Chapter 3. The survey of fish traders and processors

3.1. Summary of findings: a profile of a trader/processor of fish from Lake Victoria

3.1.1. A fish trader/processor will be male or female (if trading arid processing she ismore likely to be female). Nearly half (49%) of traders/processors are engaged inanother activity besides fish trading, mainly farming (83%: Table 21).

Table 21: Respondents' second activity to fish trading/processing

3.1.2. Most respondents (71%) trade without being involved in processing. 50% oftraders/processors deal in Nile perch, followed by 25% who deal in tilapia and 25%who deal in dagaa (Fig. 17). 49% of traders purchase their fish supplies directlyfrom fishers (Fig. 18). 83% of traders deal in only one species, and 65% say thatthey would not change to trading another (Table 22).

Fig.17: Percentaqe of respondents who trade and/or processdifferent species of fish

C,,a)

TUapia

(J)

Dagaa 140 47

159

42

31

Activity Frequency %Farming 326 40.2Making/selling industrial/consumer goods

15 1.9

Handicrafts 21 2.6Others 32 3.9No second activity 417 51.4Total 811 100

I L! I L

0% 20% 40% 60% 80% 100%

% respondents

íiJ Trade ory D Trade & process

Nile perch 237 t 143

Fig. 18: Respondents' suppliers

Wholesalers atmarkets

33%

h facto es6%

43

F he rs49%

)

Table 22: If respondents trade in a second species and if they would like to changeto trading an alternative one.

3.1.3. Traders and processors generally experience supply problems, of which the gravestare poor catches(64% out of 653 mentioned problems). This is particular problemfor Nile perch and tilapia traders (Fig. 19). Only 3% of traders citing competition asthe reason for supply irregularities blamed competition with fish firms as the reason(not shown).

3.1.4. Of 373 Nile perch traders interviewed, the largest proportion deal in smoked (47%)or fresh (37%) Nile perch. Out of 193 tilapia traders, 47% will normally deal infresh fish, followed by 32% who deal in smoked fish. 95% of dagaa traders deal inthe sun-dried product (Fig. 20).

2nd species traded? Change to trade other species?Frequency % Frequency

Yes 141 17.4 287 35.3No 671 82.6 525 64.7Total 812 100 812 100

oa)

(J)

Fiq.19: Reasons for irreqular supp'ies given by respondents (%)deaUng in various species

100%

80%

60%

40%

20%

0%

Dagaa

Tiapia

NHe perch

iFresh DSmoked DSun-dried IlFried

44

3.1.5.. Just over half of all traders (53%) will sell their produce within 20km of itspurchase location. Kenyan traders are more likely than Ugandan or Tanzaniantraders to travel over 50km to sell their fish (Fig. 21).

.7atu 21 711

36 11116 8

3287 25221

176

91 6226139 175

Poor catches Competition High prices Others

Reason

Nfle perch D Tilapia D Dagaa

Fig. 20: Respondents (%) dea Inq in various fish formats byspecies traded

0% 20% 40% 60% 80% 100%

% respondents

3.1.5. Just over half of all traders (53%) will sell their produce within 20km of itspurchase location. Kenyan traders are more likely than Ugandan or Tanzaniantraders to travel over 50km to sell their fish (Fig. 21).

Fg.21: Respondents (%)travellinq various distance rancesbetween fish source and market

45

ftessthan 20 km 021 -50km DMore than 50 km.

3.1.6. Most traders (42%) will travel to market 2-3 times per week. Very few traders( 5%) deal in more than 5 00kg of fish per trip (Table 23).

Table 23: Estimated amount of fish taken to market per trip (kg.) and number oftrips to market per week.

3.1.7. Traders/processors judge the quality of fish they buy by visual inspection (49%)gill inspection (24%) and its smell (11%; n801; Table 24). Most trader/processors(5 3%) do not take steps to maintain the quality of their fish. Of those who did, themost common technique used was to process the fish in some way or to display orpackage the fish properly (Table 25). Most trader/processors (75%) felt that thequality of the product in which they traded was good (Table 26).

No trips to market pei veekQuantity (kg) O-1 2-3 4-7 Total<500 kg 149 246 171 566>500 kg 47 29 21 97.

Total 196 275 192 663

100%

90%11380% 63 78

70%

60%

50% 67

40%30% 202

20% 131 95

10%

0%

Uganda Kenya Tanzania

Table 24: How respondents judge the quality of the fish that they buy by fishformat normally traded

Table 25: Respondents' reasons for fish spoilage by whether or not they take anyadditional steps to maintain the quality of their fish products

3.1.8. 56% of traders/processors have problems with fish spoilage. Half of those who tookextra steps to improve/maintain the quality of their product are included within thisgroup (Table 25). 99% of those indicating problcms with fish spoilage still ratedtheir product as acceptable or good (Table 26). Causes for fish spoilage wereidentified as rain (36%) poor storage (28%) and poor handling (18%; Table 25).

Table 26: How respondents rate the quality of the fish they deal in by whether ornot they are troubled by fish spoilage

3.1.9. Very few traders/processors (9%) havc had any training which they considered ofrelevance to their trade. Those who did have, trained mainly in book keeping andquality control (Untabulated data).

