The Growth of China and India:
Three Challenges for Latin
American Agriculture
D. Lederman
Development Economics Research Group, The World
Bank
November 2007
IICA Technical Workshop, San José, Costa Rica
This Presentation
• Advertisement: Forthcoming book
• Main channel: Commodity Prices
• Correlations between China and Commodity
Prices
• Co-movement between LAC production and
China/India
• Who benefits? The First Challenge
• Supply capacity. The Second Challenge
• Innovation and Diversification. The Third
Challenge
Forthcoming Book -- Outline
• “The Challenge of China’s and India’s Growth for Latin
America” (under review, forthcoming in 2008?)
– Edited by D. Lederman, M. Olarreaga & G. Perry
• In three parts
– Introduction (Lederman, Perry, Olarreaga)
– Negative impact of Chinese and Indian competition in some
industries and some LAC regions (Hanson & Robertson, Freund
& Ozden, Feenstra & Kee, and Freund)
– The growth of China and India is not a zero-sum game for LAC
(Calderón; Lederman, Olarreaga, and Soloaga; Suescún;
Cravino, Lederman, and Olarreaga)
– Factor adjustment and specialization patterns (Casacuberta &
Gandelman; Castro, Olarreaga, & Saslavsky; Lederman,
Olarreaga & Rubiano)
Our Book: The Growth of China &
India Is Not a Zero-Sum Game
• Evidence from the gravity model of trade using
aggregate non-fuel bilateral merchandise data
for LAC, 2000/4
– Large demand for LAC exports, but low supply
elasticities
– Little evidence of substitution effects in 3rd markets
• Evidence from the KCM model of MNCs using
aggregate and sectoral FCS global data,
1990/2003
– Little evidence of substitution effects in 3rd markets
• But shifting RCAs toward NRs and Knowledgeintensive industries
World Bank Commodity Price
Indexes (1990=100)
Jan/Dec 05 Jan/Dec 06 Jan/Oct 07
Price Trends: Selected
Commodities
China and Commodity Prices:
Recursive Estimates of the Correlation
between China’s I.P. and Prices
3.0
2.5
2.0
1.5
1.0
0.5
0.0
2001.12
2002.12
2003.12
2004.12
2005.12
-0.5
Agricultural Raw Materials
Beverages
Food
Metals and Minerals
Crude Petroleum
India and Commodity Prices:
Recursive Estimates of the Correlation
between China’s I.P. and Prices
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0
2001.12
2002.12
Agricultural Raw Materials
2003.12
Beverages
Food
2004.12
Metals and Minerals
2005.12
Crude Petroleum
China’s Appetite for Commodities
Share in World Consumption
Tin
Zinc
Soy bean
Aluminium
Copper
Nickel
1990
2004
Sugar
Petroleum
0
5
10
15
20
25
30
Commodity Prices, China, and
Other Global Factors
Agri
Index
Energy
Index
Ind. Met. Pre. Met.
Soy Price Copper
Index
Index
0.48
0.00
0.64
0.00
0.70
0.00
0.81
0.00
0.10
0.55
0.33
0.05
0.12
0.47
US$ vs.
EURO
0.34
0.02
0.15
0.29
0.13
0.38
0.09
0.51
-0.20
0.17
0.07
0.62
0.13
0.37
0.27
0.12
World
%YoY
0.62
0.00
-0.23
0.11
-0.20
0.15
-0.17
0.23
-0.16
0.27
0.09
0.55
0.15
0.29
0.27
0.12
0.60
0.00
USA
&YoY
0.05
0.74
0.44
0.00
0.66
0.00
0.75
0.00
0.30
0.03
0.31
0.03
0.17
0.25
0.84
0.00
-0.14
0.33
-0.09
0.53
China
%YoY
0.19
0.17
-0.32
0.02
0.14
0.34
0.03
0.81
-0.02
0.87
-0.12
0.42
-0.07
0.65
-0.04
0.82
-0.03
0.84
0.30
0.03
0.02
0.92
Oil WTI
Source: Bloomberg, %YoY: Growth on the Real GDP (annual variation)
Commodity Indexes expressed in Total Returns. Commodity Prices expressed as variations
Sample: quarterly data, 1995q1-2007q2; averages
Europe
&YoY
Share of China and India in Latin American
Trade, 1990 versus 2004
Share in exports
Share in imports
Peru
Chile
Chile
Ecuador
Argentina
Peru
Brazil
Mexico
Uruguay
Colombia
LAC
Brazil
Nicaragua
LAC
Costa Rica
Bolivia
Bolivia
Uruguay
Colombia
Nicaragua
Ecuador
Argentina
Mexico
Costa Rica
Guatemala
Venezuela
Venezuela
Guatemala
0
5
1990
10
15
0
2
2004
4
1990
6
8
10
2004
Source: Author's calculations with United Nations' Comtrade
Total LAC non-fuel merchandise exports to China and India grew by 39% and 25% per annum
respectively (in nominal USD$) during 2000-2005.
