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APPENDIXES


A - List of socio-economic indicators

A.1 GFCM/SAC/SCESS Report of the Second Session of SCESS, Rome, 15-18 May 2001).

A.2 FAO (1999) Indicators for sustainable development of marine capture fisheries. Technical Guidelines for Responsible Fisheries No.8

Appendix B: Examples of economic criteria and indicators

Criteria

Example of Indicator

Structure

Reference Point

Harvest

· landing
· by-catch

· by species; age groups
· by area
· by fishery sub-sector

· MSY
· historical level
· policy target level

Harvest capacity

· GT (decked vessels)
· No of boats (undecked ves.)
· total effort (see below)

· by fleet type
· by fishery segment
· age composition of vessels
· fishing mortality/species

· capacity or effort of MSY
· policy target level

Harvest value (in constant prices)

· total deflated value (landed price)

· by species groups
· by sub-sector & fishery

· Selected historical level

Subsidies

· Tax rebates
· Grants

· by sub-sector
· by fleets/fishery

· historical level
· zero level
· target level

Contrib. to GDP11

· Fisheries GDP/Nat. GDP

· by species groups

· historical level

Exports

· Export/Harvest value

· by species groups
· by fishery segment

· historical level

Investments

· Market or replacement value
· Depreciation
· Fleet age composition

· by fleet type
· by fishery

· historical level

Employment

· Total employment

· sub-sector
· fleet/fishery

· historical level (?)
· realistic policy target

Net returns

· (profit + rent)
· net return/ investment
· value of entitlements

· by sub-sector
· by fishery

· historical level
· MEY

Effort (mainly at fishery level)

· No of vessels; Fishing time
· Amount of gear used
· Employment

· By fishery segment
· In physical or monetary terms


A.3 Commission of the European Community (2002). STECF’s needs for socio-economic indicators. Fourteenth Report Brussels, 22-26 April 2002. Annex II Comprehensive socio-economic indicators by MS and by fleet segment

Table 1. National level indicators

Indicator

Explanation

Input Data Need

CONSUMPTION

Weight Apparent Consumption

Gross consumption of fishing products per inhabitant expressed as weight of consumed fish per inhabitant

1. Harvest Production weight
2. Aquaculture Production weight
3. Import weight
4. Export weight
5. Population

Value Apparent Consumption

Gross consumption of fishing products per inhabitant expressed as expense per inhabitant

6. Harvest Production value
7. Aquaculture Production value
8. Import value
9. Export value
5. Population

TRADE

Fish Commercial Balance

Whether exports or imports of fishing products are higher

8. Import value
9. Export value

Fish Coverage Rate

Rate of apparent consumption covered by the national production.

6. Harvest Production value
7. Aquaculture Production value
8. Import value
9. Export value

Extraversion Rate

What extent the fishing sector of a country depends upon foreign trade, both for imports and exports.

6. Harvest Production value
7. Aquaculture Production value
8. Import value
9. Export value

SOCIAL

Ratio Fish Employment

Ratio of employment created directly by the fishing industry

10. Total Employment
11. Fish Employment

MACROECONOMIC

Fish Contribution to the GNP

The importance of fishing production in the Gross National Product.

6. Harvest Production value
7. Aquaculture Production value
12. Gross Domestic Product

Ratio Harvesting Value

The importance of fishing in comparison to aquaculture in terms of income.

6. Harvest Production value
7. Aquaculture Production value

Ratio Harvesting Weight

The importance of fishing in comparison to aquaculture in terms of production weight.

1. Harvest Production weight
2. Aquaculture Production weight

Table 2. National level indicators by fleet segments

Indicator

Explanation

Input Data Need by Segment

PHYSICAL PRODUCTIVITY

Vessel Physical Productivity

the average production of each vessel in terms of weight of landings.

- Weight per vessel

Capacity Physical Productivity

the average production in terms of weight of landings for each capacity unit (GT) of the vessels.

- Weight per vessel
- GT per vessel

Power Physical Productivity

the average production in terms of weight of landings for each power unit (HP) of the vessels.

- Weight per vessel
- HP per vessel

Per vessel fishing time Physical Productivity

the average production in terms of weight of landings for each full fishing time. Is possible select the unit of fishing time (hour or day)

- Weight per vessel
- Time fishing per vessel

Man Physical Productivity

the average production in terms of weight of landings for each man employed.

- Weight per vessel
- Employment per vessel

PRODUCTIVITY

Vessel Productivity

the average production in terms of market value in the first sale for each vessel.

