Using Dairy Hubs to Improve Farmers’ Access to Milk
Markets in Kenya: Gender and its Implications
Omondi, I., Zander, K., BauerBaltenweck, I. Siegfried 2, Kerstin 3
Tropentag 2014: Bridging the Gap between Increasing Knowledge and Decreasing Resources,
Prague, Czech Republic, 17-19 September 2014
Photo: ILRI and eadairy

2
Presentation scope
Background
Methodology
Results and Implications

3
Background
Control of productive assets has a direct impact on:
• Men, women, boys and girls forge life enhancing
livelihood strategies (WB-FAO-IFAD 2009)
Men and women have different access to markets,
infrastructures and related services (WB-FAO-IFAD 2009)
Rural women face obstacles in access to resources
• These hinder their adoption of new technologies or
increasing economies of scale (Korinek 2005)

4
Background
Compared to their male
counterparts, women:
• Make crucial contribution in
agriculture and rural
development in all developing
countries
• Yet, they face more severe
constraints in accessing
productive resources, markets
and services (FAO 2011) Photo: eadairy

Background … (contd.)
5
In dairy sector,
• A major socio-economic pillar in Sub-saharan Africa (Mubiru
et al. 2007)
• Women contribute substantial labor to dairy enterprise
activities (Abdulai and Birachi 2009)
•Consequently, in pro-poor development efforts
• It is important to understand the challenges facing women
in dairy

Background … (contd.)
Analysis of factors affecting participation in dairy hubs
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FARMERS
TRANSPORTERS
FIELD DAYS
FEED SUPPLY
VILLAGE BANKS
AI & EXTENSION
HARDWARE SUPPLIERS

7
Methodology

8
Study Area

9
Sampling method and design
Structured Household Interviews
301 Households
• Household socio-economic data collected
• Farmer characteristics
• Farm Characteristics
• Participation in dairy hubs
• Farmer preferences
Hub Non-member
Households (44%)
Hub Member
Households (56%)

10
Analysis
Logit regression
i i i Y   x  e *  ~Logistic(0,1)

1 0 *




 Y max{ 0, z} i 
Yi i '  1, 
,
i n
if Y
Otherwise
• is a latent variable indexing adoption
• is the observed response for the ith farmer
• a vector of explanatory variables
*
i Y
Yi
x j

Analysis … contd
11
Censored tobit regression
iid
Y *   x  e  | x N (0, 
2 ) i i i i i
~* *

 Y max{ 0, z} i 
i i '  1, 
,
0 *
i n



Y if Y c
if Y c
Yi
i


• Y
*
is a latent variable indexing adoption
i • Yi
an observable measure of intensity of use
• x j
a vector of explanatory variables
• c is an unobservable threshold, β is a vector of unknown
parameters, and ε are residuals

12
RESULTS AND
IMPLICATIONS

13
Results
The results indicate:
• Relatively low participation of women in dairy hubs
• Female household heads:
• Are older, with more years of farming experience, than their
male counterparts;
However,
• They are worse off in education, household size, level of
education among adults in the households, and number of
income sources

Table 1: Determinants of Sale of Milk to the dairy hubs
Dependent Variable: selling milk to dairy hubs
Independent Variables Coefficient
Total milk sold by household to all channels per day 0.62** (0.13)
Household keeps exotic cattle (level of intensification -
4.41* (1.79)
advanced)
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Household keeps cross cattle (level of intensification -
emerging)
4.30* (1.92)
Household not registered in milk marketing hub) -3.01** (0.95)
Household heads years of farming experience -0.07* (0.03)
Female-headed household (Gender of household head) 2.79* (1.25)
Household size (number of household members) 0.36* (0.16)
Female household member deciding on where to sell milk -2.46* (1.23)
χ2=166.36 20df, p=0.00 log likelihood = -39.92 pseudo-R2 = 0.68
* an average day in July/Aug 2010 *, ** indicate significance at 5% and 1%, respectively
Robust standard errors are indicated in parenthesis

Table 2: Determinants of Volume of Milk Sold to the Dairy Hubs
Dependent Variable: proportion of total daily milk sales to hubs
Independent Variables Coefficient
Gender: female-headed household 0.65** (0.19)
Decision on milk sales channel: made by male 0.51** (0.15)
Decision on milk sales channel: joint male & female 0.36* (0.17)
Household not registered in EADD hub -0.74** (0.14)
Joint hub membership (both head and spouse) 0.52* (0.24)
Education: head's years of schooling 0.04* (0.02)
Household size 0.07* (0.03)
Level of intensification: keeping exotic cattle 0.52** (0.18)
Level of intensification: keeping cross cattle 0.58** (0.21)
Milk production per day 0.01* (4.7E-3)
χ2=128.44, 20df, p=0.00 log likelihood = -111.62 pseudo-R2 = 0.38
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* an average day in July/Aug 2010 *, ** indicate significance at 5% and 1%, respectively
Robust standard errors are indicated in parenthesis

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Discussion and Implication
The results reveal strong evidence of:
• Women’s apparent reluctance to participate in dairy hubs
• Arguably, due to loss of control of income from milk sales
Why participate in dairy hubs?
• Comparatively high economic endowment
• Evidenced from a propensity matching analysis
• Hub participation increased the annual cash income from sale
milk

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Discussion and Implication
The results reveals a gender puzzle that:
• Underscores the importance of intra-household
distribution of income
• Needs to be surmounted
• To ensure dairy households accrue the
benefits of collective marketing
Gender issues in the study area
• Are culturally deep-rooted
• Require careful, evidence-based approaches
Photo: eadairy

Acknowledgements
This work is financed by:
• International Livestock Research Institute (ILRI)
• German Academic Exchange Services (DAAD)
It contributes to:
• The CGIAR Research Program on livestock and Fish