NOAA Technical Memorandum NMFS 

FEBRUARY 1986 

A BUDGET SIMULATION MODEL FOR 

WEST COAST ALBACORE TROLLERS 

Samuel F. Herrick, Jr. 

Kevin L. Carlson 

NO AA-TM-NMFS-S W FC-5 7 

U.S. DEPARTMENT OF COMMERCE 

National Oceanic and Atmospheric Administration 

National Marine Fisheries Service 

Southwest Fisheries Center 
NOAA Technical Memorandum NMFS 

The National Oceanic and Atmospheric Administration (NOAA), organized in 

1970, has evolved into an agency which establishes national policies and 

manages and conserves our oceanic, coastal, and atmospheric resources. An 

organizational element within NOAA, the Office of Fisheries is responsible for 

fisheries policy and the direction of the National Marine Fisheries Service 

(NMFS). 

In addition to its formal publications, the NMFS uses the NOAA Technical 

Memorandum series to issue informal scientific and technical publications when 

complete formal review and editorial processing are not appropriate or feasible. 

Documents within this series, however, reflect sound professional work and may 

be referenced in the formal scientific and technical literature. 
NOAA ‘Technical Memorandum NMFS 

This TM series is used lor documentation and timely communlcatlon 01 preliminary results. imterim reports, or 

special purpose information. and have no1 recelved complete formal revew. editorial conlrol. of detalled edlting 

= z---9 

+rO=$ co*” 

FEBRUARY 1986 c i-c P *- 

oc, - 

A BUDGET SIMULATION MODEL FOR 

WEST COAST ALBACORE TROLLERS 

Samuel F. Herrick, Jr. 

Kevin L. Carlson 

Souithwest Fisheries Center 

National Marine Fisheries Service 

La Jolla, California 92038 

NOAA-TM-NMFS-SWFC-57 

U.S. DE!PARTMENT OF COMMERCE 

Malcolm Baldrige. Secretary 

National Oceanic and Atmospheric Administration 

Anthony J. Calio. Administtator 

National Marine Fisheries Service 

William Gi. Gordon, Assistant Administrator for Fisheries 
TABLE OF CONTENTS 

Sectron -EKE 

1 . Introduction ............................................ 1 

2 . Fl(ethodology ............................................. 2 

3 . Data .................................................... 5 

4 . Structure. Speclf icatlon. and Estimation ................ 7 

4.1 Catch and Revenue .................................. 8 

4.2 Operating Costs .................................... 14 

4.3 Fixed Costs ........................................ 17 

5 . ALBSIM Omfiguration .................................... 20 

6 . Appiicatlon ............................................. 22 

7 . Discussion .............................................. 26 

References ................................................. 29 

i 
L IST OF TABLES 

Table BxlQ 

1 Summary statlstlcs for the cost and earnlngs 

unbalanced panel data set ............................. 30 

2 Definitlons of varlable mnemonlcs ..................... 31 

3 Estimation results for catch equations ................ 33 

4 Estlmatlon results for the fuel consumption, other 

trlp costs and f lxed cost equations ................... 35 

5 Estlmatlon results for the flxed cost share 

~uatlons ............................................. 37 

6 A ALBSIM spreadsheet conf Iguratlon. user Inputs ....... 39 

B ALBSIM spreadsheet conf lguratlon. cal CUI ations ...... 40 

C ALBSIM spreadsheet conf I guration. model output ...... 41 

ii 
A Budget Slmul atlan Hodel for West Coast Albaare Trol lers 

1. Jnt roduct i on 

Private and pub1 ic f Ksheries administrators require information about 

cost and earnings of fishing vessels when evaluating alternative investment 

opportunities, gauging the economic impacts on a fishery of proposed 

pol Icy changes, and for assessing the effectiveness of existing fisheries 

pol icies. Data on the gross revenues of individual vessels a r e r o u t i n e l y 

col lected by the Pacific coast states' f i sheries agencies. On the other 

hand corresponding cost data, needed to derive estimates of the net 

earnings of vessels are not regularly collected. Data on vessel costs which 

are assembled for these purposes, are general ly collected through periodic 

cost and earnings surveys, and the results from these surveys are used to 

provide point-in-time estiimates of net returns for particular classes of 

vessels or an entire fleet. 

To extend the useful ness of cost and earnings survey data beyond the 

survey period, vessel budget (or fiscal) simulation models (budget 

simul ators) can be constructed which typical ly specify vessel costs and 

revenues as functions of variables, the val ues of which are col lected on an 

ongoing basis (e.g., vessel characteristics that are recorded during the 

annual registration process). As new val ues of the explanatory variables 

become avai I ab1 e they can be incorporated Into the model together w ith 

expenditure specific price indices -- to adjust for relative changes in 

unit costs -- to predict vessel costs and earnings outside the survey 

period. In this way vessel budget simulators provide a means of generating 
2 

predictions of net returns for years in which cost and earnings surveys are 

not con du c te d. 

This report describes the development and appl ication of a budget 

simulation model that can be used to monitor and predict the financial and 

economic performance of specific vessels or classes of vessels making up 

the north Pacific albacore fleet. The model is capable of generating 

budgetary information for west coast albacore t r o i l e r s when the values of 

key variables describing vessel characteristics, fishing activities, and 

costs of inputs (e.g. fuel) are specif led. This makes the model a 

particularly useful analytical tool for evaluating events that affect the 

f I eetls operati ng env i ronment. 

In the fol low ing sections development of the west coast albacore 

troller budget simulation model (ALBSiM) is discussed in terms of (a) 

methodology and technical 

estimation of re1 atlonshi F 

exerci si ng the model. Fi nal 

outputs are presented. 

requirements, (b) data requirements, (c) 

; and parameters, and (d) the procedures for 

y some examples of ALBSIM's capabil ities and 

2. Met hod0 I oay 

Existing vessel budget simulation models (Squires, 1985; Schel le and 

Muse, 1983; Griffinn et al., 1980) for the most part consist of 

individual, regression-estimated equations that predict a vessel's revenue, 

operating cost and fixed cost components. In princi pl e a1 I infl uences on a 

vessel's fishing performance enter these equations as Independent 

variables. In practice th!s is difficzi-t to accomplish because the number 
3 

of variables can become unmanageably large and data on many of the 

variables are either unavailable or of questionabl e qual ity. Due to these 

practical considerations, the equations that make up vessel budget 

simulators are usually formulated on an ad hoc basis and therefore prone to 

m i s s p e c i f i c a t i o n . Imp1 icit in this process is a wit I ingness to accept a 

greater risk of specification bias in order to reduce the potential ly 

greater error associated IN ith a ponderous set of independent variables. In 

cases where data I imitations are severe enough to preclude the estimation 

of revenue and cost equations, those costs and revenues are often 

approximated by incorporating historical averages, user suppl led val ues, or 

engineering relationships (technical formulas) in the budget simulator. 

Appropriate specification of the regression equations also requires an 

investigation of their functional form. In most cases some sort of 

interactive form (i.e. one in which the marginal contributions of units of 

some independent variables depend on the amounts of certain other 

independent variables) would seem advisable. This is particularly true when 

vessel characteristics enter the formulations, and underlying 

engineering/technoIogicaI constraints I imit the range of combinations of 

independent variables (e.$, a change in catch given a change in effort 

w iI I usual ly depend on vessel design characteristics). Beyond this 

consideration the choice olf functional form is an empirical question, which 

is decided on the basis {of plausibil ityJ goodness of fit, and the I ike. 

