Scholarly article on topic 'TEMPORARY REMOVAL: Measuring productivity in a shared stock fishery: A case study of the Hawaii longline fishery'

TEMPORARY REMOVAL: Measuring productivity in a shared stock fishery: A case study of the Hawaii longline fishery Academic research paper on "Economics and business"

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Abstract Fisheries productivity is the result of many factors, including endogenous and exogenous elements, such as regulation and stock condition. Understanding changes in productivity and the factors affecting that change is important to fishery management and a sustainable fishing industry. However, no study has been conducted to measure productivity change in the Hawaii longline fishery, the largest fresh bigeye tuna and swordfish producer in the United Stated. Using a Lowe productivity index, productivity change in the Hawaii longline fleet between 2000 and 2012 is measured in this study. In addition, a biomass quantity index is constructed to disentangle biomass impacts in a pelagic environment in order to arrive at an “unbiased” productivity metric. This is particularly important in the Hawaii longline fishery where catches rely mostly on transboundary (shared) stocks with little control on the total amount of extraction. As resource depletion of the transboundary stocks occurs, productivity loss may follow if less output is obtained from the same input usage, or more inputs are used to extract the same catch level from the fishery. Finally, the study compares productivity change under different fishing technologies.

Academic research paper on topic "TEMPORARY REMOVAL: Measuring productivity in a shared stock fishery: A case study of the Hawaii longline fishery"

MARINE POLICY

Marine Policy I (I

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f^Wi Marine Policy

ELSEVIER journal homepage: www.elsevier.com/locate/marpol

Measuring productivity in a shared stock fishery: A case study of the Hawaii longline fishery

Minling Pan a,n, John Walden b

a U.S. National Marine Fisheries Service, Pacific Islands Fisheries Science Center, United States b U.S. National Marine Fisheries Service, Northeast Fisheries Science Center, United States

ARTICLE INFO

ABSTRACT

Article history: Received 13 July 2015 Accepted 13 July 2015

Keywords: Productivity Lowe index

Biased and unbiased measurement Hawaii longline fishery

Fisheries productivity is the result of many factors, including endogenous and exogenous elements, such as regulation and stock condition. Understanding changes in productivity and the factors affecting that change is important to fishery management and a sustainable fishing industry. However, no study has been conducted to measure productivity change in the Hawaii longline fishery, the largest fresh bigeye tuna and swordfish producer in the U.S. Using a Lowe productivity index, productivity change in the Hawaii longline fleet between 2000 and 2012 is measured in this study. In addition, a biomass quantity index is constructed to disentangle biomass impacts in a pelagic environment in order to arrive at an "unbiased" productivity metric. This is particularly important in the Hawaii longline fishery where catches rely mostly on transboundary (shared) stocks with little control on the total amount of extraction. As resource depletion of the transboundary stocks occurs, productivity loss may follow if less output is obtained from the same input usage, or more inputs are used to extract the same catch level from the fishery. Finally, the study compares productivity change under different fishing technologies.

Published by Elsevier Ltd.

1. Introduction

Fisheries productivity is the result of many endogenous factors, such as fishing gear improvement, technical change for both electronics and engine power, and exogenous factors such as regulatory and stock conditions. Understanding the impact of both types of changes on productivity is important to fisheries management. While a domestic fishery may enjoy productivity gains from output control policies (such as catch shares) that lead to an ending of the "race to fish" and an increase of fish stock abundance [1], it may not be the case for a fishery that operates in an open ocean where fishermen face competition from foreign fisheries or different fishing gear that harvest the fish from the same stocks. This paper aims to measure the productivity change in the Hawaii longline fishery where catches are mostly from transboundary stocks, and examines the impacts of key elements, including stock conditions and fishing technology (e.g., species targeted), on productivity change. This information can contribute to fisheries management and a more sustainable fishery industry.

* Correspondence to: 1845 Wasp Blvd., Bldg. #176, Honolulu, Hawaii 96818, United States.

E-mail address: minling.pan@noaa.gov (M. Pan).

http://dx.doi.org/10.1016/j.marpol.2015.07.018 0308-597X/Published by Elsevier Ltd.

