International Trade: Measurement and Quantification
Paper Session
Sunday, Jan. 3, 2021 10:00 AM - 12:00 PM (EST)
- Chair: Andrei A. Levchenko, University of Michigan
The Quantitative Effects of Trade Policy on Industrial and Labor Location
Abstract
One justification for trade protectionism is the benefits of terms-of-trade ma- nipulation. Another reason, more central in trade policy negotiations, is the idea that trade protectionism brings industries back home. The new economic geogra- phy theory has provided intuitive insights on how the location effect of trade policy shapes welfare in the protecting country. Previous work, however, has been able to say much less about the quantitative effects of trade policy on the location of firms across space and over time, and its welfare implications. We develop a multi-country dynamic general-equilibrium trade and spatial model with forward-looking deci- sions of firms on where to locate production, forward-looking decisions of workers on where to supply labor, and endogenous capital structure accumulation. We take the model to the data using trade and production data for many locations and in- dustries, as well as using data on firms’ demographics from several data sources. We use the model to study how trade protectionism impacts the location of production across space and over time, as well as its welfare consequences. We find quantitative evidence that protection relocates production to the protected country but that this comes at the cost of higher prices and lower welfare.The Long and Short (Run) of Trade Elasticities
Abstract
We propose a novel approach to estimate the trade elasticity at various horizons. When large countries change Most Favored Nation (MFN) tariffs, small trading partners that are not in a preferential trade agreement experience plausibly exogenous tariff changes. The differential growth rates of imports from these countries relative to a control group -- countries not subject to the MFN tariff scheme -- can be used to identify the trade elasticity. We build a panel dataset combining information on product-level tariffs and trade flows covering 1995-2017, and estimate the trade elasticity at short and long horizons using local projections (Jorda,2005). Our main findings are that the elasticity of tariff-exclusive trade flows in the year following the exogenous tariff change is about -0.7, and the long-run elasticity ranges from -1.5 to -2. The welfare-relevant long-run trade elasticity is about -0.6. Our long-run estimates are smaller than typical in the literature, and it takes 7-10 years to converge to the long run, implying that (i) the welfare gains from trade are high and (ii) there are substantial market penetration costs to accessing new customers.Barriers to Global Capital Allocation
Abstract
We quantify the impact of barriers to international investment, using a novel multi-country overlapping generations model with heterogeneous investors and imperfect capital mobility. Our model yields a gravity equation for foreign asset demand, which we estimate using recently-developed foreign investment data that has been restated to account for the presence of offshore investment and financing vehicles. We show that a parsimonious implementation of the model, with four barriers (capital controls, geographic distance, institutional distance and cultural distance) can account for a large share of the observed variation in bilateral Foreign Direct Investment (FDI) and Foreign Portfolio Investment (FPI) positions. Our model predicts a significant home bias and higher rates of return on capital in emerging markets. In our benchmark calibration, we estimate that capital misallocation induced by these barriers reduces World GDP by 8.8%, compared to a situation without barriers. We also find that barriers to global capital allocation contribute significantly to cross-country inequality: the standard deviation of log capital per employee is 70% higher than it would be in a world without barriers to international investment, while the dispersion in output per employee is 39% higher.Discussant(s)
George Alessandria
,
University of Rochester
Dominick Bartelme
,
University of Michigan
Joseph Steinberg
,
University of Toronto
Julian di Giovanni
,
Federal Reserve Bank of New York
JEL Classifications
- F1 - Trade