Knowledge that Transforms

Public regulation is increasingly facing competition from “private politics” in the form of activism and corporate self-regulation. However, its effectiveness, welfare consequences, and interaction with public regulation are poorly understood. This article presents a unified dynamic framework for studying the interaction between public regulation, self-regulation, and boycotts. We show that the possibility of self-regulation saves on administrative costs, but also leads to delays. Without an active regulator, firms self-regulate to preempt or end a boycott and private politics is beneficial for activists but harmful for firms. With an active regulator, in contrast, firms self-regulate to preempt public regulation and private politics is harmful for activists but beneficial for firms. Our analysis generates a rich set of testable predictions that are consistent with the rise of private politics over time and the fact that there is more self-regulation and activism in the U.S., while public regulation continues to be more common in Europe.

Individual earnings are higher in bigger cities.We consider three reasons: spatial sorting of initially more productive workers, static advantages from workers' current location, and learning by working in bigger cities. Using rich administrative data for Spain, we find that workers in bigger cities do not have higher initial unobserved ability as reflected in fixed effects. Instead, they obtain an immediate static premium and accumulate more valuable experience. The additional value of experience in bigger cities persists after leaving and is stronger for those with higher initial ability. This explains both the higher mean and greater dispersion of earnings in bigger cities.

I present a behavioural model of a “data analyst” who extrapolates a fully specified probability distribution over observable variables from a collection of statistical data sets that cover partially overlapping sets of variables. The analyst employs an iterative extrapolation procedure, whose individual rounds are akin to the stochastic regression method of imputing missing data. Users of the procedure’s output fail to distinguish between raw and imputed data, and it functions as their practical belief. I characterize the ways in which this belief distorts the correlation structure of the underlying data generating process—focusing on cases in which the distortion can be described as the imposition of a causal model (represented by a directed acyclic graph over observable variables) on the true distribution.

We study the manipulability of stable matching mechanisms. To quantify incentives to manipulate stable mechanisms, we consider markets with random cardinal utilities, which induce ordinal preferences over match partners. We show that most agents in large matching markets are close to being indifferent of overall stable matchings. In one-to-one matching, the utility gain by manipulating a stable mechanism does not exceed the gap between utilities from the best and worst stable partners. Thus, most agents in a large market would not have significant incentives to manipulate stable mechanisms. The incentive compatibility extends to many-to-one matching when agents employ truncation strategies and capacity manipulations in a Gale—Shapley mechanism.