Download Model for economic interpretation of the ACFM advice (EIAA)

Model for economic interpretation of the ACFM advice (EIAA)
Pavel Salz (LEI) and Hans Frost (SJFI)
Abstract
One of the objectives of the Concerted Action 'Promotion of Common Methods for Economic
Assessment of EU Fisheries' is to develop a model, which would allow an evaluation of the
economic consequences of the TAC proposals formulated by ACFM. The paper will discuss
the structure of the model, the included data and the first results of applications on Danish
and Dutch fleet segments. The last section of the paper will indicate further foreseen
development of the model and explore its strong and weak points.
1.
2.
3.
4.
5.
Introduction / background
Structure of the model and the data used
Results of applications
Evaluation of the model
Conclusions
1. Introduction
Annual determination of the level of TACs set for the following year during the December
meeting of the Council of Ministers of Fisheries is based on the advice of ICES/ACFM. Since
several years this advice is provided in the form of scenarios, which meet in different degrees
the criterion of 'precautionary approach'. Biologists determine a safe level of fishing mortality
and from there they calculate an appropriate TAC. However, setting a TAC is not only a
biological, but also (and possibly primarily) a political decision. In this decision also other
considerations are taken into account. For this reason, ICES/ACFM do not provide only one
single advice, but rather they offer several options from which the decision makers can chose,
allowing for those other considerations.
Since economics of the fishing industry is certainly one of those 'other considerations', there
is need for concise economic assessment of the consequences of the biological scenarios.
The Concerted Action 'Promotion of common methods for economic assessment of EU
fisheries' (FAIR CT97-3541) collects economic information on the performance of selected
fleet segments and it has developed a model, which uses this information to evaluate the
economic consequences of the biological scenarios. This evaluation should be
complementary information to be taken into account when making TAC decisions. These
consequences are specified in terms of gross value of landings, crew share and gross cash
flow.
2. Structure of the model and data used
The model is constructed to give relative changes of gross value of landings, crew share and
gross cash flow in the on-going year (e.g. 1999), the following year (i.e.200, for which TAC
have to be set) and for the long run, which is called the 'recovered stocks situation'. The
changes are related to baseline period, namely a three year average (1996-1998).
The detailed structure of the model is presented in the flow chart in appendix 1. The model is
composed of five modules, which are mutually linked in variety of ways. The modules are:
1. Catches and TACs
2. Prices
3. Biological (long term) data
4. Fleet activity (effort)
5. Costs and revenues
The model is built on the basis of one fleet segment as a production unit, although its
formulation in Excel can now handle four segments concurrently for each EU Member State.
2.1 Catch composition
As the model is designed to carry out an economic evaluation of fish stock and catch
changes, catch compositions of pertinent fleet segments and the links to national quotas are
important in the model.
The composition of the catch of a fleet segment can be derived from:
 The total catches of one species in the European and adjacent waters,
 The shares allocated to the EU (in case of stocks shared with other countries),
 The relative stability allocation of stocks within the EU and
 The historical up-take of the species by the specific fleet segment within the national
landings.
Formally, this is included in the model starting with a detailed specification of landings
(catches) in selected years used as the base case, which multiplied with a set of prices
produces total revenue for each fleet segment:
TRbase j   Lbasei , j Pbasei , j
i
Lbasei,j: landings composition, base case, species i, fleet segment j
Pbasei,j: fish prices, base case, species i, fleet segment j
TRbasej: total revenue, base case, fleet segment j
The landings composition in the base years is then used to calculate the landings
composition in future years taking into account future quotas, relative stability and empirical
up-take:
Lfcast i , j  Qfcast i

 Lbasei , j
 nsi  nu i 
  Lbasei , j
 j
 Lbase
where nui =
Qfcasti:
nsi :
nui :
Lfcasti,j:
NQi :
j





