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Draft Plan:
What drives farm productivity: natural endowments, technology and capital,
knowledge, or policies?: A multi-country analysis of Africa and China
Robert Mendelsohn and S. Niggol Seo
Dec 3, 2008
1. Introduction
It is abundantly clear that 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 (Sachs et al.
2004). 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.
Agriculture in Africa faces still another challenge from a rapidly changing climate (Boko
et al. 2007, Kurukulasuriya et al. 2006, Seo and Mendelsohn 2008)
There are several competing theories why Sub-Saharan farmers are lagging behind
farmers elsewhere. One theory is that they lack the education and knowledge to be aware
of modern technology. Second, African farmers lack access to capital that other regions
enjoy. African farmers consequently under invest in their farms. A third theory is that
African farmers do not have the secure property rights or government policies that
provide sufficient incentives for long term investment. A final theory is that African
farmland has below average climate and soils and is a marginal investment opportunity.
This study aims to test the importance of each of these hypotheses and determine which
variables are having the largest influence on farm productivity. The paper seeks to
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explain whether productivity is a function of the inherent characteristics of the farm or
can be influenced by education, access to capital, or better policies.
In particular, the project will try to distinguish the effects of immutable agro-climate
factors from the conditioning effects of markets and household characteristics on
technology choice. This distinction is important since basic agricultural research
determines the range of feasible technologies available for a particular combination of
soils and climate, while economic factors are potentially influenced by a separate set of
policies and their interaction with public investments.
2. Theory
This study explores competing hypotheses concerning why productivity appears to be
low in Africa. The first hypothesis is that African farms have poor natural endowments
(climate and soils) which reduce productivity and make investments into inputs
unattractive. The second hypothesis to be tested is that African farmers do not have
access to capital, labor, or technology. The third hypothesis is that African farmers lack
education, experience, and access to extension. The fourth hypothesis is that African
governments have policies which discourage investment including poor property rights,
restricted trade, and crop price controls.
Let us begin with a general model of the farm. We assume that the farmer is interested
in maximizing the net revenue per unit land, π :
Max   PQ Q( I , X , L)  PI I
(1)
where PQ are the prices of outputs, Q, PI 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.
Taking the derivative of net revenue with respect to inputs reveals the following first
order conditions:
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PQ (Q / I )  PI
(2)
Rearranging terms, one can deduce the input demand function from (2):
I  I ( PQ , PI , X , L)
(3)
One can also deduce a net revenue function that is the locus of profit maximizing choices
given the exogenous variables:
   ( PQ , PI , X , L)
(4)
To the extent that each of the hypotheses we wish to test is a different exogenous
variable in (4), the empirical question is merely which of the set of exogenous variables
are most important: prices or availability of inputs, natural endowments, knowledge, or
policies. This is a straightforward test of the Ricardian model. The only difference is that
we are interested in grouping different exogenous variables to see which ones are most
influential.
The theory also suggests that we should examine the input demand functions for more
clues. Any one of these hypotheses reducing net revenues per hectare would encourage
farmers choosing lower levels of inputs. But the pattern of effects across the input
demand functions is likely to be different. Some of the exogenous variables are more
likely to restrict only specific inputs. For example, higher input prices should only affect
the input in question. Depending on whether the other inputs are substitutes or
complements, the other inputs would increase or fall. Lack of knowledge is likely to
restrict new technology. Poor property rights are likely to lower all long run investments
but not necessarily short run inputs. In contrast, poor soils or climate may make the
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marginal productivity of all inputs lower. In which case, one would see less of every
input.
Because we are looking at household as well as commercial farms, we define net
revenue broadly to include the value of own consumption. Own consumption is valued at
market prices. Unfortunately, we do not have 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.
3. Empirical Methodology
We will estimate two sets of models in this analysis. First, we will estimate Ricardian
functions to determine the net productivity of land. We will regress crop net revenue per
hectare on a set of exogenous variables that reflect each of the competing hypotheses.
We will examine linear and loglinear functional forms. To measure climate, we will
include seasonal temperature and precipitation using linear and quadratic variables. For
soils, we will include a set of measures provided by FAO for Africa. For input prices, we
will include hired wage rates. We will also include distance to nearest city (population
over 100,000) as a measure of access to capital. This is clearly not optimal because it
will also measure distance to markets. For knowledge, we will include three variables:
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. Of course, this will capture all country wide differences, not
just policy distinctions.
One test in the Ricardian analysis is whether the coefficients for a specific hypothesis
are statistically significant. A second measure of the Ricardian analysis will determine
how much of the variance in productivity across the sample is explained by each set of
variables. This will measure the relative importance of each hypothesis.
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The analysis will also estimate 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.
In addition to the study of Africa, a parallel analysis of China will also be conducted
using farm level data in that country. Instead of country level dummies, we will explore
provincial dummies. The remaining variables should be available for the Chinese data
set.
4. Data
The data for this study will rely on a survey of over 10,000 farmers 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). The study was funded by
the GEF and the World Bank and contains information about net revenues, farming
practices, and technology choices (Dinar et al. 2008). The surveys were matched with
information on soils from FAO, climate from the National Oceanic and Atmospheric
Association’s Climate Prediction Center, and hydrology from the University of Colorado.
Originally used for a series of studies on climate change, these data are well suited for
analyzing the relationship between endowments and technology choice.
The only question with the data concerns the availability of information about modern
seed varieties.
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