Gender dynamics shape opportunities in Vietnam's root crop value chains
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1. Women have less decision-making power and asset ownership compared to men, especially in intensive value chains. 2. Intensive value chains use more purchased inputs like fertilizers and hired labor, resulting in higher yields. However, extension services mainly target men. 3. Women do most of the labor in crop establishment and post-harvest handling while men do more field management. 4. Controlling for other factors, sweet potato yields are lower on female-managed farms compared to male-managed farms, indicating a gender productivity gap.
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Gender dynamics shape opportunities in Vietnam's root crop value chains
- 2. Do self-help groups improve women’s empowerment and gender equality in the fisheries and aquaculture in India? A systematic literature review Surendran Rajaratnam, Julie Newton, Cynthia McDougall and Hayrol Azril Shaffril
- 3. Research Questions • What is the state of the literature on WSHGs in the fisheries and aquaculture in India? • What are the positive and negative gender outcomes experienced by these WSHGs & their members?
- 4. 1) "Reached" through inclusive terms of value chain engagement - shorter term 2) Resources accessed and "Benefits" realized to improve HHs, community and value chain performance - intermediary term 3) "Empowerment" within HHs, community and value chain – longer term 4) "Transformation" in HHs, communities and value chain – longer term Accessing Resources 1.1 Inclusive terms of engagement in VC development 2.1 Increased access to knowledge & skills 2.2 Increased social networks 2.3 Increased access to productive resources Realizing Benefits 2.4 Increased income 2.5 Increased consumption of food (incl. fish) 3.1 Improved decision making 3.2 Economic & social VC upgrading 3.3 Increased voice & leadership 3.4 Enhanced status, recognition & confidence 4.1 Shifting attitudes & behaviors within HHs 4.2 Shifting attitudes & behaviors at the community and state level Source: Johnson et al. (2017); Danielsen et al. (2018); Danielsen et al. (forth coming) Conceptual Framework: Gender outcome typology
- 5. Findings: the state of the literature Location: Karnataka, Kerala, Tamil Nadu, Andhra Pradesh, Odisha and Mumbai Areas: Rural (21) + Sub-urban (1) + urban (1) Types of WSHGs (by association/affiliation): • Gov. programmes/schemes: vehicles of microfinance; receive subsidies from various schemes • NGOs • Women groups which are organized by the SIFFS - kept their own identities; engagement in economic activities
- 6. WSHGs and their activities: • WSHGs - previously established & engaged for the purpose of the studies. • Not all SHGs formed are actively engaged in fisheries or other livelihood activities (Vivekanandan et al. 2019; Mission Shakthi and WFP, 2020) • 30/598 SHGs in fishing villages were active (Vivekanandan et al. 2019) • In operation for a number of years • Various districts/geographical locations • Various activities along the fisheries and aquaculture value chains. Members of WSHGs • Majority of WSHG members were engaged in agriculture as their primary occupation, while fisheries were their secondary (Devi et al., 2019) • Fisherwomen who are not related to each other (Kripa & Mohamed, 2008) • Common socio-economic background; age category of members, have access to ponds (Panda et al., 2012, 2014)
- 7. 23 23 13 5 Reach Benefit Empower Transform No. of papers RQ2: Overview of findings based on the gender outcome typology
- 8. What is working? Opportunities • Access to banks and credit programs – credits and loans • Key benefits – avenue for income generation, increased income & savings, improved farming practices • Engagement in groups – mobility, stronger social cohesion, avenue for sharing ideas on livelihood issues What is not working? Challenges • Not all SHGs are able to obtain loans from banks/MFIs • Some SHGs took multiple loans from different sources (incl. private moneylenders) • No system to track the income earned by SHG members – estimation only and not actual • WSHGs were formed and managed by LSP, men (who manages the marketing and finances); paid individually; tend not to trickle down to all of WSHG members; the entrepreneurial capacities of WSHGs are not enhanced Outcome category: Benefit
