Market interactions between aquaculture and common-property fisheries: Recent evidence from the Bristol Bay sockeye salmon fishery in Alaska

ARTICLE IN PRESS Journal of Environmental Economics and Management 59 (2010) 115–128

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Market interactions between aquaculture and common-property fisheries: Recent evidence from the Bristol Bay sockeye salmon fishery in Alaska Diego Valderrama a, James L. Anderson b, a

Fisheries and Aquaculture Department, Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, 00153 Rome, Italy Department of Environmental and Natural Resource Economics, University of Rhode Island, 205 Kingston Coastal Institute, 1 Greenhouse Rd, Kingston, RI 02881, USA

b

a r t i c l e in fo

abstract

Article history: Received 26 August 2008 Available online 16 December 2009

The remarkable growth of the global salmon aquaculture industry has generated important implications for Alaskan salmon fisheries as increased supplies of farmed product have led to declines in prices of both farmed and wild species. In the particular case of Bristol Bay sockeye salmon, falling prices and declining profit margins have led to reduced participation in the limited-entry fishery. This study conducts a formal examination of market interactions between the aquaculture and commercial fishery sectors by adapting the Homans and Wilen (1997) model of regulated open access to the context of restricted access fisheries. The econometric model reveals that limited entry regulations were initially successful in extracting rents from the Bristol Bay fishery; however, these rents were gradually dissipated as a result of overcapacity and the effect of falling ex-vessel prices. The emergence of aquaculture provides a strong rationale in favor of right-based approaches to fisheries management in Alaska. & 2009 Elsevier Inc. All rights reserved.

Keywords: Market interactions Aquaculture Restricted access fisheries Bristol Bay Alaska Salmon

1. Introduction Commercial aquaculture production of species such as salmon and shrimp has increased significantly over the last three decades. In the case of salmon (including sea trout), world aquaculture production rose from 4.8 thousand metric tons (MT) to 1.58 million MT between 1977 and 2007 [14]. This impressive growth has positioned aquaculture as the dominant producer of salmon, accounting for over 60% of global supplies. Because much of its output is destined to export markets, the importance of aquaculture in the international seafood trade has grown even larger [4,25]. The growth of aquaculture worldwide has generated important implications for common-property fisheries as increased aquaculture output has led to lower prices for both farmed and wild species supplied by the capture fisheries. The wild salmon fisheries in Alaska provide an outstanding example of this type of interactions. The ex-vessel value of the sockeye salmon fishery in Bristol Bay, a river system hosting the largest runs of sockeye in the world, has decreased dramatically in recent years. The fishery remains biologically viable due to judicious management of stocks by the Alaska Department of Fish and Game (ADF&G): annual harvest has fluctuated around 60,000 MT (132 million pounds) during the last 25 years. The decrease in value is directly connected with declining sockeye ex-vessel prices: the average nominal price fell from a historic high of $4.65/kg in 1988 to a historic low of $0.93/kg in 2001 (Fig. 1). During the same time period,

 Corresponding author. Fax: +1 401 782 4766.

E-mail addresses: [email protected] (D. Valderrama), [email protected] (J.L. Anderson). 0095-0696/$ - see front matter & 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.jeem.2009.12.001

ARTICLE IN PRESS D. Valderrama, J.L. Anderson / Journal of Environmental Economics and Management 59 (2010) 115–128

4.80

100

4.00

80

3.20

60

2.40

40

1.60

20

0.80

Thousand MT

120

-

US$/kg

116

1980

1982

1984

1986

1988

1990

1992

1994

Total Landings

1996

1998

2000

2002

2004

2006

2008

Ex-vessel Price

Fig. 1. Annual landings and nominal ex-vessel price of sockeye salmon in Bristol Bay, Alaska. Source: [3].

