Knowledge that Transforms

We propose an approach for Bayesian inference in time-varying structural vector autoregressions (SVARs) identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant time-varying SVARs in which the prior and posterior densities of any sequence of structural parameters belonging to the class are invariant to orthogonal transformations of the sequence. Our methodology is new to the literature. In contrast to existing algorithms for inference based on sign restrictions, our algorithm is the first to draw from a uniform distribution over the sequences of orthogonal matrices given the reduced-form parameters. We illustrate our procedure for inference by analyzing the role played by monetary policy during the latest inflation surge.

A collective choice problem specifies a finite set of alternatives from which a group of expected utility maximizers must choose. We associate a collective pseudo market with every collective choice problem and establish the existence and efficiency of pseudo Lindahl equilibrium (PLE) allocations. We also associate a cooperative bargaining problem with every collective choice problem and define a set-valued solution concept, the ω-weighted Nash bargaining set where ω is a vector of welfare weights. We provide axioms that characterize the ω-weighted Nash bargaining set. Our main result shows that ω-weighted Nash bargaining set payoffs are also the PLE payoffs of the corresponding collective pseudo market with the same utility functions and incomes ω. We define a pseudo core for collective pseudo markets and show that pseudo Lindahl equilibria are in the pseudo core. We characterize the set of PLE outcomes of discrete allocation problems and show that they contain the set of pseudo Walrasian equilibrium outcomes.

This paper documents that the Pearl Harbor attack triggered a sharp increase in volunteer enlistment rates of American men, the magnitude of the increase was smaller for Black men than for White men and the Black–White gap was larger in counties with higher levels of racial discrimination. The results suggest that political exclusion and discrimination can undermine support for the government during critical times such as war.

We document that most dispersion in marginal revenue products of inputs occurs across plants within firms rather than between firms. This is commonly thought to reflect misallocation: dispersion is “bad”. However, we show that eliminating frictions hampering internal capital markets in a multi-plant firm model may in fact increase productivity dispersion and raise output: dispersion can be “good”. This arises as firms optimally stagger investment activity across their plants over time to avoid raising costly external finance, instead relying on reallocating internal funds. The staggering in turn generates dispersion in marginal revenue products. We use U.S. Census data on multi-plant manufacturing firms to provide empirical evidence for the model mechanism and show a quantitatively important role for good dispersion. Since there is less scope for good dispersion in emerging economies, the difference in the degree of misallocation between emerging and developed economies looks more pronounced than previously thought.

Unemployment differentials are greater between countries in the euro area than between U.S. states. In both regions, net migration responds to unemployment differentials, though the response is smaller in the euro area compared to the U.S. We use a multi-country DSGE model with cross-border migration to quantify Mundell’s hypothesis that labour mobility could substitute for independent monetary policy in a currency union. While not as effective as independent monetary policy, increased labour mobility reduces business cycle fluctuations for most countries in the euro area. However, Mundell’s conjecture does not hold uniformly. For countries that primarily face demand shocks, labour mobility stabilizes inflation and unemployment and improves welfare. If supply shocks are dominant however, labour mobility increases the cost of being in a currency union by magnifying inflation volatility.

We propose a new model of internal migration, based on persistent and spatially correlated idiosyncratic utility. The model is motivated by a new fact in the data that simple moving cost models struggle to match: the t-year interstate migration rate is proportional to the square root of t. The new model maintains the tractability and flexibility of standard migration models, but better matches the dynamics of migration, including the new fact. It has substantially different welfare implications and makes different counterfactual predictions, especially in terms of dynamic adjustment and long-run responses.

We apply classical statistical decision theory to a large class of treatment choice problems with partial identification. We show that, in a general class of problems with Gaussian likelihood, all decision rules are admissible; it is maximin-welfare optimal to ignore all data; and, for severe enough partial identification, there are infinitely many minimax-regret optimal decision rules, all of which sometimes randomize the policy recommendation. We uniquely characterize the minimax-regret optimal rule that least frequently randomizes, and show that, in some cases, it can outperform other minimax-regret optimal rules in terms of what we call profiled regret. We analyse the implications of our results in the aggregation of experimental estimates for policy adoption, extrapolation of Local Average Treatment Effects, and policy making in the presence of omitted variable bias.

Time-inconsistency may play a central role in explaining inter-temporal behaviour, particularly among poor households. However, little is known about the distribution of time-inconsistent agents, and time-preference parameters are typically not identified in standard dynamic choice models. We formulate a dynamic discrete choice model in an unobservedly heterogeneous population of possibly time-inconsistent agents. We provide conditions under which all population type probabilities and preferences for both time-consistent and sophisticated agents are point-identified and sharp set-identification results for naïve and partially sophisticated agents. Estimating the model using data from a health intervention providing insecticide-treated nets (ITNs) in rural Odisha, India, we find that about two-thirds of our sample comprises time-inconsistent agents and that both sophisticated and naïve agents are considerably present-biased. Counterfactuals show that the under-investment in ITNs attributable to present-bias leads to substantial costs that are about four times the price of an ITN.

We provide a macroeconomic theory where demand for goods has a productive role. A search friction prevents perfect matching between producers and potential customers. Larger demand induces more search, which, in turn, increases GDP and measured total factor productivity (TFP). We embed the product-market friction in a standard neoclassical model and estimate it using Bayesian techniques. Business cycles are driven by preference shocks, true technology shocks, and investment-specific shocks. Preference shocks have qualitatively similar effects as true productivity shocks. These shocks account for a large share of the fluctuations in consumption, GDP, and measured TFP and can be identified using shopping time data.

We study identification and inference in first-price auctions with risk-averse bidders and selective entry, building on a flexible framework we call the Affiliated Signal with Risk Aversion (AS-RA) model. Assuming exogenous variation in either the number of potential bidders (N) or a continuous instrument (z) shifting opportunity costs of entry, we provide a sharp characterization of the nonparametric restrictions implied by equilibrium bidding. This characterization implies that risk neutrality is nonparametrically testable. In addition, with sufficient variation in both N and z, the AS-RA model primitives are nonparametrically identified (up to a bounded constant) on their equilibrium domains. Finally, we explore new methods for inference in set-identified auction models based on Chen et al. (2018, Econometrica, vol. 86, 1965–2018), as well as novel and fast computational strategies using Mathematical Programming with Equilibrium Constraints. Simulation studies reveal the good finite-sample performance of our inference methods, which can readily be adapted to other set-identified flexible equilibrium models with parameter-dependent support.