0564 renewal Amend 80 edr SS 073010 Part B

SUPPORTING STATEMENT
AMENDMENT 80 ECONOMIC DATA REPORT (EDR)
FOR THE CATCHER/PROCESSOR NON-AFA TRAWL SECTOR
OMB CONTROL NO. 0648-0564

B. COLLECTIONS OF INFORMATION EMPLOYING STATISTICAL METHODS
1. Describe (including a numerical estimate) the potential respondent universe and any
sampling or other respondent selection method to be used. Data on the number of entities
(e.g. establishments, State and local governmental units, households, or persons) in the
universe and the corresponding sample are to be provided in tabular form. The tabulation
must also include expected response rates for the collection as a whole. If the collection has
been conducted before, provide the actual response rate achieved.
The respondent universe for the Amendment 80 EDR is a maximum of 28 non-AFA trawl
catcher/processors operating in the waters of the BSAI and Gulf of Alaska (GOA). Groundfish
harvest includes both the GOA and BSAI, therefore groundfish activity from both areas would
be included. Each catcher/processor is required to have one Amendment 80 QS permit and one
LLP license. Owners of multiple licenses and associated vessels are required annually to submit
one EDR for each licensed vessel. The year 2008 was the first full year of data required for the
Amendment 80 EDR. Each subsequent year of catch and production requires a new EDR.
The sample selection method is an annual census of all 28 vessels, as any other sampling
methodology would produce too few observations to estimate representative levels of cost,
earnings, and other outputs required for this collection. As this program is a mandatory
collection, and valuable fishing privileges will be withheld if an EDR is not submitted, we
anticipate a 100 percent response rate from QS holders. Quota shares in this program are issued
to entities, rather than vessels, and specific provisions require that each QS holder is responsible
for including data from any acquired vessel in this sector.
Non-AFA trawl catcher/processors are a closed set that includes those catcher/processors listed
in Table 31 to part 679 (see below). Each of these vessels is classified as a large entity with
greater than $4.0 million in annual gross earnings. The organizations owning and managing
these vessels routinely provide NMFS extensive data on catch by location and weight as well as
production data to both NMFS and the State of Alaska through logbooks, catch account reports,
and other collections.

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Table 31 to Part 679 – List of Amendment 80 Vessels and
LLP Licenses Originally Assigned to an Amendment 80 Vessel
Name of
Amendment 80 vessel
Alaska Juris
Alaska Ranger
Alaska Spirit
Alaska Voyager
Alaska Victory
Alaska Warrior
Alliance
American No 1
Arctic Rose
Arica
Bering Enterprise
Cape Horn
Constellation
Defender
Enterprise
Golden Fleece
Harvester Enterprise
Legacy
Ocean Alaska
Ocean Peace
Prosperity
Rebecca Irene
Seafisher
Seafreeze Alaska
Tremont
U.S. Intrepid
Unimak
Vaerdal
1

USCG
Documentation No.
569276
550138
554913
536484
569752
590350
622750
610654
931446
550139
610869
653806
640364
665983
657383
609951
584902
664882
623210
677399
615485
697637
575587
517242
529154
604439
637693
611225

LLP license number
originally assigned to the
Amendment 80 vessel
LLG 2082
LLG 2118
LLG 3043
LLG 2084
LLG 2080
LLG 2083
LLG 2905
LLG 2028
LLG 3895
LLG 2429
LLG 3744
LLG 2432
LLG 1147
LLG 3217
1
LLG 4831
LLG 2524
LLG 3741
LLG 3714
LLG 4360
LLG 2138
LLG 1802
LLG 3958
LLG 2014
LLG 4692
LLG 2785
LLG 3662
LLG 3957
LLG 1402

LLG 4831 is the LLP license originally assigned to the F/V Enterprise, USCG No. 657383.

On January 20, 2008, the Best Use Cooperative (BUC), the only Amendment 80 cooperative,
began fishing allocations under Amendment 80. BUC is comprised of the following seven
member companies, and sixteen non-AFA trawl catcher/processors. NOTE: These vessels are
listed in Table 31.

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M/V Savage

Seafisher

Length
Overall
211

Fishermen’s Finest, Inc.

