Enhancing Surveillance - Interconnectedness and Clusters - Background Paper

This paper provides additional detail for the framework discussed in “Enhancing Surveillance – Interconnectedness and Clusters” through theoretical and empirical analysis of linkages, including case studies of Saudi Arabia, the Asian supply chain, financial interconnectedness and cross-border policy dependence in banking, and the Sweden-Baltic connections. It also provides a detailed primer on network analysis.

Abstract

This paper provides additional detail for the framework discussed in “Enhancing Surveillance – Interconnectedness and Clusters” through theoretical and empirical analysis of linkages, including case studies of Saudi Arabia, the Asian supply chain, financial interconnectedness and cross-border policy dependence in banking, and the Sweden-Baltic connections. It also provides a detailed primer on network analysis.

I. How Do Global Linkages Affect The Volatility And Synchronization Of Business Cycles 1

This note provides an overview of the theoretical and empirical literature on the implications of greater trade and financial integration for the volatility and synchronization of business cycles. Theoretical models offer varying predictions depending on the extent and nature of integration (e.g., inter- vs. intra-industry trade), types of shocks (e.g., industry-level vs. common shocks), and the levels of economic and financial development (e.g., the ability to diversify risk). Empirical research reports no systematic relationship between the intensity of trade and financial linkages and output volatility. Most empirical studies suggest that trade integration has a positive impact on synchronization, but the role of financial integration remains debated.

A. Theoretical Implications

1. Varied effects. Theoretical models have different implications about how global integration should affect volatility of output and other macroeconomic aggregates. The same is true for co-movement of different macro variables. We review the literature on each of these separately.

Volatility and Synchronization of Output in Theory

2. Context matters. There is no consistent theoretical prediction across different models about how trade and financial linkages affect output volatility (Hirata, Kose, and Otrok, 2012). The effects of trade and financial integration depend, in different models, on the level of development, the nature of shocks, and the pattern of specialization.

3. Trade integration. International trade linkages generate both demand and supply-side spillovers across countries, which can increase the degree of business cycle co-movement. For example, on the demand side, an investment or consumption boom in one country can generate increased demand for imports, boosting economies abroad. On the supply side, a positive shock to output in tradable goods leads to lower prices; hence, imported inputs for other countries become cheaper.

4. Impact of specialization. Both classical and “new” trade theories imply that increased trade linkages lead to increased specialization. How does increased specialization affect the degree of co-movement? The answer depends on the nature of specialization (intra- vs. inter-industry) and the types of shocks (common vs. country-specific). If stronger trade linkages are associated with increased inter-industry specialization across countries, then the impact of increased trade depends on the nature of shocks. If industry-specific shocks are more important in driving business cycles, then co-movement is expected to decrease (Krugman, 1993). If common shocks, which might be associated with changes in demand and/or supply conditions, are more dominant than industry-specific shocks, then this would lead to a higher degree of business cycle co-movement (Frankel and Rose, 1998).

5. Financial integration. The effects of financial integration on cross-country correlations of output growth are also ambiguous in theory. Financial integration could reduce cross-country output correlations by stimulating specialization of production through the reallocation of capital in a manner consistent with countries’ comparative advantage. However, financial linkages could result in a higher degree of business cycle synchronization by generating large demand-side effects as the changes in equity prices affect the dynamics of wealth. Furthermore, contagion effects that are transmitted through financial linkages could also result in heightened cross-country spillovers of macroeconomic fluctuations (Claessens and Forbes, 2001).

6. Diversification and risk sharing. Financial integration allows relatively capital-poor emerging market economies (EMs) and other developing economies (ODEs) to diversify away from their narrow production bases, thereby reducing their output volatility. At a more advanced stage of development, however, trade and financial flows could allow for enhanced specialization (Imbs and Wacziarg, 2003). This could make advanced countries and EMs more vulnerable to industry-specific shocks and thereby could lead to higher output volatility (see Kalemli-Ozcan, Sorensen, and Yosha, 2003). That leaves open the question of whether financial integration promotes better risk sharing across countries, which we discuss next.

Consumption Risk-Sharing in Theory

7. Consumption smoothing. Basic theory has the strong prediction that, because financial integration should help countries diversify away country-specific shocks, it should result in more stable consumption patterns and stronger co-movement of consumption growth across countries. Since consumers and, by extension, economies are risk-averse, basic theoretical models predict that consumers should desire to use international financial markets to insure against income risk, thereby smoothing the effects of temporary idiosyncratic fluctuations in income on consumption. In particular, if output fluctuations are not perfectly correlated across countries, in a world with complete markets trade in financial assets can be used to delink national consumption levels from the country-specific components of output fluctuations. In turn, this should make consumption growth less volatile relative to income growth. And from a cross-country and time series perspective, increasing financial linkages should lead to lower and declining relative volatility of consumption growth (Lewis, 1999).

8. Model-specific features matter. While the benefits of international risk-sharing could be quite large, the magnitudes do depend on various model-specific features. In particular, the welfare gains in these models depend on the volatility of output shocks, the rate of relative risk aversion, the risk-adjusted growth rate and the risk free interest rate . These benefits are expected to be greater for EMs and ODEs as they are intrinsically subject to higher volatility because their production structures are less diversified than those of advanced economies. Recent research suggests that EMs and ODEs can indeed reap large benefits from international risk-sharing arrangements (Prasad et al., 2003).

B. Empirical Evidence

We organize our review of the empirical literature in parallel with our survey of the theoretical literature.

Business Cycle Synchronization

9. Impact of integration. Recent empirical studies are in general unable to provide a concrete explanation for the impact of stronger global linkages on the co-movement of business cycles. Some of these empirical studies employ cross-country or cross-region panel regressions to assess the role of global linkages on the co-movement properties of business cycles in advanced countries (Kose and Yi, 2006). While Imbs (2004) finds that the extent of financial linkages, sectoral similarity, and the volume of intra-industry trade all have a positive impact on business cycle correlations, Baxter and Kouparitsas (2005) document that international trade is the most important transmission channel for business cycle fluctuations. The results of Kose, Prasad, and Terrones (2003b) suggest that both trade and financial linkages have a positive impact on cross-country output and consumption correlations.

10. Diverse conclusions. Empirical studies do not provide a definite answer on how co-movement varies in response to changes in the volume of trade and financial flows. Findings on the temporal evolution of business cycle synchronization in response to increases in financial integration are diverse. Differences in country coverage, sample periods, aggregation methods used to create country groups, and econometric methods employed seem to contribute to these varying conclusions. Some studies find evidence of declining output correlations among advanced economies over the last three decades (Heathcote and Perri, 2004; Stock and Watson, 2003).

11. Stronger links. However, other studies document that business cycle linkages have become stronger over time. Kose, Otrok, and Whiteman (2008), employing a Bayesian dynamic factor model, find that for advanced countries business cycle correlations on average have risen since 1960. Using a longer sample of annual data (1880-2008), Bordo and Helbling (2011) also document that the degree of synchronization among advanced countries has increased over time. The evidence for a European business cycle and increasing business cycle co-movement in the EU area has also been mixed. For instance, Artis, Krolzig, and Toro (2004) find evidence of a European business cycle while Canova, Ciccarelli, and Ortega (2007) argue that, since the 1990s, there is no evidence of a specific European cycle (Hirata, Kose, and Otrok, 2011).

12. AMs and EMs. There has been a recent debate about the temporal evolution of the degree of synchronization of business cycles in advanced economies and emerging market countries. This debate focuses on the ability of EMs, especially emerging countries in the Asia-Pacific region, to decouple from a potential slowdown in the United States (Helbling et al., 2007; Kose and Prasad, 2010). Although there are many studies analyzing the decoupling potential of emerging economies using various methodologies, their results have not been conclusive so far.

13. The case against decoupling. Some studies employ simple correlations to make a case against the decoupling potential of emerging markets. For example, Flood and Rose (2009) use GDP data for 64 countries over the period 1974-2007. After de-trending these series using various filters, they analyze rolling-window correlations across advanced countries and developing economies and conclude that, while the average level of cross-correlations changes over time, there is no strong evidence that these correlations have become statistically significantly lower in the later parts of their sample. In a related study, Walti (2009) reports that the extent of co-movement of cycles across advanced countries and EMs has not changed much since the early 1980s.

14. The case for decoupling. Other studies provide evidence supporting the possibility of decoupling of business cycles in emerging markets from those in advanced countries. For example, Kose, Otrok, and Prasad (2012) examine the sources of macroeconomic fluctuations in the advanced and emerging market countries using dynamic factor models and the series of output, consumption, and investment for the 1960-2008 period. Their findings indicate that there has been a substantial convergence of business cycles among advanced economies and among emerging markets over time, but there has also been a concomitant divergence of business cycles between these two groups of countries. Mumtaz, Simonelli, and Surico (2011) also employ a dynamic factor model and report findings confirming the decoupling of business cycles using data for a group of 36 countries over a 75-year period. Other studies also provide support for the decoupling hypothesis (Dooley and Hutchison, 2009; Rossi, 2009; Fidrmuc and Korhonen, 2010).

Volatility of Output and Consumption

15. No systematic empirical relationship. Existing evidence, using a variety of regression models with different country samples and time periods, reports no systematic empirical relationship between the intensity of trade and financial linkages and output volatility. Some studies report that the ratio of consumption growth volatility to income growth volatility actually increased during the recent period of globalization for emerging market economies (Kose, Prasad, and Terrones, 2003b). Importantly, they find that the volatility of consumption rose (perhaps because of crises experienced by some of these economies) by more than income volatility did. This result runs counter to the theoretical prediction that financial integration allows countries to share income risk and smooth consumption. These authors also report that increasing financial integration is associated with rising relative volatility of consumption, albeit only up to a threshold. Beyond a certain level of financial integration, an increase in integration actually reduces the relative volatility of consumption.

