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Africa’s missed agricultural revolution: a quantitative study of the policy options

Melanie O’Gorman

Abstract

Despite the widespread diffusion of productivity-enhancing agricultural technologies the world over, agriculture in Sub-Saharan Africa has typically stagnated. This paper develops a quantitative model in order to shed light on the sources of low labor productivity in African agriculture. The model provides a vehicle for understanding the mechanisms leading to low agricultural labor productivity, in particular, how the interactions between factor endowments, government investment and technology adoption may have culminated in agricultural stagnation. I calibrate the model to data for four Sub-Saharan African economies, and use this calibrated model to provide insight into policy aimed at increasing agricultural productivity in these four countries. Policies aimed at improving rural infrastructure or productivity in the non-agricultural sectors, or allowing for land transferability, would be most effective for increasing agricultural labor productivity, and would further bring increases in household welfare for each of the countries I calibrate to.

JEL classifications: N17; O11; Q18

Corresponding author: Melanie O’Gorman, Economics, University of Winnipeg, 515 Portage Ave., Winnipeg, Manitoba R3B2E9, Canada, Phone: +(204)786-9966, e-mail:

Appendix

A Definitions and data sources for Table 1

Physical capital for agriculture is the total value (in constant 2005 $US) of equipment and machinery (tractors, harvesterthreshers, milking machines and cultivation planting/seeding implements) used in agriculture in each region, and was taken from (FAOSTAT 2012), the statistical database of the Food and Agriculture Organization (FAO). Fertilizer is the quantity of nitrogenous, phosphate and potash fertilizers in agriculture. This was also taken from FAOSTAT. To obtain capital and fertilizer per worker, I took the workforce to be the FAOSTAT’s agricultural labor force, which the FAO defines as “all persons depending for their livelihood on agriculture, hunting, fishing or forestry... [including] all persons actively engaged in agriculture and their non-working dependants” (FAOSTAT 2012). High-yielding seed diffusion is the proportion of agricultural land planted to high-yielding seed varieties across countries. It was taken from a dataset compiled by Robert Evenson at Yale University [this dataset is discussed in Evenson and Gollin (2001, 2003)]. Evenson’s dataset provides this information for 5-year intervals between 1965 and 2005, and the data covers land planted to 11 major crop seed varieties (wheat, rice, maize, sorghum, millet, barley, groundnuts, beans, lentils, cassava and potatoes).

B Common exogenous parameter values

There are a number of parameter values that can be taken as exogenous to the model (chosen directly without solving the model). In this section I discuss those parameter values that are common across the four countries I consider. These are listed in Table 9 below.

The inverse of the constant intertemporal elasticity of substitution, σ, is set to 2, given that microeconomic evidence suggests it lies between 0.5 and 3. This is discussed in Keane and Wolpin (2001) and Hubbard, Skinner, and Zeldes (1994). The stock of modern variety seed releases by the IARCs, S, was taken from Evenson and Gollin (2003). This is the sum of modern seed variety releases by the IARCs from 1965 to 1998. The shares of NARS and IARC research in a country’s agricultural research system provide guidance on the values of γ1 and γ2, respectively in the modern seed variety production function [see equation (2)]. Evenson and Gollin (2001) note that more than 35% of modern varieties in use in the developing countries originated from a direct IARC contribution, and a further 22% had an IARC-crossed parent or ancestor. This suggests a value for γ1 of 0.5, and consequently of γ2 as well.

I restrict land quality values (Q1Q4) to lie uniformly between zero and one, so that each land quality value provides a measure of “effective” land in use. The distribution of land quality, δ(Q1)–δ(Q4), is the average distribution of land qualities across Africa, taken from the Food and Agricultural Organization’s Terrastat database (2010). In this dataset type 1 denotes “not suitable for agriculture,” type 2, “marginally suitable,” type 3, “moderately suitable” and type 4, “suitable and very suitable.” High land quality is assumed to be found disproportionately on small farms in the model. This is due to the relationship that has been found between land quality and farm size in the inverse farm size productivity (IFSP) puzzle literature. The IFSP puzzle refers to the finding that smaller farms tend to have higher yields. However incorporation of data on land quality has often removed the IFSP pattern (Bhalla 1988; Bhalla and Roy 1988; Benjamin 1995; Assuncao and Braido 2004). Hence consistent with this literature, I assume that 84% of the highest (Q4) land quality farms are the very smallest land size types (Z1), and that 98% of the second-highest (Q3) land quality farms are the second smallest land size type (Z2). I set the value of total factor productivity (TFP) in the non-agricultural sector, A, to 1 for each country.

C Country-specific exogenous parameter values

The parameter values that can be chosen directly without solving the model and for which there is data at the country-level are listed in Table 10 below. Unless otherwise noted all data is for the year 2010.

Table 9

Common exogenous model parameters.

ParameterRole in modelParameter value
σCoefficient of relative risk aversion2
SNumber of modern variety seed releases by the IARCs164
γ1Share of international agricultural research in seed productivity0.5
γ2Share of national agricultural research in seed productivity0.5
Q1Q4Values for land quality0.2/0.4/0.6/0.8
δ(Q1)–δ(Q4)Distribution of land quality (FAO 2010)0.25/0.36/0.18/0.21
ATFP for non-agriculture1

The shares of income accruing to agricultural fertilizer, land and agricultural labor, the parameters ϕ, ϵ and μ respectively, are set equal to the agricultural cost shares for these factors of production for each country I calibrate to, as given in the Global Trade Analysis Project (Dimaranan 2006) database.

