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![Page 1: CARE GAAP presentation](https://reader035.fdocuments.us/reader035/viewer/2022062513/55620e35d8b42acb1e8b49a7/html5/thumbnails/1.jpg)
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
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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
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Map of study area
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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?
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Photo taken by Akram Ali, CARE Bangladesh
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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
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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?
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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
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Impacts on consumption, dairy outcomes, and assets
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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
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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
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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)
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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
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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
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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
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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
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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
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Impacts on decisionmaking and mobility
Photo credit: Akram Ali, CARE-Bangladesh
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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
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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
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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
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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
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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
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Impacts on time allocation
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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)
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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
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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