Does US partisan conflict affect US–China bilateral trade?

Accepted Manuscript Does US Partisan Conflict Affect US–China Bilateral Trade? Xiandeng Jiang, Yanlin Shi

PII:

S1059-0560(18)31055-4

DOI:

https://doi.org/10.1016/j.iref.2018.12.005

Reference:

REVECO 1725

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International Review of Economics and Finance

Please cite this article as: Jiang X. & Shi Y., Does US Partisan Conflict Affect US–China Bilateral Trade?, International Review of Economics and Finance, https://doi.org/10.1016/j.iref.2018.12.005. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Does US Partisan Conflict Affect US–China Bilateral Trade? Xiandeng Jianga,∗, Yanlin Shib a

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The School of Public Finance and Taxation, Southwestern University of Finance and Economics, 555, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, 611130, China b Department of Actuarial Studies and Business Analytics, Macquarie University, Sydney, NSW 2109, Australia

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Abstract

It is well known that the US political parties hold opposing views on many economic

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policies and foreign policies. Since China has become the largest trading partner of the US, the operation of the US–China bilateral relationship has developed into an essential and sensitive political issue that has been widely discussed in the recent US presidential election campaigns. Therefore, the understanding of the effects of partisan conflicts on the US–China trade is crucial to improving our knowledge of the international transmission

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of political uncertainty. Using a recent US partisan conflict index provided by Azzimonti (2014), we investigate those effects employing a structural VAR model and find that a one standard deviation shock to US partisan conflict is associated with a 2% increase in the US exports to China and a 2% reduction in its imports from China. Such effects are still

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significant after one year. The results are robust across various combinations of variables, models and sample periods. Further, our findings indicate that partisan conflicts in the US

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may reduce its trade deficit with China. JEL classification: E32; F42; D72 Keywords: Partisan conflict, Economic Policy Uncertainty, US–China bilateral trade, Structural VARs

∗

Corresponding author. Tel: +86 28 8709 2127 Email address: [email protected] (Xiandeng Jiang)

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1. Introduction It is well known that the political parties in the US have opposite attitudes on many economic policies and foreign policies. High levels of partisan conflict may make the two

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parties unable to reach a cooperative solution (de Figueiredo 2002). Existing literature has proved that the political instability and policy uncertainty are likely to spill over to other economies such as China (e.g. Kim, 2001; Favero and Giavazzi, 2008; Ehrman and

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Fratzscher 2009; Mumtaz and Thedorisdis, 2012). However, those studies only investigate such effect on real economic activities without confirming the direct channels. The US has

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a very close trading relationship with China. According to the recent US census, China was its largest trading partner in 2015. The total trade volume between US and China was $598.1 billion ($116.2 billion for US exports to China and $481.9 billion for US imports from China), which was equivalent to 16% of US total trade volume in 2015.1 Taking advantage of this feature of US–China trade relationship, we investigate the effect of US

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partisan conflict on its trade volume with China.

US domestic economic activities are now deeply integrated with China’s economy. Auer and Fischer (2010) and Autor et al. (2013) find that the increase in products imported from China significantly reduces the US manufacturing employment, local wages and the

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prices of goods. Trade dispute is one of today’s most controversial issue in the US–China bilateral relationship and has been widely discussed in the recent US presidential elections.

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There exists a high level of political disagreement—both within and between US parties— over the trade policy with China. Moreover, Che et al. (2016) indicate that imports from China have significant effects on US political outcomes like Congressional elections. Thus, compared with trade between US and other countries, the conflicts among US politicians are more likely to result in uncertainty between the US and China trade relationship. How would US partisan conflict affect the US and China bilateral trade? Azzimonti 1

Please refer to https://www.census.gov/ for more details.

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(2018a, 2018c) finds that greater partisan conflicts lead to lower private domestic investment in the US market. She also argues that partisan conflicts deter foreign direct investment flows to the US (Azzimonti, 2018b). Because of this negative relationship, it is likely that

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the increased US political uncertainty will have a negative effect on China’s exports to the US. Meanwhile, Handley and Limão (2017) find that a reduction in US-China trade policy uncertainty caused by China’s accession to WTO in 2001 significantly increases its subsequent imports from China. Hence, trade policy uncertainties driven by high level of

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partisan conflict may impede the China’s exports to the US.

China is well integrated into a global production chain after its WTO membership. It

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assembles intermediate components imported from foreign countries into final products that are subsequently exported to the oversea markets. Hence, there is also a great difference in the commodity compositions of the two exports. In particular, China’s exports to the US are mostly final manufactured goods such as electronic and textile products, whereas US exports to China are mainly manufacturing facilities and intermediate products (Bosworth

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and Collins, 2008). According to Koopman et al. (2008)’s estimation, the share of foreign content in manufacturing exports from China is about 40% to 50% between 2002 and 2007. The OECD–WTO report shows that 47.2% of China’s imported intermediate products

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and services were subsequently embodied in its exports in 2011. This implies that China’s imports of manufacturing facilities and intermediate products from US are possibly driven

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by its exports to US. Therefore, when greater partisan conflicts reduce China’s exports to the US, its imports from the US may be simultaneously decreased. On the other hand, Azzimonti (2014) finds that greater partisan conflicts significantly discourage investment, output, and employment in the US. Meanwhile, Azzimonti (2018a, 2018c) shows that a surge in partisan conflict leads to decrease in the US private domestic investment. Hence, it is possible that a decline in investment resulted by partisan conflicts may reduce domestic demand for products like manufacturing facilities and intermediate

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materials.2 Moreover, a range of studies find that lower domestic demands usually result in higher export sales (see, for example, Ball et al. (1966), Smyth (1968), Artus (1970, 1973), Ziberfarb (1980), Faini (1994), Sharma (2003), Esteves and Rua (2015) and Belka et

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al. (2015)). Thus, low private domestic investment caused by partisan conflicts may lead many US multinational corporations to consider China as an alternative market for their products like manufacturing facilities and intermediate materials. Consequently, shocks

market, which further stimulates US exports to China.