Japan -the primary market outlet for Bristol Bay sockeye- increased its imports of farmed Chilean coho and Chilean and Norwegian trout from around 9,000 to over 200,000 MT.1 The market share of farmed coho and trout in Japan grew from virtually zero to around 70% during 1988–2001; the market share of sockeye had declined from 67% to 30% by 2001 [7]. While other factors have affected wild salmon prices in recent years (e.g., increased production of Russian wild salmon and a prolonged recession of the Japanese economy throughout the 1990s), industry observers agree that the decline in Alaskan salmon prices has been caused primarily by competition from farmed salmon and salmon trout producers [25]. The economic relationship between wild Alaskan salmon and farmed salmon has been examined since the early 1990s [18,19] and was recently tested by Holzinger [21]. The latter study confirmed that, while worldwide farmed salmon and trout and the various wild Alaskan salmon species are not identical products, they behave as close substitutes. The econometric analyses revealed that the dramatic declines in prices of wild salmon throughout the 1990s were largely caused by increases in the volume of production in the leading farming nations (Norway and Chile). This mixed scenario combining successful biological management of fish stocks with reduced value and economic distress for fishermen has motivated discussions on the rationale of management plans focused primarily on the maximization of sustainable yield from Alaska fisheries. Industry analysts have pointed out that management should be reoriented towards improving the economic performance of the fisheries, given the strong market competition with aquaculture producers [9,20,26]. In order to meet economic objectives, rights-based approaches to fisheries management in Alaska have been proposed by some [12,26]. The Bristol Bay fishery for sockeye salmon Oncorhynchus nerka takes place in a geographically remote location in the southeastern portion of the Bering Sea in southwestern Alaska. The fishery is managed under the limited entry program instituted for Alaskan salmon fisheries since 1975. In 2008, the fishery consisted of 1,863 driftnet permits (gillnet boats) and 981 setnet permits (shore-based set netters). On average, the driftnet fleet captures around 85% of the catch while the setnet fishery harvests the remaining 15%. Official statistics indicate that the vast majority of these permits were fished during the 1980s and 1990s [3]. However, due to deteriorating economic conditions, a substantial number of drift and setnet permit holders have opted out from the fishery since 2001, which is an event with no precedent in the history of the limited-entry program. Between 2000 and 2002, the number of driftnet permits fished declined by 35%, from 1,823 to 1,184 (Fig. 2). The number of setnet permits fished declined by 26% in the same time period. The processing sector was also affected as a number of well-established firms pulled out of Bristol Bay. Participation in the drift and setnet fisheries has improved since 2003 as ex-vessel prices have recovered to some extent. However, a substantial number of fishing permits still remain unused every year.

1

Japanese imports of sockeye, in contrast, have remained relatively flat at around 50,000 MT.

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100%

300

90%

270

80%

240

70%

210

60%

180

50%

150

40%

120

30%

90

20%

60

10%

30

Permit Market Value (Thousand Dollars)

Percent Permits Fished

D. Valderrama, J.L. Anderson / Journal of Environmental Economics and Management 59 (2010) 115–128

-

0% 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 % Fished

Permit Market Value

Fig. 2. Percent of permits fished and permit market value (nominal US Dollars) in the Bristol Bay drift gillnet sockeye salmon fishery (1980–2008). Sources: [1,3].

The economic difficulties faced by the industry are mirrored in the declining market values for vessel permits. In the drift gillnet fishery, nominal permit prices in 2002 were only 8% of the respective prices in 1989 (Fig. 2). By 2008, permit prices had recovered to some extent but remained well below the levels observed in the early 1990s. In properly functioning markets, license prices act as indicators of future expected rents [36]. Given that overall expectations in Alaska are for further growth of the global aquaculture industry, permit prices are likely to remain depressed in the near and mid-term future. The Bristol Bay sockeye salmon fishery provides an appropriate empirical setting for the analysis of market interactions between aquaculture and common-property fisheries. Unlike other important salmon fishing locations such as British Columbia and the U.S. Pacific Northwest, wild salmon stocks are not physically exposed to netpen farming facilities in Bristol Bay since ocean growout of finfish is prohibited in Alaska. Similarly, salmon enhancement programs are non-existent in Bristol Bay. Interactions between salmon aquaculture and the Bristol Bay fishery occur exclusively through the market price for salmon. The formal economic analysis of market interactions between aquaculture and common-property fisheries began with Anderson [6]. In an open-access setting, he argued that the entry of competitive aquaculturists would increase natural fish stocks, reduce price, and increase supply from the commercial fishery. Despite the important implications from Anderson’s analysis, the conservation-related benefits for natural fish populations (higher stocks resulting from reduced exploitation) associated with aquaculture development have been difficult to measure empirically for a number of reasons [8]. Factors such as enhancement programs and the development of regulatory institutions in most modern fisheries have obscured the potential stock-rebuilding effects of price reductions caused by aquaculture. Given this background, the two major goals of this manuscript are (1) to extend the theoretical work of Anderson [6] by including the case of limited-entry fisheries; and (2) to examine the implications of the theoretical model against the empirical evidence available from the Bristol Bay sockeye fishery. Even though it is an illustrative case with a strong analytical appeal, the open-access context examined by Anderson [6] is not representative of most modern fisheries, which are normally managed under some sort of limited entry regulation. A more comprehensive analysis needs to make explicit consideration of the regulatory sector. To this end, the model of regulated open-access (ROA) fisheries developed by Homans and Wilen [22] (hereafter, HW) is adapted to the case of a restricted access fishery with a regulatory sector enforcing season length restrictions to keep the fish stock biomass above a sustainable threshold level. This model is then used to examine the effects of price reductions caused by aquaculture on the bioeconomic equilibrium established between the fishing and regulatory sectors. 2. The theoretical model Building on the model of seasonal fisheries introduced by Clark [11], HW [22] developed a regulated open access model of resource exploitation in which the resource stock is maintained at a safe level by the action of regulators who implement

