Abstract

Abstract We use the recent trade escalation between the USA and its trade partners to study whether retaliatory tariffs are politically targeted. We find comprehensive evidence using individual and aggregate voting data suggesting that retaliation is carefully targeted to hurt Trump. We develop a simulation approach to construct counterfactual retaliation responses allowing us to quantify the extent of political targeting while also studying potential trade-offs. China appears to place great emphasis on achieving maximal political targeting. The EU seems to have been successful in maximising political targeting while at the same time minimising the potential damage to its economy.

Highlights

  • We use the recent trade escalation between the USA and its trade partners to study whether retaliatory tariffs are politically targeted

  • The results suggest that counties that are more exposed to retaliatory tariff had higher levels of support for Trump in the 2016 presidential election

  • We find that the degree of political targeting is strongest for the European Union (EU)’s and Mexico’s retaliations

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Summary

Introduction

We use the recent trade escalation between the USA and its trade partners to study whether retaliatory tariffs are politically targeted. The EU, for example, imposed tariffs on iconic American brands like Harley-Davidson motorbikes, which are produced in Wisconsin, the home state of Speaker of the House Paul Ryan While this is suggestive, so far we know little about how countries design their retaliatory tariffs, as few trade disputes reach a stage of escalation in which threatened tariffs are imposed or retaliation measures are triggered. We know little about how countries design their retaliatory tariffs, as few trade disputes reach a stage of escalation in which threatened tariffs are imposed or retaliation measures are triggered This paper fills this gap by investigating the degree to which retaliation by the USA’s trade partners was politically targeted. Financial support from the ESRC CAGE research centre and a UKRI Open Access grant is gratefully acknowledged

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