The rollout of Wal-Mart store openings followed a pattern that radiated from the center outward, with Wal-Mart maintaining high store density and a contiguous store network all along the way. This paper estimates the benefits of such a strategy to Wal-Mart, focusing on the savings in distribution costs afforded by a dense network of stores. The paper takes a revealed preference approach, inferring the magnitude of density economies from how much sales cannibalization of closely packed stores Wal-Mart is willing to suffer to achieve density economies. The model is dynamic with rich geographic detail on the locations of stores and distribution centers. Given the enormous number of possible combinations of store-opening sequences, it is difficult to directly solve Wal-Mart's problem, making conventional approaches infeasible. The moment inequality approach is used instead and works well. The estimates show the benefits to Wal-Mart of high store density are substantial and likely extend significantly beyond savings in trucking costs.

We generalize Athey's (2001) and McAdams' (2003) results on the existence of monotone pure-strategy equilibria in Bayesian games. We allow action spaces to be compact locally complete metric semilattices and type spaces to be partially ordered probability spaces. Our proof is based on contractibility rather than convexity of best-reply sets. Several examples illustrate the scope of the result, including new applications to multi-unit auctions with risk-averse bidders.

Nous nous intéressons à l'estimation non paramétrique d'une fonction de régression instrumentale ϕ .Cette fonction est définie à l'aide de conditions de moment provenant d'un modèle économétrique structurel de la forme ( )des variables endogènes et les W des instruments.La fonction ϕ est alors la solution d'un problème inverse mal posé, et nous proposons une procédure d'estimation utilisant la régularisation de Tikhonov.Le papier analyse l'identification et la suridentification du modèle et donne les propriétés asymptotiques de l'estimateur de la régression instrumentale non paramétrique.

We study the evolution of market-oriented policies over time and across countries. We consider a model in which own and neighbors' past experiences influence policy choices through their effect on policymakers' beliefs. We estimate the model using a large panel of countries and find that it fits a large fraction of the policy choices observed in the postwar data, including the slow adoption of liberal policies. Our model also predicts that there would be reversals to state intervention if nowadays the world was hit by a shock of the size of the Great Depression.

The coefficient of relative risk aversion is a key parameter for analyses of behavior toward risk, but good estimates of this parameter do not exist. A promising place for reliable estimation is rare macroeconomic disasters, which have a major influence on the equity premium. The premium depends on the probability and size distribution of disasters, gauged by proportionate declines in per capita consumption or gross domestic product. Long-term national-accounts data for 36 countries provide a large sample of disasters of magnitude 10% or more. A power-law density provides a good fit to the size distribution, and the upper-tail exponent, α, is estimated to be around 4. A higher α signifies a thinner tail and, therefore, a lower equity premium, whereas a higher coefficient of relative risk aversion, γ, implies a higher premium. The premium is finite if α > γ. The observed premium of 5% generates an estimated γ close to 3, with a 95% confidence interval of 2 to 4. The results are robust to uncertainty about the values of the disaster probability and the equity premium, and can accommodate seemingly paradoxical situations in which the equity premium may appear to be infinite.