i, j
unless some other ratio is selected
NQi
forecast quota (ACFM), species i
national quota share of species i
national quota uptake ratio of species i
forecast landings, species i, fleet segment j
national quota of species i
Consequently the model assumes that the relative composition of landings from different fleet
segments remains constant. Also the up-take ratio is assumed constant; i.e. if a TAC or a
quota has not been fully exploited in the recent past, this under utilisation will continue also in
the near future. The constant up-take ratio is assumed to avoid impact on the economic
viability from changes in fishing patterns. But the way is it included in the model allows the uptake ration to be treated as an exogenous variable that could be changed if shifts in fishing
patterns are documented.
2.2 Price module
Given the scope of the project it is not feasible to calculate empirically the historical prices
based on landing in many different ports. Some price comparisons have, however, indicated
that the European markets are closely linked and prices of major commercial species do not
differ greatly. Therefore prices of main production countries are taken as a starting point. This
2
means for ex. Dutch prices are taken for plaice and sole, UK prices for cod, haddock and
whiting, etc.
Future prices are calculated on the basis of three variables:
 Average historic price of a species;
 Price flexibility of -0.2 for all demersale and pelagic species;
 Expected aggregate volume of catches of the species.
Aggregate volume of catches is obtained by summing all stocks of a given species into one
total. In this way, expected landings in Norway and Iceland also determine price of cod on EU
market1. Consequently, for some fleets fishing opportunities may fall due to lower TAC and
the price may drop as well.
The forecast prices P for species i and fleet segment j are calculated by use of the
subsequent formula, where  is the price flexibility rate and Q is the landing volume which in
the current model is the total EU TAC of each quota species.
Pfcasti , j 
Pbasei , j

Qbasei
 Qfcasti . It is assumed that 0
The price flexibility rate is an exogenous variable and in the current model it is decided to use
a uniform rate for all species. Quite a lot of econometric price formation work has been carried
out, and based on such studies the price flexibility rate may be adjusted in future calculation.
The magnitude of the price flexibility rate influences the economic results heavily, therefore it
is very important that the rates are used as careful as possible.
2.3 Biological information
Biological information is based on the work produced by the International Council for
Exploration of the Sea (ICES) and the Advisory Committee for Fisheries Management
(ACFM). The TACs for large number of species are proposed by ACFM each year for the
whole North East Atlantic area, and a share of the TACs are allocated to EU. The Council of
Minister of the EU then agrees on the allocation to Member States based on the principle on
the principle of relative stability.
The advise of ACFM is based on the work of a number of ICES working groups that carries
out various types of investigations into the more important species. The reports produced by
the working groups contain information about long-term development that is used as well in
the economic assessment work.
The biological information serves two purposes in the model:
1. Specification of the TAC options for the following year
2. Calculation of the long term TAC (a proxy for a sustainable / precautionary state)
Next year’s TAC
ICES/ACFM presents options for TACs according to levels of fishing mortality. One such
level represents the 'precautionary approach'. Lower fishing mortality would imply that the
stock would be saved for the future. Higher mortality (TAC) could threaten the stock into an
unsustainable position, in terms of size of spawning stock biomass (SSB) and fishing
mortality.
The model calculates the economic consequences of selecting a TAC at the level of the
precautionary fishing mortality.
1
Current version of the model takes only stocks into account, in which EU has some share. Version to be developed
in 2000 will be expanded by non-EU (though still European) stocks.
3
Long term TAC
One of the important results required by the 'policy makers' when setting a TAC is a
calculation, which would indicate what the potential long term gains could be of imposing
biologically safe TACs. In other words, what would be the profitability of the fleets in a
situation of recovered stocks? The main theoretical argument to reduce TAC being that lower
catches now will allow higher catches (and revenues) in some unknown distant future.
The potential long-term level of a TAC from a given stock can be calculated from the ACFM
reports quite simply. For many stocks the working group report presents a 'long term yield
curve', cf. figure 1, which is a relation between fishing mortality and yield per recruit in grams2.
A specific yield per recruit corresponds to the precautionary fishing mortality, which is e.g. 0,4
in the figure. The question is then how many recruits there will be in a recovered situation.
This number is set equal to the long-term average recruitment presented in the time-series
related to the stock in the pertinent ACFM reports. Multiplying the yield per recruit, in the
figure 260 grams, with the average recruitment e.g. 450 millions per year produces a TAC at
117 000 tonnes.
300
6000
250
5000
200
4000
150
3000
100
2000
50
1000
Grams
Grams
Figure 1. Long term forecast of an arbitrary species
0
0
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
Fishing mortality level F
0,8
0,9
1
Total yield per recruit at different F-levels
Total biomass per recruit at different F-levels
The question is of course whether it is reasonable to expect that most (if not all) stocks could
be in the recovered state at the same time.
The economic model does not allow for increase in the fleet in the sense that fixed costs are
kept constant. Variable cost will change in the long run to reflect the change in effort that is
required to catch the long-term quota. This assumption could be defended from the point of
view that overcapacity prevails in almost all quota species fisheries. This implies room for
more fishing days even in situations where higher stock abundances and subsequently higher
quotas require higher effort.
2.4 Fleet activity (effort)
Fishing effort in the model is measured by output. In the model it is assumed that the quota,
or the share of the quota determined by the magnitude of the up-take ratio, is always caught.
Because no severe capacity (input) restrictions prevail, and because higher stock abundance
for demersale species in particular implies higher catch rates it is reasonable to assume that
there are no such problems. The effort measure applied in the model is used to adjust
variable costs relative to changes in output and catch rates (stock abundances).
Activity of the fleet (fishing effort) is calculated based on the following factors:
2
This is the weight (yield) the recruits will grow to on average before they die or are being caught.
4




