The Macro Management of Asian Driver Related Commodity Induced Booms

The Macro Management of Asian Driver Related Commodity Induced Booms

R. Avendaño, H. Reisen & J. SantisoOECD Development Centre

All China Economics ConferenceHonk Kong, 12-14 December 2007

I The Macroeconomic Links

II The Macroeconomic Policy Challenge

III Some Recent Policy Evidence

IV Towards Export Diversification

Integration of the Asian Drivers into the world economy has shaped primary

commodity markets

Four key contributing factors until very recently:

• Global output growth Commodity prices

procyclical with growth (Beta≈1.5% for each %point of growth)

• Barter terms of trade Industrial world growth > 4%

• Lower US interest rates Higher output prospects / low storage costs

• Weakening of US dollar Denomination of raw material prices

The combined contribution of China and India to global growth is substantial

Source: Own calculation based on the IMF World Economic Outlook Database, 2007.

Africa and Latin America have benefited from this “super-cycle” in both soft and hard

commodities

Source: OECD Development Centre, based on Datastream and African Development Bank (2007), African Economic Outlook 2007, Paris, OECD

0

100

200

300

400

500

600

05-1

2-20

00

05-0

5-20

01

05-1

0-20

01

05-0

3-20

02

05-0

8-20

02

05-0

1-20

03

05-0

6-20

03

05-1

1-20

03

05-0

4-20

04

05-0

9-20

04

05-0

2-20

05

05-0

7-20

05

05-1

2-20

05

05-0

5-20

06

05-1

0-20

06

05-0

3-20

07

05-0

8-20

07

Price

Inde

x (2

000=

100)

Price of hard commodities

Petroleum Aluminium

Copper Gold

0

50

100

150

200

250

300

350

05-1

2-20

00

05-0

5-20

01

05-1

0-20

01

05-0

3-20

02

05-0

8-20

02

05-0

1-20

03

05-0

6-20

03

05-1

1-20

03

05-0

4-20

04

05-0

9-20

04

05-0

2-20

05

05-0

7-20

05

05-1

2-20

05

05-0

5-20

06

05-1

0-20

06

05-0

3-20

07

05-0

8-20

07

Price

Inde

x (2

000=

100)

Price of soft commoditiesCoffee Soyabeans Cocoa

Non-agricultural (exhaustible) vs. agricultural (renewable) resources.





The Asian Drivers pose a number of challenges for commodity-dependent

countriesVarious challenges:

1. Choice of currency regime2. Inter-temporal budget spending (reserve and asset

management)3. Counter-cyclical stance of fiscal policy

1. Currency regime– ER movement dependent on commodity prices (≈ 80%)

– Preference for managed floats for their currency:• With commodity boom, a pure float nominal appreciation

Overshooting / substitution / recession• A currency peg Short-run spike in inflation: Bond

sterilization can increase interest rates and further capital inflows.

Accounting for Balance Sheet fragilities, and guaranteeing fiscal control

2. Reserves– Greenspan-Guidotti rule: reserves should cover short term

debt– Optimal level of reserves after commodity windfall– Financial so as social costs of holding reserves (Rodrik

2006)

3. Fiscal Control– Several tools for open capital accounts:

• Mundell assignment Unstable• Fiscal policy Internal balance

• Monetary policy External imbalances

– The challenge for fiscally weak governments • Prevent ex. rate appreciation reducing demand of non-

tradables• Stabilizing demand by smoothing expenditure

Managing Public Sector Commodity Booms

Source: based on discussion in Collier (2007), “Managing commodity booms: lessons of international experience”, Oxford University, Centre for the Study of African economies, Department of Economics.

How much to save?

How much to invest at home?

How much to invest abroad vs. retire public debt?

