Suchergebnisse

Some empirical results are more likely to be published than others. Selective publication leads to biased estimates and distorted inference. We propose two approaches for identifying the conditional probability of publication as a function of a study's results, the first based on systematic replication studies and the second on meta-studies. For known conditional publication probabilities, we propose bias-corrected estimators and confidence sets. We apply our methods to recent replication studies in experimental economics and psychology, and to a meta-study on the effect of the minimum wage. When replication and meta-study data are available, we find similar results from both.(JEL C13, C90, I23, J23, J38, L82)

We consider how a firm dynamically allocates business among several suppliers to motivate them in a relational contract. The firm chooses one supplier who exerts private effort. Output is non-contractible, and each supplier observes only his own relationship with the principal. In this setting, allocation decisions constrain the transfers that can be promised to suppliers in equilibrium. Consequently, optimal allocation decisions condition on payoff-irrelevant past performance to make strong incentives credible. We construct a dynamic allocation rule that attains first-best whenever any allocation rule does. This allocation rule performs strictly better than any rule that depends only on payoff-relevant information. (JEL D21, D82, L14, L24)

In this paper we connect the discrepancy between two estimates of Fisher information, one based on the quadratic variation of the score and the other based on the negative Hessian of the log-likelihood, to weak identification. Classical asymptotic approximations assume that these two estimates are asymptotically equivalent, but we show that this equivalence fails in many weakly identified models, which can distort the behavior of the MLE. Using a stylized DSGE model we show that the discrepancy between information estimates is large when identification is weak.