Women’s Participation in the Dairy Value Chain in Tanzania and Kenya:

Benefits, Constraints and Opportunities Elizabeth Waithanji

A Presentation at The 7th African Dairy Conference and Exhibition,

Dar es Salaam, May 25th – 27th 2011 1

Outline

• Introduction

• Patterns of Dairy cattle ownership

• Dairy Marketing – Women’s Participation in Marketing

– Income management and intra-household decision making

– Women’s livestock ownership and household food security

• Opportunities and challenges in increasing women’s benefits from markets

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3

Introduction

• Livestock are productive assets as livestock and livestock products contribute to food and income security of the rural poor

• 70% of the rural poor are women (DFID 2000)

• Livestock are among the few assets that women can own

• Even where women do not control livestock, they may control products e.g. Milk

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Introduction

• Money from sales of livestock and their products constitute a most important income source for women

• Women livestock owners are more constrained than men because women have limited

– decision making powers

– access to and ownership of capital and assets

– access to information and marketing opportunities

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Introduction • Women’s control of household income from sales

is often challenged because they – are more likely to sell in informal local markets – pay higher costs than men to access information – pay male intermediaries to have some things done

• Women are, therefore, relegated to less profitable positions of small scale retailers of perishable goods in local village markets (Escola 2005)

• Because women play a key role in making choices on household food consumption, diet quality and intra-household allocation, women’s status within the household is key if good food security choices are to be made

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Objectives of Study

• To investigate the gendered patterns of dairy cattle ownership, milk marketing, dairy income management, and opportunities and constraints to milk marketing for women in Kenya and Tanzania

– To add onto the scarce information on the gender

asset gap in dairy in these two countries – To identify potential ways of closing the gender asset

gap as productivity increases in the dairy, and hopefully, other livestock value chains in both these countries

– To initiate a conversation about, and commitments towards closing the gender asset gap in dairy 7

Methodology

• The study was conducted in 5 districts in Tanzania (Kilombero, Kibaha, Gairo, Mvomero and Morogoro); and 4 districts in Kenya (Kiambu, Kajiado, Meru and Tharaka) – Factors considered in sample collection were

agricultural potential, production systems and access to markets

• Both quantitative and qualitative data were collected – Quantitative – Household survey questionnaires with a

module for household head and for female spouse in male headed households. Data analyzed statistically.

– Qualitative – gender disaggregated FGDs whereby tools such as ranking, rating and market chain maps were used. Data analyzed inductively.

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Results and Discussion

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Dairy Cattle Ownership by HH Headship

In Tanzania, FHH owned 54.5% of the number of cattle owned by MHH

0

1

2

3

4

5

6

Male headed Female headed

Dairy Cattle Numbers -Tanzania

In Kenya, FHH owned 78.2% of the number of cattle owned by MHH

0

0.5

1

1.5

2

2.5

3

Male headed Female headed

Dairy Cattle Numbers - Kenya

•The difference in numbers of dairy cattle in Tanzanian and Kenyan households might be associated with the difference in the marketing systems in these two countries •Existence of more commercialized dairy marketing systems in Kenya might explain the narrowed gap – in terms of cattle numbers owned – between MHH and FHH

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Who Markets milk Where Tanzania

0%

20%

40%

60%

80%

100%

120%

From home to other farmers Delivered to traders/shops/hotels

Men Women Joint

Kenya

0

10

20

30

40

50

60

70

Farmgateto farmers

Farmgateto traders

Delivery totraders

Villagemarket

Collectioncentre

Chillingplant

Men Women Joint

•Fewer milk-market options exist for both women and men in Tanzania than in Kenya •Women’s involvement in markets beyond the farm gate declined remarkably in both countries •Deliveries outside the farm gate are mainly done by men alone or jointly by men and women

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What determines market participation by women in Kenya

Milk

Variables Coef. z P>z

Price of eggs 0.001 -3.11 0.002

Belong to group=1 -0.501 -6.17 0.000

Age 0.000 -0.01 0.990

Transport asset 0.008 0.25 0.806

Household size -0.036 -3.81 0.000

Primary education 0.124 1.56 0.120

Secondary education 0.158 1.83 0.068

College education 0.254 2.32 0.020

Sold from home to

traders 0.038 1.05 0.294

Delivered to traders 0.081 1.5 0.133

Sold to city markets 0.136 1.43 0.153

Constant 1.469 7.99 0.000

/sigma 0.173 19.18 0.000

Eggs Variables Coefficien

t

z P>z

Price of milk 0.014 3.930 0.000

Belong to group=1 0.114 -2.510 0.012

Age -0.003 -2.060 0.040

Transport asset 0.017 0.530 0.596

Household size -0.011 -1.400 0.161

Primary education -0.093 -1.590 0.113

Secondary education -0.077 -1.210 0.225

College education -0.104 -1.170 0.242

Sold from home to

traders

0.160 3.580 0.000

Delivered to traders 0.167 3.070 0.002

Sold in village market -0.012 -0.090 0.932

Constant 0.618 4.500 0.000

/sigma 0.108304

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11.49 0

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Proportion of Milk Income Controlled by Men, Women, and Jointly in Kenya and Tanzania