•Long run saving rule•Commodity price smoothing rule

•Excess return of home investment•Construction price smoothing rule

Excess cost of public debt over global return

Three factors that might generate the adverse long-run effect:

1. Dutch disease

2. Leamer triangle

3. Volatility

Spending effect

Resource movement effect

Natural Resources

Physical and Humancapitall

Raw Labour

Primitive extraction forestry Capital-intensive

extraction, mining, permanent crops

Pulp and paperagro-business

Peasant farming,wood-working

Apparel Machinery

Farms, food, woodproducts

A

B

CD

E

F G

A

A

A





Sampling: Assessing the impact of Asian Drivers on commodity prices and demand

itcommainntcommaintcommaint ADimportADimportP _,2_,10_, _*_*

A. Price Effect

Main commodityprice

Import share ofAD on main commodity

Initial share in t=0

Note: Price equation based on a similar methodology as the one proposed in Kamin, B., Marazzi, M, Schindlerm J. “The impact of Chinese Exports on Global Import Prices, Review of International Economics, 14 (2), 2006.

Highest to lowest values taken.

B. Demand Effect

•Exports to AD / Total Exports (average 2000-2005)•Exports to AD / GDP (average 2000-2005)

Sampling: Assessing the impact of Asian Drivers on demand and prices

Country

Exports to Asian

Drivers/ Total Exports (Avg.

2003-05)

Memo: Exports to

Asian Drivers/ GDP (Avg 2003-

05)

Country

Exports to Asian

Drivers/ Total Exports (Avg.

2003-05)

Memo: Exports to

Asian Drivers/ GDP (Avg 2003-

05)

Chile 0.115 0.040 Benin 0.382 0.041Peru 0.099 0.021 Gabon 0.150 0.019Argentina 0.097 0.012 Senegal 0.141 0.035Brazil 0.068 0.010 Nigeria 0.105 0.017Uruguay 0.041 0.006 Tanzania 0.098 0.011Paraguay 0.029 0.006 Egypt 0.078 0.003

Selection Costa Rica 0.027 0.009 South Africa 0.045 0.012

Groups Panama 0.024 0.002 Mali 0.042 0.009Morocco 0.042 0.010Zambia 0.039 0.014Cameroon 0.037 0.008Madagascar 0.030 0.002

L. America 0.024 0.002 Africa 0.030 0.007

Colombia 0.009 0.002 Cote d'Ivoire 0.027 0.017Bolivia 0.009 0.002 Mozambique 0.026 0.007Mexico 0.007 0.002 Kenya 0.020 0.002Honduras 0.007 0.002 Ghana 0.016 0.006Venezuela 0.006 0.002 Tunisia 0.010 0.004

Control Guatemala 0.006 0.001 Malawi 0.010 0.003

Groups Ecuador 0.005 0.002 Mauritius 0.009 0.003Nicaragua 0.004 0.001 Uganda 0.008 0.001

Algeria 0.007 0.002Niger 0.003 0.000Burkina Faso 0.000 0.000Botswana 0.000 0.000

Latin America Africa

Selection and Control Groups

Defining the analytical framework: The Fiscal Response

4.1. Government Budget Response Function

itititititit ZTOTFgapoutputF ***_* 431210

:itF Indicator of fiscal policy (in this case government expenditure, expressed as a percentage of GDP).

:_ itgapoutput Measure of the business cycle (log deviation of real GDPFrom Hodrik-Prescott trend)

:itTOT Terms of trade (HP filtered)

:itZ Set of variable controls

Sources: World Development Indicators, International Financial Statistics,

01 procyclicality

Two periods:

1987-1999

2000-2005

Part I: Government Expenditure

Table 4a. Government Expenditure Response- All countries -

Regression Government Expenditure All Countries

Latin America Africa OECD Latin America Africa OECDoutput_gap 4.7818e-11* 5.03E-11 2.53E-13 output_gap2.53E-12 -5.8470e-10*** -3.02E-12

[1.92] [0.27] [0.17] [0.09] [2.98] [1.46]lag_gov_exp 0.7562*** 0.4753*** 0.7842*** lag_gov_exp0.3450* 0.5717*** 0.6558***

[16.73] [9.46] [16.10] [1.93] [6.75] [6.39]terms_trade 8.0764e-13* -2.7668e-12*** 2.48E-13 terms_trade-1.85E-13 2.05E-13 -4.69E-14

