Tracking Changes on Fishery 

Economic Performance

— Continuous Economic Data Collection Programs for the 

Hawaii and American Samoa Longline Fisheries 2005–2016 

Minling Pan

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Pacific Islands Fisheries Science Center

NOAA Technical Memorandum NMFS-PIFSC-73

https://doi.org/10.25923/hqhf-d906

August 2018

Tracking Changes on Fishery 

Economic Performance 

— Continuous Economic Data Collection Programs for the 

Hawaii and American Samoa Longline Fisheries 2005–2016 

Minling Pan

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

1845 Wasp Boulevard

Honolulu, HI 96818

NOAA Technical Memorandum NMFS-PIFSC-73 

August 2018 

U.S. Department of Commerce

Wilbur L. Ross, Jr., Secretary

National Oceanic and Atmospheric Administration

RDML Tim Gallaudet, Ph.D., USN Ret., Acting NOAA Administrator

National Marine Fisheries Service

Chris Oliver, Assistant Administrator for Fisheries
Recommended citation:

Pan M. 2018. Tracking Changes on Fishery Economic Performance. NOAA Tech. Memo. 

NMFS-PIFSC-73, 48 p. https://doi.org/10.25923/hqhf-d906 

Copies of this report are available from:

Science Operations Division

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

National Oceanic and Atmospheric Administration 

1845 Wasp Boulevard, Building #176 

Honolulu, Hawaii 96818 

Or online at:

https://www.pifsc.noaa.gov/library/

Cover: Photo courtesy of Minling Pan
iii

Table of Contents

Table of Contents ........................................................................................................................... iii

List of Tables .................................................................................................................................. v

List of Figures ................................................................................................................................ vi

Executive Summary ...................................................................................................................... vii

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

Need for the Continuous Economic Data Collection Programs ................................................. 1

Options for Continuous Economic Data Collection Program Designs ....................................... 2

Data Form Design ....................................................................................................................... 3

Program Protocol ........................................................................................................................ 4

Observer Training ................................................................................................................... 5

Survey Form Print-out and Distribution ................................................................................. 5

Data Collection at Sea ............................................................................................................. 5

Verification of Data Collection Status .................................................................................... 6

Data Entry ............................................................................................................................... 6

Database Management .................................................................................................................... 7

Data Entry Design for Data Quality Control .............................................................................. 7

Data Integration with Other Fisheries-Dependent Data .............................................................. 7

Economic Performance of the Hawaii Longline Fisheries, 2005-2016 ........................................ 10

Population and Response Rate .................................................................................................. 10

Metadata for the main data sources ...................................................................................... 12

Adjustments for inflation ...................................................................................................... 12

Average Trip Costs Trend ......................................................................................................... 13

Trip Cost Structure .................................................................................................................... 15

Trip Costs and Fuel Prices Trends ............................................................................................ 17

Trip Net Revenue Trends .......................................................................................................... 19

Trip Costs by Area .................................................................................................................... 21

Foreign Crew in the Fisheries ................................................................................................... 25

Economic Performance of the American Samoa Longline Fishery 2006-2016 ....................... 26

Population and Response Rate .................................................................................................. 26

Data Sources and Metadata ................................................................................................... 27

Adjustments for inflation ...................................................................................................... 27

Average Trip Costs Trend ......................................................................................................... 28
iv

Net Revenue per Set Trend ....................................................................................................... 30

Discussion on the Net Revenue Calculation ............................................................................. 32

Costs per set in the current report are lower than in the previous IR for most of the years . 33

Revenue per set varies from year to year between these two reports ................................... 33

Annual revenue aggregated by set level vs trip level ........................................................... 34

Summary and Conclusions ........................................................................................................... 35

Acknowledgements ....................................................................................................................... 36

Literature Cited ............................................................................................................................. 37

Appendices .................................................................................................................................... 38

Appendix A. Cost Data Collection Form: Hawaii Longline..................................................... 38

Appendix B. Cost Data Collection Form: American Samoa Longline ..................................... 39
v 

List of Tables

Table 1. Tasks and coordination associated with the continuous cost data collection program. .... 5

Table 2. Hawaii longline fishery population, sample size, and response rate to trip cost data 

collection, 2005-2016. ...................................................................................................... 11

Table 3. Hawaii longline fishery population, sample size, and response rate to trip cost data 

collection of tuna (deep-set) trips, 2005-2016. ................................................................. 11

Table 4. Hawaii longline fishery population, sample size, and response rate to trip cost data 

collection of swordfish (shallow-set) trips, 2005-2016. ................................................... 12

Table 5. Average trip cost, revenue, and net revenue for Hawaii longline deep-set deep-set tuna 

fishing trips (adjusted to 2016 dollars), 2005-2016. ......................................................... 15

Table 6. Average trip cost, revenue, and net revenue for Hawaii longline shallow-set swordfish 

trips (adjusted to 2016 dollars), 2005-2016. ..................................................................... 15

Table 8. Average annual fuel price and fuel costs per trip and inflation adjustors of the Honolulu 

Consumer Price Index (HCPI) (adjusted to 2016 dollars), 2005-2016. ............................ 19

Table 9. Average deep-set tuna trip costs by three areas EEZ, EPO, and WCPO (adjusted to 2016 

dollars), 2005-2016. .......................................................................................................... 23

Table 10. Average shallow-set swordfish trip costs by three areas EEZ, EPO, and WCPO 

(adjusted to 2016 dollars), 2005-2016.* ........................................................................... 24

Table 11. Number of crew and foreign crew trends per vessel in Hawaii longline fisheries (2006-

2016). ................................................................................................................................ 25

Table 12. Number of trips of American Samoa longline fishery and simple size in economic data 

collection, 2006-2016. ...................................................................................................... 27

Table 13. Costs, revenues, and net revenues per trip and per set for the American Samoa longline 

fishery (adjusted to 2016 dollar). ...................................................................................... 31

Table 14. Average fuel price in the American Samoa longline fishery, 2006-2016, (adjusted to 

2016 dollar). ...................................................................................................................... 32
vi

List of Figures

Figure 1. The flow chart of Interface Access database design. ...................................................... 9

Figure 2. The trend of average trip costs with standard deviation for Hawaii longline deep-set 

tuna trips, 2005-2016 (adjusted to 2016 dollars). ............................................................. 13

Figure 3. The trend of average trip costs with standard deviation for Hawaii longline shallow-set 

swordfish trips, 2005-2016 (adjusted to 2016 dollars). .................................................... 14

Figure 4. The cost structure of an average deep-set tuna fishing trip in 2016. ............................. 16

Figure 5. The cost structure of an average shallow-set swordfish trip in 2016. ........................... 17

Figure 6. The annual average fuel price trend with standard deviation: 2005-2016 (adjusted to 

2016 dollars). .................................................................................................................... 18

Figure 7. The quarterly average fuel price and deep-set tuna trip cost trend for the 2005-2016 

period (nominal value). ..................................................................................................... 18

Figure 8. Average Trip Net Revenue for Hawaii Longline Deep-set tuna trips (adjusted to 2016 

dollars), 2005-2016. .......................................................................................................... 20

Figure 9. Average Trip Net Revenue for Hawaii Longline Shallow-set swordfish trips (adjusted 

to 2016 dollars), 2005-2016. ............................................................................................. 20

Figure 10. The map of the three areas with different management regimes: WCPO, EPO, and 

Hawaii EEZ (map courtesy of Dr. Haiying Wang, JIMAR PIFSC). ................................ 21

Figure 11. Average trip costs by areas of Hawaii deep-set tuna trips (adjusted to 2016 dollars), 

2005-2016. ........................................................................................................................ 22

Figure 12. Average trip costs by areas of Hawaii shallow-set swordfish trips (adjusted to 2016 

dollars), 2005-2016. .......................................................................................................... 23

Figure 13. Spatial variations of Hawaii longline trip costs (2010-2012). ..................................... 24

Figure 14. Average trip costs with standard deviation in American Samoa Longline Fishery 

Average Trip Costs, 2006-2016 (adjusted to 2016 dollars). ............................................. 28

Figure 15. Cost Structure of an Average American Samoa Longline Trip in 2016. .................... 29

Figure 16. Average diesel fuel price and standard deviation reported by fishermen in the 

American Samoa longline fishery, 2006 to 2016 (adjusted to 2016 dollars).................... 29

Figure 17. Net Revenue per Set for the American Samoa Longline Fishery, 2006-2016 (adjusted 

to 2016 dollars and not including labor cost and fixed costs)........................................... 30

Figure 18. Cost per set and fuel price (both nominal) in the American Samoa longline fishery, 

2006-2016. ........................................................................................................................ 32
vii

Executive Summary

The purpose of this report is twofold. First, it provides a first-time comprehensive review of the 

establishment and implementation of continuous economic data collection programs of two 

important fisheries, the Hawaii longline fishery and the American Samoa longline fishery, 

managed under the Western Pacific Regional Fishery Management Council’s fishery 

management plans. Second, this report presents trends of the economic performance indicators 

from the beginning of the continuous economic data collection programs, 2005 being the first 

full year for the Hawaii longline fishery, and 2006 for the American Samoa longline fishery, 

through 2016. The economic performance indicators presented in this report include not only 

fishing cost data collected through the data collection programs, but also net revenue information 

resulting from the integration of primary data (from the economic data collection programs) and 

secondary economic data. In addition, since database development and management are

necessary to ensure data quality and timely products, this report illustrates the database system 

designs and management associated with products of these economic data collection programs. 