Fast growth due to demand (prices) or supply (quantities/varieties)?
0
.2
Correlation
-.4
-.2
-.2
0
Correlation
.2
.4
.4
.6
Are LA regions competing in the Same
Products as China and India?
1990
1995
2000
2005
1990
1995
Year
Corr(Andean,China)
Corr(Andean,India)
Corr(SCone,China)
Source: Author's calculations
2005
Corr(SCone,India)
-.1
-.2
0
.1
Correlation
.2
.2
.3
.4
Source: Author's calculations
0
Correlation
2000
Year
1990
1995
2000
2005
1990
1995
Year
Corr(CAmerica,China)
Source: Author's calculations
2000
2005
Year
Corr(CAmerica,India)
Corr(Mexico,China)
Corr(Mexico,India)
Source: Author's calculations
Source: Lederman, Olarreaga & Rubiano (forthcoming 2008, RWE).
Regression Results – Diverging
RCAs?
LAC
Andean
Countries
Central
America
Mexico
Southern
Cone
RCA China
-0.02
(0.04)
0.18
(0.08)*
-0.11
(0.04)**
0.09
(0.06)
-0.26
(0.05)**
RCA India
0.16
(0.03)**
-0.06
(0.06)
0.36
(0.06)**
0.03
(0.05)
0.36
(0.05)**
Bilateral net
exports of LAC
to China
-4.47e-06
(2.62e-06)
8.27e-07
(4.49e-07)
1.51e-06
(9.80e-07)
-6.71e-08
(2.21e-07)
8.63e-09
(9.52e-08)
Bilateral net
exports of LAC
to India
-1.84e-06
(3.59e-05)
1.08e-07
(1.97e-06)
-1.9e-05
(1.57e-05)
-7.94e-07
(1.64e-06)
1.01e-07
(7.95e-07)
-0.32
(0.36)
0.02
(0.37)
-0.26
(0.23)
-0.21
(0.06)**
0.09
(0.33)
R^2
0.06
0.05
0.15
0.02
0.12
Observation
s
8376
3236
2538
650
2587
Constant
Regression Results – Diverging
RCAs?
LAC
RCA China
RCA India
RCA China*unskilled
RCA China*skilled
RCA China*scien_K
RCA China*nat_res
RCA India*unskilled
RCA India*skilled
RCA India*scien_K
RCA India*nat_res
Unskilled
Skilled
Scientific Knowledge
Natural Resources
0.03
(0.05)
0.25
(0.04)**
-0.09
(0.05)
0.39
(0.06)**
0.04
(0.06)
-0.21
(0.08)*
0.25
(0.04)**
0.23
(0.05)**
-0.41
(0.05)**
-0.37
(0.05)**
0.15
(0.06)*
-0.21
(0.06)**
0.55
(0.07)**
2.44
(0.08)**
Andean
countries
0.05
(0.10)
0.31
(0.09)*
-0.23
(0.09)*
0.38
(0.12)**
0.27
(0.11)*
-0.26
(0.15)
0.15
(0.08)
0.34
(0.09)**
-0.92
(0.10)**
-0.27
(0.10)**
0.27
(0.11)**
-0.20
(0.12)
0.81
(0.13)**
2.88
(0.15)**
Central
America
-0.06
(0.07)
0.20
(0.06)*
0.07
(0.08)
0.22
(0.10)**
0.11
(0.09)
-0.49
(0.13)**
0.37
(0.07)**
0.01
(0.09)
-0.06
(0.08)
-0.18
(0.09)**
0.14
(0.09)
-0.71
(0.11)**
-0.23
(0.10)**
1.93
(0.12)**
Mexico
Southern Cone
0.12
(0.07)
0.08
(0.06)
-0.30
(0.07)**
0.42
(0.09)**
-0.02
(0.09)
0.51
(0.14)**
0.03
(0.06)
0.18
(0.07)**
-0.24
(0.08)**
-0.04
(0.08)
0.15
(0.09)
0.06
(0.10)
-0.09
(0.10)
0.21
(0.12)
0.05
(0.09)
0.27
(0.07)*
-0.11
(0.08)
0.44
(0.11)**
0.17
(0.10)
-0.01
(0.15)
0.45
(0.08)**
0.12
(0.09)
-0.16
(0.09)
-0.78
(0.10)**
-0.09
(0.09)
0.11
(0.11)
0.85
(0.13)**
2.53
(0.15)**
Who Benefits?:
The First Challenge
Who Benefits?
