Title: Rural population density, access to arable land, and small farm development: exploring linkages in
Malawi and Zambia
Authors: Jacob Ricker-Gilbert (presenting), Jordan Chamberlin, Thom Jayne
Introduction
Land has been commonly considered an abundant resource in Sub-Saharan Africa. However, nationally
representative farm surveys consistently paint a contrasting picture with the following empirical
regularities: First, evidence shows that over the past 50 years there has been a gradual but steady
decline in mean farm size as rural population growth has outstripped the growth in arable land. Second,
half or more of Africa’s smallholder farms are below 1.5 hectares in size with limited or no potential for
area expansion (Jayne et al. 2003). Most of this bottom 50% of farmers tend to produce very little
agricultural surplus, and make very little use of productivity-enhancing inputs. Third, a high proportion
of farmers perceive that it is not possible for them to acquire more land through customary land
allocation procedures, even in areas where a significant portion of land appears to be unutilized
(Stambuli 2002, Jayne et al. 2009).
Evidence now indicates that a substantial proportion of Africa’s rural population lives in relatively
densely populated areas. Recent population databases indicate that more than 30% of the rural
populations of Ethiopia, Kenya, Nigeria, Rwanda, Burundi, Uganda, and Malawi, live in areas exceeding
250 persons per square kilometre and depend largely on rainfed agriculture. These countries account
for roughly 35 percent of sub-Saharan Africa’s total population. Moreover, the effects of increasingly
crowded rural areas are not confined to those living in such areas. Hence, the question of appropriate
development strategies for densely populated rural areas is increasingly relevant to a significant portion
of Africa’s population.
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What do declining land-income relationships mean for feasible smallholder-led development pathways?
The structural transformation processes in Asia, as documented by pioneering development economists
such as Johnston and Kilby (1975) and Mellor (1976), show that a smallholder-led agricultural strategy
was necessary to rapidly reduce rural poverty and induce demographic changes associated with
structural transformation. An inclusive smallholder-led strategy is likely to provide the greatest potential
to achieve agricultural growth with broad-based reductions in rural poverty in most of sub-Saharan
Africa as well. However, it is not at all clear how such a smallholder-led agricultural strategy must be
adapted to address the limitations of very small and declining farm sizes in densely populated areas that
are dependent on rain-fed production systems with only one growing season per year.
This study quantifies how population density affects input use and output per hectare of staple crops. In
doing so, we empirically test Ester Boserup’s (1965) hypothesis that increasing population density leads
to agricultural intensification, measured through increased demand for modern inputs, such as
commercial fertilizer and increased production per hectare. We test these effects using geospatial data
on population density1. We also use recently collected household-level panel data from Malawi and
Zambia to explore how population density affects household outcomes of interest. We conduct
separate estimates for Malawi and Zambia and then compare and contrast our findings for the two
countries. As Malawi is a country of high population density and Zambia is a country of low population
density, having results from both countries provides a robust understanding of how population growth
affects smallholder intensification in different settings within the region.
1
We use gridded population databases from two sources: Columbia University’s Global Rural Urban Mapping
Project (GRUMP), and the University of Florida’s AfriPop project.
2
Testable Hypotheses
This study is motivated by the need to understand the nature and magnitude of emerging land
constraints in African agriculture, the possible impacts of status-quo policies and institutions on food
security and poverty, and the potential for institutional reforms to address these challenges.
We test the following specific hypotheses individually in both Malawi and Zambia:
1) Households in areas of high population density do not demand significantly greater quantities of
modern inputs such as chemical fertilizer than do households in areas of lower population density.
2) Households in areas of high population density do not produce significantly higher staple crop yields
then do households in areas of lower population density.
Conceptual and Empirical Framework
Conceptually, local population growth should cause demand for an agricultural good Y to shift outward
from D to D’, raising the price of Y from P to P’ ceteris paribus (see Figure 1). The increase in price should
induce a supply response where farmers adopt technology, such as chemical fertilizer, to boost
production of Y.
Figure 1: Induced Response to Supply and Demand of
an Agricultural Good Due to Population Growth
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Therefore, for household i in village j at time t, demand for modern inputs such as chemical fertilizer and
hybrid seed should be a function of the following factors:
Xijt = βРjt + Zijtδ+ cij + εijt
where X represents the level of input used by household i in village j at time t. In this study we consider
kilograms of inorganic fertilizer purchased by the household, and kilograms of hybrid maize seed
purchased as the inputs of interest. Population density measured in people per square kilometer of
arable land is denoted by P, and β represents the corresponding parameter. Other factors that affect
demand for modern inputs are denoted by Z, and δ represents the related vector of parameters. Factors
such as input and output prices are included in Z because, according the induced innovation hypothesis,
changes in relative prices drive demand for intensive inputs such as fertilizer (Hyami and Ruttan 1970).
Other factors included in Z are credit and market access, family demographics, tenure status of the
household, along with weather and other agronomic conditions
The error term in equation (1) consists of two parts; the time-constant unobservable household-level
factors that affect input demand are denoted by c, while ε represents time-varying unobserved shocks
that influence demand for inputs.
