1. 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

2. 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

3. 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)

4. 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 3 rd markets Evidence from the KCM model of MNCs using aggregate and sectoral FCS global data, 1990/2003 –Little evidence of substitution effects in 3 rd markets But shifting RCAs toward NRs and Knowledge- intensive industries

5. World Bank Commodity Price Indexes (1990=100) Jan/Dec 05 Jan/Dec 06 Jan/Oct 07

6. Price Trends: Selected Commodities

7. China and Commodity Prices: Recursive Estimates of the Correlation between China’s I.P. and Prices

8. India and Commodity Prices: Recursive Estimates of the Correlation between China’s I.P. and Prices

9. China’s Appetite for Commodities

10. Commodity Prices, China, and Other Global Factors

11. Share of China and India in Latin American Trade, 1990 versus 2004 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)?

12. Are LA regions competing in the Same Products as China and India? Source: Lederman, Olarreaga & Rubiano (forthcoming 2008, RWE).

13. Regression Results – Diverging RCAs? LACAndean Countries Central America MexicoSouthern Cone RCA China-0.02 (0.04) 0.18 (0.08)* -0.11 (0.04)** 0.09 (0.06) -0.26 (0.05)** RCA India0.16 (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) Constant-0.32 (0.36) 0.02 (0.37) -0.26 (0.23) -0.21 (0.06)** 0.09 (0.33) R^20.060.050.150.020.12 Observation s 8376323625386502587

14. Regression Results – Diverging RCAs?

15. Who Benefits?: The First Challenge

16. Who Benefits? Static Effects on Individuals (i.e., without changes in employment, quantity of sales, and quantities in consumption) 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)

17. Who Benefits? Some Data on Mexico (for 10% rise in price of corn) Source: Porto (2006).

18. Who Benefits? Data from Panama on Net Producers of Sensitive Agricultural Commodities

19. Who Benefits? Data from Guatemalan

20. An Aside on CAFTA (a decline in the price of maize, but increases in prices of fruits) and Guatemalan Indigenous Populations

21. The Supply Response: The Second Challenge

22. The Supply Response: The Gravity Model with Heterogeneity in Demand and Supply Elasticities What’s the proper estimator? We present OLS (log-linear), Poisson, & Neg Bin.

23. Gravity Results: Large Ch & Ind Demand Elasticities, Low LAC Supply Elasticities

25. Substitution Effects in 3 rd Markets? The Gravity Model with Heterogeneity in Ch/Ind Substitution, Aggregate Data, 2000/4 MEX USA CHINA Note the Four Channels through which trade can affect LAC trade with 3 rd Markets:

26. Aggregate Trade: Little (robust) Evidence of Substitution Effects in 3 rd Markets

27. 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

28. Foreign Capital: The Empirical Augmented KCM Allows for Horizontal and Vertical Motivations for FDI What’s the proper estimator? We present OLS (log-linear), Poisson, & Neg Bin. MEX USA CHINA Note the data constraint  only one channel through which China can affect FCS in LAC from 3 rd Markets:

29. Some Results: Little Evidence of Substitution Effects in the FCS Data Table 4: China and India Effects across LAC Sub Regions Aggregate DataU.S. DataU.S. Data: Manufacturing OLSPoissonNeg. Bin.OLSPoissonNeg. Bin.OLSPoissonNeg. Bin. China's Effect in Central American Countries0.450.220.130.020.200.05-0.330.27-0.06 [0.00]* *[0.06]*[0.02]**[0.47][0.31][0.14] [0.03]* *[0.29][0.56] India's Effect in Central American Countries0.340.180.020.01-0.010.010.86-0.100.16 [0.01]* *[0.45][0.70][0.64][0.83] [0.00]* *[0.89][0.46] China's Effect on Andean Countries0.480.290.180.030.110.130.070.37-0.01 [0.00]* *[0.02]**[0.00]**[0.40][0.15][0.02]**[0.73][0.59][0.94] India's Effect on Andean Countries0.350.190.04-0.02-0.010.00-0.03-0.350.17 [0.00]* *[0.06]*[0.58][0.77][0.78][0.97][0.94][0.26][0.60] China's Effect in Southern Cone's Countries0.240.200.150.080.09 0.240.430.09 [0.02]* *[0.01]**[0.00]** [0.02]**[0.03]**[0.12][0.01]**[0.63] India's Effect in Southern Cone's Countries0.590.280.15-0.020.010.07-0.37-0.580.07 [0.00]* *[0.02]**[0.00]**[0.60][0.90][0.17][0.13][0.05]*[0.78] Observations97829295 66904971 66904971 Number of Groups13111055 873603 873603 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%.

30. Innovation and Diversification: The Third Challenge

31. Export Diversification during the Growth of China and India

36. For Future Research: Not a Zero-Sum Game in Innovation and Patenting? Some Data Patents Granted by the USPTO, 1960-2003