The current turmoil is often argued to have had unprecedented global consequences (see Sugawara et al. 2010 for example). It is perhaps the severity of the universal consequences of a US-based shock, rather than the recession itself, that has drawn comparisons with the Great Depression (see also on this site Eichengreen and O’Rourke 2010).
This raises two questions:
- What can be said about the global nature of what used to be called the subprime crisis?
- Does this mean that countries have become systematically more correlated over the past couple of years, more so than at any other time?
Here I attempt to address these questions by estimating the cross-sectional distribution of bilateral cycle correlations, and tracking its changes over time. I focus on sub-periods characterised by other "global" shocks, comparing the global crisis with prominent instances of worldwide crises since 1973. I hold constant the magnitude of the shock, in order to isolate the shift in how these shocks spread across the globe.
As the subprime crisis unravelled, both goods and assets trade retreated, or at least relocated. It is an open question whether the change was a consequence of the crisis, or did actively contribute to its diffusion. World trade can fall as a result of the collapse in economic activity, and international capital can be withdrawn because recessions are a bad time to invest. Still, international financial linkages are often accused of having channelled the international diffusion of the shock. Capital is repatriated as financial intermediaries "de-leverage" their balance sheets with the result of – perhaps – exporting the crisis to borrowing, developing economies.
There is overwhelming evidence world cycles have become significantly more synchronised with the crisis, relative to the 1980's. Figure 1 reports estimates of the distribution of bilateral cycle correlations between 24 OECD countries for five sub-periods since the 1980’s. Each panel reports in thin lines the distribution estimated over the most recent period, holding the volatility of GDP constant at its pre-crisis level. It is clear that the distribution of bilateral correlations has shifted upwards for data ranges that include the last few months of 2008, and thereafter. In fact, a significant shift in the distribution occurs when January 2009 and the months that follow are included in the computation of correlations.
Figure 1. Twenty-four countries: Distribution since 1980 (NSA)
Note: The figure plots in thick plain lines the kernel estimates of the cross sectional distribution of correlation since 1980 for 24 countries and over nonoverlapping 5-year sub-periods. Dotted lines represent 90% confidence intervals. Each panel also plots in thin plain lines the distribution of correlations estimated over 2004M5-2009M4, holding volatility at its 1999M1-2003M12 level. The final panel plots the distribution of correlations estimated over 2004M5-2009M4 without volatility correction.
The shift is significantly larger now than after any of the prominent instances of world shocks since the 1970's, with the possible exception of the 1973 oil shock. That comparison is apparent from Figure 2, where estimates of the distribution of bilateral correlations are reported for eight prominent global crises since the 1970’s. The current distribution has higher first moments and skewness than estimates computed immediately after the Savings and Loans crisis, the October 1987 crash, the Nikkei crash, the European Exchange Rate Mechanism crisis, Long Term Capital Management collapse, the Nasdaq crash, or the US bankruptcies of 2002. In fact, there is no other period in the data available since 1980 that displays a similarly significant shift in the distribution of cycles synchronisation. In that sense, the current turmoil is indeed the first global recession in decades.
Figure 2. Alternatives (NSA)
Note: The figure plots in thick plain lines the distribution estimates of correlations immediately after eight alternative global shocks. Numbers between parentheses represent the number of countries used in each sample. Dotted lines represent 90% confidence intervals. Each panel also plots in thin plain lines the distribution estimates corresponding to the 2004M5-2009M4 period, holding volatility at its 2003M1-2007M12 level. The final panel plots the distribution of correlations estimated over 2004M5-2009M4 without volatility correction.
The increase in the correlation of cycles is particularly pronounced for rich OECD countries. It is at best weakly significant for cycle correlations between developing economies, or between OECD and non-OECD countries. The data therefore point to the idea of a shock that has diffused first and foremost between developed economies, while the developing world has remained relatively insulated. Including data until the end of 2008, the mode of the distribution of bilateral correlations is barely significantly positive for developing economies. In the rich world, it stands above 0.8. Yet there were no observable differences across the two samples prior to 2008. There seems to be a specificity to the diffusion mechanism that exists between rich countries. At the very least, the shock has diffused slowly to the developing world, where cycles correlations have remained sizably lower. They continue to do so, even with data running until May 2009.
How can we account for such differences across samples?
I consider two conventional determinants of business cycle correlations, or particular relevance in the current context. I compute the intensity of bilateral trade, and a measure of mutual openness to financial flows. Both before and after the crisis, rich countries are synchronised, even more so if they are trade partners. Financial openness also drives synchronisation up, albeit less significantly. This happens both in rich countries, and (albeit more weakly) in developing economies. These results are in line with the basic findings from a large literature, for instance from Frankel and Rose (1998), Fidrmuc (2001), or Imbs (2004).
But the same is not true of the change in business cycles correlations around the crisis. Business cycles became more synchronised, and that was accompanied by a reallocation of both goods and assets trade. Cycle synchronisation increased most between pairs of countries where both goods and financial trade rose, so that both margins played a significant role in channelling a US shock across the world. The channels do however differ significantly in magnitude across rich or developing economies. Amongst OECD country pairs, it is between countries with strongest financial linkages that cycles synchronised most during the crisis. But amongst non-OECD countries, it is where goods trade was highest prior to the crisis that correlation increased most.
Ultimately, the challenge raised by these findings is to explain the joint importance of goods and assets trade. The results suggest a fundamentally different margin of adjustment in response to the subprime shock across OECD and non-OECD countries. The discrepancy can reflect a more advanced stage of financial integration amongst rich economies. In developing countries, it is goods trade that is relatively unhampered, and it is therefore the dominant response to the shock. In the rich world, the global recession is associated with falling asset trade. This is perhaps because the role for multinational banks is more advanced there to start with, and de-leveraging is more prevalent.
Eichengreen, Barry and Kevin O’Rourke (2010), “What do the new data tell us?”, VoxEU.org, 8 March.
Fidrmuc, Jarko (2001), “The Endogeneity of the Optimum Currency Area Criteria, Intra-Industry Trade and EMU Enlargement”, BOFIT Discussion Papers 8/2001, Bank of Finland, Institute for Economies in Transition.
Frankel, Jeffrey and Andrew Rose (1998), “The Endogeneity of the Optimum Currency Area Criteria”, Economic Journal, 108:1009-1025.
Imbs, Jean (2004), “Trade, Finance, Specialization and Synchronization”, Review of Economics and Statistics, 86:723-734.
Sugawara, Naotaka, Victor Sulla, Ashley Taylor, Erwin R Tiongson (2010), “The crisis hits home: Stress testing households in Europe and Central Asia”, VoxEU.org, 14 April.