Trade Integration and Business Cycle Synchronization: A Reappraisal with Focus on Asia
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund

Contributor Notes

This paper reexamines the relationship between trade integration and business cycle synchronization (BCS) using new value-added trade data for 63 advanced and emerging economies during 1995–2012. In a panel framework, we identify a strong positive impact of trade intensity on BCS—conditional on various controls, global common shocks and country-pair heterogeneity—that is absent when gross trade data are used. That effect is bigger in crisis times, pointing to trade as an important crisis propagation mechanism. Bilateral intra-industry trade and trade specialization correlation also appear to increase co-movement, indicating that not only the intensity but also the type of trade matters. Finally, we show that dependence on Chinese final demand in value-added terms amplifies the international spillovers and synchronizing impact of growth shocks in China.

Abstract

This paper reexamines the relationship between trade integration and business cycle synchronization (BCS) using new value-added trade data for 63 advanced and emerging economies during 1995–2012. In a panel framework, we identify a strong positive impact of trade intensity on BCS—conditional on various controls, global common shocks and country-pair heterogeneity—that is absent when gross trade data are used. That effect is bigger in crisis times, pointing to trade as an important crisis propagation mechanism. Bilateral intra-industry trade and trade specialization correlation also appear to increase co-movement, indicating that not only the intensity but also the type of trade matters. Finally, we show that dependence on Chinese final demand in value-added terms amplifies the international spillovers and synchronizing impact of growth shocks in China.

I. Introduction

Over the past two decades, trade integration has increased rapidly within the world economy, and particularly so within Asia. Gross trade in volume terms rose at an average rate of 8 percent per annum during 1990–2012, twice the average pace outside Asia. In valued-added terms, trade increased at an average annual growth rate of over 10 percent during the same period, also double the average pace outside Asia. Not only have Asian economies traded more with one another, they have also traded differently, becoming more vertically integrated as a tight-knit supply-chain network across the region was formed. Have changes in trade patterns, in particular greater trade integration, led economies to move more in lockstep, and disproportionately so within Asia?

Theoretically, the answer to that question is a priori ambiguous, and empirically the evidence seems to be weaker than initially thought. The relationship between trade integration and business cycle synchronization (BCS) has been subject to extensive research, motivated in good part by the optimum currency area literature (OCA) that was pioneered by Mundell (1961) and McKinnon (1963) and given new impetus by Frankel and Rose (1997, 1998). A wide range of empirical papers (e.g., Frankel and Rose, 1997, 1998; Baxter and Kouparitsas, 2005; Imbs, 2004; Inklaar and others, 2008), including in an Asian context (e.g., Kumakura, 2006; Park and Shin, 2009), have found that trade intensity increases synchronization, although the magnitude of the impact varies across studies.

However, while existing studies have relied on a variety of approaches including cross-section, pooled and simultaneous equation techniques, and paid a good deal of attention to endogeneity issues, they have typically not accounted for fixed country-pair factors and common global shocks. Yet, as stressed by Kalemli-Ozcan, Papaioannou and Peydro (2013), controlling for both is required to address omitted variable bias and thereby identify a causal link. Indeed they and Abiad and others (2013) find the relationship between trade integration and BCS to be insignificant, and that between financial integration and BCS to change sign relative to a cross-section regression, when such controls are added in a panel setup. Earlier studies that accounted for country-pair heterogeneity also found weaker or no effects of overall trade intensity on BCS (Calderon, Chong and Stein, 2007; Shin and Wang, 2004), although they found the type of trade to matter.

The present paper contributes to the literature on trade integration and BCS in several ways. First, we account systematically for country-pair heterogeneity and common global shocks throughout the analysis. Second, and crucially, we depart from all existing studies by using value added instead of gross trade data, building on the recent joint OECD-WTO initiative on trade in value added. As indicated for instance by Unteroberdoerster and others (2011), and as will be illustrated below, gross trade data misrepresent trade linkages across countries amid increasingly important supply-chain networks across the globe. Using value-added trade data instead will prove crucial to identifying a robust impact of trade on BCS. Third, this paper goes beyond bilateral trade intensity and explores the BCS impact of the nature of trade and specialization, including vertical integration, trade specialization correlation, and intra-industry trade, while also controlling for other influences on BCS such as financial integration (including bank, FDI, portfolio flows) and macro-economic policy synchronization across countries. Finally, in separate but related analysis, we also examine the impact of a China shock on other economies and whether and how this shock is propagated through various trade channels.

The main findings from this paper are the following:

  • Consistent with results from other recent studies, BCS appears to spike across the globe during crises. Based on the quasi-correlation indicator proposed by Abiad and others (2013), output correlations outside of Asia peaked during the 2008/09 Global Financial Crisis (GFC), while within Asia the biggest spikes in BCS occurred during the Asian Crisis of the late 1990s. During normal times, BCS is typically much lower but has nonetheless been on an upward trend over the past two decades, particularly in Asia.

  • Based on a dynamic factor analysis, global factors appear to be playing a major role in driving business cycles in Asia and elsewhere.

  • Using a sample of 63 advanced and emerging economies spanning the last two decades, a macro panel analysis based on value-added trade data finds that bilateral trade intensity has a significant, positive, and robust effect on bilateral BCS among country pairs. Significant, though smaller effects of intra-industry trade and correlation of trade specialization are also found.

  • The impact of trade integration on BCS is greater in crisis times than in normal times, that is, trade amplifies the synchronizing impact of common shocks. This is qualitatively consistent with the concomitant finding—already featured in—that financial integration increases BCS in crisis times, even though it typically reduces it during normal times.

