Abstract
We build a weighted network of business cycle similarities across countries and assess its main quantitative properties. Business cycle similarity is measured at the annual frequency using the Euclidean distance. Network analysis is well suited to map the full set of pairwise similarities at an annual frequency. We find that the global business cycle network has become more dense and more homogeneous over time, reflecting global trends such as rising trade and financial integration. At the same time, similarity exhibits a jagged pattern, underscoring the importance of also taking into account short-term factors to explain the dynamics of global business cycle interdependence. Unlike earlier studies focused on aggregate measures of similarity, our empirical approach is able to uncover and assess both the long-term trend rise and the short-term pattern of business cycle similarity.
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Notes
The terminology varies across the literature, e.g. business cycle synchronisation, correlation, comovement, interdependence, association, or similarity. All these words are interchangeable insofar as they allow for similar business cycle dynamics to emerge either from common shocks, or from country-specific shocks spilling across national borders. In this paper, we adopt the terminology of business cycle similarity in line with the standard jargon of network analysis.
We also estimated the trend component using the Baxter-King filter as a robustness check. The resulting output gaps are very similar.
The full list of countries is available in Table 1 in the Appendix.
Some other studies have used monthly data on industrial production or the rate of unemployment as measures of the business cycle. The former is inadequate as the share of industry in the economy has been shrinking in many countries to a relatively low level. Thus, the evolution of industrial production is not representative of the dynamics of an economy as a whole. The latter is also inadequate as the rate of unemployment is known to be a lagging indicator of economic activity.
For example, Artis and Zhang (1997, 1999) have concluded that participation in the Exchange Rate Mechanism of mutually fixed exchange rates raised business cycle similarity, while Inklaar and De Haan (2001) reached the opposite conclusion using the same dataset but splitting in different sub-samples.
Mink et al. (2012) propose a comparable measure based on the idea of distance.
There is a functional correspondence between the correlation coefficient and the Euclidean distance when two time series are standardised (zero mean and unit variance). In this case, the correlation coefficient and the Euclidean distance provide the same qualitative information about similarity. See Waelti (2012) for further details.
We use the igraph package within the R statistical software to build network graphs and compute some of the quantitative characteristics of the network.
Most of the studies mentioned in the previous section build networks where the business cycles of two countries are either connected or they are not. Said differently, two countries in the network are linked by a straight line only if the similarity between them exceeds an arbitrary threshold. This binary approach is inefficient since it discards useful information contained in continuous measures of similarity.
We use the full sample of countries to assess the properties of the network in the next section.
We experimented with alternative bins. The thrust of our results remains unchanged.
We constructed an alternative network for each year using such a binary concordance index. These charts are available from the authors upon request.
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The views expressed in this paper are those of the authors and do not necessarily represent those of the Swiss National Bank. Most of the work on this paper was carried out while Amalia Repele was at the Swiss National Bank. We thank the Editor, two anonymous referees, and colleagues at the Swiss National Bank for perceptive comments and suggestions.
Appendix
Appendix
The global network of business cycle similarities, 1980–2018. Note: All links that are smaller than the median are dropped
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Repele, A., Waelti, S. Map** the Global Business Cycle Network. Open Econ Rev 32, 739–760 (2021). https://doi.org/10.1007/s11079-020-09615-1
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DOI: https://doi.org/10.1007/s11079-020-09615-1