Fishing effort in the base period (moving 3-year average, now 1996-1998) determined for
fleet segments and species
Level of TACs
Size of the spawning stock biomasses (SSB)
Stock-catch flexibility rates, pelagic species: 0.1, demersale species: 0.8
This part of the model is based on the reasoning that the relative change
(TAC t+1/TACt )/(SSB t+1/SSBt)
determines the change in effort. If the expression is less than one, then less effort is required
to take-up the TAC and vice versa. The change in effort is linear to the change in TAC, but
due to the application of the stock-flexibility rate the change in effort is less than
proportionate.
Forecast landing value of each species i subject to TAC (LVTAC) is put relative to the sum of
landing values of TAC species in the base case for each of the fleet segments. This produces
coefficients displaying the shares of the species in the landing value composition of the TACspecies. These coefficients are adjusted (multiplied) with the relationship between the
spawning stock biomasses (SSB) lifted to an exponent (the flexibility rate ). This relationship
expresses the impact on effort from the change in stock density. Finally, the coefficients
calculated for each species are added into one figure for the fleet segment showing the effort
required to catch the fleet segment’s share of the new TAC. The expression used in the
model is:
Efcast
j

 LVTACfcast i , j
 
i   LVTACbasei , j
 i
 SSBfcasti 

 

SSBbase
i

(  )


