commodity booms, africa and latin america's response to asian deman

The Macro Management of Commodity Booms: Africa and Latin America’s Response to Asian Demand

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

UNU-WIDER Project Meeting - Southern Engines of Global Growth

Johannesburg, 5-6 September 2008

I Impact Channels

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 marketsFour key contributing factors :

1. Global output growth Commodity prices procyclical with growth (≈1.5% for each point of growth)

2. Barter terms of trade Industrial world growth > 4%

3. Lower US interest rates Higher output prospects / low storage costs

4. 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

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Copper Gold

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Price of soft commoditiesCoffee Soyabeans Cocoa

Non-agricultural (depletable) vs. agricultural (replenishable) resources.

I Impact Channels




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 an 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

I Impact Channels




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.

Parameter

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

Selection and Control Groups

CountryExports to Asian

Drivers/ Total Exports (Avg.

2003-05)

Memo: Exports to Asian

Drivers/ GDP (Avg 2003-05)

Main Export CountryExports to Asian

Drivers/ Total Exports (Avg.

2003-05)

Memo: Exports to Asian

Drivers/ GDP (Avg 2003-05)

Main Export

Chile 0.115 0.040 Copper Benin 0.382 0.041 Raw cottonPeru 0.099 0.021 Metalliferous ores Gabon 0.150 0.019 Petroleum Argentina 0.097 0.012 Petroleum Senegal 0.141 0.035 Inorg acids

Selection Brazil 0.068 0.010 Iron/steel Nigeria 0.105 0.017 PetroleumGroups Uruguay 0.041 0.006 Meat Tanzania 0.098 0.011 Gold

Paraguay 0.029 0.006 Oil-seeds Egypt 0.078 0.003 Fuel oils,nesCosta Rica 0.027 0.009 Coffee South Africa 0.045 0.012 PlatinumPanama 0.024 0.002 Fish products Mali 0.042 0.009 Gold

Morocco 0.042 0.010 Inorg acidsZambia 0.039 0.014 Copper Cameroon 0.037 0.008 PetroleumMadagascar 0.030 0.002 Coffee/Vanilla

Median L. America 0.024 0.002 Africa 0.030 0.007Values

Colombia 0.009 0.002 Petroleum Cote d'Ivoire 0.027 0.017 Cocoa Bolivia 0.009 0.002 Gas Mozambique 0.026 0.007 AluminiumMexico 0.007 0.002 Petroleum Kenya 0.020 0.002 TeaHonduras 0.007 0.002 Coffee Ghana 0.016 0.006 Cocoa

Control Venezuela 0.006 0.002 Petroleum Tunisia 0.010 0.004 PetroleumGroups Guatemala 0.006 0.001 Clothing Malawi 0.010 0.003 Tobacco

Ecuador 0.005 0.002 Petroleum Mauritius 0.009 0.003 SugarNicaragua 0.004 0.001 Coffee Uganda 0.008 0.001 Coffee

Algeria 0.007 0.002 Petroleum Niger 0.003 0.000 Uranium Burkina Faso 0.000 0.000 Raw cottonBotswana 0.000 0.000 Minerals

Latin America Africa

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 (as a pourcentage 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



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



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

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

Africa

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

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

Latin America

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.

Short-term domestic debt in Emerging markets

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

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

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

I Impact Channels




The rise of China and India is also a challenge against product specialisation

n

np

HH

n

jj

11

11

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

0.00.10.20.30.4

0.50.60.70.80.9

Vene

zuela

Ecua

dor

Chile

Pana

ma

Boliv

ia

Peru

Para

guay

Hond

uras

Guya

na

Urug

uay

Colo

mbi

a

Costa

Rica

Mex

ico

Guat

emala

Braz

il

2001 2006

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

A commodity boom without diversification is a

Sword of Damocles over both regionsExport Concentration in Products for Africa

Herfindahl Hirschman Index

0.00.10.20.30.40.50.60.70.80.91.0

Ango

la

Chad

Nige

ria

Cong

o

Mali

Nige

r

Moz

ambi

que

Alge

ria

Zam

bia

Cam

eroo

n

Ghan

a

Gam

bia

Nam

ibia

Côte

d'Iv

oire

Sene

gal

Zimba

bwe

Keny

a

Sout

h Af

rica

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 towards commodity booms is very different today

2. An overall assessment of the macro response is positive: both monetary choices targeted both inflation and REER.

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

4. Evidence of specialisation revisited. Dutch disease and Beamer effects

Conclusions (II)

1. Prospective demand of Asian Drivers from mineral to agricultural A positive potential effect

6. The AD driven commodity boom shows higher resilience than expected in some African countries

7. A permanent concern: capitalise windfall revenues on infrastructure

8. The imperative of product diversification

The Macro Management of Commodity Booms: Africa and Latin America’s Response to Asian Demand

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

UNU-WIDER Project Meeting - Southern Engines of Global Growth

Johannesburg, 5-6 September 2008

commodity booms, africa and latin america's response to asian deman

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