NOAA Technical Memorandum NMFS NE 112
Data Description and Statistical Summary
of the 1983-92
Cost-Earnings Data
Base
for Northeast U.S. Commercial Fishing Vessels:
A Guide to Understanding
and Use of the Data Base
by Amy B. Gautam1 and
Andrew W. Kitts2
1National
Marine Fisheries Service, Silver Spring, MD 20910
2National Marine Fisheries
Service, Woods Hole, MA 02543
Print
publication date December 1996;
web version posted December 18, 2000
Citation: Gautam AB, Kitts AW. 1996. Data Description and Statistical Summary of the 1983-92
Cost-Earnings Data
Base for Northeast U.S. Commercial Fishing Vessels: A Guide to Understanding
and Use of the Data Base. US Dep Commer, NOAA Tech Memo NMFS NE 112; 21 p.
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Abstract
Data
from the National Marine Fisheries Service’s Capital Construction Fund
are summarized to provide a financial profile of Northeast U.S. commercial
fishing vessels. Averages for various cost categories are presented
by tonnage class, fishery, and effort level. Costs are also presented
as percentages of total revenue. Data are then evaluated for
how well they represent the Northeast fishing fleet. Potential
uses and pitfalls of using these data for economic analyses are also
discussed.
INTRODUCTION
While
vessel-level landings data are available in most Northeast U.S. commercial
fisheries, data on operating costs of fishing vessels typically are not. The
main goal of this project was to compile (from existing sources), organize,
and summarize a data base which contained vessel-level information on
revenues, landings, and expenditures for vessels operating in Northeast
U.S. fisheries during 1983-92, and which could be used for subsequent
economic analyses. This report describes the contents and potential
uses of this data base. (Note that while the data base covers 1983-92,
some of the figures in this report cover 1984-92 due to the low number
of observations in 1983.)
Financial data on the revenue and expenses of owners (sole or corporate)
of individual Northeast U.S. vessels were obtained from the National
Marine Fisheries Service’s (NMFS) Capital Construction Fund (CCF) data
base (described later). Individual vessel landings data were obtained
from the NMFS Northeast Fisheries Science Center’s (NEFSC) weighout files. Data
on individual vessel characteristics were obtained from the NEFSC master
vessel file (also described later).
The cost-earnings data base should be useful for determining economic
performance measures of various fisheries. These measures can help
to identify sources of inefficiencies in fisheries, changes in performance,
and impacts of fishery regulations. Such information will enhance
fisheries management by providing policymakers with additional knowledge
upon which to base decisions.
The specific objectives of this project were to: 1) create a panel
data set which incorporates cost, landings, and vessel characteristics
data in one file; 2) aggregate these data in various ways, and calculate
statistics which describe or identify relevant trends; 3) determine the
within-sample reliability of the data; 4) ascertain the degree to which
the group described by the data collected are representative of the Northeast
U.S. fleet; 5) summarize and make available the data in spreadsheet and
ASCII form for NMFS staff who want a general idea of cost-earnings of
Northeast U.S. vessels; 6) document the data collection and synthesis
process to provide future researchers with guidelines regarding the quantity
and quality of information which can be collected using these data sources;
7) illustrate the limitations of these data; and 8) prepare this report
as the “reference manual” for understanding and using the data.
This report has four subsequent sections. The next, or second,
section (“Data Sources and Descriptions”)
describes the three data sources and their limitations. The third
section (“Data Organization, Categorization,
and Descriptive Statistics”) generally describes the data base and
provides summary statistics in order to illustrate the quantity and quality
of data collected, and some trends in Northeast U.S. fisheries. Included
in this latter section are analyses of costs as a percent of total revenues
for various levels of disaggregation, tests for outliers in the cost
data, and correlations between reported value of landings (from weighout
data) and reported gross revenues (from tax returns) in order to evaluate
the reliability of using tax records.
The representativeness of these data is an important issue. To
address this issue, weighout data on all reporting Northeast U.S. vessels
were obtained, and the average values of several variables were compared
with the average sample values from this data base. Results of
this comparison are shown in the fourth section (“Comparison
of Sample and Population Statistics”). Finally, an assessment
of the data base is offered in the fifth section (“Discussion”). The
overall reliability of the data is discussed, and potential uses as well
as inherent pitfalls in these data are identified.
DATA SOURCES
AND DESCRIPTIONS
Three data sources are employed in the cost-earnings data base: CCF
files, NEFSC weighout files, and NEFSC master vessel file. A common
element linking all three sources is the U.S. Coast Guard (USCG) registration
number. The variables collected from each file are defined in Table
1 and Table 2. Descriptions of each file,
including its potential limitations, are discussed separately below.
