0518 renewal 03-30-11 ss Part B rev

SUPPORTING STATEMENT
ALASKA REGION BERING SEA & ALEUTIAN ISLANDS CRAB
ECONOMIC DATA REPORTS
OMB CONTROL NO. 0648-0518

B. COLLECTIONS OF INFORMATION EMPLOYING STATISTICAL METHODS
Economic Measures and Models developed with EDR data

Much of the data requested are used to compute total or average values based on a census of
plants and vessels in the years before (1998, 2001, and 2004) and after rationalization. To
compute many of these totals and averages, econometric models are required. In other cases,
statistical models may be used; and in some cases, total or average values are reported.
Examples of economic variables of interest include the following:
A. Measures not Requiring a Model
1. Measures for Harvesters not Requiring a Model
a) Distribution of average catch and ex-vessel revenue by vessel class (e.g., length class and
type), port of landing, and residence. Changes in ex-vessel prices.
Data Required: Catch and revenue information, vessel information, and vessel owner
information
b) Distribution of average variable vessel costs by vessel class (e.g., length class and type), port
of landing, and residence
Data Required: Total variable costs, by vessel, vessel characteristics, landings records
Specific Measure:
Annual Total Variable Costs = CDQ costs + IFQ costs + fuel + lube and
hydraulics + bait + food and provisions + freight costs for landed fish + lube and
hydraulic fluid + crew share payment + captain's share payment + fish taxes + pot
costs
Seasonal Variable Harvesting Costs = fuel costs + captain and crew costs +gear &
line costs + bait costs
Freight & Storage Costs = Freight costs of supplies to vessel + freight costs for
landed fish + storage costs

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c) Distribution of average quasi-rents by vessel class (e.g., length class and type), port of
landing, and residence
Data Required: Total variable costs, by vessel, vessel characteristics, landings records
Specific Measure:
Quasi-rents = Total revenue - (CDQ royalty payments + IFQ costs + fuel + lube
and hydraulics + bait + food and provisions + freight costs for landed fish + lube
and hydraulic fluid + crew share payment + captain's share payment + fish taxes)
Quasi-rents / pounds landed = QR per pound
Quasi-rents / days fished = QR per day
d) Seasonality of average catch and ex-vessel revenue by vessel class, port of landing, and
residence
Data Required: Catch, ex-vessel revenue, vessel class, port of landing, ownership,
and owner residence data
e) Catcher vessel ownership interest in BSAI crab processors and processing QS/catch history
Data Required: Processor, vessel and QS ownership data
f) Concentration of domestic and foreign ownership in the BSAI crab harvesting sector
Data Required: Vessel ownership data
g) Level and distribution of harvesting and processing sector employment and payments to labor
(number of individuals, hours/days worked, and income)
Data Required: Harvesting and processing sector employment and payments to
labor data
Specific Measures:
Labor Income = Crew share payment + Captain's share payment + IFQ holder’s
payments (where applicable), or
Labor Income = Crew share*(Total revenue - CDQ leases - IFQ leases - fuel lube and hydraulics - bait - food and provisions - freight costs for supplies - freight
costs for landed fish - fish taxes)
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)

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Average captain's share (%) & wages
Average crew share (%) & wages
Description of typical expenses deducted from crew wages
h) Degree of involvement of BSAI crab harvesters and processors in other AK fisheries
Data Required: Processor and vessel ownership data, as well as total catch,
production, and revenue data
i) Value of use right
Data Required: Information on the prices of buying and leasing quota share
j) Regional/community economic impacts (employment and income) of the BSAI crab fisheries
Data Required: Data on expenditures by location and the residence of those
involved in harvesting and processing crab, and other regional economic data are
required to develop regional economic models.
Specific Measures:
Location of employees
Location of gear purchases
Location of bait purchases
Location of fuel purchases
Location of lube and hydraulic fluid purchases
k) Observer Costs in Pre- and Post-IFQ Fisheries (Impacts of Increased Observer Coverage)
Data Required: Cost per day-at-sea, cost per pound of crab harvested, total
observer costs per fishery
l) Vessel Values Pre- and Post-IFQ
Data Required/Specific Measures: Estimated market value of vessel and gear,
estimated replacement value of vessel and gear
m) Total fish taxes by harvesting sector
Data Required/Specific Measures: taxes paid by fishermen
n) 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).

