GAF6 – Bangkok, Thailand (August 4, 2016)Alexander Kaminski, Alexander Kefi, Steven M Cole, Kate Longley, Chifuniro Somanje, Pamela Marinda, Ansen Ward, Alexander Chilala, and Gethings Chisule

A Gendered Value Chain Analysis of Post Harvest Losses in the Barotse Floodplain, Zambia

Research project site

Lake Chilwa

Research Question One

What is the gendered nature of post-harvest losses (biophysical and economic) in the value chain?

What are the social and gender constraints to post-harvest losses and does gender inequality contribute to losses?

The “social” development challenge

Types of post-harvest losses (PHL)

• Total Loss: consumed by insects (or other animals), or due to spoilage, breakage, etc.

Biophysical Loss

• Biochemical changes and processing that denatures nutrients

Nutrient losses

• Quality Loss: leads to fish sold at lower cost

• Market force loss: demand and supply changes

Economic Loss

PHL in sub-Saharan Africa• The fisheries contribution to GDP in Africa is around 1.26% • The economic and biodiversity value of fisheries is important

to 200 million people in SSA (NORAD, 2009) • Women make up 27.3% of the people engaged in fisheries-

related activities in Africa– 3.6% of fishers and 60% of processors/traders are women (de Graaf &

Garibaldi, 2014)• It is believed that PHL within small-scale fisheries in Africa are

among the highest for all the food commodities (Morrisey 1988; Hodges et al. 2011)

• Total losses are about 5% in SSA and a further 20-30% are due to quality losses (FAO, 1996; Akande & Diei-Ouadi, 2010)

The Barotse Floodplain fishery • Poorest region in Zambia – 80.4% of people living in poverty (CSO,

2012)• Migratory fishing camps where daily consumption of fish is five times

the national average (Turpie et al., 1999)• Capture fisheries contribute over 70% to mean household income in

the floodplain• About 75% of fishers are men and about 25% are women (Turpie et al.,

1999) and more than 80% of traders are women (Longley et al., 2016)• Gender inequalities are pervasive in Zambia

– Norms and power relations create gender inequalities in access to and benefits derived from the fishery (Rajaratnam et al., 2016a, 2016b; Cole et al., 2015)

• Illegal fishing contributes to dwindling fish stocks (Kolding and van Zwietan, 2014; Turpie et al., 1999; Tweddle et al., 2015)

Research methodology

BASELINE INSTRUMENTS• Exploratory Fish Loss

Assessment Method (EFLAM)

• Quantitative Fish Loss Assessment Method (QLAM)

• Gross Margins Analysis (GMA)

• Women’s Empowerment in Fisheries Index (WEFI)

RESEARCH APPROACHES• Participatory Action Research

(PAR) with women and men testing the post-harvest fish processing technologies

• Practical Needs Approach (PNA) to ensure women’s/men’s practical needs are accommodated

• Gender Transformative Approach (GTA) to challenge and address the norms/power relations that cause gender inequalities in the fishery value chain

EFLAM/QLAM

Women’s empowermentLosses Profitability

Designing and testing technologies and social change interventions to reduce PHL

Participatory action research

Gender Transformative

approach

GMA WEFI

Exploratory Fish Loss Assessment method (EFLAM)

• Focus group discussions in six fishing camps the research project is operating in

• Qualitative scoping about losses (for whom, how, why, where, when, etc.)

Quantitative Loss Assessment Method

• Sample of 176 people (33% women, 67% men) from six fishing camps– Given some fishers also processed fish [28.3% of fishers (all

men)], total sample = 206, with:• Fishers = 106 (2% women, 98% men)• Processors = 60 (40% women, 60% men)• Traders = 40 (80% women, 20% men)

• Measured physical loss and reasons for loss, by sex and node

• Measured economic loss and reasons for loss, by sex and node

Women: n=2Men: n=104

Women: n=32Men: n=8

Women: n=24Men: n=36

Fishing_x000d_ Processing_x000d_ Trading_x000d_

17.78

28.49

36.4237.98

16.64

45.95

Mean kilogram of fish per node disaggregated by sex

women men

15.09% of fishers experienced loss primarily due to spoilage (53%)

44.83% of processors (63% of women, 32% of men: p-value = 0.0228) experienced loss primarily due to breakage (82% of men, 47% of women) and over processing (33% of women, 9% of men)

30% of traders (35% of women, 13% of men: p = 0.2379) experienced loss due to breakage (64% of women, 100% of men) and spoilage (36% of women)

women

men

total

women

men

total

women

men

total

Trad

ing

Proc

essin

gFi

shin

g

0 5 10 15 20 25 30

6.98

1.56

5.9

16.5

5.35

9.97

25

2.94

3.35

Total Loss (% of catch)

Fishers experienced loss primarily due to spoilage (34%) and market forces* (55%)

Processors experienced loss primarily due to breakage (58% of women, 55% of men) and market forces (42% of women, 36% of men)

Traders experienced loss due to breakage (25% of men, 17% of women), spoilage (42% of women, 25% of men), and market forces (50% of men, 42% of women)

*Size variation, high supply, price variation

women

men

total

women

men

total

women

men

total

Trad

ing

Proc

essin

gFi

shin

g

0 5 10 15 20 25 30 35 40

25.26

25.38

25.29

34.38

19.19

25.26

18.75

18.16

18.18

Economic Loss (% of total catch)

Gross Margins Analysis

• Sample of 239 people (33% women, 67% men) from fishing camps and in town– Fishers = 113 (100% men)– Processors = 50 (70% women, 30% men)– Traders = 76 (56% women, 44% men)

• Gross margins analysis measures the difference between revenue and costs of goods sold and expressed as a percentage indicating profitability of an enterprise

Men Women Men Women MenFisher Processor Trader

12.54

30.75

39.28

27.83 25.79

Mean Price (ZMW/kg)

Men Women Men Women MenFisher Processor Trader

79.06

677.59

465.39 532.37

980.8

Mean Total Variable Costs (ZMW)

Women processors incur higher variable costs on average and receive lower prices for fish

Women traders receive better prices but incur much lower variable costs.