Under this approach the general form of the I inear regression equation for 

predicti ng the ith cost is: 

Ci jt = aiO + ail EFF + bil VES + bi$VES+EFF) + u 
4 

where: 

Cijt = the predicted expenditure on the i th 

cost item for vessel j in year t dol I ars 

EFF = a measure of vessel fishing activity or 

fishing effort 

VES = a column vector of vessel characteristics 

aiO =intercept term for the ith cost 

equation 

estimated parameter for the ith cost 

eq ua t 1 on 

ail = 

bil, biz =row vectors of estimated parameters 

for the ith cost equation 

u = error term. 

Since the equation is estimated in nominal dollars of the survey year 

(or some base year), its predictions will be in nominal dollars of the base 

year. In other years, however, the prices of various inputs and outputs may 

be different from those prevai I ing in the base year. Therefore, to obtain 

nominal dol I ar estimates for periods other than the base year the procedure 

is as follows: 

where Cij,t+n is the expenditure on the ith cost item for vessel j in 

nominal dollars of the t+n year, and Pi,t+n is the ith item specific 

conversion factor which converts base year t dollars predicted by the model 

to year t+n dol I ars. A similar approach is used to predict revenues. Net 

returns can then be predicted by taking the difference between the sum of 

predicted revenues and the sum of predicted costs. 
5 

ALBS I M i ncor por ates f unct i ons that re1 ate production, correspondi ng 

variable costs, and fixed costs to the design characteristics of the 

vessel, its home port, different species fished, and the degree to which 

the vessel i s u t i I ized on an annual basi s, al I of which are exogenous to 

the model. Such a structure al lows the ALBSIM user to select a vessel of a 

given design and specify its pattern of operation in the fishery in order 

to generate annual budgets, cash flows and other types of financial 

reports. The vessel's design is determined by its length, carrying 

capacity, and age. The pattern of operation of the vessei in the north 

Pacific albacore fishery is described by its home port, the number of days 

it spends at sea annual ly, and the amount of time it engages in f ishi ng for 

different target species, i.e., a measure of fishing effort. 

The data used to estimate ALBSlM came from several sources. Fishermen 

belonging to the Western Fishboat Owners Association, an organization 

representing west coast albacore fishermen, suppl led annual data, for one 

or more years, on their vessel's f i shing operations and corresponding 

econom ic performance. Additional vessel financial data were obtained from 

various lending institutions which finance fishing vessels. These sources 

provided 247 annual observations which contain catch, earnings, effort, 

expenditure, and physical character1 stics data from 89 west coast vessel s 

that fished primarily for- albacore mer the period 1972 through 1982. Each 

vessel is descri bed by its length, beam, gross tons, carrying capacity, 

age, and home port. After el im inati ng incomplete observations, and 

observations from non-trol I er vessel s, an unbal anced-panel cost and 
6 

earnings data set1 consisting of 127 vessel-year observations was used for 

estimation purposes. 

The catch data provided consist of the vessells annual albacore catch 

together w ith the annual catches of other species for each year the vessel 

was in the sample. Fishing effort was reported as the number of days fished 

for each species. The total number of fishing trips taken annual ly as we1 I 

as the total number of days at sea for each vessel-year were also provided. 

Vessel financial data that were furnlshed include total revenue from 

fishing and other operations, variable costs re1 ating to vessel operations, 

and the fixed costs associated with vessel ownership. Variable costs can be 

separated into those not dlrectly proportional to catch and those directly 

proportional to catch. Cost items in the former category consist of fuel 

(fuel and I ubricants), provisions (gal I ey and groceries), gear and 

suppl ies, qui pment, and other (ice, sal tr bait, etc.). Variable cost items 

that are directly proportional to catch include crew shares and payrol I 

taxes. Crew share is the hired labor expense that is based on a percentage 

of the vessel's gross ex-vessel revenues. Payrol I taxes are cal CUI ated as a 

percentage of crew shares. Flxed costs are those incurred regardless of the 

vessel's level of operation, and comprise scheduled repairs and 

maintenance, rent and moorage, insurance, interest, as we1 I as fees for 

professional serv ices, property taxes, perm its, and association dues. AI 1 

reported costs were converted to 1982 dol I ars using the US. gross national 

product, imp1 icit price deflator. This adjusts for cxeral I Inflation but 

'A combination of cross-sectional and time series data where the number of 

time series observations varies over the individuals constituting the cross 

section. 
7 

preserves the effect of relative unit price differences with regard to 

specific operating and fixed cost expenditures. Descriptive statistics for 

key variables used in the ALBSIM analysis are presented in Table 1. 

4. Structure, Spec i f i cat ion, and Es timation 

The version of ALBSIM described here is designed to predict annual 

cash flows for north Pacific troll vessels whose primary target species is 

al bacore tuna. A vessel's annual cash f I ow is an accounting of the actual 

cash out1 ays (the variable and fixed expenditures associated with owning 

and operating the vessel) that are required to generate a cash inflows 

(revenues) from fishing. The net cash flow is a measure of funds generated 

from operations that can be used to repay loans, pay taxes owed, or pay a 

wage to the vessel ownerJoperator for his labor and management. From an 

accounting standpoint it is the difference between actual expenditures and 

revenues? and can be expressed as: 

Vesse I Rev en ue 

I andl ngs * ex-vessel price 

- Operating Costs 

fuel and oil 

other 

- Labor asts 

payments to crew 

payments to skipper 

payrol I taxes 

- Fixed Colsts 

I nterest payments 

scheduled mai ntenance 

moorage and rent 

insurance 
8 

fees (professional serv icesr property taxesr 

Dermits, a nd 0th er) 

Net Cash Flow. 

ALBSIM is essential ly a col lection of regression-estimated equations 

that predict the vessel's revenues and expenditures according to the 

structure of the net cash flow account. Because the Vrue" re1 ationship 

between the dependent and independent variables is unknown for each of the 

equations that make up ALBSIM, each equation was initially formulated as a 

second degree polynomial including cross product terms. By using the 

physical characteristics and effort in I inear and non-l inear combinationsr 

the model shoul d approximate the true function.* l n s i g n i f i c a n t independent 

variables and highly correlated independent variables were then removed 

from the initial estimations in order to maximize the adjusted Rsquared 

(RBAR**Z) orr equival entlyr minimize the standard error of the estimate 

(SEE). This was done subject to a criterion of parsimonyr i.e., given two 

models with equal explanatory power) choose the simpler one. 

4.1 Catch and R evenu e 

ALBSIM employs separate equations to estimate a vessel's annual 

al bacore catch and its annual catch of other species. In doing this It is 

assumed that fishing for albacore does not occur simultaneously with 

LFrom Taylor's theorem it is possible to approximate a linear or nonl inear 

function by a polynomial of sufficient degree (see for exampler Chiang, 

1%7). However, polynomial approximations can I ead to markedly unexpected 

predictio'ns when the val ues of independent variables become extreme. 

