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Small-scale developing country
Small-scale developing country

Small-scale developing country - PowerPoint Presentation

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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 Download Presentation

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Presentation on theme: "Small-scale developing country"— Presentation transcript

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 FOUNDATIONSlide25
Slide26

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_TSlide30
Slide31

Kongowea

Fishers 81%

9

%

T

radersTraders10%VangaFishers 71%9%Traders, largeTraders, sm18%Traders, smSlide32
Slide33

% of poverty

categories in each site/actor type

Kongowea

Vanga

%

HMLHMLFishers23616135037Traders

mama

4534342472Mch

440

56

036

64

Taj

100

0

0