Transcript Slide 1

“Myths and Facts”
in African Agriculture:
What We Now Know
Christopher B. Barrett
Cornell University
African Development Bank workshop on
Structural Transformation in African
Agriculture and Rural Spaces (STAARS)
Nairobi, Kenya,
April 8, 2015
The world in which African
agriculture operates has
changed…
High and volatile food prices
Urbanization
Income growth
Soil erosion
Cell phones
Increased investment
… but our evidence base remains
often inadequate or rooted in the past.
An opportunity to update our understanding
of African agriculture!
1.
Represents 40 percent of Sub-Saharan
Africa’s population
2.
8 countries with cross-country comparable
information
3.
Strong focus on agricultural data collection,
but also consumption, expenditures, etc.
4.
Plot, household, and community level
information
5.
Nationally-representative statistics as well as
within-country (and even within-household)
analysis
6.
Statistics derived from farmers’ accounts
7.
Coupled with growing collection of georeferenced data sets
8.
Repeated visits to households (panel data)
“Myths and Facts” project objectives:
• Provide a solid, updated, and bottom-up picture of Africa’s agriculture
and farmers’ livelihoods using cross-sectional data
• Create a harmonized and easy-to-use database of core agricultural
variables for tabulation and regional cross-country benchmarking
• Build a community of practice
• Partnering institutions: World Bank, African Development Bank, Cornell
University, Food and Agriculture Organization, Maastricht School of
Management, Trento University, University of Pretoria, Yale University
• Mentorship program for young African scholars from US and African
institutions
AGRI CULTURE
IN AFRICA
T E L L I N G FAC T S
FROM MYTHS
Common wisdoms revisited…
1) Use of modern inputs remains
dismally low
2) Land, labor and capital markets
remain largely incomplete
3) Agricultural labor productivity
is low
4) Land is abundant and land
markets are poorly developed
5) Rural entrepreneurs largely
operate in survival mode.
6) Extension services are poor
7) Agroforestry is gaining traction
8) African agriculture is
intensifying
9) Women perform the bulk of
Africa’s agricultural tasks
10) Seasonality continues to
permeate rural livelihoods
11) Smallholder market
participation remains limited
12) Post harvest losses are large
13) Droughts dominate Africa’s risk
environment
14) African farmers are increasingly
diversifying their incomes
15) Agricultural commercialization
and diversification improves
nutritional outcomes
Common wisdoms revisited…
1) Use of modern inputs remains
dismally low
2) Land, labor and capital markets
remain largely incomplete
3) Agricultural labor productivity
is low
4) Land is abundant and land
markets are poorly developed
5) Rural entrepreneurs largely
operate in survival mode.
6) Extension services are poor
7) Agroforestry is gaining traction
8) African agriculture is
intensifying
9) Women perform the bulk of
Africa’s agricultural tasks
10) Seasonality continues to
permeate rural livelihoods
11) Smallholder market
participation remains limited
12) Post harvest losses are large
13) Droughts dominate Africa’s risk
environment
14) African farmers are increasingly
diversifying their incomes
15) Agricultural commercialization
and diversification improves
nutritional outcomes
1. Agricultural Inputs
Sheahan and Barrett
Ten new (or newly confirmed) facts about agricultural
input use in Africa … a preview of five
1. Modern input use may be relatively low in aggregate, but is not uniformly
low across (and within) these countries, especially for inorganic fertilizer
and agro-chemicals (although not for irrigation and mechanization).
Share of cultivating households (%) using input on
fields
90
77
80
70
56
60
50
40
30
41
33
31
17
20
10
13
8
3
17
11
3
0
Ethiopia
Malawi
Niger
any agro-chemical
Nigeria
Tanzania
inorganic fertilizer
Uganda
1. Agricultural Inputs
Sheahan and Barrett
2. There is surprisingly low correlation between the use of commonly
“paired” modern inputs at the household- and, especially, the plot-level.
This raises questions about untapped productivity gains.
Ethiopia: household level
Ethiopia: plot level
3. Farmers in East Africa do not significantly vary input application rates
according to self-perceived soil quality and erosion status.
1. Agricultural Inputs
Sheahan and Barrett
4. An inverse relationship consistently exists between farm or plot size
and input use intensity.
Nigeria: farm level
Nigeria: plot level
Local polynomial smooth
kg/ha of inorganic fertilizer applied to field
Local polynomial smooth
200
150
100
50
0
-50
0
200
150
100
50
0
1
2
3
Total hectares of land under cultivation
95% CI
lpoly smooth
kernel = epanechnikov, degree = 1, bandwidth = .34, pwidth = .51
4
0
.5
1
1.5
plot size in hectares
95% CI
lpoly smooth
kernel = epanechnikov, degree = 1, bandwidth = .2, pwidth = .3
In most cases, this relationship is more pronounced at the plot level, therefore interhousehold variation cannot explain relationship.
Suggests need to better understand intra-household agricultural input allocation
decisions.
