We develop a new approach for the identification of Pareto‐improving tax reforms. This approach yields necessary and sufficient conditions for the existence of Pareto‐improving reform directions. A main insight is that “Two brackets are enough”: When the system cannot be improved by altering tax rates in one or two income brackets, then there is no continuous reform direction that is Pareto‐improving. We also show how to check whether a given tax reform is Pareto‐improving. We use these tools to study the introduction of the Earned Income Tax Credit (EITC) in the United States in 1975. A robust finding is that, prior to the EITC, the U.S. tax‐transfer system was not Pareto‐efficient. Under plausible assumptions about behavioral responses, the 1975 reform was not Pareto‐improving. Qualitatively, though, it had the right properties: A similar reform with earnings subsidies made available to a broader range of incomes would have been Pareto‐improving.

Abstract We propose improved standard errors and an asymptotic theory for two-way clustered panels. Our theory allow for arbitrary serial dependence in the common time effects, which is excluded by existing two-way methods. Our asymptotic distribution theory is the first which allows for this level of inter-dependence. Under weak conditions, we demonstrate that OLS is asymptotically normal, our proposed variance estimator is consistent, and t-ratios are asymptotically standard normal. The results extend to two-way fixed-effect models; we argue that two-way clustering is still necessary even if two-way fixed effects are included. Simulation and empirical illustration are provided.

Instrumental variable (IV) methods are widely used to identify causal effects in models with endogenous explanatory variables. Often the instrument exclusion restriction that underlies the validity of the usual IV inference is suspect; that is, instruments are only plausibly exogenous. We present practical methods for performing inference while relaxing the exclusion restriction. We illustrate the approaches with empirical examples that examine the effect of 401(k) participation on asset accumulation, price elasticity of demand for margarine, and returns to schooling. We find that inference is informative even with a substantial relaxation of the exclusion restriction in two of the three cases.

How does transparency, a key feature of central bank design, affect monetary policy makers’ deliberations? Theory predicts a positive discipline effect and negative conformity effect. We empirically explore these effects using a natural experiment in the Federal Open Market Committee in 1993 and computational linguistics algorithms. We first find large changes in communication patterns after transparency. We then propose a difference-in-differences approach inspired by the career concerns literature, and find evidence for both effects. Finally, we construct an influence measure that suggests the discipline effect dominates.

Abstract We use decision theory to confront uncertainty that is sufficiently broad to incorporate “models as approximations.” We presume the existence of a featured collection of what we call “structured models” that have explicit substantive motivations. The decision-maker confronts uncertainty through the lens of these models, but also views these models as simplifications, and hence, as misspecified. We extend the max–min analysis under model ambiguity to incorporate the uncertainty induced by acknowledging that the models used in decision making are simplified approximations. Formally, we provide an axiomatic rationale for a decision criterion that incorporates model misspecification concerns. We then extend our analysis beyond the max-min case allowing for a more general criterion that encompasses a Bayesian formulation.

. We develop models of robust decision-making and pricing when there are contemporaneous big and small shocks. We illustrate these models using a stochasticgrowth economy. Large shocks are infrequent changes in the technological growth rate, and small shocks are continuous movements in the technology process. Large shocks evolve as a Markov jump process whereas small shocks are a Brownian motion. Robust decision-making is formalized as a two-player game. In contrast to rational expectations agents, our investors are decision-makers who treat models as approximations and fear misspecication. As an algorithmic device to enforce robustness, investors imagine a second, malevolent player, who has the ability to perturb the baseline model. We study two economies, each of which decentralizes a robust resource allocation problem with hidden growth rates. The economies dier in the manner in which the the model is viewed as an approximation. We compare the pricing implications to those that em...

In this article we characterize and estimate the process for short-term interest rates using federal funds interest rate data. We presume that we are observing a discrete-time sample of a stationary scalar diffusion. We concentrate on a class of models in which the local volatility elasticity is constant and the drift has a flexible specification. To accommodate missing observations and to break the link between “economic time” and calendar time, we model the sampling scheme as an increasing process that is not directly observed. We propose and implement two new methods for estimation. We find evidence for a volatility elasticity between one and one-half and two. When interest rates are high, local mean reversion is small and the mechanism for inducing stationarity is the increased volatility of the diffusion process.