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Data Required/Specific Measures: Cost, revenue, labor income, and compensation
structure of vessels to construct the measures given in the above section
2. Measures for Processors not Requiring a Model:
a) Distribution of processed product revenue by community and processor or processor category
(size, ownership, location)
Data Required: Product revenue information, plant and plant owner information
b) Processor ownership interest in BSAI crab catcher vessels and harvester QS/catch history
Data Required: Processor, vessel and QS ownership data
c) Concentration of domestic and foreign ownership in the BSAI crab processing sector
Data Required: Processor ownership data are required.
d) Labor Income
Specific Measures:
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
e) Product Recovery Rates (PRR) by species
PRR = Finished Pounds / Raw Pounds
f) Production
Production per Day= Finished Pounds / # of Processing Days
Production per Employee = Finished Pounds / # crab positions
g) Production sold to an affiliated company [Note: This is one of the variables specifically
requested by DOJ and FTC. The purpose of tracking production by affiliated and nonaffiliated entity is to determine the potential for anti-trust or anti-competitive behavior
through the use of quota.]
ratio of affiliated to non-affiliated prices = price per pound sold to affiliated
company / price per pound sold to non-affiliated company
% of product sold to affiliated companies = pounds of product sold to affiliated
company / total finished pounds
h) Value Added

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Specific Measures:
Value Added = Revenue - raw pounds cost
Community Impacts
Changes in crab processing employment = CPs + Floaters + Shorebased
Changes in Taxes Paid
Consolidation
Avg. Production per Plant = total finished pounds / # of plants purchasing crab
Observer costs
Observer cost as percent of revenue= Observer costs / revenue
Observer cost per day = Observer cost / # of processing days
Pre vs Post IFQ
Changes in Products Produced
Changes in grades produced
Changes in box sizes
Changes in product storage costs pre and post IFQ (expected to decline with extended
fishing seasons)
Compare processing fees charged for custom processing to variable costs of firms

Labor Income:
Labor payment
Labor Income Per Capita
Labor payment / # crab positions
Variable Costs
(packaging materials, equipment and supplies +
food and provisions +
fuel, electricity, lube and hydraulic fluid +
labor payment+
raw pounds cost)
Quasi-rents
= Value of production - (packaging materials, equipment and supplies + food and
provisions + fuel, electricity, lube and hydraulic fluid + labor payment + raw pounds cost)
Quasi-rent Measures
Quasi-rents / pounds processed
Quasi-rents / day
Changes in Inventory (by product)
= Total production - total sales - custom processed for others + custom processed for you

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We also can compute the annual costs of:
taxes
packaging materials, equipment and supplies, and re-packing costs
food and provisions
fuel, electricity, lube and hydraulic fluid
freight -- supplies
freight -- products
storage
water, sewer and waste
Note: We can compute seasonal/fishery specific costs by using information on total days spent
processing crab in each fishery.
We also can compute seasonal costs of:
Broker's fees and promotions
observer costs
B. Measures Based on Economic Models
Obviously, there are various models that analysts can choose among to construct a given
measure, and each subtle difference in the approaches often necessitates different types of data.
For example, harvesting capacity can be measured in a primal, physical framework or a dual,
cost-based framework (there are other choices which we will not elaborate on here), and both
models have different data requirements. Therefore, the goal was to consider the general types
of models that are typically used to construct the measures of excess harvesting and processing
capacity, economic returns, variable costs, and revenues. The following discussion outlines the
approach that was taken in selecting necessary data elements:
The economic models to be used are based upon the objective measures previously identified by
the Council’s Scientific and Statistical Committee (SSC) to monitor the success of the crab
rationalization program. Here we identify the method or models typically used to construct such
measures and the data required to adequately construct them.
The measures identified by the SSC are intended to allow the Council to monitor the success of
the crab rationalization program in terms of addressing the five problems currently facing the
fishery (as identified in the BSAI crab rationalization problem statement prepared by the Council
in February 2002 1). Those five problems and the summary of the problems facing the Council
are as follows:
1.
2.
3.
4.