  • Growth spillovers from China—a growing source of BCS, especially within Asia—are significant, sizeable, and larger in economies that are more dependent on final demand from China in value-added terms.

The remainder of the paper proceeds as follows: Section II presents some stylized facts about BCS, with particular focus on Asia, including results of a factor analysis on the roles played by global, regional, and sub-regional factors. Section III explores the role of the intensity and nature of trade for BCS while also covering effects of financial integration. It also features additional, related analysis of the spillovers of growth shocks in China to other economies. Section IV concludes.

II. Stylized Facts about Business Cycle Synchronization

A. Properties of Business Cycle Synchronization in Asia

Measurement

Throughout this paper, our default measurement of BCS is the instantaneous quasi-correlation measure proposed by Abiad and others (2013):

QCORRijt=(gitgi*)*(gjtgj*)σig*σjg

where QCORRijt is the quasi-correlation of real GDP growth rates of country i and j in year t, git denotes the output growth rate of country i in year t and; gi* and σig represent the mean and standard deviation of output growth rate of country i, respectively, during the sample period. The growth rate is measured as the first difference of the log of real GDP.

This measure has advantages over methods commonly used by similar studies because:

  • First, this enables the calculation of co-movement at every point in time rather than over an interval of time. By contrast, most of the literature measures output co-movement between two economies by the rolling Pearson correlation of actual or detrended growth rates between a country pair over a window period. This artificially introduces autocorrelation of the BCS time series due to a high degree overlapping observations throughout the sample. In addition, given that this paper uses annual data over the past two decades,1 the rolling correlation measure would likely be dominated by outliers during the Asian crisis and the GFC.

  • Second, the quasi-correlation measure retains some nice statistical properties. First, it can be easily shown that the period mean of the measure would asymptotically converge to the standard Pearson correlation coefficient. Second, at any point in time, the measure is not necessarily bounded between -1 and 1. As argued by Otto and others (2001) and Inklaar and others (2008), if the BCS measure lies between -1 and 1, the error terms in the regression explaining it are unlikely to be normally distributed.2

  • Finally, we calculate correlations based on actual rather than detrended growth rates, because the latter crucially depend on the choice of filtering methods and we use low-frequency data anyway (annual data over two decades).

BCS developments

Using annual data for 63 countries, including 34 advanced economies (7 of which in Asia) and 29 emerging economies (8 of which in Asia), developments of BCS in Asia and elsewhere since 1990 are depicted in Figure 1:

  • Consistent with findings from other similar studies, BCS sharply increased in crisis times. The largest spikes occurred around the GFC outside Asia, and around the Asian crisis of the late 1990s within Asia (Top left panel).

  • During normal times, BCS has been much smaller but also shown some upward trend since the 1990s around the globe, including in Asia. That increase in BCS has been particularly large in Asia and Latin America, although BCS within both regions was still considerably lower than within the euro area during the 2000s. Within Asia, BCS appears to be particularly high among ASEAN-5 economies, which include Indonesia, Malaysia, the Philippines, Singapore, Thailand (Top right panel). Similar results are obtained using the standard Pearson correlation coefficients (Bottom left panel).

  • China’s output co-movements vis-à-vis the rest of the Asia have increased, but Asian economies continue to co-move more with Japan and the United States (bottom right panel). This likely reflects the continued importance of global factors in driving business cycles across the region, as examined below.

B. The Role of Global, Regional and Sub-regional Factors

Setup

Before assessing the role of observable drivers of BCS such as trade and financial linkages using regression analysis, we employ a dynamic factor model to analyze the roles played by unobservable global, regional, sub-regional, and country factors in explaining the evolutions of business cycles.

Following the approach of Hirata, Kose, and Otrok (2013), our model is constructed as follows: 3

yti=b1iftglobal+b2iftregion(j)+b3iftsubregion(k)+b4iftcountry(i)+utiuti=+φ1iut1i+φ2iut2i+εti,WhereεtiiidN(0,σεi2)ft.=φift1.+φ2˙ft2.+ηt.,whereηt.iidN(0,ση.2)

Here, yti stands for a vector comprising growth rates of GDP, consumption, and investment; ft. stands for factors; and uti represents residuals. Each factor and residuals are assumed to follow an AR(2) process. The dataset features 34 economies including 13 in Asia, 4 in South America, 14 in Europe, and 3 in North America.

Two types of models are estimated:

  • Type I—The business cycle in each country is assumed to be influenced by three factors, namely global, regional (Asia, South America, Europe, and North America), and country factors.

  • Type II—In addition to the global, regional, and country factors, business cycles in some parts of Asia are assumed to be influenced by an additional sub-regional factor. In the first specification, Asia is divided into two regions: China supply-chain4 (China, Taiwan POC, Thailand, Korea, Philippines, and Malaysia) vs. other Asian economies. In the second specification, the region is divided into ASEAN-5 economies (Malaysia, Indonesia, the Philippines, Thailand, and Singapore) vs. others; in the third specification, Asia is divided into advanced economies (Hong Kong SAR, Japan, Korea, Singapore, and Taiwan POC) vs. others.

We use quarterly GDP, consumption, and investment from 2000Q1 to 2012Q4. The model is estimated by Bayesian Markov Chain Monte Carlo (MCMC) approach with the algorithm proposed by Carter and Kohn (1994).

Results5

Figure 2 shows the estimated factors in the model without sub-regional factors, with a 70-percent confidence interval, which appears to be rather narrow except for Latin America. The factors also seem rather intuitive in explaining recent economic fluctuations. For example, the global factor shows a steady positive value during 2003–07, consistent with the steady expansion through the period. Then the factor plummets before bouncing back, capturing the GFC and recovery in the aftermath. Meanwhile, the Asian factor shows a distinguished peak in the aftermath of the GFC, likely representing the steep recovery amidst heavy policy stimulus throughout the region at the time.