The trend data show declining economic performance of the American Samoa longline fishery. 

They also show substantial changes in bigeye tuna (deep-set) fishing and swordfish (shallow-set) 

fishing in the Hawaii longline fishery, a gradually increasing trend of the economic returns (net 

revenue) for deep-set tuna fishing, with fluctuations of economic returns for shallow-set 

swordfish fishing. Compared to 2005, the average net revenue per trip of deep-set tuna fishing 

has increased 83%, from $26,694 per trip in 2005 (adjusted to 2016 dollars) to $48,782 per trip 

in 2016. The annual average of trip net revenue of the highest year was more than twice the net 

revenue of the lowest year for shallow-set swordfish fishing (2016 vs. 2010). However, there 

was no particular downward or upward trend with respect to shallow-set swordfish fishing trip 

net revenue during the report period. 

Fuel cost is the main expenditure for both Hawaii longline and American Samoa longline trips, 

approximately 50%. The study shows that variations of trip expenditures across years 

corresponded closely to changes in fuel prices. Linking fishing cost data with vessel logbook 

data also allows us to examine fishing costs by area. The data show that average trip costs for 

EPO (Eastern Pacific Ocean) fishing trips were the highest compared to trip costs within EEZs 

(exclusive economic zones) or WCPO (Western and central Pacific Ocean) areas for both deepset tuna fishing and shallow-set swordfish fishing. The data suggest that travel distance is 

another important determinant of fishing trip cost of Hawaii longline, as EPO is farther away 

from Honolulu port compared to WCPO. 
1 

Introduction

To track the changes of economic performance of the Hawaii longline and American Samoa 

longline fisheries on a continuous basis, the economic data collection programs for these two 

fisheries were established in mid-2004 and 2006, respectively. The purpose of this report is 

twofold. The first goal is to conduct a comprehensive review and describe how the first two 

systematic data collection programs in these regions were established, especially the main 

challenges and lessons learned from the program implementation and the database development. 

The success of the program is a result of a collaborative effort between industry, the NOAA 

Observer Program, and PIFSC scientists. Second, the report presents trends in the economic 

performance (in terms of net revenue) from the beginning of the programs to 2016. Because 

2005 was the first full year for implementation of the Hawaii fisheries program, the data 

summary presented in the report covers 2005 to 2016 for the Hawaii longline fishery, and 2006 

to 2016 for the American Samoa longline fishery. 

The Hawaii and American Samoa longline fisheries are the main fisheries managed by the 

Western Pacific Regional Fishery Management Council’s (WPRFMC). These two longline 

fisheries that harvest pelagic fish, comprised 86% and 96% of the total commercial fishing 

revenue of their respective regions in 2015. While the majority of the fish landed by the Hawaii 

longline fisheries were caught outside of the United States exclusive economic zone (EEZ), the 

majority of the fish landed by the American Samoa longline fishery were caught inside of its

EEZ. The continuous economic data collection programs are add-ons to the Pacific Islands 

Regional Office (PIRO) observer program for these two fisheries providing valuable long-term 

data streams to support fishery management. Program Establishment

Need for the Continuous Economic Data Collection Programs

The need and the authorization to collect economic data are found in the Magnuson-Stevens Act

(MSA) (16 U.S.C. 1801 et seq.), the Regulatory Flexibility Act (RFA, 5 U.S.C. 601 et seq.), the 

National Environmental Policy Act (NEPA, 42 U.S.C. 4372 et seq.), and Executive Order (EO) 

12866. These statutes and Executive Order require evaluations about how fishery regulatory 

programs may affect net benefits to society and the profitability of fishing firms. In addition, 

NOAA Fisheries have developed and adopted measures of economic performance to monitor 

whether fishery regulatory programs are meeting their management goals. The key indicators of 

the economic performance measures include costs, earnings, and profitability (net revenue), as 

indicated in the NOAA Technical Memorandum where a snapshot of 2014 cost data collection 

programs across regions is provided (Thunberg et al., 2015). 

Like many fisheries in the nation, cost information of fishing operations in the Pacific Islands 

region was usually missing, although partial economic data such as fish prices and landings are 

commonly available. Before the continuous Trip Level Economic Data Collection Programs for 

the two longline fisheries were established, there was no means for the Pacific Islands Fisheries 

Science Center (PIFSC), the agency providing scientific support to the Western Pacific Regional 

Fishery Management Council (WPRFMC), to collect cost information about commercial 

fisheries on a continuous basis. Previously, the cost information of the fisheries in this region 

was collected through episodic surveys on an ad hoc basis. Episodic studies found limited 

success in tracking changes and were often outdated due to the varying nature of costs incurred 
2 

on a per trip basis and changes over time. For example, the first cost-earnings survey of the 

Hawaii longline fishery was conducted in 1994, based on the 1993 operations (Hamilton, Curtis, 

and Travis 1996). A similar cost-earnings study of the Hawaii-based longline fisheries did not 

occur until seven years later (O’Malley and Pooley 2003). 

Prior to 2000, continuous data collection programs were limited to the Atlantic highly migratory 

species (HMS) fisheries and the Northeast trip cost data collection in the observed fisheries in 

that region. Under a national initiative of the NOAA Fisheries with funding support in the 2000s, 

15 new continuous cost data collection programs were established during 2002-2012. By 2012, 

there were 18 continuous cost data collection programs established in U.S. fisheries (Thunberg et 

al. 2015). The two continuous cost data collection programs for the longline fisheries in the 

Pacific Islands described in this report were also implemented during this period. 

Options for Continuous Economic Data Collection Program Designs

According to a recent report summarizing national NOAA Fisheries cost data collection 

programs (Thunberg et al., 2015), most of the continuous economic data collection programs 

were built on existing data collection programs that were set up to collect non-economic data, 

such as observer programs or federal logbooks. Other options included mail-out surveys, 

telephone calls, in-person interviews, or websites. The cost data collection “add-on” to federal 

logbooks is mandatory, since federal logbooks are required. However, the cost data collection 

“add-on” to the federal observer programs is somewhat different. While the observers are 

required (mandated) to collect cost data from fishermen, response from the fishermen is

voluntary. 

The two continuous cost data collection programs for the longline fisheries in the PIFSC are 

“add-ons” to the NOAA Fisheries Pacific Islands Regional Office (PIRO) observer program, 

which began in Hawaii in 1994. There are benefits to using the “add-on” approach for cost data 

collections. First, it is cost effective to add-on the economic data collection to an existing and ongoing fisheries data collection program. Second, the cost data collected are concurrent and on a 

continuous basis, not after the fact. Third, the cost data can be integrated with other fisheries 

dependent data collected during the same fishing trips. It is easier from a legal standpoint to

establish a voluntary program compared to a mandated one. The federal logbook reports are 

usually mandated program requiring the forms and contents to be reviewed and approved by the 

fisheries management council. The first continuous cost data collection program in the nation, 

the Northeast trip cost data program, was implemented in 1995 by all Northeast fisheries that 

carried observers. Data are continuously collected by observers while at sea 

With the support and collaboration of the PIRO observer program, the first Continuous 

Economic Data Collection Program in the Hawaii longline fishery was implemented in August 

2004 as an add-on to the PIRO observer data collection program. The Continuous Economic 

Data Collection Program for the American Samoa longline fishery was established in 2006 

concurrently with the PIRO observer program for that fishery. 
3 

Data Form Design

Trip expenditures include the items that are commonly applied to longline fishing trips, as listed 

below. For the main cost items, such as fuel and bait, both the quantity of usage and unit price 

are collected. Other cost items include engine oil, ice, gear, provisions, and communications. For 

shallow-set swordfish fishing trips, the trip costs also included lightsticks and swordfish 

certificates. From 2004-2010, swordfish certificates represented annual effort cap of shallow-set 

fishing. As the fleet-wide effort cap, 2200 sets (the fishing line was set and hauled once a day, 

which is called a set) were equably distributed to all active longline vessels/permit holders

(including the permit holders who did not conduct any shallow-set swordfish trips). Each permit 

holder owned only 17 certificates, which were not usually sufficient for an average shallow-set 

swordfish trip, requiring the purchase of additional certificates from the other permit holders. 

