President Trump has recently escalated his ongoing trade war with China, increasing import tariffs from 10 to 25 percent. But can any country be the ‘winner’ in a trade war? In new research, Thiemo Fetzer and Carlo Schwarz look at the structure of the retaliation countries have opted for at the onset of the trade war. They find that China has been able to target its retaliatory measures to maximize economic harm to Trump-voting counties, but also that this may have come at a cost to China’s own domestic economy.
With the presidency of Donald Trump tariff barriers as an instrument of trade policy have returned. Even before he became president, during his presidential campaign Trump hinted at potential changes to US trade policy. The current trade escalation began in March 2018 when President Donald Trump raised tariff barriers for aluminium and steel imports along with tariffs for many goods originating in China. Following the announcement of the tariffs, President Donald Trump asserted in a tweet that “Trade wars are good, and easy to win”. Yet, are they?
Much of trade theory would suggest that tariff barriers have negative direct economic effects. Retaliation by other countries or trading blocs against the US’ act of escalation can further exacerbate the cost of trade disputes. This retaliation may further be specifically targeted, not only to produce economic pressures, but also political ones.
The European Union is quite transparent about its approach to trade disputes. Specifically, EU Regulation 654, published in 2014, outlines three assessment criteria for commercial policy measures in the context of a trade dispute: how effective they are at making the other country comply with international trade rules; the potential for the measure to help firms affected by the other country’s measures; and the availability of supply for the goods and services which are part of the dispute. In essence, trade policy should aim to change the trade policy of the opposing country, while minimizing harm to the own economy. In the case of Trump’s trade war, a possible way to affect the trade policy of the US would be to introduce a political cost for the Republican Party.
It is this political dimension in the choice of the retaliation response, economists have ignored thus far. In recent work, we attempt to fill this gap by investigating to what extent the retaliating countries or trading blocs managed to politically target their tariffs and if this targeting was effective in influencing political outcomes. In line with this interpretation, the chosen goods also indicate that retaliatory tariffs were picked in a way to hit important Republican states. For example, goods targeted for retaliation, such as bourbon whiskey produced in the Senate Majority Leader Mitch McConnell’s home state of Kentucky. Similarly, the Wisconsin congressional district of the then Speaker of the House Paul D. Ryan was targeted with retaliatory tariffs on cranberries and cranberry products. Similarly, China (as well as Mexico) targeted pork and soybeans, with the latter being one of the most important US agricultural export to China, which disproportionately affected Iowa, the home state of influential Republican Senate Agriculture Committee Member Senator Charles E. Grassley.
Looking at tariff effects on Republican-voting counties
In order to investigate the political targeting of tariffs, we created a county-level measure of tariff exposure.
Using this measure, we find that retaliation seems to have been targeted systematically against the Republican voter base. Figure 1 plots share of exports of a county that were affected by tariffs dependent on the Republican vote share in that county in the 2016 presidential election. It is apparent that the counties with a higher Republican vote share were also more heavily targeted by retaliatory tariffs. This provides a first indication that tariffs were indeed targeted against Republicans. Our analysis also shows that the targeting was indeed mostly calibrated to hit areas that swung to support Donald Trump in 2016 (but not to areas that swung to support any Republican candidates in 2016’s Senate or House elections). This is very indicative that those designing the retaliation response paid careful attention aiming to hit Donald Trump’s electorate specifically.
Figure 1 – Republican Vote Share and Tariff Exposure
Simulating Alternative Tariffs
Since a country’s retaliation options are constrained to the goods the country is importing from the US, a potential concern with our finding is that the observed targeting could be a result of the US export mix. Consider for example China, which is a major importer of soybeans from the US. Any retaliation bundle that includes soybeans will appear politically targeted against Republicans as soybeans are grown in Republican counties.
To asses this possibility, we used a novel simulation approach to construct a host of alternative retaliation options that the EU, China and other countries could have chosen to use against the US’ measures. This allows us to assess to what extent to which the actual retaliation is indeed targeted politically relative to a host of other conceivable options. Further, it will also allow us to shed some light on the extent to which countries may face a specific trade-off or constraints in the design of their retaliation response.