- 9. • Economic & social value chains upgrading - Women capable of taking up self- employment ventures & their group meetings became a platform to exchange ideas; discuss livelihoods issues. • Voice and leadership - SHGs & strengthening women’s economic role increased women’s voice in village & on public issues. • Enhanced status & recognition as well as confidence and self-efficacy – Gaining self-confidence & self-esteem as being part of an SHG; level of involvement in entrepreneurial activities were significantly associated with confidence building & self- esteem. Outcome category: Empowerment
- 10. Shifting attitudes within the family • Initial opposition from family, later became supportive - contribute to their household financially. • Husbands acknowledged their wives’ contribution & reported they could no longer ignore women’s wisdom & role in family decision-making process. Shifting attitudes at community and state level in recognizing these women • Leaders were recognized and given awards/fellowships for their expertise in their aquaculture venture with the nominations by government agencies who are working with them. Restrictive gender norms and attitudes of family and community members remains a challenge • Men continue to dominate village councils discussions in terms of representation • Men continue to dominate the ‘managerial’ roles in many SHGs - potentially exploitative arrangement & one which impedes independent decision making by women. Outcome category: Transform
- 11. 1. There are not many studies on WSHGs in the fisheries and aquaculture 2. Study methodologies observation: i. Type of study - cross-sectional not longitudinal – we don’t know about the change process over time ii. Intersectionality - most of the studies treats WSHGs as socio-economically homogenous iii. Gaps - lack making comparison of WSHGs with men’s groups engaged in similar activities; studies which reports on women’s empowerment lack baseline & endline, use standardized, validated tool; choice of tools to track empowerment & transformation are not always detailed/or explained. 3. Limited evidence/reporting on perverse outcome/backlash Other findings
- 13. Thank You
- 14. Understanding gender dynamics in highly intensive and low intensive value-chains for root crops in Vietnam Vanya Slavchevska (the Alliance), Nozomi Kawarazuka (CIP) PIM Webinar 10-11am EDT September 21, 2021
- 16. Project objectives/research questions • What opportunities to participate and benefit do local and export-oriented value chains provide for women and men farmers? • How far are women involved in decision-making in local and export-oriented value chains? • What factors influence the level of investment in quality planting materials, fertilizer and crop protection and implications for productivity?
- 17. Highly-intensive, export-oriented value chains Low-intensive (some localized) value chains Sweetpotato Vinh Long Lam Dong Hanoi Ha Tinh Bac Giang Southeast region South Central region Central Highlands North Mountainous Cassava Geographic typology of VCs Significant differences in value chain characteristics across provinces/regions Animal feeding Home consumption
- 18. Data -- surveys Survey completed 5 provinces 377 HHs Survey (1) Completed 26 provinces 842 HHs (552 HHs cassava farmers) Survey (2) Planned 6 provinces 132 HHs Cassava survey regions Sweetpotato survey regions
- 19. Methodology: Qualitative North Mountain Son La Southeast Tay Ninh Central coast Ha Tinh Mekong Delta Vinh Long crops Cassava Cassava SP SP Agricultural systems Agroforestry Low-land Coastal Low-land Ethnicity and gender Ethnic minority. Kinh majority Kinh majority Kinh majority Value-chain mapping Low intensive value chain Intensive value chain Low intensive value chain Intensive value chain In-depth interviews Key actors: 8 Producers: 16 Key actors: 8 Producers: 16 Key actors: 8 Producers: 16 Key actors: 8 Producers: 16
- 20. Current findings Decision making and asset ownership Agricultural input Access to training Family and hired labor Gender gaps in productivity