46

FormatInspection of: Fresh Smoked Sun-dried Others TotalThe gills 133 41 6 15 195The smell 7 53 25 2 87Touch 11 25 26 1 63Visual inspection 86 115 134 55 390Others 14 11 36 5 66Total 251 245 227 78 801

Additional quality steps takenReason for

spoilageProperpacking

Processing Properdisplay

Others Nosteps

Total

Poor handling 11 9 3 12 45 80Poor storage 7 14 7 15 56 99Rain 20 20 24 21 77 162Others 8 16 17 26 47 114No spoilage 32 31 32 58 209 n

L,

Total 78 90 83 132 434 817

Fish spoilage?Quality rating Yes No Total

Good 344 267 611Acceptable 105 86 191

PoorTotal

3 5 8

452 358 810

3.1.10. In setting their prices most (73%) of Lake Victoria's traders/processors considertheir costs as the determining factor (Table 27). Trader's/processor's incomes dovary throughout the year. There are, however, no strong common patterns of peakand low income months cited.

Table 27: Factors respndents consider when sttig their prices

3.1.11. Most trader/processors are satisfied with the current fish product that they trade in(Table 22). Of the 35% who are not satisfied it is because of the inadequate profitsachieved (Table 28).

Table 28 Res iondents' reasons for wantin to trade in an alternative fish s. ecies

Table 29: Problems ex

47

3.1.12. Traders and/or processors were asked for the worst problems they faced, and 1,291responses were given. The largest proportion of responses concerned lack ofsufficient funds with which to manage their businesses, followed by those whoexperienced transport problems (Table 29).

Mab probteProblem Frequency %

Factor - - -My costs 582 73.2No. of competitors/sellers 68 8.5Fish size 60 7.5No. of buyers 36 4.5Price set by buyer 21 2.6Quality 21 2.6Other 8 1.1

796 100

easou /0Lack of funds 202 24.9Competition too high 18 2.3Others 64 7.9Content with present species 527 64.9

811 100

Lack of funds 240 18.6Transport problems 189 14.6Market fees too high 102 7.9Poor catches 52 4.0Not enough buyers 54 4.2Others 654 50.7

1,291 100

erienced b traders and rocessors

3.2. Objectives of the survey of traders and processors

Identify and describe the peopie involved in the fish trade and processingindustry.

To investigate the impact of the export market upon the domestic fish trade.

e. To examine the participation of women in the fish trade.

d. To examine the structure and organisation of the fish trade.

4

3.3. Hypotheses for survey of fish traders and processors

Most traders deal in more than onespecies oftish

Out of our sample of 812 trader!processors, 83% did not trade in asecond species (Table 22).

Iviost fIsh traders are /inale

Of our sample of fish traders, 457(56%) were female and 360 or44% were male (Fig. 22).

Most fish processors are female

Of 237 respondents involved in fishprocessing, 33% were males(untabulated data).

Traders cannot get enough fish tosell because of competition fromindustrial processors.

Out of 653 supply problemsmentioned by the rcspondentsthere were 415 (64%) mentions ofpoor catches, 146 mentions (22%)of competition, 50 mentions (8%)of prices being too high. Only 3%of mentions referred tocompetition with fish processingplants (Fig. 19).

Traders/processors buy direct fromfIshers

Of 384 respondents, 49%identified fishers as their mainsuppliers, followed by 33% whoidentified wholesalers at marketsas suppliers, 12% who identifiedprocessors at beaches as suppliers,and 6% who obtained their flshfrom fish factories (Fig. 18).

Transport problems cause the greatestdifficulties for traders.

Of 1,291 general problems mentioned bytraders and/or processors the mostcommonly mentioned was lack of funds(19%), followed by transport problemas(15%), and market fees being too high(8%; Table 29).

Most traders deal in a small amount of Jìshbut visit markets often.

Of 566 traders and/or processors dealingin under 500 kg of fish per trip to market,the largest proportion (n246; 44%) willmake 2-3 trips to market per week. Of 97traders and/or processors who deal in over500 kg. of fish per trip, most, 47 (49%) willgo to market 0-1 times per week (Table 23).

There are few people entering into fishrradingivrocessing activities.

In fact 269 (34%) of respondents have lessthan 2 years experience trading and/orprocessing. This would seem to indicatecontinued entry into this field of work(Table 31).

Most traders do not take stej to conserve thequality offish they trade

Out of 817 respondents, 53% take no stepsto conserve/maintain the quality of theirfish (Table 25).

49

3.4. Detailed analyses

3.4.1. The sample comprised 360 males and 457 females. In Uganda, men and women aremore evenly distributed in the industry than in Kenya, where 16% of respondentswere men, and in Tanzania, where 22% of respondents were women. Regionally, atrader/processor is more likely to be a woman than a man (Fig. 22). With equalweighting between men and women, 70% of those in the fresh fish trade are men,while 71% of those involved in the sun-dried fish trade are women (Fig. 23).