Next, given that factors are orthogonal to each other in the model, we can calculate the contribution of each factor to the total variance by decomposing the variance as follows.

var(yti)=(bti)2var(ftglobal)+(b2i)2var(ftregion(j))+(b3i)2var(ftsubregion(k))+(b4i)2var(ftcountry(h))+var(uti)

Figure 3, upper panel shows the role of each factor in driving business cycles under the Type I model. Specifically, the global factor explains on average about 35 percent of output fluctuations in individual economies.6 The contribution of the global factor is lower in Asia than in North America and in Europe, but it is nonetheless larger that of the regional or country factors. Based on these results, there appears to be no evidence that Asia has been decoupling from the rest of the world over the last two decades. In other words, the region remains more globalized than regionalized.

Figure 3, lower panel shows similar results under the Type II model, which incorporates sub-regional factors. While this addition does not seem to alter the big picture, a couple of intuitive findings emerge regarding the role of global factors across various parts of Asia. In particular, global factors are found to explain a greater share of business cycle fluctuations among those countries that are part of the China supply-chain than within the rest of Asia, consistent with the view that the China supply-chain economies are comparatively more integrated with the world economy and thereby more sensitive to its fluctuations. Similarly, the global factor has higher explanatory power in advanced Asian economies than in emerging Asia, in line with the fact that the former are more deeply integrated than the latter in the global economy.

III. The Role of Trade in Driving BCS

A. A Bird’s Eye View of the Literature

In this section we begin by reviewing very briefly what economic theory postulates and what existing empirical research finds about the BCS impact of trade integration, as well as of financial integration and policy coordination whose effects will also be examined below.

Trade integration

Theoretically, the impact of trade on BCS is ambiguous:

  • On the one hand, according to traditional trade theory, openness to trade should lead to a greater specialization across countries. To the extent this holds in practice, and insofar as business cycles are dominated by industry-specific supply shocks, higher trade integration should reduce BCS.

  • On the other hand, if the patterns of specialization and trade are dominated by intra-industry trade, greater trade integration should be associated with a higher degree of output co-movement in the presence of industry-specific supply shocks. If instead demand factors are the principal drivers of business cycles, greater trade integration should also increase BCS, regardless of whether the patterns of specialization are dominated by inter- or intra-industry trade.

Given the ambiguity of the theory, the impact of trade integration on BCS is essentially an empirical question. And indeed this has been a heavily researched area, with cross-sectional regression and simultaneous equation approaches essentially finding a significant positive impact—with some disagreement regarding its magnitude—and most recent panel regression work controlling for country-pair fixed effects and common global shocks essentially finding no effect (see above).

Financial integration

Theoretically, financial integration, like trade integration, has an ambiguous impact on BCS:7

  • On the one hand, Morgan and others (2004) developed a model in which if firms in one country are hit by negative shocks to the value of their collateral or productivity, then in a more financially integrated world domestic and foreign banks would decrease lending to this country and reallocate the funds to another, thereby causing cycles to further diverge. Likewise, in the workhorse international real business cycle (RBC) model of Backus, Kehoe and Kydland (1992), capital will leave a country hit by a negative productivity shock and get reallocated elsewhere under complete financial markets, again amplifying divergence. Another argument is that if higher financial integration between countries encourages them to specialize, thereby inducing greater inter-industry trade, higher financial integration could (indirectly) reduce BCS.

  • On the other hand, if negative shocks hit the banking sector, then global banks would pull funds away from all countries across the board, thereby amplifying business cycle co-movement.

The empirical literature is not fully settled either. Kalemli-Ozcan and others (2003) find a significantly positive relationship between specialization and risk-sharing, consistent with a negative impact of financial integration on BCS. By contrast, in a simultaneous equations framework, Imbs (2006) finds a positive effect. More recent studies such as Kalemli-Ozcan, Papaioannou and Peydro (2013) identify a strong negative effect of banking integration on output co-movement, conditional on global shocks and country-pair heterogeneity. But there is evidence of a positive effect on BCS of the interaction between the GFC and banking integration, suggesting that the negative association between that form of financial integration and output co-movement is attenuated during crisis period (Abiad and others 2013; Kalemli-Ozcan, Papaioannou and Perri, 2013).

Policy coordination

Apart from trade and financial integration, policy matters for BCS. Specifically, if two countries synchronize—on purpose or not—their policies by implementing expansionary or contractionary policies at the same time, BCS between these two would be expected to rise, all else equal. Inklaar and others (2008), using data on OECD countries, confirms that similar monetary and fiscal policies have a strong impact on BCS. Similarly, Shin and Wang (2003), using data for Asian countries, find that monetary policy coordination has a significant and positive impact on BCS.

B. Methodology

Empirical strategy

Our key objective is to assess the impact of trade integration on business cycle synchronization. Given the lessons from previous studies, the core of our empirical framework lies on two elements which, combined together, distinguish our work from existing literature. First, our estimation strategy relies on panel regressions with fixed effects to account for time-invariant country-pair idiosyncratic factors and time effects to account for global common shocks affecting countries across the board. Second, we measure trade in value added, rather than gross, terms.

As already discussed, recent panel studies typically find a much weaker or insignificant BCS impact of bilateral trade intensity. One explanation might be that trade does indeed have no impact on BCS once one addresses the spurious relationship between trade integration and BCS by conditioning on common shocks or unobserved specific country-pair fixed effects.

However, another explanation might simply be that gross trade data used in recent—and indeed all—studies fail to capture properly true underlying trade linkages and interdependence across countries in a world characterized by a rapid increase in the fragmentation of production processes and a growing share of vertical trade.

