Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter February 12, 2015

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

  • Melanie O’Gorman EMAIL logo


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:


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
μ – Share of income accruing to labor in the agricultural sector
ϵ – Share of income accruing to land in the agricultural sector
Z – Agricultural land (1000s of hectares)
L – Population (1000s of people)
R – Number of full-time agricultural researchers (public sector)
X – National research expenditure [million constant $2005 (PPP)]
F – Level of infrastructure (total road length/area of country)
Z1Z4 – Land sizes (hectares)
1–θ – Ratio of ag./non-ag. wage

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––4.4–4.5
Fertilizer per farmer0.5–
Fertilizer per hectare–
Labor productivity3.41417.
Compensating variation–2.9––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––7.9
Income tax rate–2.5––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––17.7
Fertilizer per farmer1.2–
Fertilizer per hectare–1.799.90.31.798.391.5
Labor productivity3.5157.
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––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––20.1–6.6
Fertilizer per farmer0.1–
Fertilizer per hectare–3.1953,30211.60.217.820.9
Labor productivity3.
Compensating variation–0.1––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.–7.6
Income tax rate–0.1–87.9–1.10.0–20.3–0.4


Abegaz, Berhanu. 2004. “Escaping Ethiopia’s Poverty Trap: The Case for a Second Agrarian Reform.” The Journal of Modern African Studies 42 (3): 313–342.10.1017/S0022278X04000217Search in Google Scholar

Adamopoulos, Tasso. 2011. “Transportation Costs, Agricultural Productivity and Cross-country Income Differences.” International Economic Review 52 (2): 489–521.10.1111/j.1468-2354.2011.00636.xSearch in Google Scholar

Adamopoulos, Tasso, and Diego Restuccia. 2014. “The Size Distribution of Farms and International Productivity Differences.” American Economic Review 104 (6): 1667–1697.10.1257/aer.104.6.1667Search in Google Scholar

Aschauer, David. 1989. “Is Public Expenditure Productive?” Journal of Monetary Economics 23: 167–200.10.1016/0304-3932(89)90047-0Search in Google Scholar

Assuncao, Juliano J., and Luis H. B. Braido. 2004. “Testing Among Competing Explanations for The Inverse Productivity Puzzle.” Working Paper, Department of Economics, Pontifical Catholic University of Rio de Janeiro.Search in Google Scholar

Barrios, Salvador, Luisito Bertinelli, and Eric Strobl. 2008. “Trends in Rainfall and Economic Growth in Africa: A Neglected Cause of the African Growth Tragedy.” Review of Economics and Statistics 92 (2): 350–366.10.1162/rest.2010.11212Search in Google Scholar

Bellon, Mauricio R., and J. Edward Taylor. 1993. “Folk Soil Taxonomy and the Partial Adoption of New Seed Varieties.” Economic Development and Cultural Change 41 (4): 763–786.10.1086/452047Search in Google Scholar

Benjamin, D. 1995. “Can Unobserved Land Quality Explain the Inverse Productivity Relationship?” Journal of Development Economics 46: 51–84.10.1016/0304-3878(94)00048-HSearch in Google Scholar

Besley, Timothy. 1995. “Property Rights and Investment Incentives: Theory and Evidence from Ghana.” Journal of Political Economy 103 (5): 903–937.10.1086/262008Search in Google Scholar

Bhalla, S. S. 1988. “Does Land Quality Matter?” Journal of Development Economics 29: 45–62.10.1016/0304-3878(88)90070-3Search in Google Scholar

Bhalla, S. S., and P. Roy. 1988. “Mis-specification in Farm Productivity Analysis: The Role of Land Quality.” Oxford Economic Papers 40: 55–73.10.1093/oxfordjournals.oep.a041846Search in Google Scholar

Binswanger, H., and P. Pingali. 1988. “Technological Priorities for Farming in Sub-Saharan Africa.” Technical Report, World Bank Research Observer.10.1093/wbro/3.1.81Search in Google Scholar

Bloom, David E., and Jeffrey D. Sachs. 1998. “Geography, Demography, and Economic Growth in Africa.” Brookings Papers on Economic Activity 2: 207–273. Brookings Institution Press.10.2307/2534695Search in Google Scholar

Byiringiro, F., and T. Reardon. 1996. “Farm Productivity in Rwanda: Effects of Farm Size, Erosion and Soil Conservation Investments.” Agricultural Economics 15: 127–136.10.1111/j.1574-0862.1996.tb00426.xSearch in Google Scholar

Caucutt, Elizabeth M., and Krishna B. Kumar. 2008. “Africa: Is aid an Answer?” B.E. Journal of Macroeconomics: Advances in Macroeconomics 8 (1): 1–48.Search in Google Scholar

