1. Small-scale developing country fish value chains How can they inform strategies for poverty alleviation and sustainability? Presented by: Beatrice Crona Stockholm Resilience Center & Royal Swedish Academy of Sciences Matilda Thyresson, Postdoctoral researchers, SPACES Sweden Tim Daw, Senior Researcher, SRC, PI SPACES Andrew Wamukota, Postdoctoral researchers, SPACES Kenya 1

2. 22 Introduction Analysis of trade and the rise of value chain focus Aggregate analysis of trade in fish and ag commodities Critique against assumed benefit distribution despite well- known impediments (Béné, Lawton, Alisson 2010; Béné, Hersoug, Alisson 2010) Value chains as a way to examine the distribution of benefits derived from fishing - Who get’s what and why?

3. 33 Introduction Integrating value chains in fisheries research Fisheries research focused on the resource and the fishermen - relative blindness to other actors (traders, gender) Problematic from a poverty alleviation perspective Benefits from fisheries are much more than to catchers Gendered – women trader/fish friers Trade-offs between objectives and beneficiaries Most SSF are commercial Fish is dominant animal protein source but that max 30% of households catch their own (Mäkelä 2016) Value chain actors as key determinants of fishing effort (Thyresson et al 2012; 2013) Informal credit, facilitating migration (Crona et al 2010; Crona & Rosendo 2011)

4. 4 Scope and Aims Can small-scale fisheries and associated value chains be considered ’pro-poor’? 1.Who benefits and how? 2.What access limitations exist? a)Aspirations b)Needs, gaps and barriers to change 3.What opportunities are there for increasing the benefit to poorer segments of the value chain (and those not currently participating in the value chain)? 4.What are the key trade-off emerging? (social, economic, environmental)

5. 5 Methods Looking at SSF value chains Familiar story of complexity, diversity, limited data, messy data collection Many different actors Different yet often highly intertwined VCs for different types of fish/products Generally quite dynamic seasonal fluctuations & informal nature leads to more flexibility and adaptation by actors - CAS behavior

6. 6 Methods Key informant interviews Survey Kenya and Mozambique 1. Mixed reef fish 2. Octopus 3. Small pelagics

7. 7 Strong seasonality – South East (Kusi) and North East monsoon (Kaskasi) dominate fishing conditions Scale of fishery Kongowea Urban Vanga Rural Total catch Biomass levels Node/segment pop Fishers ~121~350 Sm scale traders (F) ~68~45 Sm scale traders (M) ~20~70 Large scale traders --7 Capitalization Geography Sandy lagoon, fringe reef Mangrove channels, barrier reef Urban market proximityhighlow System description INSERT PICS of sites w boats etc ~ approximate numbers as daily and seasonal fluctuations Insert catch levels biomass Capitailzation

8. Fishers (employed and independent) Fish Shops Low- income consumer s Trawlers Food Kiosk Middle- income consumer s High- income consumer s Restaurants Tourist Hotels Female small-scale traders Male small- scale traders Large-scale traders at Majengo Market Mixed Reef Fish Value Chain: Kongowea Industrial scale

9. Independent fishers Employ -ed fisher surplus Auction Consumers Employed fishers Fish shops in Mombasa Mixed Reef Fish Value Chain: Vanga small-scale male traders Large-scale traders Small-scale female traders

10. 10 Net Income = Gross income (money received when selling) – Costs (buying price* + operational costs) *for traders only Analyzed across seasons Kaskasi (calm, more productive), and Kusi (rough weather, less productive) 1. Who benefits and how? Results/Discussio n

11. 11 1. Who benefits and how? Results/Discussio n

12. 2. What access limitations exist? Results/Discussio n Examine the aspirations of actors in the value chain and then assess their needs to fulfil those aspirations (REF??) Aspirations to change (% of pop) KongoweaVanga FISHERS KongoweaVanga TRADERS Sm scale (F) Sm scale (M)Sm scale (F) Sm scale (M)Large scale (M) Have aspirations to change Have no aspirations to change

13. TRADERS Sm (F) Sm (M) Lrg (M) Sm (F) Sm (M) Lrg (M) TRADERS Results/Discussio n Specific aspirations (%) Perceived barriers to change (%) 2. What access limitations exist? FISHERS KongoweaVanga Low income/Lack capital7894 Financial instability114 High living costs35 High starting costs412 Lack equipment51 Lack skills11 Lack support10 Poor relations10 Power relations10 No barriers10 FISHERS

14. Key findings Large-scale traders are few but their net income is significantly higher than any other trader category. Due to comparatively higher volumes, as avg value/kg is no different than other traders. However, if we look at it from the perspective of the entire system - how wealth from ecosystem services flows - we see that the largest share of wealth generated by the fishery is captured by the fishers (as a group) Kongowea Urban Vanga Rural Kaskasi (calm season) Avg net (node) income X node pop / tot net income generated in the system Fishers 81% 9% Traders 10% Fishers 71% 9% Traders, large Traders, sm 18% Traders, sm

15. Who is poor? (in terms of household assets) Poverty indicators (based on household assets) Kongowea Vanga %HMLHML Fishers236116135037 Traders Sm scale (F)4534342472 Sm scale (M)4405603664 Large scale (M)***10000 Rural actors generally poorer than urban, particularly small-scale trader (male &female) (but also fishers) In both rural & urban sites, women traders under-represented in high assets category Large traders all fall in the high assets category Can small-scale fisheries and associated value chains be considered ’pro-poor’?