- 21. Figure 1. Selected sex-disaggregated indicators related to decision-making and access to resources in sweetpotato value chains, by province. Source: Authors’ estimates. Gender patterns of decision-making and asset ownership across sweetpotato VCs 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Decisions on sweetpotato Earnings from sweetpotato Planting material Varieties Purchasing planting materials Access to credit Land onwership Decisions on sweetpotato Earnings from sweetpotato Planting material Varieties Purchasing planting materials Access to credit Land onwership Decisions on sweetpotato Earnings from sweetpotato Planting material Varieties Purchasing planting materials Access to credit Land onwership Decisions on sweetpotato Earnings from sweetpotato Planting material Varieties Purchasing planting materials Access to credit Land onwership Decisions on sweetpotato Earnings from sweetpotato Planting material Varieties Purchasing planting materials Access to credit Land onwership Provinces with intensive production
- 22. Gendered patterns of decision-making in cassava production and ownership of land Figure 2. Selected sex-disaggregated indicators related to decision-making and access to resources in cassava value chains, by region. Source: Authors’ estimates. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Decision on agriculture Earnings from cass Planting material Varieties Purchasing planting materials Smart phone Access to credit Land onwership Decision on agriculture Earnings from cass Planting material Varieties Purchasing planting materials Smart phone Access to credit Land onwership Decision on agriculture Earnings from cass Planting material Varieties Purchasing planting materials Smart phone Access to credit Land onwership Decision on agriculture Earnings from cass Planting material Varieties Purchasing planting materials Smart phone Access to credit Land onwership Decision on agriculture Earnings from cass Planting material Varieties Purchasing planting materials Smart phone Access to credit Land onwership South East South Central Central Highland North Mountainous All regions Joint Female Male Most intensive production region
- 23. Use of inputs by value chain 0 5 10 15 20 25 30 -3 2 7 12 17 22 27 South East South Central Central Highland North Mountainous Land prepration (million VND/ha) Planting material (million VND/ha) Pesticide (million VND/ha) Herbicide (million VND/ha) Fertilizer (million VND/ha) Hired labor (million VND/ha) Transportation (million VND/ha) Other (million VND/ha) yield (t/ha) Highly intensive value chains use a lot of inputs • Fertilizer • Hired labor • Pesticides (sweetpotato) • Purchased planting material Yields are also higher 0 5 10 15 20 25 0 5 10 15 20 25 30 35 40 45 50 Vinh Long Lam Dong Ha Tinh Ha Noi Bac Giang Inputs costs (million VND/ha) Land prepration (million VND/ha) Planting material (million VND/ha) Pesticide (million VND/ha) Herbicide (million VND/ha) Fertilizer (million VND/ha) Hired labor (million VND/ha) Transportation (million VND/ha) Yield (t/ha)
- 24. Access to training • Extension and training services tend to be targeted at the high intensive, export oriented value chains • Men participate at higher rates in both cassava and sweetpotato trainings 0.00 0.10 0.20 0.30 0.40 0.50 0.60 Vinh Long Lam Dong Ha Tinh Ha Noi Bac Giang All regions Any sweetpotato training, % of HHs Only men participated sweetpotatoes training, % of HHs Only women participated sweetpotatoes training, % of HHs Both men and women participated sweetpotatoes training, % of HHs 0.00 0.05 0.10 0.15 0.20 South East South Central Central Highland North Mountainous All regions Any cassava training, % of HHs Only men participated cassava training, % of HHs Only women participated cassava training, % of HHs Both men and women participated cassava training, % of HHs
- 25. 0 5 10 15 20 25 30 35 40 45 50 Land preparation Planting material Planting Fertiliser application Herbicide application Weeding P&D chem cntrl P&D non-chem cntrl Harvest Transport Processing Irrigation Family labor (person days/ha) Vinh Long Female Male 0 5 10 15 20 25 30 35 40 45 50 Land preparation Planting material Planting Fertiliser application Herbicide application Weeding P&D chem cntrl P&D non-chem cntrl Harvest Transport Processing Irrigation Family labor (person days/ha) Ha Tinh Female Male Family labor 0 10 20 30 40 50 60 Field establishment Land preparation Planting material preparation Planting Fertilizer application Herbicide application Pest and disease chemical Pest and disease non-chemical Weeding Harvest Transport Chipping and drying Irrigation Family labor (person-day per ha) in South East N_Female_ha N_male_ha 0 10 20 30 40 50 60 Field establishment Land preparation Planting material preparation Planting Fertilizer application Herbicide application Pest and disease chemical Pest and disease non-chemical Weeding Harvest Transport Chipping and drying Irrigation Family labor (person-day per ha) in North Mountainous N_female_ha N_male_ha