- Value per vessel

Capacity Productivity

the average production in terms of market value in the first sale for each capacity unit installed (GT) in the vessels.

- Value per vessel
- GT per vessel

Power Productivity

the average production in terms of market value in the first sale for each power unit (HP) of the vessels.

- Weight per vessel
- HP per vessel

Per Vessel Hour Productivity

the average production in terms of market value in the first sale for each fishing hour.

- Weight per vessel
- Time fishing per vessel

Man Productivity

the average production in terms of value in the first sale for each man used.

- Weight per vessel
- Employment per vessel

SOCIAL

Employment per segment

indicates the employment in a specific segment of vessels

- Employment per vessel

Average Wage

indicates the average salary obtained by each man employed.

- Salary Cost
- Employment per vessel

MARKET

Landing Prices

(LP) represents the average market price of landings.

- Weight per vessel
- Value per vessel

INVESTMENT

Capital Employed

a measure of the value of Vessel, Licence, Quota, etc. would provide information of the relative position of the industry. Values above a discounted sum of the returns they could provide would be an indication of an unsustainable industry.

- Invested Capital

Capital Investments

% of change in capital employed over time - normally a year. It indicates the future expectations of the enterprises. Often difficult to measure empirically in other ways than using the capital employed at two different points in time and subtracting them from one another.

- bis. Invest Capital in the precedent periode

COST

Income to employees

serves to identify the return from fishing to the suppliers of labour. It may be used to compare and to estimate the effect of fishing on national and local economies. The fishing industry has traditionally been identified with low incomes. This is liable to create a shortage of skilled labour where there are alternative employment opportunities.

- Salary Cost

Opportunity Cost

the yields that the owner could obtain should he invest his money in National Debt instead of investing in his business. This means that the owner is relinquishing that potential income. There is a profit in its economic sense when the yields of the invested capital surpass the opportunity cost.

- Invested Capital
- Rate national debt

PROFITS

Gross Profit

indicates the total profits obtained by the whole of the vessel owners, once the operating costs have been deducted.

- Value per vessel
- Salary Cost
- Cost per fishing day
- Time fishing per vessel
- Yearly Fixed Costs
- Financial cost
- Indicator on Opportunity Cost

Net Profit

profitability - would provide a direct comparison with returns available elsewhere in the economy. the total earnings obtained by the whole of the owners, once the depreciation cost has been deducted.

- Depreciation
- Indicator on Gross profit

Profit Rate

indicates the percent ratio of yearly net profits plus the opportunity cost in relation with the investment. It should be borne in mind that this figure does not include the additional earnings obtained by the owner as an employee in artisanal fisheries.

- Indicator on Gross profit
- Indicator on Opportunity cost
- Invested Capital

Gross Added Value

expresses the Added Value that the segment in question contributes to the National Economy. This includes: salaries, profits, opportunity cost and depreciations.

- Salary Cost
- Depreciation
- Indicator on Gross profit
- Indicator on Opportunity Cost

Contribution to the margin

output minus variable costs is a short run indicator of the incentive for the enterprise to carry on. Given the problem of sunken capital in fisheries (capital written off in the books but still capable of producing output) this is an important indicator to assist in assessing whether schemes to reduce capacity will be effective. With low liabilities and low opportunity costs of labour and capital the incentive to carry on in the long run is determined by this indicator.

- Value per vessel
- Salary Cost
- Cost per fishing day
- Time fishing per vessel
- Yearly Fixed Costs

Return on Capital

(net profit plus interest payments relative to capital employed) - provides a simple and direct comparison of the opportunity cost of capital.

- Indicator on Net Profit
- Financial cost

MANAGEMENT

Value of Fish stock size

measured in value - gives an indication of the use of the production factor that is not subject to price determination on a market. Will indicate as to whether the output of fish is a result of surplus harvesting or a result of production factor reduction.

- Biological data on biomass
- Prices by species

Subsidies and taxes

provide information about the dependency of the industry on public support and about the GDP in factor prices.

- Data on subsidies per segment

Capacity utilization

calculation would require distinction between long run and short run, and knowledge about the state of the fish stocks as to whether they are overexploited or not. In the short run the measure disregards fish stock effects.