Capital Construction Fund Files
The CCF program, operated by NMFS’s Financial Services Division, was
designed to allow commercial fishing vessel owners to save pre-tax earnings
to purchase, construct, or reconstruct fishing vessels. Under this
program, the tax on fishing vessel income is deferred, and untaxed earnings
are put into an account, much like an IRA (Individual Retirement Account),
which is later used for vessel projects. A requirement of the program
is that participants submit their annual federal income tax returns to
the CCF program. Tax returns are kept for every year the vessel
is active in the program.
The annual tax returns provided by CCF participants yield a potentially
rich source of data on vessel-specific revenues and expenditures. Depending
on the additional financial schedules that owners submit with their basic
tax form, data elements available for collection may include some or
all of the following: 1) gross returns; 2) itemized expenditures
on fishing-related costs such as bait, docking fees, groceries, fuel & oil,
gear & supplies, ice, insurance, lumping fees, packing fees, licenses,
repairs, storage costs, water, crew shares or wages, and auction fees;
and 3) other itemized expenses such as rent, vessel depreciation, employee
benefits & pension payments, automobile expenses, bank charges, dues & membership
fees, office expenses, professional salaries, utilities, and travel & entertainment
expenses.
Several caveats should be noted with respect to these data, and any
researcher planning on using the data should be aware of them:
- The costs itemized may be based on an accountant’s classification
scheme rather than an actual breakdown of costs.
- Costs are often aggregated over vessels when more than one
vessel is owned by an individual or corporation; it is usually not
possible to allocate costs among vessels in a corporate fleet.
- Many vessels are active in more than one fishery throughout
the year, hence some costs cannot be allocated or divided among fisheries.
- The “sensitive” nature of these data limits their usefulness. Because
records are obtained from Internal Revenue Service (IRS) tax forms,
the identity of the individuals participating must be protected. While
NMFS employees have access to these files, and can match them appropriately
with other data sets, researchers outside the agency would not have
unlimited access.
- The data are not available in electronic form.
- These costs may need to be augmented for economic studies. For
example, opportunity cost may be the most relevant cost to the fishermen
or vessel owner, but this is not an accounting cost that can be itemized
on a tax return.
- As will be discussed later, most vessels that participate
in the CCF program are considered “highliners” in the fleet, i.e.,
vessels that, on average, earn the highest profits.
NEFSC Weighout Files
The NEFSC weighout files consist of voluntary, trip-level data recorded
by port agents at the end of a vessel’s fishing trip. The data
elements collected include: days absent from port, days spent fishing,
pounds of fish or invertebrates landed, value of landings, and port where
the fish or invertebrates were landed. The files are maintained
electronically and are readily accessible.
One potentially serious problem is that the data do not capture every
trip a vessel takes, so that recorded annual levels (of effort, landings,
etc.) reflect only reported levels, not necessarily actual levels.
NEFSC Master Vessel File
The master vessel file maintained by the NEFSC contains detailed data
on such features of individual vessels as: number of crew berths,
types of gear used, construction type, gross tonnage, length, year built,
and horsepower. The file is maintained electronically and is readily
accessible.
Caveats to using these data include:
- Some data in this file may not be current. That is, individual
vessels may upgrade or add engines (increased horsepower) or may make
other structural changes which are not reflected in the master vessel
file.
- The crew size variable is based on the number of berths on the vessel. Therefore,
actual crew employed is not always known nor are seasonal variations
in crew size always reflected.
DATA ORGANIZATION,
CATEGORIZATION, AND SUMMARY STATISTICS
Data Organization
The data compiled for this study were derived from 130 individual vessels
and for 375 vessel years; multiyear data exist for some vessels. Some
CCF tax returns contained data on more than one vessel; in those cases,
the costs and revenues could not be linked to a particular vessel. Although
these data remain in the data base, the data presented here exclude the
multivessel returns, leaving a total of 313 individual vessel-year records
representing 90 unique vessels.
Due to varying tax reporting techniques, missing observations exist
in many of the cost variables. In some instances, the cost was
either not incurred or not reported. Often, a cost was added to
another. For example, reported fuel expenses frequently included
oil expenses and sometimes food and ice expenses. Gear expenses
often subsumed supply expenses. The reason for such categorization
of costs by accountants and fishermen is often for convenience in calculating
crew share. In this report, fuel, oil, food, water, and ice are
considered trip expenses. Insurance, interest, taxes, maintenance,
etc., are considered fixed costs , or at least costs not assignable to
a trip.
