How Data Choices Shape Structural Gravity Estimates: Evidence from EU Membership

This paper quantifies how the construction of trade data shapes trade policy estimates in structural gravity models. Using 14 widely employed trade datasets and alternative sample restrictions, we generate 643 estimates of the EU membership trade effect from a standard structural gravity specification. We then use these estimates in a meta-regression framework to identify how data and sample choices contribute to variation in the estimates. In a Shapley decomposition, the choices explain 69.1% of the variation, with the selection of a trade dataset alone accounting for 39.4% of the explained variation. We further distinguish between decisions that affect the measurement of a common estimand and those that alter the underlying economic margin. Including domestic trade flows increases the estimated EU membership effect by 21.2 percentage points on average and nearly doubles the implied expenditure-weighted welfare gain in a four-sector general equilibrium model. Finally, we relate differences in data generating processes across datasets to estimated trade effects, showing that trade data construction is a first-order determinant of structural gravity results. This implies that conclusions from gravity based policy evaluations are sensitive to data choices and require explicit consideration of data construction as part of the empirical identification.