Predictions from polynomial approximations are superior to those from 

I i near approximations when the val ues of independent variables are closer 

to the sample means. 
9 

fishing for other species. The catch of an individual vessel can be 

expressed f unctional ly as: 

Xj+l’ 

where Y represents catch, Xi, X2 ..... xJ represent factor inputs re1 ated 

to fishing effort which {can be controlled by the owner or operator of the 

troller, and Xj+l, .... Xn denote factors beyond the control of the 

owner/operator such as the abundance of albacore and environmental 

conditions. No data are available for the uncontrollable factors, and 

therefore these factors are not included in the ALBSIM catch equation 

specifications. Furthermore, it is assumed that values of the included 

variabl es are uncorrel ated w ith the true values of the variables excl uded 

from the catch equation. 3 

’By omitting the uncontrollable factors the catch equation is subject to 

the specification error discussed above. In addition, it is very I ikely 

that val ues of the i ncl uded, control I ab1 e, ef fort-re1 ated var i ab1 es are 

i nfl uenced by expectations of al bacore abundance, environmental conditions, 

and other uncontrol I ab1 e var i ab1 es which have been om i tted from the 

equation. To t h e e x t e n t that expected val ues approach the true val ues of 

these variables, the values which actually affect catch, the I lkel ihood of 

contemporaneous correlation between the explanatory variables in the 

equation and the disturbance term (which w il I include the effect of the 

omitted uncontrol lable factors) increases. This can result in biased 

regression coefficients and bias in the standard error of the catch 

estimator. From a practical standpoint the consequences of misspeclf lcation 

and contemporaneous correl a t i o n a r e o f t e n o f f s e t t i n g . Furthermore, the 

extent of bias in the regression coefficients and standard error of the 

estimate from these sources may be of I ittle practical concern given 

re1 at ively strong-f i tting re1 ations. 
10 

Fishing effort may be defined as the product of fishing power and the 

time spent fishing. The vessel's physical characteristics, crew size and 

operator ski1 I are the major factors determining a vessel's fishing power. 

The role of physical characteristics in determining the vessel's fishing 

power seems to be re1 ated to its range of operati on. In this sense, larger, 

newer, more powerful vessel s are expected to be more productive than 01 der, 

smal I err less powerful vessel s by virtue of their bei ng ab1 e to conduct 

more fishing per unit of time. The productivity of a particular vessel w il I 

also vary with crew size and the abil ity of its operator. Because there is 

I i ttl e variab i I i ty in crew size across the sample of vessels used in the 

ALBSIM estimation and there Is no information on the re1 ative fishing 

ski I i s of their operators, these factors are excl uded from the analysis. 

Nonetheless, prof it maximizing behavior is assumed on the part of vessel 

owners and vessel operators. The individual vessel catch equations for 

ai bacore and other species take the form: 

Y = a10 + allEFF + bllVES + b12(VES * EFF) + u1 

where Y = catch of albacore (TALB) in short tons 

(tons), or catch of other species (TOTH) in 

tons (see Table 2 for definitions of the 

variables used in the ALBSIM analysis.) 

VES = vector of vessel physical characteristics 

(see Table 2). 

EFF = days fishing, or effort, for either 

albacore or other species (see Table 2.1 

VES * EFF = a vector of vessel characteristics and 

f i sh i ng effort i nteract ion terms. 
11 

In estimating the catch equation for tons of albacore all 127 

observations were usedll while the tons of other catch equation was 

estimated using 57 vessel-years of data for which there was significant 

fishing activity directed toward other species. Limited observations on 

catches and effort for some of the individual species other than albacore 

and difficulty in dissaggregating the other catches and effort for some 

vessels precluded estimating separate catch equations for all of the nonalbacore species. Thus, iin certain cases (ag. where a vessel fishes salmon 

and crab (SALMCRB)) the equation for catch of other species predicts the 

aggregate catch for a category of other species given the sum of the effort 

directed toward each. The results of the catch equation estimations are 

shown in Table 3. 

The results were used to calculate catch el asticities for the three 

independent variables entering each catch equation. A catch elasticity 

denotes the percent change in catch for a one percent change in one of the 

independent variabl es w i th al I other i ndependent variabl es he1 d constant. 

Catch elasticities for effort and carrying capacity are expected to be 

positive, but not greater than one. An elasticity greater than one suggests 

expanded use of the factor under consideration, a negative elasticity 

suggests a reduction in factor user and an elasticity in the zero to one 

range is consistent with profit maximizing behavior. It is also 

hypothes Ized that as vessel s get ol der, their product ivi ly decl I nes, w hi ch 

would be indicated by a negative catch elasticity for vessel age. 

The elasticities (E) calculated for the albacore catch equation with 

respect to changes in carrying capacity (cap), effort (dalb), and age are: 
12 

Ecap = 0.7337 

Edal b = 0.6627 

Eage = -0.0887 

These elasticities are of the expected sign and magnitude, calculated at 

the sample mean values. For example, a 10.0% increase, from the sample 

mean, in the number of days albacore fishing is expected to result in a 

6.6% increase in al bacore catch when al I the other variables remai n 

constant. 

The catch el astici ti es for tons of other species w ith respect to 

changes in carrying capacity and age are: 

The age elasticity reveals that vessels decl ine in productivity as they get 

01 d. The capaci ty el asti ci ty i ndi cates that the representative al bacore 

trol I er is of sub-opt imal size when f i shi ng for species other than 

at bacore. That is, there w I1 I be a disproportionate increase in nona1 bacore catch if carry ing capaci ty decreases. 

Effort elasticities for catches of other species are: 
13 

Edtuna/gf = 

Edsa I mon/gf = *a1 56 

= 2.9926 E dsa I mon /cra b 

Edsalmon/misc = *9175 

Edgf/misc = 1*1241 

All these elasticities have the expected sign. The magnitude of the effort 

elasticities for salmon, lobster, and salmon and other species indicates 

that these vessels would experience more than a proportional increase in 

catch for an increase in corresponding days fished. At first this suggests 

either misspecification of the equation or the fishermen in the sample are 

operating at a sub-optimal level in these catch categories -- sub-optimal 

in the sense the vessel1 would real ize a relatively greater catch by 

redirecting effort from albacore to salmon, lobster, or to any of the other 

sal mon categor i es. How ever, f i shermen may not redi rect effort tow ard these 

species due to differences in ex-vessel prices or fishing costs, or both. 

Furthermore, fishermen in the sampl e may be constrai ned from al locating 

more effort to salmon, lobster, or salmon and other species because of 

seasonal availability as well as west coast salmon and lobster fishing 

regul ations. 

The groundfish and niiscel laneous species category also has an effort 

el astici ty greater than that for ai bacore. However, because the dependent 

variable in the catch equation is tons caught rather than value, and in the 

data set the average val ue of al bacore is s u b s t a n t i a l l y greater than the 

average values for groundfish and m iscel laneous species, additional time 
14 

would be a l l o c a t e d to albacore f i s h i n g v i s a vis other species -- under 

prof it maximizing conditions -- everything else remaining unchanged. 

AI though the effort el asticities indicate the expected change in catch 

in response to a change in effort, it should be further noted that both the 

albacore equation and other catch equation exhibit constant, short-run 

catch rates; that 1 s, the catch per day for a particul ar size and age 

vessel does no1 depend on the number of days fished. Given the albacore and 

other species catch equations, a prediction of ex-vessel revenues is 

obtained by mu tiplying the predicted catches by the respective ex-vessel 

unit prices which are provided by the user of the model. In the case where 

there is an aggregate catch of non-a1 bacore species, a composite ex-vessel 

unit price is required. 

4.2 Qperat ina Cos ts 

ALBSIM incorporates an engineering-type relationship to predict fuel 

consumption where fuel consumption is dependent on the vessel's physical 

characteristics and the total amount of time the vessel spends at sea (days 

in transit plus days fishing). 