American No. 1

160

U.S. Intrepid

184

Arica

186

Cape Horn

158

Rebecca Irene

140

Tremont

125

Unimak

184

Jubilee Fisheries

Vaerdal

124

Ocean Peace

Ocean Peace

220

O’Hara Corporation

Constellation

165

Defender

124

Enterprise

124

Seafreeze Alaska

296

Legacy

132

Alliance

107

Company

Iquique U.S., L.L.C.

United States Seafoods, LLC

Vessel

2. Describe the procedures for the collection, including: the statistical methodology for
stratification and sample selection; the estimation procedure; the degree of accuracy
needed for the purpose described in the justification; any unusual problems requiring
specialized sampling procedures; and any use of periodic (less frequent than annual) data
collection cycles to reduce burden.
Given that only 28 vessels will be participating in this fishery, it is not feasible to generate
enough observations on any one of the variables without applying this collection annually. And,
as discussed above, random sampling from this population is not a viable option for statistical
reasons. Based upon the degrees of freedom and number of observations required for estimating
the statistical relationship among the variables in this collection, data in the Amendment 80 EDR
may be pooled to create a time-series of cross-sectional data in order to generate sufficient
observations for economic and statistical analysis. Although the strata to be utilized in preparing
analyses (either deterministic or statistical) of management actions for this fleet will depend on
the specific questions of interest, vessels are commonly stratified by vessel length and the
distribution and amount of catch, by species.
a. Potential dependent variables and models developed with EDR data
Much of the data requested will be used to compute total or average quasi-rents (revenues less
variable costs) based on a census of catcher/processors in the years following implementation of
this rationalization program. To understand the relationships between the vessel quasi-rents and

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the variables we collect that affect total or average quasi-rents, econometric models will be
required. Examples of some dependent and exogenous variables of interest are shown in the
following table.
Examples of some dependent and exogenous variables of interest
Estimating Dependent Variables that do not Require a Model
a) Distribution of average
Data Required
catch and processed revenue Catch, production and revenue information, vessel information, and vessel owner
by vessel length class, or
information are required. Alaska Commercial Operator’s Annual Report (COAR)
type of operation (based on
data would be used as the primary source for providing data on gross revenues paid
distribution and amount of
by processing product and species.
catch by species)
b) Distribution of average
Data Required
variable vessel costs by
Total variable costs, by vessel, vessel characteristics, landings records
vessel length class, or type
Specific Measure
of operation (based on
Annual Total Variable Costs = CDQ costs + QS costs + observer costs + fuel + lube
distribution and amount of
and hydraulics + food and provisions + freight costs for landed fish + lube and
catch by species)
hydraulic fluid + crew payment or share payment + processing materials + labor
costs for processing + packaging + freezing + captain's share payment + fish taxes
(including raw fish and local tax) + gear costs
Seasonal Variable Harvesting Costs = fuel costs + captain and crew costs + gear
costs
Freight & Storage Costs = Freight costs of supplies to vessel + freight costs for
landed fish + storage costs
c) Distribution of average
Data Required
quasi-rents by vessel length
Total variable costs, by vessel, vessel characteristics, landings records; COAR data
class, or type of operation
would be the primary source for providing data on gross revenues paid by processing
(based on distribution and
product and species
amount of catch by species)
Specific Measure
Quasi-rents = Total revenue - (CDQ royalty payments + IFQ costs + fuel + lube and
hydraulics + food and provisions + freight costs for landed fish + lube and hydraulic
fluid + crew share payment + captain's share payment + fish taxes + processing
materials + labor costs for processing + packaging + freezing)
Quasi-rents / pounds landed = QR per pound
Quasi-rents / days fished = QR per day
d) Seasonality of average
Data Required
catch and revenue by vessel Catch, processed revenue, vessel class and ownership.
class
e) Catcher processor vessel Data Required
ownership & interest in QS
Processor, vessel and QS ownership data are required.
f) Level and distribution of
Data Required
harvesting and processing
Harvesting and processing sector employment and payments to labor data are
sector employment and
required.
payments to labor (number
Specific Measures
of individuals, hours/days
Labor Income = Crew share payment + Captain's share payment + QS holder’s
worked, and income)
payments (where applicable) + processing labor payment + all other labor payment
or
Labor Income = Crew share * (Total revenue - CDQ leases - QS leases - fuel - lube
and hydraulics - bait - food and provisions - freight costs for supplies - freight costs
for landed & processed fish - fish taxes) + processing labor payment + all other labor
payment.

