- International Monetary Fund. European Dept.
- Published Date:
- April 2008
2007, “International Finance and Income Convergence: Europe Is Different,” IMF Working Paper 07/64 (Washington: International Monetary Fund).
2008, “Liquidity, Monetary Policy, and Financial Cycles,” Current Issues in Economics and Finance, Federal Reserve Bank of New York, Vol. 14 (January–February), No. 1.
2007, “Securitisation and the Bank Lending Channel,” Economic Working Paper No. 653 (Rome: Bank of Italy). Available via the Internet:www.bancaditalia.it/pubblicazioni/econo/temidi/td07/td653_07/en_td653/en_te ma_653.pdf
2007, “The Use of Mortgage Covered Bonds,” IMF Working Paper 07/20(Washington: International Monetary Fund).
2007, “Foreign Entanglements: Estimating the Source and Size of Spillovers Across Industrial Countries,” IMF Working Paper 07/182 (Washington: International Monetary Fund).
2008, “Active Monetary Policy with Direct Policy Measures” (unpublished;Washington: International Monetary Fund).
2007, “A Simple DGE Model for Inflation Targeting,” IMF Working Paper 07/197 (Washington: International Monetary Fund).
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2007, “Market Liquidity and Funding Liquidity,” NBER Working Paper No. 12939 (Cambridge,Massachusetts: National Bureau of Economic Research).
2007, “Collective Risk Management in a Flight to Quality Episode,” NBER Working Paper No. 12896 (Cambridge,Massachusetts: National Bureau of Economic Research).
2004, “Housing Markets, Wealth and the Business Cycle,” OECD Economics Department Working Paper No. 394 (Paris: Organization for Economic Cooperation and Development).
2007, “Estimating Spillover Risk Among Large EU Banks,” IMF Working Paper 07/267 (Washington: International Monetary Fund).
Committee on the Global Financial System, 2006, “Housing Finance in the Global Financial Market,” CGFS Paper No. 26 (Basel: Bank for International Settlements).
2007, “Does Risk Aversion Drive Financial Crises? Testing the Predictive Power of Empirical Indicators,” CEPII Working Paper No. 2007-02 (Paris: Centre d’Etudes Prospectives et d’Informations Internationales).
2005, “Too Much of a Good Thing? Credit Booms in Transition Economies: The Cases of Bulgaria, Romania, and Ukraine,” IMF Working Paper 05/128 (Washington: International Monetary Fund).
2007, “Determinants of House Price Dynamics in Central and Eastern Europe,” in Focus on European Economic Integration 1/07 (Vienna: Oesterreichische Nationalbank).
European Central Bank, 2008, The Euro Area Bank Lending Survey (Frankfurt am Main, January). Available via the Internet: www.ecb.int/stats/money/lend/html/index.en.html
2006, “Measuring Investors’ Risk Appetite,” International Journal of Central Banking, Vol. 2 (March), pp. 167–88.
2008, “Investors’ Risk Appetite and Global Financial Market Conditions” (unpublished; Washington: International Monetary Fund).
2004, The Mystery of Economic Growth (Cambridge, Massachusetts: Belknap Press of Harvard University Press).
2005, “Assessing and Managing Rapid Credit Growth and the Role of Supervisory and Prudential Policies,” IMF Working Paper 05/151 (Washington: International Monetary Fund).
International Monetary Fund (IMF), 2000, “Asset Prices and the Business Cycle,”Chapter 3in World Economic Outlook, World Economic and Financial Surveys (Washington), pp. 77–112.
International Monetary Fund (IMF), 2006, “Methodology for CGER Exchange Rate Assessments,” (Washington: Research Department,November).
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forthcoming, “A Global Repricing of Risk or New Episodes of Financial Contagion?” IMF Working Paper (Washington: International Monetary Fund).
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forthcoming, “Economic Growth in Croatia: Potential and Constraints,” in Financial Theory and Practice.
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forthcoming, “Current Account Deficits in EU-10: Equilibrium, Excess and EU-phoria,” IMF Working Paper (Washington: International Monetary Fund).
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2007, Growth in the Central and Eastern European Countries of the European Union, IMF Occasional Paper No. 252 (Washington: International Monetary Fund).
See the November 2007 Regional Economic Outlook: Europe, Part II, Chapter 2. Although services include tourism, which is a tradable, this is relevant primarily for Croatia and, to a lesser extent, Turkey, which have large tourism sectors. Services also include outsourcing, which, although tradable, remains a relatively small share despite rapid expansion in recent years.