16. Ambiguous evidence. Other studies, such as Bekaert, Harvey, and Lundblad (2006), find that, following equity market liberalizations, there is an outright decline in consumption volatility. Using both micro and macro datasets, Kalemli-Ozcan, Sorenson, and Volosovych (2010) examine the links between “deep” financial integration, a concept based on the idea of foreign ownership, and

business cycle volatility. They report that there is a positive association between foreign ownership and the volatility of a firm’s various outcomes, a result that extends to aggregate data as well. Differences across these studies could arise from variations in the definitions of financial integration, the measures of consumption volatility, data samples, and methodologies. Nevertheless, the evidence so far is ambiguous.

17. EMs. Why have financial flows been associated with an increase in the relative volatility of consumption in emerging market economies? One explanation is that positive productivity and output growth shocks in these countries led to consumption booms that were willingly financed by international investors. These consumption booms were accentuated by the domestic financial liberalization that many of these countries undertook at the same time that they opened up to international financial flows, thereby loosening financing constraints at both the individual and national levels. When negative shocks hit these economies, however, they rapidly lost access to international financial markets, depressing consumption. Consistent with this, a growing literature suggests that the procyclical nature of capital flows explains the adverse impact of international financial integration on consumption volatility in these economies. One manifestation of this procyclicality is the phenomenon of “sudden stops” of capital inflows (Calvo and Reinhart, 1999).

Consumption and Income Risk-Sharing

18. International consumption risk sharing. There is a rich empirical literature directly studying various dimensions of international consumption risk sharing, also in response to changes in financial integration. This literature may be divided into three categories.

  • The first category includes studies focusing on the patterns of international correlations to determine the degree of consumption risk sharing (Kose, Prasad, and Terrones, 2003a, 2009; Ambler, Cardia, and Zimmermann, 2004). The results of these studies indicate that the theoretical predictions regarding perfect risk sharing do not have much empirical support for three reasons (Backus, Kehoe, and Kydland, 1992). First, empirical studies indicate that the correlations between the consumption paths of various countries are relatively low. Second, consumption correlations are lower than those of output. Third, correlations between consumption and domestic output are generally higher than those between consumption and world output.

  • The second category tests more formally the hypothesis of perfect risk sharing with the help of regression models. In addition to the basic stylized facts reviewed above, researchers have employed more rigorous methods to test the risk sharing implications of models with financial integration. These tests generally use some versions of reduced form solutions (or first order conditions) of models and focus on the links between various measures of domestic consumption and world consumption (Cochrane, 1991; and Mace, 1991). For example, Lewis (1996) finds that the hypothesis of risk sharing is rejected for countries with few or limited capital controls. Relative to these countries, the correlations between domestic consumption and output are higher for countries with more restrictions, which suggests less risk sharing by countries in the latter group.

  • The third category of studies employs various regression models to measure the extent of risk-sharing and the impact of financial flows on the degree of risk sharing. For example, Sorenson, Yosha, Wu, and Zhu (2007) analyze the relationship between home bias and international risk sharing. They document that the extent of international risk sharing has risen during the late 1990s, while home bias in debt and equity holdings has declined in advanced countries.

19. Modest degree of international risk sharing. However, Kose, Prasad, and Terrones (2009), using a variety of empirical techniques, conclude that there is at best a modest degree of international risk sharing, and certainly nowhere near the levels predicted by theory. In addition, only advanced countries have attained better risk sharing outcomes during the recent period of globalization, with developing countries, by and large, shut out of this benefit. Even EMs, which have witnessed large increases in cross-border capital flows, have seen little change in their ability to share risk (Giannone and Reichlin, 2006; Moser, Pointner, and Scharler, 2004). The composition of flows appears to be an important factor behind the modest degree of risk sharing in EMs, as portfolio debt—the dominant form of capital inflows to these economies—does not seem to be conducive to risk sharing.

20. Inconclusive findings. Although the implications of financial integration for business cycle volatility and co-movement have substantial implications for stability and welfare, the existing theoretical studies and empirical evidence are thus far inconclusive on this issue. In particular, financial integration does not always reduce the amplitude of business and financial cycles, and may actually increase it. While risk-sharing benefits of integration are apparent in theory, it is hard to find conclusive empirical evidence in support of these benefits. Why is it so difficult to obtain sharp results about the implications of financial integration for volatility and co-movement? One potential reason could be the changes in the nature of shocks as cross-border linkages become more intensive, which we discuss next.

C. Changing Nature of Shocks and Linkages

21. Integration and shocks. Increased integration could also affect the dynamics of co-movement by changing the nature and frequency of shocks.

  • First, stronger trade and financial linkages may necessitate and lead to a higher degree of policy coordination which, in turn, raises the correlations between shocks associated with nation-specific fiscal and/or monetary policies. This could have a positive impact on the degree of business cycle synchronization (Darvas, Rose, and Szapary, 2005; Flood and Rose, 2009).

  • Second, shocks pertaining to changes in productivity could become more correlated, if rising trade and financial integration leads to an increase in knowledge and productivity spillovers across countries (Kose, Prasad, and Terrones, 2009). More financially integrated economies are able to attract relatively large foreign direct investment flows, which have the potential to generate productivity spillovers.

  • Third, increased financial integration and developments in communication technologies lead to faster dissemination of news shocks in financial markets. This could have a positive impact on the degree of business cycle synchronization if, for example, good news about the future of the domestic economy would increase domestic consumption through its impact on wealth, and if consumers in other countries, who hold stocks in the domestic country, raise demand for goods in their countries.

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II. Business Cycle Synchronicity—A Multi Dimensional Scaling Approach1

Against the backdrop of theory and empirics that to date have found no unambiguous link between interconnectedness and output synchronization, this note provides some simple stylized facts on output synchronization across groups of countries. It does so utilizing a technique known as multidimensional scaling (MDS) to map business cycle synchronicity across countries. Such maps can highlight cases where there may be tensions between the synchronicity of business cycles and macroeconomic policies (e.g., for those in monetary unions). They also draw attention to countries whose business cycles may be highly synchronized but that may not belong to an easily identifiable region. Finally, MDS mapping can help visualize how the synchronicity of business cycles has changed over time to detect whether some countries have become more or less integrated with others in a group of interest.

A. Introduction

1. An economic, not geographic, map. This note utilizes multidimensional scaling to construct a business cycle synchronicity map. A business cycle synchronicity map is a visual representation of real GDP growth correlations among a group of countries. Countries whose business cycles are more correlated (synchronous) will be located closer to each other on the map and less correlated countries will be located further away. Thus, economic relations, rather than geographic distance, determine countries’ proximity to each other.

2. Advantages. The non-metric multidimensional scaling (MDS) is an example of an ordination technique, a class of statistical methods designed to order objects such that similar objects are ordered closer to each other. Other examples of ordination techniques include principal components analysis and correspondence analysis. Some previous examples of the use of MDS include Mar-Molinero and Serrano-Cinca (2001) to model bank failure, and, most relevant for the purposes of this note, Camacho et al. (2006) to study the existence of the European business cycle. One advantage of the MDS methodology is that it requires limited assumptions on the underlying data, which makes the analysis reasonably robust to alternative assumptions. The method aims to provide a visualization for a set of high-dimensional data by giving each data point a location in a two or three-dimensional map.

B. Methodology

3. Growth correlations. We use the correlation of annual GDP growth rates as a measure of synchronicity of business cycles. The MDS algorithm begins by constructing an N by N distance matrix, where N is the number of countries in the analysis. To obtain a global picture, the analysis here focuses on two country groups: the largest 100 and 140 economies (see Figures 1 and 2). This matrix contains dissimilarity measures, i.e., measures of how dissimilar are the business cycles between two countries. We define the dissimilarity measure as one minus the correlation coefficient of two series of real GDP growth, over the sample period 1995-2010.2

4. Projecting to 2-D. The MDS algorithm optimally reduces the N-by-N synchronicity matrix into an N-by-2 matrix of coordinates such that the relative distances between them most accurately represent the relative synchronicity of business cycles. We choose a two-dimensional representation because it is easiest to interpret visually. Starting from an initial (possibly random) arrangement of the countries, the algorithm then regresses the actual dissimilarities on the distances in the two-dimensional map and minimizes the sum of squared differences between dissimilarities and the distances predicted by the regression (known as stress). The ideal solution would yield a regression with a perfect fit that is a two-dimensional map, which accurately represents the distances of the original matrix of dissimilarities. In practice, the ideal solution is not obtainable, but one can characterize the goodness of the approximation using the stress number. The MDS algorithm can be easily implemented using Matlab’s Statistics Toolbox or other statistical software.

C. Results and Policy Implications

5. Clusters of correlated economies. Global business cycle synchronicity maps reveal interesting intra and inter-regional clusters. Figures 1 and 2 show, respectively, business cycle synchronicity maps for the 100 and 140 largest economies. The results are also robust to an alternative method of representing dissimilarities between countries known as t-Distributed Stochastic Neighbor Embedding. A few interesting observations arise from the analysis:

  • Advanced economies. The core advanced economies (AE), EU countries, the U.S., the U.K., and Canada, form a tight cluster. While Ireland and Portugal are situated close to the advanced economy core, Greece is further away from its euro area neighbors than some non-euro area countries. This illustrates the persistently asynchronous business cycles among certain euro area members despite common exchange rate and monetary policy. Such tensions between policy actions and macroeconomic fundamentals as shown for the case of Greece could serve as useful warning signal in Fund surveillance.

    uA01fig01

    Synchronized Output Map: Advanced Economies

    (Multidimensional scaling of GDP growth correlations, 1995-2010)

    Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

  • East Asia. Separately, East Asian supply chain countries (Hong Kong SAR, Philippines, Malaysia, Taiwan, Province of China, Korea, Singapore, Vietnam, and Japan) are situated between China and the AE core, but they themselves do not form a tight cluster like the AE core. This illustrates that surveillance needs to take into account a variety of countries as possible sources of growth shocks, while at the same time convergence among Asian emerging markets is not nearly as significant as among the advanced economies.