2. Fishery synopsis

The Hawaii longline fishery, the largest fishery managed under the Western Pacific Fisheries Management Council, operates in the North Pacific Ocean harvesting fish inside and outside the exclusive economic zone (EEZ) of the U.S. From 2002 to 2012, the period included in this study, there were 100-129 active vessels that completed between 1162 and 1380 fishing trips annually, generating revenues ranging from $37 to $92 million per year [2]. Currently, the Hawaii longline fishery is managed under a limited entry program with 164 permits (permits are transferable) and a total allowable catch (quota) for bigeye tuna. The bigeye tuna quota was imposed on the fishery by two Regional Fisheries Management Organizations (RFMOs), the Western and Central Pacific Fisheries Commission (WCPFC), and the Inter-American Tropical Tuna Commission (IATTC), due to the overfishing status of bigeye tuna in the North Pacific Ocean [3].

Vessels in the Hawaii longline fishery are set up to conduct two types of fishing by adjusting the number of hooks on a fishing line and setting the lines and hooks to different depths in the water column. Deep-set lines target bigeye tuna and shallow-set lines target swordfish. Switching fishing targets during a trip is technically feasible. In practice, fishermen cannot set gear to target swordfish if they do not report that intent to the National Marine Fisheries Service (NMFS) prior to starting the trip because any swordfish targeted trips require 100% observer coverage. Currently, the majority of the

2 M. Pan, J. Walden / Marine Policy ■ (I

fishing vessels of the Hawaii longline fishery target bigeye tuna. About 15% of vessels target swordfish during spring and summer seasons and target bigeye tuna during the rest of the year (with one or two vessels fishing for swordfish year-around), while the other 85% of the vessels target bigeye tuna year-round. In 2012, bigeye tuna alone accounted for 67% of total revenue at $62 million for the entire fleet, while swordfish accounted for 7% of total revenue at $6 million, and other pelagic species (including yellowfin tuna, albacore tuna, moonfish, mahimahi, and pomfret) contributed 26% to total revenue [2]. All the species caught by the Hawaii longline fleet are both highly migratory and shared stocks with other countries fishing in the North and Central Pacific Ocean. While the Hawaii longline fishery was responsible for the majority of total U.S. bigeye tuna and swordfish landings, the total fish caught by all U.S. fleets comprised only a small portion of the total catch for the two species from the North Pacific. In 2012, U.S. fishermen harvested 7534 metric tons (mt) of bigeye tuna and 1477 mt of swordfish, approximately 4% and 6% respectively of the total catch from the North and Central Pacific [4].

The Hawaii longline fishery is heavily regulated. Pan [5] provided a detailed description of the main management tools/regulations implemented in the fishery. A brief summary of the management tools imposed on the fishery during the period, 2001-2012, covered in this study is provided in Table 1.

In addition to regulatory conditions, productivity of the Hawaii longline fishery may be influenced by other exogenous conditions. An important exogenous factor is that the Hawaii longline fishery faces strong competition from foreign longline fleets and purse seiners that target the same species from a common pool resource [5]. These stocks have been subjected to overfishing for over a decade [3]. Although the bigeye tuna catch by the longline fleets in the West and Central Pacific Ocean (WCPO) declined in recent years due to imposed conservation measures, the total bigeye catch still went up because bigeye catches by purse seine fisheries increased in the same region and thus the overfishing has not been halted [6]. The Western and Central Pacific Fisheries Commission (WCPFC) called for further reduction in bigeye catch limits. The total available catch to the Hawaii longline fleet declined from the current level of 3763 mt to 3554 mt for both 2015 and 2016, and will further decline to 3345 mt for 2017 [3].

Another exogenous factor is regulations intended to protect endangered species. For example, a study by [7] shows that fishery closures during 2000-2004 and sea turtle caps instituted in the Hawaii swordfish longline fishery in 2004 in order to protect endangered sea turtle species led to spillover effects. The foregone production from the Hawaii longline fishery was replaced by foreign fleets that began fishing the same grounds where the Hawaii vessels used to fish before implementation of the regulations.