 ; It is assumed that 0 1


The change in effort affects the variable fishing cost RC in a linear way. The way Efcast is
calculated implies that it is close to one. In the expression below the element Ebase,
calculated in the above expression by substituting fcast figures with base year figure, is
included in the equation to secure that effort in the forecast situation is normalised relative to
base case situation. With no data problems Ebase is always one. But because there are minor
differences between the observed landing values derived from the Annual Economic Report
(AER)3 of quota species and the landing values that are calculated in the EIAA-model taking
the empirically estimated fleet segment landings multiplied with recorded prices, effort in the
base case could differ from one, if the LVTAC in the above expression is not the same figures
in the numerator and in the denominator. This makes the model flexible with respect to the
choice of data.
The running costs RC of fleet segment j is adjusted by the change in effort caused by the
change the total allowable catches TAC of each species i and the change in the stock
abundance (density) calculated by the spawning stock biomass SSB in the base year and the
forecast year. The flexibility rate  indicates the impact on RC, through the effort change, by
the change in stock abundance. For pelagic species it is assumed that the flexibility rate
(density impact) is small because of pelagic species schooling behaviour while it is large on
demersale species.
RC fcast  RC base 
j
Efcast j
j Ebase
j
3
EAR is produced every year from the EU-supported concerted action FAIR CT97-3541 and
contains much of the data input used in the economic assessment model.
5
2.5 Revenues and Costs
Revenues
Revenues are results of the segments’ catches, which are derived from their share in national
landings of various species, and the price. The calculation is described above. The model
specifies only the most important quota species. Value of other species is set at constant
level extracted from the base years.
TRbase j  TRbasei , j
i
Total revenue on fleet segment level is one of the corner stones and is used for calculating
certain costs items. The costs are associated with total revenue and each particular species;
therefore aggregation over species is necessary. The model produces other “revenue” figures
such as net profit and contribution to gross national product.
Costs
The fishing costs are included in the model in an aggregate fashion that identifies and
emphasize short term and long term features as well the employment aspect. In the short run,
variable costs are important, in the long, run fixed costs have to be covered also. Therefore
three main cost components are distinguished:
 Crew share
 Variable costs
 Fixed costs
The variable costs are assumed to change proportionately with fishing effort, cf. above. The
variable costs of the base period are adjusted according to the future short term or long term
situation. The model includes cost on a more specific level than the one displayed. For the
base years it looks:
RCj:
CCj:
FCj:
DCj:
running costs, fleet segment j, includes fuel and other fishing days dependent costs
crew share, fleet segment j
fixed costs, fleet segment j, other than DC
depreciation and interest costs, fleet segment j
For future years the running costs, RC, are calculated from the changes in effort E, cf. below.
In many fisheries the crew share is calculated as a percentage of the difference between
gross revenues and variable costs but not in a uniform way. This method is used in the model
on an average basis for each fleet segment and allows for changes in future running costs to
be reflected in the crew share.
RC fcast j  RC base 
Efcast j
j Ebase
j
CCfcast j  CCbase j 
TRfcast j  RC fcast j
TRbasej  RC base j
FCfcast j  FCbase j
DC fcast j  DC base j
Fixed costs, depreciation, and interest payments are maintained constant throughout time.
This assumption could be justified, because the primary 'forecasts' regard only one year and
the fleet does not change very much over such a short period of time. For the long term
6
forecast this assumption could be disputed, but with reference to the capacity discussion
there is no reason to believe that fixed costs are going to increase, at least.
3. Running the model
The model translates the results into graphics presented below. Three indicators (value of
landings, crew share and gross cash flow (GF)) are presented, and the expressions look for
the future years:
TRfcast j   Lfcast i , j Pfcast i , j
i
CCfcast j  CCbase j 
TRfcast j  RC fcast j
TRbase j  RC base j
GFfcast j  TRfcast   ( RC fcast , CCfcast j , FCfcast j )
j
j
Four different scenarios are presented:
 Baseline - three years average (1996-1998). These figures are based on data collected
by the project and presented in the AER report.
 Forecast for the on-going year. The model should in the future contribute to decisionmaking on TACs. This means that the results must be available before the November
meeting of the STECF, which acts as a peer reviewer. The model is therefore run at the
end of October, when evidently only little data on the on-going year is available. Forecast
is based on the TACs and either estimated prices or indications over the first 6 months of
the year.
 Forecast for the following year is based on the TAC proposals made by ICES/ACFM,
which meet the conditions of the precautionary approach.
 Long term forecast for a situation of recovered stocks.
Below each scenario there is a verbal indication of the economic performance of the fleet
segment and the precise value of the ratio 'net profit / gross value' (NPTR):
NPTRfcast j 
TRfcast   ( RC fcast , CCfcast j , FCfcast j , DC fcast j )
j
j
TRfcast
j
The classification is derived from this ratio as follows:
 Profitable: NP/TR > 5%.
 Stable: -5% < NP/TR < 5%
 Unprofitable: NP/TR < -5%. In this situation fishing cannot continue in the long run.
Finally, the model also calculates the gross value added (GV), which is of interest to society:
GVfcast j  NPfcast j  CCfcast j  DCbase j
This economic indicator is, however, omitted from the result figures.
The model has now been used for about 10 different fleet segments in as many countries.
This means that it has to meet a large variety of conditions. In order to allow for specific local
situations, the model offers the possibility to adapt certain variable. This applies particularly to
prices and up-take ratios. As stated above a set of given prices, based on main markets for a
given species, are included in the model. However, if a specific fleet segment obtains, on the
average, substantially different landing values relative to empirical landing values, it is
7
possible to introduce a manual correction of the prices. The same applies to the up-take
ratios.
Two examples of results from model calculations are presented. The first one is for vessels
fishing with gill net belonging to Denmark, cf. figure 2. It is noted that the fleet segment is still
profitable, with reductions in cod and plaice quotas, for year 2000. In the long run with
recovered stocks based on long term biological advice an increase in the value of the
economic indicators take place. The cod TAC and subsequently the Danish cod quota was
reduced about 20% from 1999 to 2000, while the long term sustainable cod TAC is estimated
to be larger than the 1999 TAC. The development in the economic indicators for the Danish
gill net fishery then follows the expected pattern.
mln Euro
Figure 2. Denmark, gill net
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
Value of landings
Crew share
Gross cash flow
1996-98
PROFITABLE
19.5%
1999
PROFITABLE
17.5%
2000
PROFITABLE
13.3%
Recovered st.
PROFITABLE
18.7%
Common sole and plaice are by far the most important species in the Dutch beam trawl
fishery. There is a small increase in the plaice TAC from 1999 to 2000 while the Common
sole TAC is almost the same in the two years. The results shown in figure 2, and the long
term sustainable TACs are at the same level as the presents ones while the stock abundance
is estimated to be higher influencing the gross cash flow and the long term profit rate in a
positive way.