Motivated by the previously cited EU regulation 654, we also investigated whether the potential negative impact on a country or trade bloc’s own economy is also considered when selecting goods for retaliation. To do so we construct a measure of the share of imports for a specific good which originate from the US (vis-à-vis overall imports from everywhere). The implicit assumption here is that countries may find it hard to impose tariffs on US imports for products for which the US is the main supplier.
Figure 2 plots the joint distribution of these two measures, capturing the degree of political targeting on the horizontal axis and the likely measure of costs of the retaliation to one’s own country across the simulated targeting options. The horizontal and vertical lines indicate the values of these measures associated with the actually chosen bundle of goods. An ideal retaliation bundle would be located in the bottom right corner, indicating a high degree of political targeting while minimizing the potential harm to one’s own economy.
Figure 2– Trade-off in political targeting and exposure to US imports
The contour plot thus highlights a type of possibility frontier for the inherent trade-off. For the EU for example, we observe that the retaliation basket very much aims to avoid putting cost pressures on EU consumers. Out of the 1000 retaliation bundles, there exist only two that would produce lower domestic economic damage. At the same time, the retaliation bundle is clearly politically targeted. The contour plot suggests that the only way to produce a more politically targeted retaliation response would require the EU to accept hurting its consumers and firms.
For China, the situation is more mixed. Essentially, China is much more constrained in how it can design its retaliation response. While a host of bundles exist, the bulk of these bundles effectively produce zero or even negative political targeting (the mass point to the left). Hence, China needed to design a retaliation response that involved a select set of goods to produce any political targeting, which resulted in it picking bundles that are associated with the island of mass points on the right. Yet, despite bundles existing that produce an equivalent degree of political targeting, China chose a specific bundle that would likely produce significant economic damage to its own consumers and firms. While this may highlight that Chinese officials may be less concerned about its own consumers and firms, it could also simply indicate a lower degree of technical competence since alternative retaliation bundles producing an equivalent degree of political targeting.
Are Retaliatory Tariffs Effective?
Lastly, we investigated if retaliatory tariffs were successful in affecting political as well as economic outcomes. On the economic side, we found that exports hit by retaliatory tariffs declined significantly relative to exports that were not targeted by tariffs. Figure 3 shows this finding. Overall, our results suggest that each month USD 2.55 billion worth for US exports did not take place or were diverted as a result of retaliatory tariffs. We further show that also the export prices of US goods were negatively affected by the trade war. Together these results suggest that retaliatory tariffs were likely to have a negative impact on the local economy of affected counties.
More importantly, to understand if the tariffs were effective as a political instrument, we investigate whether the retaliatory tariffs were able to harm the electoral outcomes of the Republican Party in the 2018 midterm election. We find that Republicans fared worse by 1.4 – 2.7 percentage in counties most exposed to retaliation.
Figure 3 – Impact of Tariffs on US Exports
- A version of this article first appeared at VoxEU and is based on the CEPR Discussion Paper ‘Tariffs and Politics: Evidence from Trump’s trade wars’
Note: This article gives the views of the author, and not the position of USAPP – American Politics and Policy, nor the London School of Economics.
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About the authors
Thiemo Fetzer – University of Warwick
Thiemo Fetzer is an Associate Professor in the Economics department at the University of Warwick. He is also affiliated with the Centre for Economic Policy Research (CEPR), Centre for Competitive Advantage in the Global Economy (CAGE) at University of Warwick, the Spatial Economics Research Centre (SERC) at London School of Economics and the Pearson Institute at University of Chicago.
Carlo Schwarz – University of Warwick
Carlo Schwarz is a PhD student at the University of Warwick and a doctoral student of the Centre for Competitive Advantage in the Global Economy (CAGE). His research interests are in the field of applied microeconomics and political economy. In his research he combines micro-econometric techniques with machine learning and text analysis. (www.carloschwarz.eu)