Chanda, Areendam, and Carl-Johan Dalgaard. 2008. “Dual Economies and International Total Factor Productivity Differences: Channelling the Impact from Institutions, Trade, and Geography.” Economica 75 (200): 629–661.10.1111/j.1468-0335.2007.00673.xSearch in Google Scholar

Collier, Paul. 2006. Africa: Geography and Growth. Centre for the Study of African Economies, Department of Economics, Oxford University.Search in Google Scholar

Consultative Group on International Agricultural Research. 2012. “Agricultural Science and Technology Indicators.” ASTI database. in Google Scholar

Conway, Gordon. 1997. The Doubly Green Revolution. Penguin Books.Search in Google Scholar

Dalton, Timothy J., and Robert G. Guei. 2003. “Productivity Gains from Rice Genetic Enhancements in West Africa: Countries and Ecologies.” World Development 31 (2): 359–374.10.1016/S0305-750X(02)00189-4Search in Google Scholar

de Wilde, John C. 1984. Agriculture, Marketing and Pricing in Sub-Saharan Africa. African Studies Center.Search in Google Scholar

Dimaranan, Betina V. 2006. Global Trade, Assistance, and Production: The GTAP 6 Data Base. Center for Global Trade Analysis, Purdue University.Search in Google Scholar

Easterly, William, and Sergio Rebelo. 1993. “Fiscal Policy and Economic Growth: An Empirical Investigation.” Journal of Monetary Economics 32 (3): 417–458.10.1016/0304-3932(93)90025-BSearch in Google Scholar

Eswaran, Hari, Russell Almaraz, Evert van den Berg, and Paul Reich. 1997. “An Assessment of The Soil Resources of Africa in Relation to Productivity.” Geoderma 77: 1–18.10.1016/S0016-7061(97)00007-4Search in Google Scholar

Evenson, Robert, and Douglas Gollin. 2001. “The Green Revolution at The End of the Twentieth Century.” Consultative Group on International Agricultural Research (CGIAR) Technical Advisory Committee, Food and Agriculture Organization of the United Nations.Search in Google Scholar

Evenson, Robert, and Douglas Gollin. 2003. “Assessing the Impact of the Green Revolution, 1960 to 2000.” Science 300 (5620): 758–762.10.1126/science.1078710Search in Google Scholar

FAO. 2010. “Terrastat Database.” Technical Report, Food and Agriculture Organization International Instiute for Applied Systems Analysis (IIASA).Search in Google Scholar

FAOSTAT. 2012. “Agricultural Statistical Database of the Food and Agriculture Organization.” Technical Report.Search in Google Scholar

Firmin-Sellers, Kathryn, and Patrick Sellers. 1999. “Expected Failures and Unexpected Successes of Land Titling in Africa.” World Development 27 (7): 1115–1128.10.1016/S0305-750X(99)00058-3Search in Google Scholar

Food and Agriculture Organization. 2003. “The State of Food Insecurity in the World 2003: Monitoring Progress Towards the World Food Summit and Millennium Development Goals.” Technical Report, Food and Agriculture Organization (FAO), Rome, Italy.Search in Google Scholar

Freeman, Richard B., and Remco H. Oostendorp. 2000. “Wages Around the World: Pay Across Occupations and Countries.” NBER Working Papers 8058, National Bureau of Economic Research, Inc.10.3386/w8058Search in Google Scholar

Frisvold, George, and Kevin Ingram. 1995. “Sources of Agricultural Productivity Growth and Stagnation in Sub-Saharan Africa.” Agricultural Economics 13: 51–61.Search in Google Scholar

Fulginiti, Lilyan E., Richard Perrin, and Bingxin Yu. 2004. “Institutions and Agricultural Productivity in Sub-Saharan Africa.” Agricultural Economics 31 (2–3): 169–180.10.1016/j.agecon.2004.09.005Search in Google Scholar

Ghai, Dharam, and Lawrence D. Smith. 1987. Agricultural Prices, Policy, and Equity in Sub-Saharan Africa. Lynne Rienner.Search in Google Scholar

Gollin, Douglas, Stephen Parente, and Richard Rogerson. 2002. “The Role of Agriculture in Development.” American Economic Review Papers and Proceedings 92 (2): 160–164.10.1257/000282802320189177Search in Google Scholar

Gollin, Douglas, Steven L. Parente, and Richard Rogerson. 2007. “The Food Problem and The Evolution of International Income Levels.” Journal of Monetary Economics 54 (4): 1230–1255.10.1016/j.jmoneco.2006.04.002Search in Google Scholar