8
Figure 3. Netherlands, Beam trawlers, 811+ kW
300.0
mln Euro
250.0
200.0
Value of landings
150.0
Crew share
100.0
Gross cash flow
50.0
0.0
1996-98
STABLE
3.3%
1999
PROFITABLE
16.0%
2000
PROFITABLE
15.3%
Recovered st.
PROFITABLE
18.1%
Evidently, apart from the possible expected economic results of a fleet segment at one
selected set of TACs, the model offers also the opportunity to experiment with a sensitivity
analysis. This means that economic consequences of various choices of TACs can be
quantified.
3. Evaluation of the model
In many cases assumptions had to be made regarding lacking information which is essential
for the used model, e.g. the composition of costs and catches of a specific fleet segments,
price flexibility rates of certain species, etc.
For a number of areas, the management areas, for which the TAC and quotas are fixed, differ
from the ICES areas that are the basis for registration of landings from various waters.
Constant fishing patterns
The calculation requires an assumption regarding the relative shares of the various national
fleet segments in the national landings of a specific species. It is assumed that this fishing
pattern will not change from the reference year to the year for which the evaluation is made.
In other words there is no 'substitution effect'. The time, which becomes available due to
reduced effort on one stock, remains unused. It is not utilised in another fishery or another
species. For short term forecasting, when the quota changes remain within reasonable limits,
this assumption can be justified. However, over a longer time period and with more
substantial changes in the overall composition of fishing opportunities (quota and non-quota
species) the fleets will adjust their fishing pattern to the new condition.
Effort and catch of non-target species
When a TAC/quota is changed (reduced) the effort on the specific species will have to be
adjusted according to the change in TAC and the change in productivity (cpue in physical
terms, e.g. kg/kW-day). At the same time catches of other species may be affected because
of the change in effort and their cpue. These adjustments have been introduced as follows:
Effort. The fishing effort exerted on a specific species in the reference year is assumed to be
proportionate to the share of that species in the total value of the landings of the fleet
segment. Consequently, when effort is reduced for a specific species (F<1) in a scenario, the
total fishing effort of that fleet segment will be reduced by weighing the new Fs of all species
with the respective shares in value of landings. Consequently, the composition of effort of one
fleet segment by target species shifts away from the species, which is to be protected. At the
same time the role of all fleet segments fishing this species remains proportionately the same.
Non-target species. Landings of non-target species are not affected by the reduction of effort
on the target species. The implicit assumption may be that either the cpue increases or that
9
the vessels search for other fishing ground with different proportions of various species in
their total catch.
The effort influences the variable costs in the short and the long run while fixed costs are
unchanged. Variable costs are assumed to be non-linear with effort because it is assumed
that the stock abundance influences the cpue in a non-linear way. This implies that i.e. a
smaller quota requires less fishing effort to be caught and therefore lower variable costs. But
at the same time a lower stock abundance leads to a lower cpue, which offset some of the
lower effort needed to catch the lower quota. To include this assumption the model operates
with a catch-stock abundance flexibility rate, cf. appendix I about flexibility rates. The
procedure is summarised in the following items:
 Fishing mortality is changed only for a few species (quota species)
 Initial effort (base year effort) is normalised relative to the catch composition for the
relevant fleet segment
 Species effort is changed according to change in recommended landings (TACs and
F values)
 Landings per unit effort are dependent on stock abundance
 Landing flexibility rate with respect to stock abundance is assumed 0.1 for pelagic
species and 0.8 for demersale species; for the time being these are considered
appropriate estimates
 Total effort is changed as a weighted average of the landing composition
 Effort, and hence running costs, is assumed to change as a weighted average of the
stock abundances.
Live weight equivalents
As the ACFM advice is provided in live weight, all catches/landings are assumed to be live
weight equivalents. In practice some fish is landed headed/gutted so that also the respective
price information regards dead weight price per kg. At this stage the prices are unadjusted.
This leads to overestimation of forecasted values. It is considered to correct for that in future
model versions.
Quota uptake
Nominal quota, as set at the beginning of the year, is considered. However, in practice quotas
are swapped between countries, some quotas remain unutilised and/or some are exceeded.
The total effect of these changes is summarised in an uptake correction factor. This factor
allows the projected landings of the coming year to be different from the proposed quota.
Prices
Price level is adjusted to changes in the volume of landings. Future price is calculated based
on a price flexibility rate at -0.2. Consequently, value of lower quota is somewhat (20%) offset
by higher prices. General price trends could not yet be included and neither could the total
European or global catches be taken into account. A greater refinement of price elasticity by
species will be pursued in a later stage.
In the model price changes are calculated for each species (e.g. one herring and cod species
etc.) at the EU level. Landings from 3’rd countries are not included. Neither cross-price
flexibility rates are included.
4. Conclusions
It should be emphasized that the economic assessment of the ACFM advice is not a forecast
of the economic results taking all possible changes into consideration. The aim is to assess
the impact of changes in the TACs and subsequently the quotas. However, it is always
subject to discussion, where the distinction should be drawn.
Therefore, only flexibility rates that are directly linked to the change in TAC and stock
abundance are included in the model, although the magnitude of these rates could be finetuned.
10
A number of parameters in the model are treated as exogenous variables that allow for
sensitivity analyses to be carried out. These variables are related to the stock conditions.
It could be argued to included changes in various cost items, in particular, fuel prices. That
would, however, blur the impact of TAC changes that is the objective of the model. The model
could though, including such changes, be used to assess the overall economic performance
of the fleet, but that should not be mixed up with the assessment of the quota changes.
One final but important point is that the assessment model does not contain fishermen’s
behaviour. Again the argument is that the TAC changes should be viewed in the light of
changes in fishing patterns. This is a separate question. A solution of that problem would
require that changes in fishing patterns are treated simultaneously for all national fleets at the
least, and best for all fleets. Otherwise an inclusion of change in fishing patterns in the model
will cause double counting and lead to wrong results.
This problem is equal to the biological problem of species interactions. It is hardly possible
that all stock could be recovered at the same time because of species prey-predation
behaviour.
However being aware of those limits, which basically are not model limits, but rather concept
problems associated with the whole concept of fixing quotas, the model is considered a very
useful tool in the economic assessment of the ACFM TAC-advice.
11
Appendix I
Functional form and the flexibility rate:
Assuming a price-quantity function as below, the flexibility rate is equal to the exponent to the
independent variable:
Pbase  a  Qbase  a 
Pbase
Pbase
and
   a  Qbase 1