16. 16 Methods Poverty indicators List of household assets (tailored to East African context) Household survey – collected in same sites PCA (on all asset items) used to get factor loadings for assets factor loadings (weights) used to calculate a poverty for each respondent based on the assets reported

17. Bridging the gap between poverty and value chain benefits Barriers to change – gaps between aspirations and resources Lack of financial capital

18. Bridging the gap… What opportunities are there for increasing the benefit to poorer segments of the value? (and those not currently participating in the value chain) Fishers Low- income consumers Sm-scale traders (F) Sm-scale traders (M) Lrg-scale traders (M) Med/High- income consumers Sm-scale traders (F) Fishers Low- income consumers

19. Bridging the gap… Low- income consumers Sm-scale traders (F) Sm-scale traders (M) Lrg-scale traders (M) Med/High- income consumers Sm-scale traders (F) Fishers Low- income consumers 1. Lower price paid by women to fishers >> increased profit to fish fryers >> decrease fishers income >> fishers exit >> declining stocks and resource status

20. Bridging the gap… Fishers Low- income consumers Sm-scale traders (F) Sm-scale traders (M) Lrg-scale traders (M) Med/High- income consumers Sm-scale traders (F) Fishers Low- income consumers 2. Increase price paid by low-income consumers >> increase fish fryers income >> may threaten local food security for poorest segment

21. Bridging the gap… Fishers Low- income consumers Sm-scale traders (F) Sm-scale traders (M) Lrg-scale traders (M) Med/High- income consumers Sm-scale traders (F) Fishers Low- income consumers TRADE-OFFS BETWEEN VALUE CHAIN ACTORS How change would be dealt with? >> Change can be absorbed by people in the node (e.g. fishers exiting/fryers increasing their income) >> or mediated through interaction w external elements (e.g. fishing harder/influx of fish fryers as profits rise)

22. Bridging the gap… Fishers Low- income consumers Sm-scale traders (F) Sm-scale traders (M) Lrg-scale traders (M) Med/High- income consumers Sm-scale traders (F) Fishers Low- income consumers Good or bad?

23. Usefulness of integrating value chains in fisheries research and management To mitigate ‘blindness’ to other actors (traders, gender) Account for the feedback from VC dynamics and actor behavior to the resource and VC itself Highlight Benefits from fisheries are much more than to catchers Benefits gendered Trade-offs between beneficiaries depending on objectives/strategies Role of value chains dynamics in affecting local food security Ecosystem health

24. 24 Thank you! THE ERLING-PERSSON FAMILY FOUNDATION

26. Results/Discussio n Can small-scale fisheries and associated value chains be considered ’pro-poor’? Kongowea Urban Vanga Rural Kaskasi (calm season) 3. Share of benefits Traders Traders, large Traders, sm KongoweaVanga %HMLHML Fishers236116135037 Traders mama4534342472 Mch4405603664 Taj10000 Poverty indicators (based on household assets) Fishers

27. Assessing access through barriers to entry A higher percentage of fishers in Kongowea (83% of respondents) compared to Vanga (67%) had ambitions to change from how they were currently catching and selling fish (Fig. 1a). In both Vanga and Kongowea trader’s aspirations to change increased down the value chain (Fig. 1b and c). Figure 1. a) Fishers ambitions to change from what they are currently doing in Vanga and Kongowea. b) Traders ambitions to change from what they are currently doing in b) Kongowea and c)Vanga. Yes=Black, No=White. Why do 40% MK not have any aspirations? Is there a difference (demographic) in those who have/not aspirations? Needs doing – Matilda will do soonish: Rerun the access analysis /graphs w revised categorization (just 4 resp), and also including captains (or was it boat owners) as their own category Can Matilda pull out the of women (40% ) who have no aspirations (using survid) and send to Bea – that way we can link it to other demographic data

28. Analysis from T3_GrossINcome_sp_T /Gross_Cost_Net_per person

29. Analysis from T3_GrossINcome_sp_T

31. Kongowea Fishers 81% 9% Traders 10% Vanga Fishers 71% 9% Traders, large Traders, sm 18% Traders, sm

33. % of poverty categories in each site/actor type KongoweaVanga %HMLHML Fishers23616135037 Traders mama4534342472 Mch4405603664 Taj10000