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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Price of soft commoditiesCoffee Soyabeans Cocoa
Non-agricultural (depletable) vs. agricultural (replenishable) resources.
I Impact Channels
II The Macroeconomic Policy Challenge
III Some Recent Policy Evidence
IV Towards Export Diversification
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
II The Macroeconomic Policy Challenge
III Some Recent Policy Evidence
IV Towards Export Diversification
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
Part I: Government Expenditure
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
Part I: Government Expenditure
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
Note: Data on fiscal policy obtained from the Economist Intelligence Unit, the OECD African Economic Outlook and Jimenez and Tromben (2006).
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
Note: Data on fiscal policy obtained from the Economist Intelligence Unit, the OECD African Economic Outlook and Jimenez and Tromben (2006).
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.
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
II The Macroeconomic Policy Challenge
III Some Recent Policy Evidence
IV Towards Export Diversification
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