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CARE GAAP presentation

  1. 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
  2. 2. 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
  3. 3. Map of study area
  4. 4.  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?
  5. 5. Photo taken by Akram Ali, CARE Bangladesh
  6. 6. 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
  7. 7. 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?  
  8. 8. 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
  9. 9. Impacts on consumption, dairy outcomes, and assets
  10. 10. 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
  11. 11. 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
  12. 12. 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)
  13. 13. 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
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. Impacts on decisionmaking and mobility Photo credit: Akram Ali, CARE-Bangladesh
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. Impacts on time allocation
  25. 25. 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)
  26. 26. 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 dairy & household Women Men Girls Boys
  27. 27. 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