Additional information about the crew and operator on a fishing trip was also requested on the 

survey form. In recent years, the number of foreign crew employed in the fishery increased 

considerably. Additional questions on number of foreign crew were added to the survey for 

understanding this trend and status. In summary, the complete list of cost items and other 

information collected is presented below. 

• Diesel fuel cost

• Engine oil cost

• Bait cost

• Ice cost

• Gear cost (the cost of replacing gear that was lost on previous trips [i.e., hooks, line, 

swivels, sleeves, etc.])

• Provisions cost

• Communications cost (i.e., satellite imagery, email, satellite phone calls, weather faxes, 

sea charts, etc.)

• Swordfish certificates cost (only if shallow-set fishing during 2004-2010) 

• Lightsticks cost (only if shallow-set fishing)

• Type of operator (owner or captain operator)

• Number of crew (excluding captain)

• Number of foreign crew

The survey forms for the two fisheries are presented in Appendices A (Hawaii) and B (American 

Samoa). The main contents of the two forms are similar, although the survey form for the 

American Samoa longline fishery was slightly modified based on the form for the Hawaii 

longline fishery. The survey form for the Hawaii longline fisheries was translated into Korean 

and Vietnamese since the vast majority of operators in the fishery comprise three distinct ethnic 

groups: Vietnamese-American, European-American, and Korean-American. Only the English 

version is presented in the Appendix. The survey was designed for the observers to complete. 

However, some responses were given by captains, especially during the trips where the captains

were Vietnamese-American or Korean-American and language presented a challenge to 

communications.

The initial design of the form for continuous economic data collection in the Hawaii longline 

fisheries included both fixed costs and variable costs. However, a majority of the responses only 

included variable costs, without fixed cost information. In follow-up communications with the 
4 

observers and fishermen, we realized that a large portion of the vessel operators did not know the 

fixed costs because they were not the owners. The operators that were also owners often did not 

remember the actual figures for all the fixed costs since they usually did not carry their 

accounting books on board. The survey form with fixed cost questions was much longer and 

more complex and was more difficult for the observers, who were not trained economists, to 

handle the data collection. Thus, little fixed cost data were collected from the observers in the 

testing period. Therefore, the fixed cost questionnaires were dropped after a two-month trial, and 

variable trip expenditures became the sole focus of the continuous economic data collection 

program. 

Program Protocol

There are challenges in using an “add-on” approach to collecting economic data through the 

observer program. First, the PIRO Observer Program, established in 1994, was mainly designed 

to collect biological (fish catch) data and record interactions of fishing activities with protected 

species. While catch and interactions with protected species are observable, most of the 

economic data are not, so collection requires an interview with the captain or asking the captains 

to fill out the form. Observers typically did not have experience collecting data through 

interviews; and fishermen were not obligated to participate and provide economic information, 

since it is a voluntary program. Some fishermen may hesitate to give out economic information 

as these data are often viewed as private. In addition, the Observer Program is managed under 

PIRO, while the economic data collection initiatives were associated with the PIFSC. Therefore, 

it requires active commitments, communication, and collaborations across agencies to ensure and 

maintain high response rates and quality data. 

To ensure the tasks were carried out, procedures and protocols were developed collaboratively 

between PIRO and PIFSC to define duties/responsibilities for each step and the responsible 

personnel associated with each step. The main activities and procedures are described as follows 

in Table 1.
5 

Table 1. Tasks and coordination associated with the continuous cost data collection 

program. 

Tasks Detailed activities Responsible personnel

1. Observer training Provide training on the economic 

data collection

PIFSC economists

2. Survey form print-out 

and distribution 

An economic form should be 

included in the package that 

observers take to sea 

PIRO debriefers, 

observer contractor, 

observers 

3. Data collection at sea Collect trip cost data Observer on board

4. Verification of data 

collection status

Check on the status of data 

collection and send an automated 

email notification if no cost data 

collected 

PIRO debriefers

5. Data entry Pick up hardcopy of survey 

responses on a monthly basis and 

enter data 

PIFSC economists

6. Data quality control 

and data analysis

Check data quality and perform data 

analysis and data summaries 

PIFSC economists

Observer Training

Observer training on the economic add-on form is provided by PIFSC economists. Usually there 

are one or two training sessions per year and are included as part of new observer training 

sessions. The training is also provided to the debriefers who perform the acceptance check of 

data collected when observers return to the office. 

Survey Form Print-out and Distribution

The observers for the PIRO observer program are employed through a contractor (currently 

Techsea International, Inc.) rather than hired directly by the federal government. The survey 

forms are distributed to observers through the contractor. Since economic data collection is an 

add-on program, the survey form is not printed out in a booklet with other data forms. In the 

early stages of the economic data collection program, observers often did not take the economic 

data collection forms on trips, creating a source of missing data. Therefore, providing the 

observers going to sea with the economic survey form is an important step. Because the 

operators in the fishery are largely comprised of three distinct ethnic groups, the observers carry 

forms in all three languages. 

Data Collection at Sea

Observers are advised to conduct surveys with vessel operators on the return home rather than on 

the way to fishing grounds. Vessel operators are usually less busy when returning to port after 

fishing activities end, so conducting the economic survey at that time may increase the likelihood 

of vessel operators divulging information. More importantly, key items of trip expenses (such as 

fuel and bait usage) become clearer toward the end of the trip. So far, observers have quite 
6 

successfully employed this method of collecting economic data. Depending on the ethnicity and 

personality of the vessel operator, most of the observers have also found success in collecting 

data by giving the form to the operator directly. Sometimes vessel operators allow the observers 

to sort through their receipts to record costs (if the vessel operator expresses a lack of interest or 

if they are busy). The response rates for the data collection programs will be discussed later 

where the summary data for each fishery are presented. 

Verification of Data Collection Status

Though it is voluntary for vessel operators, the economic data collection program wants to 

emphasize that it is the observer’s responsibility to attempt to collect the economic data. When 

observers return from a trip, PIRO debriefers verify that all required data (including the add-on 

economic data) were collected. The overall response rate of the economic data collection over 

the observed trips was approximately 60%. If economic data were not collected, the observers 

are asked to identify the reasons and fill out the last portion of the survey form regarding the 

status of the data collection. 

Beginning on August 1, 2016, an automatic electronic notification system was developed and 

implemented to report on economic data collection status. If an observed trip returns without any 

cost data, an email notification from the observer (when he/she returned to PIRO office to enter 

data) will be sent to the responsible personnel in both PIRO and PIFSC. This notification system 

provides timely updates on the data collection status and reasons for missing data allowing

follow-up actions toward improving the data collection and coordination to be taken accordingly.

The preliminary results generated from the first 10 months of data from the automated reporting 

system indicated three main reasons economic data were not collected including: (1) “captain did 

not know” (29%), (2) “captain refused” (34%), and (3) no responses from the observers (29%, 

including 19% “observers forgot to ask” and 10% “no reports from observers”). The results 

suggest that the response rate could be further improved if the second and third issues can be 

better addressed.

Data Entry

PIFSC economists are responsible for entering economic data. Once the observers return, 

photocopies of the economic data forms are made, and staff from the PIFSC program pick up the 

photocopies from the PIRO observer program office on a regular basis (the original copies are 

kept in the PIRO observer office). In the first a few years, the economic data collected were

entered into a simple Microsoft ACCESS database without an integrated data quality control 

process. Unfortunately, many errors were discovered when summary reports were generated. To 

improve data quality and build linkages of the cost data with other fisheries-dependent data (such 

as catch and fish price data), effort has been devoted to developing an interface ACCESS 

database, which will be discussed in the next section. 
7 

Database Management

Data Entry Design for Data Quality Control

In 2016, development of an interface ACCESS database began. The effort included redesigning 

the database structure, creating data entry form with data quality control, a built-in linkage 

between the cost data and other data resources through Open Database Connectivity (ODBC), 

pre-designed queries for data integration and data summary. Thus, the interface ACCESS 

database includes many new features. For example, the data entry design includes the following 

functions: 

1) Data quality control: The cost data entered are evaluated within minimum and maximum

ranges. The data also are evaluated with fisheries-dependent data to make sure that vessel 

names, trip start and return dates are recorded and consistent with other NOAA Fisheries 

information, and trip numbers are consistent with the data recorded in the logbook for the 

same trip. This system also prevents duplicate data entries.

2) A role-based multi-user database: The database is classified with different access roles: (A) 

data entry can be performed from multiple work stations; (B) only designated manager(s) 

with an administrative access role can modify main tables and run queries or can override 

min and max ranges in a control mode; (C) data entry personnel-only access to the data entry 

form. The new data are entered into a temporary/intermediate space and then transmitted into 

the main table that is password protected and saved on a server. 

