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Using Dairy Hubs to Improve Farmers’ Access to Milk 
Markets in Kenya: Gender and its Implications 
Omondi, I., Zander, K....
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 
li...
4 
Background 
Compared to their male 
counterparts, women: 
• Make crucial contribution in 
agriculture and rural 
develo...
Background … (contd.) 
5 
In dairy sector, 
• A major socio-economic pillar in Sub-saharan Africa (Mubiru 
et al. 2007) 
•...
Background … (contd.) 
Analysis of factors affecting participation in dairy hubs 
6 
FARMERS 
TRANSPORTERS 
FIELD DAYS 
FE...
7 
Methodology
8 
Study Area
9 
Sampling method and design 
Structured Household Interviews 
301 Households 
• Household socio-economic data collected ...
10 
Analysis 
Logit regression 
i i i Y   x  e *  ~Logistic(0,1) 
 
1 0 * 
 
 
 
 
 Y max{ 0, z} i  
Yi i '  ...
Analysis … contd 
11 
Censored tobit regression 
iid 
Y *   x  e  | x N (0,  
2 ) i i i i i 
~* * 
 
 Y max{ 0, z} ...
12 
RESULTS AND 
IMPLICATIONS
13 
Results 
The results indicate: 
• Relatively low participation of women in dairy hubs 
• Female household heads: 
• Ar...
Table 1: Determinants of Sale of Milk to the dairy hubs 
Dependent Variable: selling milk to dairy hubs 
Independent Varia...
Table 2: Determinants of Volume of Milk Sold to the Dairy Hubs 
Dependent Variable: proportion of total daily milk sales t...
16 
Discussion and Implication 
The results reveal strong evidence of: 
• Women’s apparent reluctance to participate in da...
17 
Discussion and Implication 
The results reveals a gender puzzle that: 
• Underscores the importance of intra-household...
Acknowledgements 
This work is financed by: 
• International Livestock Research Institute (ILRI) 
• German Academic Exchan...
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Using dairy hubs to improve farmers’ access to milk markets in Kenya: Gender and its implications

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Presented by Immaculate Omondi, Kerstin Zander, Siegfried Bauer and Isabelle Baltenweck at the Tropentag 2014: Bridging the Gap between Increasing Knowledge and Decreasing Resources Workshop, Prague, Czech Republic, 17-19 September 2014

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Using dairy hubs to improve farmers’ access to milk markets in Kenya: Gender and its implications

  1. 1. 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. 2. 2 Presentation scope Background Methodology Results and Implications
  3. 3. 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. 4. 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
  5. 5. 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
  6. 6. Background … (contd.) Analysis of factors affecting participation in dairy hubs 6 FARMERS TRANSPORTERS FIELD DAYS FEED SUPPLY VILLAGE BANKS AI & EXTENSION HARDWARE SUPPLIERS
  7. 7. 7 Methodology
  8. 8. 8 Study Area
  9. 9. 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. 10. 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
  11. 11. 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. 12. 12 RESULTS AND IMPLICATIONS
  13. 13. 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
  14. 14. 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) 14 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
  15. 15. 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 15 * an average day in July/Aug 2010 *, ** indicate significance at 5% and 1%, respectively Robust standard errors are indicated in parenthesis
  16. 16. 16 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
  17. 17. 17 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
  18. 18. 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

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