Excellence-based Climate Change Research Prepared for the African Green Revolution Workshop Tokyo, Japan Dec 7-8, 2008

An African Green Revolution :What drives farm productivity: natural endowments, technology and capital, knowledge, or policies S. Niggol Seo, PhD Research Professor * Based on the draft proposal by Robert Mendelsohn and Niggol Seo.

Agriculture in Africa  Agriculture in Sub-Saharan Africa accounts for  70 percent of employment,  40 percent of exports and  about one-third of economic growth from 1990 to  Besides, more than 70 per cent of the continent’s poor people live in rural areas. * World Development Report (2008) 3

Low agricultural productivity  Farming in Africa is less productive than in other regions (Sachs et al 2004)  Although agriculture in other parts of the world has benefited from productivity growth owing to capital investments and the Green Revolution over the past decades, productivity has stalled or declined in many African communities  On average, value added per agricultural worker now averages around 12% below 1980 levels in Sub-Saharan Africa.  Investment rates for new technologies have declined in recent years and technology adoption rates are low when compared to other regions. 4

Economic Theory 5  A profit maximizing farmer:   where P Q are the prices of outputs, Q, P I are the prices of inputs, I, X is a vector of exogenous factors, and L is the available land to the farmer. We define the productivity of the farm in terms of π.  FOC:  Input demand functions  Net revenue function as the locus of profit maximizing choices given the exogenous variables:

Economic Theory  Empirical question is which set of exogenous variables are most important: prices or availability of inputs, natural endowments, knowledge, or policies.  Define net revenue broadly to include the value of own consumption. Own consumption is valued at market prices.  Wages for own labor: However, we do have observed hours of own labor so we can examine it as an input.  Farmers are broadly defined into two categories: a farm with a green revolution variety or a farm with a traditional variety. A famer will choose a green revolution variety if it is more profitable for the farm. 6

Empirical Model  Ricardian functions: we will regress crop net revenue per hectare on a set of exogenous variables that reflect each of the competing hypotheses.  Climate: seasonal temperature and precipitation using linear and quadratic variables.  Soils: a set of soil measures provided by FAO for Africa.  Input prices: hired wage rates.   Access to capital : distance to nearest city (population over 100,000)  Knowledge: education, experience, and extension services.  Unfortunately, we do not have a good set of policy measures. We could use rankings of governance. We could also include country dummy variables. 7

Empirical Model  Whether the coefficients for a specific hypothesis are statistically significant.  How much of the variance in productivity across the sample is explained by each set of variables.  Input demand functions for capital, irrigation, modern crop varieties, hired labor, and household labor. For inputs that are continuous, we will examine both a linear and loglinear model. For inputs that are discrete, we will use a logit model. 8

Data  Household survey of over 10,000 farmers in 11 countries in Africa - Conducted under the supervision of CEEPA at the University of Pretoria and - Yale University with the help of researchers from 11 African countries - Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya, Niger, Senegal, South Africa, Zaire, and Zimbabwe - F unded by the GEF and the World Bank - Information about net revenues, farming practices, and technology choices.  Soils from FAO  Climate from the National Oceanic and Atmospheric Association’s Climate Prediction Center  Hydrology from the University of Colorado 9

Remaining questions  Availability of information about modern seed varieties.  Institutional variables  Policy variables 10

Experiences from Africa and Latin America S. Niggol Seo 11

South American Data  The World Bank project on the impact of climate change on Latin American agriculture (Seo and Mendelsohn 2008)  Household surveys from seven countries (N=2300) Southern Cone: Argentina, Brazil, Uruguay, Chile Andean: Ecuador, Colombia, Venezuela.  Includes all the major agro-ecological zones.  Climate Data: US Department of Defense satellites, WMO  Soils: FAO 12

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African Data  Household survey of over 10,000 farmers in 11 countries in Africa - Conducted under the supervision of CEEPA at the University of Pretoria and - Yale University with the help of researchers from 11 African countries - Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya, Niger, Senegal, South Africa, Zaire, and Zimbabwe - F unded by the GEF and the World Bank - Information about net revenues, farming practices, and technology choices.  Soils from FAO  Climate from the National Oceanic and Atmospheric Association’s Climate Prediction Center  Hydrology from the University of Colorado 14

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Species ownership in South America 16 PortfolioNumber of FarmsPercentage Beef Cattle Only % Beef Cattle and Chickens233.1% Beef Cattle and Dairy Cattle527% Beef Cattle and Sheep9312.5% Beef Cattle and Pigs648.6% Chickens658.7% Dairy Cattle % Dairy Cattle and Pigs527% Pigs222.9% Sheep293.9% Pigs, Dairy Cattle and Breeding Bulls263.5% Pigs and Chickens273.6%

African Primary Species Choice 17

South American Species Choice 18

Thank you! Photo Credit: World Bank 19