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 conclu...
Drivers of demand for beer
Consumer power : Africa’s growing middle
class (313 million or 34% of the population
(ADB, 2011...
The beer market in Kenya
• East African Breweries (EABL)
(Diageo plc 51 %) has 93 % of the
market
• Strong market growth s...
The Smart Logistics business model
• Smart Logistics Solutions,
Kenyan-owned, founded
2009, contract with EABL
• Buys from...
Data and methods
Data
•A household survey in Kitui county,
semi-arid eastern Kenya .
•High poverty levels (64%) with
frequ...
Specification
Group membership
Distance to collection centre
Age
Gender
Education
Consumer/worker ratio
Household food sec...
Socio-economic profile
Variables Sellers
(n=198)
Non-sellers
(n=99)
Sig
(P value)
Members of SL groups 127 71 .000
Househo...
Decision to join SL group
Variable Coefficient S.E. Sig. (P > )
Constant -1.582 0.608 .009
TIME_CENTRE 0.000 0.003 .996
FH...
PSM results
Matching
algorithm
Mean standardized bias Sample size on
common
support
Before
matching
After matching
Caliper...
Treatment effects on treated
Variable Sample Treated Control Difference Z P > z
INCOME_PCAP ATT 46,801 49,975 -3,174
(18,9...
How inclusive….?
Group members more likely to
be older, full-time farmers, from
households headed by women,
with higher de...
How beneficial...?
Average annual income from sorghum ($116)
No significant differences in income per capita
or value of a...
How easy to scale out...?
The average annual sales volume per
Household to smart logistics (430kg)
Low Profit margins (1-2...
Preliminary conclusions
Domestic consumer markets like
sorghum beer provide opportunities
for smallholders in semi-arid ar...
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creating pro-poor value chains

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creating pro-poor value chains

  1. 1. Alastair Orr (ICRISAT) Catherine Mwema (ICRISAT) Wellington Mulinge (KARI)
  2. 2. What’s ahead…. • Why sorghum beer? • The Kenya beer market • The business model • Data and methods • Results • Some conclusions
  3. 3. 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 11/17/15 3
  4. 4. 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 11/17/15 4
  5. 5. 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? 11/17/15 5
  6. 6. 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). Methods Sellers 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 11/17/15 6
  7. 7. Specification Group membership Distance to collection centre Age Gender Education Consumer/worker ratio Household food security Occupation Farm size Sorghum sale Distance to market Qty maize production Qty sorghum production Dummy if household buys sorghum Predicted value of group membership 11/17/15 7
  8. 8. 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 .71211/17/15 8
  9. 9. 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 .04011/17/15 9
  10. 10. PSM results Matching algorithm Mean standardized bias Sample size on common support Before matching After 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 11/17/15 10
  11. 11. 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 11/17/15 11
  12. 12. 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 11/17/15 12
  13. 13. 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 capital11/17/15 13
  14. 14. 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 11/17/15 14
  15. 15. 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 11/17/15 15

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