Feodora Teti

When a country grants preferential tariffs to another, either reciprocally in a free trade agreement (FTA) or unilaterally, rules of origin (RoOs) are defined to determine whether a product is eligible for preferential treatment. RoOs exist to avoid that exports from third countries enter through the member with the lowest tariff (trade deflection). However, RoOs distort exporters' sourcing decisions and burden them with red tape.

This paper evaluates the effects of sanctions on Russia between 2014 and 2019 and the resulting countersanctions. We estimate their impact on trade in a gravity framework, allowing for treatment heterogeneity among pairs and sectors, and use the estimated elasticities in a general equilibrium analysis. We find that the sanctions decreased trade with Russia in key sectors, translating to a loss in real income in Russia by 0.3%. Full decoupling of the EU and its allies from Russia would increase this effect to over 4%. Our results emphasize the role of deep sanctions as a foreign policy instrument and international cooperation.

The Russian invasion of Ukraine in 2022 led to unprecedented sanctions aimed at denying Russia access to critical technologies essential for sustaining its war efforts. This paper examines whether trade sanctions targeting military goods were undermined by evasion, specifically through transshipment through Russia-friendly countries. We find that Russia-friendly countries are 20 pp more likely to export military goods to Russia relative to neutral countries after the war began, while exports of military goods from Western allies to these countries also increased. Our results show that weak enforcement significantly undermines the effectiveness of sanctions.

We use detailed tariff data and a dynamic trade and reallocation model with downward nominal wage rigidities to quantitatively assess the economic consequences of the recent increase in U.S. import tariffs and the responses of its trading partners. Higher tariffs trigger an expansion in U.S. manufacturing and agricultural employment, but this comes at the expense of a decline in service employment, with overall employment declining as lower real wages reduce labor-force participation. For the United States as a whole, real income falls around 0.1% by 2028, the last year we assume the high tariffs are in effect. Importantly, our analysis disaggregates the U.S. into its 50 states, while incorporating cross-state redistribution of the tariff-generated fiscal revenue, allowing us to analyze which states gain or lose more from the shock. Some of the most populous states, like California, New York, and Texas, suffer real income declines of up to 1.4%. On the flip side, 34 states gain, in some cases as much as 1.9%. Turning to cross-country results, some close U.S. trading partners—like Canada, Mexico, and Ireland—suffer the largest real income losses.

This paper reveals significant errors in a key variable in international trade: tariff rates. The issues arise from incomplete reporting, leading to measurement error from false interpolation by the data provider, the World Integrated Trade Solution (WITS), and selection bias from dropping tariffs when no trade is recorded. I develop a novel interpolation algorithm to correct these issues and construct a global tariff dataset. Reestimating recent studies relying on WITS data highlights the importance of correcting these errors. Studies using cross-country tariff variation, such as estimates of the trade elasticity, are particularly sensitive to the mishandling of the tariff data.

Modern regional trade agreements (RTAs) focus on promoting bilateral exchange mostly by lowering non-tariff barriers to trade (NTBs). But do existing RTAs actually deliver what they promise? We argue that existing results in the literature are upward-biased because of measurement error in a crucial control variable: tariff rates. Using a novel data set of high-quality tariff information, we show that, on average, NTB reductions in deep RTAs boost services trade but not goods trade. Estimating separate NTB effects for each RTA reveals strong heterogeneity: only 23% of all RTAs seem to lower NTBs; for most, we cannot find any significant effect; while 9% appear to even reduce trade, possibly because more balanced regulation evens out comparative advantages. Trade agreements that foster trade the most are the ones including non-discriminatory trade policy changes.