For example, as shown by Xing and Detert (2010) in the case of iPhone trade, in 2009 China’s value added only accounted for 3.6 percent of total trade with the United States, with the rest of the value added being reaped by Germany, Japan, Korea, the United States and other countries via their exports to China. In this case, using gross bilateral trade data vastly overstates China’s trade dependence vis-à-vis the United States, while understating other countries’ trade dependence on the U.S. trade—which is mostly indirect, via exports to China of components that are then assembled and re-exported by China to the United States (and other) markets. In order to correct for this potentially serious measurement issue, we essentially use value-added trade data throughout this paper.

Furthermore, as shown in some previous studies, not only is bilateral trade intensity important, but so are patterns of trade and specialization. Accordingly, in addition to trade intensity, we explore the BCS effects of vertical versus horizontal integration, intra-industry versus inter-industry trade, and the correlation of specialization across country pairs. While this paper is not the first one to do so, to the best of our knowledge, it is the first to compute and test for the impact of trade in value-added terms.

Data8

We begin with four trade variables: trade intensity, vertical integration, intra-industry trade, and bilateral correlation of specialization. Table 1 depicts the within correlation of the trade variables with each other. Since the magnitude of within correlation of the various trade variables is low, we will be able to introduce them in our model simultaneously without running into multi-collinearity issues.

Table 1.

Trade Variables “within” Correlations

article image
Source: IMF staff estimates.Note: The within correlations between two variables xijt and yijt are calculated as the correlation between (xit=xitxl.¯x.t¯+x..¯)and(yit=yityl.¯y.t¯+y..¯),where,xl.¯=1tΣt=1mxit,x.t¯=1tΣi=1mxitandx..¯=1t*iΣt=1m*nxiti represents a country pair.
Trade intensity

Bilateral trade intensity is the most frequently featured trade variable in the literature. We follow the standard definition, except we define it in a value-added sense using the recent OECD-WTO database on trade in value added in goods and services. Specifically,

Tijt=DVAtij+DVAtjiGDpit+GDpjt

where Tijt represents the bilateral trade intensity of country-pair i and j at time t; GDPit is the GDP of country i at time t; DVAtij denotes the domestic value added exported, both directly and indirectly, from country i to country j in year t. The indirect component includes the domestic value added exported by country i to a third country k, as intermediate inputs into the production of goods and services exported by country k to country j.

Figure 4 (upper panel) shows that trade openness in value-added terms has increased more in Asia than in other emerging regions since the mid-1990s. Likewise, intra-regional trade—measured here as the median bilateral trade intensity between Asian economy pairs—has risen most rapidly within Asia (lower panel).

Vertical integration

The next trade variable is the vertical trade integration between two countries. A priori, vertical trade could have a specific impact on BCS over and above that of trade intensity (for supportive empirical evidence using country industry-level data, see di Giovanni and Levchenko, 2010). For instance, limited or inexistent short-term substitutability of inputs could propagate shocks along a vertical supply chain in the event of disruptions in parts of it, as was evident in Asia in the wake of the earthquake and tsunami in Japan in 2011. In regression analysis we measure vertical integration between two countries by the extent to which one country’s exports in value- added terms rely on intermediate inputs from the other country. Like trade intensity, we also define it bilaterally:9

VIijt=FVAtij+FVAtjiDVAtij+DVAtji

where VIijt denotes the vertical trade integration between countries i and j; FVAtij represents the share in country i’s exports that is attributable to the (foreign) value-added content coming from country j.

Figure 5 decomposes the value of total gross exports into its domestic and foreign value-added components for various countries and regions. It shows that the share of foreign value added embedded in total exports has generally increased in Asia economies, particularly in China and in East Asia reflecting the “China supply-chain” network. However, the extent of vertical integration varies across the region: specifically, as displayed in Figure 6, value added coming from or going to China has increased across Asian economies, while that coming from or going to Japan has declined. Within ASEAN-5, vertical integration with ASEAN-5 partners has also generally increased. Figure 7 suggests that nature of integration with partners differs between China and Japan, with China specializing in downstream activities (e.g., assembling) and Japan specializing in upstream activities (providing various inputs).10 Finally, Figure 8 suggests that the United States and the European Union remain the largest final consumers of Asia’s supply chain, although the importance of final demand coming from China has increased rapidly over the past two decades.

Figure 5
Figure 5

Domestic and Foreign Value Added Embedded in Exports

(in percent of total gross exports)

Citation: IMF Working Papers 2014, 052; 10.5089/9781475522464.001.A001

Sources: OECD-WTO, Trade in Value Added database; and IMF staff estimates.
Figure 8.
Figure 8.

Value Added Exported to Partner Countries for Final Demand

(In percent of total value added exported for final demand by the country)

Citation: IMF Working Papers 2014, 052; 10.5089/9781475522464.001.A001

Sources: OECD-WTO, Trade in Value Added database; and IMF staff estimates.
Intra-industry Trade

Our third trade variable is intra-industry trade between countries. A positive coefficient on this variable would suggest that industry-specific supply shocks are an important source of business cycles fluctuations. Conceptually, intra-industry trade differs from vertical trade since it should reflect two-way trade in similar (finished or intermediate) goods, while vertical trade typically involves trade in different as many parts and components along the supply chain belong to different industries and are therefore subject to different industry-specific shocks. This justifies the inclusion of both the vertical trade integration index and the intra-industry trade measure as separate drivers of BCS in the regressions, all the more so as their (within) correlations are low in practice (see Table 1).

