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Year: 2015 Publisher: National Bureau of Economic Research
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Year: 2018 Publisher: National Bureau of Economic Research
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Year: 2020 Publisher: National Bureau of Economic Research
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Year: 2023 Publisher: National Bureau of Economic Research
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Year: 2020 Publisher: National Bureau of Economic Research
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Year: 2019 Publisher: National Bureau of Economic Research
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Year: 2020 Publisher: National Bureau of Economic Research
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Year: 2023 Publisher: National Bureau of Economic Research
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Digital
Year: 2015 Publisher: Cambridge, Mass. National Bureau of Economic Research
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We develop a methodology to construct nonparametric counterfactual predictions, free of functional-form restrictions on preferences and technology, in neoclassical models of international trade. First, we establish the equivalence between such models and reduced exchange models in which countries directly exchange factor services. This equivalence implies that, for an arbitrary change in trade costs, counterfactual changes in the factor content of trade, factor prices, and welfare only depend on the shape of a reduced factor demand system. Second, we provide sufficient conditions under which estimates of this system can be recovered nonparametrically. Together, these results offer a strict generalization of the parametric approach used in so-called gravity models. Finally, we use China's recent integration into the world economy to illustrate the feasibility and potential benefits of our approach.
Digital
Year: 2018 Publisher: Cambridge, Mass. National Bureau of Economic Research
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We study inference in shift-share regression designs, such as when a regional outcome is regressed on a weighted average of observed sectoral shocks, using regional sector shares as weights. We conduct a placebo exercise in which we estimate the effect of a shift-share regressor constructed with randomly generated sectoral shocks on actual labor market outcomes across U.S. Commuting Zones. Tests based on commonly used standard errors with 5% nominal significance level reject the null of no effect in up to 55% of the placebo samples. We use a stylized economic model to show that this overrejection problem arises because regression residuals are correlated across regions with similar sectoral shares, independently of their geographic location. We derive novel inference methods that are valid under arbitrary cross-regional correlation in the regression residuals. We show that our methods yield substantially wider confidence intervals in popular applications of shift-share regression designs.
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