Abstract This paper uncovers that input tariff reductions result in less domestic innovation, but standard models of trade ensure a positive correlation between importing and innovation. Hence, the paper develops a dynamic framework with a task-specific laboraugmenting productivity and a non-homothetic import demand system to rationalize this finding. The model implies that input liberalization enables firms to use cheaper intermediate imports as a substitute for self-made inputs, a strategy that decreases marginal production costs but also discourages firms from investing in their own inhouse varieties. Finally, the paper compares the effectiveness of trade and innovation policies in boosting aggregate productivity growth.
Abstract This paper investigates fairness perceptions of extreme income inequality generated in winner-take-all competitions. Two large-scale experiments with more than 7,000 participants from the general population of the U.S. show that extreme earnings inequality is widely accepted, even when the winner only slightly outperforms the runner-up. The effect of the winning margin on inequality acceptance is modest compared to the effect of shifting the source of inequality from luck to winning by the smallest possible margin. The experimental choices are systematically associated with broader fairness attitudes and policy preferences, including support for higher taxation of top earners and redistributive economic policy.
Abstract Predictive algorithms inform consequential decisions in settings with selective labels: outcomes are observed only for units selected by past decision makers. This creates an identification problem under unobserved confounding — when selected and unselected units differ in unobserved ways that affect outcomes. We propose a framework for robust design and evaluation of predictive algorithms that bounds how much outcomes may differ between selected and unselected units with the same observed characteristics. These bounds formalize common empirical strategies including proxy outcomes and instrumental variables. Our estimators work across bounding strategies and performance measures such as conditional likelihoods, mean squared error, and true/false positive rates. Using administrative data from a large Australian financial institution, we show that varying confounding assumptions substantially affects credit risk predictions and fairness evaluations across income groups.
Abstract We document that in response to intensified competition from China in the U.S., poor countries reduce their export prices relative to rich countries, consistent with conventional wisdom. Interestingly, however, the opposite is true for export quantities. To reconcile these facts, we develop and estimate a general equilibrium model of trade featuring (i) cross-country heterogeneity in the ability to produce high-quality goods and (ii) a two-dimensional Bertrand competition on price and quality. Our model explains the empirical facts by showing that rich countries have a comparative advantage in quality upgrading, whereas a nested model without quality cannot do so.
Abstract This paper develops a semiparametric sieve approach to estimate impulse response functions of nonlinear time series models within a broad class of structural autoregressive specifications. A two-step procedure flexibly captures nonlinearities without imposing fixed parametric forms. We establish uniform estimation guarantees and propose an iterative algorithm that makes impulse response computation straightforward. Simulation results show robustness to misspecification with only modest efficiency losses. In an application to U.S. monetary policy, we find larger GDP responses to interest rate hikes than in linear models. We also examine oil supply news shocks of varying magnitudes to assess limitations when analyzing large shocks.
