How can agricultural development strategies effectively reduce poverty in Africa?
T.S. Jayne, Kwame Yeboah, Lulama Traub, Milu Muyanga, Jordan Chamberlin, Ferdinand Meyer Ag. Learning Lunch seminar, Bill and Melinda Gates Foundation, Seattle, October 6, 2015
Organization of seminar: 1. Structural transformation – what is it? – how it is affecting African economies
2. Unpacking the smallholder farm sector 3. Implications for policies and programs to reduce poverty in Africa 2
Structural Transformation 101
4
5
6
7
Sectoral shifts in labor force: China
Source: Groningen Global Development Centre, 2013
Sectoral shifts in labor force: Ethiopia
Source: Groningen Global Development Centre, 2013
Sectoral shifts in labor force: Tanzania
Source: Groningen Global Development Centre, 2013
Sectoral shifts in labor force: Nigeria
Source: Groningen Global Development Centre, 2013
Sectoral shifts in labor force: Ghana
Source: Groningen Global Development Centre, 2013
Labor productivity per worker, Ghana
Million people
Jobs by sector, Sub-Saharan Africa
Source: World Bank (Filmer and Fox), 2014
14
Million people
Jobs by sector, Sub-Saharan Africa
Source: World Bank (Filmer and Fox), 2014
15
Features of structural transformation 1. Gradual shift of labor force from farming to off-farm 2. Pull vs. push forms of ST 3. Rise in labor productivity rising p.c. incomes 4. Rising labor wages leads to tech change in agriculture (e.g., mechanization) 5. In early phase, pace of ST related to rate of farm productivity growth 6. In middle/late phases, pace of ST related to growth in agribusiness and non-farm sectors 16
.04 Avg annual agricultural growth -.02 0 .02
Rwanda
Cameroon Malawi South Africa Mali Zambia Tanzania Togo Ethiopia Guinea-Bissau Cote d'Ivoire Ghana Congo, Rep. Guinea Madagascar Nigeria Mozambique Namibia Botswana
Burkina Faso
-.04
Senegal
-.06
-.04
-.02 0 .02 .04 Avg annual change in rural poverty Growth without poverty reduction Growth with poverty reduction
.06
Summary so far: • In early phases of countries’ development process, agricultural growth is a major driver of structural transformation • The relationship between agricultural productivity growth and poverty reduction is quite variable
18
Factors influencing the contribution of ag. productivity growth to poverty reduction: 1. Size of agriculture in overall economy 2. % of poor people engaged in agriculture 3. Distribution of assets/resources – multiplier effects from agricultural growth – how inclusive is ag. growth
19
III. Unpacking the smallholder farm sector
Crop sales by farm size over time (2011 Zmk prices) Largest smallholder farms (8%) consistently doing better
Source: MACO CFS 2000/1 to 2010/11 and authors’ computations 21
Disparities within smallholder agriculture, Zambia
Top 50% of maize sales Rest of maize sellers Households not selling maize
N=
Farm size (ha)
Asset values (US$)
Gross rev., Gross rev., maize sales crop sales (US$) (US$)
30,150 (2%)
4.1
3,703
3,199
4,213
7,324
467,320 (30%)
1.9
257
181
330
1,021
1,010,014 (67%)
1.1
129
0
128
456
Source: Central Statistical Office / IAPRI / MSU Supplemental survey
Total hh income (US$)
Rural headcount poverty rates, Zambia
23
Distance to town accessible
Agroecological Potential
High
Low
remote
% of total farm population Distance to town accessible
Agroecological Potential
• •
remote
High
Ethiopia: 9% Nigeria: 75% Tanzania: 26%
Ethiopia: 48% Nigeria: 20% Tanzania: 67%
Low
Ethiopia: 6% Nigeria: 4% Tanzania: 1%
Ethiopia: 37% Nigeria: 2% Tanzania: 7%
“accessible” defined as 100,000; high-potential defined on basis of agro-ecological zones
% of total farm population Distance to town accessible
Agroecological Potential
• •
remote
High
Ethiopia: 9% Nigeria: 75% Tanzania: 26%
Ethiopia: 48% Nigeria: 20% Tanzania: 67%
Low
Ethiopia: 6% Nigeria: 4% Tanzania: 1%
Ethiopia: 37% Nigeria: 2% Tanzania: 7%
“accessible” defined as 100,000; high-potential defined on basis of agro-ecological zones
Distance to town
Agroecological Potential
accessible
remote
High
40% commercialized
25% commercialized
Low
15% commercialized
5% semi-commercialized
Source: Paul Dorosh et al., 2013
Potential “ST” metrics 1. Extent of smallholder commercialization: – % of hhs selling > $2000 in value – HCI: value of sales / value of production
2. Wage rates
– Agricultural wage rates relative to CPI – Off-farm wage rates relative to CPI
3. 4. 5. 6.