- Time fishing per vessel
- Maximum number of sea day

B - Fleet segmentation and parameters of the socio-economic structure for the operative units

Working Group on Socio-Economic Indicators (WGSEI, Salerno, 11-13 March 2002)


Group 1

Group 2

Group 3

Non engine

all



Minor Gear

<12 meters



Trawl

<12 meters

12-24 meters

> 24 meters

Seine

<12 meters

>12 meters


Long line

>12 meters



Pelagic Trawl

>12 meters



Tuna seine

>12 meters



Dredge

>12 meters



Polyvalent

>12 meters



C - Questionnaires for the collection of socio-economic data

Several questionnaires have been presented, during the meetings on socio-economic indicators, by partners involved.

These questionnaires are aimed to collect the information required to calculate the socio-economic indicators.

In this appendix, we report the questionnaires described in the following documents:

1. Report of the AdriaMed Meeting on Socio-Economic Aspects of the Adriatic Sea Fishery Sector - Campobasso, Italy, 28th - 29th May 2001. IREPA implementation of a national observatory for monitoring techno-economic data of the Italian fleet and the evaluation of socio-economic parameters.

2. GFCM/SAC/SCESS Deuxième Groupe de travail ad hoc sur les indicateurs socio-économiques (Salerno, Italie, 11-13 mars 2002). Etude sur les indicateurs socio-économiques pour la pêche dans le Golfe de Gabès

3. GFCM-SAC-SCSS Troisième Session du Sous-Comité des sciences économiques et sociales (SCSES) (Barcelona, Spain, 6-9 May 2002). IREPA - Guidelines for sampling methodologies for socio-economic indicators.

1. Report of the AdriaMed Meeting on Socio-Economic Aspects
of the Adriatic Sea Fishery Sector - Campobasso, Italy, 28 - 29 May 2001.

IREPA implementation of a national observatory for monitoring techno-economic data of the Italian fleet and the evaluation of socio-economic parameters.

Data collection and estimates of economic parameters concerning the Italian fishing fleet is produced by IREPA (Institute for Fisheries and Aquaculture Economic Research) through a National Observatory, which dates back to the early 80’s.

Sample data are recorded by means of three specific questionnaires:

1. an annual questionnaire to record technical, dimensional and vessel - management information on the sample units and relevant socio-economic aspects (number of ship owners, their ages, their property quotas and relationships between them);

2. a quarterly questionnaire to record data on fixed and variable costs, and on social aspects of property and crew;

3. a weekly questionnaire to record information reporting activity such as fishing time and area, average number of crewmembers, gears used, quantities, prices and revenues - as per species or group of species - and trade channel for sales.

In brief, the most important annual, monthly and weekly information recorded are the following:

Annual information

· name

· gross registered tonnage (GRT)

· maritime district where the boat has been registered, (coastal area/sector)

· gross tonnage (GT) based on London Convention (Reg. EC 2930/86)

· first year of service (therefore, age)

· horsepower (kW)

· authorised fishing gears

· engine make, location and type of propeller

· maritime district from where the ship departed for fishing

· communication engine

· maritime district where the product is landed

· navigation engine

· type of association and year of its creation

· fish location engine

· number of shipowners, their ages, their property quotas and relationships between them

· conservation equipment

· type of association and year of its creation

· employment contract used

· length overall and length between perpendiculars



Quarterly information

· name

· fish transport cost

· month

· other running cost

· maritime district where the boat has been registered (coastal area/sector)

· labour share, wages and social insurance

· fuel (total and unit value)

· ordinary maintenance

· cost of nets

· extraordinary vessel maintenance

· cost of bait

· extraordinary hull maintenance

· cordage and ropes

· extraordinary engine maintenance

· food

· vessel insurance

· boxes and ice

· tax and other fiscal costs

· commercialisation costs

· bank charges

· other running costs

· other vessel costs


Weekly information

· Name

· Non fishing days for bad weather

· Week

· Non fishing days for rest, repair and other

· Maritime district where the boat has been registered

· Hulls

· Engine used

· Average time (in hours) for each single trip

· Gear used

· Minimum and maximum fishing area’s distance perpendicular to coast line

· Average crew

· Maritime district from where the ship departs

· Fishing days

· Maritime district where the product is sold

· Total hours at sea (navigation and fishing)

· For each single species or group of species landed: quantity, prices, income and commercial channel (wholesaler, fish market, retail dealer, others).

It is also to be noted that the input of data for the single vessel is fully computerized; the software, specifically designed for the survey’s objectives, is logically structured and also includes crosscheck programs to avoid partial or inconsistent filling of the questionnaire.

2. GFCM/SAC/SCESS Deuxième Groupe de travail ad hoc sur les indicateurs socio-économiques (Salerno, Italie, 11-13 mars 2002).

