Knowledge that Transforms

Most empirical work in economics has considered only a narrow set of measures as meaningful and useful to characterize individual behavior, a restriction justified by the difficulties in collecting a wider set. However, this approach often forces the use of strong assumptions to estimate the parameters that inform individual behavior and identify causal links. In this paper, we argue that a more flexible and broader approach to measurement could be extremely useful and allow the estimation of richer and more realistic models that rest on weaker identifying assumptions. We argue that the design of measurement tools should interact with, and depend on, the models economists use. Measurement is not a substitute for rigorous theory, it is an important complement to it, and should be developed in parallel to it. We illustrate these arguments with a model of parental behavior estimated on pilot data that combines conventional measures with novel ones.

We consider social learning in a changing world. With changing states, societies can be responsive only if agents regularly act upon fresh information, which significantly limits the value of observational learning. When the state is close to persistent, a consensus whereby most agents choose the same action typically emerges. However, the consensus action is not perfectly correlated with the state, because societies exhibit inertia following state changes. When signals are precise enough, learning is incomplete, even if agents draw large samples of past actions, as actions then become too correlated within samples, thereby reducing informativeness and welfare.

We propose a theory of how market power affects wage inequality. We ask how goods and labor market power jointly determine the level of wages, the skill premium, and wage inequality. We then use detailed microdata from the U.S. Census Bureau between 1997 and 2016 to estimate the parameters of labor supply, technology, and the market structure. We find that a less competitive market structure lowers the average wage of high‐skilled workers by 11.3%, and of low‐skilled workers by 12.2%, contributes 8.1% to the rise in the skill premium, and accounts for 54.8% of the increase in between‐establishment wage variance.

This paper theoretically and empirically studies the interaction of search and application approval in credit markets. Risky borrowers internalize the probability that their application is rejected and behave as if they had high search costs. Thus, “overpayment” may be a poor proxy for consumer sophistication since it partly represents rational search in response to rejections. Contrary to standard search models, our model implies (1) endogenous adverse selection through the search and application approval process, (2) a possibly non‐monotone or non‐decreasing relationship between search and realized interest, default, and application approval rates, and (3) search costs estimated from transaction prices alone are biased. We find support for the model's predictions using a unique data set detailing search behavior of mortgage borrowers. Estimating the model, we find that screening is informative and search is costly. Counterfactual analyses reveal that tightening lending standards and discrimination through application rejection both increase equilibrium interest rates. This increase in realized interest rates is in part due to strategic complementarity in bank rate setting.