3) Classification of data completeness: After data are entered, they are categorized (A, B, or C) 

based on the completeness of the data collected on each trip: (A) “completed” if all of the 

cost-related items are collected; (B) “bait and fuel” if at least these two key cost items are 

collected but missing part or all of the other cost items; (C) “other incomplete” for all others 

(including those missing bait and fuel information) with few or no cost-related data collected.

4) Imputation methods for the missing cost items: The database has a built-in function to 

estimate the costs for certain items for those trips with the key cost information (such as bait 

and fuel costs), labeled as “bait and fuel” trips in the previous step. Fuel and bait costs 

comprise approximately 70% of the total trip costs. However, about 38% of the trips with 

fuel cost and bait cost information missed at least one other item. The database has a built-in 

a function to add missing values using the average value of the completed trips. A raw data 

table, without any filled value is kept in a separate file from the tables that have been 

modified. Thus, users may choose different data tables to use based on their research needs. 

Data Integration with Other Fisheries-Dependent Data

Another new feature of the interface ACCESS database is the built-in linkages with other 

fisheries-dependent data. The built-in linkages make integration among the cost, fish catch, and 

fish sales data much easier and more effective. The three types of data are linked together in the 

same database allowing data summary products, such as costs, revenues, and net revenues, to be 

generated in a timely manner. The flow chart below shows the database structure and data flow 

from various data sources. Because the interface ACCESS was built with Oracle compliant sync 

function and pre-defined queries, the database and data summaries can be updated with the data 

sources within the PIFSC domain. 
8 

AS the flow chart (Figure 1) shows, the Interface ACCESS database is composed of several 

elements (four boxes): 1) Trip Cost Data Entry, 2) Trip Level Catch and Effort Data from 

logbook data stored in PIFSC domain, 3) Fish Sale Data generated from Hawaii Division of 

Aquatic Resources (DAR) data from PIFSC domain, 4) Relational/Interface ACCESS database 

with functions of data sync, queries, and reports, etc. 

The functions of the first box, Trip Cost Data Entry, were descripted in the previous section 

“Data Entry Design for Data Quality Control.” Users who have access to the first box (performs 

data entry) do not require access to the other three boxes as the database is set up as role-based 

multi-user. The main function of the second box is "data sync", acquiring trip level logbook data 

(such as fishing effort and catch data) stored in PIFSC domain. The raw data of the fishermen 

logbooks (for both Hawaii longline and American Samoa longline fisheries) are at set level, 

because the two longline fisheries usually have multiple sets in one trip. The set level data were 

aggregated into trip level data by a pre-designed “data sync” query. The trip level data are then 

linked and uploaded into the ACCESS database through “Open Database Connectivity” 

(ODBC), a function in ACCESS. Thus, the trip level fishery data in the interface ACCESS 

database are updated constantly as the trip level data are directly linked to the logbook dataset in 

PIFSC domain where data are updated constantly. Similar to the second box, the function of the 

third box is "data sync" function to pull fish sale data from the DAR dataset stored in PIFSC 

domain to the interface ACCESS database through ODBC. Finally, the fourth box merges data 

from the three sources and conducts data summary and analysis. The data summaries are 

produced by pre-design queries.
9 

Figure 1. The flow chart of Interface Access database design.
10

Economic Performance of the Hawaii Longline Fisheries, 2005-2016

Population and Response Rate

The economic data collection program for the Hawaii-based longline fishery covers both the 

deep-set (targeting bigeye tuna) and shallow-set (targeting swordfish) components of the fishery. 

The Hawaii-based longline fishery landed over 39.8 million pounds of pelagic fish, valued at 

$112.8 million in 2016, with 142 active longline vessels. The number of active vessels in the 

fishery had been stable from 2004 to 2012, ranging from 123 to 129, but increased gradually in 

recent years, up to 142 in 2016. The fishery is managed under a limited entry program with 164 

permits and is subject to bigeye tuna catch limits imposed by two Regional Fisheries 

Management Organizations: The Western and Central Pacific Fisheries Commission and the 

Inter-American Tropical Tuna Commission.

The statistical sample design of the economic data collection program is defined by the PIRO 

observer program. In the early period of the observer program (1994-1999), observer coverage 

was approximately 5% but has since increased. By 2004, when the economic data collection 

program was started, the observer coverage rate in the Hawaii fisheries was up to 25%.1 Since 

then, observers are required aboard 100% of Hawaii-based pelagic longline vessels targeting 

swordfish (shallow-set) and 20% of vessels targeting tuna (deep-set). American Samoa-based 

longline vessels targeting tuna (albacore), require 20% coverage. Observer samples are randomly 

chosen through a sample design created by a PIFSC scientist. Both the current high coverage rate 

and the continuity of the observer program allow the economic add-on survey to provide a timely 

and informative cross-sectional analysis of the variable cost figures for vessels of varying targets 

(shallow-set to target swordfish or deep-set to target bigeye tuna) and vessel sizes on a 

continuous basis. Table 2 lists the population, sample size, and response rate for the cost data 

collection program of the Hawaii longline fishery. 

The response rate of the cost data collection was high in general, 61% for the entire period from 

2005 to 2016. Only trips with completed data or with key cost items (fuel and bait costs) 

collected are counted for the number of trips with economic data in the response rate calculation. 

The coverage rate of trips with cost data among all longline trips was 15.4% for the entire period 

from 2005 to 2016. The details for each individual year are listed in Table 2. Table 3 and Table 4

show the population, sample size, and response rates for tuna (deep-set) trips and swordfish 

(shallow-set) trips, respectively. Overall, the response rate for shallow-set swordfish trips to 

observers is higher than the deep-set tuna trips.

 

1 http://www.fpir.noaa.gov/OBS/obs_hi_ll_ds_rprts.html
11

Table 2. Hawaii longline fishery population, sample size, and response rate to trip cost 

data collection, 2005-2016.

Table 3. Hawaii longline fishery population, sample size, and response rate to trip cost 

data collection of tuna (deep-set) trips, 2005-2016.

Year

Number of 

active 

vessels

Total Tuna 

trips

Total 

observed 

tuna trips

Tuna trips 

with cost 

data

Response rate 

to observed 

trips

% of trips with 

cost data to 

the total trips

2005 124 1377 364 208 57% 15%

2006 127 1300 282 184 65% 14%

2007 129 1382 270 181 67% 13%

2008 127 1314 288 174 60% 13%

2009 127 1221 250 141 56% 12%

2010 122 1216 250 134 54% 11%

2011 129 1260 253 148 58% 12%

2012 128 1309 263 148 56% 11%

2013 135 1334 273 148 54% 11%

2014 139 1301 257 161 63% 12%

2015 143 1377 273 148 54% 11%

2016 142 1411 283 141 50% 10%
12

Table 4. Hawaii longline fishery population, sample size, and response rate to trip cost 

data collection of swordfish (shallow-set) trips, 2005-2016. 

Year

Number of 

active 

vessels

Total 

Swordfish 

trips

Total observed 

swordfish trips

Swordfish 

trips with 

cost data

Response rate 

to observed 

trips

% of trips with 

cost data to 

the total trips

2005 31 98 98 57 58% 58%

2006 35 60 60 44 73% 73%

2007 27 79 79 61 77% 77%

2008 27 82 82 67 82% 82%

2009 28 103 103 68 66% 66%

2010 28 106 106 85 80% 80%

2011 20 83 83 65 78% 78%

2012 18 75 75 50 67% 67%

2013 15 51 51 35 69% 69%

2014 20 78 78 53 68% 68%

2015 22 58 58 55 95% 95%

2016 13 43 43 21 49% 49%

Metadata for the main data sources 

The main data presented in this report are sourced from the primary data collection efforts by 

PIFSC economists as discussed in the previous sections. Secondary data, such as landings and 

fish price information collected by other programs in PIFSC and PIRO, are also used in this 

report. The full metadata records for the data sources are available at InPort, the NOAA Fisheries

Enterprise Data Management Program (https://inport.nmfs.noaa.gov/inport). The links to the 

metadata records in InPort are listed in parentheses for each associated data source as follows: 

1) Trip Level Cost Data Collection Program of the Hawaii Longline Fishery 

(https://inport.nmfs.noaa.gov/inport/item/5662) 

2) The Hawaii Longline Observer program (https://inport.nmfs.noaa.gov/inport/item/16865) 

and the annual summary of observer trips 

(http://www.fpir.noaa.gov/OBS/obs_qrtrly_annual_rprts.html)

3) Hawaii Longline Logbook (https://inport.nmfs.noaa.gov/inport/item/2721)

4) Hawaii DAR Dealer Reporting System Data 

(https://inport.nmfs.noaa.gov/inport/item/5610)

Adjustments for inflation

The prices, costs, and net revenue trends in this report will be presented in both nominal and 

inflation-adjusted 2016 dollars. The general level of prices in the economy increase over time 

(i.e., inflation) and as a result, a dollar cannot buy as much today as it could in the past. To make 

valid comparisons of monetary estimates over time, a price index is used to remove the effects of 

changes in the general level of prices. A price index is used to convert “nominal” or unadjusted 

values into “real” or inflation-adjusted values so that valid comparisons of monetary estimates 

across time can be made. Many price indices can be used to convert nominal into real values, 

with the Consumer Price Index (CPI) being the most widely used and best known of these 
13

indexes. This report uses the Honolulu Consumer Price Index (HCPI) to convert nominal into 

real values for the Hawaii longline fishery. The use of the HCPI is consistent with other technical 

reports, such as plan team reports from PIFSC to the Council. 

