This paper discusses the effects on the financial markets of the several rounds of tariff hikes during the 2018–19 US-China trade war. It illustrates that US firms that are more dependent on exports to and imports from China have lower stock prices around the announcement date, while the expectation of weakened Chinese import competition due to US tariffs plays an economically minimal role. Firms with indirect exposure to US-China trade through domestic supply chains also experience more negative stock returns, with the magnitude of the effects sometimes even greater than the direct trade exposure. The differential responses are not due to firms’ short-term overreactions, and are observed two months after the initial announcement in March 2018.
A notable feature of globalization in the past few decades has been the unprecedented reorganization of economic activities across regions, firms, and workers. This reorganization has been driven by the establishment of numerous complex global value chains, which have enhanced the connectivity between firms and, hence, nations. Although the resulting increase in the interdependence of firms and nations has allowed for greater sharing of economic benefits (Acemoglu et al. 2016b), it has also amplified the propagation of shocks across complex production networks and thus increased macroeconomic uncertainty (Acemoglu et al. 2016a; Barrot and Sauvagnat 2017; Carvalho et al. 2017; Ozdagli and Weber 2017; Pasten, Schoenle, and Weber 2019).
Methodology
We use a novel dataset that reports firms’ intertwining input-output relationships, together with various datasets on companies’ financial outcomes and international trade, to assess a US (Chinese) firm’s direct exposure to imports from and exports to China (the US), as well as US firms’ indirect exposure to trade with China through their engagement in global value chains. In particular, we construct several measures of the indirect exposure to trade with China, using firm-level production networks and trade data. In constructing the measures, we follow Acemoglu et al. (2016a), who analyze how shocks are amplified and propagated through industry input-output links including buyer and supplier linkages.Figure 1. Firm Production Networks: Customer Side
As illustrated in Panel B of Figure 1, US firm A has three US customers, among which firms B and C have Chinese firms as their suppliers. The tariff hikes increase the cost of the Chinese inputs for B and C, potentially leading to a decline in their total production and the demand for goods produced by firm A. In contrast, if the intermediate goods produced by Chinese firms E and F can be sufficiently substituted by goods produced by US. firm A, then the tariff hike may also increase the demand for the goods produced by firm A and boost its sales. The same product network of GE is plotted in Panel D of Figure 1, where the blue nodes indicate GE customers that have outsourced input from China.
Evidence from the Financial Market
We estimate the effects of the trade-war announcements on firms’ stock values, through both direct and indirect exposure to US-China trade. In the three-day window surrounding March 22, 2018, US publicly listed companies that export more to (or import more from) China experienced lower stock returns. Specifically, in that three-day period, after controlling for standard firm-level characteristics and industry fixed effects, we find that a 10% increase in a firm’s share of sales to China is associated with a 0.5% lower average cumulative abnormal stock return (CAR), while firms that directly source inputs from China have a 0.6% lower average cumulative abnormal stock return than those that do not. These results imply that US tariffs were perceived to substantially raise the prices of imported inputs from China, and thus US companies’ production costs.Firms’ indirect exposure to US-China trade also matters. Our event-study regressions show that the coefficients on the average revenue from China across a firm’s customers and suppliers are both statistically and economically significant, after their direct exposure measures are included in the regression. Specifically, we find that a 10% increase in indirect sales exposure through buyers is associated with 1.1% lower CAR over the three days surrounding March 22. Our regression results also show that a 10% increase in indirect sales exposure to suppliers is associated with 0.9% lower CAR. The effects remain significant when the indirect measures based on buyers and suppliers are jointly estimated in the regression and when industry fixed effects are included. The estimated coefficients in the regression model suggest the overall indirect exposure to sales in China has a larger impact than direct exposure.
Conclusion
In this paper, we examine the effects on the financial markets of the Trump administration’s announcement of a trade war against China on March 22, 2018. We find that US firms that are more dependent on exports to and imports from China have lower stock prices in the short window around the time of the announcement. We further examine whether firms’ indirect exposure to trade with China through their domestic supply chains may also affect their responses to various tariff announcements. As predicted by our theoretical model, we find more negative responses by firms that have greater indirect exposure to exports to and imports from China through their domestic supply chains, even after controlling for the firms’ direct output and input exposure in baseline regressions.
References
Acemoglu, Daron, Vasco M. Carvalho, Asuman Ozdaglar, and Alireza Tahbaz-Salehi. 2012. “The Network Origins of Aggregate Fluctuations.” Econometrica, 80(5), 1977–2016. https://doi.org/10.3982/ECTA9623.
Acemoglu, Daron, Ufuk Akcigit, and William Revill Kerr. 2016a. “Networks and the Macroeconomy: An Empirical Exploration.” NBER Macroeconomics Annual, 30(1): 276–335. https://doi.org/10.1086/685961.
Acemoglu, Daron, Simon Johnson, Amir Kermani, James Kwak, and Todd Mitton. 2016b. “The Value of Connections in Turbulent Times: Evidence from the United States. ”Journal of Financial Economics, 121(2), 368–91. https://doi.org/10.1016/j.jfineco.2015.10.001.
Baldwin, Richard. 2011. “Trade and Industrialisation after Globalisation’s 2nd Unbundling: How Building and Joining a Supply Chain Are Different and Why It Matters.” NBER Working Paper No.17716. https://www.nber.org/papers/w17716.
Barrot, Jean-Noel, and Julien Sauvagnat. 2016. “Input Specificity and the Propagation of Idiosyncratic Shocks in Production Networks.” Quarterly Journal of Economics 131(3), 1543–92. https://doi.org/10.1093/qje/qjw018.
Carvalho, Vasco M., Makoto Nirei, Yukiko U. Saito, and Alireza Tahbaz-Salehi. 2017. “Supply Chain Disruptions: Evidence from the Great East Japan Earthquake.” Northwestern University Working Paper. https://doi.org/10.2139/ssrn.2893221.
Grossman, Gene M., and Esteban Rossi-Hansberg. 2006. “The Rise of Offshoring: It’s Not Wine for Cloth Anymore.” Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, 59–102. https://www.princeton.edu/~erossi/RO.pdf.
Huang, Yi, Chen Lin, Sibo Liu, and Heiwai Tang. 2018. “Trade Linkages and Firm Value: Evidence from the 2018 US-China ‘Trade War.’” https://doi.org/10.2139/ssrn.3227972.
Johnson, RobertC., and Guillermo Noguera. 2012. “Accounting for Intermediates: Production Sharing and Trade in Value Added.” Journal of International Economics, 86(2), 224–36. https://doi.org/10.1016/j.jinteco.2011.10.003.
Ozdagli, Ali, and Michael Weber. 2017. “Monetary Policy through Production Networks: Evidence from the Stock Market.” NBER Working Paper No.23424. https://www.nber.org/papers/w23424.
Pasten, Ernesto, Raphael Schoenle, and Michael Weber. 2019. “The Propagation of Monetary Policy Shocks in a Heterogeneous Production Economy.” Journal of Monetary Economics, forthcoming. https://doi.org/10.1016/j.jmoneco.2019.10.001.