- 26. 0 2 4 6 8 10 12 Field establishment Land preparation Planting material preparation Planting Fertilizer application Herbicide application Pest and disease chemical Pest and disease non-chemical Weeding Harvest Transport Chipping and drying Irrigation Hired labor (person days/ha) South East N_HiF_ha N_HiM_ha 0 2 4 6 8 10 12 Field establishment Land preparation Planting material preparation Planting Fertilizer application Herbicide application Pest and disease chemical Pest and disease non-chemical Weeding Harvest Transport Chipping and drying Irrigation Hired labor (person days/ha) North Mountainous N_HiF_ha N_HiM_ha Hired labor 0 5 10 15 20 25 Land preparation Planting material Planting Fertiliser application Herbicide application Weeding Pest and disease chemical control Pest and disease non-chemical control Harvest Transport Processing Irrigation Hired labor (person days/ha) in Vinh Long Female Male 0 2 4 6 8 10 12 14 16 18 20 Land preparation Planting material Planting Fertiliser application Herbicide application Weeding Pest and disease chemical control Pest and disease non-chemical control Harvest Transport Processing Irrigation Hired labor (person days/ha) in Ha Tinh Female Male
- 27. Gender gap in productivity in sweetpotato: Naïve regressions v (1) (2) (3) (4) VARIABLES Yield (in logs) Yield (in logs) Yield (in logs) Yield (in logs) Female managed -0.645*** -0.214* -0.150 -0.122 (0.134) (0.111) (0.122) (0.149) Jointly managed -0.264** -0.0235 -0.0157 -0.0678 (0.122) (0.0871) (0.0864) (0.0998) Ha Tinh -1.100*** -0.816*** -0.738** (0.137) (0.252) (0.311) Lam Dong -0.198 -0.170 0.147 (0.122) (0.129) (0.169) Ha Noi -0.456*** -0.163 -0.148 (0.119) (0.260) (0.295) Bac Giang -0.276 -0.0282 -0.190 (0.218) (0.265) (0.264) Sweet potato area (ln) No No Yes Yes Wealth quintiles No No No Yes Other controls No No No Yes Observations 373 373 373 365 R-squared 0.078 0.275 0.287 0.369 Standard errors clustered at the village in parentheses *** p<0.01, ** p<0.05, * p<0.1
- 28. Gender gap in productivity in cassava: Naïve regressions (1) (2) (3) (4) (5) VARIABLES Yield (in logs) Yield (in logs) Yield (in logs) Yield (in logs) Yield (in logs) Female or jointly managed -0.104 -0.00943 -0.0226 -0.0159 0.0333 (0.0764) (0.0771) (0.0788) (0.0785) (0.0718) South Central -0.0158 -0.105 -0.0509 0.0101 (0.0884) (0.120) (0.115) (0.131) Central Highlands -0.381** -0.433** -0.280 -0.263 (0.153) (0.164) (0.192) (0.182) North Mountainous -0.511*** -0.639*** -0.603*** -0.489** (0.135) (0.174) (0.176) (0.204) Cassava area (ln) No No Yes Yes Yes Wealth quintile No No No Yes Yes Other controls No No No No Yes Observations 541 541 541 541 541 R-squared 0.003 0.081 0.086 0.099 0.237 Standard errors clustered at the village level in parentheses *** p<0.01, ** p<0.05, * p<0.1
- 29. Root crops’ multiple utilization remains particularly important for women processing trading Export Dried chips business Local seed trading business Animal feeding – animal selling Local market (vines and roots) Firewood, roots & leaves for home consumption Seed and roots exchange for reciprocal support Priorities: High-input High-yield Clean seed When roots crops are used for multiple purposes, women’s priorities are more likely to be reflected in the household decisions on agricultural input and varieties to grow.
- 30. 1. What opportunities to participate and benefit do local and export-oriented value chains provide for women and men farmers? Intensive value chains are dominated by men. In this case, women have more control and interest in other crops, livestock or off-farm work. It is not necessarily to have women empowerment in all crops evenly. Local value chains with post-harvest technologies have a great potential for women. Gift giving and exchange (vines, roots, planting materials) is a means of receiving support in times of needs. This is often neglected in economic analyses. Preliminary conclusions
- 31. Preliminary conclusions 2. How far are women involved in decision-making in local and export-oriented value value chain? Women have more decision-making power in local and less-intensive value chains. Male labour-migration contest - a great potential to empower women. In export-oriented value chains, women are not necessarily excluded but joint decision-making is nuanced. Men have more power in technical decisions.