Resource conservation, utilization, and management problems;
Bycatch and its associated mortalities, and potential landing deadloss;
Excess harvesting and processing capacity, as well as low economic returns;
Lack of economic stability for harvesters, processors and coastal communities; and

1

North Pacific Fisheries Management Council, 2002. Minutes of the June, 2002 NPFMC
Meeting, Dutch Harbor, Alaska, pp. 22. http://fakr.noaa.gov/npfmc/minutes/Council602.pdf.
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5. High levels of occupational loss of life and injury.
The problem facing the Council, in the continuing process of comprehensive
rationalization, is to develop a management program which slows the race for fish,
reduces bycatch and its associated mortalities, provides for conservation to increase the
efficacy of crab rebuilding strategies, addresses the social and economic concerns of
communities, maintains healthy harvesting and processing sectors, and promotes
efficiency and safety in the harvesting sector. Any such system should seek to achieve
equity between the harvesting and processing sectors, including healthy, stable and
competitive markets.
The Objective Measures
This section discusses the economic objective measures that will likely need to be computed, and
the corresponding economic data that is needed (some of which must be elicited through the
Economic Data Reports, or EDRs). For a majority of the measures elaborated on below, the
required data is discussed in the context of the vessel or plant (and at times, the firm), depending
on the measure. Measures that are primarily production based (capacity utilization, productivity,
and efficiency) are best constructed with data from the vessel or plant level. Such a focus allows
the analyst to more directly identify the link between inputs used to catch or process fish and the
quantity of fish or product forms obtained, respectively. Characterizing this link, and how it
changes, is a key part in assessing the changes in economic performance that arise under
rationalization. However, because the production process of one vessel or plant is at times only
one component of the overall business structure, instances arise in which the firm (which may
own one or more vessels, plants, or both) is the natural unit of observation.
Therefore, in addition to the individual measures discussed below, ownership data are required to
link each piece of the overall puzzle. This data allows one to assimilate the individual effects
into the likely overall” effect of crab rationalization on the residual claimants of the operations
we observe on a piece-by-piece basis. It also allows analysts to monitor structural changes not
reflected directly in performance- or profit-based measures, such as changes in the concentration
of domestic and foreign ownership in the harvesting and processing sectors, the structure of
ownership (including proprietorships, publicly traded corporations and privately held
corporations), and the relationships both within firms, (i.e., the amount and nature of vertical and
horizontal integration) and among firms.
Although vessel-, plant-, or firm-level detail is needed to adequately construct many of the
model-based measures discussed below, there are some simple averages for which aggregate
(e.g., sector-level) data can likely provide an adequate representation. One underlying problem
with using aggregated data for modeling purposes, however, is that the conditions under which
the aggregate data accurately represents the individual firms’ production technologies and
decisions is quite restrictive. The result is a model with unrealistic assumptions which may bias
the resulting measures (aggregation issues constitute a large branch of economic theory).
Furthermore, if the aggregation is too extreme, the information that can be obtained from a
model will not allow the analyst to adequately explain the source or cause of any changes. In
other cases, the lack of a sufficient number of observations (i.e., data on each vessel, plant, or
firm operating in a given time period) may preclude estimation of the model typically used to
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construct a particular measure. Finally, aggregate data cannot be used to determine whether
most fishermen and processors will have benefited from crab rationalization. For example,
aggregate processor profits could increase even though the profits for the majority of the
processors decreased.
Problems, Measures, and Data
The measures identified by the SSC are intended to allow the Council to monitor the success of
the CR Program in terms of addressing the five problems currently facing the fishery (as
identified in the BSAI crab rationalization problem statement prepared by the Council in
February 2002 ). Those five problems facing the Council are as follows:
1.
2.
3.
4.
5.

Resource conservation, utilization, and management problems;
Bycatch and its associated mortalities, and potential landing deadloss;
Excess harvesting and processing capacity, as well as low economic returns;
Lack of economic stability for harvesters, processors and coastal communities; and
High levels of occupational loss of life and injury.

This discussion does not address the specific data needed to analyze problems 1), 2), and 5)
identified by the Council as the primary data required are not necessarily economic in nature and
therefore not requested in the EDRs under consideration. However, some of the objective
measures discussed for problems 3) and 4), and the data used therein, may be useful in
monitoring the success of the crab rationalization program with regard to problems 1), 2), and 5).

Problem #3, Excess Harvesting and Processing Capacity and Low Economic Returns
Measures:
a) Harvesting capacity and capacity utilization
Data Required: Typically, the analysis of capacity and capacity utilization is based upon
the cost structure of the vessel, and examines whether the observed level of catch
coincides with the least-cost level, given the capital stock. This process requires one to
compile information on all significant variable costs (labor, fuel, bait, pots, etc.),
including the price of all variable inputs and the quantities used, and estimate a cost
function at the vessel level. A measure of the capital stock is also required, and is often
expressed as the dollar value of the vessel and equipment onboard, or with proxies such
as vessel characteristics [length, tonnage, horsepower, etc.]. One can then model the
relationship between output (total catch, by species), input prices, and cost. Capacity is
underutilized if production is currently less than the level at which total average costs are
minimized, given the existing capital stock. The opposite is true if current output exceeds
such a level. Further extensions of the model allow one to directly compute the
contribution of the capital stock in production and thus, provide an alternative measure of
the extent to which capital is being utilized.