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to the US partisan conflicts are likely to increase the sales of US products in the Chinese

In this paper, we systematically test the impact of US partisan conflict on the US–

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China trade using the “partisan conflict index” proposed in Azzimonti (2014) (for the US only).3 This index is a novel indicator of partisan conflicts and captures uncertainty about politicians’ future policy choices (Azzimonti and Talbert, 2014). In terms of the methodology, we employ the structural vector autoregressive (VAR) model with bilateral settings (see Sims and Zha (2006), Sin (2015) for examples). Our results suggest that a one stan-

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dard deviation shock to US partisan conflict is positively associated with the US exports to China but negatively related to its imports from China. Such effects are still significant after twelve months. Compared with US economic policy uncertainty, a shock to the

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US partisan conflict induces more persistent influences on the US–China bilateral trade relationship. Additionally, we demonstrate that a shock to US partisan conflict can signif-

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icantly increase the Chinese industrial production index after fifteen months. This finding is entirely distinct from Cheng et al. (2016) who suggest a negative relationship between 2

The purchase of manufacturing facilities is classified as non-residential investment, and the purchase of the intermediate materials is classified as inventories in the national accounts. Both of them are counted as private domestic investment. 3 It is worth noticing that (Azzimonti, 2018b) provides another version of PCI: Trade Partisan Conflict Index (TPCI) that mainly captures the partisan conflicts in trade policies. Generally, the US-China bilateral trade is affected by US domestic economic policies as well as trade policies. Therefore, compared to TPCI, the regular PCI can capture a more completed effect of partisan conflicts on the US–China bilateral trade. In this paper, we use PCI in our main analysis and employ TPCI in the robustness checks. Their results are largely consistent.

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shocks to US partisan conflict and industrial production in the Euro area. The contributions of this paper are threefold. First, to the best of our knowledge, this paper is the first study examining the effects of US domestic partisan conflicts on the

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US–China trade relationship. Consistent with implications of relevant literature (see, for example, Handley (2014, 2015); Handley and Limão (2017)), our empirical results indicate the important role played by the partisan conflicts of US in its bilateral trade with China. In addition, most of the existing research on the US political uncertainty focuses on its shocks

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to real economic activities in other countries (see, for example, Colombo (2013), Cheng et al (2016), Stockhammar and Österholm(2016)). As a complement to them, we investigate

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such shocks to the trade volume between the US and China. Finally, our results can help improve the understanding of the international transmission of political uncertainty. Differing from most of the existing literature (Kim, 2001; Favero and Giavazzi, 2008; Ehrman and Fratzscher 2009; Mumtaz and Thedorisdis, 2012) that focuses on studying the spill over effect between US political and policy uncertainty in other OECD countries, we demon-

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strate that US partisan conflicts also tend to affect its trade with major emerging markets like China.

The remainder of this paper is organized as follows. Section 2 explains the empirical

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model. Section 3 presents our dataset. Section 4 discusses the results and Section 5 concludes the paper.

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2. The empirical model

Since the seminal work of Sims (1980), multivariate data analysis using VAR-type models has evolved to become a standard instrument in empirical economic and financial studies (see Sims and Zha (2006), Sin (2015) for examples). VAR models explain the endogenous variables solely by their own history, distinct from the covariates. In contrast, a structural VAR (SVAR) model allows the explicit modeling of contemporaneous interdependence between dependent variables. This type of models is used to bypass the shortcomings of the

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VAR (Pfaff, 2008). The SVAR model is derived from a reduced form VAR model with the following speci-

yt = A(L)yt−p + ΨDt + µt

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fication (Sims and Zha, 2006): (1)

where p denotes the order of the VAR model, yt is an n × 1 vector of endogenous variables, A(L) is an autoregressive lag-polynomial, Dt is an n×1 vector of deterministic components,

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and µt is an n × 1 vector of reduced form residuals. As Dt is unaffected by shocks to the

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system, we can safely ignore it, which leads to the following SVAR model: Ayt = A∗ (L)yt−p + Bεt

(2)

Here, the matrix A is used to model the contemporaneous relationships, and matrix B contains the structural form parameters of the model. εt is an n × 1 vector of structural disturbances and var(εt ) = Λ, where Λ is a diagonal matrix with the variance of structural

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disturbances being the diagonal elements.

As noted by Sims and Zha (2006), the shocks cannot be observed directly, which demands imposing some restrictions. The common practice is to multiply Equation (2) by

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follows.

