International Food Policy Research Institute (IFPRI) and Ethiopian Development Research Institute (EDRI) Seminar Series. April 05, 2013. Addis Ababa University
Africa's Adaptation to Land Constraints: Is the Continent Different
1. 1
Adaptation to land constraints:
Is Africa different?
Derek Headey
International Food Policy Research Institute (IFPRI)
Thom Jayne
Michigan State University (MSU)
2. Outline
1. About the project
2. Introduction (background on existing theory &
evidence)
3. Expanding land use (extensification)
4. Intensifying agriculture
5. Reducing fertility rates
6. Diversifying out of agriculture
7. Conclusions
3. This paper is part of a Bill & Melinda Gates Foundation
project on emerging land issues in African agriculture
The motivation for the project was the observation of
various puzzles of Africa agriculture: apparent land
abundance in Africa, but much of Africa has major
land constraints, and very, very small farms
In addition to five African case studies (Ethiopia
included), we decided to look at the cross-country
evidence on agricultural intensification
That is what I am presenting today
About the project
4. Some 215 years ago, Malthus argued that pop. growth
cyclically outstrips agricultural productivity
Strong assumptions: high exogenous fertility rates,
land constraints, zero ag. productivity growth
In much of the world, economic history has not been
kind to Malthus, because of “induced innovations”
Whilst “induced innovation” is associated with Hayami
and Ruttan, plenty of prior research looked at
particular elements of induced innovation
More generally, “responding to incentives” is at the
heart of economic theories
1. Introduction
5. Land expansion
Malthus’ theory depends on land constraints, but
people have been adept at expanding the land frontier
through colonialization, tech. and infrastructure
e.g. recent surge in global food prices has prompted
“land grabs” in Africa & land expansion more generally
Agricultural research in Brazil led to massive land
expansion in 1990s and 2000s (opening the cerrado)
Of course, for specific countries, land expansion may
not be an option
1. Introduction – Land constraints
6. Agricultural intensification
Boserup (1964): as land to labor ratios shrink, people
intensify agricultural production – use more inputs
per hectare to get more output per hectare
Boserup described transition from land-abundant
technologies (slash-and- burn, long fallow) to land-
scarce technologies (short fallow, adoption of plow,
increased fertilizer use, irrigation)
She also emphasized increased labor inputs, and
transition from communal to private property rights
Binswanger et al generalized the theory in 1980s
1. Introduction - intensification
7. 1980s saw substantial empirical literature
Broadly supports Boserup’s theory, but lots of
complexity
Binswanger emphasizes that land constraints interact
with access to markets, and agroecological factors
For example, irrigation and high rainfall allow multiple
cropping – not possible in all agroecologies, however
Market access can be an driver of intensification, but
might also interact with land constraints
And institutions matter – e.g. literature in 1990s
unfavorably compared Ethiopia to Kenya
1. Introduction - intensification
8. Policy-induced intensification
One weakness of Boserup’s theory is that endogenous
intensification takes place over the long run
But Africa’s population has doubled in last 40 years
Hence, much of the ag-economics literature focuses on
policy-induced intensification - e.g. Green Revolution
Of course, many scientific successes in agriculture
But Binswanger emphasized that adoption of
technologies is typically a function of land-labor ratios,
agroecology and market access (“Boserup matters!”)
1. Introduction - intensification
9. Reducing fertility rates
Massive economic & demographic lit. on fertility
Economics sees fertility as a choice variable
If land is becoming constraint (and labor is not), then
farmers will have less children . . . all else equal
But children serve other purposes (consumption
goods, old age security), so fertility response to land
constraints may be low
Moreover, demographic literature emphasizes “supply”
constraints: family planning, female education, etc
Not obvious there is a strong endogenous mechanism
1. Introduction - intensification
10. Diversifying out of agriculture
Major omission from 1980s literature was discussion
of nonfarm economy, which is large in many countries
If land is a constraint, why not migrate?
Of course, farmers do migrate, but viability of
migration in domestic economy is a general
equilibrium issue: are there nonfarm jobs?
Rural nonfarm economy (RNFE) often felt to be driven
by agric. productivity, infrastructure, education
Policies matter: RNFE does not spontaneously emerge
1. Introduction - intensification
11. The African context
What about international migration?
Has boomed in last 20 years: remittances to LDCs
grown by 1600% from 1990 to 2010.
Moreover, not just small islands: Philippines, Pakistan
and Bangladesh hugely dependent on remittances, and
they are all much larger than most African countries
Are land constraints driving rural people to explore
international migration as a way out of farming?
1. Introduction - intensification
12. So we have 4 adaptations to land constraints
In this paper we focus on international evidence, and
on whether and how Africa adapts to land constraints
Why be especially concerned about Malthus in SSA?