  • Latin American EMs. It is notable that most of the large Latin American EMs (except Mexico) are situated closer to the Asian EM cluster than to the AE core. This possibly reflects increased importance of commodity trade with China, the precise composition and extent of which may merit further exploration. It also sheds light on the stylized facts on AE and EM business cycle synchronicity as outlined in Note I. While AE business cycle synchronization has been close, EMs’ cycles have been somewhat less synchronized than AEs, although more synchronized with each other. This could be for a variety of factors, as discussed in Note I.

    uA01fig02

    Synchronized Output Map: Emerging Markets

    (Multidimensional scaling of GDP growth correlations, 1995-2010)

    Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

  • Commodity exporters. For the oil producers, there is some weak clustering around Saudi Arabia. However, the majority of oil producers are fairly scattered around the AE and Asian/Latin American cluster. This highlights two interesting observations. First, while most of the oil producing countries face similar terms of trade shocks and have a common exchange rate policy that is pegged to the U.S. dollar, the map illustrates their business cycles are not that similar, suggesting another instance of tension between policy and fundamentals to be further explored in surveillance. Second, we also see that most of the oil producers are situated closer to the large Asian EMs (China and India) than the AE core, suggesting the former’s increasing importance as a source of demand.

    uA01fig03

    Synchronized Output Map: Oil Producers

    (Multidimensional scaling of GDP growth correlations, 1995-2010)

    Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

  • Low income countries (LICs). Finally, there is no observable clustering among LICs, which reflects the importance of idiosyncratic shocks (political cycle and weather pattern, etc.) to these countries business cycles.

D. An Application to Common Currency Areas

6. Euro Area. Since euro adoption, business cycles within the Euro Area (EA) have become largely synchronized, around a core comprising Germany, France, Italy, and some northern European countries (see Figure 3, upper panels). However, peripheral countries such as Greece, Ireland, Portugal, and Spain have remained located well outside that EA core in terms of business cycle synchronicity and unit labor cost convergence. Recent entrants into the European Union have also moved closer to the Euro Area, reflecting increased trading ties.

  • Structural fissures through the EA core. Despite largely convergent macroeconomic policies, close trade and financial links, structural fissures run right through the EA core. On some measures, such as unit labor costs, France and Italy are far removed from Germany (see Figure 3, lower LHS panel). On current account balances, Germany forms one group with Austria and the Netherlands, while Belgium, Italy, and France join Greece, Ireland, Portugal, and Spain to form another (see Figure 3, lower RHS panel). The split in current account dynamics reflects the divergence in the countries’ national saving-investment balance and this reveals deeper structural differences among core EA countries.

7. West and Central African Monetary Union. The synchronicity map of West and Central African Monetary Union (which also have a fixed exchange rate between each other) shows that West African Monetary Union is slightly more homogeneous (see Figure 4). Note that the map is centered on the World real GDP series to analyze not only how close the countries are relative to each other, but also how they relate to the world business cycle. It is notable how the Central African Monetary Union splits up into two sub-clusters which are relatively distant from each other. Such patterns may merit further exploration in surveillance activities, as they call attention to possibly divergent macroeconomic outcomes against the backdrop of a common exchange rate policy.

8. Eastern Caribbean Union. The synchronicity map for Eastern Caribbean Union shows that the correlations between the member countries have increased considerably in the last 5 years, presumably due to the economic crisis (see Figure 5). In the earlier map in particular, there again seem to be two sub-clusters rather than one. Surveillance can further delve into the implications of such patterns, including the roles of natural disasters, fiscal space, and differences in income.

Figure 1.
Figure 1.

Map of Business Cycle Synchronicity Using Multi-Dimensional Scaling (MDS) (1995-2010) 1/

(Red dots denote G20 countries)

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

1/ Countries that are located closer together on the map have a higher degree of synchronization. Some country names have been shortened to improve readability of the chart.
Figure 2.
Figure 2.

Map of Business Cycle Synchronicity Using MDS for 140 Countries (1995-2010) 1/

(Red dots denote G20 countries)

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

1/ Countries that are located closer together on the map have a higher degree of synchronization.
Figure 3.
Figure 3.

European Business Cycles Synchronization and Macro-imbalances

Since the adoption of the Euro, European business cycles have moved closer centered around Germany, while peripheralcountries remained outside the EA core.

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

Source: WEO and Staff calculations.1/ The synchronization maps are computed based on the MDS method, where countries that are located closer together on the map have a higher degree of synchronization.
Figure 4.
Figure 4.

West (blue) and Central African Monetary (red) Union

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

Figure 5.
Figure 5.

Eastern Carribbean Currency Union

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

Sources: WEO; and Fund staff calculations.

References

  • Camacho, Maximo, Perez-Quiros, Gabriel, Saiz, Lorena (2006). “Are European Business Cycles Close Enough to be Just One?”. Journal of European Dynamics and Control, Vol. 30, pp. 1687-1706

    • Search Google Scholar
    • Export Citation
  • Mar-Molinero, Cecilio, Serrano-Cinca, Carlos (2001), “Bank Failure: A Multidimensional scaling Approach”. European Journal of Finance, Vol. 7, pp. 165-183.

    • Search Google Scholar
    • Export Citation

III. Cluster Analysis: Framework And Application1

To map the architecture of cross-border trade and financial interconnectedness, this note uses a cluster analysis method called Clique Percolation. This method characterizes today’s interconnected system as one that comprises a “core” set of economies, “clusters” or groups such as the Asian supply chain within which economies are closely connected to each other, and “gatekeepers”, which are individual economies or themselves clusters of economies that connect various parts of the system to one another. Gatekeepers can play important roles in propagating shocks. Understanding this architecture is a step toward better analyzing the transmittal and spillover of shocks and events and toward assessing how the system could be made more robust to shocks.

A. Motivation

1. A systemic perspective. Analyzing the cross-border propagation of shocks and of the systemic impact of policies is complex. While bilateral trade and financial connections are comprehensible, formally analyzing the chain of relations—direct and indirect—across a large number of countries is virtually intractable. There is a dimensionality problem related to the density of country-to-country relationships (across sectors, types of goods, and assets, etc.). Production chains and financial markets are deeply intertwined; as such, aggregation without double counting, or inappropriately specifying traditional models so as to avoid over or under estimation becomes daunting. Hence, traditional models have tended to simplify by studying the problem from the lens of a single country unit against the rest of the world or an aggregated basket of partners. They have rarely focused on understanding the structure of the flows between countries as a whole, the role of particular countries or flows in amplifying or dampening shocks, and most importantly how policies in certain countries can propagate and become systemic.

2. Framework. Network analysis, or graph theory—in particular centrality and cluster analysis—provides a theoretic framework which can help shed light on the complexity arising when trade and financial flows are viewed in the context of an aggregate network. Graph theory allows us to construct and analyze networks where any number of vertices are joined by edges. The edges between any two vertices can be directed (i.e., explicitly denoting a flow from one vertex to another) or weighted (i.e., reflecting some value relative to other edges). Taking trade and financial flows as networks, each country is a vertex (v), and the flow of any type of goods, service, or financial contract from one country to another constitutes an edge (e) which can be treated as either directed or weighted. Moreover, graph theory allows us to construct and analyze multigraphs, i.e. where more than one edge joins the same two vertices (multiple trade and financial links).

3. Structure of the note. The remainder of this note is organized as follows. Section B below briefly explains how we have built a network of aggregated trade and financial flows. Section C then develops the first steps in understanding the structure of the network, and shows the need for cluster analysis. Section D further develops the concept of clusters and explains the importance of appropriately choosing the identification technique, as well as our chosen methodology. Section E presents the results and explains the implications of a cluster analysis driven understanding of the aggregated trade and financial flows network. Section F presents some potential applications of cluster analysis to Fund surveillance.

B. A Network of Global Trade and Financial Flows

Building Blocks and Graph Construction

4. Data. We begin by constructing a series of n × n matrices (A) each respectively containing bilateral relationships for aggregate trade flows, consolidated and locational cross-border banking exposures (as collected by the Bank for International Settlements), as well as cross-border portfolio and direct investment exposures (from the Fund’s Coordinated Portfolio Investment Survey and Coordinated Direct Investment Survey, respectively). All data are taken at end-2009. The i and j entries in each respective A matrix reflect the raw values corresponding to the bilateral interaction between every two countries. In the case of trade, A contains the sum of exports and imports recorded bilaterally (hence Aij = Aji), while elsewhere the values reflect the gross claims of one country on another (hence Aij ≠ Aji). A contains 0 values on the diagonal (as internal trade and financial flows are not considered in our analysis).

5. Transforming the data. The entries of each A matrix are subsequently transformed into (wj) which represents the weight of the trade or financial flow between i and j in all flows out of i to the rest of the world. In cases where wij ≠ wji and neither one is zero, the weights are averaged to take the relative importance of the link for both countries. The upper triangular part of each matrix is taken to reflect the weighted edges between each two countries. The respectively weighted A matrices are then concatenated into a two dimensional array, wherein each entry represents the non-zero elements of the weighted A matrices. Finally, as the concatenated array will contain multiple edges between most country pairings, we coalesce the repeated edges by taking their geometric average. This results in a structure G = (V,E) where G is a connected and weighted graph consisting of a vertex (country) set V={d1, ..., dV}, and an edge set E ⊂ V × V (representing the weight of aggregated flows between countries) such that each edge {i,j} corresponds to a set of two adjacent vertices d1, d2 in V, and a wij.