[1.79] [2.70] [1.36] [0.89] [0.32] [0.23]Observations 207 318 195 Observations89 144 72Number of id_gen 16 25 15 Number of id_gen16 25 15R-squared 0.6 0.28 0.65 R-squared0.07 0.31 0.47Absolute value of t statistics in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

1987-1999 2000-2005

Procyclicality

Fiscal control

Regression Government Expenditure Selection Group

Latin America Africa Latin America Africaoutput_gap 3.8198e-11** 2.05E-10 output_gap1.0716E-11 -2.14E-10

[2.25] [1.06] [0.24] [1.29]lag_gov_exp 0.7746*** 0.4904*** lag_gov_exp0.6225* 0.2270***

[13.30] [7.33] [1.79] [2.71]terms_trade -4.2171E-13 -2.6502E-12 terms_trade-1.8463E-13 3.3567E-12

[0.51] [1.02] [0.41] [0.78]Observations 103 151 Observations45 66Number of id_gen 8 12 Number of id_gen8 12R-squared 0.68 0.3 R-squared0.1 0.14Absolute value of t statistics in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

1987-1999 2000-2005


Regression Government Expenditure Control Group

Latin America Africa OECD Latin America Africa OECDoutput_gap 7.2943E-11 -1.1254e-09** 2.53E-13 -1.0326E-11 -1.5932e-09*** -3.02E-12

[1.18] [2.07] [0.17] [0.43] [4.00] [1.46]lag_gov_exp 0.7533*** 0.4353*** 0.7842*** 0.11 1.0084*** 0.6558***

[11.48] [5.76] [16.10] [0.83] [8.04] [6.39]terms_trade 9.5359E-13 -2.9224e-12** 2.48E-13 -2.0351E-13 9E-14 -4.69E-14

[1.55] [2.54] [1.36] [1.40] [0.15] [0.23]Observations 104 167 195 44 78 72Number of id_gen 8 13 15 8 13 15R-squared 0.59 0.29 0.65 0.12 0.58 0.47Absolute value of t statistics in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

2000-20051987-1999


Latin America Africa OECD Latin America Africa OECDAll countries P N N N C NSelection Group P N N NControl Group N C N C

Summary Government Expenditure1987-1999 2000-2005

Note: P=procyclality, C=counter-cyclicality, N=fiscal neutrality

Part II: Budget balance (% of GDP)

Regression Budget Balance All Countries

Latin America Africa OECD Latin America Africa OECDoutput_gap 5.45E-11 -3.62E-10 9.14E-13 output_gap2.76E-11 1.05e-09*** 1.70e-11**

[1.49] [0.60] [0.22] [1.10] [3.57] [2.33]lag_budg_bal 0.28*** 0.1232 0.85*** lag_budg_bal0.45*** 0.08 0.33**

[3.71] [1.31] [16.75] [4.07] [0.97] [2.49]terms_trade 1.00e-12** 4.70e-11*** 8.02E-13 terms_trade2.84E-13 2.23E-14 1.19E-12

[2.42] [3.23] [1.30] [1.39] [0.02] [1.37]Observations 146 140 167 Observations90 138 84Number of id_gen 15 23 15 Number of id_gen15 23 14R-squared 0.19 0.13 0.66 R-squared0.27 0.11 0.23Absolute value of t statistics in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

1987-1999 2000-2005

Note: Data on fiscal policy obtained from the Economist Intelligence Unit, the OECD African Economic Outlook and Jimenez and Tromben (2006).