Many reasons:
1. Very poor, and poverty still heavily rural: history of
famine & drought; progress might be deceptive
2. Rural poverty closely associated with small farms;
most Africa farms have a few hectares or less
3. Low inherent agric. potential (incl. low irrigation)
1. Introduction
13. 5. Rapid population growth (double by 2050); suggests
that farm sizes will only get smaller
6. Climate change: secular changes in climate, but also
likelihood of more shocks
7. Very limited success with industrialization; urban
jobs mostly in low-wage informal services sector
1. Introduction
15. Our overarching objective is to assess international
experience in these 4 adaptations to land pressures
There is a large literature exploring Boserup’s
hypothesis, as well as policy-induced intensification
There is much smaller literature on land expansion
There is essentially no literature on farm sizes &
fertility rates
And there is some indirect literature on farms sizes,
rural nonfarm activity and migration
For each of these adaptations, we also ask whether
Africa is different, and why?
1. Introduction
16. In terms of data and methods, we make use of:
1. FAOSTAT ag production and land data;
2. Census (FAO) and survey data on farm size
distributions
3. DHS data on rural fertility rates & occupations
4. Some WB data on remittances
We combine these data in an unusually rich data set
on agricultural and rural development
(though we also acknowledge that some of the
numbers are fairly speculative)
1. Introduction
17. On methods, our approach is necessarily exploratory
Establishing causation is an under-recognized
problem with Boserup’s theory
Problems of simultaneity, omitted variables, selection
biases, parameter heterogeneity. Some examples:
1. Agroecological (AE) factors & market access jointly determine
settlement patterns and intensification
2. Boserupian intensification depends on AE potential
3. Unsuccessful intensification encourages out-migration
4. Policies promote intensification, discourages out-migration
IV rarely plausible in cross-country setting, but we do
make an effort to add as many controls as possible
18. If farm sizes are shrinking, why not expand land use?
Africa is typically thought of as land abundant, but
this neglects the heterogeneity within Africa
2. Land expansion
Region Period
Hectares per
agric. worker
(FAO)
Hectares per
holding
(censuses)
Used land as %
of potentially
cultivable land
Africa - high
densityb (n=5)
1970s 0.84 1.99 32.7
2000s 0.58 1.23 43.8
Africa - low densityb
(n=11)
1970s 1.65 2.65 17.2
2000s 1.37 2.82 24.7
South Asia 1970s 0.78 2.01 129.5
(n=5) 2000s 0.55 1.19 135.9
China & S.E. Asia 1970s 0.80 2.08 71.2
(n=4) 2000s 0.68 1.58 83.0
19. Several important facts & mysteries emerge from
census, FAO and FAO-IIASA data:
1. Farm sizes are shrinking in high-density Africa.
2. Some high-density countries still have unused land,
but smallholders face major constraints to using that
land (e.g. Ethiopia, Madagascar).
3. Even in countries with unused land (e.g. Ethiopia),
there are major constraints to using new lands:
different agronomics, disease burdens, infrastructure
4. Farm sizes are unchanged (on average) in low
density Africa, but still very small on average
2. Land expansion
20. 3. Agricultural intensification
In the framework above, the most welfare-relevant
indicator of intensification is just output per hectare
Boserup focused more on cropping intensity, and the
ag-econ profession & CGIAR looks a lot at yields
But switching to high value crops is obviously also a
potentially important adaptation, especially in SSA.
So I’m going to show you a series of graphs, and then
some more formal econometric tests.
Note that I also decompose agricultural output per
hectare into cereal yields, cereal cropping intensity
and high value non-cereals
25. Regression No. R1 R2 R3 R4
Dep. var.
Agric. output
per ha
Cereal output
per ha
Cereal crop
intensity
Non-cereal
output per ha
Population density 0.33*** 0.18*** 0.20*** 0.28***
Density*Africa -0.11** -0.23*** -0.01 -0.01
Road density 0.14*** 0.09** -0.03 0.19***
Number of ports 0.14*** 0.21*** 0.03 0.15***
Urban agglom (%) 0.29*** -0.09 0.31*** 0.31***
Regional fixed effects? Yes Yes Yes Yes
Sign of SSA dummies? + in E.Africa Zero Neg. + in E.Africa
AE controls Yes Yes Yes Yes
No. Obs 243 243 243 243
R-square 0.8 0.74 0.67 0.79
Table 4. Log-log estimates of agricultural value per hectare
and its three components
26. Regression No. R1 R2 R3 R4
Dep. var.