We assume a Cobb-Douglas technology with two factors, capital and labor, and with constant returns to scale: Y(t) = A(t) F[K(t), L(t)]. Y is real GDP; A is an index of the level of technology, or TFP; K is capital; and L is employment. Contributions to growth are then computed according to y(t) = a(t) + αk(t) + (1−α)l(t), where α is the share of rental payments to capital in total income and (1−α) is the share of wage payments to labor in total income, assuming competitive product markets, and lowercase letters indicate growth rates. a(t) is estimated as a residual, and although assumed to measure productivity improvements, it also captures production forces in addition to capital and labor, as well as possible measurement errors. For more details, see Barro and Sala-í-Martin (2004).
This is consistent with Schadler and others (2006), who find that TFP growth in central-eastern Europe has been higher than in other emerging economies, including east Asia and Latin America. Some caution in interpreting these results is in order as it is difficult to estimate capital stocks for transition economies. Investment data are not reliably available before the 1990s so an ad hoc assumption needs to be made about the starting value of the capital stock. We assumed that the initial ratio of capital to GDP in the European transition economies is somewhere between the average level in the low- and middle-income economies in the world in 1995. This leads to a range of TFP estimates for each country that may be more plausible than estimates based on short investment time series. The data in Figure 26 show the middle of these ranges for TFP growth and the growth contribution of capital. The income shares were taken from previous IMF country studies, or assumed to be equal to the average in the region when country data were not available.
Table 8 includes factors that the literature has found to determine potential economic growth and productivity. Although the statistical and relative economic significance of many of these determinants is still subject to discussion, these factors can indicate the growth prospects of a country or region and provide guidance to policy. For more details, see Barro and Sala-í-Martin (2004); George, Oxley, and Carlaw (2004); Helpman (2004); and Aghion and Durlauf (2005).
Assuming the euro area is the region to which emerging Europe is converging, the convergence pace of the latter is conditional on its reform progress compared with that of the euro area.
Southeastern Europe (SEE) comprises Albania, Bosnia and Herzegovina, Bulgaria, Macedonia, FYR, Romania, and Serbia; central-eastern Europe (CEE) comprises the Czech Republic, Hungary, Poland, and the Slovak Republic; the Baltics comprises Estonia, Latvia, and Lithuania; and the CIS area comprises Belarus, Russia, Moldova, and Ukraine.
See the November 2007 Regional Economic Outlook: Europe, Part II, Chapter 3.
For a detailed discussion of the state of the financial sector in emerging Europe, its health and development prospects, the implications and sustainability of fast credit growth, and the sector’s resilience to shocks, see Duenwald, Gueorguiev, and Schaechter (2005) for Bulgaria, Romania, and Ukraine; Hilbers and others (2005) for central-eastern Europe; Sorsa and others (2007) for southeastern Europe; and IMF Country Reports 06/392 for Poland, 06/285 for Albania, 07/269 for Bosnia and Herzegovina, 07/390 for Bulgaria, 07/82 for Croatia, 06/354 for Latvia, 06/169 for Romania, 08/55 for Serbia, and 06/414 for new EU members.
Firm conclusions on the degree of overheating are difficult to draw as the level of potential output is hard to pin down.
The results are based on a common methodology for all emerging European countries. Although this is useful for cross-country comparisons, it leaves out some country-specific aspects. However, IMF country reports, some of which are referenced in this chapter, address country-specific issues in more detail.
CGER stands for the Consultative Group on Exchange Rate Issues, which was established in the IMF in 1995 to strengthen its capacity to assess current account positions and exchange rate levels. The CGER assessments are based on three complementary approaches: the macroeconomic balance approach, the reduced-form equilibrium real exchange rate approach, and the external sustainability approach. For more details, see Isard and Faruqee (1998); Isard and others (2001); and IMF (2006).
For example, IMF Country Report 07/255 for Estonia.
The CGER approach may underestimate the equilibrium current account deficit in a setting of EU convergence and rapid financial integration. Abiad, Leigh, and Mody (2007) take this into account in their empirical model and find financial integration to be the main determinant of the current account balances in Europe.
The differences between predicted and actual current account deficits are largely explained by cyclical factors. For a detailed discussion of these simulations for Lithuania, see Ohnsorge (forthcoming).