• Most (63%) of the milk income was controlled jointly in both countries

• In Tanzania, women controlled more (31%) of the remaining income than men

• In Kenya, men controlled more (21%) of the remaining income than women

• This gendered difference in milk income control in both countries could be attributed to the fact that milk production in Kenya is more commercialized and markets more formalized than in Tanzania

0

10

20

30

40

50

60

70

Men Women Joint

Kenya Tanzania

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Decision Making on Livestock and their products

Tanzania - number and amount of animals and products to keep

Kenya - number and amount of animals and products to keep

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Decision Making on use of Income from Sales of Livestock and their Products

Tanzania- use of money from sale of livestock and livestock products

Kenya - use of money from sale of livestock and livestock products

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Patterns of Decision Making

• In both countries, women made sole decisions more than men for milk and poultry products and the income accrued from these

• If a woman can make a decision on what commodity to keep and how much to produce, then she is most likely to have control over income from the sale of that commodity

• This finding suggests that an intervention in dairy and poultry is likely to have more women benefitting than an intervention in other livestock value chains

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Women’s Livestock Ownership and Household Food Security

Influence of women's livestock ownership on HDDS and MIHFP in Tanzania

HDDS MIHFP

HH where

women

own

livestock

HH where

women do

not own

livestock

T-values

HH where

women

own

livestock

HH where

women do

not own

livestock

T-values

Dairy cattle 0.69 0.55 1.44

(0.151) 11 8.77 3.67 (0.047)

Exotic chicken 0.58 0.55 2.8 (0.006) 11.5 8.77 5.08 (0.077)

Local chicken 0.63 0.55 0.92

(0.365) 8.84 8.67

0.416

(0.679)

Goats 0.51 0.56 0.35

(0.781) 8.5 8.83 0.51 (0.617)

Influence of women's livestock ownership on HDDS and MIHFP in Kenya

HDDS MIHFP

HH where

women own

livestock

HH where

women do not

own livestock

t-values HH where

women own

livestock

HH where

women do not own

livestock

t-values

Dairy cattle 0.73 0.65 3.105*** 4.3 5.8 2.272**

Exotic chicken

0.82 0.66 4.376*** 3.7 5.5 1.689

Local chicken 0.71 0.66 2.118** 5.3 5.4 0.242

Goats 0.61 0.69 2.564** 5.1 5.4 0.403

HDDS – Household Dietary Diversity Score; MIHFP – Months of Inadequate Household Food Provisioning

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Women’s Livestock Ownership and Household Food Security

• Results on the difference in HDDS were more dramatic in Kenya than Tanzania where the differences were significant between all households where women owned livestock and those where women did not own livestock – Households where women own livestock have access to

more diverse foods

• Results on the differences in MHIFP were significant only in households where women owned dairy cattle in Kenya. The pattern on MIHFP was less clear in Tanzania – When women own dairy cattle, the households are

likely to experience fewer months of food inadequacy 18

Opportunities for Women’s Increased Participation in Dairy Markets

• It appears like with an increase in commercialization of dairy production and market formalization, the inter-household asset gap – in terms of dairy cattle numbers – narrows ( 22.8% in Kenya and 45.5% in Tanzania)

• Intervention in dairy development projects are most likely to benefit women directly since women are able to control production and income obtained from sale of milk.

• For women’s participation in the dairy industry to be sustained, it appears necessary to integrate gender in projects since under the current circumstances, women’s participation diminishes remarkably beyond selling at the farm gate

• Joint milk income control does not appear to be affected by degree of commercialization of production or market formalization: the “jointness” concept needs to be explored and if beneficial for women, it should be exploited during dairy development interventions

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Challenges for Women’s Increased Participation in Dairy Markets

• Commercialization of production and formalizing markets of most farm commodities is associated with marginalization of women from the markets of these commodities – Can milk be an exception? – How?

• Formal markets are mostly located in urban areas and most

farms in rural areas. Women participate poorly beyond the farm gate. Consequently; – “Mainstream” production commercialization and market

formalization interventions are doomed to fail if they do not address gender issues

– and innovative ways of involving rural women in formal farm commodity markets must be established urgently

• The “jointness” concept remains a methodological and

discursive challenge owing to its nuanced as well as fluid nature 20

Acknowledgements

• Participating Tanzanian and Kenyan Farmers

• Data collection Field teams

• ILRI PGI Team

• IDRC

• FORD Foundation

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Thank You

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