Static Effects on Individuals (i.e., without changes in
employment, quantity of sales, and quantities in consumption)
Wi , s , c  ( wi , s , c  qi , s , c  ci.s , c )  Ps
Dynamic Effects on Individuals (i.e., with changes in
employment, quantity of sales, and quantities in consumption
caused by the change in prices of commodities)
W  ( w  q  c )  P  (w / P)  P  (q / P)  P  (c / P)  P
Who Benefits? Some Data on Mexico
(for 10% rise in price of corn)
Source: Porto (2006).
Who Benefits? Data from Panama on Net
Producers of Sensitive Agricultural
Commodities
Total Population
Indigenous Population
Share of net
Share of product
Agricultural
Share of
Share of total sales in gross
Income in total net Indigenous
Population
Agriculture Income. Income.
Population
Milk
0.86%
40.9%
43.4%
0.00%
Cheese
Meat
0.08%
4.5%
54.0%
0.00%
3.17%
50.8%
40.6%
5.10%
Poultry
2.67%
11.8%
32.8%
7.20%
Rice
1.32%
20.9%
44.1%
Maize
3.54%
13.2%
Pork
2.23%
Tomato
Total Net Producers
Moderate Poverty
Share of net
Share of product
Agricultural
sales in gross
Income in total
Agriculture Income. net Income.
0.0%
Share of product
Share of net
Share of Poor sales in gross
Agricultural Income in
Population
Agriculture Income. total net Income.
0.0%
0.45%
41.0%
40.4%
0.0%
0.0%
33.2%
47.4%
0.03%
0.5%
66.8%
3.40%
38.9%
7.4%
47.3%
39.3%
4.99%
8.7%
3.39%
35.8%
13.3%
36.8%
2.57%
18.7%
34.5%
43.9%
5.28%
8.8%
33.8%
5.98%
10.1%
22.7%
36.1%
33.8%
4.04%
11.2%
44.9%
3.57%
14.7%
0.19%
41.4%
34.8%
26.1%
0.16%
0.2%
8.8%
0.22%
32.8%
3.80%
20.3%
60.4%
43.5%
5.96%
36.3%
49.2%
4.52%
43.2%
50.8%
Who Benefits? Data from
Guatemalan
An Aside on CAFTA (a decline in the price of
maize, but increases in prices of fruits) and
Guatemalan Indigenous Populations
The Supply Response:
The Second Challenge
The Supply Response: The Gravity Model with
Heterogeneity in Demand and Supply Elasticities



 

M ijt  Yit Y jt Dij Bij  ij Linderijt e
 i d i  j d j  t d t
M ijt  Yit  d i China d jR Yit  R Y jt  d i China d jR Y jt  R Dij Bij  ij

R

R
Linderijt  d i China d jR Lindert ijt  R e

 i d i  j d j  t d t
R
What’s the proper estimator?
We present OLS (log-linear), Poisson, & Neg Bin.