1. Agricultural Inputs
Sheahan and Barrett
5. National-level factors explain nearly half of the farm-level variation in
inorganic fertilizer and agro-chemical use.
Variation in household-level inorganic fertilizer use
Categories of variables
Bio-physical variables: rain, soil,
elevation, maximum greenness, agroecological zones
Socio-economic variables:
consumption level, sex of household
head, household size and dependency
ratio
Farm operation characteristic
variables: farm size, number of crops,
type of crops
Market and accessibility variables:
distance to market and road, prices of
fertilizer and main grain
Country dummy variables
Shapley value
24
• Ultimately interested to learn where
most of the variation in input use comes
from: biophysical, infrastructure, market, socioeconomic, or policy-specific variables?
4
• Binary use at household level (avoids
bias from survey design)
16
11
• 45 percent of variation in inorganic
fertilizer use can be explained by
country level (similar for agro-chem)
45
Suggests the policy and operating environments facilitated by
governments and regional processes (e.g., CAADP) are critically
important for ushering in a Green Revolution in Sub-Saharan Africa.
2. Factor Markets
Dillon and Barrett
Agricultural Factor Markets in Sub-Saharan Africa:
An Updated View with Formal Tests for Market
Failure … a preview
1.
Provide a summary overview of land and labor market participation
in Ethiopia, Malawi, Niger, Tanzania, and Uganda
2.
Implement a simple test of market failures in data from five African
countries (testing whether the separation hypothesis holds)
Main findings: in spite of widespread participation in rural
land and labor markets, we strongly reject the null hypothesis
of complete and competitive markets in all study countries …
both input market participation and failure are widespread
2. Factor Markets
Dillon and Barrett
Labor markets exist and are active.
Percent of agricultural households hiring labor
Country
Activity
Ethiopia
Cultivation
Harvest
Overall
Non-harvest
Harvest
Overall
Preparation
Cultivation
Harvest
Overall
Planting
Weeding
Fertilizing
Harvest
Overall
Overall
Malawi
Niger
Tanzania
Uganda
Number of
households
3091
2666
2666
2605
2605
2605
2339
2339
2339
2339
2630
2630
2630
2630
2630
2109
Percent hiring
workers
18.5%
20.9%
30.2%
32.6%
16.0%
42.0%
19.5%
37.4%
18.6%
47.8%
18.5%
18.9%
2.6%
16.0%
30.8%
46.8%
2. Factor Markets
Dillon and Barrett
Land markets exist and are active, too.
N
Participation in land rental markets
Ethiopia
Malawi
Niger
Tanzania
3094
2666
2339
2630
Uganda
2135
Household rents
land out
6.10%
0.90%
1.20%
3.40%
0.40%
Household rents
land in
19.50%
13.10%
7.30%
6.20%
18.10%
Household rents
or borrows land in
30.30%
28.40%
27.70%
23.20%
36.60%
Clearly these markets have sufficient transactors to be competitive. But
adequate transactional density is merely a necessary condition for the
separation hypothesis to hold.
2. Factor Markets
Dillon and Barrett
However, clear evidence of market failure across all countries and
multiple specifications (Benjamin 1992, Udry 1999):
Log area (acres)
Log median wage
Log HH size
R-squared
N
OLS regression results of farm labor use
Ethiopia
Malawi
Niger
Tanzania
Uganda
0.489***
0.528***
0.343***
0.444***
0.379***
-0.04
0.036
-0.051
0.379***
-0.055
0.33
2499
-0.048
-0.121**
-0.052
0.399***
-0.061
0.278
2556
-0.026
-0.155
-0.107
0.635***
-0.061
0.301
2183
-0.027
-0.077
-0.065
0.399***
-0.043
0.321
2346
-0.033
0.012
-0.043
0.211***
-0.044
0.312
2047
But it’s not clear which markets are failing (next step), nor why (search,
supervision or transactions costs? That’s the next phase of research …
3. Women in Agriculture
Christiaensen, Kilic, Palacios-López
How Much Do Women in Africa
Contribute to Agriculture? … a preview
Common rhetoric:
“….women are responsible for 6080 [percent] of the agricultural
labour supplied on the continent
of Africa.” (UNECA, 1972; FAO,
1995)
Female share of agricultural labor
documented in LSMS-ISA surveys:
Uganda 56%
Tanzania 53%
Malawi 52%
Nigeria 37%
Women produce 60 to 80 percent
of the food in developing countries
and 50 percent of the world’s food
supply. (Momsen, 1991)
Ethiopia 29%
Niger 24%
Cross-country average 40%
3. Women in Agriculture
Christiaensen, Kilic, Palacios-López
Agricultural activities appear to be gendered.
Female share of agricultural labor by activity
Activity
Land Preparation
Planting, Weeding
Harvesting
Total
Tanzania Malawi Niger Uganda
52
53
54
53
53
53
51
52
18
25
28
24
56
Northern Southern
Ethiopia
Nigeria Nigeria
31
31
34
32
51
51
51
51
26
26
37
29
• Women are relatively more involved in harvesting and
less in land preparation in the countries in which men
have the higher share of agricultural labor.
