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

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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

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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?

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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

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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

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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

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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

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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

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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

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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

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