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A−1 . This leads to a relationship between the reduced form and structural disturbances as

µt = A−1 Bεt

(3)

We follow Sims and Zha (2006) and estimate the AB model proposed by Amisano and Giannini (1997). This allows us to write Equation (3) as Aµt = Bεt

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Following Colombo (2013) and Cheng et al. (2016), the effects of a partisan conflict shock are estimated through a two-country SVAR model. Using similar variables considered in those studies, in Equation (2), we have an endogenous vector yt = [U SP Ct , U SEXt , 6

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U SCP It , U SIPt , U SF F Rt , U SEP Ut , CN EXt , CN CP It , CN IPt , CN OIRt , CN EP Ut ]0 , including the US partisan conflict index (U SP Ct ), the US exports to China (U SEXt ), the US consumer price index (U SCP It ), the US industrial production index (U SIPt ),

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the federal funds rate (U SF F Rt ), the US economic policy uncertainty index (U SEP Ut ), the Chinese exports to the US (CN EXt ), the Chinese consumer price index (CN CP It ), the Chinese industrial production index (CN IPt ), the Chinese overnight interest rate (CN OIRt ) and the Chinese economic policy uncertainty index (CN EP Ut ). In particular,

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U SP Ct refers to the news-based partisan conflict index developed by Azzimonti (2014). We construct U SEP Ut and CN EP Ut using the news-based components of the economic

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policy uncertainty (EPU) indexes developed by Baker et al. (2016).

All variables, except for the federal funds rate and the Chinese overnight interest rate, are expressed in log values. Also, we select the optimal number of lags in the SVAR model by combining an initial lag selection based on various information criteria and test for no serial correlation in the error terms. Our SVAR (2) model includes equation-specific constants and

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linear trends and is estimated by an ordinary maximum likelihood estimation. To recover the structural shocks from the residuals, we follow a standard Cholesky decomposition approach. In line with Colombo (2013) and Cheng et al. (2016), we place the US block

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before the Chinese block and place policy uncertainty last within each block.

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3. Data and sample

In this paper, the US Partisan conflict index (U SP C) is sourced from the Real Time Data Center of the Federal Reserve Bank of Philadelphia. The index is a novel indicator measuring the degree of political disagreement about government policies among US politicians at the federal level. U SP C is computed monthly using a semantic search approach and captures the frequency of such disagreement being reported in major US newspapers, including the Washington Post, New York Times, Los Angeles Times, Chicago Tribune, and Wall Street Journal. Additionally, U SP C is a proxy for those disagreements among

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political parties, Congress and the President in a given period. Higher index values indicate greater partisan conflicts in the US. The US and Chinese economic policy uncertainty (U SEP U and CN EP U ) data

are

obtained

from

the

Economic

Policy

Uncertainty

website

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indexes

(http://www.policyuncertainty.com). Such indexes are calculated following the methodology in Baker et al. (2016). More specifically, those indexes measure the economic policy uncertainty of a country by considering three main components: the frequency of news

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articles about policy-related economic uncertainty; the number of upcoming expropriation of tax code provisions at the federal level; and the degree of disagreement among economic

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forecasters over future macroeconomic indicators.

The Chinese industrial production index data are sourced from the National Bureau of Statistics of China (http://www.stats.gov.cn).

The US industrial production

index and other variables are available from the Federal Reserve Bank of St. (https://www.stlouisfed.org).

Louis

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In terms of sample frequency and period, we follow Colombo (2013) and Cheng et al. (2016) to use monthly data from January 2002 to June 2008. The starting date is based on the date when China became a WTO member. The ending date helps ensure that we avoid

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any non-linearities that might be a result of the financial crisis (Cheng et al., 2016).4 The U SP C, U SEP U and CN EP U indexes over this period are plotted in Fig. 1 (in original

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values). The descriptive statistics of all the endogenous variables used in this paper are summarized in Table 1.

In Fig. 1, we can observe that a movement of U SP C is associated with a movement of the U SEP U in the same direction. Their correlation of 0.4315 suggests that both series are positively correlated with moderate strength. It is worth mentioning that CN EP U is 4 We also extend our sample to cover the global financial crisis (June 2008 to July 2011) and most recent periods (up to December 2016). As demonstrated in Fig. 4 and described in Section 4.2, our major results are generally robust by considering such extension.

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150

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100

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2005

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2006

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2007

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Fig. 1. US partisan conflict index and US and Chinese economic policy uncertainty indexes: 2002–2008.

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associated with U SP C. Their positive correlation of 0.3110 is slightly weaker than that between U SEP U and U SP C. Not surprisingly, U SEP U and CN EP U are also positively correlated. Their correlation is considerably strong at around 0.63. Additionally, U SEP U

volatile.

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and CN EP U seem to vary at similar levels over time, whereas CN EP U is much more

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In Table 1, it can be seen that although the central tendencies of U SEP U and CN EP U are close to each other, CN EP U has a relatively larger standard deviation (0.6029, compared with that of U SEP U at 0.3271). This suggests that the Chinese economic policy uncertainty is more volatile than its US counterpart, which is consistent with our observation above. In terms of the industrial production index, the story is quite different. First, CN IP is overall higher than the U SIP . On average, CN IP is around 0.2 larger than U SIP . Also, the standard deviation of U SIP is almost two times larger than that of CN IP . Comparing the U SEX and CN EX, it seems that China’s exports to the US are 9

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Table 1 Descriptive statistics

M ean

Std.Dev.

