The primary motivation behind quantitative work in international trade and many other fields is to shed light on the economic consequences of policy changes and other shocks. To help assess and potentially strengthen the credibility of such quantitative predictions, we introduce an IV-based goodness-of-fit measure that provides the basis for testing causal predictions in arbitrary general equilibrium environments as well as for estimating the average misspecification in these predictions. As an illustration of how to use the measure in practice, we revisit the welfare consequences of the U.S.-China trade war predicted by Fajgelbaum et al. (2020).

We study inference in shift-share regression designs, such as when a regional outcome is regressed on a weighted average of 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, independent of their geographic location. We derive novel inference methods that are valid under arbitrary cross-regional correlation in the regression residuals. We show using popular applications of shift-share designs that our methods may lead to substantially wider confidence intervals in practice.

The earnings of individuals depend on the demand for the factor services they supply. International trade may therefore affect earnings inequality because either (i) foreign consumers and firms demand domestic factor services in different proportions than domestic consumers and firms do, an export channel; or (ii) domestic consumers and firms change their demand for domestic factor services in response to the availability of foreign goods, an import channel. Building on this idea, we develop new measures of export and import exposure at the individual level and provide estimates of their incidence across the earnings distribution. The key input fed into our empirical analysis is a unique administrative data set from Ecuador that merges firm-to-firm transaction data, employer-employee matched data, owner-firm matched data, and firm-level customs transaction records. We find that export exposure is pro-middle class, import exposure is pro-rich, and in terms of overall incidence, the import channel is the dominant force. As a result, earnings inequality in Ecuador is higher than it would be in the absence of trade.