Gravity Results: Large Ch & Ind
Demand Elasticities, Low LAC Supply
Elasticities
Table 1. Trade Demand and Supply Elasticities of GDP for LAC-China
Trade – Non-Fuel Merchandise Trade Data
OLS
Poisson
Negative Binomial
Estimated
Estimated
Coeficient P-Value Coeficient P-Value
Estimated
Coeficient
P-Value
Andean Countries
Ow n supply
0.51
0.00
0.28
0.14
0.38
0.19
China demand
3.40
0.00
3.01
0.00
4.42
0.00
Ow n supply
0.15
0.19
-0.11
0.52
-0.81
0.24
China demand
3.32
0.00
3.04
0.00
4.49
0.00
Ow n supply
-0.03
0.89
-0.97
0.01
-2.10
0.00
China demand
3.20
0.00
2.95
0.00
4.25
0.00
Ow n supply
0.28
0.01
-0.03
0.70
-0.09
0.58
China demand
3.59
0.00
3.19
0.00
4.69
0.00
Caribbean Countries
Central Am erica/Mexico
Southern Cone
Observations
21480
21480
21480
Gravity Results: Large Ch & Ind
Demand Elasticities, Low LAC Supply
Elasticities
Table 2. Trade Demand and Supply Elasticities of GDP for LAC-India Trade
– Non-Fuel Merchandise Trade Data
OLS
Poisson
Negative Binomial
Estimated
Estimated
Coeficient P-Value Coeficient P-Value
Estimated
Coeficient
P-Value
Andean Countries
Ow n supply
0.29
0.35
0.28
0.25
-0.27
0.56
India demand
1.84
0.00
1.62
0.00
2.99
0.00
Ow n supply
-0.26
0.02
-0.21
0.21
-1.47
0.04
India demand
1.87
0.00
1.55
0.00
2.78
0.00
Ow n supply
-0.34
0.08
-1.40
0.00
-2.47
0.00
India demand
1.76
0.00
1.74
0.00
2.72
0.00
Ow n supply
0.39
0.00
-0.08
0.21
-0.09
0.50
India demand
1.78
0.00
1.88
0.00
2.90
0.00
Caribbean Countries
Central Am erica/Mexico
Southern Cone
Observations
21480
21480
21480
Substitution Effects in 3rd Markets? The Gravity
Model with Heterogeneity in Ch/Ind Substitution,
Aggregate Data, 2000/4
M ijt  Yit Y jt Dij Bij ij Linderijt e
 i d i  j d j  t d t




X China
M
X
M
, z ,t
China, z ,t
China, j ,t
China, j ,t
R
R
R
R
d
X
d
M
d
X
d
M
 jR China, z,t  jR China, z,t  jR China, j ,t  jR China, j ,t
R
R
R
R
Note the Four Channels through which trade can affect LAC trade with 3rd
Markets:
USA
MEX
CHINA
Aggregate Trade: Little (robust)
Evidence of Substitution Effects in 3rd
Markets
Table 3: Impact of China's Trade Flows on LAC Non-Fuel Exports to Third
Countries
Negative
OLS
Poisson
Binom ial
Estimated
Estimated
Estimated
Coeficient P-Value Coeficient P-Value Coeficient P-Value
Andean Countries
China exports to third countries
0.06
0.10
0.11
0.38
0.14
0.15
China imports from third countries
0.01
0.65
0.10
0.30
0.06
0.38
China Exports to Andean
-0.07
0.25
0.21
0.25
0.03
0.83
China Imports from Andean
-0.05
0.10
0.21
0.00
0.03
0.64
China exports to third countries
-0.14
0.00
0.14
0.31
-0.06
0.74
China imports from third countries
-0.04
0.27
0.08
0.33
0.04
0.76
China Exports Caribbean
-0.04
0.66
0.27
0.29
0.15
0.67
China Imports from Caribbean
0.00
0.82
0.02
0.46
0.09
0.03
China exports to third countries
0.00
0.91
0.85
0.00
0.16
0.19
China imports from third countries
-0.04
0.15
-0.25
0.00
0.00
0.98
China Exports to Central America
-0.03
0.31
-0.04
0.71
0.01
0.93
China Imports from Central America
0.03
0.10
0.06
0.40
0.10
0.08
China exports to third countries
0.21
0.00
0.02
0.87
0.14
0.14
China imports from third countries
0.02
0.51
0.19
0.05
0.06
0.33
China Exports to Southern Cone
0.05
0.56
0.05
0.72
0.30
0.08
China Imports from Southern Cone
0.02
0.64
0.45
0.00
0.21
0.09
Caribbean Countries
Central Am erica/Mexico
Southern Cone
Observations
15440
15440
15440
Foreign Capital: The Growth of China and India Is Not
a Zero-Sum Game for LAC – Some Data
Foreign Capital Stocks (FCS) as a Share of GDP in LAC Relative to China and
India, 2003
20
15
10
5
0
na
i
t
n
ge
r
A
il
z
a
Br
ile
h
C
ia
la
or
ca
b
i
d
a
a
R
lv
em
lom
t
ta
a
o
a
s
S
C
Co
Gu
El
China and Hong Kong
India
xic
e
M
o
ela
u
ez
n
Ve
Foreign Capital: The Empirical Augmented KCM
Allows for Horizontal and Vertical Motivations for
FDI
FCSijt  f ( SUMGDPijt , GDPDIFSQij,t , SKDIFFijt , SKDIFF _ NEGijt , F _ COST jt , T _ COSTit ,T _ COST jt , DISTijt ,
FCSiChinat, FCS _ LACiChinat.FCSiIndiat, FCS _ LACiIndiat)
Note the data constraint  only one channel through which China can affect
FCS in LAC from 3rd Markets:
USA
MEX
CHINA
What’s the proper estimator?