Formally, bilateral intra-industry trade can be measured by the Grubel and Lloyd (1975) index, IITijt, for a country pair i-j in year t:

IITijt=1[Σh=1n|Xtij,hMtij,h|Σh=1n(Xtij,h+Mtij,h)]

where Xtij,h(Mtij,h) are the exports from (imports to) country i to (from) country j in industry h. The higher the index value, the greater the share of intra-industry trade relative to inter-industry trade between the two countries. It is well-known that the Grubel-Lloyd index is sensitive to the granularity of the trade classification. Regressions were run using alternative Standard International Trade Classifications (SITC), at the three- or five-digit level and for all goods or manufacturing goods only. Results were very consistent and robust across these alternative measures. In light of this, we only show those using the SITC three-digit classification, which is also the one used to compute the trade specialization correlation index described further below. Figure 9 shows that intra-industry trade has moderately increased in Asia, while only slightly increased elsewhere.

Figure 9.
Figure 9.

Degree of Intra-Industry Trade

(Median Bilateral Grubel-Lloyd Index; ranges from 0 to 1)

Citation: IMF Working Papers 2014, 052; 10.5089/9781475522464.001.A001

Sources: United Nations, COMTRADE database; and IMF staff estimates.Note Calculated as period medians of the median country pairs in each group.
Trade specialization correlation

Lastly, we consider the trade specialization correlation of a country pair. The measure, which comes from UNCTAD, looks at the similarity of the basket of goods the two countries trade with the world. This measure effectively captures the importance of pure industry-specific shocks as it does not require any bilateral trade to take place between the partner countries. A significant positive coefficient on this variable would imply that countries engaging in similar trade, regardless of whether they trade with each other, would tend to co-move owing to industry-specific shocks.

Formally, the measure is defined as:

TSCijt=Σh=1n(TSIti,hTSItI,h¯)(TSItj,hTSItJ,h¯)Σh=1n(TSIti,hTSItI,h¯)2(TSItj,hTSItJ,h¯)2

such that TSIti,h=Xti,hMti,hXti,h+Mti,h

where TSCijt represents the trade specialization correlation between countries i and j in year t, TSIti,h denotes the trade specialization index of country i for year t in industry h, TSItI,h¯ is the average trade specialization of country i over all n industries in year t. Xti,h(Mti,h) is the measure of gross exports (imports) of country i in industry h to (from) all its trading partners. Figure 10 shows that trade specialization correlation has declined noticeably in Asia, while remaining broadly constant elsewhere. The decline in the specialization correlations within Asia may reflect greater vertical specialization of different economies along the supply chain, which may have increased the complementarity—and therefore reduced the similarity—of their production structures.

Figure 10.
Figure 10.

Correlation of Trade Specializations

(Median Bilateral Trade Specialization Correlations)

Citation: IMF Working Papers 2014, 052; 10.5089/9781475522464.001.A001

Source: United Nations, UNCTADstat database.Note: Calculated as period medians of the median country pairs in each group.
Other explanatory variables

While this paper focuses on the impact of trade integration on BCS, other drivers of BCS are also taken into account. As discussed above, financial integration matters, and in particular banking integration—defined as the share of the stock of bilateral assets and liabilities between countries relative to the sum of the two countries’ external assets and liabilities vis-à-vis the entire world. Figure 11 shows that banking integration has increased across the globe, with the faster rate of increase outside Asia. Other financial integration variables controlled for in certain specifications include portfolio and FDI integration. These series, which are available over a much shorter sample, are described in Appendix II.

Figure 11.
Figure 11.

Median Bilateral Banking Integration

(In percent of total external position with the world)

Citation: IMF Working Papers 2014, 052; 10.5089/9781475522464.001.A001

Sources: Bank for International Settlements; and IMF staff estimates.Note: Calculated as period medians of the median available country pairs in each group (see Appendix II for the variable definition).

In addition to financial variables, we also control for policy coordination variables, including fiscal policy synchronization, monetary policy synchronization, and exchange rate policy synchronization. The measures used for fiscal, monetary and exchange rate policies in each economy are the structural fiscal balance purged from the cycle—to focus as much as possible on the synchronization of fiscal shocks rather than of fiscal policies, and thereby address potential reverse causality—, the real policy rate purged from the cycle, and the rigidity of the nominal bilateral exchange rate vis-à-vis the other country in the pair, respectively (see Appendix II for details).

The main model

We begin with a baseline model that focuses exclusively on the impact of trade and specialization on BCS:

QCORRijt=αij+αt+f(TRADEijt1)+εijt(1)

where QCORRijt is the instantaneous quasi-correlation (as defined in the previous section) between country-pair i and j at time t; αij is the country-pair fixed effect, which accounts for fixed factors such as gravity-type variables or other unobservable time-invariant idiosyncratic factors specific to country-pair i and j; αt is a time effect, which accounts for time-varying common factors affecting all countries. TRADE captures the four trade and specialization variables mentioned previously—namely trade intensity, vertical integration, intra-industry trade, and trade specialization correlation.

Once we estimate the baseline model, in order to check the robustness of the trade variables, we augment the baseline model by adding controls for financial integration and policy synchronization among partner countries:

QCORRijt=αij+αt+f(TRADEijt1,FINANCEijt1,POLICYijt1)+εijt(2)

where FINANCE includes (all or some of the) financial integration variable and POLICY includes policy synchronization variables.

Endogeneity

Endogeneity is a standard concern in this type of regression. Specifically, trade might be endogenous in the sense that BCS may be driven by some omitted or unobservable variables that are correlated with trade; or there might be reverse causality as higher BCS may induce greater trade intensity between more synchronized countries. In this context, OLS would be both inconsistent and biased.