- 32. 3. What factors influence the level of investment in quality planting materials, fertilizer and crop protection and implications for productivity? Women-led farms may be intentionally kept with low investment: questioning the argument of “women lack access to finance, knowledge…. “. Productivity may not be a priority for women farmers (e.g. taste, varieties for animal feeding) Preliminary conclusions
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- 34. Annex: Table 1. Basic characteristics of sweetpotato sample Vinh Long Lam Dong Ha Tinh Ha Noi Bac Giang All regions Farmer and household characteristic Gender of respondent (1=male) 0.92 0.66 0.27 0.18 0.31 0.47 Gender of household head (1= male) 0.97 0.79 0.81 0.77 0.93 0.85 Age of respondent (years) 50.10 46.23 56.24 56.22 55.50 52.75 HH ethinicity (1= Kinh) 0.99 0.56 1.00 1.00 1.00 0.90 The highest education level in the household 10.36 12.47 10.20 11.59 11.71 11.28 Year of education of household head 5.99 7.26 7.71 7.90 7.77 7.33 Farm characteristic Agr land own area (ha) 0.98 1.57 1.69 0.23 0.31 1.00 Agr_rented area (ha) 0.51 1.06 0.70 0.07 0.01 0.49 Agr_borrowed area(ha) 0.03 0.10 0.08 0.07 0.08 0.07 Sweet potatoes area (ha) 1.43 1.30 0.07 0.07 0.16 0.63 72 82 78 73 72 377 • In highly intensive, export-oriented value chains (Vinh Long and Lam Dang) the respondents were primarily men. • In low intensive, localized value chains (Hanoi, Ha Tinh and Bac Giang), the respondents were largely women. • A significant difference in land area dedicated to sweetpotato between highly and less intensive value chains.
- 35. Annex: Table 2. Basic characteristics of cassava sample South East South Central Central Highland North Mountainous All regions Farmer and household characteristics Gender of respondent (1=male) 0.76 0.62 0.69 0.57 0.66 Gender of household head 0.91 0.81 0.91 0.89 0.88 Age of household head 50.13 53.20 47.12 50.49 50.12 HH ethnicity (1= Kinh) 0.98 0.76 0.23 0.26 0.53 The highest education level in the household 10.52 10.55 9.55 9.29 9.94 Year of education of household head 7.17 6.84 5.15 5.79 6.17 Farm characteristics Agr land own area (ha) 4.09 1.26 2.51 1.41 2.27 Agr_rented area (ha) 5.16 0.22 0.01 0.03 1.19 Agr_borrowed area(ha) 0.72 0.02 0.00 0.03 0.17 Cassava area (ha) 6.68 0.78 1.31 0.31 2.10 121 135 157 139 552 The highly intensive, export-oriented cassava chains (Southeast and South-Central regions) are dominated by the ethnic majority group (Kinh), better educated farmers, and larger farms.
- 36. Helpers, Employees or Owners: Opportunities for women’s empowerment in agricultural value chains Jennifer Twyman Manuel Moreno, Jenny Wiegel, Juliana Muriel, Monica Chavarro, Carlos Suazo, and Judith Castro PIM Webinar - Webinar 1: Gender dynamics in value chains beyond production node and a single commodity focus: Findings September 21, 2021
- 37. Background Most gender and value chain studies focus on understanding women’s participation, benefits from, and empowerment across different links of one value chain. Few look across multiple value chains. Many gender and agricultural studies focus on on-farm production, and not women’s roles in other nodes of the value chain, such as processing, which can occur on-farm/at home or in processing plants. Women, in agricultural settings, often identify themselves as “helpers” supporting their husbands, especially with on-farm production. However, women can also be employees, owners, or managers.
- 38. Study objective & Research questions The main objective of this study is to explore women's empowerment across two nodes of two value chains in order to identify opportunities and barriers that women face to be involved as employees, owners, and/or managers in the production and processing nodes of different kinds of agricultural value chains. 1. What is the relative performance of different agricultural value chains in terms of women’s empowerment? 2. What factors influence women's empowerment in the different value chains and the different nodes of these value chains? 3. What opportunities and barriers exist for women in terms of economic opportunities (and/or assuming different roles such as employee, manager, or owner)?
