Can dairy value chain projects change gender norms in rural

Bangladesh? Lessons from the CARE-Bangladesh

Strengthening the Dairy Value Chain Project

Agnes R. Quisumbing

Shalini Roy

Jemimah Njuki

Kakuly Tanvin

Elizabeth Waithanji

Overall objective of the SDVC project

Goal: Double the dairy-related incomes of smallholder farmers in northwest Bangladesh by addressing the major challenges to improving smallholder participation in the value chain by

• Mobilizing farmers through formation of small holder dairy farmer groups

• Building capacities of selected farmer group leaders, dairy collectors, livestock health workers, AI workers

• Increasing access to milk markets and productivity enhancing inputs

Targeted Beneficiaries: 36,400 smallholder dairy farmers of NorthWest Bangladesh

• with weak dairy value chains

• prone to natural disasters such as floods

• functionally landless (less than 0.5 acres of cultivable land)

• earning about USD 20 – 30 equivalent per month

Map of study area

Women traditionally have responsibility for dairy cows Many SDVC dairy farmers, farmer group leaders,

value chain actors and service providers are women (85 percent of the 36,400 producers; 71 percent of the 3425 farmer group leaders; 22 percent of 201 livestock health workers ,9 percent of the 333 trained milk collectors and 52 AI workers)

Deliberate effort to increase women’s representation in nontraditional dairy activities (livestock health workers)

Training directed to women dairy producers, farmer leaders; formation of savings groups 

Setting up of milk collection points within the village

How did SDVC take gender into account?

Photo taken by Akram Ali, CARE Bangladesh

Study Design Longitudinal quant impact evaluation (2008 and 2012);

propensity weighted regressions

Based on household survey with detailed questions on gender and assets

• Treatment group

• Control: same unions (with chilling plant) but not SDVC area

Qualitative research on gender related topics including ownership and control over agricultural assets

Study sample selected from Phase 1 (early) beneficiaries; program has subsequently been modified and so our results don’t reflect program modifications

Key Questions

Questions Quant Qual

Did the SDVCP increase women’s and/or men’s ownership of assets? What types of assets?

Did increases in some types of assets change gender norms around ownership/control of those assets?

Did participation in specific nodes of the dairy value chain change gender norms regarding decisionmaking in these areas?

Where there time costs? What were the tradeoffs involved?

Quick summary of results Impacts were not felt on expenditures and most dairy-related

outcomes, but on assets, their composition, and ownership (if you weren’t looking for it, you wouldn’t find this impact!)

There was some indication of increases in women’s asset ownership, but through joint ownership. Control of dairy animals and income from dairy still mostly male

There is some indication that women’s decisionmaking and mobility increased, around points of involvement with dairy value chain

Most of the time burden of dairying was borne by adult women, with time possibly diverted from child feeding and care

Impacts on consumption, dairy outcomes, and assets

Outcome variables Impacts relative to nonparticipants in unions with chilling plants

Consumption outcomes

Household consumption expenditures (tk) 215.66

Monthly household nonfood expenditure (tk)

138.04

Monthly household food expenditure (tk) 70.34

Impacts of the project on consumption were not significant

Propensity-weighted ANCOVA regressions; *p<0.10, ** p < 0.05, *** p<0.01

Limited impact on dairy outcomes, but there was increased formal market channel participation

Outcome variables Impacts relative to nonparticipants in unions with chilling plants

Proportion owning cows 0.05

Proportion producing milk 0.06

Proportion selling milk 0.02

Milk production (liters/hh/day) -0.96

Share with crossbred cows -0.06

Ln (value of cows) -0.02

Productivity per cow (hhs owning cows)

-0.46

Whether household sold milk in formal sector

0.24***Propensity-weighted ANCOVA regressions; *p<0.10, ** p < 0.05, *** p<0.01

Baseline asset ownership in participant households was mostly in the form of livestock

Livestock assets Non-livestock assets Total household assets

0.00

10,000.00

20,000.00

30,000.00

40,000.00

50,000.00

60,000.00

Value of assets owned among participant HHs at baseline (Taka)

Baseline descriptives on sex-disaggregated livestock ownership in participant households

Cows Goats Poultry0

0.5

1

1.5

2

2.5

3

Number of livestock owned among participant HHs at baseline

Husband Wife Joint

Cows Goats Poultry0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Value of livestock owned among partic-ipant HHs at baseline (Taka)

Husband Wife Joint

Although women tended to perform dairy maintenance / milking…

• Men tended to own more cows (high-value livestock)• Women tended to own more poultry (low-value livestock)• Considerable joint ownership of all livestock assets

Baseline descriptives on sex-disaggregated non-livestock ownership in participant households

• Men tended to own more consumer durables, agricultural and non-agricultural productive assets, and land

• Women only tended to own more jewelry• Considerable joint ownership of all non-livestock assets except land

Consumer durables

Jewelry Ag prod Non-ag prod0.00

500.001,000.001,500.002,000.002,500.003,000.003,500.004,000.004,500.00

Value of non-livestock assets owned among participant HHs at baseline (Tk)

Husband Wife Joint

Land0

20406080

Area of land owned among participant HHs

at baseline (dec-imals)

Husband Wife

Joint

Weak or insignificant program impacts on livestock assets, with small magnitudes

Household

Male Female Joint

Livestock holdings (number)

Cattle –0.169 0.072 –0.039 –0.252

Goats 0.213* 0.086 –0.002 0.029

Poultry –0.332 0.110 –0.237 –0.206

Livestock holdings (value)