Etude sur les indicateurs socio-économiques pour la pêche dans le Golfe de Gabès

INSTITUT NATIONAL DES SCIENCES ET TECHNOLOGIES DE LA MER

Étude de Cas - Golfe de Gabès
Pour l’estimation des indicateurs socio-économiques de la pêche
Questionnaire

A) Données techniques des bateaux

· Nom et matricule du bateau


· Nombre de marins à bord (en général)


· Longueur du bateau (mètre)


· Quels sont les engins à bord


· Chalut (C), Senne Tournante (ST), Filet Maillant Invisible (FMI), Trémail à poissons (TP), Trémail à crevette (TC) Trémail à seiche (TS), Palangre de Surface (PS), Palangre de fond (PF), Autres (AU),


· Puissance en CV


· TJB


· Quelle est la distance maximale habituellement atteinte à partir de la côte (miles)


· Nombre d’heures de travail par sortie (en comptant les heures de travail dans le port, dans le marché et autres)


· Nombre de sorties par mois


· Si ce nombre est différent pour chaque mois, quel est le nombre de sorties approximatif par mois durant toute l’année



Jan

Fev

Mar

Avr

Mai

Jun

Jul

Août

Sep

Oct

Nov

Dec















B) Données sur les coûts

· Après la vente, quelles sont les choses déduites avant la distribution des parts: Carburant (C), Vivres (V), Glace (GL), Appât (A ), Lubrifiants (L)


· Quel est le pourcentage de la part de l’équipage, en incluant le propriétaire s’il est pêcheur

· Quel est le coût d’un plein de gasoil

· Combien de sorties peut assurer un plein de gasoil

· Quels sont les dépenses par jour (par sortie) de pêche, en dehors du carburant


Appâts



Vivres



Lubrificant



Autres


· Quelle est la valeur approximative de votre bateau à son état actuel, y compris les engins de pêche, les équipements électroniques (GPS, Sondeur, Radar, Radio, etc.) et les équipements de pêche (Treuils, Power block).


· Quel est le coût annuel pour maintenir le bateau opérationnel (assurance, poste au port, licences, papiers, entretiens routiniers et réparations du moteur de la coque et des engins de pêche, etc.)



C) Données sur les débarquements

· Quel est la production mensuelle approximative en kg, si ces débarquement connaissent une grande variation dans l’année, indiquer l’évolution mensuelle dans le tableau ci-dessous



Jan

Fév

Mar

Avr

Mai

Jun

Jul

Aoû

Sep

Oct

Nov

Dec














· Valeur de la totalité des ventes pour l’année précédente


Nom de l’enquêteur:



Port:



Date de l’enquête:


3. GFCM-SAC- SCSS Troisième Session du Sous-Comité des sciences économiques et sociales (SCSES) (Barcelona, Spain, 6-9 May 2002).

IREPA - Guidelines for sampling methodologies for socio-economic indicators. Appendix A

A simple questionnaire has been developed based on questionnaires used in other surveys; primarily those undertaken by the IREPA.

Year

____________

Quarter

____________


Name of vessel

___________

GRT

___________

Length

___________

kW

___________

Vessel age

___________

Company type

___________


MU FAO

___________

Port

___________

Principal gear

___________


Revenues

Description

Value

Notes

from the sale of fish



other sources of revenue such as insurance claims, compensation and government assistance




Fixed costs

Description

Value

Notes

Social security contributions and charges



Routine maintenance, hull and engine



Non-routine maintenance, hull



Non-routine maintenance, engine



Non-routine maintenance (other)



Vessel insurance



Miscellaneous taxes and dues



Interest charges (loans, etc.)




Production costs




Description

Unit cost

Value

Notes

Fuel




Lubricants




Purchase of nets




Purchase of bait




Ropes and warps





Selling costs




Description

Unit cost

Value

Notes

Boxes




Ice




Fish market or wholesaler




Transport of catches





Labour costs



Number employed

Grade of crew member

Average net monthly pay

.....




How many crew (excluding the skipper) are normally employed on the vessel during a typical trip?

....................


Number of owners: ..................
ownership shares: ..................

Are you (the vessel’s owner) also the skipper of the vessel?
Yes, full-time [] Yes, part-time [] No []


What is the market value of your vessel (including equipment and license value)?
................