To address the missing data problem, some costs in this report have
been summarized by group. All variables collected or created from
the master vessel and weighout files are provided in Table
1. All variables collected or created from the CCF files are
provided in Table 2.
Data Categorization
The data base reflects a fairly heterogenous fishing fleet. This
heterogeneity of vessels is particularly noticeable in three categories: tonnage
class, fishery or gear type, and effort level. Before descriptive
statistics on cost and earnings variables are presented, summary vessel
information in these three categories is described.
Tonnage Class
Three tonnage classes have been identified: class 2 refers to
5 to less than 50 gross registered tons (GRT), class 3 refers to 50 to
less than 150 GRT, and class 4 refers to 150 and greater GRT. Of
the 90 unique vessels in the data set, the majority are class 4, followed
closely by class 3; fewer than 15% are in class 2.
Similarly, the majority of vessel years are in class 4, and the smallest
number in class 2. Table 3 presents the
number and percentages of unique vessels and vessel years by tonnage
class.
Fishery or Gear Type
Ten fishery or gear types have been identified. Because vessels
can and often do participate in more than one fishery during a year,
each vessel’s primary fishery was considered to be the fishery in which
it earned the highest revenue during the year. Table
4 shows the numbers and percentages of unique vessels and vessel
years by gear type.
The number of vessel years is higher than the number of unique vessels
for nine of the 10 gear types because some vessels’ principal fishery
changed from one year to the next. Therefore, some double counting
occurred.
Otter trawls and scallop dredges constitute the majority of observations. Together,
these two gear types represent 70% of the unique single-vessel observations
and 76% of the vessel-year observations. Longlines and surfclam/ocean
quahog dredges are the next most frequent gear types, collectively contributing
14% of the vessel-year observations. The low number of observations
in several other gear types suggests that data for these gear are probably
not suitable for detailed analyses. Only otter trawls and scallop
dredges provide enough information for fishery-level analysis.
Effort Level
Another way to categorize observations is by a measure of effort such
as the number of days that a vessel spends at sea per year. Three
classes were chosen to break up the range of days absent (DAB): DAB
class 1 if a vessel spent fewer than 110 days at sea in a year, DAB class
2 if a vessel spent between 110 and 219 days; and DAB class 3 if a vessel
spent 220 or more days. Table 5 shows
the numbers and percentages of unique vessels and vessel years by effort
level.
As Table 5 shows, the majority of observations consist of vessels which
spent 110-219 days at sea, or roughly one- to two-thirds of the year. The
division of observations in the other two effort levels is skewed towards
DAB class 1. Since the number of days at sea is determined through
the voluntary weighout collection system, there may be some vessels (or
vessel years) categorized in DAB class 2 which were actually at sea more
than 220 days, and some vessels in DAB class 1 which were absent more
than 110 days.
Summary Statistics
Table 6 contains the mean, standard deviation,
and minimum and maximum values of all variables in the data set. The
means and standard deviations of several key variables are presented
in Table 7, Table 8, and
Table 9,
distinguishing among fishery, effort level, and tonnage class, respectively. Those
key variables are: 1) vessel characteristics (gross registered
tons, length, horsepower, year the vessel was built, and number of crew
members); 2) weighout data (annual quantity & value of harvest, number
of crew members, annual days absent & days fished, and trips taken
per year); and 3) CCF variables (trip costs, other variable operating
expenses, indivisible operating expenses, gross revenue, insurance costs,
crew shares, interest payments, salaries, and employee benefits).
Average deflated costs for all vessels are shown in Figure
1. Averages for trip costs, indivisible operating expenses,
crew shares, salaries, and interest payments declined over the 1984-92
study period, while other variable operating expenses and employee
benefits remained fairly constant. The average number of fishing
trips taken and the average days fished during the period both increased
as depicted in Figure 2. Average real
total costs and gross revenues decreased as shown in Figure
3.
Reported costs are averages for those vessels that reported a particular
cost. As can be seen from the number of observations in Table
6, most cost variables have missing values. In the case of
fuel or crew share, it is expected that all vessels incur these costs,
thus these averages can safely be applied to all vessels. However,
certain costs such as employee benefits are not incurred by all vessels. In
this case, missing values should be treated as zeros. So, for certain
costs, it may not be accurate to apply the reported average to all vessels,
but only to vessels that normally incur that cost.
Costs as a Percentage of Total Revenues
The ratio of costs to revenues can provide insight on the relative importance
of particular categories of costs to fishing operations. The following
section evaluates costs as a percentage of revenue for various data categorizations. To
more accurately describe the cost characteristics of groups of vessels,
missing values in the data are treated as zero.