The Structure of an Aggregated Trade and Financial Flows Graph

6. A dense center. As expected, the network representing trade and financial flows displays a high density of interactions. However, the distribution of edges between vertices does not seem to be uniform across the entire network. In particular, there seem to be a very dense set of relationships occurring at the center of the network, which peter-out as we travel toward the periphery. Moreover, there are some peripheral vertices which are linked to other peripheral vertices directly (see Figure 1 below).

Figure 1.
Figure 1.

Global Trade and Financial Flows Graph

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

7. Architecture and function. Global trade and financial relationships are in large part associated with economic activity undertaken between, and through, countries at the “core” (red vertices in Figure 1). Moreover, this suggests that economic and financial conditions in the core are likely to have material impact on the remainder of the network. However, the variation in density and the seemingly different functional role of some vertices vis-à-vis others suggests that a level of complexity exists within this graph system which would need to be further understood. Our concern then becomes how to systematically deconstruct the graph system in such a way that—irrespective of its complexity—we are able to further understand the underlying structure and functional characteristics of its building blocks (i.e., the vertices and how they are inter-related). Toward that end, we explore below various techniques and metrics.

C. Quantifying and Rank-Ordering Centrality

8. “Core” and “periphery”. Understanding which vertices belong at the core of the graph and which are peripheral is a necessary first step. Further, uncovering the correct order of the most important vertices for the graph as a whole vis-à-vis other important vertices, is also crucial. Together, these two pieces of information will later allow us to understand how the dynamics of the complex relationships unfold from the core outward, and more importantly from one peripheral part of the graph—via the core—to another.

9. Centrality measures. A methodologically robust way of quantifying the relative importance of vertices in the graph is to calculate a series of centrality measures as detailed below:

  • Degree centrality: quantifies the number of connections any given vertex has to all others in the network. A vertex degree can be computed respectively as the sum of: incoming links (in degree); outgoing links (out degree); or all links (degree).

  • Closeness centrality: measures the mean of the shortest path in terms of the number of paths, between one vertex and all others.

  • Random walk betweenness centrality quantifies the importance of each vertex in relaying flows amongst all others. This can be approximated by the expected number of times that a random walk (or trade or financial flows) between any starting and ending vertex will pass through an intermediate set of vertices averaged over all starting and ending vertices.

  • Eigenvector centrality: defines both the number and the quality of the connections any given vertex has within the network. Vertices with a large number of connections with lower connection weights may receive a lower eigenvector centrality value relative to points with fewer connections but with higher connection weights.

10. Most central economies. Based on the combination of these centrality measures, and for the available date used, we have identified the economies that display the highest centrality (higher than the 90th percentile for each measure). As evident in Table 1, the core of the trade and financial flows graph consists of 22 economies. In rank order of centrality, three distinct tiers appear at the core; the first two are dominated by Euro Area economies as well as Canada, China, Japan, Korea, Switzerland, the United Kingdom, and the United States. The final tier is largely made up of EMs along with Australia, Denmark, and Sweden.

Table 1.

Most Central Economies in Trade and Financial Flows

article image

11. Unpacking the system. What does centrality tell us that we do not already know? Understanding the core of the trade and financial flows graph, and how it is structured (i.e., who is at the core and in what ranking), confirms some existing priors, while revealing new results. Most importantly, it prompts us to address further issues concerning the complexity of the entire graph system. Revisiting Figure 1 above, along with the high centrality and core-tiering of specific economies, we can posit that the presence of some of these economies at the core reflects their high level of interaction in particular dimensions (trade, financial, or both) of the data we use. For instance:

  • The presence of China along with several of its key trading partners at the core is indicative of its role in the Asian—and global—supply chains.

  • The presence of key global financial intermediation centers reflects the importance of countries such as the United Kingdom, United States, and Switzerland in global financial flows, portfolio allocation, and management.

  • Similarly, the presence of key EMs confirms the well known prior that, as such economies continue developing, they are becoming regional magnets for financial and trade flows.

  • Overall, the core of the trade and financial flows graph in large part reflects many of the constituent economies in the Group of Twenty (G20).

12. Some surprises. Our measures of centrality also contain somewhat surprising results. Taking membership in the G20 as a representation of existing priors on identifying industrialized and developing countries that play the most systemically important role, we see that Argentina, Brazil, Indonesia, Saudi Arabia, Russia, and Turkey are absent, while Denmark, Sweden, Switzerland, Taiwan Province of China, and Thailand are part of the core. How can we explain the absence of oil exporting or significant emerging market economies from the core, while others (sufficiently proxied by other countries such as Austria-Germany and Belgium-France) appear? In addressing this, we are forced to seek a more nuanced understanding of the structural and functional properties of the trade and financial flows graph system. One meaningful way of doing this is via uncovering its underlying community (cluster) organization and the role of specific vertices within it.

D. From Networks (Graph) to Clusters

Cluster analysis lends itself to systematically uncovering and interpreting the natural organization of the vertices (countries) represented in any graph. In our case, we are concerned with how to understand country groupings—whether in the core or periphery—and in particular which countries form cohesive communities.

The Appropriate Cluster-Identification Methodology

13. Away from non-overlapping clusters. Cluster identification is essentially a problem of finding an unknown number of cohesive groups that are the underlying building blocks of a broader graph system. This can be done by imposing constraints on a maximizing problem; however, such constraints risk producing results that ignore the very complexity we are seeking to understand. Most cluster identification methods rely on partitioning algorithms which identify separable non-overlapping, non-nested communities as shown in Figure 2 below. While such algorithms successfully underscore the cohesive or hierarchical nature of some sets of links between groups of vertices, the less dense regions of links between groups become irrelevant. Adjacency between groups is not a trivial feature in trade and financial flows. As will be explained in detail in subsequent sections, adjacency between clusters can be utilized to define overlaps in clustering.

Figure 2.
Figure 2.

Architecture of Separable, Non-Overlapping Communities

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

14. Toward overlapping clusters. While the tractability and efficiency of traditional partitioning algorithms are attractive, they are subject to some constraints on the number, size, or shape of the clusters to be identified. For example, such methods necessitate defining ex-ante the number of clusters into which the graph should be partitioned. In effect, the choice of the number of clusters to identify will have a non-trivial effect on the results; e.g., China would only be associated with the Asian supply chain economies and not with the United States, Germany would be associated only with large Euro Area economies and not with other EU economies. Hence, multiple valid solutions may be expected. As such, they are significantly limiting for our purposes of further understanding the underlying complexity of the graph system. Also, the partitioning usually focuses on cutting the graph system into clusters while minimizing the cost of edge elimination, thus, any single vertex can only belong to one cluster. This ignores the implications of overlapping or nested communities, and dilutes the potential for a richer understanding of the particular vertices which facilitate such overlaps and, therefore, of the set of interactions across economies.

Cluster Identification with Minimal Constraints

15. Clique percolation method. A recently developed cluster identification algorithm called the Clique Percolation Method (CPM) provides a feasible way of identifying clusters without imposing the constraints mentioned previously. In other words, it allows us to identify an unknown number of potentially overlapping clusters (i.e., cohesive country groups) with varied membership size. Utilizing CPM, we model the interaction amongst countries using global trade and financial flows to map out potential shock transmission paths between countries. Country groups which share very dense connections (based on trade, BIS, and portfolio flow data) are considered “clustered.” Clusters share overlap regions which are facilitated by a specific country or group of countries’ membership in multiple clusters. Membership in multiple clusters may allow countries to act as unique links through which the effects of economic or financial conditions may be transmitted from one cluster to another. Owing to their capacity to act as transmitters (and potentially amplifiers or mitigators) of shocks, countries comprising the cluster overlap regions are categorized as “gatekeepers.” The theoretical underpinnings of CPM are detailed in Annex 1. Box 1 provides a step-by-step demonstration of how clusters are identified.

A Primer on Cluster Identification

This box provides a step-by-step guide on how clusters are identified. It is useful to begin with a few necessary glossary terms:

  • A graph is a mathematical representation of a system. A convenient way of understanding the system is uncovering its underlying structurally cohesive building blocks or components (i.e. clusters or cliques).

  • Strictly defined clusters within a graph are the set of complete sub-graphs in which all nodes are connected to one another.

  • The number of member vertices in a cluster can be used to identify the “order” k of the cluster (i.e. a cluster of 3 vertices is a 3-clique).

  • Clusters are adjacent if they share k-1 vertices.

  • A cluster chain is the union of adjacent clusters of a given cluster order.

  • If two or more clusters are connected via a cluster chain they are considered connected clusters.

  • Maximally connected clusters are uncovered by identifying the union of all connected clusters via template rolling (see Annex 1).

Identifying maximally connected clusters. This includes four steps. First, as discussed in Annex 1 we begin by identifying the following: (i) critical regime threshold associated with maximal cluster identification; (ii) edge weight threshold for reconstructing the graph system; and (iii) the appropriate order (i.e. k) range for the graph system. Next, having identified the appropriate range for k, we begin with the largest value for k and extract the clusters associated with that cluster order (i.e. removing vertices and edges identified as clustered) until we either run out of vertices or clusters of that order. We then iteratively repeat this cluster enumeration process for other k values. Thirdly, for each k value we represent the identified overlapping clusters in a new graph matrix (similar to previously explained in section B). This is called the clique overlap matrix as it represents the overlapping clusters in the original graph system. The values in this matrix represent the number of common vertices amongst each cluster pairing. For each cluster order (i.e. k) we reconstruct the overlap matrix to include only the overlap regions associated with the k-th order. Finally, after enumerating the cluster membership of each vertex in the graph along with the associated edges responsible for membership in the various clusters, we simply reconstruct the original graph system with the added benefit of identifying structurally driven overlaps and divisions.

