This paper investigates what features of an economy determine whether convergence under learning is fast or slow. In all of the models that we consider, people's beliefs about model outcomes are central determinants of those outcomes. We argue that under certain circumstances, convergence of a...

This paper proposes a new way of displaying and analyzing macroeconomic time series to form recession forecasts. The proposed data displays contain the last three years of each expansion. These allow observers to see for themselves what is different about the last year before recession. Based on...

After the Covid-shock in March 2020, stock prices declined abruptly, reflecting both the deterioration of investors' expectations of economic activity as well as the surge in aggregate risk aversion. In the following months however, whereas economic activity remained sluggish, equity markets...

We study capital-skill complementarity in a multi-sector framework featuring firm-specific, multi-factor production functions and allowing for firm-specific factor-price wedges. We characterize the elasticity of the skill premium to the price of capital equipment in terms of firm-level...

We study a sequential experimentation model with endogenous feedback. Agents choose between a safe and risky action, the latter generating stochastic rewards. When making this choice, each agent is selfishly motivated (myopic). However, agents can disclose their experiences to a public record,...

The central question we address in this paper is: what can an analyst infer from choice data about what a decision maker has learned? The key constraint we impose, which is shared across models of Bayesian learning, is that any learning must be rationalizable. To implement this constraint, we...

We provide semiparametric identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: i) known heterogeneity, ii) initially unknown heterogeneity that may be revealed over time, and iii) transitory uncertainty. We consider a...

We develop and estimate a model of consumer search with spatial learning. Consumers make inferences from previously searched objects to unsearched objects that are nearby in attribute space, generating path dependence in search sequences. The estimated model rationalizes patterns in data on...

We provide evidence of a powerful barrier to social learning: people are much less sensitive to information others discover compared to equally-relevant information they discover themselves. In a series of incentivized lab experiments, we ask participants to guess the color composition of balls...

Adaptation to dynamic conditions requires a certain degree of diversity. If all agents take the best current action, learning that the underlying state has changed and behavior should adapt will be slower. Diversity is harder to maintain when there is fast communication between agents, because...