Fiq.22: Respondents' gender by country

1«iT1fJ H i I LLÍ11 ilTanzania 155 43

Kenyaoo

Eo4-rC,,

LL

Sun-dried

Smoked

Fresh

0%

I H I I

t )I i

50

Maie

Fg.23: Proportion of males and females tradinq ¡n fresh, smokedand sun-dried fish (weighted in favour of males by 1 .19)

I Li I I I I I

169 70

Male D Female

197

20% 40% 60% 80% 100%

% respondents

127

I I I I

Uganda 155 H 145

0% 20% 40% 60% 80% 100%

% respondents

50 269

3.4.2. 71% of respondents oniy trade fish and do not process it (n808). Only 29%of respondents are involved in processing fish in some way (untabulated data).Most of these are women. Just 22% of men are involved in processing at all(n=355: untabulated data). Fish processing occurs mainly at beaches, where 49% ofthose who are involved in processing their fish product were interviewed (n=237).22% of those who trade only were encountered at beaches, while the remaining78% were interviewed at in-land markets (n=571; Table 30).

Table 30: Interview locations by respondent's status

3.4.3. Out of 803 respondents, the largest proportion had been involved in their trade forfive or more years (44%). Most (51%) of those processing their fish had beeninvolved for less than five years (Table 31). With the sample equally weightedbetween the sexes, 58% of those who have traded for less than two years arewomen, while 63% of those who have worked from 2 to 5 years are men. Thelargest proportion (43%) of (weighted) men have worked for more than five years,while a similar proportion of women (44%) have also traded fish for more than fiveyears (Fig. 24). Of 269 respondents who have traded for less than two years, only23% do any processing at all, possibly because processing requires a larger initialinvestment than does trading alone (Table 31).

Fig.24: Respondents' gender by time spent trading (weighted infavour of males by 1.19)

100%

° 80%

60%

40%

20% - 126

0%

Less than 2 years

17679

134

2 -5 years

Time range

D Male D Female

51

202

197

5 years plus

Interview locationStatus Beach Inland, small mkt. Inland, large mkt. TotalTrade only 127 191 253 571Trade &process 116 81 40 237Total 243 272 293 808

Table 31: Respondent's status by time spent trading main species

3.4.4. 77% of traders and processors have dealt in only a single species of fish. Of 178respondents who had changed fish species, 50% had changed to trading orprocessing Nile perch, 28% to tilapia and 22% to dagaa (Table 32). Of 178 reasonsprovided for why respondents had changed the species in which they traded, themost common was that they believed that they were going to obtain better suppliesof fish, gaining 56% of mentions. Expectations of greater profits obtained 28% ofmentions, while 65% of respondents did not want to change the species of fish inwhich they are currently involved (Table 33). Of 269 respondents who did want tochange to trading an alternative species, 71 % claimed to be unable to do so becauseof 'lack of funds'. Of those who wanted to change species, 54% traded Nile perch,20% traded tilapia, while the remaining 26% traded dagaa (Table 33).

Table 32: If respondents have ever changed the species in which they trade by mainspecies traded

Table 33: If respondents want to change to trading an alternative species

3.3.5. Half of all fish traded by respondents was Nile perch, followed by tilapia (25%)and dagaa (25%; n763). Kenya, Uganda and Tanzania share the Nile perch tradeby roughly a third each. Tanzania holds just 9% of the tilapia trade, while Kenyaaccounts for 46% of the regional dagaa trade (n=1 87, Fig. 25), 72% of all dagaatraded being carried out by women (n187; Fig. 26).

52

Time spent tradingStatus <2 yrs. 2-5 yrs. > 5 yrs TotalTrade onlyTrade & process

20861

121 239 56860 114 235

Tota! 269 181 353 803

SpeciesChange species? Nile

perchTilapia Dagaa Total

Yes, but cannofduè to lack of funds 103 36 53 192Yes, but cannot for 'other' reasons 43 19 15 77No: content with present species 236 139 119 494TOtal 382 194 187 763

Reason for changeSpecies Availability Profitability Others No change TotalNile perch 58 19 12 293 382Tilapia 26 16 7 145 194

Dagaa 15 15 10 145 185

Total 99 50 29 583 761

Dagaa

C,)a)

Tilapia(J)

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

86

I I I

40

Fig.25: Main species traded by country

186

196

Nile perch

1 I

Uganda D Kenya D Tanzania

Fig.26: Species traded by respondents' gender

101

Tilapia

Species

82

135

52

Dagaa

17

Nile perch 122 129

Ï t I

0% 20% 40% 60% 80% 100%

% respondents

D Male D Female

53

3.4.6. Out of 752 traders and/or processors, 31% dealt in fresh fish, while the remaining5 16 respondents dealt in processed fish. Of this latter group, 45% dealt in Nileperch, 20% in tilapia and 35% in dagaa. Out of 373 Nile perch traders, 63% dealt inprocessed fish, mainly smoked fish (47%). 95% of dagaa traders dealt in a sun-dried product. Just over half (53%) of those trading in tilapia dealt in a processedproduct (Fig. 20).

3.4.7. Regionally, the distribution of fish formats amongst trader/processors was fairlyeven between those who traded in fresh fish (32%), smoked fish (31 %) and sun-dried fish (29%; n=795). Between countries, however, there are large differences.Of 228 respondents interviewed in the region trading in sun-dried fish, 48% werelocated in Kenya, while out of 246 traders who dealt in smoked fish, 47% wereencountered in Uganda. Out of 69 respondents who dealt in fried fish, 72% wereKenyans (Fig. 27).