Since the data used to estimate ALBSIM do not include observations on 

fuel consumption, the annual fuel expenditure reported for each vessel was 

divided by the corresponding annual average diesel fuel price for the 

Pacific region (Bureau of Labor Statistics, 1975 - 1983) to obtain 

estimates of actual fuel consumption. The fuel consumption re1 ationship is 

expressed as: 
15 

where 

FC = a20 + a21DSEA + b21VES + b22(VES * DSEA) 

+ u2 

FC = fuel consumption in gallons 

DSEA = nunber of days vessel is at sea. 

Results of estimating the fuel consumption equation are presented in 

Table 4. As anticipated, fuel consumption increases with the size 

(represented by capacity and length) and age of the vessel and the amount 

of time it spends at sea. Fuel cost is derived by multiplying predicted 

fuel consumption by the fuel price furnished by the model user. 

Variable expenditures on items other than fuel and labor include 

provisions, gear, ice, bait and other miscellaneous variable costs. ALBSIM 

predicts these costs, in 1,982 dol Iars, as a group through an equation which 

specifies totalr non-fuelr non-labor, operating costs as a function of the 

vessel's physical characteristics, the amount of time it spends at sea, the 

species targeted in addition to albacore, and its home port. This 

re1 ationshi p is expressed as: 

TRIP = a30 + a31DSEA + b31VES + b32PORTS + 

b33SPECIES -I- bj4(VES * DSEA) + ~3 

w her e TRIP = total non-fuelr non-labor operating 

costs in 1982 dol I ars 

PORTS = vector of dummy var i ab1 es for home 

port I ocation (see Tab1 e 2) 
16 

SPECIES = a vector of dummy variables denoting 

the species or species categories 

targeted other than albacore (see 

Table 2) 

Estimation results for the other trip costs equation are shown in 

Table 4. With the two sets of dummy variables, the constant term represents 

a vessel home ported in the San Diego - Los Angel es port area that only 

f i shes for al bacore. Not unexpected I yr trip costs increase w i th i ncreases 

in vessel size and age, as well as increases in the amount of time spent at 

sea. Trip costs are highest for vessels home ported in Hawaiir wherer of 

the port areas consideredr the cost of I iving is the highest. For vessels 

that fish al bacore and other speciesr trip costs w it I be greatest for those 

also fishing salmon. 

Labor costs include the payments to operator and crew and the 

associated payrol I taxes. For al bacore trol I ers the crew is typi cal ly 

remunerated v ia a share system, whichr for the sampl e of vessels used to 

estimate ALBSIM, corresponds to a percentage of the vessells gross 

revenues. As a percentage of gross revenues there was a signif icant 

difference in crew share according to size of the vessel: for vessels 75 

feet or greater in length, crew share averaged 31% of gross revenues; for 

vessels less than 75 feet, crew share averaged 21%. These shares also 

included any payment to the vessel operator. PayroI I taxes are calculated 

as a percentage of crew earnings at the rates of 4% and 2% respectively for 

the two vessel size classes. ALBSIM preaicts a vessel's labor expense based 

on the predicted gross revenues for the vessel and the appropriate crew 

share and payrol I tax rate. 
17 

4.3 F ixed Costs 

ALBSIM predicts vessel fixed COStS8 or the costs associated with 

owning the vessel, using a two stage procedure. The first stage cons sts of 

predicting the vessel's total fixed costs, I.a, the sum of the ind vidual 

cost i tems under the fixed cost heading in the cash flow account shown on 

page 7. To do this, ALBSlM incorporates a total fixed costs equation which 

relates total fixed costs to the vessel's physical characteri sti cs, home 

port8 the amount of time it spends at sea annual ly8 and the species fished 

other than al bacore. In a strict senser f ixed costs should be insensitive 

to the amount of time the vessel is at sea or spends fishing. However8 for 

some of the costs categorized as fixed (e.g. scheduled repairs and 

mal ntenance, or moorage:) it seems reasonabl e to expect some correl ation 

between the amount of the expenditure and the time spent at sea, and the 

time spent fishing. A prel iminary analysis of the data supported this 

proposition. Theref ore, these variabl es are i ncl uded i n the total f i xed 

cost specification to enhance the model's predictive power. The total 

fixed costs equation takes the form: 

where FIXED = total fixed costs in 1982 dollars 

Results for the total fixed costs estimation are shown in Table 4. As 

expected8 costs of vessel ownershi p i ncrease w ith vessel size and the 

intensity of vessel use. Fixed costs are significantly higher for vessels 
18 

home ported in Hawaii and for those that fish groundfish and miscellaneous 

species in addition to a1 bacwe. 

The second stage of the fixed cost analysis conslsts of estimating 

expenditures for each of the i tems of fixed cost: i nterest, mai ntenance, 

moorage, insurance, and fees. These costs are estimated on a share basis 

using a system of equations that expresses the relative expenditure for 

each fixed cost item as a function of vessel characteristics, vessel 

activity, home port, and non-a1 bacore target species. The system is 

estimated under the restriction that the shares must sum to unity. The 

system of fixed cost share equations can be represented in compact form as: 

SHRi = ai0 + ailVES + ai2EFF + bilPORTS+ 

bi2SPECIES + uj 

i = lr2, - -,5 

subject to FSHRi = 1.0 

where 

1 

SHR1 = interest expense as a proportion 

of total fixed costs 

SHR2 = maintenance expense as a 

proportion of total fixed 

costs 

SHR3 = moorage expense as a proportion 

of total fixed costs 

SHR4 = fees as a proportion of total 

fixed costs 

SH% = insurance expense as a 

proportionoftotal fixed costs. 

Empirical ly, if the prediction error for one of the cost shares is 

positive, then the prediction errw of some other sharek) must necessarily 

be negative. This imp1 ies that ordinary least squares is not the 
19 

be negative. This imp1 ies that ordinary least squares is not the 

appropriate estimation technique, but instead an estimation procedure w hlch 

uses the additional information about the correlation between errors should 

be used. Zel I ner's Seemingly Unrelated Regression estimation is such a 

procedure and is used here. The correl ation of the errors can be expl ai ned 

behaviourly by noting $hat fishermen may decide to trade-off higher 

expenses in one category for lower expenses in another. The share 

equations were first estimated using ordinary least squares and then the 

error covariance matrix was used to increase the efficiency of estimation. 

This is done as a system with one equation dropped. In this case, the 

insurance equation was dr~pped.~ With the constraint that the five shares 

add up to one (1.01, only four shares need to be estimated (see Pindyk and 

Rubinfel d, 1976). Results of the fixed cost share estimations are presented 

in Table 5. 

An estimate of each fixed cost expenditure, in 1982 dol I ars, is then 

derived by multlplying ,the estimated total fixed costs by each of the 

respective shares with the insurance expenditure calculated as a residual. 

The vessel's net cash flow is estimated by then taking the difference 

between total vessel revenue and the sum of operating, labor and fixed 

costs. The way in which the catch, variable cost, and fixed cost equations 

are conf igured to form AL13SIM is described n the next section. 