- 39. Context Rural Opportunities Project • Dry Corridor of Honduras • Value chain project seeking to increase women’s economic empowerment Cashew – export market Dairy – cheese for local markets Two Nodes: • Production (raw products: cashew nuts, milk) • Processing (processing raw product into marketable product for consumers)
- 40. Methods Qualitative • Participatory workshops • FGDs • Interviews. Quantitative • A-WEAI – 4 surveys, 1 per node per VC. • Regression analysis • Descriptive statistics & cross-tabs
- 41. Results: Women’s Participation/Roles in the Value Chain Manager Person involved in making most of the decisions related to the value chain node. Helpers Person who does any activities (work) related to the value chain node but are not managers; they may make some decisions but not the majority. Not involved Do not participate in the value chain node activities. 44% 49% 72% 51% 56% 33% 20% 15% 0% 18% 9% 34% CASHEW PRODUCTION CASHEW PROCESSING DAIRY PRODUCTION DAIRY PROCESSING Managers Helpers Not involved
- 42. Results: A-WEAI by Value Chain Node
- 43. Percent of women reaching “empowerment” in each indicator, by value chain and node. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Producers Processors Producers Processors Producers Processors Producers Processors Producers Processors Producers Processors Workload Group membership Access to and decisions on credit Ownership of assets Input in production decisions Control over use of income % of empowered women Cashew 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Producers Processors Producers Processors Producers Processors Producers Processors Producers Processors Producers Processors Group membership Workload Access to and decisions on credit Ownership of assets Control over use of income Input in production decisions % of empowered women Dairy
- 44. Indicator Contributions to Women’s Disempowerment 2.0% 20.6% 11.6% 2.0% 37.9% 25.9% Cashew Production Input in production decisions Ownership of assets Access to and decisions on credit Control over use of income Group membership Workload 5.7% 3.8% 8.7% 38.6% 42.0% Cashew Processing Input in production decisions Ownership of assets Access to and decisions on credit Control over use of income Group membership Workload 7.1% 9.3% 7.6% 18.4% 29.8% 27.8% Dairy Production Input in production decisions Ownership of assets Access to and decisions on credit Control over use of income Group membership Workload 2.9% 2.9% 9.5% 0.7% 43.8% 40.1% Dairy Processing Input in production decisions Ownership of assets Access to and decisions on credit Control over use of income Group membership Workload
- 45. Some qualitative results related to workloads • “El trabajo del marañón no es un trabajo tan pesado como para que lo pueda hacer sólo un hombre…” • Cashew work isn’t so tough that only a man can do it. • “No es que es fácil, sino que la mujer tiene que tener más capacidades … [Una mujer] que tiene su hogar, que tiene hijos, no tiene el tiempo de andar como el varón. Entonces se le hace un poco más difícil a la mujer.” • It is not that it is easy, but that the woman has to have more capabilities…. [A woman] who has a home, who has children, does not have the time to walk around like a man. … So, it makes it a little more difficult for women.
- 46. OLS Regression Analysis: Factors Related to Women’s Empowerment (A-WEAI 5DE Scores) Model 1 Model 2 Role (base category = Manager) Helper -0.038* -0.047** Not involved -0.048 -0.046 Value chain: cashew 0.138*** Node: production 0.044** Value Chain*Node (base category = cashew production) Cashew processing -0.145*** Dairy production -0.203*** Dairy processing -0.169*** Land Ownership 0.054*** 0.069*** Control Variables Yes Yes Observations 537 537 R-squared 0.150 0.182 * p<.05; ** p<.01; *** p<.001
- 47. Conclusions • Results suggest that women’s empowerment differs by value chain, node, and women’s role in them. • Women in cashew production (export market) are more empowered, according to A-WEAI, than women in other value chain nodes. • Women in production are more empowered than women in processing node. • Workload and group membership are two key contributors to women’s disempowerment across both value chains and both nodes. • This is especially relevant for women in the processing nodes of both value chains. • Limited access to credit is also contributing to disempowerment. • For women in dairy production, control of income is another important contribution to women’s disempowerment.
- 48. Thank you! Jennifer Twyman Manuel Moreno, Jenny Wiegel, Juliana Muriel, Monica Chavarro, Carlos Suazo, and Judith Castro Email manuel.moreno@cgiar.com j.wiegel@cgiar.org
Editor's Notes
- The study provides a comparative analysis women’s and men’s opportunities and constraints to participate in and benefit in the localized and the globalized value chains of two root crops, which have become major industrial crops and multi-billion dollar industries in SEA. It contributes insights into how the CGIAR addresses gender issues in the processes of increased commercialisation and globalization of agri-food systems Southeast Asia’s on-going shift of root crops from local to commercial value chains can provide many implications for other crops in Africa and other regions where commercialization has only recently started. HH survey (2) is one remaining region out of 5 for the cassava survey which could not be surveyed in 2020 because farmers had already harvested all their cassava; the survey included DNA fingerprinting and disease surveillance components, which require that farmers are ideally visited around 3-4 months after planting. We can proceed with the analyses even without that region and will add it whenever the data collection are complete. What terminology to use? Localized/local and globalized/export-oriented/upgraded?
- Value chains differ significantly across provinces and regions. It therefore difficult to analyze the characteristics of low intensive and high intensive value chains without taking into account the regional/territorial characteristics.