Fig. 27: Reqional fish formats traded by respondents by country

100%

90% -80% -70%

60%

50% -- 99

40% --30%

20% -- 7610%

0%

50 -i 40 1577

55

141

54

110

50

78

D Uganda D Kenya DTanzania

3.4.8. In Tanzania, 42% of respondents traded in fresh fish (n182; Fig. 27). Out of 166Tanzanian respondents interviewed, 66% of them dealt in Nile perch. Out of 84Kenyan respondents trading in fresh fish, 55% dealt in Nile perch (Table 34). Ofthe 68 Kenyan tilapia traders, over half (52%) dealt in processed tilapia. Out of 85Kenyan dagaa traders, just 7% did not deal in the sun-dried product. Of 141Uganda traders dealing in smoked fish, 65% dealt in Nile perch, followed by tilapia(34%; Table 34).

Fresh Smoked Sun-dried Fried

Fish format

Table 34: Format in which fish is traded by country

3.4.9. Traders judge the quality of the fish they buy in a variety of ways. Out of 801respondents, the most common way of assessing the quality of fish was via a visualexamination (46%). Out of the 251 regional respondents who dealt in fresh fish,53% checked the quality of the fish they bought by examining its gills. Of 245traders dealing in smoked fish, 47% gave the fish they bought a visual inspection,as did 59% of the 227 respondents dealing in sun-dried fish (Table 24). Just 1% ofrespondents claim that the quality of the fish they trade is poor (Table 26).

3.4.10. From our sample of 817 respondents, 53% did not do anything to maintain thequality of their fish product. For 383 respondents who did do something tomaintain the quality of their fish, the most common methods were to process thefish, pack it properly or 'display' it properly (Table 25).

3.4.11. Of 816 respondents interviewed, 56% claimed to be affected by fish spoilageproblems. Of 300 Ugandans interviewed, 56% were untroubled by spoilage(compared to 62% of Kenyans and 65% of Tanzanians; Fig. 28). Of 402respondents who explained their fish spoilage difficulties, 36% attributed them torain, followed by 22% who attributed them to inadequate storage facilities. 51% ofthe 125 respondents who dealt in sun-dried fish products attributed their spoilageproblems to rain. Of 131 respondents involved in the smoked fish trade, 34% werealso affected by rain, while the most commonly cited source of fish spoilage for the146 involved in the fresh fish trade were 'poor storage' facilities (such asrefrigeration; Table 35).

55

Fish speciesNile perch Tilapia Dagaa Total

UgandaFresh 33 'f-, 76

Smoked 92 48 1 141

Sun-dried 3 3 59 65

KenyaFresh 46 33 S 84

Smoked 34 13 1 48

Sun-dried 4 22 79 105

TanzaniaFresh 60 15 1 76

Smoked 49 1 50

Sun-dried i 38 40

Total 322 179 184 685

Fig. 28: Proportion of respondents who experience fish spoilaqe bycountry

56

Spoilage D No spoilage

Table 35: Reasons for fish spoilage from respondents dealing in fresh, smoked andsun-dried fish products

3.4.12. Of 781 respondents, the largest proportion (44%) make two or three trips to marketper week. Not surprisingly, the largest proportion of those who make the trip tomarket betwén four and seven times a week deal in fresh fish, while those whodeal in processed fish go to market less frequently (Fig. 29).

3.3.13. Of 770 respondents, 49% obtain their supplies directly from fishers, followed by33% who obtain their supplies from wholesalers at markets. 75% of those whotrade in fresh fish gain their supplies directly from fishers, while 43% of those whotrade in smoked fish and 41% of those who deal in sun-dried fish obtain theirsupplies from wholesalers at markets (Fig. 30).

fish formatReason for spoilage Fresh Smoked Sun-dried TotalPoor handling 23 29 17 69

Poor storage facilities 50 16 21 87

Rain 34 45 64 143

Other 39 41 23 103

Total 146 131 125 402

100%

80% 122 69

169

60%

40%128197

20% 131

0%

Uganda Kenya Tanzania

100%90%

80%70%60%50%40%30%20%10%0%

Fig.29: Ranges of respondents1 trips to markets by main fishformat traded

Sun-driedo4-

Smoked

Fig. 30: Respondents fish suppliers by fish format normallytraded

1HHL I IM

Fried 2E

Fresh

0%

89 15

57

34

I I

t

I I I -.

20% 40% 60% 80% 100%

% respondents

Fishers Factories Processors at beach D Wholesalers at rnkts.

Fresh Smoked Sun-dried Fried

Fish format

Oto i times 2 to 3 times 4 to 5 times

180 11

Fiq. 31: Whether or not resondents have reqular supplies

0% 20% 40% 60% 80% 100%

% respondents

D Regular supplies D Irregular supplies

3.3.14. Of 825 respondents, 65% claim to suffer from irregular fish supplies. Over half(54%) of these are Nile perch traders (Fig. 31). Respondents provided 653responses to explain these problems. The most common of these was poor catches(64%). High supply prices obtained 8% of responses, while high levels ofcompetition gained 22% of replies. Of the 144 responses relating to competition,86% referred to competition between traders and the remainder to competition withfish firms (Fig. 19).