Third, a vast marine protected area, named the Papahanau-mokuakea Marine National Monument, was established by U.S. President George W. Bush on June 15, 2006, which shrunk the fishing grounds in the EEZ that used to be available to the Hawaii longline fishery [9]. The monument encompasses an area of approximately 139,793 square miles (362,061 km2) in the Northwestern Hawaiian Archipelago. This amounts to 23% of the total Hawaiian Archipelago EEZ where no fishing activities are permitted. Historically, 9% of the catch of bigeye and 17% of swordfish came from the EEZ of this Northwestern Hawaiian Archipelago area [8]. In addition, the False Killer Whale Take Reduction Team [10] established a "Southern Exclusion Zone" (SEZ) south of the Main Hawaiian Islands to prevent fishery interaction (bycatch) with False Killer Whales in 2012. Whenever the deep-set longline fishery reaches a specific level of observed false killer whale by-catch, the area will be closed for deep-set fishing.

Catch data show that Hawaii longline fishing increased its dependence on resources outside the U.S. EEZ. For example, in 2012 among the total bigeye tuna landed (kept) by the Hawaii longline fishery, 65% were caught outside of the U.S. EEZ (including the Hawaiian Islands EEZ and the Pacific Remote Islands Area EEZ), which is a 21% increase compared to 44% in 2002 [6].

Cost-earnings studies show the Hawaii longline fishery garners a small profit margin and that margin has tended to decline as operating costs have increased in recent years [5]. Because operating costs increased, small productivity gains in fishing industries can be important for maintaining or increasing profit levels for individual vessels. Therefore, it is important to understand productivity changes and factors that may affect the changes. The objective of this study is to measure productivity change in the Hawaii longline fishery over time and to distinguish the effect of technical efficiency (the rate of inputs converted into outputs) and the effects of resource abundance on that productivity.

Although there have been several economic studies on the productivity of the Hawaii longline fishery [11-13], all of the previous studies focused on examining the productivity performance in a static setting using a single year of data. Little attention has been given to measuring productivity changes in the fishery over time. Moreover, biomass was not considered as a variable in these previous studies. Productivity measures without considering the impact of biomass can be biased [14], as biomass changes may influence fishery industry productivity, often resulting in higher or lower outputs for the same amount of inputs. This paper provides the first comprehensive estimate of productivity changes in the Hawaii longline fishery where catches are mostly from trans-boundary stocks.

Table 1

Main management tools employed during 2001-2012.

Management tools employed

Related fishery

April 2001 Partial closure of certain waters in November 1999, and a complete shutdown of the swordfish fishery in April 2001 due to the Swordfish

concerns of turtle interactions with the fishery April 2004 Reopen of swordfish fishing with a series of regulations (100% observed, cap of fishing effort, caps of sea turtles, bait and gear Swordfish modifications...)

2004 Bigeye total allowable catch (TAC) in EPO imposed by the 1ATTC beging in 2004 and it only applied to vessels that were longer Bigeye tuna

than 24 m. The annual TAC was 150 mt from 2004 to 2006, and 500 mt after 2007 to present. Jun 20006 A vast marine protected area, named Pahanaumokuakea Marine National Monument, was established in Northwestern Ha- Swordfish and bigeye waiian 1slands.

2009-2010 Bigeye TAC imposed in WCPO in 2009 and the annual TAC was 3763 mt. The bigeye fishery was closed for two days before New Bigeye tuna

Year in 2009 and closed on November 22 when the catch limit was reached. 2011-2012 The Hawaii fishery had no effective bigeye TAC in 2011 or 2012 because the Hawaii fishery was able to attribute a part of the Bigeye tuna

bigeye catch to American Samoa or another U.S. Pacific territory November 2012 NMFS revised the sea turtle catch caps (limits) for leatherback turtles from 16 to 26, and for loggerhead turtles from 17 to 34 Swordfish December 2012 Established a "Southern Exclusion Zone" (SEZ) south of the Main Hawaiian Islands to prevent fishery interaction (bycatch) with Bigeye tuna False Killer Whales