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Data Summary: Variable input prices and quantities purchased, capital quantities, and
catch quantities (by species) are required.
Model to be estimated: econometric cost function or data envelopment analysis
b) Processing capacity and capacity utilization
Data Required: The same approach and data requirements would apply in assessing
processing capacity and capacity utilization (although the specific inputs used and
outputs produced are different). It can be more difficult, however, to quantify the capital
stock for processors, as is evidenced by conversations with industry. Respondents will be
asked to provide the assessed value of plant and equipment, which can be used as a proxy
for the capital stock. And, given the panel nature of the data, fixed effects estimators may
be used to in part account for the fixed, unobserved differences between plants that may
be attributable to the differences in the capital stock.
Data Summary: Variable input prices and quantities purchased, capital quantities, and
production quantities by species and product form are required.
Model to be estimated: econometric cost function or data envelopment analysis.
Analyses related to excess capacity and capacity utilization will likely be based on a cost
function specification. In this model, total variable costs are regressed upon the outputs,
the relevant variable input prices, quasi-fixed inputs, and environmental attributes (such
as stock sizes) that may shift or twist the production possibilities frontier (and thus the
costs of harvesting or processing a unit of crab).
For harvesting operations, the specification will be Variable Costs = f (W, Y; X, Ω),
where W is a vector of input prices including bait, fuel, and crew; Y is a vector of outputs
including catch levels for the relevant crab species; X is a vector of quasi-fixed inputs
including the number of pots, vessel length, vessel tonnage, and vessel horsepower; and
Ω is a vector of environmental variables such as stock sizes for the various crab species.
This regression will be undertaken using a flexible functional form in order to minimize a
priori restrictions on the production technology, recognizing the trade-offs between
increased flexibility and approximation capabilities with the requisite degrees of freedom
required for reasonable bounds on parameter estimates. Please see the discussion paper
“Performance Measures for the Bering Sea and Aleutian Islands Crab Rationalization
Programs: Data and Other Considerations” for a further discussion.
c) Harvesting sector quasirent (total revenue - total variable cost)
Data Required: This measure is comprised of total revenues less total variable cost. The
Council has restricted us to focus solely on crab operations, which implies that we will
not have a complete picture of each vessel’s overall economic activities, and thus cannot
adequately apportion all of their fixed costs across fisheries. By focusing on quasirents,
we can avoid introducing this potentially significant source of error.
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If one wants to understand the source of any change in quasirents at the most basic level,
one needs separate measures of total revenues and total variable costs. However, without
details on total catch, and the prices and quantities of variable inputs, and quantities of
quasi-fixed inputs, one cannot tell if variable costs changed due to changes in catch
levels, effort (variable input) levels, or input prices, or quasi-fixed inputs. Furthermore,
without detail on the quantities sold and prices received, for each species, one cannot tell
if changes in revenue are attributable to changes in price, quality, or total catch.
Thus, without the above information, changes in quasi-rent cannot be explained and
increased production or cost efficiency cannot be discerned from exogenous market
impacts. The data components described above can also be used to construct predictive
models that assess the likely change in production patterns, revenues, and costs in
response to market shocks and/or regulations.
Data Summary: Variable input prices and quantities purchased, quasi-fixed inputs, total
catch quantities and prices received, by species are required.
Model to be estimated: econometric restricted profit function.
d) Processing sector quasirent
Data Required: essentially the same type of information is required as for harvesters,
which is discussed in c) above (with the obvious qualification that the respective variable
inputs are likely to be different and revenue data should include product form, by species,
quantity produced, and price received).
Data Summary: Variable input prices and quantities purchased (including fish purchases
by species), quasi-fixed inputs, total production, by species and product form, and prices
received for each product are required.
Model to be estimated: econometric restricted profit function.
e) Processor or Harvester Productivity:
Data Required: The measurement of productivity essentially involves the quantity of
inputs required to produce a unit of output. The inputs included in the model should
consist of those that directly contribute to the quantity of output one can produce. In the
simplest terms, a single-input productivity measure such as labor productivity is
computed as the ratio of output to labor hours. These measures are quite limited,
however, in that they fail to account for the use of other inputs in production. That is, the
ratio of total output to labor hours may have increased over time for a particular plant or
vessel, but this may be due to increased use of automation (so the decreased labor use has
been offset by increased capital expenditures). Therefore, total factor productivity
measures are preferred, which account for the use of, and substitution among, all inputs
in production. Because the contribution (and cost) of a one-unit change in each factor of
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production can differ widely, each input’s share of the total cost of production is needed
as a weight when accounting for the changes in input use. There are other metrics used
for productivity measurement, such as Malmquist indices, which do not require the cost
data or the associated competitive market assumptions.
Summary: Direct inputs in production (quantities used and for some models, the cost of
each), total catch or processed product quantities, by species are required.
Model to be estimated: Tornqvist total factor productivity index, Malmquist index, or
econometric transformation function.
f) Technical Harvesting Efficiency
Data Required: The measurement of efficiency can be undertaken in several ways to
identify different notions of efficiency. Technical efficiency is similar to productivity in
that it relates to the quantity of inputs used to obtain a given bundle of output(s).
Essentially, productivity measurement involves computing how the skill with which
inputs are converted to outputs progresses (or regresses) over several periods of time, and
technical efficiency measurement involves analyzing each firm’s relative proficiency in
production processes within each period.
Data Summary: Direct inputs in production and total catch quantities by species are
required.
Model to be estimated: an econometric production frontier model, or a non-parametric
data envelopment analysis model may be used to estimate technical harvesting efficiency.
g) Allocative Harvesting Efficiency:
Data Required: The measurement of input-allocative efficiency pertains to the degree to
which one minimizes costs of producing a given level of output by choosing an optimal
proportion of inputs, given their relative costs and contributions to production. In more
familiar terms, cost savings afforded by eliminating the race for crab are likely to
increase input-allocative efficiency. Output-allocative efficiency reflects the degree to
which one chooses the optimal mix of outputs (here, catch or finished product, for
harvesting and processing models, respectively), given the respective market prices and
opportunity costs of targeting (or processing) one species (or product) instead of another.
Loosely speaking, measures of input (output) allocative efficiency can be thought of as
the extent to which one minimizes (maximizes) the cost of (revenue from) a given level
of outputs (inputs). Note that one can be input-allocatively efficient and outputallocatively inefficient, or vice-versa. Similarly, one can be technically efficient and
allocatively inefficient. The point here is that each measure captures a different aspect of
production, and each can be affected in different ways from changing institutional or
regulatory environments.