We address this concern in three ways. First, the inclusion of country-pair fixed effects addresses endogeneity problems insofar as omitted variables or the transmission mechanism through which BCS affects trade are time-invariant, such as, for example, geographical proximity, culture, etc. Second, the use of lagged trade intensity variables in an annual panel should further mitigate reverse causality.11 Third, following most studies, we also resort to instrumental variables for both trade intensity and vertical integration. Specifically, for trade intensity, we use a set of time-varying gravity variables, comprising (see Appendix II for details): i) the product of the real GDP of the two countries; ii) a WTO membership dummy; iii) the degree of trade cooperation between countries; iv) a geographical distance index; and v) the average import tariff of the two countries. For vertical trade integration, in addition to the five variables above, we add as instruments the average import tariff on intermediate goods—which should correlate negatively with the bilateral intensity of intermediate goods trade—and the difference in real per capita GDP levels between the two countries—which empirically correlates well with the share of intermediate goods in their bilateral trade, as vertical integration tends to exploit factor proportion/price differences across countries, with the China supply-chain providing a typical illustration.

C. Results

Baseline regressions—Trade only

Table 2 presents the results of our baseline model with trade variables only (i.e., Equation 1). Due to the presence of serial correlation, standard errors are clustered at country-pair level in all models (including subsequent tables), to allow for autocorrelation and arbitrary heteroskedasticity for each pair (Kalemli-Ozcan, Papaioannou and Peydro, 2013). In order to illustrate the importance of using value-added trade data rather than gross trade data, column 1 of Table 2 shows that trade intensity in gross terms is not significant in explaining co-movement of business cycles once country-pair and time-fixed effects are accounted for. By contrast, column 2 shows that trade intensity in value-added terms has a highly significant and positive effect on BCS. While we do not report below any other regressions including gross trade data, these consistently show an insignificant coefficient, in contrast with the significant and robust impact of value-added trade.

Table 2.

Business Cycle Synchronization and Trade Integration

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Source: IMF staff estimates.Note: Standard errors, clustered at country-pair level, are given in parentheses. The estimated model is: QCORRijt = αij + αt + f(TRADEijt-1) + ɛijt. * p<0.10, ** p<0.05, *** p<0.01.

Column 3 shows the instrumental variable regression for the model in column 2, confirming that trade intensity is significant and positive. Somewhat surprisingly—insofar as any reverse causality would be expected to be such that higher BCS increases trade intensity—but consistent with findings in earlier studies, the instrumented regressions yield a higher coefficient estimate than do OLS regressions. In column 4, intra-industry trade and trade specialization correlation measures are added, and both have the expected positive coefficients, with statistical significance at 1 the percent level. This also holds true in column 5 when trade intensity is instrumented.

However, we fail to find a robust specific impact of vertical trade integration over and above that of trade intensity. As shown in column 6, when the vertical trade integration measure is added, the former three trade variables continue to be positive and significant, but vertical integration enters negatively. However, the latter finding is not robust across alternative specifications, for example, it becomes positive and significant when we remove time fixed effects from the model while all other trade variables in the model keep their sign and significance, or it changes sign when we instrument trade intensity and vertical integration, in ways that depend on the instruments used (results not shown, available upon request).12 Therefore we discard this variable in the remainder of the paper and leave this issue for future work. For instance, it could well be that vertical integration creates co-movement only by propagating up and down the international supply chain only specific shocks such as natural disasters (in the presence of limited short-term substitutability of intermediate inputs used in production processes). This may have been the case for instance with the earthquake and tsunami that affected Japan in 2011, which adversely affected the industrial output of Japan’s downstream trade partners in the regional supply chain. Recent IMF work (IMF, 2013) finds some evidence that growing intermediate goods trade within the “German-Central European supply chain” (Germany, Czech Republic, Hungary, Poland and the Slovak Republic) has increased the transmission of external shocks to this group of countries.

Augmented regressions—Controlling for financial integration and policy synchronization

Next, we add financial integration and policy synchronization controls to the model (Equation 2). As shown in Table 3, overall trade intensity stays positive and significant. Consistent with recent literature, the coefficient on banking integration is negative significant but here, in contrast to these papers, trade intensity remains positive and significant, in both OLS and IV regressions (columns 1–2). In columns 3–4, we add the intra-industry trade and trade specialization correlation indicators. While trade specialization correlation stays significant, intra-industry trade becomes insignificant albeit remaining positive. In column 5, we add portfolio and foreign-direct investment (FDI) integration to column 1. All three financial variables have the expected negative sign, though the results are fairly weak, possibly due to short sample size.

Table 3.

Business Cycle Synchronization and Trade Integration-Augmented Models

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Source: IMF staff estimates.Note: Standard errors, clustered at country-pair level, are given in parentheses. The estimated model is: QCORRijt = αij + αt + f(TRADEijt-1, FINANCEijt-1, POLICYijt-1) + ɛijt. * p<0.10, ** p<0.05, *** p<0.01

Broadly similar results hold true when we add policy synchronization variables, as shown in columns 6 and 7. Trade intensity and banking integration are significant while results for other trade variables are weak. As regards the policy coordination variables themselves, monetary policy, fiscal policy and exchange rate policy synchronization all appear to be significant determinants of business cycle co-movements.

Assessing the impact of trade and finance on BCS during crisis vs. tranquil times

We also test whether trade integration and banking integration have a higher impact on BCS during crisis times than during tranquil times. For this purpose, we include interactions between trade and banking integration variables, on the one hand, and all time dummies on the other. Results are presented in Table 4. The first two columns are the same as in Table 3 to facilitate comparisons. The next two columns incorporate interactions into the models featured in the first two columns.

Table 4.