M. Pan, J. Walden / Marine Policy ■ (■■■■) Ill-Ill 3

3. Methodology

In fisheries, productivity is defined as the relationship between the quantity of fish produced and the amount of inputs used to harvest the fish [1]. Thus, productivity change can be measured as the ratio of the change in output (fish) produced to the change in inputs used to achieve the outputs. Productivity change is the quantity component of profitability change, meaning it measures the change in the ratio of outputs produced by the firm, to the change in inputs used to produce the outputs, compared to a base year or previous period. The Lowe index was selected for this study because it was identified as an index which had important properties from index number theory [15], and could be easily constructed using standard spreadsheet software. The Lowe index measures multi-factor productivity (MFP), meaning it is constructed using multiple outputs and multiple inputs. O'Donnell [15] recently demonstrated that the Lowe index satisfies all economically-relevant axioms from index theory. Recently, NOAA fisheries used the Lowe index to measure productivity change across all U.S. catch share fisheries, in order to evaluate whether catch share management leads to an improvement of productivity, as expected. Since this study uses the same analytical approach, a detailed treatment of the Lowe index is not repeated herein but can be found in [1].

There are several advantages in constructing the Lowe index to measure productivity change in fisheries. First, the Lowe index is a 'basket type' index, and can include multiple outputs and inputs, which is usually the situation for fishing vessels that typically land a mixture of species using several different inputs. Secondly, the output and input indices are constructed using fixed prices for each input or output in the basket. Prices do not have to come from the period under study, which is an advantage when price data are not routinely collected. In fisheries, regular collection of price data particularly for input prices is often an issue. Finally, the index is easy to construct using readily available spreadsheet programs, and the measure of productivity change can be made at the vessel, fleet, or fishery level. This is in contrast to the Malm-quist index (MI) introduced by [16], which has been used to measure productivity change in fisheries [17], and which only needs data on quantities. However, in order to construct the MI, one needs to run four different linear programming models needing a large number of observations, and often balanced panel data. For this fishery, the fleet size varies over time with vessels entering and exiting the fishery each year [18]. Additionally, a large number of vessels exited after a regulation that banned swordfish harvesting in the summer of 2000, only to return in late 2004 after the fishery reopened. Therefore, to form a balanced panel data across long periods of time in the fishery would exclude a significant amount of observations. The Lowe index as constructed here is an aggregated index calculated at the fishery level (aggregation occurs over all vessels) that avoids computational problems associated with changes in fleet size over time [1].

Finally, biomass data is used to calculate a separate biomass index which is used to adjust the productivity indices to yield a measure of 'unbiased' productivity. In the case of a fishery, the traditional productivity metric of output/input will be biased unless the influence of biomass is separated from the index [19]. To adjust the "biased" productivity measure, [1] developed a "Lowe" biomass index, assuming that greater biomass will produce higher outputs for a given level of inputs than a smaller biomass. This study uses adult biomass to construct the biomass index since catchability may have a closer relationship with adult biomass compared to biomass in general. For multiple species fisheries, the Lowe biomass index is constructed by using the biomass and the share value of landings of each species where the value of all shares sums to 1.

1n this study, the Lowe index is constructed in the following steps: (1) Define inputs and outputs relevant to the fishery; (2) Estimate input and output quantities and choose a standardized price (fixed price) for each input and output; (3) Calculate aggregated input and output at the vessel-level and fleet-wide on annual basis; (4) Select a base year against which productivity change is measured; (5) Calculate the Lowe input index, (e.g. the ratio of annual aggregated input in a year to the annual aggregated input of the base year) and the Lowe output index (e.g. the ratio of fleet-wide annual aggregated output in a year to the fleet-wide annual aggregated output of the base year); (6) Calculate the Lowe productivity change index, that is, the ratio of the Lowe output index to the Lowe input index; and (7) Adjust by the biomass index to yield a value of biomass adjusted productivity.

4. Data

The definition of inputs and outputs relevant to the fishing industry is consistent with the general production function

Y = F (L, K; B)

where Y denotes total output, L is the labor input, K is the capital input, and B stands for the biomass condition. For any given combination of labor and capital, output is conditional on biomass denoted as B. Thus to construct the Lowe index for this study data were needed for both quantities and prices for outputs and inputs and for biomass.