3. Women in Agriculture
Christiaensen, Kilic, Palacios-López
No systematic evidence of gender differences in youth’s
engagement in agriculture.
Female share of agricultural labor by age group
Age
Northern Southern
Tanzania Malawi Niger Uganda
Ethiopia
Category
Nigeria Nigeria
0-15
52
49 25 54
30
46
43
15-30
51
54 27 55
32
54
30
30-45
56
52 24 56
38
61
30
45-60
52
52 23 61
29
50
26
60+
51
52 10 54
21
40
15
Total
53
52 24 56
32
51
29
3. Women in Agriculture
Christiaensen, Kilic, Palacios-López
No strong case to disproportionately focus on gender if total
agricultural supply is the objective.
Gender-based yield gaps range from
13% in Uganda to 25% in Malawi
(World Bank, ONE Campaign 2014)
due to lower use of improved
technologies and lower returns to
those technologies.
If 40% of laborers (the female portion)
increased their output by 13-25%, then
closing this gap this would only
contribute to a 5-10% increase in total
agricultural production.
4. Post-harvest Losses
Kaminski and Christiaensen
Post Harvest Loss in Sub-Saharan Africa:
What do farmers say? … a preview
Common wisdoms about post harvest losses:
“Worldwide 32 % of all food produced is lost. In SSA, it amounts to 37%.”
(FAO, 2011)
Post harvest losses for cereals alone is estimated at 20.5% (FAO, 2011).
Goal of this analysis: Focus on farmer-reported post harvest
losses of maize (more perishable than sorghum and millet) in
East Africa.
4. Post-harvest Losses
Kaminski and Christiaensen
Proportions (%)
UG 2009-10
Post harvest losses, portion
5.9
of harvest (unconditional)
Probability of reporting post
21.5
harvest losses
Post harvest losses, portion
27.4
of harvest (conditional)
# maize producing hhs
1,853
TZ 2010-11
TZ 2008-09
MW 2010-11
2.9
4.4
1.4
14.7
19.0
6.8
19.7
23.1
20.6
1,520
1,301
10,331
• Between 7 (Malawi) and 22 (Uganda) percent of maize farmers report to
incur on-farm PHL for maize, losing between 20 to 27 percent of their
harvest.
• Adds up to between 1 and 6 percent of total national maize
harvest.
• Insects and rodents/pests (biotic factors) as the most important reasons
for reported losses.
4. Post-harvest Losses
Kaminski and Christiaensen
Correlates of self-reported post harvest maize losses:
• Increases with
• Humidity and temperature
• Declines with
•
•
•
•
Seasonal price gap
Proximity to market place
Post primary education
Female headed households
• Not associated with
• Poverty
• Rural/urban areas
4. Post-harvest Losses
Kaminski and Christiaensen
Use of improved storage technology is low.
UG 2009-10
TZ 2010-11
TZ 2008-09
MWI 2010-11
Traditional storage
1.4
24.8
19.2
17.9
Improved storage
0.6
11.5
5.9
2.0
Spraying/ smoking
63.1
49.0
37.2
10.8
• Uptake of improved grain storage facilities (modern stores, improved
local structures, air-tight drums) is minimal.
• Crop protection sprays and smoking, however, are widely used, further
adding to the already higher than expected agro-chemical use on fields.
• Taken together, these facts suggest limited aggregate food supply gains to
attempting reductions in on-farm loss or storage technology promotion.
5. Labor and Productivity
McCullough
Time use and labor productivity in Africa
… a preview
• Labor allocation and productivity are key features of structural
transformation because they describe the incentives households
face when making decisions about time use
5. Labor and Productivity
McCullough
• Micro-measures of labor shares are relatively similar to national
accounts-based measures
• However, note that hours based measures offer lower agriculture
shares than participation based measures do
5. Labor and Productivity
McCullough
• Agricultural workers supply far fewer hours of labor per year than do
workers in other sectors
• Individuals participating in other sectors also work in agriculture; the
reverse is much less true of primarily agriculture workers
5. Labor and Productivity
McCullough
• Labor productivity gaps are pronounced when only focusing on
primary sector of participation
• But gaps virtually vanish or are reversed when accounting for hours
worked (except Tanzania)
• What look like productivity gaps (left) could actually just be
employment gaps
• Highlights the continued importance of agriculture in Africa’s
structural transformation and the need to interrogate the data
Concluding remarks
•
Solid descriptive statistics are key to guide policy debates
•
We’ve learned a lot about the current status of African agriculture
and livelihoods from this new descriptive evidence.
•
Times are changing, and so is rural Africa. We must keep up to date
•
Now, with emerging panel data, we have the opportunity to further
contribute to evidence-based policy making by moving from
descriptive work to research on the causes of change.
Thank you
Thank you for your time, interest and comments!