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

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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)

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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

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Background … (contd.)

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

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Background … (contd.)

Analysis of factors affecting participation in dairy hubs

TRANSPORTERS

FARMERS

FIELD DAYS

FEED SUPPLY

AI & EXTENSION

VILLAGE BANKS

HARDWARE SUPPLIERS

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Methodology

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Study Area

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Sampling method and design

• Household socio-economic data collected• Farmer characteristics• Farm Characteristics• Participation in dairy hubs• Farmer preferences

Structured Household Interviews

301 Households

Hub Non-member Households (44%)

Hub Member Households (56%)

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Analysis

Logit regression

• is a latent variable indexing adoption• is the observed response for the ith farmer• a vector of explanatory variables

iii exY * )1,0(~ Logistic

niOtherwise

YifiY

i ,,1'01 *

},0max{ zYi

*iY

iY

j x

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Analysis … contd

Censored tobit regression

• is a latent variable indexing adoption• an observable measure of intensity of use• a vector of explanatory variables• c is an unobservable threshold, β is a vector of unknown

parameters, and ε are residuals

iii exY * ),0(| 2~ Nxiid

ii

nicYif

cYifYiY

i

ii ,,1'0 *

**

},0max{ zYi

*iY

iY

j x

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RESULTS AND IMPLICATIONS

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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

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Table 1: Determinants of Sale of Milk to the 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 - advanced) 4.41* (1.79)

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

Dependent Variable: selling milk to dairy hubs

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Table 2: Determinants of Volume of Milk Sold to the Dairy 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

* an average day in July/Aug 2010 *, ** indicate significance at 5% and 1%, respectively Robust standard errors are indicated in parenthesis

Dependent Variable: proportion of total daily milk sales to hubs

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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

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

Acknowledgements