M edian

Q1

Q3

U SP Ct U SEXt U SCP It U SIPt U SF F Rt U SEP Ut CN EXt CN CP It CN IPt CN OIRt CN EP Ut

4.5969 8.0398 4.4805 4.5428 2.8202 4.6068 9.6015 4.4995 4.7237 4.2851 4.5289

0.2634 0.8671 0.1405 0.0926 2.3555 0.3213 0.7876 0.1268 0.0344 2.3589 0.6029

4.5094 8.0906 4.4769 4.5589 2.2800 4.5322 9.8063 4.4512 4.7256 3.2500 4.5432

4.4180 7.1944 4.3521 4.5086 0.1600 4.3540 8.9081 4.3992 4.6959 3.2400 4.2191

4.8004 8.8377 4.6053 4.6123 5.2500 4.8333 10.2917 4.5947 4.7519 4.1400 4.9335

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V ariables

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Note: This table presents the descriptive statistics of the endogenous variables used in this paper, including the US partisan conflict index (U SP Ct ), the US export to China (U SEXt ), the US consumer price index (U SCP It ), the US industrial production index (U SIPt ), the federal funds rate (U SF F Rt ), the US economic policy uncertainty index (U SEP Ut ), the Chinese export to US (CN EXt ), the Chinese consumer price index (CN CP It ), the Chinese industrial production index (CN IPt ), the Chinese overnight interest rate (CN OIRt ) and the Chinese economic policy uncertainty index (CN EP Ut ). All variables, except for the U SF F Rt and CN OIRt , are expressed in log values. Std.Dev. is the standard deviation. Q1 and Q3 are the 1st and 3rd quartiles, respectively. greater than the US exports to China, with a comparatively smaller variation. The US and

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China have very similar results with respect to the consumer price index. Finally, the US federal funds rate is only about half of the Chinese overnight interest rate on average. The

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variations in those rates are very similar. 4. Empirical results 4.1. Impulse responses

To analyze how our variables react over time to various shocks, we fit the SVAR(2) model as described in Section 2 and then estimate the impulse responses. The estimated responses to a one standard deviation positive shock to U SP C are shown in Fig. 2. It is observed that the extent of this shock is 12% (the effect at time 0). We consider responses up to 24

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months and display the estimates along with the 68% error bands as recommended by Sims and Zha (1999). Similar to Cheng et al. (2016), the confidence intervals are constructed via 2000 bootstrapped replications.

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The underlying dynamics of this model are more nuanced. There is a short-lived reduction in US economic policy uncertainty after a positive partisan conflict shock. This result is consistent with that found by Cheng et al. (2016) and is seemingly consistent with Azzimonti’ s (2014) hypothesis. More specifically, it is hypothesized that relatively higher

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levels of partisan conflict can be associated with lower levels of economic policy uncertainty because investors and consumers expect little change in economic policy in the short term.

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More importantly, our model predicts that a positive shock to U SP C exerts a significant positive (negative) effect on US exports to (imports from) China. The effect of U SP C on U SEX is immediate at around 2% after one month and dies out very quickly. It becomes positively significant again after eleven months with an increase by 0.5% and lasts until the twentieth month. In contrast, the effect of U SP C on CN EX is negative. It also becomes

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significant after one month and reaches a maximum at around -2%. Similarly, this effect only lasts for two months, but it turns significant again at the ninth month and lasts until the seventeen month. For the second period with significant effects (between the ninth

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and seventeenth months), the influence of U SP C is much weaker, with a local maximum around -0.8% at the twelfth month. These results may indicate that a high level of partisan

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conflict will increase (decrease) US exports to (imports from) China, thus reducing the US trade deficit with China. As for the Chinese industrial production, it seems that a shock to the U SP C can only significantly affect CN IP after fifteen months. This effect is positive and lasts over the twenty-fourth month with a maximum around 0.4%. We are also interested in knowing the effects of U SEP U on other endogenous variables. Fig. 3 displays the impulse responses to a one standard deviation shock (a rise of 11%) to the U SEP U index. These results are largely consistent with Colombo (2013) and Cheng et al. (2016). It can be observed that a positive shock to the U SEP U can also significantly 11

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Fig. 2. Impulse responses to a one standard deviation positive shock to US partisan conflict.

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Fig. 3. Impulse responses to a one standard deviation positive shock to US policy uncertainty.

exert an immediate positive effect on the US exports to China, with a maximum contraction at around 3% after one month. The effect only lasts for one month, after which U SEP U cannot significantly affect U SEX. For the CN EX, the effect of U SEP U is negative and also reaches its maximum of -3% after one month. It becomes significant again at the fourth month horizon, and this short-lived effect only lasts for another two months, with a local maximum around -1.5%. There are two observed differences between the influences of U SP C and U SEP U on U SEX and CN EX. First, the maximum effects of U SEP U

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are comparatively larger than those of U SP C. Second, U SP C can affect U SEX and CN EX in the longer run (after twelve months), whereas U SEP U can only affect the trade immediately. Additionally, the effects of U SEP U are not persistent. As for the

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Chinese industrial production index, a shock to the U SEP U can significantly increase CN IP between the fourth and sixteenth months, with a maximum around 1.5%. 4.2. Robustness Checks

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To check the robustness of our results with respect to the shocks of U SP C, we follow Colombo (2013) and Cheng et al. (2016) and consider various changes in our model and sample settings separately.

