Luis ServénThe World Bank

BarcelonaMarch 2006

Growth and welfare effects of macroeconomic volatility

Figure 1. Standard Deviation 1970-2001

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16

Std Dev of Real GDP Growth

Std

De

v o

f R

ea

l Pri

va

te C

on

su

mp

tio

n G

row

th

Developing Countries

Industrial Countries

Source: WDI - World Bank.

Output volatility and consumption volatility

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 linksBoth limit risk sharing / smoothing of shocks Pro-cyclical macro policies that amplify fluctuations

Volatility of Terms of Trade Growth (Regional Medians)

0

2

4

6

8

10

12

14

16

18

20

IndustrializedEconomies

East Asia andPacific 7

Latin Americaand the

Caribbean

Middle Eastand North

Africa

South Asia Sub-SaharanAfrica

Other EastAsia andPacific

(In

Per

cen

t) 1960s

1970s

1980s

1990s

Source: Montiel and Serven (2005)

Fiscal volatility

Volatility of public consumption growth (medians by group)

0

2

4

6

8

10

12

Low income Midlde income Industrial All developing

60s 70s 80s 90s

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 !

Why are LDCs more volatile ?

ZMB

MDGGMB

GHA

BFA

ZWE

TGOSLE

MWI

NGA

CIV

ZAF

COGNER

SENKENBWA

IDNPNG

PHL

KOR

THA

MYS TUR

ISRSWE

NLDDNK

ITA

JPN

NORAUS

IRL

USA CHE

PRT

FRAESP

ISL

AUTBEL

CANGRC

FIN

GBR

NIC

PRY

COLTTO

CHLURY

VEN

DOM

ECU

GTM

PER

SLV

ARG

BOL

HTI

HND

PAN

CRI

BRA

MEX

JAM

TUN

EGY

SYR

JOR

IRN

MAR

INDPAK

BGDLKA

0.0

2.0

4.0

6V

ola

tility

of

ou

tpu

t ga

p

.2 .3 .4 .5 .6 .7Overall Regulation Index

Correlation: 0.41***

Overall RegulationMicro regulation and macro volatility

Source: Loayza, Oviedo and Servén 2005

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)

0

10

20

30

40

50

60

70

80

61-70 71-80 81-90 91-00

Figure II.2. Structure of GDP Growth Volatility (percent, mean of 77 developing countries)

Normal Extreme Crisis BoomSources: Hnatkovska and Loayza (2004); authors' calculations.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.

Source: Montiel and Serven (2005)

Normal and extreme GDP growth volatility(percent of total volatility, average of 77 LDCs)

Figure II.10: Developing Countries: Exchange Rate Crises, 1963-2002 (relative frequency, percent)

0

5

10

15

20

25

30

35

19

63

19

65

19

67

19

69

19

71

19

73

19

75

19

77

19

79

19

81

19

83

19

85

19

87

19

89

19

91

19

93

19

95

19

97

19

99

20

01

%

LDC (77) MIDDLE (41) LOW (33)

Source: IMF-IFS. Note: For this figure an exchange rate 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.

Source: Montiel and Serven (2005)

Exchange rate collapses(% of LDCs undergoing a Frankel-Rose exchange rate crisis)

Growth collapsesRecessions lowering real GDP by over 5 percent

(annual frequency, 77 countries)

0

5

10

15

20

25

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

low income middle income all developing industrial

Source: Montiel and Serven (2005)

Source: Montiel and Serven (2005)

Sudden stops in capital flows (relative frequency in percent)

0

10

20

30

40

50

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

low income middle income All developing

Sudden stops(% of LDCs undergoing a sudden stop)

Incidence of systemic banking crises (number of developing countries in crisis, per year)

0

2

4

6

8

10

12

14

16

18

20

22

19

81

19

82

19

83

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

Nu

mb

er

of

Cri

sis

ALL DEVELOPING (60) MIDDLE (35) LOW (24)Source: Caprio and Klingebiel (2003).

Banking crises

(number of LDCs undergoing a systemic crisis)

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

Source: Montiel and Serven (2005)

Procyclicality of Public Consumption(rolling 15-year windows, medians)

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

DEV (41) G7 IND Non G7 (14)

Fiscal procyclicality(procyclicality of public consumption, 15-year rolling windows,

group medians)

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

1. Self-Protection 2. Self-Insurance

3. Hedging / Insurance

Source of Volatility

Terms of trade

Trade diversification (possibly away from comparative advantage)

Commodity stabilization funds

Commodity-linked options / futures

Capital flows

Strict current account limits [“precautionary recessions”]

Capital controls

Liquidity hoarding Contingent credit

lines

Managing macro volatility

Three options for dealing with external shocks:

In practice, few instruments to achieve # 3, so countries resort to # 1-2

$ billion

0

200

400

600

800

1000

1200

1400

1600

1800

1999 2000 2001 2002 2003 2004

Low-income countries

Other middle-income countries

China

Developing-country foreign exchange reserves

0 6 12 18 24

Pakistan

Argentina

Russian Federation

Brazil

China

Indonesia

Egypt

India

Venezuela, Rep Bol de

Reserves as months of imports

Developing-country foreign exchange reserves

0

5

10

15

20

25

30

35

%

All (154) EAP (21) ECA (44) LAC (30) MENA (11) SAS (7) SSA (41)

Foreign Reserve / GDP by Region

1997-98 1999-00 2001-02 2003-04

Developing-country foreign exchange reserves

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 ex-ante – 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)

Source: Loayza, Oviedo and Servén 2005

Micro regulation and macro volatility

Micro regulation and macro volatility

Source: Loayza, Oviedo and Servén 2005

COUNTRY

Argentina 8.13%Brazil 9.49%Chile 5.96%Colombia 4.87%Mexico 5.69%Peru 6.34%Venezuela, RB 6.52%Jamaica 26.39%

United States 0.31%Germany 0.98%Japan 0.59%Spain 0.70%Ireland 1.28%

Singapore 6.98%Thailand 10.28%Pakistan 3.93%Korea, Rep. 9.57%India 1.01%

Estimated Welfare Gains from Diversification

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).