creating pro-poor value chains
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Transcript of creating pro-poor value chains
Alastair Orr (ICRISAT)
Catherine Mwema (ICRISAT)
Wellington Mulinge (KARI)
What’s ahead….
• Why sorghum beer?• The Kenya beer market• The business model• Data and methods• Results• Some conclusions
Drivers of demand for beer
Consumer power : Africa’s growing middle class (313 million or 34% of the population (ADB, 2011).
Urbanisation: 55 African cities with populations over 1 million
Slowing beer markets in developed countries
Competition between 4 multinational Companies with 90% of African beer market
Sorghum beer targeted at ‘aspirational’ consumers trading up from illicit brews ($3 billion market)
Barley- Rising prices and import duties
05/01/233
The beer market in Kenya
• East African Breweries (EABL) (Diageo plc 51 %) has 93 % of the market
• Strong market growth since 2000• ‘Senator’ keg sorghum beer
launched in 2004• No excise duty until 2013• One-third price of malted beers• Senator Kenya’s best-selling beer
by volume, 35 % of EABL revenues
• EABL sorghum demand expected to reach 60,000t by 2015
05/01/23 4
The Smart Logistics business model
• Smart Logistics Solutions, Kenyan-owned, founded 2009, contract with EABL
• Buys from small scale farmer groups and appointed agents
• Sorghum aggregated in collection centres
• SL transports to EABL• Payments from 1-4 Wks
through bank or mpesa• Pays 26 US cents/kg compared
to 7 cents paid by local traders
Three research questions:
How inclusive is this business model?
What are the benefits for smallholders?
Can it be scaled out?
05/01/23 5
Data and methods
Data•A household survey in Kitui county, semi-arid eastern Kenya .
•High poverty levels (64%) with frequent droughts.
•Multi-stage stratified sampling used to select 150 members & 150 non-members of Smart Logistics groups
•2012 crop year (short and long rains).
MethodsSellers to Smart Logistics include both members and non members
Propensity Score Matching (PSM) of sellers, non-sellers
Selling influenced by membership of Smart Logistics group
Use predictive value of membership as independent variable for participation in sorghum sales
05/01/23 6
Specification
Group membership
Distance to collection centreAgeGenderEducationConsumer/worker ratioHousehold food securityOccupationFarm size
Sorghum sale
Distance to marketQty maize productionQty sorghum productionDummy if household buys sorghumPredicted value of group membership
05/01/23 7
Socio-economic profile
Variables Sellers(n=198)
Non-sellers(n=99)
Sig(P value)
Members of SL groups 127 71 .000
Household size 6.5 6.2 .306
De facto female-headed households (no.) 88 32 .045
Adults >15yrs full time in sorghum production (no) 1.9 1.9 .746
Crop production, 2011-2012
Total land planted (acres) 5.0 5.0 .935
Area planted to sorghum (acres) 1.2 0.9 .000
Total maize production (kg) 841 732 .445
Total sorghum production (kg) 463 337 .455
Households buying maize (no.) 162 70 .037
Total household income (000 Ksh) 255 324 .050
Income per capita (000 Ksh) 46 58 .049
Income from crops (000 Ksh) 53 50 .774
Income from livestock (000 Ksh) 131 181 .021
Value of household assets (000 Ksh) 115 121 .71205/01/23 8
Decision to join SL group
Variable Coefficient S.E. Sig. (P > )
Constant -1.582 0.608 .009
TIME_CENTRE 0.000 0.003 .996
FHH_DEFACTO 0.613** 0.272 .024
AGESQ 0.000** 0.000 .011
SCHOOLYRS 0.110** 0.039 .005
CWRATIO 0.248** 0.121 .040
BUYMAIZE -0.100*** 0.035 .005
FARMER 0.691** 0.286 .016
LAND_PADULT -0.122** 0.058 .034
LAND_PCAPITA -0.280** 0.136 .04005/01/23 9
PSM results
Matching algorithm
Mean standardized bias Sample size on common supportBefore
matchingAfter matching
Caliper (bandwidth 0.01)
12.1 6.5 267
Kernel (bandwidth 0.06)
12.1 13.4 276
Nearest neighbor with replacement (k=1)
12.1 14.5 276
Nearest neighbor without replacement (k=1)
12.1 18.6 182.5 .6 .7 .8 .9
Propensity Score
Untreated Treated
05/01/23 10
Treatment effects on treated
Variable Sample Treated Control Difference Z P > z
INCOME_PCAP ATT 46,801 49,975 -3,174
(18,922)
-0.57 0.571
INCREASE_ASSETS ATT 28,204 39,332 -11,128
(9,169)
-1.28 0.200
SCHOOL_FEES ATT 34,832 49,177 -14,334
(27,063)
-1.70* 0.090
CHANGE IN ECONOMIC CONDITION
ATT 0.85 0.66 0.18
(0.070)
2.39** 0.017
SELL SORGHUM AS COPING STRATEGY IN DROUGHT
ATT 0.51 0.31 0.21
(0.078)
2.03** 0.042
05/01/23 11
How inclusive….?
Group members more likely to be older, full-time farmers, from households headed by women, with higher dependency ratios, and less land per adult member of the household…
The business model is inclusive because poorer households have fewer alternative opportunities for cash income
Better-off households don’t join because they have more opportunities to earn cash income, and less time to attend group meetings
05/01/23 12
How beneficial...?
Average annual income from sorghum ($116)
No significant differences in income per capita or value of assets bought since 2009
Significant differences in perceived improvement in economic condition since 2009, and in selling sorghum as a coping strategy, increasing resilience to climatic shocks.
Sellers spent significantly less than non-sellers on school fees ($400 compared to $565)
But two-thirds of sellers ranked expenditure on school fees and materials as most important use of sorghum income.
Benefits from sorghum are being invested in human capital05/01/23 13
How easy to scale out...?
The average annual sales volume per Household to smart logistics (430kg)
Low Profit margins (1-2 US cents/kg)
Erratic supply: sales fall in drought years as households prioritize food security
Smart Logistics reaches about 3,000 growers
In 2012,Kenya’s sorghum growers supplied only 8,000 t of the 24,000 required by EABL
05/01/23 14
Preliminary conclusions
Domestic consumer markets like sorghum beer provide opportunities for smallholders in semi-arid areas
Poorer households can participate
Benefits invested in human capital
Low yields, small sales volumes, drought, limit the scope for scaling out
05/01/23 15