- We draw on both quantitative and qualitative data sources. The quantitative analyses are based on a HH survey with 552 cassava producing households from 4 cassava producing regions and with 377 sweetpotato-producing HHs in 5 provinces.
- We’ve planned four qualitative case studies. Because of the recent worsening of the COVID-19 situation, we have only conducted two of the case studies and today’s presentation largely draw on these two case studies which were in the less intensive production systems for both cassava and sweetpotato.
- The first two panels are the highly intensive regions. The last three are the less intensive ones. Blue color is male only decision-making, orange is women-only and grey is joint decision-making. Most decisions in highly intensive, export oriented value chains are dominated by men (the first two provinces). Women, jointly with their spouses are reported to have a say in overall decisions about sweetpotato in these highly commercial value chains, but technical decisions such as around selecting planting material and crop varieties, purchasing seeds and other inputs are all take almost exclusively by men, especially in Vinh Long. Women’s participation in decision-making is starkly different in the other three provinces where value chains are less intensive and more localized. In those value chains, women make nearly all decisions with little or no input from men. Nevertheless, across all provinces, men continue to enjoy a higher access to and ownership of key productive resources such as land and credit.
- The gendered patterns of decision-making are quite different in cassava value chains. To remind, grey is joint decision-making. While men seem to dominate decision-making in the most highly intensive value chains in the Southeast Region, a large share of cassava producers across all regions, and particularly the South central and the Central highlands take decisions jointly with their spouse. In few households, across all regions decisions around cassava production are taken by women only. Only in the North mountainous region, a non-negligible share of cassava producers are exclusively women. The north mountainous region is where cassava has other uses besides being sold fresh to the factories for further processing into chips or starch. This is important and we will come back to this later with the qualitative analyses too.
- We now proceed to look at how input use differs across the more localized and the export oriented value chains. The top graph is the inputs and yields of sweetpotato. The bottom one has the same information for cassava. It is immediately clear that the value of inputs used per hectare are significantly higher in the more export-oriented value chains. In sweetpotato, pesticide, hired labor and fertilized are the main cost drivers. Across all provinces, a non-negligible share of households also purchase planting material. In export-oriented value chains, the main cost drivers are fertilizer and hired labor. The cost of planting material in somewhat negligible. The main source of planting material in nearly all regions is “own farm”. Yields are also quite different across value chains. Higher use of inputs are also associated with higher yields and this is clear on the graph. What is interesting to notice is that yields in Ha tinh are particular low. And this is largely explained by the type of variety grown and final use – in Ha Tinh farmers grow sweetpotato for their leaves, which are consumed, rather than for their roots.
- Extension and training services tend to be targeted at the high intensive, export oriented value chains Men participate at higher rates in both cassava and sweetpotato trainings, even in localized value chains.
- Highly commercialized value chains (Vinh Long and Lam Dang) use significantly less family labor than the localized value chains (Ha Tinh and Hanoi). Although both men and women tend to take part in most activities, women supply significantly more labor than men in all activities. Highly commercialized value chains such as those of Vinh Long and even Lam Dang use significantly less family labor than the localized value chains in Ha Tinh and Hanoi. (Note that the scale of x-axis differs across graphs.) Both men and women are somewhat equally likely to engage in all activities in the commercial value chains. While there is no processing in vinh Long and sweetpotato are directly exported fresh, there is a lot more processing in lam Dang and Ha Tinh, and processing tends to be domeinated by women by men also take part. The localized value chains in Ha Tinh and Ha Noi rely on a lot more family labor, both male and female, than the highly commercial value chains. Although both men and women tend to take part in most activities, women supply significantly more labor than men in all activities.
- There is a lot more hiring of labor in export-oriented value chains (the top panels) than in the more localized value chains (the bottom panels).
- We also look at the gender gap in a traditional regression framework. The dependent variable is yield and the explanatory variable of interest is whether the production is managed by women, or jointly (male management is the base category). What we see is that.
- The study provides a comparative analysis women’s and men’s opportunities and constraints to participate in and benefit in the localized and the globalized value chains of two root crops, which have become major industrial crops and multi-billion dollar industries in SEA. It contributes insights into how the CGIAR addresses gender issues in the processes of increased commercialisation and globalization of agri-food systems Southeast Asia’s on-going shift of root crops from local to commercial value chains can provide many implications for other crops in Africa and other regions where commercialization has only recently started. HH survey (2) is one remaining region out of 5 for the cassava survey which could not be surveyed in 2020 because farmers had already harvested all their cassava; the survey included DNA fingerprinting and disease surveillance components, which require that farmers are ideally visited around 3-4 months after planting. We can proceed with the analyses even without that region and will add it whenever the data collection are complete. What terminology to use? Localized/local and globalized/export-oriented/upgraded?
