Annex I. Modeling Frameworks
Spillover scenarios have been simulated using: (i) the Global Integrated Monetary and Fiscal Model (GIMF) and two modules of the Flexible System of Global Models (FSGM): G20MOD and EUROMOD; (ii) the GPM model; and (iii) the G35-S model.9
Annex II. Spillovers to Low-Income Countries11
Robust growth in the low-income countries (LICs) is likely to continue in the near term. However, notwithstanding strong growth, in most LICs macroeconomic buffers are being reconstituted slowly, limiting the scope for countercyclical policies in the case of a negative shock to the global economy. A better-than-expected global outlook would present opportunities to accelerate the pace of building buffers. The macroeconomic implications from the simulations of two scenarios reported below—a negative one and an upside rebalancing scenario—illustrate these points.
46. Scenario 1. A protracted slowdown in the euro area: Under this scenario, LICs could face additional external financing needs amounting to $7 billion by end 2014 and $10 billion by end 2015 in the absence of policy adjustment, with most of the additional needs concentrated in several Sub-Saharan African (SSA) countries. Reserve coverage in the median LIC could decline to 3.0 months of imports, from 3.7 months at end 2012, and the share of LICs with reserve coverage below 3 months of imports would almost double. At the same time, the pace of fiscal consolidation would generally be slower than that expected under the baseline. If policies had to adjust to offset such rising vulnerabilities or in the absence of financing, the growth impact would be much more significant than shown in Chart 1. balances are small, the beneficial effects on the external balances would be larger. As a result, reserve coverage in the median LIC could improve to 4 months of imports by end 2015, although some countries could lose out, leading to additional external financing needs of about $2 billion concentrated in a very few oil-exporting countries.
48. Finally, the recent strengthening of trade and investment links between SSA countries and China has made the former more susceptible to spillovers from and demand fluctuations in the latter.12 China’s rapid investment-led economic growth has had positive spillovers to exports of SSA countries: a one percentage increase in China’s domestic investment growth is associated with an average of a 0.3 percentage point increase in SSA’s export growth rate, with an even larger impact (about 0.4 percentage point) for resource-rich countries. Moreover, low-income countries are more vulnerable to fluctuations in investment demand from China than other SSA countries.
The lower bound estimates come from simulations using the FSGM notably as presented in the April 2012 WEO “weak policies” scenario, where banks tighten lending standards and constrain credit growth to rebuild buffers and where sovereign yields temporarily rise by 100 basis points, with fiscal consolidation 1 percent higher in 2012 and 2013. The upper bound comes from using a G35 simulation assuming a 450 basis point increase in long-term government bond yields in the euro area periphery with high financial market contagion to the rest of the world.
See 2012 Spillover Report, at http://www.imf.org/external/np/pp/eng/2012/070912.pdf (page 10)
Earlier analysis of the impact from unconventional monetary policies, with emphasis on the domestic impact, was presented in Unconventional Monetary Policies—Recent Experience and Prospects; April 18, 2013.
The intuition behind the slightly negative spillovers from QQME in this model is that its announcement triggered a relatively sharp yen depreciation and a drop in equity prices everywhere but in Japan, likely reflecting concerns about loss of competitiveness (further discussed in paragraphs 12 and 13). The growth impact is short lived, fading after a year. Given the more limited number of observations, however, the QQME impact estimates are less robust than the others. Moreover, full implementation of the Abenomics package would generate positive net spillovers (see below).
See CP, section IX.25 and 26. Estimates are based either on G35-S simulations—a model that likely overestimates the adverse impact on economies with a high share of services trade, as it only imperfectly accounts for the latter; or on the FSGM, as noted.
The magnitude of the global output loss depends heavily on the degree of contagion to long-term rates in the rest of the world. The correlations assumed here reflect historical correlations. Using FSGM, but with similar contagion effects outside the United States and Japan to the 200 basis point increase in both countries’ short-term sovereign risk premium, the April 2013 WEO scenario estimated a peak impact on global GDP of 2.7 percent after two years.
See “Does Micro-Pru Leak? Evidence form a U.K. Policy Experiment”, Aiyar, Calomiris, and Wieladek, Bank of England, 2012. Risks may also migrate toward the non regulated financial sector. In the United Kingdom, all the major banks are highly and increasingly sensitive to risks of distress in shadow banks. Thus, problems in shadow banks could also have large adverse spillovers.
There will also be cases, e.g., in a downswing, where the interest of different financial systems not only do not overlap but conflict. For example, supervisor of country A may press its banks to disengage from country B where the economy is in trouble and therefore credit risk is sharply higher. This would bolster financial stability in A but make it worse in B. Short of a collective agreement to avoid such a vicious circle, as happened in the first phase of the crisis with the “ Vienna Initiative”, only a supervisor assessing systemic stability at the multi-country level can remedy such a conflict. Recognition of this problem is one of the drivers of Europe’s efforts toward a single supervisory mechanism.
These models were presented at a technical seminar on spillover methodologies at the margins of the 2013 Spring Meetings, with participants from all five systemic economies and major countries affected by spillovers.
Vitek, F. (2013), Policy analysis and forecasting in the world economy: A panel dynamic stochastic general equilibrium approach, International Monetary Fund Working Paper, forthcoming.
The analysis of the impact of the spillover scenarios on LICs was prepared by Marco Arena, Vera Kehayova, and Svitlana Maslova.
Drummond, P. and E. Liu (2013), Africa’s Rising Exposure to China: How Large are Spillovers through Trade, International Monetary Fund Working Paper, forthcoming.