Business Cycle Synchronization and Trade Integration-Crisis vs. Non-crisis times

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Source: IMF staff estimates.Note: Standard errors, clustered at country-pair level, are given in parentheses. GFC stands for Global Financial Crisis. * p<0.10, ** p<0.05, *** p<0.01.

Both OLS (column 3) and IV (column 4) estimates indicate that the impact of trade integration is greater during crisis times than during normal times. This is shown here only for the interaction between trade intensity and the GFC, but the interactions between trade and other sharp global slowdowns (not reported here) also turn out to be typically positive and significant. While endogeneity might a priori be a concern given that the GFC and major crises led to a contraction in trade, this issue is addressed here by lagging trade intensity by one year and instrumenting it. Likewise, and in line with Abiad and others (2013) or Kalemli-Ozcan, Papaioannou and Perri (2013), banking integration has a less negative—and indeed positive13—impact on BCS during crisis periods. This result is consistent with contagion through the banking channel in crisis times, particularly during a global banking crisis such as the GFC.14

D. Interpretation

What are the quantitative implications from the econometric results? Figure 12 provides an illustrative interpretation of the magnitude of the estimated coefficients across various specifications. It suggests that if a country pair moves from the 25th to the 75th percentile of the cross-country distribution of trade intensity—which is equivalent to an increase in trade intensity by about 12 percentage points, similar for instance to the increase in trade intensity between the Philippines and Thailand in the past 12 years—the quasi-correlation of the growth rates would increase by around 0.2. This impact is sizeable given the median correlation of around 0.1 across the sample. A similar variation of the intraindustry trade and trade specialization correlation indicators has a much smaller effect. Finally, a similar movement along the distribution of banking integration would slightly reduce co-movement. During crisis times, however, both trade intensity and banking integration have a high positive impact on business cycle synchronizations in both Asia and elsewhere. As a result, during a crisis, moving from the 25th to the 75th percentile of the distribution of trade intensity raises the quasi-correlation by almost 0.25, compared with an impact of 0.2 during normal times. And in the case of financial integration, crises shift its estimated sign and turn it into a contagion channel: moving from the 25th to the 75th percentile of the cross-country distribution of financial integration implies an increase in the quasi-correlation by some 0.25 in crisis times, in sharp contrast to its small negative association with BCS during tranquil times.

Figure 12.
Figure 12.

Illustrative Impact of Explanatory Variables on Co-movement

(Estimated impact on BCS of Moving from the 25th Percentile to 75th Percentile of the Cross-country Distribution of the Variable Considered)

Citation: IMF Working Papers 2014, 052; 10.5089/9781475522464.001.A001

Source: IMF staff estimates.Note: Given model uncertainty, the coefficients used to compute the contribution of each explanatory variable are the median values across the models in columns (1) and (3) in Tables 3 and 4.

How much of the actual change in BCS over time can be attributed to these various explanatory variables? Figure 13 shows that the trend rise in trade intensity has made an important contribution to the trend rise in BCS during tranquil times. This is especially the case in Asia, where the increase in trade intensity and BCS has both been the largest. Other factors have played a fairly insignificant role in driving BCS, as they have changed little over time. In Asia, the degree of intra-industry trade remains largely unchanged from 15 years ago, while trade specialization correlation appears to have decreased. However, the results in Table 4 (not used in Figure 13) suggest that the trend rise in banking integration made a large contribution to the spike in BCS observed during the GFC.

Figure 13.
Figure 13.

Estimated Contribution of Trade and Other Variables to the Trend Increase in BCS: Asia and Non-Asia

(Change in median quasi-correlation between 1999–2005 and 2006–12 (excl. 2009) and contributions of the explanatory variables)

Citation: IMF Working Papers 2014, 052; 10.5089/9781475522464.001.A001

Source: IMF staff estimates.Note: Given model uncertainty, the coefficients used to compute the contribution of each explanatory variable are the median values across the models in columns (1) and (3) in Table 3.

E. An Additional Model: Assessing China Spillovers

Setup

China’s importance in international trade has increased rapidly in the last two decades and numerous studies have demonstrated its profound spillovers on other countries, not least in Asia. Here we add to this literature by studying the international spillovers of a growth shock in China and how they may vary with the strength of bilateral trade linkages with China.

Formally, along the lines of Abiad and others (2013), we estimate the following cross-country time-series panel regression on quarterly data:

Δyit=αi+βt+φ1(l)shockchina,t+φ2(l)shockchina,tTradeLinkichinat,t1+φ3(l)TradeLinkichina,t1+Xitβ+εit

Where Δyit is the change in the log of quarterly real GDP of country i at time t, αi is a country fixed effect, shockchina,t is the China growth shock in quarter t, and Xit includes controls for bilateral banking integration with China as well as global factors that affect growth like the world oil price and global financial uncertainty (measured by the VIX).

TradeLinkichina,t captures trade linkages between China and country i. Alternative trade variables are tested for, as discussed below. All are constructed at a quarterly frequency by interpolating available end-year observations using quarterly fluctuations of bilateral gross trade. Positive coefficients ϕ2 would imply that the trade variable considered is a propagation mechanism for growth shocks originating from China.

Following Morgan and others (2004), the China growth shock is computed simply as the residual growth rate that remains after removing China’s average growth rate over the sample period and the average growth rate of all countries during a given quarter, that is, after estimating the panel regression:

git=αi+αt+υit

where git is the quarterly growth rate of country i in year t; αi and αt are country and time dummies, and residual growth ϑ1t^ is the growth shock for country i (and that for China for i=China). The regression is estimated over the period 1995Q1–2012Q4 for the 63 countries in the sample.