Average Trip Costs Trend

The Hawaii longline fishery conducts two types of fishing; deep-set targets bigeye tuna, and 

shallow-set targets swordfish. Most of the vessels target only tuna although some vessels switch 

between these two types of fishing depending on the season. Vessel operators need to declare the 

type of their fishing trip before departing and report that to the NOAA Fisheries office. Since the 

reopening of the fishery in 2004, all shallow-set swordfish trips are required to carry a NOAA 

Fisheries observer on broad. The average cost for this type of trip is usually higher than a deepset tuna trip as it is frequently longer. The average length for shallow-set swordfish trips was 32 

days during the 2005-2016 period, while it was 22 days for deep-set tuna trips (based on the 

logbook information during the period). Therefore, this study assessed the economic 

performance for these two components of the fishery separately. Please note that the trip costs in 

this report do not include labor. Figure 2 and Figure 3 show the trend of average trip costs and 

one standard deviation for the deep-set tuna and shallow-set swordfish trips, respectively for 

2005-2016. Detailed data on trip costs for the two types of fishing are presented in Table 5 and 

Table 6.

Figure 2. The trend of average trip costs with standard deviation for Hawaii longline 

deep-set tuna trips, 2005-2016 (adjusted to 2016 dollars). 
14

Figure 3. The trend of average trip costs with standard deviation for Hawaii longline 

shallow-set swordfish trips, 2005-2016 (adjusted to 2016 dollars).

Figure 2 shows the trend of average trip costs for Hawaii longline deep-set tuna fishing trips 

between 2005-2016 (adjusted to 2016 dollars). Since 2005, trip costs for Hawaii longline deepset tuna fishing increased steadily to 2012, and the average trip cost increased 36%, from 

$22,207 per trip in 2005 to $31,842 per trip in 2012. The trip costs then slightly decreased in 

2013 and 2014, then dropped substantially in 2015 and 2016. The average deep-set tuna trip cost 

of $31,842 at the peak in 2012, dropped to $26,398 and then $24,242 in 2015 and 2016, 

respectively. Compared to the peak in 2012, the tuna trip costs in 2016 had decreased by 24%. 

Trip costs in 2008 were high due to a sharp rise in fuel prices which made up 62% of total trip 

costs that year. Overall, the rise and fall of trip costs have been due, primarily, to changes in fuel 

prices.

Shallow-set swordfish fishing trips are usually longer than deep-set tuna fishing trips, as 

mentioned previously, even when the trips are carried out by the same vessel, which makes them 

more expensive in general. For example, the costs of an average shallow-set swordfish trip were 

65% higher in 2016. Figure 3 shows the trend of Hawaii longline shallow-set swordfish trip

costs, 2005-2016 (adjusted to 2016 dollars). The average shallow-set swordfish trip costs were 

relatively steady during the 2005-2010 period, but were particularly high in 2008, and again in 

2011 and 2012. At its peak in 2011 and 2012, the average shallow-set swordfish trip cost totaled 

over $61,000, while the average deep-set tuna trip cost peaked at $31,824 in 2012. Fuel made up 

over 60% of trip costs in 2008 and 2011. 
15

Table 5. Average trip cost, revenue, and net revenue for Hawaii longline deep-set deepset tuna fishing trips (adjusted to 2016 dollars), 2005-2016. 

Table 6. Average trip cost, revenue, and net revenue for Hawaii longline shallow-set 

swordfish trips (adjusted to 2016 dollars), 2005-2016.

Trip Cost Structure

Fuel accounted for the largest share of total fishing trip costs (non-labor items) from 2005-2016, 

ranging from 44% to 62% for deep-set tuna fishing trips and 43% to 63% for shallow-set 

swordfish fishing trips. Figure 4 and Figure 5 show the cost structures of an average deep-set 

tuna trip and shallow-set swordfish trip, respectively, in 2016. At that time, fuel comprised 44% 

of deep-set tuna trip costs. Bait was the second largest item, making up 27% of the cost. 

Therefore, if information for at least bait and fuel was gathered, the trip was defined as 

successful in terms of economic data collected. Otherwise, it was defined as an “incomplete” trip 
16

(not counted as a response in the response rate calculation). The other costs of deep-set tuna trips 

included gear (12%) and provisions (11%). The remaining 6% of the trip costs were for engine 

oil, ice, and commutations, each accounting for 2% of the deep-set tuna trip costs. 

The trip cost structure for shallow-set swordfish fishing was similar to deep-set tuna fishing. On 

average for the period of 2005-2016, the fuel cost comprised 54% of total trip expenditures for 

both types of trips. In 2016, fuel made up 43% of shallow-set swordfish trip costs. Bait was the 

second largest item, making up 20% of those trip costs. The third largest item for shallow-set 

swordfish trip costs was lightsticks, comprising 15% of trip costs in 2016. Deep-set tuna fishing 

does not use these. Gear and provisions also were substantial portions of shallow-set swordfish 

trip costs, each comprising 9% of trip costs. The other two small cost items were engine oil and 

commutations, each accounting for 2% of the shallow-set swordfish trip. While ice made up 2% 

of deep-set tuna trip costs, it is zero or negligible for shallow-set swordfish trips since those 

vessels are equipped with icemakers. Even on deep-set tuna trips, ice costs have declined over 

time (from 6% in 2005 to 2% in 2016) because more vessels are equipped with icemakers in the 

Hawaii longline fleet.

Figure 4. The cost structure of an average deep-set tuna fishing trip in 2016.
17

Figure 5. The cost structure of an average shallow-set swordfish trip in 2016.

Trip Costs and Fuel Prices Trends

In the previous section, we indicated that the fluctuations in trip costs over time are correlated 

with fluctuations in fuel prices. Figure 6 shows the trend of the annual average fuel price paid by 

fishermen (recorded through the data collection program) during the period of 2005-2016 

(adjusted to 2016 dollars), along with the standard deviation of the average fuel price. The data 

for Figure 6 are presented in Error! Reference source not found.. The first peak in average trip 

costs appeared in 2008, when fuel prices spiked to an all-time high of $4.20 per gallon. Fuel 

prices went down substantially in 2015 and 2016, with the lowest fuel price during the reporting 

period occurring in 2016. As a result, the proportion of fuel cost to total trip costs in 2016 was 

the lowest recorded for both deep-set tuna trips and shallow-set swordfish trips. 

Figure 7 shows the quarterly average fuel price (nominal) and deep-set tuna trip cost (nominal) 

trends for 2005-2016. Both time series data moved up and down synchronously. Since fuel price 

changes are an exogenous variable that is beyond the control of fishermen, the uncertainty of fuel 

markets unavoidably brings uncertainty to the economic performance of the fisheries. This report 

does not present quarterly fuel prices and trip cost figures for shallow-set swordfish trips due to 

the limited and unevenly distributed (across seasons) observations. 
18

Figure 6. The annual average fuel price trend with standard deviation: 2005-2016

(adjusted to 2016 dollars).

Figure 7. The quarterly average fuel price and deep-set tuna trip cost trend for the 2005-

2016 period (nominal value).
19

Table 7. Average annual fuel price and fuel costs per trip and inflation adjustors of the 

Honolulu Consumer Price Index (HCPI) (adjusted to 2016 dollars), 2005-2016.

Trip Net Revenue Trends 

Figure 8 and Figure 9 present the trip net revenue for the Hawaii deep-set (tuna) fishery and the 

shallow-set (swordfish) fishery, respectively. In this analysis, we define trip net revenue as trip 

revenue less trip costs (not including labor cost). In some economics literature, the net revenue, 

revenue less variable costs, is called “quasi-rent” rather than profit.

2

 The fishing trip costs used 

in calculating trip net revenue do not include labor (payment to captain and crew) or fixed costs 

(e.g., insurance and major repairs). In the following analyses, we present the average trip net 

revenue by trip type: deep-set in Figure 8, and shallow-set in Figure 9. The detailed data for the 

two figures are also presented in Table 5 and Table 6.