Regression Budget Balance Selection Group

Latin America Africa Latin America Africaoutput_gap 5.76E-11 -1.16E-09 output_gap1.65E-11 1.09e-09***

[1.19] [1.40] [0.60] [3.63]lag_budg_bal 0.24** 0.1 lag_budg_bal0.42*** 0.11

[2.12] [0.67] [3.06] [1.05]terms_trade 2.08e-12** 4.45e-11** terms_trade9.95e-13*** -1.95e-11***

[2.34] [2.48] [2.90] [2.87]Observations 75 58 Observations48 72Number of id_gen 8 12 Number of id_gen8 12R-squared 0.15 0.21 R-squared0.42 0.27Absolute value of t statistics in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

1987-1999 2000-2005


Part II: Budget balance (as a pourcentage of GDP)

Regression Budget Balance Control Group

Latin America Africa OECD Latin America Africa OECDoutput_gap 5.11E-11 2.2330e-09** 9.14E-13 4.93E-11 1.20e-09* 1.70e-11**

[0.94] [2.28] [0.22] [1.04] [1.79] [2.33]lag_budg_bal 0.29*** 0.08 0.85*** 0.44** 0.15 0.33**

[2.78] [0.73] [16.75] [2.55] [1.16] [2.49]terms_trade 8.51E-13 1.7810e-10*** 8.02E-13 1.53E-14 2.73E-13 1.19E-12

[1.55] [2.75] [1.30] [0.06] [0.26] [1.37]Observations 71 82 167 42 66 84Number of id_gen 7 11 15 7 11 14R-squared 0.21 0.15 0.66 0.22 0.08 0.23Absolute value of t statistics in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

2000-20051987-1999


Part II: Budget balance (as a pourcentage of GDP)

Respecting the Guidotti-Greenspan Rule: Higher Reserves, Lower Debt

Source: Computed on the basis of World Bank Global Development Finance Database.

The AD boom has reduced vulnerability to speculative attacks on emerging markets

Source: Computed on the basis of World Bank Global Development Finance Database.

A different risk debt management compared to the 1970s: debt composition and maturities

Source: Blommestein and Santiso, 2007, based on IMF Global Stability Report, 2006.

Inflation and Real Effective Exchange rates : No clear boom-effect in selection countries

-5

0

5

10

15

20

25

30

0

20

40

60

80

100

120

1995

1997

1999

2001

2003

2005

Argentina

REER Inflation (right axis)

0

10

20

30

40

50

60

70

0

20

40

60

80

100

120

1995

1997

1999

2001

2003

2005

Brazil


0123456789

0

20

40

60

80

100

120

1995

1997

1999

2001

2003

2005

Chile


0

5

10

15

20

25

8486889092949698

100102104106

1995

1997

1999

2001

2003

2005

Costa Rica


0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

80

85

90

95

100

105

110

115

1995

1997

1999

2001

2003

2005

Panama


0

2

4

6

8

10

12

14

16

0

20

40

60

80

100

120

1995

1997

1999

2001

2003

2005

Paraguay


0

2

4

6

8

10

12

14

80

85

90

95

100

105

1995

1997

1999

2001

2003

2005

Peru


051015202530354045

0

20

40

60

80

100

120

1995

1997

1999

2001

2003

2005

Uruguay


Source: Authors, 2007; based on Economist Intelligence Unit and IMF Statistical Yearbook, 2007.

Sterilized foreign exchange intervention to accommodate ER appreciation.

Real appreciation has been observed in Africa during the studied period: Zambia

Benin

02468

10121416

1995

1997

1999

2001

2003

2005


Cameroon

949698

100102104106108110112114

1995

1997

1999

2001

2003

2005

0

2

4

6

8

10


Egypt

0

20

40

60

80

100

120

140

1995

1997

1999

2001

2003

2005

024681012141618


Morocco

868890

92949698

100102

1995

1997

1999

2001

2003

2005

0

1

2

3

4

5

6

7


South Africa

0

20

40

60

80

100

120

140

1995

1997

1999

2001

2003

2005

0

2

4

6

8

10


Nigeria

0

50

100

150

200

250

1995

1997

1999

2001

2003

2005

01020304050607080


Senegal

85

90

95

100

105

110

1995

1997

1999

2001

2003

2005

-10123456789


Tanzania

0

0.2

0.4

0.6

0.8

1

1.2

1995

1997

1999

2001

2003

2005

0

5

10

15

20

25

30


Zambia

020406080

100120140160

1995

1997

1999

2001

2003

2005

0

10

20

30

40

50


Source: Authors, 2007; based on Economist Intelligence Unit and IMF Statistical Yearbook, 2007.

