How geographic characteristics affect farming practices Workshop on An African Green Revolution Tokyo December 7-8, 2008

Overview  Motivation for the research  Theory behind the approach  Methodology Comparison with an alternative approach  Results from a proof (disproof?) of concept exercise

Why the Asian Green Revolution is not relevant for Africa  The geographies of Asia and Africa are different Soils Climate Water and hydrology Natural transport systems Proximity of production centers to consumption centers

Consequences  Relative prices between output and purchased inputs are lower Transportation costs are higher  Farm-gate price for outputs lower  Farm-gate price for fertilizer higher  Technologies that were successful in Asia require a different geography  So need to find a different set of technologies or invent new ones

Endogenous growth theory  Farmers assess their own situation Decision environment  Act rationally and choose farming methods that suit their needs Technology  In practice we observe multiple ways of producing because there is great heterogeneity in farms and farmers

Getting to a production function  From a mathematical perspective the problem is to solve for the control variables, conditional on initial conditions and a set of state variables The solution to control variables is always the same for a given set of state variables; they optimize out.  Endogenous growth theory, if you have good measures of the state variables, then they define the technology choice If not then observed input choices are a combination of technologies

Geography of crop revenue in Ecuador

Annual precipitation

Fertilizer use

So how can we measure this?  Econometric approach  Problem finding comparable detailed data to make cross country comparisons Value of REPEAT and the data developed by the World Bank, Yale and China

Spatial-matching approach  Place a grid over map of yield for two countries and break the map into thousands of cells Asia and Africa  Do the same for maps with geographic information  Domino analogy Yield on one side, geography description on other  Put the Africa bits in a bag and leave Asia bits out on a table  Draw an Africa bit out, find best match best on geography.  Flip the pieces over and take the difference in yield and write it down.  Put the Asia piece back and start over  Calculate a mean difference and standard deviation  If the difference is not significant, then geography tells the whole story

Propensity matching  Use probit or logit to estimated predicted probability of being in the “treated” group Maize area in Ethiopia Equivalent to a weighted index of treatment group characteristics Other approaches use alternative weighting methods  Find closes match outside of “treated” group Maize area NOT in Ethiopia Perfect match is predicted probability based on probit parameters and “non-treated” geography variables  Calculated difference yields and standard error  Test of difference in yields from moving a maize farm in Ethiopia to best match in Uganda, Tanzania or Kenya

Spatial map of maize yields for Ethiopia, Kenya, Uganda and Tanzania

Temperature map with boundaries

Population density map

Probit results for Ethiopia

Results suggest that yields on farms with similar geography outside of Ethiopia are higher than inside Ethiopia

If this works  Some notion about policies that deal directly with geographic hurdles Infrastructure, new technologies  And policies that target markets and households Extension, household support programs and market support programs  Country by country outcomes likely to differ and regions within countries might differ  Side product, a matching of areas for more detailed comparative studies