We study the profitability of traders in two fully electronic and highly liquid markets: the Dow and Standard & Poor׳s 500 e-mini futures markets. Using unique information that identify counterparties to a transaction, we show and seek to explain the fact that the network pattern of trades captures the relations between behavior in the market and returns. Our approach includes a simple representation of how much a shock is amplified by the network and how widely it is transmitted. This representation provides a possible shorthand for understanding the consequences of a fat-finger trade, a withdrawing of liquidity, or other market shock.

We develop a model that shows that, at the Nash equilibrium, the outcome of each individual embedded in a network is proportional to his/her Katz-Bonacich centrality measure. This measure takes into account both direct and indirect friends of each individual, but puts less weight to his/her distant friends. We then bring the model to the data using a very detailed dataset of adolescent friendship networks. We show that, after controlling for observable individual characteristics and unobservable network specific factors, a standard deviation increase in the Katz-Bonacich centrality increases the pupil school performance by more than 7% of one standard deviation.

We study the effect of hearing cases alongside female judicial colleagues on the probability that a federal judge hires a female law clerk. Federal judges are assigned to judicial panels at random and have few limitations on their choices of clerks. Using a unique dataset of federal case records merged with judicial hiring information, we find a significant effect of the fraction of copanelists who are female on a male judge’s likelihood of hiring a female clerk. This finding suggests that increases in the diversity of the upper rungs of a profession can create opportunities at the entry level.

We present a model of endogenous network formation to recover unobserved social networks using only observable outcomes. We propose a novel equilibrium concept that allows for a sharp characterization of equilibrium behaviour and that yields a unique prediction under testable conditions. While the equilibrium is characterized by a large number of non-linear equations, we show that it can be efficiently employed to recover the networks by an appropriately designed approximate Bayesian computation method. We apply the model to recover the network of social links between lawmakers in the U.S. Congress using data from the 109th to 113th legislatures. We show that social connections are important for legislators’ productivities, and we identify some of the key determinants of network centralities in the U.S. Congress.