ARTICLE IN PRESS D. Valderrama, J.L. Anderson / Journal of Environmental Economics and Management 59 (2010) 115–128

Effort: E

118

Industry Curve E = E(T; X0, P, v, f, q) A

Agency Curve T = T(E; X0, S∗, q)

Tmin

Season Length: T

Fig. 3. Joint regulated equilibrium in the model of the regulated open access fishery (source: [22]).

target harvest levels according to a safe stock concept. In salmon fisheries, this concept is closely related to the notion of escapement goals, which guides the formulation of management plans in Alaska. In the regulated open access model, harvest quotas are implemented by setting season lengths, conditioned on the level of effort committed by the industry. In turn, the industry enters the fishery until rents2 are dissipated, conditioned on season length regulations. A joint economic equilibrium determines the final fishing capacity, season length, and harvest level outcomes. By means of an instantaneous Schaefer-type harvest function [15,34], HW assume that fish biomass declines by the fishing rate within a single season according to the equation X_ ðtÞ ¼ qEXðtÞ;

ð1Þ

where X is the biomass level in period t of a given season, q is the catchability parameter, and E is a measure of fishing capacity. Under these assumptions, total cumulative harvest H for the industry over a season of length T is given by HðTÞ ¼ X0 XðTÞ ¼ X0 ð1expqET Þ:

ð2Þ

In the case of salmon fisheries, this is akin to the expression Total Harvest ¼ Total Run minus Escapement. Assuming linear variable and fixed costs, total industry rents for a season of length T can be written as Rents ¼ ½PX 0 ð1expqET Þ½vET þ fE;

ð3Þ

where P is ex-vessel price, X0 is the biomass size at time t ¼ 0 (or initial run size), and v and f are the variable and fixed cost coefficients, respectively. Regulators are concerned with setting the season length T in such a way that a specific escapement goal ðS Þ is met; i.e., season length T must be short enough to ensure that a minimum number of salmon escape to the upstream spawning locations. In other words, S ¼ X0 expqET . Therefore, regulators choose T according to the equation   1 X0 T¼ ð4Þ ln  : S qE Eqs. (3) and (4) determine a joint regulated open access equilibrium, which occurs at an effort level E and season length T as depicted at the intersection A of the industry and regulatory agency curves (given by Eqs. (3) and (4), respectively) shown in Fig. 3. The industry curve slopes upward over the relevant section of Fig. 3 because higher levels of effort can be supported by the fishery when the fishing season T is lengthened (i.e., a greater biomass is available for harvesting). Conversely, the agency curve slopes downward because tradeoffs between season length T and effort level E must be made in order to meet the escapement goal S . Season length must exceed a lower bound (Tmin in Fig. 3) to attract the first unit of effort: at Tmin , sufficient revenue is generated by the first unit of capacity to recoup fixed costs f. 2.1. The limited entry fishery By assuming that effort E represents the number of vessels in a homogeneous fishery, the regulated open access model can be adapted to examine the case of a limited entry fishery, with an upper cap on the number of vessels granted a fishing permit. Fig. 4 illustrates the HW model with the level of effort restricted to be less than or equal to ELE . Assuming that other 2

Rents are defined as the difference between industry revenues and industry costs, accruing over a given fishing season.