3.3.15. Of 658 respondents, 86% do not trade in more than 500 kg. of fish per trip tomarket, and 52% of them do not trade more than 20 km. from their fish supplysource. Of the 95 who claim to deal in more than 501 kg. of fish per trip to market,55% will sell their fish at markets more than 50 km. from their supply sources(Table 36).

Table 36: Quantity of fish respondent takes to market by distance to market

3.3.16. Respondents face a great number of problems with respect to the facilities availabieto them in their trade. Out of 628 respondents, transport problems were mentionedby 51% - either the poor state of access roads, or the lack of transport. Most of the183 Tanzanian respondents (62%) considered this to be the worst problem that theyfaced (Table 37).

58

Distance range (km)Kilo range <20 21 - 50 > 59 Total<500 kg 342 101 120 563>501kg 31 12 52 95Total 373 113 172 658

(I)a)

(J)

Dagaa 97

Tilapia

Nile perch

I I I II I

2

99

146

I I i f

290

Table 37: Respondents' problems with the facilities available to them by country

59

3.3.17. For 796 respondents, the principal influencing factor in the determination of theirprices was their costs (73%), followed by the number of competitors (9%) (Table27).

3.3.18. Incomes to traders and processors fluctuate throughout the year depending on anumber of factors. When questioned as to what these were, respondents gave 1,133responses for why their incomes might 'peak' during certain periods, and 1,130responses as to why they might drop during other periods. The largest proportion ofresponses for 'peak' periods were high catches and low catches, each obtaining14% of responses. In the latter case, traders' incomes peaked because they hadplenty of fish to sell, while in the former they peaked because there was little fishand they were able to obtain higher prices per unit. In the case of income decline,catch variations again provided the core of responses (29%), while seasons also hadan important affect on incomes declining (Tables 38-39).

Table 38: Factors affectin res iondents' incomes during '.eak' eriods

iactor ire uenc71Lots of buyers 6.3

Few buyers 35 3.1

High catches 159 14.1Low catches 156 13.8High prices 81 7.1Low prices elsewhere 11 0.9Consumers harvesg 385 33.9Holidays 22 1.9Accessible roads 13 1.1

No food but fish 40 3.5Dry season 24 2.1Others 136 12.1iI 1133 100

Problem UgmdaConty

TanzaniaKeiya TotalTransport 106 103 113 322Storage 35 19 28 82Market fees 73 51 16 140Others 44 14 26 84il aiL 258 187 183 628

Table 39: Factors affecting respondents' incomes during 'low' periods

3.3.20. The sample discussed here does not do justice to the regional trade in Nile perchskeletons ('frames'). As the industriul processing sector grows, so higherproportions of the Nile perch landed goes to these factories. In Tanzania in 1996,Gibbon (1997) estimates that 80 per cent of the Nile perch landed was bought bythe fish filleting factories. As the industrial component of Lake Victoria's fisherygrows, so its by-product production also increases. Throughout the region, the tradein Nile perch frames appears to be largely restricted to urban areas and 'centres',where demand for very cheap fish products is high from low income groups. InUganda, middlemen collect the frames from the processing factories and deliverthem to urban centres by road. The dagaa trade in Uganda remains poor, with muchof the country's dagaa catch probably moving to neighbouring countries. TheUgandan trade in Nile perch frames appears, therefore, to fill that demand nichecreated by low income groups for cheap fish, but who are as yet unwilling toconsume dagaa for whatever reason. Observational data suggest that almost all theNile perch frames from Ugandan filleting factories is disposed of on the localmarket.

In Tanzania, the trade in frames appears to be largely seasonal, peaking during thedry seasons and in the month or two immediately prior to harvest when farmproduce supplies are running low. In Kenya, much has been written about the risein the production of processed Nile perch frames (Abila, 1994, 1995, 1996; Abilaand Jansen, 1997; Jansen, 1997), which centres on Kisumu and for which demandexists in urban markets elsewhere in the country.

Suggestions have been made that the international trade in Nile perch fillets has ledto the sidelining of small-scale fish traders unable to compete with the factories(Jansen, 1997; Abila and Jansen, 1997; Medard and Wilson 1996). The growth ofthe animal feeds industry has led to the channeling of some of Kenya's dagaa catchlo animal feeds industries, as well as Nile perch by-products (Abila and Jansen,1997). As these developments occur, the implication made is that demand fromthese large-scale processing plants represents a net loss of fish to the LakeVictorian fish consumer, and even a possible contribution to regional malnutrition(Ssale et al. 1991; Jansen, 1997).

60

Facf Frequency %Too few buyers 84 7.4Seasonal variations 154 116Low catches 165 14.6High catches 159 14.1

Before harvest, so no money 108 9.6After harvest, so no need for fish 106 9.4Low prices elsewhere 71 6.3School fees due/term-time costs high 39 3.5Others 244 21.6

1130 100

Overall, this study does indicate that there would appear to be a general consensuswithin the regional market for Lake Victoria's fish that declines have occurredwithin catches. As would be expected, this has resulted in price increases on localfish markets which consumers have indicatcd may sometimes push fish out of theirreach. The international trade in Nile perch may, at certain times of the year, alsocontribute to increases in local prices. At other times of the year, when Nile perchcatches are high, the affect of the international trade in Nile perch on local priceswould appear to be minimal.