- The study provides a comparative analysis women’s and men’s opportunities and constraints to participate in and benefit in the localized and the globalized value chains of two root crops, which have become major industrial crops and multi-billion dollar industries in SEA. It contributes insights into how the CGIAR addresses gender issues in the processes of increased commercialisation and globalization of agri-food systems Southeast Asia’s on-going shift of root crops from local to commercial value chains can provide many implications for other crops in Africa and other regions where commercialization has only recently started. HH survey (2) is one remaining region out of 5 for the cassava survey which could not be surveyed in 2020 because farmers had already harvested all their cassava; the survey included DNA fingerprinting and disease surveillance components, which require that farmers are ideally visited around 3-4 months after planting. We can proceed with the analyses even without that region and will add it whenever the data collection are complete. What terminology to use? Localized/local and globalized/export-oriented/upgraded?
- The study provides a comparative analysis women’s and men’s opportunities and constraints to participate in and benefit in the localized and the globalized value chains of two root crops, which have become major industrial crops and multi-billion dollar industries in SEA. It contributes insights into how the CGIAR addresses gender issues in the processes of increased commercialisation and globalization of agri-food systems Southeast Asia’s on-going shift of root crops from local to commercial value chains can provide many implications for other crops in Africa and other regions where commercialization has only recently started. HH survey (2) is one remaining region out of 5 for the cassava survey which could not be surveyed in 2020 because farmers had already harvested all their cassava; the survey included DNA fingerprinting and disease surveillance components, which require that farmers are ideally visited around 3-4 months after planting. We can proceed with the analyses even without that region and will add it whenever the data collection are complete. What terminology to use? Localized/local and globalized/export-oriented/upgraded?
- Motivation: Does this identification impact the opportunities they have in different nodes of different value chains, and women’s empowerment?
- Motivation: Does this identification impact the opportunities they have in different nodes of different value chains, and women’s empowerment?
- In terms of employees and owners, we found very little variation across the sample. There were very few people who identified as employees. For ownership, we had most data about landownership and nearly everyone was a landowner. We also explored ownership of assets important for each node of the value chains but found similar problems. So, we decided to only consider managers and helpers for this categorization. We also include landownership as a variable in the regression.
- The score of A-WEAI in the cashew chain is better than the dairy chain. Cashew producers have the high A-WEAI score (0.897). Dairy procesors have the low A-WEAI score (0.749). Overall, this graph shows higher WE in the cashew VC, and higher WE in the production nodes than the processing nodes. indicators. These results are a bit surprising. From field observations and discussions with the project team, I had assumed that women in cashew (and in general in production) were the least empowered.
- The bar charts on the previous slide showed the % of women meeting the empowerment threshold for each indicator. These pie charts show each indicator’s contribution to disempowerment. So, in the previous slide we saw that access to and control over credit was the least achieved empowerment indicator. However, in the pie charts we see that this indicator (dark green piece) does not contribute much to disempowerment because of the weighting of the different indicators. And, while qualitative research and conversations with partners on the ground suggest low levels of income, especially in the production node of the cashew value chain, control over income (yellow) does not contribute much to women’s disempowerment, except in dairy production. The drivers of women’s disempowerment seem to be the purple pieces – group membership and workload – across both nodes of both value chains.
- The model: Dependent Variable: Women’s disempowerment (WEAI 5DE indicator) Ranges from 0 to 1 0 indicates fully empowered (in all indicators/domains) 1 indicates completely disempowered (in all indicators/domains) Key Variables of Interest Role (Helper or Manager) Value Chain (Cashew or Dairy) Node (Production or Processing) Landowner Control Variables Age, education Spouse’s age and education Other on-farm activities Off-farm activities Results: Helpers are more disempowered than managers. Managers are more empowered than helpers. Women in the cashew value chain are less disempowered than those in the dairy chain. Women in cashew are more empowered than those in dairy. Men in production are less disempowered than men in processing node. Men in production are more empowered than those in processing. Older men and women are less disempowered than younger men and women. Older men and women are more empowered. Landowners are less disempowered than non-owners. Landowners are more empowered than non-owner.