4The choice as to which equation is dropped is arbitrary. However, 

ordinary least squares results showed that the insurance share equation had 

the lowest predictive power; therefore, that equation was dropped from the 

Seem i n g I y Unrel ated Regress i on est I mat i on. 
20 

5. AI BSlM Conf iau rat ion 

To perform the sequence of calculations required to predict an 

al bacore trol ler's net cash flow, ALBSIM was implemented using a 

m lcrocomputer based el ectroni c spreadsheet (Tab1 es 6A-6C). To conduct an 

ALBSIM simulation the user must specify the following: 

vesse I character i st i cs 

length (feet) 

capacity (short tons) 

age (years) 

home port 

trip characateri sti cs 

days at sea 

days f ishing albacore 

days fishing other species 

fuel price ($/gal) 

ex-vessel al bacore price ($/ton) 

ex-vessel %othert1 price ($/tonI5 

other species or species category 

year 

Once these inputs are entered into the spreadsheet, the program calculates 

revenues, costs and the vessel's net cash f I ow i n nom1 nal dol I ars of the 

year specif led. User input is denoted by rt***lr in Tab1 e 6A. This user 

suppl ied information is used in the calculations of other variables and in 

the equations shown in Table 6B 

The spreadsheet has been set up so that most of the information needed 

as input and the final output fits onto the computer screen. The 

calculations being made in Table 68 are off the screen, but the output from 

the equations can be adjusted by the user. For example, Table 6C shows the 

output for a hypothetical vessel fishing for albacore, tuna, and groundfish 
21 

out of southern Cal ifornia. (The spreadsheet nomencl ature rep1 aces the 

variable names used above with their cell location in the spreadsheet 

matrix - ag. the vessell's carrying capacity, CAP, becomes 83 when entered 

into the spreadsheet.) If in the example the model user felt that the 

predicted catch of the other species was inappropriate for the vessel under 

consideration, by entering the appl icable amount of the other catch into 

cel I C20, cel Is D20, D26, F28, F29, and F30, the cel Is containing revenue 

and cash flow output w iI I automatical ly be changed. 

'In the case where the %other" catch is a mixture of west coast commercial 

species, ALBSIM has been exercised using a weighted average price for these 

species. 
22 

6. Appli cat i on 

To demonstrate ALBSIM several cases from the data set that were not 

used in the estimations are analyzed. 

Case 1. A vessel between 50 and 60 feet long, with a capacity between 20 

and 30 short tons, and age between 25 and 30 years in 1982. The 

vessel was at sea more than 130 days, and fished more than 120 days 

for al bacore only In 1982. The ex-vessel price of al bacore for this 

case is $1,387 per short ton, and the price of fuel is $.94 per 

gal ton. 

Catches (st) 

a I bacore 

Revenues 

al bacore 

Operating Costs 

fuel 

trip 

I abor 

Sub-tota I 

Fixed Costs 

I nterest 

mai ntenance 

moor age 

insurance 

fees 

Sub-tota I 

Total Costs 

Reported 

46 .O 

$62 ,3 50 

12,500 

8,325 

19,305 

40,130 

13,870 

2,000 

800 

4,000 

300 

20 , 970 

61,100 

Fred icted 

44.5 

$61,735 

11,786 

10,543 

12,656 

34,985 

12,248 

19,731 

2,579 

15,949 

2,169 

52,676 

87 966 1 

Net Cash Flow. 1,250 -25,926 

I n order to mal ntai n conf ident i al 1 ty, the spec1 f IC vessel 

characteristics for each case are not revealed. 
23 

Case 2. A vessel between 40 and 50 feet long, with a capacity between 10 

and 20 short tons, and age between 25 and 35 years in 1981. This 

vessel was at sea for more than 120 days, fished albacore more than 

60 days, and fished salmon more than 60 days in 1981. The ex-vessel 

price of al bacore used in this case is $1,800 per short ton, the 

ex-vessel price of salmon used is $5,040 per short ton, and the 

price of fuel used is 

Catches (st) 

al bacore 

sa I mon 

Revenues 

a I bacor e! 

sa I mon 

Tota I 

Operating Costs 

fuel 

trip 

I abor 

Sub-tolta I 

Fixed Costs 

i nterest 

mai ntenance 

moor age 

insurance 

fees 

Sub-tota I 

Total Costs 

$.85 per gal Ion. 

Reported 

27.5 

4.2 

60,733 

6 ,360 

11,530 

8,544 

26,434 

12,275 

6 ,013 

6 00 

37 0 

2 ,450 

21,708 

4891 42 

Pred i cteci 

19.9 

6.3 

$35 987 4 

31,802 

67,676 

7,377 

7,555 

13,813 

28 97 45 

6 ,200 

10 1403 

1,681 

5,586 

1,790 

25,660 

54,405 

Net Cash Flow 12,591 13,271 

(1981 dol I ars) 
24 

Case 3. A 55 to 65 foot vessel w ith a capacl ty between 40 and 50 short 

tons, and age between 1 and 5 years in 1982. The vessel was at sea 

for more than 150 days, and fished a1 bacore only for more than 120 

days in 1982. In this case $1,387 per short ton is used for the exvessel price of albacore, and 6.94 per gallon for the price of 

fuel. 

Catches (st) 

a1 bacore 

Revenues 

a1 bacore 

Operating Costs 

fuel 

trip 

I abor 

Sub-total 

Fixed Costs 

i nterest 

mal ntenance 

moor age 

insurance 

fees 

Sub-tota I 

Total Costs 

Reported 

65.0 

$77 ,7 53 

26,569 

4,468 

--o-- 

31 ,307 

17,905 

10 ,050 

1,798 

3,789 

8,842 

43,384 

74,691 

Fred i cted 

63.6 

$8891 99 

22,580 

9,497 

18,081 

50,158 

20 r6 87 

24,715 

4,382 

18,238 

3,221 

71,243 

121,401 

Net Cash Flow 4,242 -33,202 
25 

Case 4. A vessel between 70 and 80 feet long, with a capacity between 60 

and 70 short tons, and age between 10 and 20 years in 1980. The 

vessel was at sea for more than 200 days, and f ished albacore more 

than 160 days, and other tuna more than 20 days in 1980. In this 

case the ex-vessel price of al bacore used is $1 ,659 per short ton, 

the ex-vessel of other tuna used is $1,600 per short ton, and the 

price of fuel used is 

Catches (st) 

al bacore 

other tuna 

Revenues 

albacore 

other tuina 

Tota I 

Operating Costs 

fuel 

trl p 

I abor 

Sub-total 

Fixed Costs 

i nterest 

mal ntenance 

moorage 

insurance 

fees 

Sub-tota I 

Total Cost 

5.68 per gal Ion 

ReDorted 

83 .O 

0.3 

$1 29,300 

26 $7 81 

9,737 

30,538 

67,056 

101576 

37,466 

4% 

9 ,304 

2,491 

60 ,333 

1279389 

Fred i cted 

84.2 

--O-- 

$139,605 --o-- 

139,605 

28,817 

8,188 

28,6 19 

65,624 

26 ,202 

59,102 

4,464 

6,390 

--O-- 

96,158 

162,082 

Net Cash Flow 1,911 -22,477 

(1980 dol I iars) 
26 

7. Discussi OR 

The demonstration cases reveal some of the strengths and weaknesses of 

the ALBSIM model. The model is most adept at predicting each vessel's 

al bacore catch, which is not unexpected given the re1 atively high overal I 

goodness of fit measure (R2) for the al bacore catch equation (relatively 

high considering the data used to estimate the equations is dominated by 

cross-sectional observations). Moreover, no probl ems associated w i th 

heteroscedast i ci ty -- a mmmon occurrence w hen us i ng cross-sect i on data -- 

were detected in the albacore catch equation, which if present, would 

I ikely contribute toward overstating the significance of the equation's 

predictive ab1 I i ty. 