D - Implementation of the bethel method (SAS ® HML)

/* SOFTWARE TO CALCULATE THE SAMPLE SIZE WITH THE BETHEL METHOD*/

/* THE ESTIMATES PER STRATUM - SUM - THE VARIANCES PER STRATUM - VAR AND THE POPULATION SIZE - N - ARE REPORTED IN THE FILE NAMED DATIN */

%MACRO BETHEL(DATIN,DATOUT,STR,SUM,VAR,N,ERR,NITER,CONV);


/* RICHIEDE LA SPECIFICA DEL NOME DEL FILE DI INPUT, NOME DEL FILE */

/* DI OUTPUT, NOME DELLA VAR CHE INDICA LO STRATO, NOME DELLE VARS */

/* DI CUI SI CONOSCE LA STIMA, NOME DELLE VARIANZE, NUMEROSITA’ */

/* DELLA POP. PER STRATO, ERRORI ATTESI PER CIASCUNA VAR, NUMERO */

/* MAX DI ITERAZ, CRITERIO DI CONVERGENZA */


PROC IML;

USE &DATIN;

READ ALL VAR {&SUM} INTO SOMME; READ ALL VAR {&VAR} INTO VARIA;

READ ALL VAR {&N} INTO N; READ ALL VAR {&STR} INTO STR;

ERR={&ERR};

STATS=ERR;

AIJ=(N##2)#VARIA#(1/(((SOMME[+,]##2)#(ERR##2))+(N#VARIA)[+,]));

ALFA=J(NCOL(AIJ),1,1/(NCOL(AIJ)));

DO

I=1 TO &NITER UNTIL(DIF< &CONV);


X=1/(SQRT(AIJ*ALFA)*((SQRT(AIJ*ALFA))[+,])+1E-20);


NALFA=(ALFA#(T(AIJ)*X)##2)#(1/(ALFA#(T(AIJ)*X)##2)[+,]);


DIF=MAX(ABS(NALFA-ALFA));


ALFA=NALFA;

END;

NH=CEIL(1/X);

VARI2=(N/NH)#(N-NH)#VARIA;

ERR=(SQRT(VARI2[+,])/SOMME[+,]);

* può dare errore se NH>N;

STATS=STATS//ERR;

NH=(NH<>J(NROW(AIJ),1,2))><N;

NHS=(N-NH)><NH;

* trova il minore elemento per elemento;

VARIA=(N/NH)#(N-NH)#VARIA;

ERR=(SQRT(VARIA[+,])/SOMME[+,]);

* [+,] somma per riga prendi tutte le colonne;

STATS=STATS//ERR;

STATS=STATS//T(ALFA);

NH=STR||NH||NHS||N;

* affianca tutti gli elementi elencati;

CREATE STATS VAR {&SUM};APPEND FROM STATS;

CREATE &DATOUT VAR {&STR NH NHS &N};

APPEND FROM NH;

QUIT;

PROC TRANSPOSE DATA=STATS OUT=STATS;

DATA STATS;SET STATS;

ATTRIB COL1 LABEL=‘ERRORI RICHIESTI’


COL2 LABEL=‘ERRORI OTTENUTI SENZA CORREZIONI’


COL3 LABEL=‘ERRORI OTTENUTI CON CORREZIONI’


COL4 LABEL=‘IMPORTANZA DELLA VAR. SULLA DIM. CAMP.’

_NAME_ LABEL=‘NOME VARIABILE’;

PROC PRINT DATA=STATS NOOBS LABEL;

VAR _NAME_ COL1 COL2 COL3 COL4;

DATA &DATOUT;SET &DATOUT;

ATTRIB &STR LABEL=‘CODICE DI STRATO’


NH LABEL=‘NUMEROSITA CAMPIONE’


NHS LABEL=‘NUMEROSITA EVENTUALE SUPPLETIVO’


&N LABEL=‘NUMEROSITA POPOLAZIONE’


_NAME_ LABEL=‘NOME VARIABILE’;

PROC PRINT DATA=&DATOUT NOOBS LABEL;

VAR &STR &N NH NHS;

SUM &N NH NHS;

%MEND;


OPTIONS MPRINT MTRACE SYMBOLGEN;

%BETHEL(IREPA.VARIANZ1,IREPA.NBETH31,STR,S1 S2 S3 S4 S5 S6 S7 S8 S9 S10,V1 V2 V3 V4 V5 V6 V7 V8 V9 V10,

_N,0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03,300,0.000001);

RUN;


/* %MACRO BETHEL(DATIN,DATOUT,STR,SUM,VAR,N,ERR,NITER,CONV); */

/* elenco elementi necessari a far girare la macro

E - Optimal sample size and the differences Between neyman and Bethel methods - an example

Suppose to define a sample survey in order to estimate the total revenues of the fleet per group of species. Our target variables are:

- revenues of crustaceans
- revenues of molluscs
- revenues of other fishes
- revenues of anchovies.