In general, the returns on deep-set tuna fishing increased steadily for the 2005-2016 period 

(adjusted to 2016 dollars), although some years experienced a drop in net revenue compared to 

previous years (Table 5). The average trip net revenue of deep-set tuna fishing has increased 

83%, from $26,694 per trip in 2005 to $48,782 per trip in 2016. Revenue per trip in 2016 was the 

highest during the entire 2005-2016 period, and trip costs were low as a result of reduced fuel 

costs during the report period. 

The trend of returns on shallow-set swordfish fishing shows a different pattern than deep-set tuna 

fishing. The average shallow-set swordfish trip net revenue was usually greater than the average 

deep-set tuna trip net revenue. In some years, such as 2009, 2010, 2014, and 2015, the average 

net revenue for a shallow-set swordfish trip was approximately $35,000. In other years (2005, 

2008, 2013, and 2016), the average net revenue was over $55,000, more than 50% higher 

compared to years with low returns. Table 6 shows the detailed cost and revenue figures for 

 

2 http://www.economicsdiscussion.net/rent/quasi-rent/quick-notes-on-quasi-rent-with-diagram/17197
20

shallow-set swordfish trips. As with deep-set tuna fishing, 2016 was a particularly good year for 

shallow-set swordfish fishing with the lowest trip costs and the highest trip revenue ($73,000). 

Figure 8. Average Trip Net Revenue for Hawaii Longline Deep-set tuna trips (adjusted to 

2016 dollars), 2005-2016. 

Figure 9. Average Trip Net Revenue for Hawaii Longline Shallow-set swordfish trips 

(adjusted to 2016 dollars), 2005-2016. 
21

Trip Costs by Area

Linking fishing cost data with vessel logbook data allows us to examine costs by area. The 

fishing grounds of the Hawaii longline fisheries encompass broad regions, and span three areas 

with different management regimes: the Hawaiian Islands Exclusive Economic Zone (EEZ), the 

Western and Central Pacific Ocean (WCPO), and the Eastern Pacific Ocean (EPO). The EPO 

and WCPO are basically divided by the 150°W longitude in the Hawaii longline fishing ground. 

Figures 10 and 11 show the average trip costs by the three areas. The three areas and their 

boundaries are illustrated in Figure 10. A Hawaii longline fishing trip usually lasts over two 

weeks and might fish across all three areas on the same trip. A trip was assigned to a specific 

area in the report based on the following definitions and priorities: 1) a fishing trip with at least 

one set fished in the EPO is defined as an EPO trip; 2) a fishing trip with at least one set fished in 

the Hawaiian Islands (including the Northwestern Hawaii Islands and the Main Hawaii islands) 

EEZ is defined as an EEZ trip; and 3) a fishing trip without any sets in the Hawaii EEZ or the 

EPO is defined as a WCPO trip. Under these definitions and priorities, an EPO trip fished in the 

EPO might have also fished in both the WCPO and the EEZ during the trip; an EEZ trip fished in 

the EEZ might also have fished in the WCPO (but not in the EPO) during the trip; and a WCPO 

trip fished only in the WCPO area. 

Figure 10. The map of the three areas with different management regimes: WCPO, EPO, 

and Hawaii EEZ (map courtesy of Dr. Haiying Wang, JIMAR PIFSC). 

The average deep-set tuna trip costs for EPO fishing trips were the highest compared to trip costs 

in the EEZ or WCPO. The average deep-set tuna trip costs of EEZ trips were higher compared to 

trips to the WCPO. One may expect that the fishing costs within EEZ are lower than the other 
22

areas due to its proximity to fishing ports. However, since Hawaii EEZ covers the entire 

Hawaiian Archipelago that stretches 1,500 miles northwest of the island of Hawaii to Midway 

Island, the travel distance alone from Honolulu to further north of the Northwestern Hawaii 

Islands EEZ can be longer than the entire expanse of fishing ground in WCPO. Shallow-set 

swordfish trip costs across areas show similar patterns to deep-set tuna fishing. The magnitude of 

differences of shallow-set swordfish fishing costs among the three areas was less compared to 

the differences of deep-set tuna trip costs. Trip costs in the Hawaii EEZ and WCPO for shallowset swordfish fishing were actually quite similar. Most years, the average trip costs of shallow-set 

swordfish trips in the EEZ were higher than in the WCPO; however, in some years the average 

costs in the EEZ were lower than in the WCPO. The detailed data to support Figure 11 and 

Figure 12 are in Table 8 and Table 9, respectively. 

The Hawaiian volcanic island chain of 137 islands spans more than 1,500 miles from the island 

of Hawaii (the Big Island) in the southeast to Kure Atoll in the northwest. The detailed spatial 

variations of fishing costs in the EEZ can be examined by linking trip costs to the actual 

distances between fishing grounds and the departure ports. The maps, published in the brochure 

titled Hawaii Longline Trip Expenditure 2004-2012 (PIFSC 2014) illustrated the spatial 

variations of Hawaii Longline trip costs for both deep-set tuna trips and shallow-set swordfish 

trips, respectively (see Figure 13). The maps were developed based on the estimated cost 

functions using the trip cost data from our continuous economic data collection program and 

vessel logbook data for all the longline trips during the 2010 to 2012 period. Kalberg and Pan 

(2016) presented the detailed method for the estimations of the fishing trip costs with the 

associated distances from port to fishing grounds.

Figure 11. Average trip costs by areas of Hawaii deep-set tuna trips (adjusted to 2016 

dollars), 2005-2016.
23

Figure 12. Average trip costs by areas of Hawaii shallow-set swordfish trips (adjusted to 

2016 dollars), 2005-2016.

Table 8. Average deep-set tuna trip costs by three areas EEZ, EPO, and WCPO (adjusted 

to 2016 dollars), 2005-2016.
24

Table 9. Average shallow-set swordfish trip costs by three areas EEZ, EPO, and WCPO 

(adjusted to 2016 dollars), 2005-2016.*

*The figures of WCPO in 2005 and 2015 are not presented because the number of trips were less than 3. 

Trip costs 

of EPO

StDev of 

trip costs

No. of 

Trips

 Trip costs 

of EEZ 

 StDev of 

trip costs 

 No. of 

Trips 

Trip costs 

of WCPO

StDev of 

trip costs

No. of 

trips

2005 1.34 52,616 11,587 7 45,543 11,349 41 38,412 9 8,225

2006 1.27 61,942 14,830 8 43,104 12,212 36

2007 1.21 46,410 11,641 20 48,936 10,701 36 43,138 5 13,815

2008 1.16 67,735 17,210 24 52,689 11,409 38 59,688 5 15,437

2009 1.15 48,429 8,678 24 41,343 7,162 40 32,085 4 9,772

2010 1.13 52,659 11,180 37 42,479 10,335 41 45,271 7 16,799

2011 1.09 62,681 12,851 35 60,823 11,270 26 56,172 4 12,331

2012 1.06 65,298 11,604 21 59,166 10,870 26 50,663 3 6,319

2013 1.05 57,658 9,592 19 45,241 10,028 13 45,186 3 9,199

2014 1.03 57,524 17,402 33 47,117 14,524 17 43,361 3 10,838

2015 1.02 44,243 9,869 35 39,221 11,399 16 41,756 3 7,198

2016 1.00 40,733 13,169 13 38,427 5 7,365

 Year 

 HCPI 

adjustor 

EPO EEZ WCPO only

Figure 13. Spatial variations of Hawaii longline trip costs (2010-2012). 
25

Foreign Crew in the Fisheries

In addition to trip cost data, information on the total number of crew employed is also collected 

in the economic data collection program. Since employing foreign crew became more common 

in the Hawaii longline fisheries, the number of foreign crew vs. local crew was added into the 

questionnaire in 2006. However, the data collected for total numbers of crew before and after 

2011 were inconsistent due to the change in the survey forms. Some vessels included a captain as 

a “crew member” while others (especially vessels with owner operators) did not. Thus, the 

number of crew was not comparable from vessel to vessel. To address this issue, the survey form 

was revised in 2011 to specify that the number of crew “DOES NOT include” the captain so that 

the number of fishermen on board is the number of “crew” plus captain making these data 

comparable across vessels after 2011. Table 10 presents the trends in the number of crew 

(available since 2011) and foreign crew (available since 2006). It is required that vessel operators 

are U.S. citizens. The figures in Table 10 show that the number of crew per trip was stable over 

the years, averaging five (not including the captain) per vessel. The number of foreign crew 

increased over the past 11 years. In 2016, the foreign crew was 94% of the total, while in 2011, 

foreign crew was about 88%. Assuming the total number of crew did not change over the 2006 to 

2016 period, the percentage of foreign crew was about 81% in 2006. Due to these high ratios,

any policy changes related to the employment of foreign crew in U.S. fishing industry would 

greatly affect the fishery.