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First, we examine the robustness against sample period variation. This is tested by extending our sample to include the 2008 global financial crisis and most recent periods (up to December 2016). The estimated impulse estimates are plotted in Fig. 4. The longer sample suggests that shocks to U SP C only significantly affect the short-term US exports to China. Also, the magnitude reduces to 1.5%. This indicates that the overall impact

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of U SP C on U SEX is not as strong as that for 2002–2008. As for the US imports from China, shocks to U SP C has an everlasting significant negative influence with a quickly dying out positive short-term impact. It is also worth noticing that the short-term impact

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of U SP C on U SEP U is positive and thus opposite from our main result. This difference may be caused by a potential structural change to impact of U SP C on U SEP U during

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the 2008 global financial crisis period. Second, the robustness of our findings are checked against the selection of Partisan Conflict Index variable. More specifically, we test the effects of shocks to US Trade Partisan Index (U ST P C) on U SEX and CN EX, which are discussed in a recent study of Azzimonti (2018b). In Fig. 5, it is demonstrated that this effect on U SEX is quite similar to that of shocks to U SP C on U SEX. The only difference is that the former lasts longer than the latter. However, U ST P C affects CN EX much more significantly than U SP C. For one thing, the impact of shocks to U ST P C on CN EX is negatively everlasting. For another, 14

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12

SC

9

9

1.0

6

6

0.5

3

1

percent

0.005

pp −0.005 −0.015

−1.0 0

3

USEPU

2

1.5 1.0 0.5 0.0

percent

6

0.015

USIP

3

−2.0

6

RI PT

0.0 3

−1.0 −1.5

0.5

1.0

percent

percent

1.5

10 8 6

percent

4 2 0 0

percent

USCPI

0.5

USEX 2.0

USPC

0

3

6

9

12

15

18

21

24

EP

Notes: In this robustness check, we extend the sample to cover the financial crisis and more recent periods (June 2008 to Dec 2016).The error bands correspond to 68% intervals.

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Fig. 4. Robustness check: 2002–2016.

the magnitude of this impact can be up to -8%, whereas that of shocks to U SP C on CN EX is below -2%.

Although comprehensive explanations of those differences are out of the scope of this paper, we provide some potential reasons as follows. As argued in Section 1, increase of U SP C affects U SEX and CN EX via its influence on foreign direct and domestic investment in US. Thus, one explanation is that investors was affected by news differently during normal and volatile periods. Ho et al. (2013) study asset volatility within and 15

USCPI

3

6

9

12

15

18

21

24

−5 −10

percent 0

6

9

12

15

18

21

24

USFER

3

6

9

12

15

18

21

24

18

21

24

18

21

24

USEPU

6

−0.06

−6

−0.10

−4

−15

−10

pp

percent

M AN U

−5

−0.02

4 2 0 −2

percent

0

0

0.02

USIP

3

SC

0

0

3

6

9

12

15

18

21

24

0

3

9

12

15

18

21

24

0

3

6

9

CNCPI

12

15

CNIP

0

3

6

9

12

15

18

21

24

0

3

6

9

−4 −6 12

15

18

21

24

6

9

12

3

6

9

12

15

5

10

CNEPU

percent

pp

−5

−0.04 −0.08

AC C 3

0

0

EP

0.00

0.02

CNOIR

0

0

percent

−2

0

percent

−10

−5

TE D

−5

percent

2

0

5

4

6

CNEX

6

−20

−10

−2

−15

0

0

2

percent

20 10

percent

4

30

0

6

40

5

USEX

8

USPC

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15

18

21

24

0

3

6

9

12

15

18

21

24

Notes: In this robustness check, we present the impact of USTPC to compare that of USPC.The error bands correspond to 68% intervals. Fig. 5. Robustness check: Impact of USTPC

16

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USEX

USCPI

9

12

15

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0

3

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USFER

6

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15

18

21

24

6

9

12

15

18

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24

18

21

24

18

21

24

USEPU

0.5

percent

−1.5 −2.5

0

3

CNEX

6

9

12

15

18

21

24

0

CNCPI

3

6

9

12

15

CNIP

0

3

6

9

12

15

18

21

24

0

3

6

9

15

18

21

24

9

12

3

6

9

12

15

1.0

1.5

2.0

CNEPU

percent

EP 6

0

0.0

0.5

0.000 −0.005

pp

−0.010

−0.5

−0.015

AC C 3

1.5 0.5 −0.5 0.0

12

CNOIR

0

1.0

percent

0.5

percent

−2.0

−0.5

−1.5

TE D

−1.0

0.0

−0.5

2.0

0.0

2.5

1.0

0.5

3

3.0

3

0

24

M AN U

−0.5

−0.010

0.0

−0.005

pp

0.5

0.000

1.0

0.005

1.5

USIP

0

percent

6

SC

6

1.0

3

−0.5 0.0

0

percent

−2.0

−2

0

−1

−1.5

−1.0

percent

1 0

percent

5

percent

10

−0.5

2

0.0

3

USPC

15

18

21

24

0

3

6

9

12

15

18

21

24

Notes: In this robustness check, we employ a SVAR(5) model instead of the SVAR(2).The error bands correspond to 68% intervals. Fig. 6. Robustness check: the SVAR(5) model