4.1. Output Y

The value of catches is defined as the output Y in this study. Catches in the Hawaii longline fishery typically feature multiple species that receive different market prices. The catches are classified into three species groups, including bigeye tuna, swordfish, and other species. The targeted species are separated from the rest of the species because bigeye tuna and swordfish prices are usually higher than the other species and they are the main catches of the fishery. The "standardized" prices for the three species were generated for the period of 2005-2012 from the sample of individual vessels included in the study. The price for bigeye, swordfish, and the others are respectively $322.23, $373.21, and $68.78 per fish. These prices are held constant for all years. The total number of fish was the number of fish recorded in the mandatory fishermen's logbooks submitted to NMFS. Multiplying the fixed output prices by aggregate quantities yields the aggregated value of output for 2002-2012 presented in the third column of Table 2. The last (fifth) column of Table 2 presents the total number of vessels in the aggregations. While the study intended to include all active vessels, only a few vessels without complete input or output data were excluded from the study.

4.2. Capital (K)

Capital (K) as an input needs both a measure of the quantity of capital, and a price for the capital input, which is measured in this study as "user cost". A fishing vessel is the single capital component used here. Generally, the capital value is the average of the beginning and ending book value for the asset (vessel). However, those types of detailed data were unavailable. Using a median price of a sample of advertisements from ten steel hulled trawl vessels, the capital value for each vessel was set at $1571 per foot of vessel length times the vessel length.1 The annual user cost of

1 Prices paid for steel hulled fishing vessels obtained from http://www.ocean

M. Pan, J. Walden / Marine Policy ■ (l

Table 2

Output and input in the Hawaii longline fishery for all trips.

Year Outputa Labora Capitala Total inputa No. of vessels

2002 55,302,518 15,383,517 1,403,270 16,786,786 97

2003 49,025,268 15,756,020 1,567,432 17,323,453 107

2004 63,556,533 17,991,699 1,801,623 19,793,321 123

2005 63,875,621 19,892,798 1,807,597 21,700,395 123

2006 58,269,13 20,502,387 1,873,628 22,376,014 127

2007 72,754,358 23,124,369 1,912,712 25,037,081 129

2008 73,797,815 23,437,290 1,920,916 25,358,206 128

2009 61,003,297 24,423,893 1,899,901 26,323,794 126

2010 67,710,360 24,077,553 1,866,560 25,944,113 123

2011 75,273,686 25,135,627 1,922,241 27,057,869 127

2012 77,943,490 27,015,628 1,937,955 28,953,583 129

a Data reported in 2010 constant dollars using the GDP implicit price deflator.

capital (price) is comprised of three components. The first reflects a price that must be paid to the owner of an asset to prevent it from being sold (opportunity cost); the second component reflects depreciation and, the third component is re-investment in the asset [1]. Since information about re-investment is not available in the Hawaii longline fishery, only depreciation was included. The opportunity cost of capital is measured by the rate on BAA rated bonds. Depreciation was set at 6% of vessel value based on rates established by the Bureau of Economic Analysis (BEA). The aggregated user cost of capital for 2002-2012 is presented in the third column of Table 2.

4.3. Labor (L)

Time series data on labor costs for individual vessels was not available. Labor (L) cost was estimated by the crew size reported on mandatory logbooks multiplied by number of days at sea, and by a daily wage per crew ($/day), following the approach by [1]. The price for labor was estimated by the average hourly earnings of production and nonsupervisory employees, which was $18.21 for 2005 (the base year of the study), 2 The hourly wage was multiplied by 8 h to convert to a daily rate, as the daily opportunity cost of crew labor. The aggregated labor cost for 2002-2012 is presented in the second column of Table 2.

4.4. Biomass index (B)

Data for annual estimates of adult biomass in mt were obtained from stock assessments documents [20] and [21]. The biomass from these assessment reports for stock regions 2 and 4 were used in this study since these stock areas correspond to where the Hawaii longline fleet operates. "Prices" for the Lowe biomass index were calculated as the value shares of each species in 2005 (Bigeye equals 0.63, swordfish equals.09, "other" equals 0.28). Multiplying prices by quantities from the stock assessment yields an aggregate value for biomass in each year. The biomass index is constructed as the inverse of a usual Lowe quantity index as the biomass value at base year is in the numerator rather than the denominator [1]. Therefore, an increase in biomass between the baseline period (base year) and any other year is represented by a biomass index value below 1.00, while a biomass index value above 1.00 indicates a decrease in biomass between the base year and any other year.