Fertilizers
per hectare
Cattle/oxen
per hectare
Irrigation per
hectare
Capital per
hectare
Population density 0.76*** 0.42*** 0.59*** 0.24***
Density*Africa -0.32** 0.15* -0.47*** -0.10***
Road density -0.08 0.31*** 0.04 0.07**
Number of ports 0.50*** 0.07 0.24*** 0.12***
Urban agglom (%) 0.38 0.03 0.24** -0.03
Regional fixed effects Yes Yes Yes Yes
Sign of SSA dummies? Zero Neg. Zero Zero
AE controls Yes Yes Yes Yes
No. Obs 0.73 0.77 0.92 0.77
R-square 0.69 0.74 0.91 0.73
Table 5. Log-log estimates of specific agricultural inputs
27. Stylized facts Potential explanations
Lowproductivityofcerealssector
Low fertilizer
application
Agronomic constraints (e.g. low soil fertility) Poor
management practices, low human capital High transport
costs (see regression 1 in Table 4); Low rates of subsidization
(structural adjustment)
Low adoption
of improved
varieties
More varied agroecological conditions and crop mix
Lower returns because of limited use of other inputs (e.g.
irrigation); Lower investment in R&D
Low use of
plough/ tractors
Tsetse fly in humid tropics Feed/land constraints in some
densely populated areas
Low rates of
irrigation
Hydrological constraints; High costs of implementation and
maintenance; Poor access to markets limits benefits
Noncereals
High non-cereal
output per
hectare
Agroecological suitability; Colonial introduction of cash crops;
Non-perishable cash crops (cotton, coffee, cocoa, tea,
tobacco) not limited by poor infrastructure and isolation
Table 7. Potential explanations of Africa’s agricultural
intensification trajectory
28. 02468
0 500 1000 1500
Rural population density (person per sq km)
Non-Africa gradient
African gradient
Figure 3. Rural fertility rates and rural population density
3. Reducing rural fertility rates
30. Figure 5. Unmet contraception needs (%) and rural population density in Africa
BEN
BEN
BEN
BFA
BFA
CMR
CMR
CMR
TCD
COM
ZAR
COG
CIV
ERI
ERI
ETH
ETH
GAB
GHA
GHA
GHA
GHA
GIN
GIN
KEN
KEN
KEN
LSO
LBR
MDG
MDG
MWI
MWI
MWI
MLI
MLI
MOZ
MOZ
NAMNAM
NER
NER
NER
NGA
NGA
NGA
NGA
RWA
RWA
RWA
SEN
SEN
SLE
TZA
TZA
TZA
TZA
TGO
ZMB
ZMB
ZMB
152025303540
0 100 200 300 400
Rural population density (person per sq km)
Sources
31. Regression number 1 2 3 4
Dependent variable Actual fertility Actual fertility Desired
fertility
Desired
fertility
Model Linear Log-log Linear Log-log
b/se b/se b/se b/se
Pop density (per 100 m2) -0.14*** -0.09*** -0.11*** 0.00
Density*Africa 0.05 0.09*** -0.34*** -0.07***
Female sec. education (%) -0.02*** -0.05*** -0.01** -0.08***
Ag. output per worker, log -0.58*** -0.13*** 0.01 0.06***
Africa dummy 1.25*** -0.15 2.13*** 0.67***
Number of observations 165 165 164 164
R-square 0.75 0.76 0.77 0.81
Table 8. Elasticities between rural fertility indicators
& rural population density
32. 4. Nonfarm diversification
Much neglected in 1980s literature on Boserup
Subsequent literature on both RNFE and migration &
remittances shows that RNF income is big
But not much specific literature looking at pop density
On RNF activity, often suggested there is a U-shaped
relationship between farm size and RNFE: landless
poor are pushed into RNFE, rich are pulled in
Very difficult to look at rural-urban migration
Int. remittances have boomed in last 10 years,
particularly in densely population South Asia – now
22% of rural income in Bangladesh
33. High density Africa Low density Africa Other LDCs
Country W M Country W M Country W M
Benin 50.4 23.7 Burkina Faso 12.9 8.1 BGD 53.4 44.5
Congo (DRC) 14.0 23.5 Chad 13.7 9.6 Bolivia 71.4 25.9
Ethiopia 34.3 9.7 Cote d'Ivoire 31.7 22.1 Cambodia 36.0
Kenya 47.1 37.3 Ghana 50.1 26.6 Egypt 69.4
Madagascar 17.8 15.3 Mali 44.6 16.0 Guatemala 79.1
Malawi 41.5 36.0 Mozambique 5.2 23.0 Haiti 24.0 19.0
Nigeria 65.5 37.0 Niger 60.2 35.8 India 22.4
Rwanda 7.3 14.2 Senegal 63.7 37.1 Indonesia 59.2 39.5
Sierra Leone 25.2 20.1 Tanzania 7.2 10.5 Nepal 90.5 34.2
Uganda 15.5 20.3 Zambia 30.1 19.5 Philippines 16.2 42.6
Table 9. Speculative estimates of rural nonfarm
employment shares for men and women in the 2000s
34. Regression No. R1 R2 R3 R4 R5 R6
Sample Women Women Women Men Men Men
