Land O Lakes GAAP Presentation January 2013


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Presentation given by Land O Lakes at GAAP final technical workshop in Addis Ababa, January 2013

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  • Check differences in comparison groups
  • Will draw on qual and quant in each section
  • --a few people seem to have it clear.Jointness example (own one together, each own some)
  • Human capital Which hh lost its cow?
  • Emphasize jointowenrshipWomen’s share of assets in non-recipient households dropped dramatically from 2008 to 2011. Need to look more at this to see why.
  • Receiving cattle increases toalhh assets but does not increase disparity. Seems inconsistent with qual results that all cows went to men. If no assets in index, then we see primary trainee female has significant negative relationship with change in total hh assets
  • Need better measures of consumption. Looked at fraction of sales but since people bought before this could actually go down.
  • Both men and women said that the introduction of the dairy cow enabled them to become more diligent planners because of its demand on their time. Men noted that the increase in their workload had necessitated them to employ labourers to attend to other duties as they and their wives attended to the dairy cow. Women had to juggle between cropping and dairying activities. In addition, women noted that specific household members had to be assigned specific chores. Women had to leave very specific instructions, on the cow, to the children and house help before going to tend the crops. Women could no longer stay away from home for long because the cow required them to come back and chop and mix feed, and feed and water it.Women’s activities tied them to the home more than men’s activities, which is typical in almost all societies whereby women’s productive roles are tied to their child rearing and other reproductive activities and performed close to home (Brown 1970). The fact that men could hire labourers and women could not – women juggled chores and delegated to family and existing workers – could suggest that men had more money or accessed money to pay labourers more easily than women. The additional cow-related activities that took much of the beneficiary time were gendered. For men and women, cutting and chopping grass and mixing this green forage with hay was most time consuming. For men only, selling milk and looking for milk customers took the most time. For women only, fetching water and watering the cows as well as the general care work was a main time-consuming added responsibility. In this division of labour, men were involved in both near and away from home activities more than women who were only involved in near home activities.
  • results consistent with all, all went up but especially women
  • possible drop
  • Both cow and time with cow have positive impact on male labor
  • while they don’t talk about milk productin, it is clear that the majority of production comes from improved cows
  • What is jointness (maybe clear) This question was only asked in 2012. All respondents had cows then. We can’t say how much this changed, but we do know that income is higher now than it was pre-improved cows
  • Number of cows is weighted by hh size.Need to check consistency of these numbers because they seem not to add up.
  • just describe
  • Link to qualitative result on importance of women of training women since they are the ones who do the work ,and produce milk of higher quality
  • Land O Lakes GAAP Presentation January 2013

    1. 1. The gender impacts of the Land O’Lakes -Manica Smallholder Dairy Development Program (MSDDP) —preliminary findings Marinho Nhambeto, Liz Waithanji, Nancy Johnson, Lizz Hutchinson, Martha Rogers, Edna Ogwangi, Mimoso Agostinho GAAP Final Technical Workshop ILRI-Addis Ababa 10 January 2013
    2. 2. Background Funded by United States Department of Agriculture (USDA) Location and duration of program: 2009- 2012, Manica Province, Mozambique Objectives: 1) rebuild Mozambique’s dairy industry and 2) increase incomes for smallholder farmers in a dairy value chain.
    3. 3.  Program distributed 500 improved dairy cows to 327 qualified beneficiary households and trained 2 members per household in dairy management
    4. 4. GAAP ResearchHypothesis/Questions for MSDDP1. What direct and indirect benefits and constraints do women experience in a gender- blind asset distribution program?2. What decision-making roles do women play in the management of dairy cows?3. Who controls the benefits (milk, income) from the project?