(footnote continued) marine.com accessed 28.05.13.

2 Wages rates obtained from http://alfred.stlouisfed.org/series7seid = CEU0500000008&cid —32306.

5. Results

5.1. Results for all swordfish and tuna trips

The Lowe input index for a year was the aggregate inputs for the year divided by the aggregate inputs for the base year. The base year was set as 2005 in this study because it was the first year when swordfish fishing operated for the entire year after it was closed in 2000 and reopened at the end of 2004. An input index value above 1.00 in a year means that inputs in that particular year were higher than that in 2005. Similarly, an output index value above 1.00 in a year means that output for that particular year was higher than that in 2005. The Lowe productivity index is the ratio of the output quantity index to the input quantity index. If an index value is above 1.00 it means that productivity growth is positive and the fishery is getting more output from a given level of inputs, compared to 2005. However, if the index value is below 1.00, the opposite is true.

The unadjusted Lowe productivity index shows a declining trend since 2005 (Table 3). Between 2006 and 2012 all index values were less than 1.00. Before 2005, the mean, unadjusted, productivity index for the Hawaii longline fishery was 1.06, and after 2005 it was 0.91. The unadjusted productivity indices were then adjusted by the biomass index (Table 3). As seen, the biomass indices from 2007 to 2012 have a value more than 1.00, signaling a decrease in fish resources for the Hawaii longline fishery during the period from 2007 to 2012. After adjusting for the biomass impact, the biomass adjusted Lowe productivity index shows a different trend (Fig. 1). Between 2002 and 2009, the unadjusted and biomass adjusted indices tracked each other quite closely, but after 2009 they diverged. The adjusted productivity leapt from its lowest point of 0.83 in 2009 and moved upward to 1.11 in 2011 and 1.16 in 2012, implying that the productivity index shows 11% and 16% increases in 2011 and 2012, respectively. This suggests an improvement of productivity of the fishery in the face of a declining trend in biomass. Without making this adjustment to the original productivity measure, it would appear that the fishery had much poorer performance than the biomass adjusted measure suggests.

5.2. Results for bigeye tuna trips only

As previously noted, there are two types of fishing in the Hawaii longline fishery. Although the fishing gear used for both types of fishing is the same, the resources and regulations facing the two types of fishing are different during their operations. In addition, the exogenous elements such as biomass condition between these two main species are different. The bigeye biomass value shows a decline while the swordfish stock was relatively stable (Fig. 2). To

Table 3

Output and input in the Hawaii longline fishery for all trips.

Year Output Input Biomass un- Biomass Biomass ad-

index index adjusted Lowe index justed Lowe

index index

2002 0.87 0.77 1.12 0.99 1.10

2003 0.77 0.80 0.96 1.08 1.04

2004 1.00 0.91 1.09 0.99 1.08

2005 1.00 1.00 1.00 1.00 1.00

2006 0.91 1.03 0.88 0.97 0.85

2007 1.14 1.15 0.99 1.00 0.99

2008 1.16 1.17 0.99 1.01 1.00

2009 0.96 1.21 0.79 1.05 0.83

2010 1.06 1.20 0.89 1.09 0.97

2011 1.18 1.25 0.95 1.18 1.11

2012 1.22 1.33 0.91 1.27 1.16

M. Pan, J. Waiden | Marine Policy l (l

Table 5

Output, input, and multi-factor productivity - tuna target only.

Fig. 1. Adjusted and unadjusted biased and unbiased Lowe index for all trips (base year=2005).

Fig. 2. The biomass trends of bigeye tuna and swordfish in the Hawaii longline fishing grounds, 2002 and 2012.

Table 4

Output and input in the Hawaii longline fishery tuna trips.