16. Richer network architecture. A much richer differentiation among groups of countries emerges in Figure 3 relative to the earlier presentation. In the first instance, CPM clearly yields 11 distinct clusters of varying sizes. The graph is also now separable into two clearly identifiable core regions, one surrounding the United States and China, and one surrounding Italy and Germany. Moreover, we can also identify a group of countries such as France, The Netherlands, and the United Kingdom which seem to sit in between both core regions. The next section elaborates on key implications of cluster-based analysis and concepts.

Figure 3.
Figure 3.

Preliminary Clustering based on Clique Percolation Method

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

E. Cluster Analysis Results and Implications

Some economies serve as gatekeepers—linking different clusters and therefore transmitting (passing through, amplifying, or dampening) shocks. Groups of economies can together also play a gatekeeper role. The method allows for distinguishing individual gatekeepers and clusters of gatekeepers.

Gatekeeper Countries Versus Gatekeeper Clusters

17. Cluster-to-cluster relationships. CPM allows us to further understand cluster-to-cluster relationships. There is a threshold parameter, k, for defining cluster size (see Annex 1). For values of k between 5 and 8 which have been previously identified as the critical percolation region, a single cluster arises at the 8-clique level, which includes Australia, China, Indonesia, Japan, Malaysia, Singapore, Thailand, and the United States. We also identify three clusters at the 7-clique level, two of which are overlapping, while one stands separated.

18. Gatekeeper economies. Combining the clusters resulting from the 7 and 8 clique levels produces an interesting initial result. As evident in Figure 4, European economies appear clustered together, along with Tunisia and Algeria. The inclusion of Tunisia and Algeria in this cluster is on account of trade relations and remittances, while, China, Japan, Korea, and the United States act as gatekeeper countries linking key Middle Eastern economies and Pakistan to Asia. Also, on account of being members of adjacent clusters Korea, India, and Brunei are also clustered together despite having no connecting edges. In many respects, these are non-trivial results as they explicate clearly both the importance of relationship dynamics within developing Asian economies, as well as the role of their gatekeepers in linking them to oil exporters. Moreover, the analysis presents a unique result for the gatekeepers pertaining to Hong Kong SAR (see group K in Table 1 of main paper). Traditionally, Hong Kong SAR is viewed as a financial hub which is responsible for intermediating Asian savings. The results of our analysis contextualize further and add granularity by explicating the idea that the dynamic amongst the Asian economies (which are subject by varying degrees to capital flow restrictions) and the United States may potentially be the driving factor behind the role of Hong Kong (i.e., the gatekeepers’ real economic context drives the role of the equity financing hub).

Figure 4.
Figure 4.

Country Level View of First Clusters

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

19. Gatekeeper clusters. There is also a clear internal organizational structure between clusters themselves (see Figure 5). Looking at the maximal overlap between clusters which takes place at the 6-clique level followed by the 5-clique level, we respectively identify 12 and 8 overlapping clusters containing 72 and 133 countries. The number of countries which are members in each cluster varies. For example at the 5-clique level, we see that cluster 1 is a giant cluster containing 117 countries. Moreover, we also see that the clusters are not only clustered into different yet overlapping groups. At first glance, the overlap between clusters appears to involve particular clusters (red circles). This suggests that the clusters situated in overlap region are gatekeeper clusters.

Figure 5.
Figure 5.

Structure of Clusters

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

20. Gatekeeper economies in gatekeeper clusters. We can also expect that gatekeeper clusters contain as members a higher than average level of gatekeeper countries. To test this hypothesis, we have developed a metric to gauge the relative involvement of gatekeeper countries in any particular cluster. This is done by measuring the weighted contributions of cluster members to each cluster’s overlap with other clusters relative to the average overlap between clusters over the entire graph, after normalizing to account for variation in cluster size. In effect we are rank ordering the clusters in terms of the contribution of their member countries to the density of adjacencies over the entire graph. This intuitively implies that we are rebuilding the graph structure from the cluster level by prioritizing those clusters whose members act as gatekeepers. According to this metric, we are able to confirm the status of cluster #11 under the 6-clique level as a top gatekeeper cluster in the graph, while cluster #7 and #4 rank higher than nearly half the other clusters (see Figure 6).

Figure 6.
Figure 6.

Gate-Keeper Score at the Cluster Level

(Rank ordered by increasing involvement of gatekeeper economies)

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

21. Characteristics of gatekeepers. What are the characteristics of gatekeeper clusters? A gatekeeper cluster is a grouping of countries through which shock transmission can be mapped out. One potential implication of this is that the membership of the clusters which act as gatekeeper clusters contain specific countries or groups of countries which display particular characteristics that allow them to facilitate the gatekeeping role for the entire cluster. To confirm this, we focus on the country membership of cluster #s 4, 7, and 11 under the 6-clique regime.

22. Centrality and gatekeepers. It is clear that the majority of the country membership, in two of these three clusters, consists of very large and important economies (see Figure 7). In effect, the significant centrality of these countries, and their combined links, allow them easy connectivity to the remainder of the system. This becomes an even more intuitively acceptable statement, when we consider that the United States, The Netherlands, China, and Germany at the intersection of these three gatekeeper clusters. An immediate implication of these results is that core economies act (via different core regions) as gatekeepers for multiple other important economies which in turn provide a gatekeeping function throughout the entire graph. Based on this intuition we rebuild the graph representing global trade and financial flows based on the gatekeeping score of each cluster (see Figure 8).

Figure 7.
Figure 7.

Gatekeepers: An Illustration

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

Figure 8.
Figure 8.

Final Architecture

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

23. Shock propagation. These findings shed insight on shock propagation within and across clusters. An important result of this exercise is reaching the understanding that while cluster #1 under the 5-clique level is a very large and seemingly important cluster, the gatekeeping function of the majority of its important gatekeeper countries is already accounted for in other clusters. However, it is also important to note that while this particular giant cluster does not rank highly, it does allow us to identify two important pieces of information. First, it inherently identifies the weakly gatekeeping countries which may link strongly peripheral countries to the rest of the network. Second, it also helps us identify how peripheral countries may be linked to one another thus linking smaller but more important clusters indirectly. Without this final piece of the puzzle we would remain without a clear understanding of which parts of the periphery rely on the core of the graph directly and which other parts also reside in cluster chains.

24. An illustration. An illustration of the gatekeeper role and potential for shock propagation is provided in Table 2. Countries playing a gatekeeper role are identified in groups along with the countries for which they play a gatekeeping role. Keeping in mind that i) a gatekeeper links countries together which may not appear naturally clustered; and ii) the transmission of shocks may take place between clusters through gatekeepers, some key elements of the results are as follows:

Table 2.

Structural Features of Global Trade and Financial Flows Network: Gatekeepers

article image
  • Countries which are gatekeepers are coherently linked to other gatekeepers thereby creating an overlap cascade from the core of the system to the periphery. This implies that a shock transmitted from any one point must go through multiple “gates” to spread throughout the system. However, this also implies a vast potential for negative feedback effects within and between clusters.

  • Potential transmission channels are strongest from Germany and Italy to Europe, CEESE, CIS, and parts of the Middle East.

  • The United States is a key conduit for spillovers elsewhere.

  • In addition to any first round direct effects of European stress in Italy or Germany on the U.S.—if not stemmed—additional feedback effects are likely to be transmitted from other European countries to both core economies such as the U.S. as well as peripheral regions.

F. Putting it All Together—Cluster-Based Surveillance

25. Analyzing shock propagation and vulnerabilities. Cluster analysis provides a potentially useful tool in identifying country vulnerabilities, such as those related to the Euro zone. Applying the same methodology elaborated above, cluster membership and gatekeeper roles are illustrated in Figures 9 and 10. The main take-away from the analysis is that Italy is not only a “gatekeeper” for core Europe, becoming a conduit of significant shocks to Belgium, France, Germany, and the Netherlands, but it acts as a “gatekeeper” for five other areas: (i) the CEESE region; (ii) CIS economies; (iii) emerging Middle Eastern economies such as Tunisia and Jordan; and (iv) the United States. In turn, as core European economies are closely interconnected, a shock that emanates from Italy would also have secondary reverberation effects between the various core European countries, as well as the CEESE economies which are also connected to the core European economies. The analysis also suggests a few other interesting results:

Figure 9.
Figure 9.

Gatekeepers and Shock Propagation Channels

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

  • Unlike core Europe, and the CEESE economies, the CIS economies may not be as susceptible to secondary reverberation effects.

  • However, certain African economies (Burkina Faso, Cameroon, Gabon, and Senegal) are susceptible to two distinct channels of reverberation aside of the direct impact from Italy— one is their interconnectedness with France, and the other is their interconnectedness amongst each other.

  • The U.S. (rather than China) is the larger source of shock transmission from Italy to both Latin America and to Asia. The implication is that U.S. policies to counteract a shock from Italy are likely to have a greater positive impact on global stability at the margin, than isolated actions from China alone. However, concerted action by both will bring the greatest gains, as China is a “gatekeeper” to emerging Asia.

  • The U.K. appears as a shock absorber from Europe. Owing to its unique role as a global intermediator of U.S. dollar assets, it acts as a conduit for shocks to the U.S. However, the real effects are manifested in the economies which issued or whose financial sectors utilized them as funding vehicles (see Figure 10).

    Figure 10.
    Figure 10.

    European Shock Propagation Channels 1/2/

    Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

    1/ A shock is mechanically applied where the value of all links are uniformly reduced (15 percentage points) to proxy for a global GDP shock. The resulting system is analyzed in the figure.2/ The two numbers specified for each country are the share of the envisioned shock transmitted to it (i.e., incoming from others) on the left, and the share of its effects on others on the right.