Agribusiness GDP to agricultural production GDP Growth over time in mean off-farm incomes Growth over time in mean farm incomes Rural and urban poverty rates
Main conclusions:
1. For the next 2-3 decades, most Africans will be engaged in farming – Rule of thumb: 1. 2. 3.
~ 10% African farmers are already commercialized and contributing to ST ~ 30% --with appropriate support -- have potential to produce moderate/high surplus production value ~ 60% : difficult to “move the needle” directly, but raising their productivity will contribute indirectly to poverty reduction and growth and metrics – –
Productivity growth reduces their food expenditures greater disposable incomes for non-farm goods Generally the more inclusive is ag growth, the greater the multipliers
Main conclusions (ii):
2.
Agriculture will remain central for generating the income and employment multipliers to reduce national poverty rates –
Necessary but not sufficient condition
–
Other conditions: lower costs of commerce, policies encouraging equitable growth, sustainable forms of intensification
3.
Hence need to encourage inclusive ag growth (within reason) as an intermediate objective in the process of economic transformation
4.
The strategies to engage smallholders are different in the 2x2 matrix
Some farmers are better marketing negotiators than others Farm-Gate Prices in Remote Villages in Rumphi District versus Retail Maize Prices in Rumphi District
80.00
80.00
70.00
70.00
60.00
60.00
50.00
50.00 MKW
MKW
Farm-Gate Prices in Accessible Villages in Rumphi District versus Retail Maize Prices in Rumphi District
40.00
40.00
30.00
30.00
20.00
20.00
10.00
10.00
0.00
0.00
Mean Retail Prices
Farm-Gate Prices
Mean Retail Prices
Farm-Gate Prices 35
Six “mega-trends” 1. Youth bulge/labor force expansion 2. Growth in non-farm employment – urbanization - rising labor productivity 3. Food consumption outstripping production (region is deficit in most food products) 4. Rise of investor farmers rising share of African farmland under MS/LS production 5. Rising land scarcity price of land rising relative to other inputs soil mining land degradation
6. Climate change
Total area under cultivation by 5-100 ha farmers 1000 2000 3000 4000
Correlation between district medium-scale landholdings and measures of tractor use, northern/central Ghana
0
50 100 Number of households owning tractors in Districts
150
Trend #1:
Youth Bulge/ Labor Force Expansion [80+] [75-79]
Male
[70-74] [65-69]
Female
[60-64] [55-59] [50-54] [45-49] [40-44] [35-39] [30-34] [25-29] [20-24]
44% < 15 years old
[15-19] [10-14] [5-9] [0-4]
-10%
-8%
-6%
Source: UN Pop Council, 2013
-4%
-2%
0%
2%
4%
6%
8%
10%
38
Motivating fact: looming employment challenge in SSA Total Rural Population (millions) 1000
India
900
China
800
SSA
700 600
Other South Asia
500 400 300
South-East Asia
200 100
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
0
Source: UN 2013 39
African Population by Income Class: excluding North Africa and South Africa 4.8%
2010
13.4%
Poor ($0 - $4) Middle-class ($4 - $20) Rich (>$20) 81.8%
Source: Potts, 2012: calculated from the AfDB (2010)
Net Grain Exports for Southern Africa
-4
Millions of Tons -2
0
Net Export for Southern African (without South Africa)
1980
1985
1990 Maize Rice
1995 Year
2000
2005
2010
Wheat Total Net Export
Source: FAO, 2015
Net Grain Exports for East Africa
-6
Millions of Tons -4 -2
