Knowledge that Transforms

Unlike gender or race, class background is rarely a focus of research on career progression, or of DEI efforts in elite occupations. Should it be? In this paper, we document a large class gap in career progression in one occupation—U.S. tenure‐track academia—using parental education to proxy for class background. First‐generation college graduates are 10% less likely to be tenured at an R1, are tenured at institutions ranked 11% lower, earn 3% less, and report 5% lower job satisfaction, than their former PhD classmates (from the same institution and field) with a parent with a non‐PhD graduate degree. Neither selection out of academia nor different preferences explain this gap; differential research productivity also plays little role. Instead, likely drivers are differences in cultural and social capital. We also find a class gap in career progression for PhDs who work in industry, suggesting this phenomenon generalizes outside academia.

I document that the right tail of the firm size distribution systematically thickens with economic development. To rationalize this fact, I develop a parsimonious idea search model in which both aggregate growth and the firm size distribution are endogenously determined. The model features an asymptotic balanced growth path along which Gibrat's law holds at each date, and the right tail of the firm size distribution thickens monotonically toward Zipf's law. The model also implies that policies favoring large firms can improve welfare by better utilizing the diffusion externalities arising from idea search.

We study instrumental variables models characterized by: (i) Unobserved heterogeneity consisting of potential outcomes and response types that describe how the instrument determines treatment choice; (ii) Conditional independence of the instrument and the unobserved heterogeneity; and (iii) Convex restrictions on the distribution of unobserved heterogeneity. We show certain causal parameters are identified in these models if and only if a version of the kappa of Abadie (2003) exists. Our identification results are constructive in yielding estimating moment conditions. Focusing on a leading special case, we develop asymptotically normal estimators based on a doubly robust version of these moment conditions.

This paper develops a macroeconomic model that combines an incomplete‐markets overlapping‐generations economy with a job ladder featuring sequential wage bargaining, endogenous search effort of employed and non‐employed workers, and differences in match quality. With these ingredients, our model provides a joint microfoundation for the three main inputs in aggregate production: capital, employment, and labor efficiency. The calibrated model offers a good fit to the empirical age profiles of search activity, job‐finding rates, wages, and savings. We use the model to analyze the impact of tax and transfer policies for labor market dynamics and aggregate economic activity via capital, employment, and labor efficiency channels. Lower unemployment benefits and a less progressive tax schedule bring about welfare losses for a newborn worker which are mainly driven by higher consumption risk and costlier search effort; both policies have differential effects along the age, income, and wealth dimensions.

Personalizing policies can theoretically increase their effectiveness. However, personalization is difficult when individual types are unobservable and the preferences of policymakers and individuals are not aligned, which could cause individuals to misreport their type. Mechanism design offers a strategy to overcome this issue: offer an “incentive‐compatible” menu of policy choices designed to induce participants to select the variant intended for their type. Using a field experiment that personalized incentives for exercise among 6,800 adults with diabetes and hypertension in urban India, we show that personalizing with an incentive‐compatible choice menu substantially improves program performance, increasing the treatment effect of incentives on exercise by 80% without increasing incentive costs relative to a one‐size‐fits‐all benchmark. Offering choice achieves similar performance to personalizing with an extensive set of observable variables, but without the same data requirements.

We construct a dynamic integrated assessment model of climate and the economy with very high geographic resolution. Migration is free within, but not allowed across, countries. The model parameterization uses a wealth of data, including the distribution of output, population, energy sources and use, and estimates of the local damages from climate change. It implies very large geographic dispersion in damages from warming. We conduct three kinds of policy experiments. In one, we note that a modest, uniform carbon tax limits global warming and damages around the world substantially. In a second experiment, we let the poorest countries not tax carbon, while the rest compensate by setting higher taxes; the efficiency losses are large. In a final experiment, we find that fast green technology growth alone is a poor substitute for carbon taxes, whether globally available or not.

We consider impulse response inference in a locally misspecified vector autoregression (VAR) model. The conventional local projection (LP) confidence interval has correct coverage even when the misspecification is so large that it can be detected with probability approaching 1. This result follows from a “double robustness” property analogous to that of popular partially linear regression estimators. By contrast, the conventional VAR confidence interval with short‐to‐moderate lag length can severely undercover for misspecification that is small, difficult to detect statistically, and cannot be ruled out based on economic theory. The VAR confidence interval has robust coverage if, and only if, the lag length is so large that the interval is as wide as the LP interval.