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Growth and welfare effects of macroeconomic volatility Luis Servén The World Bank Barcelona March 2006 Figure 1. Standard Deviation 1970-2001 Output volatility and consumption volatility Std Dev of Real Private Consumption Growth 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 Developing Countries 0.02 Industrial Countries 0.00 0.00 0.02 0.04 0.06 0.08 Std Dev of Real GDP Growth Source: WDI - World Bank. 0.10 0.12 0.14 0.16 Why are LDCs more volatile ? Three broad ingredients: External shocks – often bigger in LDCs (e.g., terms of trade) Domestic shocks – e.g., fiscal policy volatility (higher in LDCs) [Fatás – Mihov] Weaker “shock absorbers” – especially financial – part of the problem rather than the solution: Shallow domestic financial systems Weak international financial links Both limit risk sharing / smoothing of shocks Pro-cyclical macro policies that amplify fluctuations Volatility of Terms of Trade Growth (Regional Medians) 20 18 16 (In Percent) 14 12 1960s 1970s 10 1980s 8 1990s 6 4 2 0 Industrialized Econom ies East Asia and Pacific 7 Latin Am erica and the Caribbean Middle East and North Africa South Asia Sub-Saharan Africa Other East Asia and Pacific Fiscal volatility Volatility of public consumption growth (medians by group) 12 10 8 6 4 2 0 Low income Midlde income 60s Source: Montiel and Serven (2005) 70s Industrial 80s 90s All developing Why are LDCs more volatile ? Bigger external shocks + macro policy shocks + lower financial development -- each accounts for about 1/3 of “excess volatility” of LAC over OECD (WB 2001) More recent emphasis on micro-policies for shock absorption: microeconomic regulation (higher in LDCs) may hamper the reallocation of resources following shocks Empirically, evidence that tighter regulation (product, labor) may raise aggregate volatility [Loayza et al] – likely the opposite of what regulation intended ! .06 Micro regulation and macro volatility Overall Regulation Correlation: 0.41*** SLE TGO MWI .04 IDN PNG THA MYS KORTUR ZMB ARG PER .02 URY CHL HTI ZWE MAR JOR VEN NER MEX ECU NICCIV IRN HND DOM BRA PHL SEN NGA TUN PAK PAN IND PRY GMBMDG BOL CRI SYR BFA EGY 0 COL TTO PRT FIN BWA IRLISL SWE KEN JPN CAN ISRZAF SLV AUS GBR NOR BEL ESP GRC DNK JAM ITA AUT FRA USA CHE NLD LKA BGD GHA GTM COG .2 .3 .4 .5 Overall Regulation Index Source: Loayza, Oviedo and Servén 2005 .6 .7 Volatility and crises Some evidence that “crisis volatility” [extreme adverse realizations] has become more important in LDCs: High incidence of extreme events in the 1990s (growth collapses, sudden stops, banking crises…) Both consumption and output growth display higher skewness in the 1990s than before Crisis volatility accounts for a rising portion of overall volatility (which has itself declined) Normal and extreme GDP growth volatility Figure II.2. Structure of GDP Growth Volatility (percent of mean totalofvolatility, average (percent, 77 developing countries) of 77 LDCs) 80 70 60 50 40 30 20 10 0 61-70 71-80 Normal 81-90 Extreme Sources: Hnatkovska and Loayza (2004); authors' calculations. Crisis 91-00 Boom Source: Montiel and Serven (2005) Notes: Total volatility = Normal + Extreme; Extreme = Crisis + Boom. Extreme shocks are defined as those exceeding two standard deviations of output growth over the respective decade. Exchange rate collapses Figure II.10: Developing Countries: Exchange Rate Crises, 1963-2002 (% of LDCs undergoing a Frankel-Rose (relative frequency, percent) exchange rate crisis) 35 30 25 20 % 15 10 5 LDC (77) MIDDLE (41) 2001 1999 1997 1995 1993 1991 1989 1987 1985 1983 1981 1979 1977 1975 1973 1971 1969 1967 1965 1963 0 LOW (33) Source: IMF-IFS. Source: Servenrate (2005) Note: ForMontiel this figureand an exchange crisis is defined as in Frankel and Rose (1996): a depreciation of the (average) nominal exchange rate that (a) exceeds 25 percent, (b) exceeds the preceding year’s rate of nominal depreciation by at least 10 percent, and (c) is at least three years apart from any previous crisis. The countries featured are those for which data is available over the entire period shown. Growth collapses Recessions lowering real GDP by over 5 percent (annual frequency, 77 countries) 25 20 15 low income middle income Source: Montiel and Serven (2005) all developing industrial 2002 1999 1996 1993 1990 1987 1984 1981 1978 1975 1972 1969 1966 1963 10 5 0 Sudden stops in capital Sudden stopsflows (relative frequency in percent) (% of LDCs undergoing a sudden stop) 50 40 30 20 10 19 78 19 80 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 0 low income Source: Montiel and Serven (2005) middle income All developing Banking crises of systemic banking crises (number ofIncidence LDCs undergoing a year) systemic crisis) (number of developing countries in crisis, per 22 20 18 14 12 10 8 6 4 2 ALL DEVELOPING (60) Source: Caprio and Klingebiel (2003). MIDDLE (35) LOW (24) 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 0 1981 Number of Crisis 16 Volatility and crises Empirically, extreme volatility more