Men traders have very high variable cost

Men Women Men Women MenFisher Processor Trader

21.49

2.56

5.5

13.7912.21

Mean Gross Margin (%)

100% men 70% women 56% women

WEFI• Adapted from the women’s empowerment in agriculture index

(WEAI) (IFPRI, 2012)• Sample of 151 people (39% women, 61% men) • Measured access to assets, decision-making powers, individual

leadership capabilities, gender attitudes and allocation of time• Primary reason women are in the fishing camps:

– 73% to trade fish– 15% to process fish– 7% to fish– 5% to do something else

• Primary reason men are in the fishing camps:– 90% to fish– 9% to trade fish– 1% to do something else

Went fish

ing in la

st 12 m

onths

Decisions o

n fishing

Spending m

oney from fish

ing

Processi

ng in la

st 12 m

onths

Decisions o

n processi

ng

Spending m

oney from proce

ssing

Trading fi

sh in

last

12 months

Decisions o

n trading

Spending m

oney from tra

ding0

10

20

30

40

50

60

70

80

90

100

Participation of women/men in key nodes of the fishery value chain and their own decision-making powers

Women

Men

Access to assets• A larger percentage of women’s households own locally-produced

fishing equipment (e.g., fishing baskets) compared to men’s (46% versus 30%)

• A larger percentage of men’s households own externally-produced fishing equipment (e.g., seine nets, hooks) compared to women’s (86% versus 34%) – Importantly, 0% of women own such gears themselves, while 62% of

men do • No women make their own decisions to sell, rent/give away or

purchase new externally-produced fishing gears, while majority of men do

• Similar results for women and men regarding processing and trading

Individual leadership in the camps

• 51% of women felt very comfortable speaking in public to help decide on projects or issues affecting the fishing camp, compared to 83% of men.

• 56% of women felt very comfortable speaking in public to protest the use of illegal fishing gears or activities, compared to 87% of men.

Gender attitudes

• Women have more gender equal attitudes than men (p-value = 0.0019)

• A greater percentage of men compared to women feel that women should not get involved in fishing and women should primarily be the ones who clean and process fish

• More men than women feel that they should primarily be the ones who control the earnings obtained from the sale of fish

• A greater percentage of men compared to women felt men should primarily be the ones who transport fish to a market for sale

• Women and men almost equally believe that women should primarily be the ones who prepare meals (including fish)

Gender Attitudes MeanWomen 19.98*Men 18.11

*Perfect gender equal attitude score = 24, perfect gender unequal attitude score = 8

Sleeping a

nd resti

ngEati

ng

Fishing

Fish proce

ssing

Fish tra

ding

Transp

orting fish

to m

arket

Gardening,

farming,

and/o

r live

stock

rearing

Other Acti

vties e

.g. buyin

g groce

ries

Cooking

Domestic w

ork

Care fo

r child

ren, adults

, elderly

Sports

Drinkin

g alco

hol0

1

2

3

4

5

6

7

8

9

Time allocation (average hours per day)

Women

Men

Conclusions• Women face higher physical and economic losses than

men• Women incur smaller gross margins in processing node• A greater percentage of men make individual decisions on

many fishing-, processing-, and trading-related processes• Gender attitudes about women’s and men’s involvement

in key activities in (and outside) the fishery value chain and their allocation of time devoted to paid and unpaid (e.g., home-based) activities may influence women’ abilities (in particular) to process higher-quality fish with minimal losses

Drivers of PHL• Complex no doubt• Poverty and marginalization, lack

of access to improved technologies and markets, climate change, etc.

BUT also…

• Women’s access to resources, lack of individual decision-making powers, socially-assigned roles and gender attitudes, and time allocation (especially carrying out unpaid tasks)

How the project is utilizing these results

Social and gender analysis

to highlight harmful norms, behaviors and

power relations in a PHL context

Design and test innovations that

address that cause gender inequalities

and prohibit women from

processing higher quality fish

Reduced gender gaps

Greater adoption and utilization of technologies

Improved gender relations

More sustained

development impact for all

Research output Outcomes Impact

Figure: Gender transformative impact pathway to change

Participatory action researchCommunity defines

the problem and together plan

actions

Undertake actions

Observe and analyse the

results

Reflect on what the

results meanPAR on Post Harvest Fish Losses

Undertake varietal trials with farmers

Observe and analyse results

Reflect on what the results

mean

Plan trials

Undertake action with

group

Observe and analyse results

Reflect on what the

results mean

Plan new actions

PAR on Solar Drier

PAR on gender roles

PHL-reducing technologies

Using PAR to implement technologies

Gender transformative communication tool

Dramas are performed in fishing camps

Questions are presented to PAR groups

Actions to address the

harmful social and gender norms are carried out

Key insights• Post-harvest fish losses literature lacks gendered analyses• Gender norms and power relations influence how, when,

where fish get processed and as a corollary contribute to post-harvest losses in the value chain, and especially for women occupying the processing node

• Gender transformative approaches and post-harvest fish processing technologies should be tested/promoted together to address both the technical and social issues that lead to PHL

• PAR can be a vehicle for implementation by building ownership of the research and developing capacities

Acknowledgments

• The research project is funded by IDRC and ACIAR under the Cultivate Africa’s Future (CultiAF) fund

• The Zambia component of the research project is being implemented by the Department of Fisheries, University of Zambia, WorldFish, and NoNo Enterprises

Thank You!