Gollin, Douglas, David Lagakos, and Michael Waugh. 2014. “Selection, Agriculture, and Cross-Country Productivity Differences.” American Economic Review 2 (103): 948–980.Search in Google Scholar

Haggblade, Steven, and Peter B. Hazell. 1988. “Prospects for Equitable Growth in Rural Sub-Saharan Africa.” Agriculture and Rural Development Department Working Paper, World Bank, April 1988. WPS 8.Search in Google Scholar

Haggblade, Steven, and Peter B. R. Hazell. 2010. Successes in African Agriculture: Lessons for the Future. International Food Policy Research Institute – IFPRI Issue Brief 63.Search in Google Scholar

Harvey, Charles. 1988. Agricultural Pricing Policy in Africa: Four Country Case Studies. Macmillan Publishers Limited.Search in Google Scholar

Hazell, Peter. 2004. International Development Committee – Minutes of Evidence. United Kingdom Parliament.Search in Google Scholar

Herrendorf, Berthold, James A. Schmitz Jr., and Arilton Teixeira. 2009. Transportation and Development: Insights From the U.S., 1840–1860. Federal Reserve Bank of Minneapolis, Staff Report: 425.Search in Google Scholar

Hiebert, Dean L., 1974. “Risk, Learning and the Adoption of Fertilizer Responsive Seed Varieties.” Economic Journal 56 (4): 764–768.10.2307/1239305Search in Google Scholar

Hubbard, Glenn R., Jonathan Skinner, and Stephen P. Zeldes. 1994. “The Importance of Precautionary Motives In Explaining Individual and Aggregate Saving.” Carnegie-Rochester Conference Series in Public Policy 40: 59–125.10.1016/0167-2231(94)90004-3Search in Google Scholar

IFPRI. 2010. Statistics of Public Expenditure for Economic Development (speed database). Development Strategy and Governance Division, International Food Policy Research Institute.Search in Google Scholar

Institute of Development Studies. 2005. “New Directions for African Agriculture.” IDS Bulletin 36 (2): 1–12.10.1111/j.1759-5436.2005.tb00188.xSearch in Google Scholar

Jacoby, Hanan G., and Bart Minten. 2007. “Is Land Titling in Sub-Saharan Africa Cost-Effective? Evidence from Madagascar.” The World Bank Economic Review 21 (3): 461–485.10.1093/wber/lhm011Search in Google Scholar

Jaeger, William K. 1992. “The Economic Effects of Economic Policies on African Agriculture.” World Bank Discussion Papers: African Technical Department Series. The World Bank.Search in Google Scholar

Jayne, T. S., Takashi Yamano, Michael T. Weber, David Tschirley, Rui Benfica, Anthony Chapoto, and Ballard Zulu. 2003. “Smallholder Income and Land Distribution in Africa: Implications for Poverty Reduction Strategies.” Food Policy 28: 253–275.10.1016/S0306-9192(03)00046-0Search in Google Scholar

Johnson, Daniel, and Robert E. Evenson. 2000. “How Far Away is Africa? Technological Spillovers to Agriculture and Productivity.” American Journal of Agricultural Economics 82 (3): 743–749.10.1111/0002-9092.00073Search in Google Scholar

Johnson, Michael E., and William A. Masters. 2004. “Complementarity and Sequencing of Innovations: New Varieties and Mechanized Processing for Cassava in West Africa.” Economics of Innovation and New Technology 13 (1): 19–31.10.1080/1043859042000156011Search in Google Scholar

Johnston, B. F., and J. W. Mellor. 1961. “The Role of Agriculture in Economic Development.” American Economic Review 51 (4): 566–593.Search in Google Scholar

Karanja, D. D., M. Renkow, and E. W. Crawford. 2003. “Welfare Effects of Maize Technologies in Marginal and High Potential Regions of Kenya.” Agricultural Economics 29 (3): 331–341.10.1111/j.1574-0862.2003.tb00169.xSearch in Google Scholar

Kayizzi-Mugerwa, Steve. 1998. “A Review of Macroeconomic Impediments to Technology Adoption in African Agriculture.” African Development Review 10 (1): 211–225.10.1111/j.1467-8268.1998.tb00105.xSearch in Google Scholar

Keane, Michael, and Kenneth Wolpin. 2001. “The Effect of Parental Transfers and Borrowing Constraints on Educational Attaintment.” International Economic Review 42 (4): 1051–1103.10.1111/1468-2354.00146Search in Google Scholar

Kilkenny, Maureen. 1998. “Transport Costs and Rural Development.” Journal of Regional Science 38 (2): 293–312.10.1111/1467-9787.00093Search in Google Scholar