Year Output

Labor*

Capital

Total Input No. of vessels

2002 55,081,416 15,368,220 1,403,270 16,771,490 97

2003 49,025,268 15,756,02 1,567,432 17,323,453 107

2004 63,224,108 17,819,942 1,801,623 19,621,565 123

2005 57,637,078 18,162,120 1,807,597 19,969,717 123

2006 53,350,084 19,061,507 1,873,628 20,935,135 127

2007 65,583,809 21,046,827 1,912,712 22,959,539 129

2008 66,538,099 21,289,675 1,903,541 23,193,216 127

2009 53,741,845 21,741,924 1,899,901 23,641,826 126

2010 60,989,982 21,196,731 1,831,262 23,027,993 121

2011 68,585,141 22,571,514 1,922,241 24,493,755 127

2012 72,699,945 24,585,977 1,922,641 26,508,618 128

further understand the productivity of the two fishing methods, the bigeye tuna targeted fishing trips were examined separately for their productivity changes. Swordfish fishing were not examined independently because the fishery experienced a period of being closed, and the data series is not complete during the full period of 2002-2012 of this study.

Table 4 presents the aggregated output and aggregated input data for the tuna fishing trips only, while the Lowe indices derived from these data are shown in Table 5. When swordfish sectors are excluded from the analysis, the average Lowe index for tuna trips during the entire period was 0.98, which was slightly higher than the average Lowe index with all trips, 0.96. 1n terms of the trend,

Year Output Input Biomass un- Bigeye bio- Biomass ad-

index index adjusted Lowe mass index justed Lowe

index index

2002 0.96 0.84 1.14 0.98 1.12

2003 0.85 0.87 0.98 1.12 1.10

2004 1.10 0.98 1.12 0.99 1.11

2005 1.00 1.00 1.00 1.00 1.00

2006 0.93 1.05 0.88 0.95 0.84

2007 1.14 1.15 0.99 1.00 0.99

2008 1.15 1.16 0.99 1.00 0.99

2009 0.93 1.18 0.79 1.07 0.84

2010 1.06 1.15 0.92 1.14 1.05

2011 1.19 1.23 0.97 1.27 1.23

2012 1.26 1.33 0.95 1.43 1.36

Table 6

Comparison of Lowe index before and after the 2005 base year for all trips, and tuna trips.

Biomass index Unadjusted Lowe index Adjusted Lowe index

All trips 2002- 1.02 1.06 1.07

2005 1.00 1.00 1.00

2006- 1.08 0.91 0.99

Tuna trips 2002- 1.03 1.08 1.11

2005 1.00 1.00 1.00

2006- 1.12 0.93 1.04

the unadjusted Lowe index before the base year of 2005 is above or near 1.00, but they were all less than 1.00 after 2005. This implies that the productivity of the tuna fishery declined after 2005.

Table 6 shows the Lowe index before and after the base year for all trips, and for tuna only trips. For tuna trips only, the average value of the Lowe index before and after 2005 was 1.08 and 0.93, respectively, while the figure for all trips was 1.06 and 0.91, respectively. Based on the Lowe index, depletion of the bigeye tuna resource shows a great impact on the productivity change. As Table 4 shows, the biomass of bigeye tuna increased from its low in 2003 to its peak in 2006 and then exhibited a declining trend since 2007. The biomass level in 2006 and 2007 dropped to a level similar to that of 2005. From 2009 to 2012, the bigeye tuna biomass index continuously dropped, the Lowe biomass index was 1.07, 1.14, 1.27, and 1.43, respectively for the four years. The 2012 index of 1.43 represents a 43% decline in adult biomass, compared to the 2005 value. When the productivity measure for the tuna-only trips is adjusted for the biomass effect, it moves in a different direction compared to unadjusted productivity. Fig. 3 illustrates the trends of the adjusted and unadjusted productivity measures for tuna fishing.