The catch equation for the other species does not perform as we1 1. To 

a I arge extent this can be attributed to I imi ted observations on other 

catches, correspondi ng effort, the diversity of species maki ng up the other 

catch, and great variabil ity of other catch for the same vessel year to 

year, as we1 I as accross vessels having similar physical characteristics 

and patterns of operation. Therefore, there Is a lack of robustness when 

making predictions of other catch from observations outside the range of 

those used to estimate the model. 

t must also be recognized that when predicting ex-vessel revenue, 

diffc:ences In actual versus predicted revenues reflect not only 

differences between adual and predicted catches, but also differences in 

actual and user provided ex-vessel prices. Thus, erroneous catch 

predictions can be offsQt inadvertently by compensatory error in the ex- 
27 

vessel prices selected by the model user when predicting ex-vessel 

r even u e s . 

Of the operating expenses, ALBSIM predicts fuel cost w ithin 15% of the 

values reported in the four cases examined. The fuel consumption equation 

also has a relatively high R2, the significance of which does not appear to 

be affected by heteroscedasticity. However, just as revenue cal CUI ations 

depend on the user suppl ied ex-vessel prices, predicted fuel expenditures 

depend not only on predicted fuel consumption, but on the user suppl led 

fuel price as we1 1. Predlictions of labor cost are derived directly from 

estimates of ex-vessel revenue and therefore are as precise as estimated 

catches, user suppl led ex-vessel prices and the correspondence of the 

vessel's actual labor share system to that incorporated in the model. The 

remaining operating Cost!; are predicted as a group through the other trip 

costs equation. In the cases analyzed, the predicted other trip costs vary 

significantly from the reported values. These results are not surprising 

si nce vessel s w i th s im i I i3r physical characteri sti cs and operating patterns 

(even the same vessels year toyear) exhibit considerablevariabil ity in 

the costs which fall in this category. This is borne by the goodness of fit 

measure for the other trip costs equation which is relatively low even 

recognizing the dominance of cross-sectional observations in the data set. 

Based on the cases examined and the goodness of fit measures, ALBSIM 

is weakest in predicting vessel fixed costs. As in the prediction of other 

trip costs, this weakness is probably due to a great deal of variabil ity in 

the observations on fixed costs in both dimensions of the data set used to 

estimate the total fixed cost and the fixed cost share equations. 
20 

Furthermore, problems of heteroscedasticity were detected in estimating 

the total fixed cost and fixed cost share equations, which likely result in 

upward bias in the goodness of fit test statistics for these equations. 

The varlation in vessel costs and revenues left unexplained by ALBSIM 

can be partly ascribed to disparity in accounting methods, changes in tax 

codes, inconstant f i nanci al markets, and the preferences of i nd ivi dual 

vessel owners. Even though ALBSIM does not y iel d perfect predictions of 

costs, revenues, and cash flows -- point estimates of revenues or costs 

will always be questionable -- it does provide reasonable revenue and cost 

parameters which can be useful when attempting to measure the marginal 

value of fishingeffort. In thisregardALBSIMcanfurnish fishermenand 

fisheries administrators with useful information regarding changes in the 

operati ng environment of west coast al bacore trol I ers. 
29 

REFER EN CES 

Chiang, C C. 1967. Fundamental. Methods nf MathemaL t F conom ics. McGrawHi I I, New York, 690 p. 

Griffen, W. L., J. Cato, J. Gates and F. Prochaska. 1980. Soc i oeconom i c 

budget simulator. Texas A&M Research Foundation, Department of 

Agricul tural Econom ics, Texas Agricultural Experiment Station, Texas A8M 

University, Cot I ege Station, Texas, 246 p. 

Pi ndyck, R S. and D. L. Rub1 nfel d. 1976. Modelsiml Fconom i c 

Forecasts. McGraw-Hi I I, New York, 576 p. 

Schel I e, K. and 8. Muse. 1983. A suggested methodol ogy for estimating 

the distribution of operating costs and net returns in Alaska's salmon 

fisheries. AI aska Commercial Fisheries Entry Commission Report 83-9, 22 

Po 

Squi res, D. 985. Review of f isheries budget simulation models. National 

Oceanic and Atmospheric Admi nistration, National Marl ne Fisherles 

Service, Southwest Fisheries Center, La Jol la, Cal ifornia (in Press). 
30 

Table 1. Summary statistics for the cost and earnings 

unbal anced-panel data set. 

Number of Standard 

Variable Observations Mean Deviation Minimum Maximum 

n 

AI ba cor e 1 27 32.4 

Other s peci es 57 12.5 

Effort 

Days fished 

al bacore 127 73.7 

Days fished 

other 57 56.9 

Days at sea 127 115.3 

Phvs ical Cha racteristics 

Length overal I 

(feet) 127 51 .7 

Capacity (tons) 127 25.5 

Age (years) 127 19.4 

Costs ($1,000 I s) 

Fixed 127 13.2 

Fue I 1 27 11.3 

Trip 127 7.5 

Labor 1 27 8.7 

Cost Shares (%I 

Fees 127 7 .O 

Equipment and 

mai ntenance 127 42.5 

Interest 1 27 20.5 

I nsur ance 127 22.7 

Moor a ge 127 7.3 

23.6 

29.7 

34.8 

43.8 

39.2 

7.3 

11.8 

14.6 

11.2 

7.8 

4.5 

10.7 

7.2 

20.1 

20.4 

14.2 

6.9 

.2 

.3 

5 .O 

20 .o 

12.0 

32 .O 

3 .O 

.o 

1.5 

3.2 

1.1 

.o 

.O 

.o 

.o 

.o 

.o 

104.5 

203 .O 

172.0 

300 .O 

210 .o 

7 5.0 

70 .O 

58.0 

52.2 

40.6 

26.5 

56.9 

51 .7 

89.4 

65.8 

82.2 

33.1 
31 

Table 2. Definitions of variable mnemonics. 

Dependent Var i ab1 es : 

EQ MA 

FC 

FEES 

FIXED 

I NTE 

INSU 

LABOR 

MOOR 

TALB 

TOTH 

TRIP 

= qui pment and mal ntenance share of fixed costs 

= fuel consumption in gallons 

= fees and miscel I aneous as a share of fixed costs 

= total fixed costs in thousands of 1982 dol I ars 

= interest cost share of fixed costs 

= insurance share of fixed costs 

= labor cost incl uding payrol I tax in thousands of 1982 

= moorage and rent as share of fixed costs 

= catch of albacore In short tons 

= catch 04 other species in short tons 

= trip closts (ice, bait, gear, gal I ey, etc.) 

dol I ars 

thousands of 1982 dol I ars 

Predetenni ned Variables: 

EFF = a measure of vessel fishing activity or fishing effort 

such as: 

DALB = days 

DALBSQ = DALB 

DF I SH = tota 

DGFMIS = days 

DLOBSTER = days 

DSALM = days 

DSALMCRB = days 

DSALEF = days 

of al bacore f ishi ng * DALB 

fishing groundfish and miscel I aneous species 

f ish i ng I obster 

fishing salmon 

fishing salmon and crab 

fishing salmon and groundfish 

number of days fished all species 

DSALMMIS 

DS EA = days at sea steaming, idle, and fishing 

DSEADALB = DSEA * DALB 

DSEASQ = DSEA * DSEA 

DTUN A 

DTUNAGF 

= days f ishiig salmon and miscel laneous species 

(herring, squi d, etc. 1 

= days fishing other tuna 

= days fishing tuna and groundfish 

PORTS1 = vector of dummy variables for hane port location: 

HI = Hawaii 

MPN P = unspecif led, or more than one area indicated 

NCCA = northern or central Cal ifornia 

ORWA = Oregon or Washington 

SPECIES2 = a vector of dummy variables denoting the species or 

species catagory targeted other than albacore 

GFMl S = groundf ish and m iscel I aneous speci es 

LOBSTER = lobster 

SAL M = salmon 

SALMCRB = salmon and crab 

SALKF = sal mon and groundf ish 
32 

Table 2. (continued) 

SALMMIS = sal mon and m iscel I aneous species (herring, 

squ i d, etc.) 