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Examples of some dependent and exogenous variables of interest

g) Degree of involvement
of non-AFA trawl
catcher/processor sector in
other AK fisheries
h) Observer Costs in QS
Fisheries (Impacts of
Increased Observer
Coverage)
i) Total fishing and
processing taxes including
fee collection
j) Changes in Fleet
Composition (comparison of
cost, revenue and
compensation structure of
vessels exiting the fleet
versus those staying, based
on the measures given in
this section)
k) Product Recovery Rates
(PRR) by species
l) Production
m) Consolidation
n) Observer costs

Where applicable
Averaged daily Wage = Labor Payment / # of Processing Days
$ per Hour = Labor Payment / Total Man-hours
Labor as % of Revenue = labor payment / value of product
Labor as % of variable costs = labor payment / variable costs
Labor Income Per Capita = Labor income / # of crew earning shares
Average number of harvesting crew per vessel by season (by geographic region of
employee residence)
Average captain's share (%) & wages
Average crew share (%) & wages
Description of typical expenses deducted from crew wages
Data Required
Catcher Processor and vessel ownership data, as well as total catch, production, and
revenue data are required.
Data Required
Cost per day-at-sea by individual. Number of days purchased per season from data
collected by the observer program.

Data Required/Specific Measures
Taxes, use fees paid by catcher/processors
Data Required/Specific Measures
Cost, revenue, labor income, and compensation structure of vessels to construct the
measures given in the above section.

PRR = Finished Pounds / Raw Pounds
Production per Day = Finished Pounds / # of Processing Days
Production Per Employee = Finished Pounds / # catcher/processor positions
Avg. Production per catcher/processor = total processed pounds / # of catcher/
processors producing groundfish.
Observer cost as percent of revenue= Observer costs / revenue
Observer cost per day = Observer cost / # of processing days

b. Estimating Dependent Variables that Require a Model
Economic theory is concerned with explaining the relationships among economic variables (e.g.,
input quantities and prices, output quantities and prices) and using that information to explain,
evaluate, and/or predict production, allocation, and distribution decisions. This process typically
involves specifying a ‘model’ that characterizes the salient aspects of a particular process or
decision. The chosen model defines the general relationships to be examined, and within the
model, observed choices, outcomes and factors (i.e., data) are used to provide information
regarding the relationships of interest.

5

AFSC analysts use the data contained within the completed and verified EDRs to construct
statistical models that characterize the determinants and factors affecting the costs and revenues
of vessels within each stratum. The benefit of using statistical models to characterize the
relationship between costs or revenues and the factors that influence them is that the models may
initially be used to analyze the way in which economic performance changes after the immediate
implementation of the program.
If the Council makes adjustments to the program at a later date, analysts will be able to observe
the changes in quasi-rents not attributable to the factors that have historically been the
predominant statistical determinants to draw conclusions about the impact of the adjustments.
That is, these statistical techniques can be used to disentangle the influence of particular
economic variables on quasi-rents from “policy” or “management” variables that change directly
as a result of managers’ choices over policies or regulations. Examples of economic variables
would be the prices of fuel, materials, or other inputs used in fishing and processing. Variables
that can be altered directly by fishery managers or regulation are the length of fishery openings
by statistical area and species, the amount of allocation of a species to a sector, or individual
vessels or persons in a sector.
The data collected in the EDRs are used to develop both cost and quasi-rent that characterize the
relationships between fishing and processing activities and their economic impacts. In order to
estimate such functions one needs vessel-level information on variable costs of operation and
gross earnings. These variables will form the basis for the dependent part of the statistical
model, while the other data collected on input quantities, catch, and prices will be used as
exogenous variables. The analysts will determine the exact specification of the cost and quasirent functions based upon the questions desired by fishery managers, the number of observations
available, and the perceived quality or accuracy of the collected data.
Econometric Methods. The primary and most common approach for estimating and
specifying cost and quasi-rent functions is with econometric methods. This approach examines
the multivariate statistical relationships between short- run costs or quasi-rents and exogenous
variables, using choices or decisions made by economic agents over target species and fishing
location. Observed behavior over time and strata may be merged with other data to infer how
management actions impact quasi-rents. This analysis would include data on catch by species
and area, data on the value of retaining catch of a given species, and data on species with lower
market value. Error and regression statistics may be generated from econometric models to
indicate the level of statistical significance of estimated parameters. Given the number of
variables that could be included in any of these models, we are not prepared at this time to
provide quantitative standards of accuracy for each parameter included in the EDR. The level of
accuracy required in any given independent data value for estimating a particular dependent
variable may vary greatly from one dependent variable to another.

