We document that the plants belonging to small and mid-sized firms are geographically concentrated, while large firms are much more dispersed. These differences are sizable; firms with 2 plants have a dispersion that is 5 log points lower than predicted by industry location patterns, while the corresponding figure is less than 2 log points for firms with 40 plants and less than a half for firms with 100 or more plants. These patterns are qualitatively robust across industries, time periods, and alternative specifications. We also find that plants that are farther from the firm center employ significantly less workers than closer plants within the same firm, and that this effect is attenuated in large firms. These findings suggest that large firms face lower costs of geographic expansion.
Industry agglomeration can be indicative of agglomeration forces and is correlated with firm outcomes. Because multi-plant firms tend cluster their establishments in space, industry agglomeration could in part be driven by forces internal to the firm rather than across-firm spillovers. We propose and implement a decomposition of the industry agglomeration measures into within and across-firm components using U.S. census microdata. The within-firm component makes a small contribution to observed industry agglomeration for most industries and spatial scales, but accounts for 20% or more of observed agglomeration at short spatial scales for a subset of industries.
The Textbook Case for Industrial Policy: Theory Meets Data, (with Arnaud Costinot, Dave Donaldson and Andres Rodriguez-Clare) (August 2019, R&R at JPE)
The textbook case for industrial policy is well understood. If some sectors are subject to external economies of scale, whereas others are not, a government should subsidize the first group of sectors at the expense of the second. The empirical relevance of this argument, however, remains unclear. In this paper we develop a strategy to estimate sector-level economies of scale and evaluate the gains from such policy interventions in an open economy. Our benchmark results point towards significant and heterogeneous economies of scale across manufacturing sectors, but only modest gains from industrial policy, below 1% of GDP on average. Though these gains can be larger in some of the alternative environments that we consider, they are always smaller than the gains from optimal trade policy.
This paper estimates the impact of foreign sectoral demand and supply shocks on real income. Our empirical strategy is based on a first order approximation to a wide class of small open economy models that feature sector- level gravity in trade flows. The framework allows us to measure foreign shocks and characterize their impact on income in terms of reduced-form elasticities. We use machine learning techniques to group 4-digit manufacturing sectors into a smaller number of clusters, and show that the cluster-level income elasticities can be estimated using high-dimensional statistical techniques. We find clear evidence of heterogeneity in the income elasticities of different foreign shocks. Foreign demand shocks in complex intermediate and capital goods have large impacts on real income, and both supply and demand shocks in capital goods have particularly large impacts on growth for poor countries. Counterfactual exercises show that both comparative advantage and geography play a quantitatively large role in how foreign shocks affect real income.
Trade Costs and Economic Geography: Evidence from the US, (March 2018, R&R at ReStat)
This paper shows that for a wide class of economic geography models the positive implications of trade costs are captured by two reduced form elasticities: the elasticities of wages and population with respect to market access. It develops a novel IV approach to consistently estimate these elasticities using exogenous changes in the incomes of each location's trading partners. The approach is implemented using data from U.S. MSAs and finds that wages and especially employment are quite sensitive to differences in market access across cities. Counterfactual simulations indicate that reducing trade costs would result in large population shifts from the Northeast toward the South and West, along with a flattening of the city size distribution.
Specialization is a powerful source of productivity gains, but how production networks at the industry level are related to aggregate productivity in the data is an open question. We construct a database of input-output tables covering a broad spectrum of countries and times, develop a theoretical framework to derive an econometric specification, and document a strong and robust relationship between the strength of industry linkages and aggregate productivity. We then calibrate a multisector neoclassical model and use alternative identification assumptions to extract an industry-level measure of distortions in intermediate input choices. We compute the aggregate losses from these distortions for each country in our sample and find that they are quantitatively consistent with the relationship between industry linkages and aggregate productivity in the data. Our estimates imply that the TFP gains from eliminating these distortions are modest but significant, averaging roughly 10% for middle and low income countries.
Econ 641 (International Trade), Fall 2015, Fall 2016, Fall 2017, Fall 2020
Econ 441 (International Trade), Winter 2016, Fall 2016, Fall 2017, Winter 2020, Fall 2020
Econ 340 (International Economics), Winter 2020, Fall 2020