It is not clear that the international trade in Nile perch contributes to localmalnutrition. Local traders appear to be willing to invest in the Nile perch fisheryand do not identify competition with large scale filleting factories as a major factorinfluencing their fish supplies. Consumers, similarly, appear able to obtain Nileperch regularly. It is not clear if the Nile perch obtained is predominantly rejectsand/or under-sized fish unacceptable to the factories.

This study has not obtained any evidence to suggest that the growth of the animalfeeds industry has resulted in declines in the availability of &igaa on local markets.Variations in local dagaa availability are the result of the lunar cycle (the maindagaa catch being obtained when there is no full moon); and the rains when dagaacan often not be dried sufficiently well to interest local markets. In thesecircumstances, much dagaa is sold to the animal feeds industry.

61

3.5. The survey of traders and processors: sampling strategies field difficulties, possibleareas of bias and rcornrnendations

3,5.1. The sample selection strategy.

The organisation of survey sites amongst beach markets, large and small inlandmarkets gave good sampling points and highlights how the hinterland is served bythe beaches.

Surveying in Bukoba Region was restricted to landing sites. Time and persoirnelconstraints in Tanzania meant that the three surveys h.d to be run simultaneouslyand therefore only beach locations were used for fishers, trader/processors andconsumers. One of these landing sites, however, also comprised a large market,and one small market was slightly inland.

For Kenya arid Uganda the target quota was 33 trader/processors from each surveysite, while for Tanzania it was 16 respondents per survey site.

The quotas set for Kenya and Uganda were 300 trader/processors each and 150 forTanzania. Actual interviews completed were 319 for Kenya, 300 for Uganda and198 for Tanzania.

3.5.2. Sample selection strategy and the dynamics offleidwork.

Sorne confusion arose because of the distinctions between 1arge' and 'small'inland markets. A market could be a 'large' market on one day arid a 'small' one onanother. This prompted difficulties in selecting markets.

Some markets were served by so many supply beaches that identifying marketingchains became extremely complicated and confusing.

In Kenya, some beaches could not be reached by car. Logistical constraints meantthat islands wcre not visited, thereby excluding potential survey sites andrespondents from the study.

There was difficulty in fitting visits to inland markets which occurred only once perweek into our interview schedule.

3.5.3. Fieldwork

Beaches that had previously been identified as study locations sometimes had to bedropped when access roads flooded and the beaches could not be reached. If thisoccurred, the closest accessible beach was chosen as an alternative, provided theyfell within the same geographical region.

Traders were very busy and often had little time to be interviewed. On manyoccasions, interviews would be terminated half way through. If this occurred, theinterview was discarded and another potential respondent approached.

62

e. Occasionally, when the survey teams arrived at beaches, Fisheries Departmentofficers were found to have recently departed from them. If the officers had beenseizing illege.1 nets, the survey teams were treated with suspicion and sometimeshostility if they were suspected of having provided the FD with information onillegal nets.

3.5,4. Respondents fypical reOction to the survey questionnaires,

Q6 (marital status): respondents sometimes felt that they were being interviewedabout personal details they did not wish to disclose.

Q7 (family size): respondents were occasionally suspicious as to why researcherswanted to know how large their families were. This could result in the interviewbeing terminated immediately. On other occasions, the respondent simply declinedto respond lo the question and the relevant space was left blank. In tables containedin this report, which show that the total population is less than 817 have beenaffected by such missing values.

e. Q9 (when did you first start working in the fish trade?): those respondents who hadworked in the trade for a long time sometimes could not remember precisely whenthey had started, and their responses were actually guesses.

Ql i and subsections (supply sources, main suppliers, where fish sold, distancefrom source to market and means of transport): in many eases there was no single'main' source of fish, and respondents had difficulties arriving at clear responses tothis question. In addition, respondents often did not know what the distance wasbetween their source of supply and their markets, leading to speculative responses.

Q 13 (quantity of fish dealt in per week): respondents often speculated whenanswering this question because they did not deal in kilo weights, but number ofbaskets, number of sacks etc. Conversion into kilogramme weights are estimates asa result. In addition, the data generated is of little use given that the initial rangesset within the question were too high. As such, almost 70% of respondents agreedthat they dealt in less than 500 kg of fish per trip to market.

3.5.5. Possible areas of bi as

Q6(marital status) and Q7 (family size) for the reasons outlined in Section 4, theanswers gained could sometimes flot be relied upon.

Q9 (when did you first start working in the fish trade and what fish species did youcommence with?): respondents occasionally appeared not have started with a fishspecies for any particular reason - "I started with any fish that was available to me"was a typical response.

e. Qil (supply sources, main suppliers, where fish sold, distance from source tomarket and means of transport): see explanation in Section 4 above.

d. Q21 (income levels during peak periods): responses gained were rarely Consistentbecause 'peak' and 'low' periods amongst respondents varied so considerably.