6

3. Describe the methods used to maximize response rates and to deal with non-response.
The accuracy and reliability of the information collected must be shown to be adequate for
the intended uses. For collections based on sampling, a special justification must be
provided if they will not yield "reliable" data that can be generalized to the universe
studied.
Each of the QS holders operating a catcher/processor in this fishery is required to submit an
annual EDR. All of these respondents will be applying for one or more QS. Because this is a
mandatory collection, and valuable fishing privileges will be withheld if an EDR is not
submitted, we anticipate a 100 percent response rate from QS holders.
Measures to verify the accuracy of the EDR data were developed by NMFS economists and
analysts to ascertain anomalies, outliers, and other deviations from averaged variables. The
principle means to verify data is consultation between NMFS and the submitter when questions
arise regarding data. NMFS requests oral or written confirmation of data submissions and
requests copies of or review documents or statements that would substantiate data submissions.
The person submitting the EDR would need to respond within 20 days of the inquiry for
information. Responses after 20 days could be considered untimely and could result in a
violation and enforcement action.
NMFS amends data in the EDR through this audit verification. NMFS may retain a professional
auditor/accounting specialist who would review and request financial documents substantiating
economic data that is questioned. NOAA guidelines for the Data Quality Act will be followed
and estimates without an adequate statistical basis will not be used.
Enforcement of the data collection program is different from enforcement programs used to
ensure that accurate landings are reported. It is critical that landings data are reported in an
accurate and timely manner, especially under a QS system, to properly monitor catch and
remaining quota. However, because it is unlikely that the economic data will be used for inseason management, it is anticipated that persons submitting the data will have an opportunity to
correct omissions and errors before any enforcement action would be taken. Giving the person
submitting data a chance to correct problems is considered important because of the complexities
associated with generating these data. Only if the agency and the person submitting the data
cannot reach a solution would the enforcement agency be contacted. The intent of this program
is to ensure that accurate data are collected without being overly burdensome on industry due to
unintended errors.
A discussion of four scenarios will be presented to reflect the analysts’ understanding of how the
enforcement program would function. The four scenarios are:
1. No information is provided on an EDR;
2. Partial information is provided on an EDR;

7

3. NMFS has questions regarding the accuracy of the data that has been submitted
on an EDR; and
4. A random audit to verify the data does not agree with data submitted in the EDR.
In the first two cases, the person would be contacted by NMFS (or a NMFS contractor) and
asked to fulfill his/her obligation to provide the required information. If the problem is resolved
and the requested data are provided, no other action would be taken. If that person does not
comply with the request, the collecting agency would notify enforcement that the person is not
complying with the requirement to provide the data. Enforcement would then use their
discretion regarding the best method to achieve compliance. Those methods would likely
include fines or loss of quota and could include criminal prosecution.
In the third case, questions may arise when, for example, information provided by one company
is much different than that provided by similar companies. These data would only be called into
question when obvious differences are encountered. Should these cases arise, the agency
collecting the data would request that the person providing the data double check the
information. Any reporting errors could be corrected at that time. If the person submitting the
data indicates that the data are accurate and the agency still has questions regarding the data, that
firm’s data could be audited. It is anticipated that the review of data would be conducted by an
accounting firm selected jointly by the agency and members of industry. Only when that firm
refuses to comply with the collecting agency’s attempts to verify the accuracy of the data would
enforcement be contacted. Once contacted, enforcement would once again use their discretion
on how to achieve compliance.
In the fourth case, an audit reports different information than that contained in the EDR. The
audit procedure is a verification protocol similar to that which was envisioned for use in the
pollock data collection program developed by NMFS and PSMFC. During the design of this
process, input from certified public accountants was solicited in order to develop a verification
process that is less costly and cumbersome than a typical audit procedure. That protocol
involves using an accounting firm, agreed upon by the agency and industry, to conduct review of
certain elements of the data provided.
Since some of the information requested in the EDRs may not be maintained by companies and
must be calculated, it is possible that differences between the audited data from financial
statements and EDR data may arise. In that case the person filling out the form would be asked
to show how his/her numbers were derived. If the explanation resolves the problem, there would
be no further action needed. If questions remained, the agency would continue to work with the
providers of the data. Only when an impasse is reached would enforcement be called upon to
resolve the issue. It is hoped that this system would help to prevent abuse of the verification and
enforcement authority.
In summary, members of the non-AFA trawl catcher/processor sector will be contacted and
given the opportunity to explain and/or correct any problems with the data, which are not willful
and intentional attempts to mislead, before enforcement actions are taken. Agency staff does not
view enforcement of this program as they would a quota monitoring program. Because these