TUNA = other tuna 

TUNAGF = tuna and groundf Ish 

VES = a vector of vessel characteristics 

ffiE = vessel age in years 

AG ESQ = AGE * AGE 

CAP = vessel capacity In tons 

CAPAGE = CAP * AGE 

CA PSQ = CAP * CAP 

L EN = vessel overall length in feet 

LENCAP = LEN * CAP 

LENSQ = LEN * LEN 

VES * EFF = a vector of vessel characteristics and fishing 

effort interaction terms 

CAPDALB = CAP * DALB 

CAPDSEA = CAP * DSEA 

'Sari Diego - Los Angeles home port area Is the base case. 

'AI bacore only is the base case. 
33 

Table 3. Estimation results for catch equations. 

AI bacore (TALB) Other Species (TOTH) 

R**2 

RBAR**2 

SS R 

SEE 

F-STATISTIC 

.63 453739 

.6 22 5550 1 

25669.91 5 

1 4.50 5487 

52.94 

.5 8 90 97 58 

.4 9977 0 97 

201 27.064 

20.9 17 573 

5.9952 

Independent 

Var i ab I e Coeff icie nt Coef f i c I ent 

CONSTANT -16.12975 

(-1.876489)b 

14.57281 

( .3619939) 

CAP 1 .209455 

(3.62%48) a 

- .3377 5 17 

(-.2430338) 

.1251164 E-0 1 

( .77 937 1 8E-02) 

AGE - . 1 4846 1 9 

(-1 .442262)~ 

DALB .3862112 

(4.912684)a 

- .37256%E-02 

(-1.379043)c 

CAPDALB 

CAPAGE - .2196737E-O1 

( - .3363192 1 

DTUNA .170190 1 

(1.357840)~ 

.9767663 E-0 1 

( .4%0257 1 

DS AL M 

DL OBSTER .3058222E-01 

(.1042846) 

.5002390E-02 

( .6542058E-01) 

DTUNAGF 

.9767663 E-0 1 

( .4%0257 1 

DSALMGF 

DSALMCRB .5023546 

( 2.037 248) b 

,61131 52 

(1.601 580)~ 

DSALMM I S 
34 

Tab1 e 3. (continued) 

DGFM I S 1.350161 

(5.062020)a 

Notes: t-stati sti cs i n parentheses 

a - significant at the 99% level 

b - signif icant at the 95% level 

c - significant at the 90% level 
35 

Table 4. Estimation results for the fuel consumption, other trip 

costs and fixed cost equations. 

Fue I Other tr I p Fixed 

consumption (FC) costs (TRIP) costs (FIXED) 

R**2 .62361131 

RBAf?**2 .601470;80 

SSR 291 2.7 520 

SEE 4.947 41 42 

F-STATISTIC 28.1647 

Independent 

yar iabl e 

CONSTANT 

NCCA 

ORWA 

HI 

MPNP 

LEN 

LENSQ 

CAP 

LENCAP 

AG E 

AG ESQ 

CA PAG E 

Coefflcm 

-7.968425 

( - .9 83 581 3 1 

.35827193 

.26 492 967 

1625.4098 

3.84401 52 

16.1241 

Coeff icie nt 

-2.921635 

(-1 .055954) 

-1.8921 12 

( -1 .7 10221 1 b 

.3708714 

(.3153586) 

2.496002 

(1.671421 IC 

-5.862331 

(-2.3451 87 1 b 

2447698 

( 1.501 044)~ 

.1417694E-02 

(2.498973)a 

.5149040 

( 1.981 41 5) b 

-.1181099E-01 

(-3.243791 )a 

.1985989 

(1.548924)~ 

-5731 '104E-02 

(-2.469882)a 

.86 3 587 3E-0 1 

(2.371 923)a 

.54562%3 

.47 47 6453 

7213.21 23 

8.1 3487 92 

7.6986 

Coeff lcie nt 

-9.91 5595 

(-1 .961920)b 

-1 .697079 

(w.7211754) 

2 A8552 

(1.123641 1 

14.17605 

(4.41 0325)a 

-9.2277 03 

(-1.562270)~ 

1 .632974 

( 3 376 136 1 a 

-.1577351 E-01 

(-2.746255)a 

.127 6294 E-0 1 

(3.200879)a 

- .23 47 007 E-0 1 

(-3.740727)a 
36 

Table 4. (continued) 

.817 03 12E-0 1 

(1.976501 )b 

.5950 403 E-0 1 

(2.399861 )a 

DS EA - .3 97 826 OE-0 1 

(-1 .401754)~ 

- .2363157 E-03 

(-1 .407286 IC 

DS EASQ 

.46 5 1 364 E-0 2 

( 3.6 87 877 1 a 

CAPDSEA 

-5.21 9284 

(-1 .625701 IC 

-1 2.40637 

(-1.768125)b 

TUNA 

SALM 1 .021486 

(.9252243) 

-3.850858 

( -1 .7 4837 2) b 

LCBSTER -1.900998 

( - .7 923983 1 

-12.04875 

( -2.292806 1 b 

TUNAGF -3.760706 

(-1 .450345)~ 

-1 8.82242 

( -3.427 295 1 a 

SALMGF -2.125618 

(-.7416811) 

3.077885 

(.4932753) 

SALMCRB -8.591 21 5 

( -3 . 1 91 1 09) a 

-16.27070 

(-2.1 51 564) b 

SALMM I S -4.661 404 

(-1 .929932) b 

-2 39591 

( - .50766 13 1 

GFMIS - .3355561 

(-.1664704) 

6.21 8109 

( 1.427443 IC 

Notes: t - s t a t i s t I cs I n parentheses 

a - significant at the 9% level 

b - signif lcant at the 95% level 

c - signif icant at the 90% I eve1 
37 

Table 5. Estlmatlon results for the fixed cost share equations. 

mal ntenance Moor age Fees I nterest 

(EQMA) ( MOOR) ( FEES 1 (INTE) 