Figure 4 shows the trend in costs as a percentage
of gross revenue for all vessels over the 1984-92 study period. Other
variable operating expenses show a slightly upward trend, interest payments
show a slightly downward trend, while trip costs, indivisible operating
expenses, crew shares, and salaries do not exhibit clear trends.
Costs as a percentage of gross revenue, averaged over the 1984-92 study
period, for the major gear types -- scallop dredges and otter trawls
-- are shown in Figure 5. Trip costs
for otter trawlers and scallop dredgers were 18% and 17% of gross revenue,
respectively. Other variable operating expenses for otter trawlers
and scallop dredgers were 8% and 7% of gross revenue, respectively, while
indivisible operating expenses were 17% and 16%, respectively. Crew
shares, salaries, and interest payments were calculated as a percentage
of gross revenue also. For otter trawlers, crew shares were 37%
of gross revenue, salaries were 3%, and interest payments were 6%. For
scallop dredgers, crew shares were by far the largest expense at 40%,
salaries were 2%, and interest payments were 4%.
Breakdown of costs as a percentage of gross revenue by tonnage class
and effort level is shown in Figure 6 and Figure 7,
respectively. Trip costs, indivisible operating expenses, interest
payments, crew shares, and salaries were greater for larger vessels,
while other variable operating expenses decreased with vessel size. Crew
shares increased with effort. Trip costs were greater for more
active vessels. Other variable operating expenses decreased from
DAB class 1 to 2, but increased slightly from class 2 to 3. Other
costs remained rather constant relative to effort level.
Individual Cost Shares Relative to Average
Cost Shares
To examine the data for outliers, the share of each individual cost
item as a percentage of total cost was calculated for every observation. Actual
shares were then compared with the average share of the same cost item
over all observations. Following Herrick et al. (1992),
a two-standard-deviation rule was used to evaluate each observation. The
formula used was:
where Cij refers to cost item i of vessel j, and TCj refers
to the total costs of vessel j. If the absolute value of Sij was
greater than two standard deviations from the average share for that cost
item,
the expenditure on i for vessel j was considered an outlier.
The analysis was also performed for the aggregate variables of trip costs,
other variable operating expenses, and indivisible operating expenses for each
observation. All average shares were calculated by tonnage class, under
the assumption that vessels in different tonnage classes have significantly
different expenses (see Figure 6). Table
10 shows the results for the three aggregate cost categories. Some
recorded expenditures were determined to be outlying values. Table
11 shows the results of the tests of individual cost items.
Gross Revenue Versus Landings Values
To evaluate the consistency of CCF data with weighout data, we compared gross
revenue from the tax return files with the annual landings values from the
weighout files. These were the only comparable variables in the two files. All
values were deflated using the gross domestic product implicit price deflator. Figure
8 illustrates the pattern of gross revenue and landings values during 1984-92. A
strong downward trend is evident in both variables over the period. Average
reported gross revenue was higher than average landings values in all years. This
is likely due in part to the voluntary reporting of landings data.
Figure 9 shows the trends in average gross revenue
and landings values by tonnage-class-3 and -4 vessels. (There were not
enough observations on tonnage-class-2 vessels to include this category.) Again,
average gross revenue was greater than average landings values in all years,
with the exception of 1992 for class-4 vessels. Revenue of class-4 vessels
has been declining since 1987, while revenue of class-3 vessels has been rising
since 1988. As expected, class-4 vessel revenue was higher than that
of class-3 vessels. Paired comparison t-tests indicate that gross revenue
for class-3 vessels was not significantly different from landings values (P > 0.05). For
class-4 vessels, gross revenue did not significantly differ from landings values
in 5 of the 9 yr.
Given that most observations were from otter trawlers and scallop dredgers,
the landings values and gross revenue of these vessels over time were examined. Figure
10 shows the mean real landings values and gross revenue for these two
gear types. Paired t-tests reveal that gross revenue and landings values
differed significantly in only 2 of 8 yr (1990 and 1991) for trawlers, and
in none of the 9 yr (P > 0.05) for dredgers.
Finally, the means of gross revenue and landings values were calculated by
effort levels, as defined by days absent. For DAB-class-1 vessels, means
could only be calculated for 1986-92; of these 7 yr, values were significantly
different (P < 0.05) in 5 yr. For DAB-class-2 vessels, values were
significantly different only in 1988. For DAB-class-3 vessels, values
were different only in 1991. Trends in mean real landings values and
gross revenue are shown in Figure 11 for DAB-class-2
vessels. (There are too few observations to present trends for DAB-class
-1 and-3 vessels.)
