The Smoot‐​Hawley Trade War

11/03/2021

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Kris James Mitchener, Kirsten Wandschneider, and Kevin Hjortshøj O’Rourke | Cato Institute

In the words of Robert J. Samuelson, “The ghost of Smoot‐​Hawley seems to haunt President Trump.” As fears of a trade war between the United States and China grew after the U.S. presidential election of 2016, many commentators drew this link between the signing of the Smoot‐​Hawley Tariff Act of 1930 and recent trade disputes. And the consensus was that the trade wars of the 1930s were an ominous portent of what might await the world if Donald Trump’s protectionist impulses were not checked.

Empirical and theoretical interest in understanding the effects of trade wars has surged in response to the recent U.S.-China trade war. The fast‐​moving literature focuses on the effects of the tariff increases of 2018–2019 on U.S. manufacturing employment, producer prices, and capital expenditure of firms as well as consumer welfare losses in the form of higher prices and nearly complete pass‐​through.

This was by no means the first trade war in which the United States was a combatant. However, while economists have for decades used the tariff wars sparked by the Smoot‐​Hawley legislation of June 1930 as a cautionary tale of what can go wrong when protectionism gets out of hand, remarkably little quantitative research has been conducted on the Smoot‐​Hawley trade war. Even more surprisingly, perhaps for nonspecialists, the general conclusion of quantitative economic historians who have explored the effects of 1930s protectionism is that it had less impact than was traditionally thought. The point is straightforward: the collapse in gross domestic product during the Great Depression was so large that, on its own, it can explain the bulk of the trade collapse of 1929–1933; there is relatively little left over to explain the decline in trade. Our work aims to fill this gap in the literature. We estimate the quantitative impact of the Smoot‐​Hawley trade war on trade flows and conclude that it was big.

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