Qbase
Qbase
Pbase
Qbase Pbase
Qbase f (Qbase)
flex  Pbase 



Qbase Pbase Qbase f (Qbase) Qbase
Qbase
Qbase
By proper substituti on we get flex =
   a  Qbase 1  

a  Qbase
This result also applies to landing-stock abundance flexibility rate if a similar functional form is
assumed.
12
Appendix II. Structure of the model
13
Appendix 3. Definitions of economic indicators
Value of landings
The value of landing is the gross revenue of a vessel (total revenue) evidently determined by
annual volume of catches of each species and the prices, which those species fetch. The
time, which can be spent at sea, and the productivity achieved per unit of time (catch per unit
of effort) determine the annual volume.
Gross cash flow
Gross cash flow is the direct money income to the vessel that is available to pay interest and
instalments (depreciation) on loans. It is expressed:
Gross cash flow = Value of landings - variable costs - crew share - (fixed cost – interest depreciation); or
Gross cash flow = gross value added – crew share
Variable costs
Variable costs are varying directly with effort i.e. fuel, provision, repair. When effort, exerted
on a certain stock, is reduced due to lower F (or TAC), the total variable costs of that fleet
segment are reduced relative to weight of the reduced species in the segment's landing
composition, cf. above about effort.
Fixed costs
Fixed costs are kept constant and are assumed not to vary with effort i.e. interest payments
and depreciation, insurance, administration etc.. This is justified because in the short run no
changes in the invested capital can be expected.
Crew share
Crew share is a percentage of the difference between gross revenue and variable costs.
Net result
Net result = gross revenues – variable costs – fixed costs – crew share
Gross value added
Gross value added is a socio-economic indicator expressing the remuneration of labour and
capital:
Gross value added = depreciation costs + interest + crew share + net profit, or
Gross value added = Gross revenues – all expenses excl. crew share, instalments and
interest payments (depreciation) on loans.
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