Knowledge that Transforms

The view that empirical strategies in economics should be transparent and credible now goes almost without saying. By revealing for whom particular instrumental variables (IV) estimates are valid, the local average treatment effects (LATE) framework helped make this so. This lecture uses empirical examples, mostly involving effects of charter and exam school attendance, to illustrate the value of the LATE framework for causal inference. LATE distinguishes independence conditions satisfied by random assignment from more controversial exclusion restrictions. A surprising exclusion restriction is shown to explain why enrollment at Chicago exam schools reduces student achievement. I also make two broader points: IV exclusion restrictions formalize commitment to clear and consistent explanations of reduced‐form causal effects; the credibility revolution in applied econometrics owes at least as much to compelling empirical analyses as to methodological insights.

Discrete location, occupation, skill, and hours choices of workers underpin their incomes. This paper analyzes the optimal taxation of discrete income‐generating choice. It derives optimal tax equations and Pareto test inequalities for mixed logit choice environments that can accommodate discrete and unstructured choice sets, rich preference heterogeneity, and complex aggregate cross‐substitution patterns between choices. These equations explicitly connect optimal taxes to societal redistributive goals and private substitution behavior, with the latter encoded as a substitution matrix that describes cross‐sensitivities of choice distributions to tax‐induced utility variation. In repeated mixed logit settings, the substitution matrix is exactly the Markov matrix of shock‐induced agent transitions across choices. We describe implications of this equivalence for evaluation of prevailing tax designs and the structural estimation of optimal policy mixed logit models. We apply our results to two salient examples: spatial taxation and taxation of couples.

This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency of the ML estimator and local asymptotic normality for the models under general conditions, which allow for autoregressive dynamics in the observable process, Markov regime sequences with covariate‐dependent transition matrices, and possible model misspecification. A Monte Carlo study examines the finite‐sample properties of the ML estimator in correctly specified and misspecified models. An empirical application is also discussed.

We document that between 50% and 70% of changes in the U.S. wage structure over the last four decades are accounted for by relative wage declines of worker groups specialized in routine tasks in industries experiencing rapid automation. We develop a conceptual framework where tasks across industries are allocated to different types of labor and capital. Automation technologies expand the set of tasks performed by capital, displacing certain worker groups from jobs for which they have comparative advantage. This framework yields a simple equation linking wage changes of a demographic group to the task displacement it experiences. We report robust evidence in favor of this relationship and show that regression models incorporating task displacement explain much of the changes in education wage differentials between 1980 and 2016. The negative relationship between wage changes and task displacement is unaffected when we control for changes in market power, deunionization, and other forms of capital deepening and technology unrelated to automation. We also propose a methodology for evaluating the full general equilibrium effects of automation, which incorporate induced changes in industry composition and ripple effects due to task reallocation across different groups. Our quantitative evaluation explains how major changes in wage inequality can go hand‐in‐hand with modest productivity gains.

Many firms in developing countries could be too small to adopt modern technology embodied in expensive production machines. This paper shows that rental market interactions allow these small firms to increase their effective scale and mechanize production. We conduct a survey of manufacturing firms in Uganda, which uncovers an active rental market for large machines between small firms in informal clusters. We then build an equilibrium model of firm behavior and estimate it with our data. We find that the rental market is quantitatively important for mechanization and productivity since it provides a workaround for other market imperfections that keep firms small. The rental market also shapes the effectiveness of development policies to foster mechanization, such as subsidies to purchase machines. Overall, our results point to the importance of taking into account firm‐to‐firm interactions within informal clusters to understand technology adoption in low income countries: focusing on the small scale of firms in isolation might be misleading.

How can one determine whether a treatment, such as the introduction of a social program or trade shock, alters agents' incentives to form links in a network? This paper proposes analogs of a two‐sample Kolmogorov–Smirnov test, widely used in the literature to test the null hypothesis of no treatment effects, for network data. It first specifies a testing problem in which the null hypothesis is that two networks are drawn from the same random graph model. It then describes two randomization tests based on the magnitude of the difference between the networks' adjacency matrices as measured by the 2 → 2 and ∞ → 1 operator norms. Power properties of the tests are examined analytically, in simulation, and through two real‐world applications. A key finding is that the test based on the ∞ → 1 norm can be much more powerful for the kinds of sparse and degree‐heterogeneous networks common in economics.

United States households' consumption expenditures and car purchases collapsed during the Great Recession and more so than income changes would have predicted. Using CEX data, we show that both the extensive and the intensive car spending margins contracted sharply in the Great Recession. We also document significant cross‐cohort differences in the impact of the Great Recession including a stronger reduction in car spending by younger cohorts. We draw inference on the sources of the Great Recession by investigating which shocks can explain household choices in a 60 period life‐cycle model with idiosyncratic and aggregate shocks fitted to aggregate and life‐cycle moments. We find that the Great Recession was caused by a combination of large aggregate income and wealth shocks, while cross‐cohort adjustment patterns imply a role for life‐cycle income profile shocks. We also find a role for car loan premia shocks in accounting for car spending and car loans.

We study optimal control problems in the multivariate linear‐quadratic‐Gaussian framework under rational inattention. We propose a three‐step procedure to solve this problem using semidefinite programming and derive the optimal signal structure without strong prior restrictions. We analyze both the transition dynamics of the optimal posterior covariance matrix and its steady state. We characterize the optimal information structure for some special cases and develop numerical algorithms for general cases. Applying our methods to solve three multivariate economic models, we obtain some results qualitatively different from the literature.

Airfares fluctuate due to demand shocks and intertemporal variation in willingness to pay. I estimate a model of dynamic airline pricing accounting for both sources of price adjustments using flight‐level data. I use the model estimates to evaluate the welfare effects of dynamic airline pricing. Relative to uniform pricing, dynamic pricing benefits early‐arriving, leisure consumers at the expense of late‐arriving, business travelers. Although dynamic pricing ensures seat availability for business travelers, these consumers are then charged higher prices. When aggregated over markets, welfare is higher under dynamic pricing than under uniform pricing. The direction of the welfare effect at the market level depends on whether dynamic price adjustments are mainly driven by demand shocks or by changes in the overall demand elasticity.