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119

Industry Curve - ROA E = E(T; X0, P, v, f, q) eROA

ELE

Industry Curve - LE eLE

Agency Curve T = T(E; X0, S, q)

Season Length: T Fig. 4. The case of the limited entry fishery. ROA ¼ Regulated open access. LE ¼ Limited entry.

regulations are in place to prevent significant capital stuffing, it is clear that the limited-entry requirement has the potential to re-capture some of the rents dissipated under regulated open access. Equilibrium eLE in Fig. 4 implies a lower level of effort and a longer fishing season T as compared to eROA . The vertical distance between the two industry curves provides a measure of the level of rents generated by the limited-entry regulation. An important implication from the HW model is that the industry curve (ROA) will shift downwards with reductions in price, all other factors held constant. If the price reduction is significant, the ROA curve may drift below the limited-entry constraint (ELE ). In such a case, participation in the fishery will fall below 100% (not all fishing permits will be used) and rents will again be dissipated. In other words, the fishery will return to a regulated open access equilibrium (eB in Fig. 5) if the limited-entry cap remains fixed at the same level. The limited entry fishery model presented in Fig. 5 provides a theoretical basis to the phenomenon of unused fishing permits observed in Bristol Bay since 2001 (Fig. 2). The emergence of aquaculture has depressed ex-vessel prices to the point that the underlying ROA industry curve has shifted below the limited-entry restriction. In other words, current limited-entry regulations are failing to create any significant wealth from the fishery as declining prices have substantially reduced the level of effort that can be supported by the resource. The presence of the regulatory sector ensures that escapement goals are still met, meaning that harvest levels remain constant (assuming all other parameters also remain constant) even when rents have been completely dissipated. 3. An econometric model for the Bristol Bay drift gillnet fishery An econometric model was formulated to determine whether the empirical evidence available from the Bristol Bay drift gillnet fishery conforms to the theoretical model defined by (3) and (4) and illustrated in Fig. 5. The Bristol Bay fishery consists of five major river systems (Naknek-Kvichak, Egegik, Ugashik, Nushagak, and Togiak). Each river system is managed independently by the ADF&G, meaning that different escapement goals are defined for each river according to the unique biological characteristics of its salmon runs. The ADF&G collects information on size of runs, escapement and harvest levels, length of fishing season, and levels of active effort for each river system [3,33]. The ADF&G also reports average ex-vessel prices for the fishery. Table 1 summarizes the biological and economic variables used in the econometric model. Given the differences in salmon run characteristics and management objectives pursued by the ADF&G, the model was estimated separately for each river system, using data from the period 1980–2006.3 Data on ex-vessel prices (P), size of runs (X0 ), realized escapement (S),4 and length of fishing season (T) were obtained from the Bristol Bay Area Annual Management Reports [3]. Fishing opening schedules were consulted for the determination of fishing season length. Fishing openings with daily harvests of fewer than 1,000 fish, which tend to occur towards the end of the season,5 were not considered in the computation of season length. Although the total number of permitted vessels remained relatively constant for all of Bristol Bay over the study period, the level of effort in each river system varied every year because vessels are relatively mobile among river systems. 3

The complete dataset is available at JEEM’s online archive of supplementary material, which can be accessed at http://www.aere.org/journals/. For simplicity purposes, the model assumes that realized escapement is equivalent to the targeted escapement goal S in (4). While most realized escapement falls within the escapement goal ranges defined by the ADF&G, actual escapement might occasionally fall outside this range. For the purposes of this analysis, this distinction is unimportant. 5 The timing of salmon runs in Bristol Bay is such that most harvesting takes place over the course of a few weeks in June and July. 4

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Effort: E

120

ELE

Industry Curve A - ROA E = E(T; X0, PA, v, f, q)

Agency Curve T = T(E; X0, S, q)

Industry Curve - LE

PA > PB

eA eB

Industry Curve B - ROA E = E(T; X0, PB, v, f, q) Season Length: T Fig. 5. Effect of a reduction in price from PA to PB in the limited entry fishery model. All other model parameters are held constant. ROA ¼ Regulated open access. LE ¼ Limited entry.