Matsumoto, Tomoya, and Takashi Yamano. 2009. “Soil Fertility, Fertilizer, and the Maize Green Revolution in East Africa.” Policy Research Working Paper 5158, World Bank.10.1596/1813-9450-5158Search in Google Scholar

Mwangi, Wilfred. 1996. “Low Use of Fertilizers and Low Productivity in Sub-Saharan Africa.” CIMMYT, Natural Resources Group Paper 96-05.Search in Google Scholar

Ndjeunga, J., and M. S. Bantilan. 2005. “Uptake of Improved Technologies in the Semi-Arid Tropics of West Africa: Why is Agricultural Transformation Lagging Behind?” Journal of Agricultural and Development Economics 2 (1): 85–102.Search in Google Scholar

Norton, Roger D., 2004. Agricultural Development Policy: Concepts and Experiences. Hoboken, NJ: Wiley.Search in Google Scholar

Pardey, Philip G., Johannes Roseboom, and Shenggen Fan. 1998. “Trends in Financing Asian and Australian Agricultural Research.” In: (S.R. Tabor, W. Janssen, and, H. Bruneau, eds.) Financing Agricultural Research: A Sourcebook, The Hague, the Netherlands: International Service for National Agricultural Research, pp. 342–356.Search in Google Scholar

Rattso, Jorn, and Hildegunn E. Stokke. 2007. “A Growth Model for South Africa.” South African Journal of Economics 75 (4): 616–630.10.1111/j.1813-6982.2007.00140.xSearch in Google Scholar

Rattso, Jorn, and Ragnar Torvik. 2003. “Interactions Between Agriculture and Industry: Theoretical Analysis of the Consequences of Discriminating Agriculture in Sub-Saharan Africa.” Review of Development Economics 7 (1): 138–151.10.1111/1467-9361.00181Search in Google Scholar

Restuccia, Diego, Dennis Tao Yang, and Xiaodong Zhu. 2008. “Agriculture and Aggregate Productivity: A Quantitative Cross-Country Analysis.” Journal of Monetary Economics 55 (2): 234–250.10.1016/j.jmoneco.2007.11.006Search in Google Scholar

Ripoll, Marla and Juan Carlos Cordoba. 2006. Agriculture, Aggregation, Wage Gaps, and Cross-Country Income Differences. Manuscript. Available at: in Google Scholar

Sanchez-Robles, Blanca. 1998. “Infrastructure Investment and Growth: Some Empirical Evidence.” Contemporary Economic Policy 16 (1): 98–108.10.1111/j.1465-7287.1998.tb00504.xSearch in Google Scholar

Smale, Melinda, and Thom Jayne. 2003. “Maize in Eastern and Southern Africa: Seeds of Success in Retrospect.” International Food Policy Research Institute, Environment and Production Technology Division Discussion Paper #97.Search in Google Scholar

The World Bank. 1994. World Development Report 1994: Infrastructure for Development. World Bank.Search in Google Scholar

The World Bank. 2005. “World Development Indicators.” Technical Report.Search in Google Scholar

Thissen, Mark, and Robert Lensink. 2001. “Macroeconomic Effects of a Currency Devaluation in Egypt: An Analysis With a Computable General Equilibrium Model with Financial Markets and Forward-Looking Expectations.” Journal of Policy Modeling 23 (4): 411–419.10.1016/S0161-8938(01)00056-4Search in Google Scholar

Tiffen, Mary. 2003. “Transition in Sub-Saharan Africa: Agriculture, Urbanization and Income Growth.” World Development 31 (8): 1343–1366.10.1016/S0305-750X(03)00088-3Search in Google Scholar

Van Tassel, Eric. 2004. “Credit Access and Transferable Land Rights.” Oxford Economic Papers 56 (1): 151–166.10.1093/oep/56.1.151Search in Google Scholar

Vollrath, Dietrich. 2009. “How Important are Dual Economy Effects for Aggregate Productivity?” Journal of Development Economics 88 (2): 325–334.10.1016/j.jdeveco.2008.03.004Search in Google Scholar

Winter-Nelson, Alex, and Anna Temu. 2005. “Impacts of Prices and Transactions Costs on Input Usage In a Liberalizing Economy: Evidence from Tanzanian Coffee Growers.” Agricultural Economics 33 (3): 243–253.10.1111/j.1574-0864.2005.00064.xSearch in Google Scholar

Wobst, Peter. 2001. “Structural Adjustment and Intersectoral Shifts in Tanzania: A Computable General Equilibrium Analysis.” International Food Policy Research Institute, Research Report 117.Search in Google Scholar

Wood, Adrian. 2002. Could Africa be like America? Manuscript. Available at: in Google Scholar

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

©2015 by De Gruyter

Downloaded on 24.2.2024 from
Scroll to top button