63

Respondents frejuently felt that 'all the months are bad'. In addition, confusionalso arose because respondents sometimes equated peaks' as being periods whenthere was lots of fish as opposed to periods when incomes were high.

e. Q23 (do you sometimes have children helping you in this business?): rcspondentswould not admit to ever gaining assistance from children in their business for fearof possible repercussions. This section was therefore excluded from the data set.

3.5.6. Questionnaire testing

The questionnaire was tested but in hindsight further revisions could have beenmade.

3.5. 7. Questions that were inefJè clive and phrasing dffìculties.

Ql 1 ilvlain supp1iers: respondents named only a single supply source leaving outany other possible intermediaries.

Q13: (Quantity per trip): traders typically did not trade in kilos, and so responseswere often ambiguous and possibly incorrect. Pre-survey weights and measuresshouid have been defined for the various units of sale, although units are not alwaysstandard. This problem had been identified prior to the interviews.

017 (Fish sjoi1age): most respondents insisted that they never had problems withfish spoilage because they were suspicious that researchers might report them toFisheries Department officials for selling poor quality fish. Additionally, sornequestioning occurred during the dry season when fish spoilage was less likely, andrespondents may subsequently have reported thtt they did not suffer from it.

J. Q20: (Estimated income: establishing incomes was nearly impossible.Respondents would insist that they earned no profit at all and that much of the timethey sold their product at purchase price at source. Incornc was usually understoodas cash in hand at the thue of the interview and not as a sum of earnings over aperiod of time.

e. 021: (Peak/low periods): this question revealed no particular trends. Envisionedtrends related to climatic patterns - such as drought, rainy seasons etc.Respondents, however, typia11y provided months as their answers for which therewas no climatic paiallel.

3.5.8. Sensitive areas jòr questioning

a. Q6 (Marital status), Q7 (Family size). Ql 3 (quantities of fish dealt in): traders weresensitive to this because they were worried about being taxed for VAT. Q17 (fishspoilage), Q20 (gross income),

64

3.5.9. Recommendations for future survey design

Survey instruments should be designed in such a way that key information can beobtained in a short time given the time constraints imposed by busy traders.

Where possible, survey visits should be planned to avoid coinciding visits byfisheries officers.

e. Quantitative information - such as kilos converted from no. of sacks - should beregarded as estimates only.

d. Traders and processors may have been more informative if some inducementscould have been offered to them.

3.5.10. Interview sites for the survey offish traders and processors

65

J Desig,ueiwUgandaZone ii Kyotera market Large inland market2 Kyabakuza market Small inland market3 Kinoni market Small inland market4 Bukakata market Beach marketZone 25 Kayabwe market Small inland market6 Mpenj a market Small inland market7 Owino market Large inland market8 Ggaba landing Beach marketZone 39 Kakira market Small inland market10 Mauge market Small inland market11 Waka waka beach Beach market12 Busia central market Large inland marketI(enyZone 413 Busia Large inland market14 Sisenye beach Beach market15 Funyala Small inland market16 Osieko beach Beach market17 Bondo Large inland market18 Akala Small inland marketZone 519 Nyakwere Small inland market20 Sondu Large inland market21 Oyugis Large inland market22 Miti-mbili beach Beach market23 Balarawi beach Beach market24 Nyangweso Small inland market

66

Zone 625 Rongo Large inland market26 Ndiwa Small inland market27 Son Beach market28 Matoso Beach market29 Namba Kodero Small inland market30 Migori Large inland market

Zone 731 Juakali beach Beach market32 Shirati market Small inland market33 Nyang'ombe beach Beach market34 Busanga beach Beach market35 Tarime Large inland market36 Mgango beach Beach market37 Bwai beach Beach market38 Suguti beach Beach market39 Kiabakari market Small inland marketZone 840 Rugezi beach Beach market41 Nansio beach Beach market42 Nansio market Small inland market43 Ihale beach Beach market44 Mahaha market Large inland market45 Magu market Small inland market46 Kayenze beach Beach market47 Igombe beach Beach market48 Kirumba market Large inland marketZone 949 Muganza beach Large inland market50 Kifungu beach Beach market51 Kyamkwikwi beach and market Beach market52 Jeshi market Small inland market53 Kemondo market Large inland market54 Igabilo beach Beach market55 Malahe beach Beach market56 Luhanga beach Beach market57 Kemondo beach Beach market58 Biharamolo (not on map) Beach market

FIG 32 MAP OF STUDY SITES FOR TRADERS AND PROCESSORS

ZONE2Kamp ala

I, j-6

iitehhe

(

----T VICTORIA

0)Bukoha

LAKE

KeyNational frontier linesSurvey zaiie linesScale 2.4cm:6Okm

ZONE8

JinjaZONE3

/11 rrfij

j

' ril

'7 ---? 1';

rwa11Za I --L

I_ J

67

-- -i24

L26 ì-

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1 '32ìÌus ornar-j 1Z ) 4Dzo7