The amount of agricultural land is taken from FAOSTAT and is the sum of arable land, land for permanent crops, and land for permanent pastures. Land according to this definition is termed “Agricultural Area” in this database (FAOSTAT 2012). The population for each country is taken from the World Development Indicators (The World Bank 2005).

The number of full-time agricultural researchers, R, is taken from the Agricultural Science and Technology Indicators (ASTI) (ASTI 2012). This figure consists of crop, livestock, forestry and fisheries researchers working in government, semi-public, and academic agencies. The level of national research expenditure, X, is also from the ASTI database (ASTI 2012). This includes capital expenditures for things such as labs and equipment, as well as salaries of researchers. ASTI data is for the year 2009 rather than 2010.

I set the level of infrastructure equal to total kilometers of roads over total area of each country. Both of these variables were taken from the World Development Indicators (The World Bank 2005). Average farm sizes were taken from Haggblade and Hazell (1988) and Jayne et al. (2003). I set the ratio of the agricultural to the non-agricultural wage using industry average wages from the OWW database (Freeman and Oostendorp 2000) from the late 1990s to the early 2000s. Within this dataset I take the agricultural wage to be the average for farm or plantation workers, and the non-agricultural wage to be the average for all other occupations.

Table 10

Country-specific exogenous model parameters.

Parameter – role in model
Burkina FasoKenyaRwandaZambia
ϕ – Share of income accruing to fertilizer in the agricultural sector
0.3170.3170.3170.301
μ – Share of income accruing to labor in the agricultural sector
0.5490.5490.5490.578
ϵ – Share of income accruing to land in the agricultural sector
0.1340.1340.1340.121
Z – Agricultural land (1000s of hectares)
12,07027,3201830234,368
L – Population (1000s of people)
155,40440,90910,83713,217
R – Number of full-time agricultural researchers (public sector)
239.851011.46104.2208.5
X – National research expenditure [million constant $2005 (PPP)]
19.38171.4818.148.06
F – Level of infrastructure (total road length/area of country)
0.0560.1120.5680.123
Z1Z4 – Land sizes (hectares)
1.35/2.69/4.31/10.50.52/1.1/2.0/7.10.45/0.97/1.7/4.00.78/1.7/3.1/8.7
1–θ – Ratio of ag./non-ag. wage
0.50.240.430.48

D Results of counterfactual scenarios for countries other than Rwanda

Below I present the results of the policy experiments or counterfactual scenarios for Burkina Faso, Kenya and Zambia.

Table 11

Results of counterfactual scenario for Burkina Faso.

VariableImproved land qualityLand transferabilityIncreased international agricultural researchIncreased agricultural researchersIncreased non-agricultural TFPIncreased infrastructure
Figures represent average change relative to the benchmark economy (%)
Share of labor in agriculture–2.8–98.90.00.0–4.4–4.5
Fertilizer per farmer0.5–3.50.00.035.135.1
Fertilizer per hectare–2.292.10.00.029.228.9
Labor productivity3.41417.40.00.08.68.6
Compensating variation–2.9–65.10.00.0–29.4–5.9
% of Land planted to the modern seed variety0.01149.70.00.0214.4143.6
Relative price of the agricultural good–3.2–93.40.00.015.0–7.9
Income tax rate–2.5–36.80.00.0–23.8–3.8

Table 12

Results of counterfactual scenario for Kenya.

VariableImproved land qualityLand transferabilityIncreased international agricultural researchIncreased agricultural researchersIncreased non-agricultural TFPIncreased infrastructure
Figures represent average change relative to the benchmark economy (%)
Share of labor in agriculture–3.3–98.60.00.023.0–17.7
Fertilizer per farmer1.2–7.00.41.260.062.5
Fertilizer per hectare–1.799.90.31.798.391.5
Labor productivity3.5157.21.60.18.58.6
Compensating variation–1.7–51.5–1.3–0.5–32.0–9.1
% of Land planted to the modern seed variety0.061.3165.70.0165.7165.7
Relative price of the agricultural good–3.3–61.1–1.50.015.2–7.8
Income tax rate–0.1–41.9–16.0–0.9–29.3–10.3

Table 13

Results of counterfactual scenario for Zambia.

VariableImproved land qualityLand transferabilityIncreased international agricultural researchIncreased agricultural researchersIncreased non-agricultural TFPIncreased infrastructure
Figures represent average change relative to the benchmark economy (%)
Share of labor in agriculture–3.2–91.70.00.0–20.1–6.6
Fertilizer per farmer0.1–2.58.30.131.029.5
Fertilizer per hectare–3.1953,30211.60.217.820.9
Labor productivity3.134.02.20.010.19.2
Compensating variation–0.1–97.40.00.0–27.70.8
% of Land planted to the modern seed variety–8.31027.4143.62.9143.6143.6
Relative price of the agricultural good–3.0–4851.30.00.015.0–7.6
Income tax rate–0.1–87.9–1.10.0–20.3–0.4

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Published Online: 2015-2-12
Published in Print: 2015-7-1

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