harmful for growth than “normal” volatility [Hnatkovska / Loayza] “Threshold effects”: volatility hampers growth only when large enough. Aggregate volatility-investment link negative only for high levels of volatility Large adverse shocks more likely to make liquidity constraints binding (and prevent restructuring) Deep recessions more likely to lead to asset destruction Volatility and crises Crises often the result of domestic policies / rigidities magnifying external shocks [e.g., Argentina] Some major crises of the 1990s [Gen 3] unlike those of the 1980s: multiple equilibria under financial fragilities – e.g., currency or time mismatches making banks and firms vulnerable to BoP runs and RER collapses Emphasis on “crisis-proofing”: reducing fragilities and increasing flexibility Managing macro volatility A strategy with several components: Reduce domestic policy-induced macro volatility – e.g., fiscal volatility: fiscal institutions / rules [“Fiscal Responsibility Laws”] Strengthen shock absorbers: Countercyclical policies [e.g., Chile] Reduce financial fragility by limiting mismatches – in banks’ portfolios as well as their borrowers’ Move away from rigid exchange rate regimes Enhance micro-flexibility – along with safety nets – to adjust to shocks Fiscal procyclicality Procyclicality of Public Consumption (procyclicality of public consumption, 15-year rolling windows, (rolling 15-year windows, medians) group medians) 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 -0.2 DEV (41) Source: Montiel and Serven (2005) G7 IND Non G7 (14) 20 00 19 99 19 98 19 97 19 96 19 95 19 94 19 93 19 92 19 91 19 90 19 89 19 88 19 87 19 86 19 85 19 84 19 83 19 82 -0.1 19 81 19 80 0.0 Managing macro volatility …but after achieving all this still need to deal with external shocks. Three general options (Ehrlich-Becker): 1. Self-protection: lower exposure to risk (e.g., by limiting specialization, “precautionary recessions”) 2. Self-insurance: transfer resources across time (e.g., commodity stabilization funds, foreign reserve accumulation) 3. Insurance / hedging: transfer resources across states of the world Managing macro volatility Three options for dealing with external shocks: 1. Self-Protection 2. Self-Insurance 3. Hedging / Insurance Source of Volatility Trade diversification (possibly away from Commodity Terms of trade stabilization funds comparative advantage) Strict current account limits [“precautionary Capital flows Liquidity hoarding recessions”] Capital controls Commodity-linked options / futures Contingent credit lines In practice, few instruments to achieve # 3, so countries resort to # 1-2 Developing-country foreign exchange reserves $ billion 1800 Low-income countries 1600 Other middle-income countries 1400 China 1200 1000 800 600 400 200 0 1999 2000 2001 2002 2003 2004 Developing-country foreign exchange reserves Venezuela, Rep Bol de India Egypt Indonesia China Brazil Russian Federation Argentina Pakistan 0 6 12 18 Reserves as months of imports 24 Developing-country foreign exchange reserves Foreign Reserve / GDP by Region 35 30 25 20 % 15 10 5 0 All (154) EAP (21) ECA (44) 1997-98 LAC (30) 1999-00 2001-02 MENA (11) 2003-04 SAS (7) SSA (41) Managing macro volatility Holding massive stocks of cash involves a huge cost in terms of growth and consumption. Big payoff to the development of new instruments to hedge aggregate volatility exante – bigger than to developing ex-post crisis resolution mechanisms Even imperfect hedging by trading instruments linked to world financial indicators (high yield spread, commodity prices…) can be a big help (Caballero-Panageas 2005) Sudden stops: Self-insurance vs hedging Source: Caballero and Panageas 2005 Managing macro volatility Why so few hedging instruments ? Moral hazard (e.g., in GDP-linked securities) Coordination problems in creating new markets Potential role for IFIs: A basic step: countercyclical lending (and aid stability) More lending in local currency – remove RER risk Room for contingent credit lines ? Lead the creation and trading of new financial instruments for hedging End Domestic vs foreign factors Across LDCs (unlike OECD), gov size not related negatively to volatility – gov is source of shocks (Suescún) Fatas-Mihov: (discretionary) fiscal volatility reduces LR growth [fiscal volatility is driven by political constraints] Pro-cyclicality not driven by political constraints (but seems to matter less than volatility) Micro regulation and macro volatility Source: Loayza, Oviedo and Servén 2005 Micro regulation and macro volatility Source: Loayza, Oviedo and Servén 2005 Estimated Welfare Gains from Diversification COUNTRY Argentina Brazil Chile Colombia Mexico Peru Venezuela, RB Jamaica United States Germany Japan Spain Ireland Singapore Thailand Pakistan Korea, Rep. India 8.13% 9.49% 5.96% 4.87% 5.69% 6.34% 6.52% 26.39% 0.31% 0.98% 0.59% 0.70% 1.28% 6.98% 10.28% 3.93% 9.57% 1.01% Note: Variances are over sample period 1990-01. Time horizon is 35 years. Source: World Bank staff calculations based on Arthanasoulis and van Wicoop (2000).