0
Net Export for Eastern Africa
1980
1985
1990 Maize Rice
1995 Year
2000
2005
2010
Wheat Total Net Export
Source: FAO, 2015
Net Grain Exports for West Africa
-15
Millions of Tonnes -10 -5
0
Net Export for Western Africa
1980
1985
1990 Maize Rice
1995 Year
2000
2005
2010
Wheat Total Net Export
Source: FAO, 2015
Food balances in sub-Saharan Africa
Source: FAOSTAT, 2014. acknowledgements to Holgar Matthey
44
The importance of SOM
45
Review of maize-fertilizer response rates on farmer-managed fields Study
country
Agronomic response rate (kgs maize per kg N)
W/E/S Africa
10-14
Sheahan et al (2013)
Kenya
14-21
Marenya and Barrett (2009)
Kenya
17.6
Liverpool-Tasie (2015)
Nigeria
8.0
Burke (2012)
Zambia
9.6
Snapp et al (2013)
Malawi
7.1 to 11.0
Holden and Lunduka (2011)
Malawi
11.3
Minten et al (2013)
Ethiopia
11.7
Pan and Christiaensen (2012)
Tanzania
11.8
Mather et al (2015)
Tanzania
5.7 to 7.8
Morris et al (2007)
46
Manifestations of land scarcity • • • •
Increased cropping intensities Inadequate crop rotation Loss of soil organic matter Low crop response to fertilizer unless sustainable intensification practices are adopted
47
Primary activities of youth (15-25 years of age)
Source: Yeboah and Jayne (2015) using most recently available IPUMS and LSMS surveys
48
Variation in farmers’ efficiency of fertilizer use on maize, Agroecological Zone IIa, Zambia 5
Percent of farms
4
3
2
1
0 0
5
10
15
20
25
30
35
Marginal product (kgs / kg nitrogen)
Note: Zone IIa is a relatively high-potential zone suitable for intensive maize production
49
F2. Trends in fertilizer use and cropping intensity Nitrogen application per hectare
0.1
India 2009
0.08 Thailand 2009
0.06
0.04
0.02
Kenya Uganda
0 0.5
Malawi 2009 Nigeria 2009
0.6 0.7 0.8 0.9 Cereal cropping intensity (area harvested/area planted at least once)
1
F3. Trends in Irrigation and cropping intensity 40 India 2009
Irrigated crop area (% total)
35
Thailand 2009
30 25 20 15 10 5 0
Uganda 0.5
Kenya
Malawi
Nigeria
0.6 0.7 0.8 0.9 Cereal cropping intensity (area harvested/area planted at least once)
1
Agricultural intensification Agricultural output per hecatre (2005 int. dollars) 4000 6000 0 2000
EGY
CRI
Ag output per hectare
JOR CHL LBN COL
CHN
other VNM
ECU URY JAM ARM UZB PHL VEN DOM BRA MYS PER MKD TKM PAK THA GTM MMR GEO ALB SOM TJK SWZ IRN PAN KGZ TUR HND IDN SLV MEX LAO IND ARG PRK BLR SRB PRY SYR BTN MRT KEN LTU AZE ROM MDG BWA MNE FJI BIH BDI NGA MNG BOL CIV HTI KHM MWI LVA GHA MAR ZAF GUY TUN LBR BEN BGR UGA COG UKR NIC TZA CMR AGO SDN MDA NAM DZA LBY GIN TMP GAB ETH ZAR IRQ COM MLI GNBSLE CAF ERI RUS AFG LSO MOZ ZMB ZWE KAZ SENBFA TCD TGO GMB NER
0
BGD NPL LKA RWA
Africa
200 400 600 Agricultural population density (person per sq km)
800
1500
Agricultural intensification EGY
Cereal output per hectare ($/ha) 500 1000
Cereal output per hectare
VNM
DOM
GUY
IDN CHN MMR
MYS CRI CHL COL URY PER ECU FJI UZB
BGD LKA
LAO
THA
other PHL
KHM
NPL
Africa RWA
0
ARGMDG PRK VEN BRA IND ALB PAN PAK MEX TKM BIHSRB LTU LVA ZAF IRN BTN AZE LBRSLV MKD TJK BLR ARM KGZ GIN MWI PRY GTM TUR SLE COM NIC CIV GNB ZMB LBN UKR AFG BGR BOL GEO RUS TMP KEN HND TZA CMR UGA BDI SYR GAB NGA ROM ETH GHA IRQ TGO HTI JAM BEN BFA MDACAF SEN COG ZAR GMB MOZ JOR ZWE AGO SWZ MAR LSO
0
200 400 600 Agricultural population density (person per sq km)
800
Clustering of rural populations: Zambia
Clustering of rural populations: Kenya