  • China does not receive shocks from others. Compared to the rest of the core/gatekeepers, China’s density lies more within Asia. Stickiness in trade relationships therefore is likely to mute the impact of certain shocks on China. However, if the Chinese economy were to come under severe stress, it would produce material effects on the countries in its clusters including the United States.

26. Caveat. This analysis is aimed at mechanically mapping out the potential transmission routes through which a shock would propagate between clusters and how the corresponding feedback loops might play out within each cluster in the first instance. This should not be interpreted as a quantitative gauge of the relative intensity of shocks between countries, given the need to subsequently assess policy space and action. But as a first step to understanding the impact of a shock on the system, the analysis of potentially insightful.

IV. External Linkages And Policy Constraints In Saudi Arabia1

Saudi Arabia’s interconnections with the global economy, and the constraints that these linkages impose on domestic policy choices, continue to evolve. Specifically, fiscal policy is constrained by developments in the global oil market while monetary policy is guided by the U.S. dollar peg. Over the past couple of decades, two important developments have occurred. First, growing oil needs from EMEs has become increasingly important for oil market dynamics. Second, financial sector development in Saudi Arabia has strengthened the monetary transmission mechanism. The former implies greater influence of EMEs economic fluctuations on Saudi oil export revenues, while the latter suggests greater influence of U.S. monetary policy on the Saudi non-oil sector. Hence, situations in which global oil prices move counter-cyclically with the U.S. business cycle have become increasingly likely and could generate tension between policy objectives.

A. Introduction

1. Backdrop. On September 18, 2007, the U.S. Federal Reserve lowered its policy rate from 5.25 percent, the highest rate since March 2001, to 4.75 percent, citing tightening credit conditions and an ongoing housing market correction. By the end of October 2008, the federal funds target rate had been reduced to just one percent. During the same time period, crude oil prices surged from over $65 per barrel in mid-2007 to over $130 per barrel by the summer of 2008, partly driven by increased demand from EMEs. The situation in Saudi Arabia was quite different from that in the U.S. The rise in oil prices between 2004 and 2007 had raised oil revenues to the government which had been reflected in increased spending, leading to higher economic growth, but also rising inflationary pressure. Nonetheless, in order to prevent speculative capital inflows and maintain the exchange rate peg to the U.S. dollar the Saudi Arabian Monetary Agency (SAMA) cut its policy rate from 5 percent in October of 2007 to 2 percent by mid-2008. Annual credit growth increased from 6 percent in early 2007 to over 30 percent in July 2008, while higher world commodity prices coupled with a depreciating dollar further contributed to inflationary pressure, resulting in double digit inflation by mid-2008. This episode clearly illustrates how global interconnectedness affected policy trade-offs and had a significant impact on the Saudi Arabian economy.

2. Purpose and motivation. This note examines the evolution of interconnectedness and its impact on policy constraints in Saudi Arabia. Given the country’s dependence on oil, growing linkages with developing Asia, and the peg to the U.S. dollar, the note focuses on three external factors: (i) global oil prices, (ii) U.S. business cycle, and (iii) developing Asia’s business cycle. The motivation for examining Saudi Arabia is not only because it is an interesting case in its own right, but also because it illustrates many features that are common across resource rich economies. In particular, Saudi Arabia exhibits a low degree of economic diversification with the oil sector accounting for over half of GDP and oil exports accounting for over 80 percent of export receipts.

Furthermore, as oil revenues primarily accrue to the government, the public sector plays a central and dominant role in the non-oil economy. Finally, with the exchange rate pegged to the U.S. dollar and with a relatively open financial account, interest rate policy closely follows that of the U.S. Federal Reserve. All these characteristics can be found in many other resource rich countries albeit at varying degrees.

3. Results. The correlation of the Saudi business cycle with the U.S. has shifted over the past three decades with supply driven oil shocks causing a divergence in business cycle dynamics in the 1980s, while demand driven oil shocks in the 2000s—reflecting high growth in developing Asia— resulted in a convergence. It is further argued that the pass-through from global oil prices to fiscal spending has fallen over the past three decades, possibly accounting for the observed reduction in output volatility. Finally, based on empirical evidence, it is shown that credit dynamics are becoming increasingly more relevant for non-oil economic activity and that the importance of U.S. interest rate policy has likely risen in the 2000s.

4. Implications. Given the commitment to the fixed exchange rate and the on-going financial deepening, synchronization of the domestic and U.S. business cycles is likely to become increasingly relevant for the stabilizing impact of monetary policy. At the same time, the degree of structural interconnectedness with developing Asia has increased over time through growing trade flows and Asia’s rising influence in the global oil market. Tensions between policy objectives are therefore more likely to arise when global oil prices and the Asian business cycle move counter-cyclically with the U.S. business cycle. Going forward, these tensions highlight the importance for Saudi Arabia of strong fiscal management as well as further refining macro prudential instruments to influence monetary conditions independent of interest rate policy.

B. Business Cycles and Global Oil Prices

5. A glance at business cycle synchronization.Figures 1 and 2 compare annually de-trended U.S. and developing Asia’s real GDP with real non-oil GDP of Saudi Arabia.2 The data cover the three decades from 1980 to 2010. Figure 3 compares fluctuations in Saudi non-oil GDP with the average global oil price. Four observations immediately stand out. First, there is a clear negative relationship between U.S. and Saudi Arabian economic fluctuations in the 1980s. This negative correlation is later reversed and a positive relationship emerges in the mid-1990s. Second, economic fluctuations in developing Asia do not appear to be well correlated with Saudi non-oil GDP in the first half of the sample, but become positively correlated at the end of the 1990s and throughout the 2000s. Third, oil prices tend to be positively correlated with Saudi non-oil GDP throughout the whole sample period. Finally, volatility in Saudi non-oil GDP falls significantly in the 2000s. This also appears to be true in comparison to the U.S. and developing Asia.

Figure 1.
Figure 1.

Cyclical GDP of United States and Saudi Arabia

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

Sources: National authorities and author’s calculations
Figure 2.
Figure 2.

Cyclical GDP of developing Asia and SaudiArabia

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

Sources: National authorities and author’s calculations
Figure 3.
Figure 3.

Oil price and Saudi Arabia non-oil GDP

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

Sources: National authorities and author’s calculations

6. A simple correlation analysis. The conditional relationship between the three external factors and the Saudi non-oil economy can be examined via a simple regression with Saudi Arabia’s non-oil GDP as the dependent variable and U.S. and developing Asia’s real GDP together with the average oil price as independent variables. All variables are de-trended using the HP-filter. The assumption of exogeneity of the explanatory variables is fairly non-controversial as economic fluctuations in the U.S. and developing Asia and movements in the global oil price are unlikely to be affected by Saudi non-oil GDP. Given the observed reversal in business cycle correlations, the regression analysis is conducted on the full sample as well as for two subsamples i.e., 1980-1995 and 1996-2010. The results (shown in Table 1) are in broad agreement with the observations from figures 1-3. When the full sample is used, U.S. real GDP is negatively and statistically significant related to Saudi non-oil GDP. When splitting the sample, the negative relationship only holds for the 1981-1995 period while it turns positive in the 1996-2010 period. As expected, developing Asia’s real GDP is not statistically significant when using the full sample, but positive and statistically significant for the 1996-2010 period. Finally, the oil price has a positive impact on non-oil GDP in the first period, but is neither economically nor statistically significant in the second period. The latter may reflect both that oil prices were primarily driven by global demand—captured through the Asian and U.S. business cycle dynamics—and that the pass-through from oil revenues to fiscal spending has declined (section III).

Table 1.

Relevance of external factors for the Saudi non-oil economy

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All variables were detrended using the HP filter

Sources: National authorities and author’s calculations

7. Demand and supply driven oil shocks. What could explain the divergence between U.S. and Saudi business cycles in 1980s and subsequent correlation reversal in the late 1990s and 2000s? To a large extent the answer is related to whether the oil price cycle was supply or demand driven. For Saudi Arabia, as a net oil exporter, it is largely irrelevant (at least in the initial stage) whether a rise in the global oil price is due to a positive demand shock or a negative supply shock. Both will cause oil export revenues to rise. However, for a net oil importer such as the U.S. the economic impact of a supply driven or demand driven oil shock is quite different. For instance, a rise in the oil price due to a supply shock increases the cost of production, leads to output contraction and raises prices. Thus, one possible explanation for the observed reversal of business cycle correlations is that oil price shocks were primarily supply driven in the 1980s and early 1990s, while demand driven in the late 1990s and 2000s. Box 1 explores this explanation in more detail.

Demand and Supply Driven Shocks and Business Cycle Correlations

The 1980s and early 1990s. Three major oil supply disruptions occurred between 1978 and 1991: (i) the Iranian Revolution (1978-1979), Iraq’s invasion of Iran (1980-1981), and the first Gulf war (1990). The effect of these shocks on the Saudi Arabian economy was significant. The rise in the oil price in 1978-1981 caused oil export revenues in Saudi Arabia to increase by over 90 percent from approximately $58 billion in 1978 to $111 billion in 1981. This sizable windfall was partly spent as fiscal spending rose by 41 percent over the same time period and partly saved as international reserves. As a result, non-oil growth increased from 6 percent in 1979 to 10 percent in 1981.

