily reflect the true state of nature. Nonetheless, agents still find others' choices informative, and base their decisions partly on others' behavior. Consequently, bad choices can be perpetuated: aggregate uncertainty makes agents herd on the inferior technology with positive probability. I derive the optimal decision rule when agents sample exactly two individuals. I also present examples with herds on the inferior technology for arbitrarily large sample sizes. (JEL C72, D83)

We present a natural environment that sustains full cooperation in one-shot social dilemmas among a finite number of self-interested agents. Players sequentially decide whether to contribute to a public good. They do not know their position in the sequence, but observe the actions of some predecessors. Since agents realise that their own action may be observed, they have an incentive to contribute in order to induce potential successors to also do so. Full contribution can then emerge in equilibrium. The same environment leads to full cooperation in the prisoners' dilemma.