Population density 0.47 0.09 0.15 -0.33 -0.32 -0.31
Density*Africa -0.19** -0.22** -0.15* 0.03 -0.02 -0.02
Africa dummy -0.25 0.1 0.04 -0.43 0.09 0.09
Sec. educ. by gender 0.03 0.11 0.35*** 0.35***
Road density 0.14* 0.15** 0.17* 0.17*
Electricity 0.20** -0.07 0.09 0.09
Ag. Output/worker, log 0.46*** 0.01
No. Obs. 162 122 95 74 74 74
R-square 0.2 0.53 0.24 0.55 0.55 0.55
Table 11. Elasticities between RNF employment indicators
and rural population density for women and men
35. Figure 6. National remittances earnings (% GDP) and
rural population density
DZA
ARG
BGD
BEN
BOL
BRA
BFA
BDI
KHM
CMR
CHL
CHN
COL
COG
CRI
CIV
DOM
ECU
EGY
SLV
ETH
GHA
GTM
GIN
HTI
HND
IND
IDN
IRN
IRQ
JOR
KEN
LAO
LBN
LBR
LBY
MYS
MLI
MEX
MAR
MOZ
NPL
NIC
NER
NGA
PAK
PAN
PRY
PER
PHL
RWA
SEN
SLE
ZAF
LKA
SDN
SYR
TZA
THA
TGO
TUN
UGA
URY
VEN
VNM
ZMB
05
10152025
0 500 1000 1500
Rural population density (person per sq km)
36. Estimator OLS Robust OLS Robust
Structure Levels (logs) First difference Levels (logs) First difference
Density variable Agricultural Agricultural Rural Rural
Population density 0.25*** 0.97** 0.31*** 1.17***
Population density*Africa 0.05 -0.94 0.04 -1.22**
Total population -0.24*** -1.31** -0.23*** -0.82
Lagged remittances -0.21*** -0.24***
Lagged population density 0.06 0.06
West Africa dummy -0.67* -0.49
Central Africa dummy -1.55*** -1.40***
East Africa dummy -0.90** -0.74*
Southern Africa dummy 0.14 0.24
1977-87 dummy 0.15 0.12
1987-97 dummy 0.33* -0.09 0.28* -0.06
1997-2007 dummy 0.79*** 0.19 0.72*** 0.24*
Number of observations 231 147 231 159
R-square 0.39 147 0.4 0.22
Table 11. Estimating elasticities between national
remittance earnings (% GDP) and population density
37. 5. Conclusions
Land pressures are severe in much of Africa, esp. high
density SSA, where small farms are getting smaller,
and will continue to get smaller as pop. grows
Yet history shows that rural people are generally adept
at adapting to mounting land pressures.
Ag intensification is only part of the adaptation
The question we posed is whether Africa is different
In many ways, the answer is yes . . .
38. Adaptation 1 – Agricultural Intensification
Africa has intensified agriculture, but largely
through high value non-perishable crops (HVCs)
Much less historical success with cereals, and much
less potential given limited potential for irrigation
Should we shift emphasis of research and development
strategies from cereals to HVCs?
CGIAR, for example, barely looks at cash crops like
coffee, tea, cotton, cocoa, tobacco (even though cash
buys food!)
5. Conclusions
39. Adaptation 2 – Reducing fertility rates
Higher densities (smaller farms) apepar to lead to a
desired reduction in fertility in Africa
But desired reductions are not met by access to
contraceptive technologies
High-density East Africa now shows mixed policies
Ethiopia & Rwanda are investing in family planning
(*), but Museveni (Uganda) has resisted family
planning (population growth is “a great resource”)
Asian experience suggests FP yields high returns
5. Conclusions
40. Adaptation 3 – Nonfarm diversification
Weak evidence, but evidence that is there suggests
that nonfarm sector doesn’t just grow without
engines like education, infrastructure, agriculture
(also true for African cities?)
Boom in overseas migration and remittances is new,
and unexpected.
20 years ago, BGD and Pakistan were regarded as too
big to benefit from remittances. Not true now.
Why isn’t Africa getting more remittances?
5. Conclusions
41. Finally, we ask whether the results we find warrant a
re-think in the way high density countries pursue
rural development
Are SSA countries thinking through the implications
of rural pop. growth for farm sizes and rural welfare?
Do SSA countries need rural development strategies
that are more integrated with respect to smallholder
intensification, commercial farms, family planning,
migration and rural nonfarm development?
What are the costs of not doing so?
5. Conclusions
Editor's Notes
(e.g. Egypt’s 8000 years of experience with irrigation has surely influenced migration and fertility decisions)