17

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USCPI

USIP

1

percent

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−1 −2

percent

1 0

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0

USEPU 10 0

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0

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CNCPI

0

3

6

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24

18

21

24

CNIP

−2

−3

−1

−1

0

percent

percent

0

−1 −2

percent

1

1

M AN U

0

2

2

CNEX

12

6

SC

−0.03 3

5

pp

−0.02

−0.01

percent

0.00

3 2 1

percent

0 −1 −2

0

3

USPC

0.01

4

USFER

9

RI PT

−2

−1

−4

−1

0

−3

0

percent

2

0

3

3

1

USEX

12

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24

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12

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0

3

6

9

12

15

3

0

3

TE D

2

0.00

pp

−0.01 −0.02 −0.03

9

CNEPU

0.01

CNOIR

6

6

9

12

15

18

21

24

1

9

percent

6

0

3

−1

0

0

3

6

9

12

15

18

21

24

EP

Notes: In this robustness check, We order US partisan conflict index last in the US block, which leads to yt = [U SEXt , U SCP It , U SIPt , U SF F Rt , U SEP Ut , U SP Ct , CN EXt , CN CP It , CN IPt , CN OIRt , CN EP Ut ]0 . The error bands correspond to 68% intervals.

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Fig. 7. Robustness check: reorder the endogenous variables

out of the 2008 GFC period. Their results suggest that macro news has more substantial impact on asset volatility persistence in the turbulent state. Therefore, it is expected that influence of macro news like US partisan conflict could be more persistent during the 2008 GFC period. As a result, increased U SP C level during volatile time may make FDI to US further deterred, leading to an everlasting negative impact on CN EX. Additionally, the market liquidity is lower, whereas market risks are much higher during the 2008 GFC period. 18

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The overall level of investment can be much lower and hence reduces the responsiveness of U SEX to U SP C. This is supported by a range of studies including Amiti and Weinstein (2011), Chor and Manova (2012) and Easton et al. (2016). Such research demonstrates

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that the international trading volumes are largely reduced during the 2008 GFC period, which may be caused by declines in investment efficiency. In contrast, results of Shi et al. (2016) imply that marginal impact of macro news on asset volatility can be weaker in the volatile regime. This may partially explain the lower impact of U SP C on CN EP U , when

SC

2008 GFC period is considered.

Third, we use a SVAR(5) model instead of the SVAR(2) to test the model robustness. As

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demonstrated in Fig. 6, the results produced by SVAR(5) are strongly consistent with those of SVAR(2). The only difference is the magnitudes of estimated impact are slightly weaker in SVAR(5). For instance, the maximum impact of shocks to U SP C on U SEX is reduced to 1.5%, compared to 2% estimated in the SVAR(2) model. We also note that the variable U SF ER presents some different patterns after we using SAVR(5) model instead SVAR(2)

TE D

model. The only significant difference is that the short-term positive impact disappears now, whereas the significant intermediate-term negative impact is still consistent with our previous findings. This suggests the short-term positive impact of shocks to U SP C on

EP

U SF ER is not quite robust.

Next, we reorder the endogenous variables by placing the U SP C last in the US block of

AC C

variables. The rationale of this robustness check is due to the technical fact that SVAR is constructed using Cholesky decomposition, which is sensitive to the ordering of variables. The results shown in Fig. 7 are almost identical to those displayed in Fig. 2, suggesting that our observations are robust against the ordering of variables. Finally, we include additional endogenous variables to examine the robustness of our main findings. Those variables are individually added into our baseline model, including the S&P 500 index, Shanghai stock exchange index, the US consumer sentiment index and the Chinese yuan (CNY) against US dollar (USD) nominal exchange rate. The corresponding 19

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SP500

USEX

1 0

percent

0

percent 0

3

6

9

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18

21

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18

21

24

18

21

24

−2

−0.01 3

6

SC

−1

0.00

0

1

percent

0.01

pp

2

0.02

0 −1

percent

−2 −3 −4 0

3

USFER

3

1

12

USIP

0.03

USCPI

3

RI PT

−2

0

−2

−1

−1

5

percent

10

2

1

3

USPC

0

3

6

12

15

18

21

CNEX

24

0

3

6

9

12

15

CNCPI

−1

−3

−3

3

6

9

12

15

18

21

24

0

3

9

12

15

18

21

24

0

CNOIR

3

6

9

12

15

CNEPU

1

percent

0

pp

−0.01

−2

−0.03

−1

−0.02

TE D

0 −1

percent

1

0.00

2

2

0.01

CNIP

6

3

0

1

percent

0

−1

percent

−2

−2

−1

percent

0

2

M AN U

0

1

3

USEPU

9

0

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24

0

3

6

9

12

15

18

21

24

0

3

6

9

12

15

EP

Notes: In this robustness check, we include the S&P 500 index (SP 500) in yt , which leads to yt = [U SP Ct , SP 500t , U SEXt , U SCP It , U SIPt , U SF F Rt , U SEP Ut , CN EXt , CN CP It , CN IPt , CN OIRt , CN EP Ut ]0 . The error bands correspond to 68% intervals.