The regression equation is cait = αt + βt(yit – yt) + γXit + εit, where cait is the current account balance of country i at time t, Χt is a common time effect, yit is the log per capita GDP of country i at time t, yt is the log of the average per capita GDP in the euro area, and Χit is a set of other control variables for country i at time t, including the age dependency ratio and real GDP growth. We would expect that the larger the income gap of an emerging European economy from the advanced European economies, the higher the age dependency ratio, and the stronger the current growth cycle, the larger its current account deficit. The coefficient of relative income varies over time. As argued in Blanchard and Giavazzi (2002), financial integration in Europe has increased substantially in recent years, allowing emerging economies to borrow more, invest more, and save less during convergence, and leading to larger current account deficits over time. The sample includes all European economies, for the period 1976–2006 (beginning in the mid-1990s for most transition economies). For earlier applications of this model to Latvia, Lithuania, and Hungary, see IMF Country Reports 06/354, 05/122, and 05/215, respectively.
According to the latest IMF World Economic Outlook projections, Estonia is expected to be within this band by 2008, Latvia by 2009, and Bulgaria by 2010 (IMF, 2008b).
See the November 2007 Regional Economic Outlook: Europe, Part II, Chapter 2.
Net external debt, which adjusts for private sector foreign assets, is considerably lower in most countries. In Latvia, for example, where external debt is the highest in emerging Europe, net debt was estimated at about 52 percent of GDP in 2007—the ratio of net short-term debt to foreign reserves was estimated at 98 percent. Although a large share of foreign assets could provide some buffer during external shocks, possible mismatches between asset owners and debtors suggest that there is no immunity.
The path of external debt as a share of GDP is determined by the following process: where d is the ratio of external debt to GDP, r is the effective nominal interest rate on external debt, g is the rate of real GDP growth, tb is the noninterest current account balance in percent of GDP, and ϱ is the change in the domestic GDP deflator in euros: where πt is domestic GDP deflator inflation and εt is the rate of nominal appreciation. This methodology assumes no further accumulation of foreign reserves. For an application of this framework to Lithuania, see IMF Country Report 05/122.
See IMF Country Reports 07/82 for Croatia, 06/419 for Estonia, 05/277, 06/354 for Latvia, and 06/379 for Hungary.
For example, an estimated 80 percent of loans in foreign currencies are made to unhedged borrowers in Croatia.
For more details, see Hilbers and others (2005).
The Taylor rule is defined as the sum of the output gap, the equilibrium interest rate (assumed to be equal to potential growth estimated using the Hodrick-Prescott filter), expected inflation (assumed to be equal to actual inflation in the past three years), and the inflation gap (assumed to be equal to actual inflation minus an inflation target, which is taken to be the 2 percent ECB target plus 1.5 percent from Balassa-Samuelson effects).
The result that monetary conditions are loose does not suggest that monetary policy is loose (this conclusion would be particularly wrong for emerging European economies with fixed exchange rates, where there is no independent monetary policy). It suggests, rather, that tightening policies in emerging Europe have not been effective in tightening monetary conditions (which can be loose even in a country with a currency board arrangement). This interpretation of the result is derived from the fact that the Taylor rule calculations in this chapter include the lending interest rate, instead of the policy interest rate. The latter is relevant for determining whether monetary policy is loose, while the former is relevant for determining whether monetary (lending) conditions are loose.
The monetary conditions index is equal to 100 in 2003 and is the weighted sum of the changes in the real lending interest rates and in the real effective exchange rates, with weights equal to 0.75 and 0.25, respectively.
Potential output and the output gap are measured using the Hodrick-Prescott filter or IMF staff estimates based on alternative methodologies for each country. Potential growth estimates based on the growth model in the earlier section cannot be used because they cannot determine the level of potential output. The potential output estimates based on the Hodrick-Prescott filter may be overestimated, as growth has been well above potential based on the growth model estimates in the period considered.
Although infrastructure needs would justify structural deficits in parts of emerging Europe, projections for rising health and pension spending due to aging populations would call for structural surpluses.
These incentives include mortgage interest deductibility of varying generosity (Croatia, the Czech Republic, Estonia, Hungary, Lithuania, and Poland); housing subsidies, including interest rate subsidies and saving bonuses (Hungary, Croatia, the Czech Republic, and Poland); and exemption of primary residences from property tax and capital gains tax (in all countries).