Results

Estimation results for the China growth spillovers specification above are presented in Table 5.15 Trade is a significant transmission channel (column 1). As expected regarding a growth shock originating from China, statistical significance increases when only value added exported by country i to China is considered—that is, ignoring the value added exported by China to country i (column 2). Finally, also consistent with priors, the estimated effect becomes larger when only the value added “exported” to China for final demand purposes—that is, the value added that “stays” in China—is considered: the impact (on growth spillovers from China to a given country i) of 1 percentage point of GDP of value added exported by country i to China for final demand purposes is estimated to be over twice as large as the effect of 1 percentage point of GDP of value added exported to China for any purpose—final demand or re-export (column 3). These results are robust to the inclusion of bilateral banking integration with China as a control variable (results not reported).

Table 5.

China’s Spillover Effect Identified via Various Trade Linkages

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Source: IMF staff estimates.Note: F statistics of joint significance of contemporaneous and lagged values of the interaction term, based on standard errors clustered at country level, are reported in parentheses. Coefficients shown are the sum of the contemporaneous and lagged coefficients. All models also include time trends. The estimated model is Δyit = αi +βt+ ∅1 (l) shocktchina+ ∅2 (l) shocktchina * TradeLinkiChina,t-1 + ∅3 (l) TradeLinkiChina,t-1 + Xjt’ +β+ ɛit. shocktchina is the growth shock originating from China. * p<0.10, ** p<0.05, *** p<0.01

Based on column 3 in Table 5, Figure 14 suggests that China’s growth shocks have sizeable international spillovers. Specifically, a 1 percentage increase in China’s growth is estimated to raise GDP growth in the median Asian economy by over 0.3 percentage point after a year, and in the median non-Asian economy by about 0.15 percentage point at the same horizon. These estimates come fairly close to those in Ahuja and Nabar (2012) based on a macro panel approach, but they are larger than these authors’ results using a FAVAR approach, and also larger than the spillovers that typically come out of global DSGE model simulations.

Figure 14.
Figure 14.

Estimated Impact of 1 Percent Growth Surprise in China on Partner Country Growth

(Median GDP growth impact after one year; in percentage points)

Citation: IMF Working Papers 2014, 052; 10.5089/9781475522464.001.A001

Source: IMF staff estimates.Note: Estimates based on column (3) in Table 5.

IV. Concluding Remarks and Policy Implications

The impact of trade integration on BCS has been extensively researched over the years, with the most careful, recent papers casting doubt on earlier evidence of a strong positive effect. In this paper, we contribute to existing literature by relying for the first time on value-added trade data, as well as by taking into account the impact of a host of trade-related variables and other influences on BCS in a panel setup that also controls for global common shocks and country-pair heterogeneity.

It turns out that using value-added trade data is key to finding any robust impact of trade intensity on BCS, consistent with the fact that gross trade data—used in all previous literature—poorly capture trade linkages between countries in a world characterized by growing international fragmentation of production processes. The impact of trade is greater in crisis times, echoing the greater positive impact of financial integration during such periods. We also find some positive effects on BCS of intra-industry trade and trade specialization correlation between country pairs, but no significant impact of vertical integration over and above that of trade intensity. Finally, macro-economic policy synchronization also matters for BCS.

Spikes in BCS over the past two decades have largely coincided with major global or regional shocks such as the GFC or the Asian crisis of the late 1990s. Nonetheless, there has been some small trend increase in BCS even outside these periods, especially within Asia. Our analysis implies that growing international trade integration was one of the explanatory factors, accounting for about a fourth of the trend rise in BCS within Asia, for instance. And during the GFC, it appears that greater financial integration, and to a lesser extent greater trade integration, amplified the spike in BCS, most likely reflecting increased international spillovers across countries. In separate analysis, we find that growth shocks in China have sizeable international spillovers that vary across countries depending on their dependence (in value-added terms) on final demand in China—spillovers are therefore stronger in Asia than elsewhere.

Perhaps the most important implication of these results is that, all else equal, BCS among economies would be expected to continue to rise in the future as world economic integration increases. Trade integration would be a driving force in normal times, and an amplification mechanism in crisis times. Any growth shocks in China would also induce more synchronization as the importance of China as a source of final demand for the rest of the world grows bigger. Finally, future increases in financial integration—especially in emerging economies where it continues to lag behind trade integration, and some catch-up could therefore be expected—could also strengthen spillovers and synchronization in crisis periods, even though they may well reduce co-movement in tranquil times.

Trade Integration and Business Cycle Synchronization: A Reappraisal with Focus on Asia
Author: Mr. Romain A Duval, Mr. Kevin C Cheng, Kum Hwa Oh, Richa Saraf, and Miss Dulani Seneviratne
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    Domestic and Foreign Value Added Embedded in Exports

    (in percent of total gross exports)

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    Value Added Exported to Partner Countries for Final Demand

    (In percent of total value added exported for final demand by the country)

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    Degree of Intra-Industry Trade

    (Median Bilateral Grubel-Lloyd Index; ranges from 0 to 1)

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    Correlation of Trade Specializations

    (Median Bilateral Trade Specialization Correlations)

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    Median Bilateral Banking Integration

    (In percent of total external position with the world)

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    Illustrative Impact of Explanatory Variables on Co-movement

    (Estimated impact on BCS of Moving from the 25th Percentile to 75th Percentile of the Cross-country Distribution of the Variable Considered)

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    Estimated Contribution of Trade and Other Variables to the Trend Increase in BCS: Asia and Non-Asia

    (Change in median quasi-correlation between 1999–2005 and 2006–12 (excl. 2009) and contributions of the explanatory variables)

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    Estimated Impact of 1 Percent Growth Surprise in China on Partner Country Growth

    (Median GDP growth impact after one year; in percentage points)