Table 10. Number of crew and foreign crew trends per vessel in Hawaii longline fisheries 

(2006-2016). 

Year

Total number of 

crew (not including 

captain)

Number of Foreign 

crew

% foreign 

crew

2006 4.1

2007 3.9

2008 4.1

2009 4.0

2010 4.1

2011 5.0 4.4 88%

2012 4.9 4.3 89%

2013 4.7 4.4 93%

2014 5.1 4.8 94%

2015 5.2 4.8 92%

2016 5.1 4.8 94%
26

Economic Performance of the American Samoa Longline Fishery 2006-2016

This section covers the long-term trend of trip and set level fishing costs, revenues, and net 

returns for the period 2006 to 2016 in the American Samoa longline fishery. The fishing cost 

data were collected through the Continuous Economic Data Collection Program similar to the 

program that collects Hawaii longline fisheries costs data. The trip revenues have been estimated 

using algorithms (the number of fish kept (not sold), fish size, fish price, and market 

decomposition) that are consistent with those from PIFSC and WPacFIN. The report shows a 

downward trend in economic performance of the American Samoa longline fishery for this

period. 

American Samoa longline fleet uses deep-set longline gear and mainly targets albacore. Unlike 

the shallow-set fishery, the deep-set does not have regulations regarding the time of day that the 

gear may be set. However, it is more common for fishermen of the American Samoa longline 

fleet to set their gear early in the day and haul in the afternoon, mainly to improve their catch 

rates. During the reported period (2006-2016), the fishery declined in both number of active 

vessels and revenue. In 2016, there were a total of 17 active longline vessels that landed 3.9 

million pounds of fish valued at $4.0 million. Albacore was 84% of the total revenue in 2016. 

The highest peak of revenue was $19 million ($13.9 million nominal) in 2006. 

Population and Response Rate 

The observer add-on economic data collection program was implemented in 2006, coincident 

with start of the PIRO observer program in the American Samoa longline fishery. Table 11. 

Number of trips of American Samoa longline fishery and simple size in economic data 

collection, 2006-2016. shows the total number of longline trips taken by the American Samoa 

longline fishery, total observed trips, the total number of trips with economic data collected, and 

the response rate of the cost data collection. The economic data collection program has been 

improved over time in terms of the coverage rate (total trips with cost data to the total fishing 

trips).

In the beginning, fishing cost data collection relied solely on the PIRO observer program. Due to 

limited observer coverage and unstable response rates of economic data collection, the average 

number of trips with economic data collected was less than 7 per year during the period of 2006 

to 2011. Though 2008 had 82% response rates from the observer program, the total number of 

trips with economic data collected was only 6% for that year. The average response rate on the 

observed trips was 43% in American Samoa fishery during the 2006-2011 period. However, in 

2011, there were 50 fishing trips with observers on board, but only 10 trips with economic data 

collected, resulting in a 20% response rate and only 7% of total longline trips covered. In order 

to increase the sample size, we conducted in-person surveys with fishermen in addition to 

collecting cost data through the observer program. Since 2012, PIFSC economists have traveled 

to American Samoa annually and conducted in-person interviews with fishermen, owners, or 

agents to collect cost data. The number of trips with economic data collected averaged less than 

7 per year from 2006 to 2011. That figure improved to 21 trips per year on average for 2012 to 

2016, as we began in-person surveys with the fishermen or the agents who manage the fishing 

business. Therefore, the coverage rate went from 5% on average for 2006 to 2011, to an average 

of 23% for 2012 to 2016.
27

Table 11. Number of trips of American Samoa longline fishery and simple size in 

economic data collection, 2006-2016. 

*In-person surveys conducted with fishermen by PIFSC economists, in addition to collecting cost data through the 

observer program.

** Due to limited number of observations, 2007 data are confidential and not presented. 

Data Sources and Metadata

The main data presented in this report are sourced from the primary data collection efforts as 

discussed in the previous sections. The secondary data collected by other programs in PIFSC 

were also used in this report. The full metadata records for the data sources collected by PIFSC 

are available at InPort, the NOAA Fisheries Enterprise Data Management Program

(https://inport.nmfs.noaa.gov/inport). The links to the metadata records in InPort are listed for 

each associated data source as follows: 

1) Trip-Level Cost Data Collection Program of the American Samoa Longline Fishery: 

https://inport.nmfs.noaa.gov/inport/item/10373

2) Revenue per trip was calculated using annual revenue and the number of sets collected by 

PIFSC’s WPacFIN Program and published at: 

http://www.pifsc.noaa.gov/wpacfin/as/Pages/as_data_5.php, where data are sourced from (A) the 

American Samoa Boat-based Creel Survey: https://inport.nmfs.noaa.gov/inport/item/5612 and 

(B) American Samoa Longline Logbook: https://inport.nmfs.noaa.gov/inport/item/1775 

Adjustments for inflation

The price, cost, and net revenue trends in this report will be presented in both nominal and 

inflation-adjusted 2016 dollars by American Samoa CPI (CPI sources: American Samoa 

Statistical Yearbooks, 2006-2016).
28

Average Trip Costs Trend

Figure 13 presents the average per-trip costs and the respective standard deviation for 2006 to 

2016. The figure shows two components, the fuel cost (blue) and other costs (light blue), for the 

average trip expenditure. Other costs include engine oil, bait, freezer operating costs (Freon), 

gear, provisions, communications, and miscellaneous items. Labor costs are not collected in the 

Continuous Economic Data Collection Program of PIFSC; therefore, labor costs are not included 

in the trip expenditures in Figure 13. Data from 2017 are excluded from the figure in accordance 

with the NOAA Fisheries rule on confidential data.

Figure 14. Average trip costs with standard deviation in American Samoa Longline 

Fishery Average Trip Costs, 2006-2016 (adjusted to 2016 dollars).

Figure 14 shows fluctuations in trip expenditures during the data collection period caused mainly 

by variations in fuel costs. Because there was only one observation in 2007, no data were 

presented for that year. Three years (2008, 2012, 2013) with average (adjusted) trip costs over 

$50,000 had high fuel prices, as shown in Figure 16. 

Trip Cost Structure 

Fuel costs usually comprised over 50% of trip costs but were lower in 2016 due to low fuel 

prices that year (the lowest of all the years studied). Figure 14 shows the cost structure for an 

average trip in 2016. 
29

The average diesel fuel price (and the respective standard deviation) presented in Figure 16 was 

collected by the Continuous Economic Data Collection Program and has been adjusted to 2016 

dollars. The fuel price from 2011 to 2014 was also high at over $3.50 per gallon, and then it 

dropped in 2015 and 2016. The fuel price in 2016 was $1.85 per gallon, the lowest price since 

data collection began in 2006. The data (fuel price and standard deviation) supporting Figure 15

are presented in Table 12. 

Figure 15. Cost Structure of an Average American Samoa Longline Trip in 2016.

Figure 16. Average diesel fuel price and standard deviation reported by fishermen in the 

American Samoa longline fishery, 2006 to 2016 (adjusted to 2016 dollars).
30

Net Revenue per Set Trend

The average trip length (total trip days) for the American Samoa longline fleet varied 

substantially over the years, although the fishing style and main target were still the same. This 

may reflect true variation in fishing operations, or it may be due to statistical uncertainty 

resulting from very small sample sizes in the economic data collection, especially in the early 

years when the continuous data collection program was being established and the sample size 

was limited. Therefore, the cost and net return per set, instead of the cost per trip, may be a better 

index for comparisons across the years. Figure 17 shows net revenue per set for this fishery from 

2006 to 2016. Data from 2007 are excluded from the figure in accordance with the NOAA 

Fisheries rule on confidential data. Net revenue is defined as revenue after subtracting trip costs, 

but trip costs do not include labor costs or fixed costs (such as dry dock and major repairs). 

Based on this definition, net revenue is the amount of earnings that can be distributed among the 

captain, crew, and vessel owners. However, if fixed costs per set are also considered, the net 

returns in certain years (2013, 2014, and 2016) would be negative (Pan 2017). 

Figure 17. Net Revenue per Set for the American Samoa Longline Fishery, 2006-2016 

(adjusted to 2016 dollars and not including labor cost and fixed costs).

The trend of net revenue per set (Figure 17) illustrates the poor economic performance of the 

American Samoa fishery in recent years since 2012. During the report period, net revenue per set 

fluctuated but reflects a sharp declining trend after 2009. Table 12 shows detailed figures for 

costs, revenues, and net revenues per trip and per set from 2006 to 2016. 