Contrary to Saudi Arabia, the impact on the U.S. economy was far from favorable. Although many factors contributed to the U.S. recessions of 1979-1980 and 1981-1982, the rise in the price of oil is generally viewed as a significant contributor. The economic downturn in the U.S. and Europe in the early 1980s led to a sharp decline in oil consumption. To support the high oil price, OPEC assigned production quotas to each member. However, the bulk of the burden fell on Saudi Arabia which operated as the swing producer and was committed to the official price system. As a result, Saudi oil production fell sharply from 10 mbd in 1980 to about 3 mbd in 1985. Hence, at a time when the U.S. economy was recovering, the favorable conditions in Saudi Arabia began to deteriorate. Oil export revenues fell from $111 billion in 1981 to $11 billion in 1986 and spending fell from $84 billion to $37 billion over the same time period. The effect on growth was substantial as non-oil GDP contracted by 1.2 percent in 1984 and by 5.7 percent in 1986. However, with abandonment of its role as the swing producer in OPEC, Saudi Arabia’s oil revenues slowly began to recover, and the non-oil economy began to expand by the end of the decade.

By the end of the 1980s the U.S. economy was at its business cycle peak, but a financial crisis coupled with tighter monetary policy started to weigh on the economy and the economy fell into a recession in 1990. The oil price shock following Iraq’s invasion of Kuwait in August 1990 came therefore at an unfortunate point in time for the U.S. economy and likely worsened the downturn. Meanwhile, the oil price spike—coupled with an increase in Saudi Arabian oil production to compensate for the disruption in global supply—increased oil revenues in Saudi Arabia and further helped the domestic economy in its post-1986 recovery as non-oil growth rose above 5 percent in 1992.

The late 1990s and 2000s. Although several events occurred in the late 1990s and 2000s that had a significant impact on the global oil prices (e.g., the Asian crisis, the OPEC meeting in 1999, the recession in 2001 and the second Gulf War in 2003), the most striking characteristic in oil price dynamics has been the consistent upward trend since 1998.

Hamilton (2009) and other observers have attributed this upward trend to the strong growth performance of developing Asia and its impact on global oil demand. Indeed, the sharp increase in crude oil consumption in China, the main consumer within the block of developing Asian economies, is particularly impressive. Since mid 1990s crude consumption in China has increased from 3 mbd to above 8 mbd in 2010. The country’s share of global consumption increased from 4 percent to 10 percent over the same time period. Meanwhile the share of global crude oil consumption of developing Asia as a whole increased from 11 percent in 1995 to over 19 percent in 2010.

The rising demand for oil by developing Asia was particularly apparent in the period 2004-2008 as the price of oil climbed from an average of $40 per barrel to over $130 per barrel. During this time period developing Asia accounted for over 43 percent of the global increase in crude oil consumption while North America and Europe combined for 21 percent. Another contributing factor to the sharp rise in oil prices during this time period was the stability in global oil production. While the global economy grew by over 19 percent from 2004 to 2008, total oil production only rose by 1.8 percent (from 80.6 mbd to 82.0 mbd).

As oil price dynamics in the 2000s began to primarily reflect demand forces, the oil price cycle became increasingly pro-cyclical. This, in turn, implied that the Saudi non-oil GDP began to co-move positively with both the U.S. and developing Asia’s GDP The sharp increase in oil revenues in the latter part of 2000s translated into stable annual non-oil growth rates of 4 and 5 percent, the highest since the early 1980s.

Another argument for the increased relative importance of demand dynamics in the 1990s and 2000s is that Saudi Arabia has internalized the impact of negative supply shocks on revenue volatility by investing in, and using, spare capacity to smooth oil price dynamics (e.g., the Gulf Wars and the Libyan crisis).

C. Fiscal Policy and Oil Revenue Volatility

8. Fiscal policy. With government spending amounting to over 80 percent of non-oil GDP and the public sector taking a central role in the economy, fiscal policy constitutes the main driving force behind non-oil growth. The principal task for fiscal policy has been to balance development goals (i.e., invest in social and economic infrastructure and promote economic diversification) with macroeconomic stability in an environment of volatile oil revenues. To do so the government has engaged in counter-cyclical fiscal policy with respect to the oil price cycle. That is, when oil prices are low the government either draws down on international reserves or issues debt to finance its expenditure, and when oil prices are high part of the surplus is used to retire existing debt and build up reserves. Hence, by conducting counter-cyclical policy, the government attempts to smooth fiscal spending over time.

9. Outcome. How successful has the Saudi government been in its attempt to smooth spending in the face of volatile oil revenues? Table 2 summarizes the volatility in the growth rates of oil revenues and fiscal spending and the corresponding correlation coefficient for each of the past three decades. Several broad observations can be made. First, there is a clear positive relationship between oil revenue and spending growth over the past three decades. Second, oil revenue volatility was high in the 1980s, declined in 1990s, and rose again in the 2000s. Spending volatility, on the other hand, has fallen consistently over the past three decades. Finally, the correlation between revenue and spending was high in the 1980s and 1990s, but fell significantly in the 2000s. Hence, it appears that the fiscal authorities have been gradually more successful in smoothing spending despite continued oil revenue volatility. One major difference between the 1980s and 2000s was that oil revenues were declining for a significant portion of the 1980s while the opposite was true in 2000s. There might thus be an asymmetrical response to increases versus decreases in oil revenues. This could potentially explain why non-oil GDP in the 2000s was less volatile than in previous decades.

Table 2.

Volatility and correlation of oil revenue, spending and non-oil growth

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Sources: National authorities and author’s calculations

D. Financial Deepening and Monetary Policy

10. Bank dominance. As in most emerging economies, the financial system in Saudi Arabia is dominated by commercial banks. As a consequence, monetary policy primarily influences economic activity through two channels; the exchange rate and bank lending. However, with the U.S. dollar peg and the open financial account, SAMA’s ability to affect the economy through the exchange rate and the short-term interest rate is limited. Al-Jasser and Banafe (1999) lay out the channels of the monetary transmission mechanism in Saudi Arabia. They argue that the interest and credit channels are likely to be weak due to the presence of government controlled Specialized Credit Institutions (SCIs), lack of financial leverage, and imperfect pass-through of the policy rate to the lending rate due to imperfect competition.

11. Financial development. Since 1999, however, the banking system in Saudi Arabia has grown significantly in size while the relative importance of SCIs has declined (see Figures 4 and 5). Furthermore, SAMA has taken steps to liberalize the banking system and allow for increased competition (SAMA, 2003). An increased presence of foreign banks has also emerged, following Saudi Arabia’s accession to the WTO (Ramady, 2010). Thus, as the financial system has deepened and the frictions identified by Al-Jasser and Banafe (1999) have loosened up, it is reasonable to assume that the effectiveness of monetary policy has increased over time.

Figure 4.
Figure 4.

Credit by commercial banks

(Share of non-oil GDP)

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

Source: Saudi Arabian Monetary Agency
Figure 5.
Figure 5.

Credit by Specialized Credit Institutions (SCIs)

(Share of non-oil GDP)

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

Source: Saudi Arabian Monetary Agency

12. Credit and non-oil GDP. The first step in assessing the evolving relevance of monetary policy is to examine whether credit has become increasingly more important to business cycle dynamics.

  • Granger causality test. The test addresses the following question: Can real credit help forecast fluctuations in non-oil GDP (and vice versa) and has its ability to do so changed over time? Based on annual de-trended data of real credit and non-oil GDP from 1980 to 2010 the answer is no. That is, real credit does not Granger cause non-oil GDP and vice versa. However, when splitting the sample, results change significantly. In particular, for the sample period 1996-2010, the null of no Granger causality is rejected, supporting the notion that real credit has become more important to the non-oil economy over time.

  • Impulse responses. To further investigate the relationship between bank credit and non-oil economic activity over time, a simple bi-variate vector autoregressive model (VAR) was constructed. The model was estimated with two lags as suggested by the AIC lag order selection criteria. The identification scheme assumes that non-oil GDP reacts contemporaneously with a shock to credit, but not the reverse. The results do not change markedly if the reverse ordering is used. Again, the model is first estimated for the full sample and then for the subsamples 1980-1995 and 1996-2010. For the full sample, the response of non-oil GDP to a one standard deviation shock to real credit is positive in the first two years, but insignificant. The dynamics change substantially when the sample is split. Non-oil GDP still responds positively initially to real credit shocks in both sub-periods. However, although the magnitude of the response is smaller, the positive effect is more prolonged and statistically significant in the 1996-2010 period (see Figures 6 and 7).

    Figure 6.
    Figure 6.

    Response of non-oil GDP to a one std real credit shock (1980-1995)

    Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

    Sources: Author’s calculations
    Figure 7.
    Figure 7.

    Response of non-oil GDP to a one std real credit shock (1996-2010)

    Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

    Sources: Author’s calculations

  • Variance decomposition. The results from the corresponding variance decomposition shows that that real credit explains more of the forecast error variance of non-oil GDP in the 1996-2010 period (58 percent after 5 years) compared to the 1980-1995 period (40 percent after 5 years). Again, this seems to indicate an increased relevance of credit for the non-oil economy.

13. Interest rates and real credit. The results from the VAR using annual data indicate evidence in favor of increased relevance of real credit for non-oil activity. But how has the imported interest rate policy affected real credit over time?

  • Vector autoregressive model: To answer this question a monthly VAR was specified with credit and the CPI as endogenous variables and the 3-month LIBOR, oil price and an international food price index as exogenous variables. The VAR is estimated by log-differencing the variables (except for the LIBOR). The main objective is to test whether the LIBOR significantly impacts credit and inflation. Table 3 displays the results from the VAR with respect to the exogenous variables. The full sample is 1997:1 to 2008:9. The end date was picked to exclude the global financial crisis as it represents a structural break in U.S. monetary policy as well as a sharp disruption in the overall economic environment. Furthermore, the model was estimated for two sub samples (1997:1-2003:12 and 2003:9-2008:9) to evaluate the evolution of the interest rate channel over time.

    Table 3.

    The impact of LIBOR on credit and inflation

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    Note: The optimal length criteria was chosen by considering five different lag order selection criteria.