    5. 5. Methods1) Qualitative - April 2011, 15 single-gender FGDs with members of 15 of 17 farmer groups - April 2012 - 6 single-gender FGD from 3 dairy associations in the Vanduzi Dairy Cooperative 2011 Focus Group Discussions Average # Groups Average # Groups Participants- without Cows participants- with Cows Cows (Anticipating) AnticipatingFemale FGD 5 6.8 2 9.5Male FGD 6 7.2 2 12
    6. 6. 2) Quantitative analysis - 3 households surveys conducted: April, 2009 (LOL), April-May 2011 (GAAP), April-May 2012 (LOL+GAAP) - Different populations surveyed in each round. For impact evaluation, we focus on at beneficiary households in 2011 and 2012 rounds
    7. 7. Comparison groups For assets we can look at changes between 2008 and 2011 based on recall For all other outcome variables, the comparison groups are: 1) Received cows vs. still waiting 2) Length of time since cow was received (months) 3) Households that had a woman trained 4) Households where woman was primary trainee
    8. 8. Outline of results Assets Food security Dairy production Milk sales and income
    9. 9. Assets• Qualitative focused on understanding asset ownership and control• Quantitative: 2 measures – Total household assets • Non-land asset index – Women’s share of individually-owned assets • Value of asset index for women/(value for men + value for women)—joint asset no included • Higher means less disparity
    10. 10. Qualitative findings• Men and women use same asset categories— domestic, productive, transport• In general, people struggled to differentiate between ownership, control and use• Most people said that in male-headed households, men make decisions – Decision making powering is “bigger” than claim to ownership – But claim to ownership is one factor that may influence decision-making
    11. 11. • Completion of training increased women’s self-esteem and confidence – Their ability to take care of cows was recognized – Joint decision making with husbands was enhanced
    12. 12. Descriptive statistics on assets From 2011 survey, with retrospective asset question 2008 2011 Non Recipients Non Recipients Recipients Recipients 73.84 84.77 80.16 110.96 Household asset index (n=125 ) (100.10) (92.07) (87.96) (115.73) .39 .13 0.17 .18 Women’s share of assets (n= 125 ) (.92) (.43) (.36) (.33) From beneficiaries in 2011 and 2012 surveys Non Recipients Recipients All beneficiary households Mean S.D Mean S.D Mean S.DHousehold asset index 80.16 87.96 100.91 100.20 99.04 99.15Women’s share of assets 0.17 0.36 0.21 0.33 0.20 0.33Number of observations 25 198 223
    13. 13. Determinants of change in total and women’s share of assets, 2008-2011 Change in total HH Change women’s share assets of assetsHH received cattle 16.6760** 18.3263*** 0.3514 0.3065 (6.4327) (6.2075) (0.3012) (0.2754)Primary or secondary dairy trainee wasfemale 2.3529 -0.0882 (5.6049) (0.1345)Primary dairy trainee was female -2.8895 0.1164 (8.1888) (0.1250)Observations 102 102 63 63 Estimates controlled for household and community characteristics
    14. 14. Food security• Participants in qualitative studies perceived that improved family nutrition was a major benefit of the program• 2 quantitative measures – # of months of adequate food provisioning – Dietary diversity index – No pre-project baseline data so we compared early v late beneficiaries in 2011 and 2012 surveys Non-recipients Recipients All beneficiaries Mean S.D Mean S.D Mean S.DMonths of adequate HH food 10.48 2.24 11.18 1.48 11.10 1.59provisioningHousehold dietary diversity score 5.80 2.61 6.42 2.60 6.35 2.60
    15. 15. Determinants of Months of adequate householdfood provisioningHH received cattle 2.0155* 1.9502* (1.0900) (1.0710)Months since HH received first cow 0.0551** 0.0546** (0.0236) (0.0250)Primary or secondary dairy trainee was -0.2868 -0.2302female (0.4058) (0.3578)Primary dairy trainee was female -0.5537 -0.3955 (0.4206) (0.4072)Observations 163 153 163 153Estimates controlled for household and community characteristics
    16. 16. Determinants of dietary diversity scoreHH received cattle 1.8731*** 2.0621*** (0.5473) (0.6050)Months since HH received first cow -0.0421 -0.0469 (0.0517) (0.0517)Primary or secondary dairy trainee was female 0.7773 0.5972 (0.6946) (0.8497)Primary dairy trainee was female 0.1567 -0.0526 (1.0005) (1.0373)Observations 163 153 163 153 Estimates controlled for household and community characteristics
    17. 17. Dairy cow management• Qualitative analysis looked at who does what and who makes decisions• Quantitative analysis focused on total cost and household labor use in dairy
    18. 18. Key qualitative findings• Dairy activities are gendered, with some overlap• “Both men and women said that the introduction of the dairy cow enabled them to become more diligent planners because of its demand on their time.” – Everyone’s responsibilities increased with improved cow – Women may be most affected because they had to stay home more and could not hire help the way men did
    19. 19. • Men traditionally make decisions about cows but “women are developing an interest in improved cows” – Women are gaining authority and decision making power around dairy• At household level women are more concerned with milk quality – Many said the benefit from training related to hygiene
