1. 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
Workshop on Gender and Agriculture: A focus on Bangladesh
18 June 2014, Lakeshore Hotel, Gulshan, Dhaka

3. 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

4. Map of study area

5.  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?

6. Photo taken by Akram Ali, CARE Bangladesh

7. 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

8. 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?
 

9. 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

10. Impacts on consumption, dairy outcomes,
and assets

11. 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

12. 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

13. Baseline asset ownership in participant
households was mostly in the form of livestock
0.00
10,000.00
20,000.00
30,000.00
40,000.00
50,000.00
60,000.00
Livestock assets Non-livestock assets Total household assets
Value of assets owned among participant HHs at baseline
(Taka)

14. Baseline descriptives on sex-disaggregated
livestock ownership in participant households
0
0.5
1
1.5
2
2.5
3
Cows Goats Poultry
Number of livestock owned among
participant HHs at baseline
Husband Wife Joint
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
Cows Goats Poultry
Value of livestock owned among participant
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

15. 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
0.00
500.00
1,000.00
1,500.00
2,000.00
2,500.00
3,000.00
3,500.00
4,000.00
4,500.00
Consumer
durables
Jewelry Ag prod Non-ag prod
Value of non-livestock assets owned among
participant HHs at baseline (Tk)
Husband Wife Joint
0
10
20
30
40
50
60
70
Land
Area of land owned
among participant
HHs at baseline
(decimals)
Husband Wife Joint

16. 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 –3,796.393 603.722 1,911.730
Goats 320.328* 199.594 –62.991 51.148
Poultry 23.078 23.622 0.522 –14.648
Propensity-weighted ANCOVA regressions; *p<0.10, ** p < 0.05, *** p<0.01

17. 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

18. 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

19. Impacts on decisionmaking and mobility
Photo credit: Akram Ali, CARE-Bangladesh

20. Some positive impacts on women’s role in dairy
decisionmaking
Outcome Husband Wife Other 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 –0.014
Decision to give milk
to children
0.059 –0.055 0.008** –0.009

21. 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

22. There were additional impacts on mobility, particularly
in relation to value chain services
Who 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

23. 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

24. 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

25. Impacts on time allocation

26. 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)

27. 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
0 10 20 30 40 50 60 70
Total dairy
Total household
Total dairy & household
Women Men Girls Boys

28. 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