The poor economic performance measured by net revenue per set was a result of either higher 

cost per set, lower revenue per year, or a combination of both factors. For example, in 2013, the 

cost of $1,527 per set was second highest during the period, while the revenue per set was 
31

$2,051, the lowest in all the years up to that point. Due to both these factors, the net revenue per 

set in 2013 was the lowest for the entire report period. 

Table 12. Costs, revenues, and net revenues per trip and per set for the American Samoa 

longline fishery (adjusted to 2016 dollar).

While the cost per set fluctuated over the period and increased substantially in 2012 and 2013, 

the revenue per set was in a generally declining trend for the study period. Compared to 2013 

and 2014 when the net return was negative after accounting for fixed costs (fixed costs are not 

shown in Table 1), the economic performance of the American Samoa longline fleet in 2015 

improved slightly but turned negative again in 2016.

The cost per set across the years was indicative of changes in fuel prices. Figure 18 shows the 

cost per set (nominal) and fuel price in the American Samoa longline fishery, 2006-2016. The 

fuel price data for Figure 18 are included in Table 13. Over the period, these two time-series rise 

and fall together, signifying that they are highly correlated.
32

Figure 18. Cost per set and fuel price (both nominal) in the American Samoa longline 

fishery, 2006-2016.

Table 13. Average fuel price in the American Samoa longline fishery, 2006-2016, 

(adjusted to 2016 dollar). 

Discussion on the Net Revenue Calculation 

An earlier report (PIFSC Internal Report IR-15-015) provided to the Western Pacific Fishery 

Management Council covered the period 2006 to 2014, offering insight into the American or

Samoan longline fishery’s collapse in 2013. Although both the previous report and this one show 

a declining trend in the net returns during that period, there are some variations between the two. 

The differences resulted from several factors as discussed below.
33

Costs per set in the current report are lower than in the previous IR for most of the years 

The number of set counts per trip documented in these two reports came from two different data 

sources. Thus, there was potential for the cost per set in the same year to differ, although this was 

not the case. In the PIFSC Internal Report IR-15-015, the number of set counts per trip was 

generated using the PIRO observer reports, while in the current report, the number of set counts 

per trip was generated using the NOAA Fisheries vessel logbooks.3 We found discrepancy on 

trip definitions from these two data sources, and the average number of sets on the observed trips 

was lower than that on the logbook trips. The logbooks define a trip as ending when the fish 

caught are landed. Often, a trip in the American Samoa longline fishery was extensive requiring 

the vessel to make one or multiple port stops (return to dock to get supplies or for repairs) 

without unloading any of their catch. Then, the trip might continue without the observer 

remaining on board. That observed trip was recorded as ended before the fish were landed. The

fishing trip might have continued with a new observer trip number assigned as the disrupted trip 

continued. Thus, the number of set counts in a vessel logbook was usually greater than the 

number of set counts in the PIRO observer data. 

The definition of a fishing trip for the purpose of economic performance assessment is consistent 

with the logbook definition. As a result, the costs per set in the current report are generally lower 

than that reported in the previous report, IR-15-015. 

For example, the average number of sets per trip recorded in 2013 was 22 in the previous IR 

(based on the observer data), while the average number of sets per trip for the same year in the 

current report (based on the logbook data) was 35 sets per trip. Thus, the costs per set for 2013 in 

the current report ($1,527) are substantially lower than that in the previous IR, although, the 

average costs per trip in the two reports are similar. 

Revenue per set varies from year to year between these two reports

The revenue per set was not affected by set counts since the revenue per set was calculated the 

same way in both reports; the total revenue fleet-wide divided by the total number of sets fleetwide. However, there are different algorithms used within the PIFSC for estimating fleet 

revenue. Revenue figures for the same year reported from different sources can vary, or the 

revenue figures for the same year are subject to change, depending on the date of publication. 

In 2016, the economics program developed an interface ACCESS database system to link and 

summarize costs and revenues of the American Samoa longline fishery, as described in the 

section above on database management. The interface ACCESS database system follows one of 

the WPacFIN algorithms to estimate trip level revenue. We used the number of fish kept by 

fishermen (not sold) from the logbooks, prices for the fish sold to the cannery that were collected 

by the PIFSC economics program and PIRO observer programs, and the fish size and market 

decomposition from WPacFIN to generate fish weight and revenue. The revenue figures 

generated from the algorithm in some years are different from the figures in the previous IR, 

where revenue figures were sourced from the plan team reports published on the PIFSC website. 

 

3 https://inport.nmfs.noaa.gov/inport/item/1775
34

Annual revenue aggregated by set level vs trip level 

The interface ACCESS database system uses the same algorithm as the WPacFIN. The sum of 

revenue from multiple years (e.g., 2006 to 2016) in the interface ACCESS database is similar to 

the sum according to WPacFIN, though revenue values for individual years are not similar 

between the two sources. That is mainly because the WPacFIN generated annual revenues by 

aggregating revenue at the set level, while the interface ACCESS database system generated 

annual revenues at the trip level. 
35

Summary and Conclusions 

In summary, the continuous economic data collection programs of the longline fisheries in 

Hawaii and American Samoa are a success, with over 60% response rate on the observed trips in 

Hawaii longline fisheries and 43% in American Samoa fishery. The data collected provides 

insights into the two longline fisheries and supports fisheries management. 

From the data series of 2005 to 2016, we can observe economic performances of two important 

commercial fisheries of this region. The trend data show the downward economic performance 

of the American Samoa longline fishery. The trend data also show substantial changes in deepset tuna fishing and shallow-set swordfish fishing in the Hawaii longline fishery during the 

reporting period, a gradually increasing trend of the economic returns (net revenue) for deep-set 

tuna fishing, and fluctuations in the economic returns for shallow-set swordfish fishing. The 

difference of the annual average economic returns between highest and lowest for shallow-set 

swordfish fishing was also demonstrated. However, there was no particular downward or upward 

trend for shallow-set swordfish fishing during the report period. 

We were able to identify and monitor a key element associated with the uncertainty of the 

economic performance of the fisheries. Fuel is the main cost item of trip expenditures for both 

Hawaii longline and American Samoa longline, approximately 50%. Linking fishing cost data 

with vessel logbook data also allows us to examine fishing costs by areas. The data summary 

results suggested that the average trip costs for EPO fishing trips were the highest compared to 

trip costs in the EEZ or WCPO areas for both deep-set tuna fishing and shallow-set swordfish

fishing. 

Finally, the success of the continuous economic data collection programs requires active 

commitments across agencies. In addition, good data management is an important step to ensure 

data quality. The development of the interface ACCESS database not only supports data quality 

control but also allows cost data integration with other fisheries dependent data. More 

importantly, the interface database system provides an opportunity to conduct further research 

with integrated fisheries data that can advance ecosystems science.

The economic data collection programs are expected to persist. While continuing to improve the 

database and further develop advanced automated reporting tools and summaries for more timely 

analysis and website publication, we also plan to conduct a pilot project for a mobile tablet-based 

data collection form for in-person surveys conducted by the PIFSC economists in the American 

Samoa longline fishery.
36

Acknowledgements

The continuous economic data collection programs were established and are maintained through 

collaboration between the Economics Program of the Pacific Islands Fisheries Science Center 

(PIFSC) and the Pacific Islands Regional Office (PIRO) Observer Program. The success of the 

programs is a result of a great team effort over a long period of time. The author, the lead of the 

team, greatly appreciates the support from PIRO’s Observer Program and its staff, John Kelly, 

Kevin Busscher, Joseph Arceneaux, and Eric Forney in the Hawaii office, and Steve Kostelnik 

and Michael Marsik in the Pago Pago office, as well as all the debriefers with the PIRO observer 

programs. Special thanks to Eric Forney for his assistance in developing the auto-notification 

system on the economic data collection status of observer trips that was implemented on 

August 1, 2016.

I would also like to thank the support of my colleagues in the Socioeconomic Program, past and 

present, including Jonathon Sweeney, Kolter Kalberg, Timothy Ming, and Rick Chesler, for their 

support and assistance in data collection in the field and database management. PIFSC JIMAR 

researcher Dr. Haiying Wang and database developers Marci A. Sato and Troy Kanemura 

contributed greatly to the development of the Interface ACCESS database. Their efforts are 

highly appreciated. Special thanks to Michelle McGregor, my economist fellow at PIRO, for her 

thorough review and valuable comments on the manuscript. Also, thanks to the two external 

reviewers for their valuable comments and feedback. 

The participation of fishermen in the two continuous data collection programs is voluntary, and 

the programs would not succeed without the generous support of the vessel owners and 

operators. Their friendly and generous cooperation is the solid foundation for the success of the 

data collection programs.
37

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Appendices

Appendix A. Cost Data Collection Form: Hawaii Longline
39

Appendix B. Cost Data Collection Form: American Samoa Longline