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Fig. 8. Robustness check: including the S&P 500 index

20

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SSE

USEX

1

percent

3

6

9

12

15

18

21

24

0

6

9

15

18

21

24

0

percent

0.02

pp 0.01

0

1

0 −1 −2

percent

9

12

15

18

21

24

0

3

6

12

15

18

21

CNEX

24

0

12

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18

21

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3

6

9

12

15

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18

21

24

18

21

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−3

3

−4

−1

−2

−3

0

1

percent

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percent

0 −1

percent

2

M AN U

0

2 1

9

CNCPI

1

USEPU

9

6

SC

6

−0.01

3

−1

0.00

−3 −4 0

3

USFER

0.03

1

12

USIP

0.04

USCPI

3

2

0

RI PT

−2

−3

0

−1

0

−1

percent

−2

5

percent

2

0

10

3

1

4

USPC

12

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24

0

3

12

15

18

21

24

pp

−0.04

3

6

9

12

15

3

6

18

21

24

9

12

15

CNEPU

3

−0.02

TE D

0

0

2

0.00

2 1 0 −1

percent

9

CNOIR

0.01

CNIP

6

0

3

6

9

1

9

percent

6

0

3

−1

0

12

15

18

21

24

0

3

6

9

12

15

AC C

EP

Notes: In this robustness check, we include the Shanghai stock exchange index (SSE) in yt , which leads to yt = [U SP Ct , SSE, U SEXt , U SCP It , U SIPt , U SF F Rt , U SEP Ut , CN EXt , CN CP It , CN IPt , CN OIRt , CN EP Ut ]0 . The error bands correspond to 68% intervals. Fig. 9. Robustness check: including the Shanghai stock exchange index

21

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USCSI

USEX

1 0

percent

1

percent 3

6

9

12

15

18

21

24

0

9

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18

21

24

0

USIP 2

percent

0.02

pp

0

0.01

3

6

9

12

15

18

21

24

9

12

15

18

21

24

18

21

24

18

21

24

18

21

24

−1

0

3

6

9

12

15

18

21

CNEX

24

0

3

6

9

12

15

CNCPI

3

6

9

12

15

18

21

24

3

6

9

12

15

18

21

24

0

3

6

CNOIR

9

12

15

CNEPU

3

6

9

12

15

18

21

0

3

6

9

1

percent

0

−0.03 −0.02 −0.01

24

−1

−2 0

TE D

0

pp

1

0.00

2

2

0.01

3

1

percent

0

CNIP

−1

percent

0 −1

−3 0

M AN U

−2

−1

percent

−1 −2

percent

0

0

2

1

1

USEPU

6

SC

0.00 −0.01

−3 0

3

USFER

0.03

0 −1 −2

percent

6

0.04

USCPI

3

1

0

RI PT

−2

0

−1

−1

0

5

percent

2

10

2

3

3

USPC

12

15

18

21

24

0

3

6

9

12

15

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EP

Notes: In this robustness check, we include the US Consumer Sentiment Index (U SCSI) in yt , which leads to yt = [U SP Ct , U SCSI, U SEXt , U SCP It , U SIPt , U SF F Rt , U SEP Ut , CN EXt , CN CP It , CN IPt , CN OIRt , CN EP Ut ]0 . The error bands correspond to 68% intervals. Fig. 10. Robustness check: including the US consumer sentiment index

22

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EXR

USEX

2

percent

0

3

6

9

12

15

18

21

24

0

3

6

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21

24

0

USIP

percent

9

12

15

18

21

24

9

12

15

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24

18

21

24

18

21

24

18

21

24

−2

0

3

6

9

12

15

18

21

CNEX

24

0

3

6

9

12

15

CNCPI

2 −1

−5

−3

−2

−4

0

1

percent

−1 −3

−2

percent

0 −1

M AN U

1

0

3

2

1

USEPU

percent

6

SC

−1

pp

0.01 0.00

6

−0.01

3

0

1

0.03 0.02

0 −1 −2

percent

−3 −4 0

3

USFER

1

USCPI

12

RI PT

−2

0 −5

0

2

percent

1

5 0

percent

3

4

10

4

USPC

0

3

6

9

12

15

18

21

24

0

3

9

12

15

18

21

24

0

3

6

CNOIR

9

12

15

CNEPU

6

9

12

15

18

21

24

6

9

2

3

3

−1

0

−0.005

0

1

percent

0.005 3

−0.015

0

TE D

pp

0 −2

−1

percent

1

4

CNIP

6

12

15

18

21

24

0

3

6

9

12

15

AC C

EP

Notes: In this robustness check, we include the CNY/USD nominal exchange rate (EXR) in yt , which leads to yt = [U SP Ct , EXR, U SEXt , U SCP It , U SIPt , U SF F Rt , U SEP Ut , CN EXt , CN CP It , CN IPt , CN OIRt , CN EP Ut ]0 . The error bands correspond to 68% intervals. Fig. 11. Robustness check: including the CNY/USD nominal exchange rate

results are reported in Figs. 8–11. Despite some deviations in the magnitudes, all results are largely consistent with those discussed in Fig. 2. To sum up, regarding the impact of US Partisan Conflicts on US exports to or imports from China, all modifications examined in this section do not materially change our observations in Fig. 2. Although the specific estimates vary to limited extent, we consistently demonstrate that shocks to U SP C can positively affect U SEX but negatively influence 23

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CN EX.5 Hence, our conclusions in Section 4.1 are mostly robust under various scenarios. 4.3. Forecast-error-variance decomposition (FEVD) Forecast-error-variance decomposition (FEVD) is a technique that allows us to analyze

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the contribution of one variable to the multi-step FEV of other variables. We consider the FEVDs of U SEX, CN EX, CN IP and CN EP U due to the shocks of U SP C, U SEP U and CN EP U in Table 2.