Estimating the impact of Asian Drivers’demand on inflation

itititit growthgovADortlatdev exp__*_exp*inf_ 210

:_exp itADort Export share of country i to Asian drivers

:exp__ itgrowthgov Government expenditure growth

:inf_ itlatdev Diff. between inflation in year i and average inflation

Sources: International financial statistics, Datastream and World Integrated Trade System (WITS) database. Hausman tested fixed-effect estimator for both samples.

In Africa, fiscal policy has played an important role in explaining inflation

deviations

Inflation Deviation and Exports to Asian Drivers

- Selection Group -

Inflation Deviation vs Export AD 2000-2005 Selection Group

Latin America Africaexport_ad -2.90E+01 -2.96E+00

[0.67] [0.29]gov_exp_growth 0.2 0.05**

[1.07] [2.40]Observations 4.60E+01 6.40E+01Number of id_gen 8 12R-squared 0.04 0.1Absolute value of t statistics in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

2000-2005

Inflation Deviation and Exports to Asian Drivers

- Control Group -

Inflation Deviation vs Export AD 2000-2005 Control Group

Latin America Africaexport_ad -4.98E+02 2.05E+01

[0.85] [0.21]gov_exp_growth 0.12 0.31*

[0.25] [1.94]Observations 4.50E+01 5.90E+01Number of id_gen 8 11R-squared 0.03 0.08Absolute value of t statistics in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

2000-2005




IV Export Diversification

The rise of China and India is a challenge for export diversification

n

np

HH

n

jj

11

1

1

2

Note: Herfindahl-Hirschmann index calculated as , where represents

the market share of country j on the exports of country i in its total exports .

iijj Xxp /

Export Concentration in Products for Latin AmericaHerfindahl Hirschman Index

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9Ve

nezu

ela

Ecua

dor

Chile

Pana

ma

Boliv

ia

Peru

Para

guay

Hon

dura

s

Guy

ana

Uru

guay

Colo

mbi

a

Cost

a Ri

ca

Mex

ico

Gua

tem

ala

Braz

il

2001 2006

Source: Latin American Economic Outlook 2008, OECD Development Centre. Based on data from Comtrade, World Integrated Trade Database, 2007.

The shadow side of the commodity boom: Towards export concentration

Export Concentration in Products for AfricaHerfindahl Hirschman Index

0.00.10.20.30.40.50.60.70.80.91.0

Ang

ola

Chad

Nig

eria

Cong

o

Mal

i

Nig

er

Moz

ambi

que

Alg

eria

Zam

bia

Cam

eroo

n

Gha

na

Gam

bia

Nam

ibia

Côte

d'Iv

oire

Sene

gal

Zim

babw

e

Keny

a

Sout

h A

fric

a

Tuni

sia

Mor

occo

2000 2005

Source: African Economic Outlook 2007, OECD Development Centre. Based on data from Comtrade, PC-TAS and World Integrated Trade Database, 2007.

Conclusions

1. The macro challenge faced by Latin America and Africa: commodity booms are longer term, not just cyclical.

1. An overall assessment about the macro response is positive: targeting both inflation and REER, and fiscal control in Africa.

1. Short term benefits (prices, proceeds) both also debt reduction, broader client base, reduced vulnerability

1. Evidence of specialisation revisited. Dutch disease and Leamer effects

Conclusions (II)

• Prospective demand of AD driver from mineral to agricultural A positive effect on vertical diversification?

1. The AD driven commodity boom shows higher resilience on some African countries than expected

1. A permanent concern: capitalise windfall revenues on infrastructure

2. The imperatives of product diversification

The Macro Management of Asian Driver Related Commodity Induced Booms

R. Avendaño, H. Reisen & J.SantisoOECD Development Centre

All China Economics ConferenceHonk Kong, 12-14 December 2007

Documents

The Macro Management of Asian Driver Related Commodity Induced Booms