  • Results: When using the full sample the LIBOR is negatively correlated with credit growth and positively related to inflation, but the net effect of a rise in the LIBOR would be a decline in real credit growth. However, none of the exogenous variables are statistically significant. When the sample is split, statistical significance emerges in both sub-samples. In the first period a statistically significant relationship between LIBOR and credit growth is not established. However, for the later period LIBOR has a negative and statistically significantly impact on credit growth. Interestingly, the reverse is true for LIBOR and CPI inflation. In the first period LIBOR has a negative and statistically significant effect on inflation, while the relationship breaks down in the second period. Note that an increase in LIBOR on real credit growth is positive in the first period, but negative in the second. The period 2003:9-2008:9 also shows some significance in terms of other exogenous variables. As expected, the oil price has a positive impact on credit growth and international food prices have a positive impact on inflation. Perhaps more surprisingly is that the nominal effective exchange rate is positively and significantly correlated with credit growth.

E. Conclusion

14. Summary of results. This paper examined the evolution of Saudi Arabia’s interconnectedness with the global economy and the constraints that these linkages impose on domestic policy. Two important developments over the past couple of decades were emphasized. First, growing oil needs from EMEs has become increasingly important for oil market dynamics. Second, financial sector development in Saudi Arabia has strengthened the monetary transmission mechanism. The former implies greater influence of EMEs economic fluctuations on Saudi oil export revenues, while the latter suggests greater influence of U.S. monetary policy on the Saudi non-oil sector. It was also argued that fiscal policy has been increasingly more successful to smooth spending despite continued volatility in oil revenues, possibly accounting for the lower output volatility in 2000s.

15. Going forward. As external links continue to evolve it is imperative to understand the implications for domestic policy. Given Saudi Arabia’s growing interconnectedness with developing Asia (e.g., China and India) and the continued commitment to the U.S. dollar peg, tension between policy objectives is likely to arise when global oil prices move counter-cyclically with the U.S. business cycle. For instance, the collapse of Libya’s oil exports in 2011 boosted oil revenues and fiscal spending in Saudi Arabia. At the same time, the debt crisis in Europe raised concerns of a global economic downturn. The combination of expansionary fiscal policy and accommodative monetary policy in the U.S. may again cause inflationary pressure to rise. These developments underline the importance for Saudi Arabia to effectively use fiscal policy as a stabilizing tool and to further refine macro prudential instruments to influence monetary conditions independent of interest rate policy.

References

  • Al-Jasser, M. and Ahmed Banafe, 1999, “Monetary Policy Operating Procedures in Emerging Market Economies.” BIS Papers, No. 5.

  • Hamilton, J., 2009, “Causes and Consequences of the Oil Shock 2007-08,” Brookings Papers on Economic Activity, Vol. 2009, pp. 215-261.

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  • Ramady, M. A., 2010, The Saudi Arabian Economy: Policies, Achievements and Chaiienges. New York, Springer International.

  • Saudi Arabian Monetary Agency, 2003, “A Case Study On Globalization and the Role of Institution Building in the Financial Sector in Saudi Arabia”, Prepared for the G20 Finance and Central Bank Deputies’ Meeting, May 26, 2004, Mexico City.

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V. Asian Supply Chain: A Case Study1

This note seeks to examine the implications of increased trade linkages on policy setting in the Asian supply chain economies. With a vertically integrated production structure, particularly for machinery and electronics, the Asian supply chain appears closely linked to both the global electronics and to China’s business cycle. China is at the ‘core’ of the cluster and a ‘gatekeeper’. It therefore may have an important role as a propagator, and dampener, of shocks, and a bearing on the policies employed by the other cluster members. Taking a cluster-view thus can shed light on policy issues.

A. Background

1. The cluster. The Asian cluster comprises, for the purposes of this note, economies identified on the basis of strong trade and (to a lesser extent) financial ties in the region. These include: China, Hong Kong SAR, Indonesia, Korea, Japan, Malaysia, Philippines, Singapore, Thailand, and Vietnam.

2. Structure of the cluster. Given its economic size and growing trade intensities with the cluster, China has been playing an increasingly “central” role in the cluster (see text figure).2 China also connects the Asian cluster to other overlapping clusters due to its centrality to global trade (see text figure). The cluster analysis points to China as a ‘gatekeeper’. As such, it could play an important role in transmitting shocks across the cluster or insulating the cluster from global shocks, provided it has sufficient policy buffers to act as a “circuit-breaker.” As financial centers with numerous linkages to other countries outside the Asian supply chain, Hong Kong SAR and Singapore play important roles in linking the Asian trade cluster to global financial centers and intermediating financial flows from a wider set of countries to the rest of the region. Hong Kong SAR and Singapore may also act as conduits through which financial shocks can spill over into trade shocks across the cluster.

China’s Relative Economic Size and Bilateral Trade intensity

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

Source: WEO, DOTS. Region includes: Hong Kong, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore, Thailand, and Vietnam.

3. Preliminary findings. The Asian cluster is underpinned by a vertical production structure that links the output and export structures of the economies in the region, but not necessarily their monetary policy frameworks or exchange rate regimes. The latter, in theory, should provide an additional policy lever not available in the other clusters, such as in the Baltics or Central and Eastern Europe. But, in practice, countries could be internalizing—to varying degrees—the policies and actions taken not only in China, but also other countries in the cluster. This could affect the policy space and options of any single country and conditions its responses. It also underlines the scope for cooperation and for the Fund to potentially bring a cluster-based lens to its surveillance work.

Figure 1.
Figure 1.

Asian Trade Cluster

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

4. Analytical approach. This note combines network-based cluster analysis with correlation analysis and a narrative approach to shed light on the potential policy implications of belonging to the cluster. The next section documents the evolution of trade and financial linkages, i.e., the fundamentals, underpinning the cluster. Section III examines the impact of these fundamentals on business cycle synchronization. Section IV looks into the effect of belonging to the cluster on policy choices by focusing on the experience of a sub-set of economies in the period following China’s exchange rate reforms up to the crisis (mid-2005 to 2008).

B. Trade and Financial Linkages

Trade Linkages

5. Growing intra-regional and intra-industry trade. Intra-regional trade (measured by imports) among cluster economies has grown at a much faster pace compared to that with the United States and European Union, to reach a peak of 17 percent of GDP in 2008 (see Figure 2). Trade between China and other cluster economies is also mostly of the intra-industry type, especially in the electronics and machinery sectors, as illustrated by the Grubel-Lloyd index (which takes the value of zero if no products in the same category are both exported and imported and one if all trade is intra-industry). The extent of intra-industry trade has also increased over time, against a declining trend in China’s trade with the world. Almost half of China’s imports of capital and intermediate goods are from cluster countries, and 40 percent of its exports are destined to them.

Figure 2.
Figure 2.

Asia: Growing Intra-Regional and Intra-Industry Trade

Citation: Policy Papers 2012, 016; 10.5089/9781498340823.007.A001

6. Export dependence. Countries in the cluster are heavily dependent on exports. Exports as a share of GDP exceed 100 percent in Singapore, Hong Kong SAR, and Malaysia, although they account for less than 3 percent each of total world exports. In contrast, whereas exports account for only 27 percent of GDP, Chinese exports account for close to 11 percent of global exports (text figure). China is therefore an important outlet for exports of cluster economies. Moreover, and with the exception of Vietnam and Hong Kong SAR, most cluster countries had fairly small trade balances (either deficit or surplus) with China up until the crisis in 2008. Given this overall export dependence, economies in the cluster could be sensitive to exchange rate changes vis-à-vis China.

7. Convergence in export structures. The export similarity index (ESI) (calculated at the 6-digit product level) is a common indicator to gauge export competitiveness, defined following Finger and Kreinin (1979), and takes the value of 1 for identical export structures and zero otherwise (See Riad et al, 2012 for details). Export structures in cluster countries are fairly similar to China’s, and similarity is much higher for individual subsectors such as electronics and other manufactures (text figure). At the same time, given vertical production structures, a high ESI value could also reflect complementarity (see Riad et al., 2012 for details). Electronics trade is particularly important for the region, accounting for about 25 percent of China’s total imports and exports in 2010; of electronics imports, almost half come from cluster partners. Imports and exports of electronics between China and the region have grown remarkably; in contrast, the G7 (excluding Japan) remains important for China on export side, but significantly less so on the import side. With emerging Asia competing with China in electronics and manufactures and to preserve market share, they may be less willing to allow too much flexibility in their currencies, especially vis-à-vis China.

Financial Linkages

8. Growing financial links. While Hong Kong SAR is an important channel of financial flows in and out of China, Singapore is a hub of the Asian Dollar Market (ADM) intermediating financial flows from a wider set of countries not only in Asia (such as Malaysia and India), but also from the main oil producers such as Kuwait and Saudi Arabia to the rest of the region (see also Table 4 in Chapter VI). In addition to the role of Hong Kong SAR and Singapore as equity and fixed income hubs facilitating Asian trade, an important aspect of financial linkages throughout East Asia is through FDI (Table 1), and likely also asset and equity holdings by government-owned corporations or sovereign wealth funds (SWF). While very limited information is available, asset and equity holdings by government-owned corporations suggest an additional layer of (non-trivial) financial linkages in the region. For the three SWFs in Singapore (Temasek), China (China Investment Corporation), and Korea (Korea Investment Corporation), investments in Asia were about 45 percent, 30 percent, and over 15 percent (of total reported portfolio by value) as of end-2010.

Table 4.

Singapore-South East Asia Cluster as of end-2010

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Sources: Bank for International Settlements; and staff calculations.Note: The numbers in Table 4 are drawn from data on the geographic distribution of holdings reported confidentially by Singaporean banks to the Bank for International Settlements (BIS) in combination with information available in Table 6a of the BIS’s locational banking statistics.
Table 1.

Foreign Direct Investment

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Source: CDIS.