We present OLS (log-linear), Poisson, & Neg Bin.
Some Results: Little Evidence of
Substitution Effects in the FCS Data
Table 4: China and India Effects across LAC Sub Regions
Aggregate Data
U.S. Data
U.S. Data: Manufacturing
OLS
Poisson
Neg. Bin.
OLS
Poisson
Neg. Bin.
OLS
Poisson
Neg. Bin.
0.45
0.22
0.13
0.02
0.20
0.05
-0.33
0.27
-0.06
[0.00]*
*
[0.06]*
[0.02]**
[0.47]
[0.31]
[0.14]
[0.03]*
*
[0.29]
[0.56]
0.34
0.18
0.02
0.01
-0.01
0.01
0.86
-0.10
0.16
[0.01]*
*
[0.45]
[0.70]
[0.64]
[0.83]
[0.83]
[0.00]*
*
[0.89]
[0.46]
0.48
0.29
0.18
0.03
0.11
0.13
0.07
0.37
-0.01
[0.00]*
*
[0.02]**
[0.00]**
[0.40]
[0.15]
[0.02]**
[0.73]
[0.59]
[0.94]
0.35
0.19
0.04
-0.02
-0.01
0.00
-0.03
-0.35
0.17
[0.00]*
*
[0.06]*
[0.58]
[0.77]
[0.78]
[0.97]
[0.94]
[0.26]
[0.60]
0.24
0.20
0.15
0.08
0.09
0.09
0.24
0.43
0.09
[0.02]*
*
[0.01]**
[0.00]**
[0.00]*
*
[0.02]**
[0.03]**
[0.12]
[0.01]**
[0.63]
0.59
0.28
0.15
-0.02
0.01
0.07
-0.37
-0.58
0.07
[0.00]*
*
[0.02]**
[0.00]**
[0.60]
[0.90]
[0.17]
[0.13]
[0.05]*
[0.78]
Observations
9782
9295
9295
6690
4971
4971
6690
4971
4971
Number of Groups
1311
1055
1055
873
603
603
873
603
603
China's Effect in Central American Countries
India's Effect in Central American Countries
China's Effect on Andean Countries
India's Effect on Andean Countries
China's Effect in Southern Cone's Countries
India's Effect in Southern Cone's Countries
p-values in brackets correspond to the F-test of the null hypothesis that the effect = 0. All estimates come from estimations of the fully specified CKM, but other parameter
estimates are not reported. * significant at 5%; ** significant at 1%.
Innovation and Diversification:
The Third Challenge
Export Diversification during the
Growth of China and India
0.12
0.1
0.08
0.06
0.04
0.02
Herfindahl
Herfindahl excluiding ISIC 220 and 353
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
0
1990
Herfindahl Index of Concentration
Latin America & Caribbean
Export Diversification during the
Growth of China and India
0.35
0.3
0.25
0.2
0.15
0.1
0.05
Herfindahl
Herfindahl excluiding ISIC 220 and 353
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
0
1990
Hefindahl Index of Concentration
Andean
Export Diversification during the
Growth of China and India
0.12
0.1
0.08
0.06
0.04
0.02
Herfindahl
Herfindahl excluiding ISIC 220 and 353
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
0
1990
Herfindahl Index of Concentration
Southern Cone
Export Diversification during the
Growth of China and India
Mexico
0.2
0.15
0.1
0.05
Herfindahl
Herfindahl excluiding ISIC 220 and 353
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
0
Export Diversification during the
Growth of China and India
Central America
0.35
0.3
0.25
0.2
0.15
0.1
0.05
Herfindahl
Herfindahl excluiding ISIC 220 and 353
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
0
For Future Research: Not a Zero-Sum Game
in Innovation and Patenting? Some Data
Patents Granted by the USPTO, 1960-2003
450
350
300
250
200
150
100
50
Year
China
India
LAC
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
1973
1971
1969
1967
1965
0
1963
Number of patents
400