The comparison of the adjusted and unadjusted productivity measures shows the significant impact of biomass in the productivity of tuna fishing in the Hawaii longline fishery. For example, the bigeye tuna biomass in 2003 was relatively low, with Lowe biomass index 1.12, while it was near 1.00 for 2002 and 2004. The unadjusted index of 2003 was much lower than its neighborhood years and they were 1.14, 0.98,1.12 for 2002, 2003, and 2004 respectively. However, after adjusting for the biomass index, the productivity measure for 2003 was up to 1.10. Apparently, the productivity of tuna fishing from 2002 to 2004 was static and high. During the years 2005-2009, both unadjusted and

M. Pan, J. Waiden | Marine Policy l (I

Reopen swordfish fishing 1

-♦-Unbiased -X-Biased

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Fig. 3. Biased and unbiased Lowe index for tuna trips 2002-2012 (base year—2005).

adjusted productivity measures went down and in 2009 they both reached time series lows of 0.79 and 0.84, respectively. Productivity was improved after 2009. The adjusted productivity shows an increase from its lowest point of 0.84, in 2009, rising continuously to 1.05, 1.23, and 1.36 in 2010, 2011 and 2012 respectively, suggesting a rapid increase of biomass-adjusted productivity of the tuna fishery in recent years. Without such an improvement in productivity, the tuna fishery would have had much poorer performance due to the depletion of the fish resources. The average adjusted (unbiased) productivity index after 2005 was 1.11, which just offset the negative impact of a biomass decline of 12% (average biomass index for the period 2006-2012). Thus, the unadjusted productivity was able to remain stable at around 1.00.

recent years went up sharply, increasing from 1.07 in 2009 to 1.43 in 2012, which implies significant declining biomass of bigeye tuna. In same period, the embodied fishing productivity improved, as the unbiased productivity index (the adjusted Lowe index) went up steadily from 0.84 in 2009 to 1.36 in 2012. Biomass as an exogenous factor seems to play a significant role in the productivity changes in the Hawaii longline tuna fishery, given the transboundary nature of the resource and the displacement of effort into non-EEZ waters of the fishery. The resource depletion of the transboundary stocks and the adoption of turtle caps resulted in decreased productivity because fishermen had lower output with the same input or had to go further to fish resulting in higher input use.

During the study time period, especially after 2010, tuna fishing became more efficient. The endogenous productivity improvement has kept the fishery stable in terms of output to input ratio. Without such an improvement in productivity, the tuna fishery would have had much poorer performance due to the depletion of the fish resources. In addition, tuna fishing seems to be more efficient compared to swordfish fishing. The productivity measure when swordfish fishing activities were included was lower compared to estimated productivity for bigeye tuna-only fishing activities. Regulations may have impacted the productivity of the fishery. When the swordfish fishery was reopened after it was closed from 2002 to 2004, the fishery may have become less productive due to the policy restrictions which forced vessels to leave the fishery, and then possibly re-entered on a new learning curve. The "learning by doing" component of productivity change after a fishery re-opens is a topic for further research.

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6. Discussion and conclusions

As illustrated in Fig. 3, productivity in the tuna fishery for the period of 2005-2009 was relatively lower compared to the other years. The reason is unclear but it may be a result of fisheries management policy changes. The most obvious change in April 2004 was the reopening of the swordfish fishery after being closed for four years. While the swordfish fishing was closed during 2000-2004, the majority of the swordfish vessels moved to California and these vessels then returned after the fishery was reopened [22]. The number of fishing vessels in 2003 was 107 and about 16 vessels regularly moved back to Hawaii by the end of 2004 (Although the active vessels for 2003 and 2004 are the same, as showed in Table 4, but these returning vessels only operated limited time in Hawaii during 2004). The fishermen who used to target swordfish had to switch to tuna fishing if they remained in Hawaii. As the swordfish fishery was restricted by a series of regulations, including effort limits, which were about half of the historical level, and sea turtle interaction caps [5], swordfish fishing may have become less efficient due to both the turtle interaction caps and fishing effort caps. In addition, the returning swordfish fishermen also switched their target to tuna fishing seasonally or even completely changed to tuna fishing year-round. It was possible that a learning curve applied for the fishermen who were new to tuna fishing. On the other hand, the opposite direction of bigeye tuna price and swordfish price while bigeye price increased and swordfish price declined [5] may have provided incentive for the fishermen to focus on bigeye tuna fishing.

Overall, unadjusted productivity in the Hawaii longline fishery showed a declining trend since 2005. However, once biomass change was used to adjust the index values, the negative productivity change turned positive. The bigeye tuna biomass index in

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