ZONE i_____73i/ i

1j

LAKE VICTORTA FISHERIES RESEARCH PROJECTTraders & Processors Questionnaire

Section A: Basic Information

Name of enumerator

I Case no

When started

<22-5> 5 yearS

Species

[I] N ile perchTilapiaDagaaFulu

Orod ud fhri

[I] Fresh[2j Smoked[3] Sun-dried[41 Fried

(i8

Saltednoasied

[i Others

15-17

2 Countiy code

3 Zone code

4 District code 4

Section B: Main Questionnaire

5 Respondenf s gender: [1] Male Go to Q 62hI Female

6 Marital status: [I] Married 6

SingleWidowedDivorcedSeparated

7 Family size: [1] No. wives 7-10No. of co-wivesNo. of children

8 Respondent's status [I ] Fish tradei' only(dominant activity): [2] l:ísh trader and processor

[3] Fish processor onlyFish frame traderFish ftanie trader and processorOther (specify)

9 When did you first get involved in this business and in what species/productwere you dealing in when you started:

When started Fish type/product Production forni 12-14

1. 500 kg.2.501 700 kg.3.701-900 kg.4.>901 kg.

69

12 Do you get regular supplies?Yes (Go to Q13)No (Give two most important reasons)

[1] Not enough fish available2J Competition from other traders

[3 Competition from fish finns[4] Transport problems[5J Prices too high[6] Others

18

Source ofsupply

Main supplier Where do yousell

Distance fromsource tomarket (km)

Means cftransport

Source Main supplier: Where do Distance to Means of transport:of [1] Fishermen you sell: market: [1] By footsupply: [2] Fish factories Insert 1. <20 km. [2J Bicycleinsert [3] Processors at place 2.21-50 [3] Public transportplace beach name. km. [4] Own transportname. [4] Wholesalers at

markets3. > 50 km. [5] Hired truck! vehicle

[6] Hired bicycle! donkey[5] Others [7] Hired labour

[8] Boatlferry[9] By air110] By rail

13 What is the average quantity of fish you dea! in per wcek (If more than onespecies, record 2 most important; - monthly qty. column to be filled in afterexercise).

Species! Product No. trips per weekto market

Qty per trip (Kg.) Total qty. permonth

33-39

Total quantity per month: 40-44

10 Have you ever changed the fish [1] Yes; why? [1] Availabilityspecies in which you trade? [2J Profitability

[31 Demand[4] Others

[2] No (go to Ql 1).11 Complete the following table with the codes provided below it.

19

20-24

25-29

30

31-32

15 How do you judge the quality of the fish you buy (2 options only)?

Quality identification

GillsEyesSmell

[41! Uy touchBy tasteVisua' inspection.

70

SatisfactionGoodAcceptablePoor

16 Do you take ally further steps to maintain the quality of your fish?

[1] Yes (Select 2 options only):

IcingPackagingRapid transfer to marketProcessingProper displayOthers

[2] No(GotoQl7)

17 Do you have problems with fish supplies?

[1] Yes (Select 3 options only):

Poor handlingStorageRainfallLack of display facilitiesOthers:

[2] No(GotoQl8)

46-49

56

57-5 8

59

60-62

Facility Problem[]Transport12] Packaging[3] Storage14] Market fee[5] License[6] Other

Product form Quality Identification Rating[1J Fresh[2] Smoked[3] Sun-dried[4] Fried[5] Salted161 Roasted[7] Others

14 Do you have problems with the facilities you use in the course of your 45

business?

[1] Yes (Provide 2 problems) [2] No (go to Q15)

50-53

54-5 5

18 Have you ever had any trading of relevance to your business?

[1] Yes (Select 2 options only)

Book keeping and accountingFish proccSsingQuality controlGroup organisation

[51 Others:

[2] No(GotoQl9)

19 What factors do you consider in setting your prices? (1 option only)

My costsNo. competitorsNo. of buyersPrice offered by buyerThe fish's qualityThe fish's sizeMy locationOther:

20 What are you peak/low periods and your estimated income during theseperiods? (2 periods per year only).

21 What factors affect your income during peak/low periods (3 factors only)?

22 Is there another species/product that you would like to deal in but cannot?

[1] Yes (provide single most important reason):

Low profits on present speciesCompetition too high on present speciesTaxes too high on present speciesNo private transportOther:

[2] No (go to Q23)

71

54-55

56

57-58

59-60

Peak period(months/season)

Estimatedgross income

Low period(months/season)

Estimatedgross income

L

Peak periods Low periods1. 1.

2. 2.

3. 3.

6 1-63

64-66

57

58

2.

o

72

23 Do children sometimes assist you in your business?

[1] Yes; give the following details for 2 of thechildren:

[2] No (Go to Q24) 59

How old are they?6O61

[1] <6yrs.[2] 7-l2yres[3] 13-l7yrs.

How much do you pay him/her? 62-63

[1] I pay liin-ilher Shs. per day[2] 1 pay him!her Shs. per month[3] No pay because s/lie are offspring[4] No pay because I assist him/her[5] Other

24 Besides the fish trade, are you involved in any other activity? 64

[1] Yes (select i most important) [2] No (Go to Q25)

[] Agriculture 65

[2] Trading industrial/consumer goods[3] Handicraft business[4] Others:

25 Please provide the 3 worst problems you face ¡n your business: 66-68