We stratify our target population per fleet segment (because we know that there is, at some extends, a correlation between segments and target groups of species). The stratification is as follow:

segment

Nh1

purse seines

235

dredges

835

small scale fishery

12425

multipurpose vessels

3564

trawls

2364

tuna fleet

212

midwater pair trawls

145

Total

19780

What must be the sample size in order to have a maximum error of our estimates not higher than, for instance, 5% with a confidence level of 95%? To answer this question we can apply the Neyman formula (see paragraph 4.3), but, as our target variables are four, we have to apply this method four times:

crustaceans - revenues

segment

Nh

Sh2

Nh*Sh2

Sh

Nh*Sh

nh-5%

purse seines

235

2660,5

220824,2

51,6

4281,2

8

dredges

835

0,0

0,0

0,0

0,0

0

small scale fishery

12425

686,4

608782,8

59,0

58073,2

105

multipurpose vessels

3564

27023,6

10254979,9

405,9

145590,8

180

trawls

2364

80634,5

29624128,9

706,1

194462,0

221

tuna fleet

212

0,0

0,0

0,0

0,0

0

midwater pair trawls

145

5,5

298,0

2,3

126,8

25

Total

19780

111029,9

40711713,7

1229,2

403148,8

539

other fishes - revenues

segment

Nh

Sh2

Nh*Sh2

Sh

Nh*Sh

nh-5%

purse seines

235

40499,6

1601939,5

390,4

15117,6

8

dredges

835

0,0

0,0

0,0

0,0

0

small scale fishery

12425

11640,5

15201704,2

300,2

352201,6

165

multipurpose vessels

3564

73240,6

26638037,8

678,3

243427,1

124

trawls

2364

110333,0

35197945,5

870,8

222044,1

113

tuna fleet

212

83411,7

5320063,7

529,3

32960,4

17

midwater pair trawls

145

800,8

37013,6

50,0

2062,9

9

Total

19780

319982,2

84004549,0

2826,5

868861,8

436

molluscs - revenues

segment

Nh

Sh2

Nh*Sh2

Sh

Nh*Sh

nh-5%

purse seines

235

78,3

6481,0

9,6

749,6

25

dredges

835

3237,0

829282,9

70,6

15548,6

28

small scale fishery

12425

3036,6

2571116,7

139,8

134984,7

194

multipurpose vessels

3564

7780,5

2792485,3

260,4

87990,1

159

trawls

2364

14379,0

2566988,5

334,0

64506,0

117

tuna fleet

212

0,0

0,0

0,0

0,0

0

midwater pair trawls

145

0,0

0,0

0,0

0,0

0

Total

19780

28523,8

8766668,2

818,6

303902,5

523

anchovies - revenues

segment

Nh

Sh2

Nh*Sh2

Sh

Nh*Sh

nh-5%

purse seines

235

130797,7

5607901,6

905,2

34180,8

132

dredges

835

0,0

0,0

0,0

0,0

0

small scale fishery

12425

1436,6

2040314,0

69,7

105012,1

145

multipurpose vessels

3564

85,8

62171,7

16,6

10176,4

39

trawls

2364

277,1

23508,8

21,6

2196,6

8

tuna fleet

212

0,0

0,0

0,0

0,0

0

midwater pair trawls

145

30253,6

1237186,5

303,4

11978,6

46

Total

19780

162850,9

8971082,6

1316,5

163544,5

371

In this way, for each variable we obtain a different sample size. In order to respect our assumption (maximum error 5% of the total values) we must consider the maximum value for each segment:

segment

Neyman

Bethel

purse seines

132

95

dredges

28

19

small scale fishery

194

125

multipurpose vessels

180

99

trawls

221

165

tuna fleet

17

12

midwater pair trawls

46

32

Total

818

547

The Bethel method has been applied on the same data using the SAS implementation (see Appendix D) and the results are reported in the previous table. Applying the Bethel method, we will obtain a lower sample size (547 units instead of 818 units). In fact, the Bethel method considers the four variables all together and therefore it minimises the variances taking into account the constraints all together.

The Bethel method requires the same basic data as Neyman, and it can be implemented on the basis of any software (see appendix D for an example).


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