Just as population growth may induce farmers to adopt modern inputs, it may also cause staple crop
production to increase per hectare. Therefore for household i in village j at time t, output supply of
staple crops can be modeled as a function of the following factors:
1) Yijt = ρРjt + Zijtα + bij + vijt
where Y represents production in kilograms per hectare of staple crops (maize, for southern Africa). Just
as in equation (1) population density is denoted by P, and here ρ represents the corresponding
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parameter. In addition other factors that affect output supply are denoted by Z, and α represents the
related vector of parameters. The error term in equation (2) consists of two parts just as in equation (1).
The time-constant unobservable household-level factors that affect output supply are denoted by b,
while v represents time-varying unobserved shocks that influence output supply.
Data
Smallholder household data for Zambia come from the Supplemental Surveys carried out by the
Zambian Central Statistical Office (CSO) in association with the Zambian Ministry of Agriculture and
Cooperatives (MACO) and Michigan State University’s Food Security Research Project (FSRP). These
surveys are linked with the 2000 Post Harvest Survey for small and medium scale holdings. A consistent
panel of 4340 smallholder households was surveyed in each of the Supplemental Survey waves, which
took place in 2001, 2004 and 2008. The survey is nationally representative and the sampling frame
includes villages in 70 of Zambia’s 72 Districts.
Household data for Malawi come from three farm household surveys conducted by the Government of
Malawi’s National Statistical Office. The first wave of data comes from the Second Integrated Household
Survey (IHHS2) in Malawi collected during the 2002/03 and 2003/04 growing seasons. The IHHS2
surveyed households in 26 districts in Malawi and in total 11,280 households were interviewed. The
second wave of data comes from the 2007 Agricultural Inputs Support Survey (AISS1) conducted after
the 2006/07 growing season. The budget for AISS1 was much smaller than the budget for IHHS2 and of
the 11,280 households interviewed in IHHS2 only 3,485 of them lived in enumeration areas that were
re-sampled in 2007. Of these 3,485 households 2,968 were re-interviewed in 2007, which gives us an
attrition rate of 14.8%.
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The third wave of data comes from the 2009 Agricultural Inputs Support Survey II (AISS2) conducted
after the 2008/09 growing season. The AISS2 survey had a subsequently smaller budget than the AISS1
survey in 2007, so of the 2,968 households first sampled in 2003 and again in 2007, 1,642 of them lived
in enumeration areas that were revisited in 2009. Of the 1,642 households in revisited areas, 1,375 were
found for re-interview in 2009, which gives us an attrition rate of 16.3% between 2007 and 2009.
Note that the first year of the panel, while drawn from the same survey IHHS2, covers two different
years. Since weknow in which of the two years each household was surveyed, we address this issue by
including a year dummy for each of the two years in the first survey and using the second survey as the
control year. Furthermore since the time difference is just a single year one would not expect there to
be many unobservable changes that vary over that time.
For both countries, the household data were augmented by spatial data on infrastructure, population,
terrain, land cover, and climate. These data come from various data sources, described fully in the final
paper. The data were brought into a common geographic information system (GIS) framework, where
they were transformed in ways described in the essays and appendices.
Results & Conclusions
We find evidence that input demand, and output supply are markedly constrained by access to land as
proxied by rural population density. However, the relationship between population density and these
outcomes is nonlinear: in lower density areas, increases in density are associated with increases in these
outcomes; in higher density areas, rising densities are negatively associated with these outcomes. This
nonlinearity is well represented by a second-order polynomial expression which allows us to identify
critical thresholds of population density. Controlling for agricultural production potential and market
access infrastructure, the turning points in many of the relationships we examine is in the range of 150-
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200 persons per square kilometer (p/km2). Smallholders dwelling in areas with densities exceeding 300
p/km2 appear to be especially constrained by access to land. Policies targeting economic growth and
poverty reduction in rural areas may need to be differentiated on the basis of such thresholds.
As an example of the patterns that are emerging in our analysis, Figure 1 shows household maize
production for smallholders in Malawi and Zambia, plotted against local rural population densities, and
controlling for other household and community-level endowments. We will present a full econometric
analysis in the final paper and presentation.
References
Boserup, Ester. 1965. The Conditions of Agricultural Growth: The Economics of Agrarian Change under
Population Pressure. London: Allen & Unwin.
Hayami, Yujiro and V. W. Ruttan. 1970. Agricultural Productivity Differences among Countries. The
American Economic Review, Vol. 60, No. 5 (Dec., 1970), pp. 895-911.
Jayne, T.S. T. Yamano, M. Weber, D. Tschirley, R. Benfica, A. Chapoto, and B. Zulu. 2003. Smallholder
Income and Land Distribution in Africa: Implications for Poverty Reduction Strategies. Food Policy,
28(3): 253-275.
Johnston, B.F. and P. Kilby. 1975. Agriculture and Structural Transformation: Economic Strategies in
Late-Developing Countries. New York: Oxford University Press.
Mellor, J. 1976. The New Economics of Growth: A Strategy for India and the Developing World. Ithaca:
Cornell University Press.
Stambuli, K. 2002. Elitist Land and Agricultural Policies and the Food Problem in Malawi. Journal of
Malawi Society – Historical and Scientific, 55(2): 34-83.
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Figure 1: Bivariate relationship between household maize production and rural population density
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Maize production(kg/hh)
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