    20. 20. Cost of production and labor for recipients and non recipient households Non-recipients Recipients All beneficiaries Mean S.D Mean S.D Mean S.DTotal dairy costs in last month(MZM) 46.56 101.7 581.0 740.3 539.7 725.8Total HH male labor hours ondairy activities 4.50 10.42 18.48 16.04 16.91 16.11Total HH female labor hours ondairy activities 1.15 3.29 18.13 21.33 16.23 20.83Total HH child labor hours on dairyactivities 3.34 10.05 19.27 19.33 17.49 19.18
    21. 21. Determinants of total dairy costs last month(MZM)HH received cattle 257.432** 246.44*** (98.4012) (82.8502)Primary or secondary dairy traineewas female -84.1539 -93.3024 (185.263) (198.960)Months since HH received first cow 10.6395 12.1344 (12.4131) (12.2075)Primary dairy trainee was female 90.0300 132.6012 (365.883) (363.653)Observations 161 152 161 152Estimates controlled for household and community characteristics
    22. 22. Determinants of household male labor (hours spent on dairy)HH received cattle 11.4581** 11.3406** (4.7583) (4.9038)Primary or secondary dairy trainee wasfemale -0.3805 1.2055 (3.5829) (3.7896)Months since HH received first cow 0.4406** 0.4577** (0.1817) (0.1929)Primary dairy trainee was female 2.7140 4.4576 (7.2095) (6.4491)Observations 163 153 163 153Estimates controlled for household and community characteristics
    23. 23. Determinants of household female labor (hours spent on dairy)HH received cattle 17.991*** 17.341*** (6.2307) (6.4795)Primary or secondary dairy trainee wasfemale -2.7002 2.3768 (4.8822) (3.9246)Months since HH received first cattle 1.8959*** 1.9117*** (0.3022) (0.3163)Primary dairy trainee was female -1.3249 5.7621 (10.7911) (9.3753)Observations 163 153 163 153Estimates controlled for household and community characteristics
    24. 24. Determinants of household child labor (hours spent on dairy)HH received cattle 14.092*** 14.130*** (4.6111) (4.7187)Primary or secondary dairy trainee wasfemale 0.6609 3.1349 (3.8385) (4.2271)Months since HH received first cattle 0.8903*** 0.9711*** (0.2416) (0.2817)Primary dairy trainee was female 13.8598 17.7639 (13.5895) (12.4791)Observations 163 153 163 153Estimates controlled for household and community characteristics
    25. 25. Milk production and income• Qualitative analysis explored: Who makes decisions about milk consumption and sale and who control income from milk• Quantitative analysis looked at impact on: – Quantity of milk sold – Dairy income – Income control in 2012 (descriptive only)
    26. 26. Findings from qualitative• “All milk worth talking about is from improved cows.”• Morning milk sold to milk collection center (MCC), generally by men• Evening milk consumed or sold locally, generally by women
    27. 27. Decision maker on dairy income, 2012 survey
    28. 28. Milk sales and income for recipients and non-recipients Non-recipients Recipients All beneficiaries Mean S.D Mean S.D Mean S.DLiters of milk sold or bartered inlast month 4.69 18.75 112.41 154.66 104.00 151.35Money received from milk sales inlast month (MZM) 60.00 232.4 1540.8 2051.8 1430.8 2012.7
    29. 29. Determinants of liters of milk sold to MCC village or bartered last month (MZM)HH received cattle 73.7820*** 75.0157*** (27.5877) (27.4968)Primary or secondary dairy trainee wasfemale 8.6903 6.9587 (32.1106) (34.6512)Months since HH received first cattle 0.0196 0.2898 (2.7204) (2.7834)Primary dairy trainee was female 47.0947 50.5377 (54.3564) (56.4264)Observations 158 149 158 149Estimates controlled for household and community characteristics
    30. 30. Determinants of income from milk sales (MZM)HH received cattle 1247.42** 1244.97** (515.3749) (611.6042)Primary or secondary dairy trainee wasfemale 1053.341** 1019.53** (460.2119) (498.0601)Months since HH received first cattle -11.4963 -14.5877 (34.3267) (34.3921)Primary dairy trainee was female 618.9363 617.5980 (821.3166) (827.2487)Observations 155 147 155 147 Estimates controlled for household and community characteristics
    31. 31. Some conclusions• Overarching: Gender roles are shaped by socio- cultural, economic and political norms• Assets – Care must be taken when interpreting “ownership” data – As expected, receipt of a cow improves households assets. Does not appear to increase gender asset gap. – People say that putting the cow in both names or in woman’s name will have no practical effect but this should be tested• Food security – Receipt of cow seems to have positive impact on food security and nutrition, though mechanisms not clear
    32. 32. • Management – Male HH heads traditionally make decisions, but there is evidence that women are getting more involved in management – Costs increase with high-producing cows • hypothesis that household needs 2-5 improved cows to profitable—this is something to watch in next phase of project – Impacts on women’s labor need to be monitored carefully, especially in early stages of commercialization• Production and income – Production and income increase with receipt of improved cow – Men control most of dairy income – Having a female trainee is associated with higher income, possibly through better milk quality
    33. 33. Thank you!