8

data are not being collected in “real” time, there is the opportunity to resolve occasional
problems as part of the data collection system. The program was developed to collect the best
information possible. Analyses of the Amendment 80 rationalization program will be conducted,
to minimize the burden on industry and minimize the need for enforcement actions.
4. Describe any tests of procedures or methods to be undertaken. Tests are encouraged as
effective means to refine collections, but if ten or more test respondents are involved OMB
must give prior approval.
The Council held two industry meetings in 2006 to review and recommend data to be collected
in the EDRs. While this did not result in a formal pretest of the data reports, several fields in the
data forms were significantly revised. In addition, some members of the non-AFA trawl
catcher/processor sector have voluntarily submitted individual comments on previous versions of
this data form.
The AFSC held two half-day workshops to review the Amendment 80 EDR with members of
industry on January 23, 2009 and February 17, 2009; these meetings were held at the Best Use
Cooperative (BUC) offices. In August, 2009, AFSC met with the BUC cooperative manager
and BUC legal counsel regarding the conduct of the validation audit review of Amendment 80
EDR submissions, followed by several subsequent telephone consultations with one or both of
them.
AFSC conducted a meeting in 2010 with the one cooperative, BUC, to review the EDR. AFSC
scheduled a meeting in late January 2010 to consult with the sole Amendment 80 participant that
is not a member of BUC, Fishing Company of Alaska (FCA), but the meeting was cancelled by
FCA and not rescheduled.
5. Provide the name and telephone number of individuals consulted on the statistical
aspects of the design, and the name of the agency unit, contractor(s), grantee(s), or other
person(s) who will actually collect and/or analyze the information for the agency.
Mark Fina, Ph.D.
Economist
North Pacific Fisheries Management Council
PH: (907) 271-2809
Internet Address: mark.fina@noaa.gov
Jeff Hartman
NOAA/NMFS, Alaska Region, Sustainable Fisheries Division
PH: (907) 586-7228
Internet Address: jeff.hartman@noaa.gov

9

Tracy Buck
Supervisor of Permits
NOAA/NMFS, Alaska Region
Internet Address: tracy.buck@noaa.gov
Person(s) Who Will Actually Collect the Information for the Agency.
Dave Colpo
Program Manager
Pacific States Marine Fisheries Commission
PH: (503) 595-3100
Internet Address: Dave_Colpo@psmfc.org
Geana Tyler
Pacific States Marine Fisheries Commission
PH: (503) 595-3100
Internet Address: Geana_Tyler@psmfc.org
Person(s) Who Will Actually Analyze the Information for the Agency
Ron Felthoven, Ph.D.
Program Manager, Economics and Social Sciences Research Program
NOAA/NMFS, Alaska Fisheries Science Center
PH: (206) 526-4114
Internet Address: Ron.Felthoven@noaa.gov
Brian Garber-Yonts, Ph.D.
Research Economist
NOAA/NMFS, Alaska Fisheries Science Center
PH: (206) 526-6301
Internet Address: Brian.Garber-yonts@noaa.gov
Alan Haynie, Ph.D.
Economist
NOAA/NMFS, Alaska Fisheries Science Center
PH: (206) 526-4253
Internet Address: Alan.Haynie@noaa.gov

10