fish value chains How can they inform strategies for poverty alleviation and sustainability Presented by Beatrice Crona Stockholm Resilience Center amp Royal Swedish Academy of ID: 541495
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Slide1
Small-scale developing country
fish value chains
How
can they
inform strategies
for poverty alleviation and sustainability?Presented by: Beatrice CronaStockholm Resilience Center & Royal Swedish Academy of SciencesMatilda Thyresson, Postdoctoral researchers, SPACES SwedenTim Daw, Senior Researcher, SRC, PI SPACESAndrew Wamukota, Postdoctoral researchers, SPACES Kenya
1Slide2
2
2
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?Slide3
3
3
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 catchersGendered – women trader/fish friersTrade-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)Slide4
4
Scope and Aims
Can
small-
scale
fisheries and associated value chains be considered ’pro-
poor’?
Who benefits and how?What access limitations exist?AspirationsNeeds, gaps and barriers to change
What opportunities
are there
for increasing the benefit
to
poorer
segments
of
the
value
chain
(and
those
not
currently
participating
in the
value
chain
)?
What
are
the
key
trade
-off
emerging
? (social,
economic
,
environmental
)Slide5
5
Methods
Looking at SSF value chains
Familiar story of complexity, diversity, limited data, messy data
collection
Many different actorsDifferent yet often highly intertwined VCs for different types of fish/productsGenerally quite dynamic
seasonal fluctuations & informal nature leads to more flexibility and adaptation by actors - CAS
behaviorSlide6
6
Methods
Key informant interviews
Survey
Kenya and Mozambique
1. Mixed reef fish
Total
Fishers418Traders208Total 6262. Octopus 3. Small
pelagics
Kongowea
Urban
Vanga
Rural
Fishers
88
117
Traders
65
58
Sm
scale
traders (F)
47
25
Sm
scale
traders (M)
18
28
Large
scale
traders
0
5Slide7
7
Strong
seasonality
– South
East (Kusi) and North East monsoon (Kaskasi) dominate
fishing
conditionsScale of fishery Kongowea UrbanVanga
RuralTotal catch
Biomass levelsNode/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
m
arket
proximity
high
low
System description
INSERT PICS of sites w boats
etc
~ approximate numbers as daily and seasonal fluctuations
Insert catch levels
biomass
CapitailzationSlide8
Fishers (employed and independent)
Fish Shops
Low-income consumers
Trawlers
Food Kiosk
Middle-income consumersHigh-income consumersRestaurants
Tourist Hotels
Female small-scale traders
Male small-scale traders
Large-scale traders at
Majengo
Market
Mixed Reef Fish Value Chain:
Kongowea
Industrial scaleSlide9
Independent fishers
Employ-
ed
fisher surplus
Auction
ConsumersEmployed fishersFish shops in Mombasa
Mixed Reef Fish Value Chain:
Vanga
small-scale male traders
Large-scale traders
S
mall-scale female traders Slide10
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/DiscussionSlide11
11
1. Who
benefits and how?
Results/DiscussionSlide12
2
. What access limitations exist?
Results/Discussion
Examine the aspirations
of
actors in the value chain and then assess their needs to fulfil those aspirations (REF??)Aspirations to change (% of pop)KongoweaVangaFISHERS
Kongowea
VangaTRADERSSm scale (F)Sm scale (M)Sm scale (F)Sm scale (M)Large scale (M)
Have aspirations to change
Have no aspirations to changeSlide13
TRADERS
Sm (F)
Sm (F)
Sm (M)
Sm (M)
Lrg (M)Sm (F)Sm (F)Sm (M)Sm (M)Lrg (M)TRADERSResults/DiscussionSpecific aspirations (%)
Perceived barriers to change (%)
2. What access limitations exist? FISHERS
Kongowea
Vanga
Low income/Lack capital
78
94
Financial instability
11
4
High living costs
3
5
High starting costs
4
12
Lack equipment
5
1
Lack skills
1
1
Lack support
1
0
Poor relations
1
0
Power relations
1
0
No barriers
1
0
FISHERSSlide14
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
%
T
raders
Traders
10%
Fishers 71%
9
%
Traders, large
Traders,
sm
18%
Traders,
smSlide15
Who
is poor?
(in
terms
of
household assets)Poverty indicators (based on household assets) Kongowea Vanga %
H
MLHM
L
Fishers
23
61
16
13
50
37
Traders
Sm scale (F)
4
53
43
4
24
72
Sm scale (M)
44
0
56
0
36
64
Large
scale (M)
*
*
*
100
0
0
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
h
igh
assets
category
Large
traders all fall in the
high
assets
category
Can
small-
scale
fisheries
and
associated
value
chains
be
considered
’pro-
poor
’?Slide16
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 assetsfactor
loadings (weights) used to calculate a poverty for each respondent based on the assets reportedSlide17
Bridging the gap between poverty and value chain benefits
Barriers
to
change – gaps between aspirations and resourcesLack of financial capitalSlide18
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)FishersLow-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 Slide19
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 statusSlide20
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 segmentSlide21
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)Slide22
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? Slide23
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
H
ighlight
Benefits from fisheries are much more than to catchersBenefits genderedTrade-offs between beneficiaries depending on objectives/strategiesRole of value chains dynamics in affecting local food securityEcosystem healthSlide24
24
Thank you!
THE ERLING-PERSSON FAMILY FOUNDATIONSlide25Slide26
Results/Discussion
Can
small-
scale
fisheries and associated value chains be considered ’pro-poor’?KongoweaUrbanVanga
Rural
Kaskasi (calm season)
3. Share of benefits
T
raders
Traders
Traders, large
Traders,
sm
Traders,
sm
Kongowea
Vanga
%
H
M
L
H
M
L
Fishers
23
61
16
13
50
37
Traders
mama
4
53
43
4
24
72
Mch
44
0
56
0
36
64
Taj
100
0
0
Poverty indicators (based on household assets)
Fishers
FishersSlide27
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 categoryCan 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 dataSlide28
Analysis from T3_GrossINcome_sp_T
Analysis from T3_GrossINcome_sp_T
/Gross_Cost_Net_per personSlide29
Analysis from T3_GrossINcome_sp_TSlide30Slide31
Kongowea
Fishers 81%
9
%
T
radersTraders10%VangaFishers 71%9%Traders, largeTraders, sm18%Traders, smSlide32Slide33
% of poverty
categories in each site/actor type
Kongowea
Vanga
%
HMLHMLFishers23616135037Traders
mama
4534342472Mch
440
56
036
64
Taj
100
0
0