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 species; age
groups |
· MSY |
Harvest capacity |
· GT (decked vessels) |
· by fleet type |
· capacity or effort of
MSY |
Harvest value (in constant prices) |
· total deflated value (landed price) |
· by species groups |
· Selected historical level |
Subsidies |
· Tax rebates |
· by sub-sector |
· historical level |
Contrib. to GDP11 |
· Fisheries GDP/Nat. GDP |
· by species groups |
· historical level |
Exports |
· Export/Harvest value |
· by species groups |
· historical level |
Investments |
· Market or replacement
value |
· by fleet type |
· historical level |
Employment |
· Total employment |
· sub-sector |
· historical level (?) |
Net returns |
· (profit + rent) |
· by sub-sector |
· historical level |
Effort (mainly at fishery level) |
· No of vessels; Fishing
time |
· By fishery segment |
|
A.3 Commission of the European Community (2002). STECFs 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 |
Value Apparent Consumption |
Gross consumption of fishing products per inhabitant expressed as expense per inhabitant |
6. Harvest Production value |
TRADE |
||
Fish Commercial Balance |
Whether exports or imports of fishing products are higher |
8. Import value |
Fish Coverage Rate |
Rate of apparent consumption covered by the national production. |
6. Harvest Production value |
Extraversion Rate |
What extent the fishing sector of a country depends upon foreign trade, both for imports and exports. |
6. Harvest Production value |
SOCIAL |
||
Ratio Fish Employment |
Ratio of employment created directly by the fishing industry |
10. Total Employment |
MACROECONOMIC |
||
Fish Contribution to the GNP |
The importance of fishing production in the Gross National Product. |
6. Harvest Production value |
Ratio Harvesting Value |
The importance of fishing in comparison to aquaculture in terms of income. |
6. Harvest Production value |
Ratio Harvesting Weight |
The importance of fishing in comparison to aquaculture in terms of production weight. |
1. Harvest 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 |
|
Power Physical Productivity |
the average production in terms of weight of landings for each power unit (HP) of the vessels. |
- Weight 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 |
|
Man Physical Productivity |
the average production in terms of weight of landings for each man employed. |
- Weight 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 |
|
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 |
|
Per Vessel Hour Productivity |
the average production in terms of market value in the first sale for each fishing hour. |
- Weight per vessel |
|
Man Productivity |
the average production in terms of value in the first sale for each man used. |
- Weight 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 |
|
MARKET |
|||
Landing Prices |
(LP) represents the average market price of landings. |
- Weight 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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
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 |
|
|
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 80s.
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 areas 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 surveys 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 lestimation 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 dheures 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 lannée |
|
|||||||||||
|
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 léquipage, en incluant le propriétaire sil est pêcheur |
||
· Quel est le coût dun 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 lannée, indiquer lévolution 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 lannée précédente |
|||||||||||||
|
Nom de lenquêteur: |
|
|||||||||||
|
Port: |
|
|||||||||||
|
Date de lenquête: |
|
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? .................... |
Are you (the vessels owner) also the skipper of
the vessel? |
What is the market value of your vessel (including
equipment and license value)? |
/* 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 |
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).