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Data Summary: The quantities of direct inputs in production and their costs, total catch
(or processed product, for processing models) quantities and prices by species are
required.
Model to be estimated: allocative harvesting efficiency may be assessed by estimating an
econometric cost function model or a non-parametric data envelopment analysis model.
h) Processing sector productivity and efficiency
Data Required: The basic data required to measure productivity and efficiency in
the processing sector is the same as in the harvesting sector -- only the definition
of direct inputs and outputs changes. See e), f) and g) above for a description of
the measures, models, and data.

Problem #4, Lack of Economic Stability for Harvesters, Processors and Coastal
Communities
Many of the measures listed for Problem 3 (both the model-based measures and simple averages
or totals) are well suited to assess the success of the crab rationalization program in increasing
economic stability for harvesters and processors. This can be accomplished by examining each
vessel or plant’s annual profit or quasi-rents, and calculating measures of variation for pre- and
post-rationalization periods. The detail afforded in the data used to construct c), d), e) and f) also
allows one to account for exogenous market effects (or varying stock levels) that may affect
stability. That is, one can ascertain whether economic stability or viability is more likely in the
rationalized fishery (relative to pre-rationalization) when market shocks are prevalent. Stability
can also be analyzed by designating vessels or plants into strata of interest (based on size, species
composition, regional designation, etc.) and presenting the mean values for the group (along with
indicators of the variation within that group) for each year. Such an approach will preserve
confidentiality, yet allow for the most accurate and informative measures of stability and the
distribution of income among and between harvesters and processors.