Overall, gross revenue reported on annual tax returns reflects the same trends
found in landings values obtained from the weighout data base. The two
series are not significantly different after controlling for variation due
to fishery, vessel size, or effort level.
Although average gross revenue and average landings values were not significantly
different, for some observations the values were quite different. Depending
on the kind of analysis, these particular observations may need to be excluded.
COMPARISON OF SAMPLE AND POPULATION
STATISTICS
Table 12 compares average revenues, landings, days
absent, days fished, number of trips, and vessel characteristics of all Northeast
vessels, as reported in the NEFSC weighout files, with those in the cost-earnings
data base for 1983-92. The landings value variable is deflated to remove
inflation distortions. A given vessel in a given year is treated as a
unique observation regardless of whether it appears in other years. Table
13 and Table 14 compare averages otter trawlers and
scallop dredgers, respectively.
Table 13 and Table 14 indicate
that vessels sampled from the CCF program are, on average, newer, bigger, more
powerful vessels which are at sea more days per year than the average vessel
in the weighout data base. Therefore, one would expect the average CCF
vessel to have higher landings and greater revenues. However, a
comparison of profits cannot be made because cost data are not available for
the weighout vessels. A reasonable assumption could be made that the
average CCF vessel earns a higher profit than the average weighout vessel since
the nature of the CCF program is to put aside earnings for new projects. Presumably,
the vessels most likely to participate in the program are those consistently
returning profits.
DISCUSSION
Potential
Uses
Information on fishing vessel costs is normally difficult to obtain. Periodically,
surveys are undertaken by a university or a consulting firm, but these
usually provide only snapshots of the total financial situation. The
NMFS Northeast Region does not routinely collect vessel financial information. While
data on revenue or landings values are typically available, revenue data
alone do not adequately capture the viability of fishing operations;
that is, information on costs is necessary for evaluating the overall
profitability of operations. One important feature of the CCF tax
returns is that vessels can be tracked over several years. This
is useful for assessing how certain costs, and hence profitability, change
over time. These, in turn, could be used in part to assess the
economic health of particular fisheries.
A significant use of this information has been and can be to evaluate
the potential benefits and costs of various fishery management measures. For
example, the analysis of Amendment #5 to the Northeast Multispecies Fishery
Management Plan used CCF tax returns to calculate the impact of various
regulatory schemes by evaluating expected changes in benefits and costs
from each of the schemes. Determining expenditures and profits
by tonnage class, gear type, and effort level (e.g., days absent)
can help identify sources of inefficiency in a fishery, and may also
be used to test for economies of scale. This information is useful
to managers in making resource allocation decisions.
Finally, general relationships between vessel characteristics, effort
levels, and expenditures on various cost items can be evaluated with
these data. This information is often used in bioeconomic models.
Potential Pitfalls
Before drawing conclusions from these data, one must fully understand
their limitations. Most caveats have been addressed in the course
of the report, but it is worth reiterating that these data are based
on tax returns. Costs are reported to the IRS in such a way as
to minimize tax liability. For example, there are a host of techniques
used to calculate depreciation expenses and these can vary significantly
from one return to the next.
A significant shortcoming of the data set is that it does not fully
represent the population of Northeast fishing vessels. Compared
to all vessels in the NEFSC weighout system (which also does not represent
the population, but is a closer approximation), the vessels participating
in the CCF program can be considered the fleet “highliners.” Hence,
analyses based on these data may show that the average vessel is better
able to withstand financial difficulties than may actually be the case.
Finally, the data base is of limited value for gear types other than
scallop dredges and otter trawls. As long as one is interested
in these fisheries, the data provide a reasonably good description of
ongoing trends.
REFERENCES
CITED
Herrick, S.; Lee, J.G.; Squires,
D. 1992. Documentation for the West Coast fishing
fleet cost-earnings data base. Southwest Fish. Sci. Cent.
Admin. Rep. LJ-92-23. Available from: National Marine Fisheries
Service, P.O. Box 271, La Jolla, CA 92038-0271.
Acronyms |
| CCF |
= |
[NMFS] Capital Construction Fund |
| DAB |
= |
days absent [from port] |
| GRT |
= |
gross registered tonnage |
| IRA |
= |
individual retirement account |
| IRS |
= |
[U.S. Department of the Treasury]
Internal Revenue Service |
| NEFSC |
= |
[NMFS] Northeast Fisheries Science
Center |
| NMFS |
= |
[NOAA] National Marine Fisheries
Service |
| TSD |
= |
two-standard-deviation [test for
outlying data] |
| USCG |
= |
U.S. [Department of Transportation]
Coast Guard |
