Knowledge that Transforms

Advances in communication and transportation technologies have the potential to bring people closer together and create a “global village.” However, they also allow heterogeneous agents to segregate along special interests, which gives rise to communities fragmented by type rather than by geography. We show that lower communication costs should always decrease separation between individual agents even as group-based separation increases. Each measure of separation is pertinent for distinct types of social interaction. A group-based measure captures the diversity of group preferences that can have an impact on the provision of public goods. While an individual measure correlates with the speed of information transmission through the social network that affects, for example, learning about job opportunities and new technologies. We test the model by looking at coauthoring between academic economists before and during the rise of the Internet in the 1990s.

We investigate the regulation of labor markets through employment, collective relations, and social security laws in 85 countries. We find that the political power of the left is associated with more stringent labor regulations and more generous social security systems, and that socialist, French, and Scandinavian legal origin countries have sharply higher levels of labor regulation than do common law countries. However, the effects of legal origins are larger, and explain more of the variation in regulations, than those of politics. Heavier regulation of labor is associated with lower labor force participation and higher unemployment, especially of the young. These results are most naturally consistent with legal theories, according to which countries have pervasive regulatory styles inherited from the transplantation of legal systems.

I analyze a sequential bargaining model in which players are optimistic about their bargaining power (measured as the probability of making offers), but learn as they play the game. I show that there exists a uniquely predetermined settlement date, such that in equilibrium the players always reach an agreement at that date, but never reach one before it. Given any discount rate, if the learning is sufficiently slow, the players agree immediately. I show that, for any speed of learning, the agreement is delayed arbitrarily long, provided that the players are sufficiently patient. Therefore, although excessive optimism alone cannot cause delay, it can cause long delays if the players are expected to learn.

This paper investigates the effect of (potential) market size on entry of new drugs and pharmaceutical innovation. Focusing on exogenous changes driven by U. S. demographic trends, we find a large effect of potential market size on the entry of nongeneric drugs and new molecular entities. These effects are generally robust to controlling for a variety of supply-side factors and changes in the technology of pharmaceutical research.

Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its “effect” as well as the standard error of this estimate. These conventional DD standard errors severely understate the standard deviation of the estimators: we find an “effect” significant at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the time-series process do not perform well. Bootstrap (taking into account the autocorrelation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variance-covariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a “pre”- and “post”-period and explicitly takes into account the effective sample size works well even for small numbers of states.

Most transplanted kidneys are from cadavers, but there are also many transplants from live donors. Recently, there have started to be kidney exchanges involving two donor-patient pairs such that each donor cannot give a kidney to the intended recipient because of immunological incompatibility, but each patient can receive a kidney from the other donor. Exchanges are also made in which a donor-patient pair makes a donation to someone waiting for a cadaver kidney, in return for the patient in the pair receiving high priority for a compatible cadaver kidney when one becomes available. There are stringent legal/ethical constraints on how exchanges can be conducted. We explore how larger scale exchanges of these kinds can be arranged efficiently and incentive compatibly, within existing constraints. The problem resembles some of the “housing” problems studied in the mechanism design literature for indivisible goods, with the novel feature that while live donor kidneys can be assigned simultaneously, cadaver kidneys cannot. In addition to studying the theoretical properties of the proposed kidney exchange, we present simulation results suggesting that the welfare gains from larger scale exchange would be substantial, both in increased number of feasible live donation transplants, and in improved match quality of transplanted kidneys.

Players in a game are “in equilibrium” if they are rational, and accurately predict other players' strategies. In many experiments, however, players are not in equilibrium. An alternative is “cognitive hierarchy” (CH) theory, where each player assumes that his strategy is the most sophisticated. The CH model has inductively defined strategic categories: step 0 players randomize; and step k thinkers best-respond, assuming that other players are distributed over step 0 through step k - 1. This model fits empirical data, and explains why equilibrium theory predicts behavior well in some games and poorly in others. An average of 1.5 steps fits data from many games.