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 potential respondent universe is approximately 90 (84 full EDR, 6 certification only) catcher
vessel owners, 5 (all full EDR) catcher processors, 29 (16 full EDR, 13 certification only)
shoreside processors, and 8 (2 full EDR, 6 certification only) inshore stationary floating
processors. For catcher vessel operations on average, two individual persons may collaborate to
provide the data to complete an EDR, though in most cases there will only be one party
submitting the data.
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This data collection process will take the form of a census. Therefore, all vessel and plant
owners are required to fill out the EDRs. The response rate is expected to be 100 percent, as
non-compliance carries with it two severe penalties. First, no IFQ or IPQ will be granted to any
vessel or plant owner that does not complete the EDR. Second, enforcement can levy fines
against any individual who does not comply with the law.
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.
Respondents submitted both historical and annual EDRs at the beginning of the CR Program.
Currently, only annual EDRs are collected from all vessels and plant owners participating in crab
fisheries during each year. Owners of these vessels and plants are identified through fish tickets,
Alaska Commercial Operator Annual Reports (COARs), and crab quota share holder data. We
will not be sampling from these populations, but rather compiling a census for all years.
With the response (produced from completed and verified data forms), AFSC analysts will
construct statistical models for estimating key variable cost values for each strata. This data will
also be used to develop cost functions from this data and to estimate average variable costs of
operations, average gross earnings, and quasi-rents. Other data on purchases by cost category
may be developed to estimate changes in purchases and regional economic impacts before and
after the CR Program is implemented. Several methods are available to estimate these outputs.
The analysts will select the best methods based on an assessment of the response sample, the
census data (from mandatory data forms) of other sectors, and other data.

3. Describe the methods used to maximize response rates and to deal with nonresponse.
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 owners and leaseholders in the BSAI crab harvesting and processing sectors is
required to submit an annual EDR. Most of these potential respondents will also be applying for
one or more crab fishing or processing permits that are required to participate in the CR
Program. All persons who are owners and/or leaseholders of vessels and processing operations
must submit an EDR to obtain one of these crab fishing or processing permits. The response to
mandatory data requirements should be very high, because the continued opportunity to use
these permits has substantial value. We are anticipating response rates of 95-100 percent.
NMFS has taken substantial efforts to obtain high response rates and to verify that data
submitted is accurate and complete. For example, we have prepared (either ourselves or through
a contractor) annual reports documenting the accuracy with which the information for each
variable collected has been reported. Problems were pointed out and subsequently addressed by
13

making minor changes to the wording of problematic questions. We have hired an accountant to
independently assess the quality of the reported data (through detailed financial audits) and
found that the reported data are of sufficient quality to support analysis of the crab rationalization
program. We have taken public comment on the data quality at the Council meetings as well as
other “town hall” style meetings with fishery participants. We have also held meetings with
NOAA data quality specialists to make sure we have followed all rules and protocols for
ensuring the accuracy and quality of these data.
Enforcement of the data collection program with regard to non-compliance has been 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 an IFQ system, to
properly monitor catch and remaining quota. However, because it is unlikely that the economic
data will be used for in-season management, it is anticipated persons submitting the data have
been given 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 for unintended errors.
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 appointed an industry technical committee that met in 2001 and 2002 to review and
recommend data to be collected in the EDRs. While this did not result in a formal pretest of the
data reports, representatives from each fishery and the crab processing sectors participated in
seven day-long meetings during that period. Responses from those meetings resulted in draft
EDR data forms referenced in the P. L. No. 108-199. Following congressional action on P. L.
No. 108-199, a focus group meeting consisting of a small number (less than a total of ten) of
industry participants was held at the AFSC. Participants in the focus group met to evaluate the
draft data forms and identify the optimum years between 1998 and 2004 from which to select
historical data from each of the four crab sectors. As a result of the review, several data forms
were significantly revised.
Since the EDR program has been in place, informal testing has taken place by meeting with EDR
submitters to discuss ways in which the forms used to request information could be improved.
The accountants that perform the data quality audits, as well as PSMFC (who administers the
data collection) also document ways in which the EDRs could be clarified and we have used this
information to clarify instructions and variable definitions.

14

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.
Ron Felthoven
Economist
NMFS WASC Route: F/AKC3
PH: (206) 526-4114
Internet Address: ron.felthoven@noaa.gov
Brian Garber-Yonts
Economist
NMFS WASC Route: F/AKC3
PH: (206)526-6301
Internet Address: Brian.Garber-Yonts@noaa.gov
Joe Terry
Economist
NMFS WASC Route: F/SWC
PH: (858) 546-7197
Internet Address: joe.terry@noaa.gov
Dave Colpo
Program Manager
Pacific States Marine Fisheries Commission
PH: (503) 595-3100
Internet Address: frontoffice@psmfc.org

15