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).

The textbook case for industrial policy is well understood: sectors with relatively large external economies of scale should be subsidized at the expense of other sectors. Little is known, however, about the magnitude of the welfare gains from such interventions. We develop an empirical strategy that leverages commonly available trade data to estimate sector-level economies of scale and, in turn, to quantify the gains from optimal industrial policy in a general equilibrium environment. Our results point toward significant economies of scale across manufacturing sectors but gains from industrial policy that are hardly transformative, even among the most open economies.