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From Table 2, we can see that a US partisan conflict shock plays a non-trivial role in explaining fluctuations in the U SEX and CN EX. It can account for over 3% (7%) of variation in U SEX (CN EX) at the twelfth (eighteenth) month horizon. Also, the shock

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of U SP C can explain up to 3% and 10% of changes in CN IP and CN EP U , respectively. A US economic policy uncertainty shock is relatively less (more) important in explaining the variations in the US exports to (imports from) China. The shock of U SEP U accounts for approximately 2% and 12% of the changes of U SEX and CN EX, respectively. It is also interesting to notice that this shock can explain around 2.8% and 7% of the changes

U SP C effects.

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of CN IP and CN EP U , respectively. Both of them are smaller than the corresponding

Additionally, Table 2 shows that, in general, a Chinese economic policy uncertainty

EP

shock is more (less) relevant than its US counterpart in explaining the FEV of U SEX (CN EX). More precisely, a shock of CN EP U can account for up to 4.5% and 1.7% of the

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changes in U SEX and CN EX, respectively. However, CN EP U can only explain 1.8% of the variation in CN IP , which is less than such effects of U SP C and U SEP U . To check the robustness of the FEVD results, we also consider the scenarios described in Section 4.2. The new estimates are largely consistent with the observations in Table 2 and are available upon request. 5

Under the same scenarios, the new results with respect to the U SEP U are also strongly consistent with those observed in Fig. 3. The plots can be found in the Appendices.

24

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U SEX

Horizon

CN EX

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Table 2 Forecast error variance decomposition of Chinese variables due to U SP C, U SEP U and CN EP U shocks

CN IP

U SP C

U SEP U

CN EP U

U SP C

U SEP U

CN EP U

U SP C

1 6 12 18 24

0.0003 2.3864 3.1737 3.2059 3.3089

0.0000 1.6621 1.7205 1.7939 1.8058

0.0000 4.5454 4.4949 4.4781 4.4641

4.5902 5.6564 6.6358 7.8365 8.8662

1.5570 11.2789 12.2644 11.5174 10.6827

0.0000 1.7124 1.6168 1.5501 1.4954

0.0798 2.4629 2.6220 2.8508 3.2229

U SEP U

CN EP U

U SP C

U SEP U

CN EP U

2.5550 2.7355 2.8155 2.8103 2.7682

0.0000 1.7569 1.8047 1.7797 1.7582

0.0564 6.4532 8.5657 8.8601 10.1813

2.2064 7.3135 6.8820 7.1647 6.6845

79.1664 51.5478 45.4306 42.9342 39.6284

M AN U

SC

(in months)

CN EP U

Note: This table presents the FEVD of the selected Chinese variables due to the U SP C, U SEP U and CN EP U shocks. The unit is displayed in percentage. Please see Table 1 for explanations of the variables.

AC C

EP

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25

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5. Concluding remarks The US is the world’s largest and most influential economy. Its political instability and policy uncertainty may not only affect the US domestic economy, but also influence

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the global economy. It is acknowledged in the existing literature that those instability and uncertainty can affect real economic activities in other countries such as China. However, no research has been done to examine if such effect significantly exists on the trade performance

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between US and its major trading partners. This paper examines how US partisan conflict shocks affect the trade volume with China—its largest trading partner.

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According to the existing evidence, a shock to the US partisan conflicts may induce a decrease in the US domestic demand. This may further reduce its imports from China and exports of intermediate products to China. However, decline in the domestic demand may lead US firms to allocate more resources to alternative markets like China and expand their product sales in those markets. Therefore, this may increase the US exports to China.

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Motivated by those implications, we use a recent US partisan conflict index provided by Azzimonti (2014) and employ SVAR models to investigate the effects of US partisan conflict shocks on the US–China trade relationship. Our empirical results suggest that US partisan conflict shocks have a positive effect on its exports to China, but a negative effect on its

EP

imports from China.

Further, we demonstrate that compared with the effects of US policy uncertainty shocks,

AC C

those of US partisan conflict shocks are more persistent on the US–China bilateral trade. Hence, our results are consistent with those of relevant studies and can complement and contribute to them by providing direct empirical evidence. More specifically, the effects of US partisan conflicts can spill over to other economies like China through trade channels. Our results suggest such spill-over may possibly increase US exports to those countries but reduce its imports from them. The exact effects and potential differences among the trade between US and other areas beyond China remain for the future work.

26

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Supplementary materials Additional robustness check results can be found in the on-line appendix.

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Acknowledgment An early version of this paper was presented in the 4th International Conference on the Chinese Economy: Past, Present and Future at Tsinghua University. The authors wish to thank Donald Lien, the anonymous referees, and the conference participants for their

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EP

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M AN U

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helpful comments and suggestions which have greatly improved the quality of the paper.

27

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Amiti M., Weinstein D.E. (2011). Exports and financial shocks. The Quarterly Journal of Economics, 126(4), 1841-1877. Artus, J. R. (1970). The short-term effects of domestic demand pressure on British exports performance. International Monetary Fund Staff Papers 17, 247-274.

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Artus, J. R. (1973). The short-run effects on domestic demand pressure on export delivery delays for machinery. Journal of International Economics 3(1), 21-36. Auer, R. A., Fischer, A. M. (2010). The effect of low-wage import competition on U.S. inflationary pressure. Journal of Monetary Economics 57(4), 491-503. Autor, D., Dorn, D., Hanson, G. (2013). The China syndrome: Local labor market effects of import competition in the United States. American Economic Review 103(6), 2121-68.

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Azzimonti, M. (2014). Partisan conflict. Working Paper No. 14-19. Federal Reserve Bank of Philadelphia.

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