Equipment and 

R**2 .3 1337738 25041045 .36906188 .15384837 

RBAR**2 . 1 91 45373 .19959082 .32052818 . 1 1 8883 42 

SSR 34881 .091 4472.2670 41 54.381 0 444% .559 

SEE 18.055235 6.1 563437 5.95881 95 19.167931 

F-STAT 2.5706 4.9272 7.6055 4.3984 

Independent 

var iab I eCo eff lclent Coeff Ici ent Coefflclent Coefficient 

CONSTANT 40.82162 17.68847 191.8860 -1 8.21 926 

N CCA 

OWA 

HI 

MPNP 

LEN 

L EN SQ 

CAP 

CAPSQ 

L ENCAP 

DALB 

DALB SQ 

DS EA 

( .266652911 (5.124115)a 

2.755549 

( .3 2333 85 11 

- .3466680fi-01 

(-289323611 

-3 .899325 

(-3.1 M437)a 

-5.800285 

(-4.1 12610)a 

-1 .629772 

( - .92 1 7 6 93 1 

.352607 1 

(.1192260) 

-2.81 4087 - .4912478E-01 

(-.489414611 (-.9154492) 

- .7 40 1 1 37 to2 

( - .134415411 

.4061211 If-01 

(.2513100:l 

241 5872 

(1.448545)~: 

- .1669324fi-02 

(-1 .636608)~: 

- .7 187638 -.1183%8 

(-3.675634)a (-2.207452)b 

(3.358062)a (-1.910008)b 

-9.7 2367 4 

(-3.1 05848)a 

.1242809 

(2.859161 )a 

5.7 82401 

( 2.76046 2) a 

.4150320 E-01 

( 2.081 202) b 

- .1415324 .61902%E-03 

(-2.41 B7O)a ( 2901 954) 

. 1 3 29500 E-0 2 

(3.265458)a 

.5 933 0 47 

(3.6551 55)a 
38 

Tab1 e 5. (continued) 

DS EASQ .1663341 E-02 .3532269E-03 .9525925E-O3 -.1531904E-O2 

(2.284692)b (1 .56957O)c (6 .DO6 13) (-2 .n4227 )b 

DS EADALB 

CAPDSEA .7 51 5524 E-02 

(2.036661)b 

DF I SH 

AGE 

AGESQ .4 87 83 52 E-0 2 

( 1.802329) b 

TUNA 8.92 4066 

( .7424904) 

SALM 1.875647 

( .5403068) 

LOBSTER 12 .OW46 

(1.823646)b 

TUNPI;F 12.41 405 

(1.638682)~ 

SALMGF -3.352830 

( - .3573529) 

SALMCRB -23 29745 

(-2.350708) b 

SALMMIS 9.080517 

( 1.27091 2) 

GFMl S -6.050616 

(-.9835183) 

- .2076056E-02 

( -4.950053 1 a 

- .237 03 87 E-0 2 

(-1 .742910)b 

.1171808 

(2.84 1 203 )a 

- .7550187E-O1 

(-1.825798) b 

- . 1 963304 E-0 1 

(-.1690914) 

Notes : t-stati sti cs In parentheses 

a - slgnif lcant at the 99% level 

b - significant at the 95% level 

c - signif lcant at the 90% level 
39 

Table (6~. ALBSIM Spreadsheet configuration, user inputs. 

I A II B II C II D 1 

1 I MSIM 

21 la: *** year: *** 

31 cap: *** 

41 age: *** PRICES: 

51 dsea: *** fuel: *** 

61 dab: *** albacore: *** 

71 doth: *** other: *** 

81HOME FORT: 

91 sd&la: *** (choose 

10 I n&cca: *** one) 11 I orwa: *** 

12 I hj: *** 

13 I CATQI : 

14 I 

15 I 

16 I 

17 I 

18 I 

19 1 

20 I 

21 I 

22 I 

23 I 

24 I 

25 I 

26 I 

27 I 

28 I 

29 I 

albacore : 

other species 

(ore choice) 

tuna: *** 

salmon: *** 

lobster: *** 

tuna&*: *** 

Salm&gf: *** 

salmbcrb: *** 

salmisc: *** 

gf&misc: *** 
4C 

NUl mr( 

00 

I* *m 

28 

93 

+It; 

mm 

mr- UlO 

In. 

W 

m 

N 

In 

51 

n 

cb 

51 

m T 

W 

0 *' t .-I 

5% 

B 

m+ Wrl Qlm 

r(n 

9 

I 

N 

W 

rl 

F! 

w .. 

rm 0 

I 

3 

N 

M 

8 

I 

U 
41 

Table 6C. ALBSIM Spreadsheet configuration model output. 

I A II B II C I1 D II 

1 IATBsIM 

21 len: 53 year : 85 

31 Cap: 25 

41 age: 10 PRICES: 

51 dsea: 125 fuel: .93 

61 dalb: 80 albacore: 1200.00 

71 doth: 30 

81HM FORT: 

91 &&la: 1 

101 n&cca: 0 

111 orma: 0 

12 I hi: 0 

13 ICM'U-I: 

141 albacore: 

15 I other species 

16 I (one choice) 

17 1 tuna: 0 

181 salmon: 0 

191 lobster: 0 

201 tuna&(#: 1 

211 Salmgf: 0 

221 salm&crb: 0 

231 salmisc: 0 

241 gf&misc: 0 

25 I 

26 I 

27 I 

28 I 

29 I 

other: 800.00 

(choose 

On€!) 

tons: $ 

36.05 43264.80 

.oo .oo . 00 .oo 

.oo .oo 

.91 726.13 

.oo -00 

.oo -00 

-00 .oo . 00 -00 

WEWE: 43990.93 

CASH FLm: 

E I1 F I! G I 

am: 

fuel: 12281.05 

trip: 4684.07 

fixed: 20802.50 

fees: 814.80 

moor: 1737.58 

eqma: 10297.71 

inte : 5059.97 

insu: 2892.44 

gross 6223.31 

- labor 9018.14 

301 = net -2794.83 
1 

RECENT TECHNICAL MEMORANDUMS 

Copies of this and other NOAA Technical Memorandums are available from the National Technical 

Information Service, 5285 Port Royal Road, Springfield, VA 22167. Paper copies vary in price. 

Microfiche copies cost $3.50. Recent issues of NOAA Technical Memorandums from the NMFS 

Southwest Fisheries Center are listed below: 

NOAA TM-NMFS SWFC 47 

48 

49 

50 

51 

52 

53 

54 

55 

56 

Hawaiian Monk Seal population research, Lisianski Island, 1 982. 

H.S. STONE 

(August 1984) 

Interpreting spotted dolphin age distributions. 

J. BARLOW and A.A. HOHN 

(August 1984) 

Observations of the Hawaiian Monk Seal on Laysan Island from 

1977 through 1980. 

B.W. JOHNSON and P.A. JOHNSON 

(October 1984) 

Hawaiian Monk Seal observations on French Frigate Shoals, 1980. 

P.A. JOHNSON and B.W. JOHNSON 

(October 1984) 

Estimating dolphin juvenile survival rates from the proportion of 

calves nursing. 

T. POLACHECK 

(October 1984) 

Operational plan for NMFS albacore program. 

R.H. PARRISH, et al. 

(May 1 985) 

Albacore fishing and windspeed. 

P.N. SUND 

(June 1985) 

Proceedings of the workshop on the fate and impact of marine 

debris, 27-29, November 1 985, Honolulu, Hawaii. 

R.S. SHOMURA and H.O. YOSHIDA (Editors) 

(August 1985) 

The Hawaiian Monk Seal and Green Turtle on Necker Island, 1983. 

R.J. MORROW and E.K. BUELNA 

(November 1985) 

Proportions of species of dolphin in the eastern tropical Pacific. 

J. BARLOW and R.S. HOLT 

(January 1986)