The impact of intra-industry trade on business cycle synchronization in East Asia
Introduction
The export-oriented growth path of East Asian economies highlights trade as a leading candidate of business cycle transmission. Could Asian emerging economies be decoupled from the European Union (EU) and the United States? How much does international trade transmission affect business cycle synchronization? Would greater trade flows between two countries cause greater business cycle synchronization? This paper analyzes these questions, utilizing standard approaches based mainly on the framework of Shin and Wang (2003). Data from eleven Asian countries, the Euro zone and the United States are used to discuss and determine trade integration and business cycle synchronization.
The discussion of business cycle co-movement originated with a series of correlation studies. The basic measure of co-movement between time series is a classical correlation, which is also commonly used in business cycle correlation research. At the same time, there is a longstanding concern regarding transmission channels through which business cycle fluctuations in one country are transmitted to other countries. The issue of business cycle synchronization is also relevant in the context of the possible formation of a currency union within East Asia, which has been revived in the wake of the Asian Crisis. A great deal of literature has been motivated by the implementation of optimum currency area (OCA)1 criteria in the context of the pros and cons of regional monetary union or greater regional policy coordination (Willett, Permpoon, & Wihlborg, 2010). Based on the OCA argument of Mundell (1961), my empirical research begins by testing that countries with closer trade links tend to have more tightly correlated business cycles (Frankel & Rose, 1998).
As vertical specialization increases in East Asia, it is expected that the links in business cycles among East Asian countries will become much closer due to sector-specific shocks, although inter-industry trade and intra-industry trade lead business cycles across trading countries to move in opposite directions. We define East Asia to include nine emerging economies (China (Mainland), Hong Kong, Taiwan, Singapore, South Korea, the Philippines, Thailand, Malaysia, Indonesia and, one industrial economy (Japan) and India (due to its impressive growth rate). The criteria for selecting this set were data availability and the uncertainty of these countries in other studies as major representatives of Asian emerging economies.
Section snippets
Theoretical norms
Theoretically, increased trade can lead business cycles across trading partners to shift in opposite directions (Shin & Wang, 2003). In terms of international trade and cross-country convergence, intra-industry trade, especially vertical intra-industry trade, is the major source contributing to the convergence of business cycles. Statistically, 80% of this convergence is due to vertical intra-industry trade and 20% is due to horizontal intra-industry trade (Luis & Maria, 2007). On one hand,
The Frankel and Rose model
Eichengreen (1992), Kenen (1969) and Krugman (1993) argue that as trade linkages increase, greater specialization of inter-industry trade occurs, resulting in less synchronization of business cycles. However, Frankel and Rose (1998) argue that if intra-industry trade is more pronounced than inter-industry trade, business cycles will become more positively correlated as trade become more integrated. They use thirty years of data for twenty industrialized countries and the following regression
Data description
There are at least four different channels affecting business cycle co-movements: inter-industry trade, intra-industry trade (horizontal-commodity trade vs. vertical-fragmentation trade), demand spillovers and policy correlations. In addition, capital flows can also be relevant. The first channel implies that increased trade leads to less synchronization of business cycle fluctuations, while the other three channels indicate increased trade would induce more business cycle fluctuation
Results for standard approaches analysis
Generally speaking and in most cases, the coefficients for intra-industry trade stay positive and significant at the 10% significance level in pooling regression and panel regression with random effects. However, the coefficients of trade intensity have both positive and negative signs for different measures WX, WM, and WT. The coefficients for the three policy variables — fiscal policy correlation measure, monetary policy correlation measure and exchange rate movement measure — on the whole,
Conclusion and policy implications
By using standard correlation approaches, the positive and important role played by IIT has been confirmed in both pooling regressions and panel regressions with random effects, and the results are supported by using Hausman test. That is, the coefficients for IIT are consistently positive and statistically significant at the 5% significance level, in most cases, indicating that IIT has a positive and significant weight in explaining business cycle synchronization. For the trade intensity
Acknowledgements
This article was supported by National Natural Science Foundation of China (Grant No.71503284).
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