Cattle –431.163 –3,796.39

3603.722 1,911.730

Goats 320.328* 199.594 –62.991 51.148

Poultry 23.078 23.622 0.522 –14.648Propensity-weighted ANCOVA regressions; *p<0.10, ** p < 0.05, *** p<0.01

Weak impacts on non-livestock assets, but of fairly large magnitude, suggesting joint income diversification outside dairy

Household

Male Female Joint

Agricultural productive assets (Tk)

1,303.246* 940.329 183.395 –95.315

Nonagricultural productive assets (Tk)

452.581* 253.683 60.187 127.737**

Consumption assets (Tk)

4,874.666 347.580 70.948 485.543

Jewelry (Tk) 3,401.685 1,625.968 –19.080 1,365.358

Land (decimals) 7.646 6.916 0.479 –0.183

Findings from qualitative work among program participants

The intervention resulted in an increase in assets owned by HH

Cattle were the main asset that increased owing to increase in milk income (note: different from quantitative work)

Assets mainly controlled by men

Joint assets purchased and controlled jointly, but men’s decisions take higher priority than women’s and their decisions are final

Women unlikely to inherit land, most women believe that they should but the Hindu law prevents them from inheriting

Impacts on decisionmaking and mobility

Photo credit: Akram Ali, CARE-Bangladesh

Some positive impacts on women’s role in dairy decisionmaking

Outcome Husband WifeOther male

Other female

Decision to buy a cow –0.001 0.020 0.009 –0.008

Decisions on dairy-related expenses (feed, livestock)

–0.033 0.055** 0.013 –0.018

What type of feed to provide –0.081 0.103** 0.005 –0.022

Whether to provide vaccinations 0.003 0.016 0.015* –0.031

Where to purchase inputs and services –0.017 0.037* 0.013 –0.030

How to use income from dairy sales –0.047 0.067 0.004 –0.020

Decision to sell milk 0.030 0.000 –0.002 –0.014Decision to give milk to children 0.059 –0.055 0.008** –0.009

Impacts on non-dairy decisionnmaking

Program did not affect who decided on most categories of household expenditures

Program increased the proportion of households in which both the woman and her husband were primary decisionmakers on whether to take a loan, or in which women participated in the decision to take the loan

There were additional impacts on mobility, particularly in relation to value chain servicesWho decides whether woman can go by herself to:

She herself Husband Both

Another person

She participates (solely or

jointly)NGO training outside community

0.021 0.006 0.105** 0.008** 0.126*

NGO training in community

0.041 0.025 0.074 0.006* 0.114

Milk collection point outside community

–0.023 0.047 0.057 0.014** 0.033

Visit livestock health worker

–0.051 0.049 0.084 0.011** 0.033

Friends outside the community

–0.028 –0.138 0.003 0.003 0.138

The bazaar or market –0.052 0.036 0.013** 0.013** 0.063

Hospital/clinic/doctor 0.010 –0.100 0.007* 0.007* 0.071

Cinema/fair/theater –0.023 0.032 0.005* 0.005* 0.029

Insights from qualitative work Culture of seclusion determined who sold milk from

where – women sold milk mainly from home and men delivered milk to the market

Other factors that determined who controlled income from milk were who received the money, how much money and the intended expenditure purpose of the money

Generally women received less money, and controlled money for smaller investments than men

Impacts on mobility

Quant: Greater acceptance of women’s going to places related to value chain in program areas (input dealers, milk collection points, whether inside or outside the village)

Qual: Women’s seclusion determined their engagement in training and the type of training they received

Women were more involved in the training if it was conducted at or near home

Women were trained more than men in activities that could be conducted at home (e.g. production), whereas men were trained in activities that could be conducted outside the home (e.g. marketing – milk collection and transportation)

Owning skills in disease control enhanced women’s mobility

Impacts on time allocation

Impacts on time allocation Adult women appear to increase time on dairy activities (e.g.,

cleaning of milking area, taking animals for AI), decrease time on household activities (including child feeding and care)

Adult men and young boys appear to somewhat increase time to dairy activities as well

Young girls appear to somewhat increase time to household activities but not enough to compensate decrease in adult women’s time

 Household overall

Adult Women

Adult Men

Young Girls

Young Boys

Weekly hours in past 30 days

         

Feeding young children -1.225* –1.347** 0.037 0.083** 0.002  (0.675) (0.671) (0.024) (0.039) (0.002)Looking after young children -1.612* –1.574* 0.079 –0.119 0.003  (0.824) (0.835) (0.057) (0.249) (0.003)

Cooking-0.479 –0.913 0.132**

0.315***

–0.014

  (1.011) (1.004) (0.066) (0.115) (0.052)

Impacts on time allocation In absolute terms, adult women still contribute the largest

amount of time in the household to both dairy-related and household maintenance activities.

Results suggest that adult women are likely to experience disproportionate time burden from program participation, diverting time from household activities such as child feeding and care

Total weekly hours over dairy and household activities in the past 30 days at endline

Total dairy

Total household

Total dairy & household

0 10 20 30 40 50 60 70

Women Men Girls Boys

Main messages

• Overall value of assets not changed

• However, apparent reallocation of asset portfolio toward agricultural and non-agricultural productive assets

• The gender asset gap still persists, although there is an increase in joint assets.

• Gender norms regarding mobility and decisionmaking are changing around some value chain activities

• Decisionmaking is still mostly male, particularly around higher-return activities (involving cash)

• Most of the time burden of dairy activities is borne by adult females, with possible unintended consequence of reducing time for child feeding and care