Table 1 Biological and economic variables in the econometric model of the drift gillnet sockeye salmon fishery, Bristol Bay, Alaska. Variable

Name

Units

X0 S T E P w hp

Size of the annual run Realized escapement Length of fishing season Number of vessels Nominal ex-vessel price Average weight of an individual fish Average horsepower of vessels

Millions of fish Millions of fish Thousand hours Thousand vessels US$/fish lb per fish Thousand hp

Therefore, rivers with stronger runs tend to attract a greater number of boats, given their larger potential for rents. The strength of runs changes by river system and by year. An important implication of effort mobility is that rents tend to be equalized across river systems over any given fishing season. Effort data for the econometric model were obtained from effort tables published by the ADF&G in a 2006 Special Publication [33]. Eqs. (3) and (4) form the theoretical foundation for the econometric model, with the variables E and T defined as endogenous regressors. Because the limited-entry constraint was binding from 1980 through 2000 (Fig. 2), a Simultaneous Tobit formulation is required to account for the censored nature of the endogenous regressor E in (3) and (4). Based on the assumption of rent equalization across river systems, observations for effort E were interpreted as being censored across all rivers during the period 1980–2000. Observations from 2001 through 2006 were assumed to be not censored given that a substantial number of fishing permits remained unused. 3.1. Fishing costs in Bristol Bay In addition to the censored nature of effort, the analysis of restricted-access fisheries is further complicated by the phenomenon of capital stuffing. Because limited-entry regulations leave the underlying open-access incentive structure intact, some rents are still dissipated by the excessive use of unregulated inputs.6 Capital stuffing in the Bristol Bay drift gillnet fishery has been documented primarily through increases in the average horsepower of vessels. From 1980 through 2007, average horsepower steadily increased by more than 60%, from 210 hp to approximately 350 hp [2]. The most important implication of capital stuffing for our analysis is the gradual change in the structure of total industry costs, relative to Homans and Wilen’s static cost formulation (see Eq. (3)). We address the phenomenon of capital stuffing by embedding an empirical cost function estimated for the Bristol Bay drift gillnet fishery by the Alaska Commercial Fisheries Entry Commission (CFEC) [35, Chapter 4], in place of the original linear cost formulation in (3). The CFEC cost equation was derived from individual boat data provided by a 2002 survey of 6 This phenomenon corresponds to the Class II Common Property problem discussed by Munro and Scott [30]: limitations on total catch and/or selected dimensions of effort may prevent resource decline but will exacerbate a ‘‘race for fish’’ whereby fishermen compete to harvest their largest share of the resource before the season ends. This competition brings excess capital into the fishery without a concomitant increase in total revenue, leading to a net loss of resource rents.

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121

operating vessels in the fishery [10]; the information from individual surveys was merged with CFEC data on the relevant fishing operations. The total set of observations covered the 1983–2003 time period and was confined to permits that were used by a single person fishing a single vessel during the year, which includes the vast majority of permits in Bristol Bay. The CFEC study modeled total costs per vessel (R03TCV, measured in real 2003 dollars) partially as a cubic cost function of total pounds harvested (lbs). Because crew shares for crew other than the skipper are a direct function of gross earnings, the operation’s gross earnings (R03GE, measured in real 2003 dollars) were included as an explanatory variable. Other explanatory variables included statistical weeks with landings (weeks), the horsepower of the vessel (hp), and dummy variables for vessels with wood hulls (DW) or fiberglass hulls (DFG). The estimated total cost function follows below: R03TCV ¼

6562:31 ð45:83Þ

þ ð0:06034 lbsÞ

ð1:11579E7 lbs Þ ð7:47Þ

ð385:64Þ

ð23:59Þ

þ ð9:2185E14 lbs Þ

þ ð2236:84 weeksÞ

þ ð30:35 hpÞ

ð4:39Þ

ð150:52Þ

ð132:66Þ

ð7942:66 DWÞ ð82:49Þ

ð1548:90 DFGÞ ð23:62Þ:

3

2

þ ð0:30671 R03GEÞ

ð5Þ

Adjusted R2 ¼ 0:9481; n ¼ 35 454; t-value in parentheses. Based on Eq. (5), which describes costs per vessel, a total industry costs function for Bristol Bay was derived by assuming a fleet of homogeneous vessels. Total pounds harvested per vessel (lbs in (5)) were modeled as wX 0 ð1expqET Þ=E (see Eq. (2)) while gross earnings per vessel were represented as PwX 0 ð1expqET Þ=E, where w denotes the average weight in pounds of an individual fish. The variable weeks in CFEC Eq. (5) was modeled in terms of season length T. The CFEC’s Permits and Vessel database [2] was consulted to obtain historical data on average vessel horsepower and the share of vessels with wood and fiberglass hulls in the drift gillnet fleet (see Table 1 for units). The resulting equation estimates total costs for an homogeneous fleet (R03TCF, measured in million real 2003 dollars) based on average vessel characteristics: R03TCF ¼

6:5623E

þ 0:30671 PX 0 ð1expqET Þ

þ 0:06034wX 0 ð1expqET Þ

A þ

B

þ ð13:31 ETÞ

þð30:35 E  hpÞ

ð7:94 E  DWÞ

ð1:55 E  DFGÞ;

ð6Þ

where A ¼ 1:11579E4

½wX 0 ð1expqET Þ2 ; E

and B ¼ 9:2185E8

½wX 0 ð1expqET Þ3 : E2

Total costs estimated in (6) (R03TCF) were converted from real 2003 dollars to nominal dollars.7 The resulting mathematical expression, denoted as Tot_Costst , was embedded in the econometric model. Despite its apparent complexity, Eq. (6) provides approximately linear estimates of total costs for any relevant range of effort. This occurs primarily because regulators ensure that total harvest from the fleet wX 0 ð1expqET Þ remain constant regardless of the level of effort entering the fishery.8 By including average vessel horsepower as a component of costs, Eq. (6) accounts for the impact of increasing engine size on the total structure of costs. Because the cost function is embedded, cost parameters such as v and f in (3) are not estimated directly from the model.9 Given that the parameters of the CFEC cost function (5) are subject to sampling variability, the standard errors of the simultaneous Tobit model were obtained through bootstrapping [13]. 3.2. The simultaneous Tobit model For the years when the limited-entry constraint is binding (1980–2000), the econometric model is given by the system of equations   1 X0t ; ð7Þ e1t ¼ Tt  ln St qEt

e2t o½PX 0t ð1expqEt Tt ÞTot_Costst ; 7

ð8Þ

The underlying ROA model in (3) and (4) assumes that annual nominal rents are driven down to zero. An Appendix with further details is available at the online archive. Nevertheless, it must be reminded that total fishing costs are partially a function of the catchability coefficient q, which is estimated from the econometric model. 8 9

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where e1t and e2t are error terms; Tot_Costst are as estimated by Eq. (6) after converting to nominal values; all other parameters are as defined previously. The comparison operator o is introduced in (8) to indicate that total rents are greater than zero because the endogenous regressor E has been censored to a level lower than that leading to complete rent dissipation. When the limited-entry constraint is not binding (2001–2006 period), the resulting system of equations corresponds exactly to the HW regulated open access model:   1 X0t ; ð9Þ e1t ¼ Tt  ln St qEt

e2t ¼ ½PX 0t ð1expqEt Tt ÞTot_Costst :

ð10Þ

The simultaneous Tobit model is estimated using a Full Information Maximum Likelihood procedure [27]. If f ðe1 ; e2 Þ is the joint density of e1 and e2 , then the likelihood function is given by       Y Z PX 0 ð1expqET ÞTot_Costs  Y  1 X0 1 X0 ; e2 de2  ; PX 0 ð1expqET ÞTot_Costs ; f T ln f T ln ð11Þ Lðq; KÞ ¼ S S qE qE 1 EoE

E¼E

where E ¼ E and E oE denote the set of censored (1980–2000) and uncensored (2001–2006) observations, respectively. 2 K is the covariance matrix of the equation error terms: K ¼ Covðe1t ; e2t Þ ¼ ½rss11s2 rss12s2 . 2 Estimation of (11) is complicated by the presence of the integral term in the RHS. However, this expression can be simplified by writing f ðe1 ; e2 Þ as f ðe1 Þ  f ðe2 je1 Þ. Note that e2 je1 is normal, with mean ðrs2 =s1 Þe1 and variance s22 ð1r2 Þ [27]. Therefore, the integral term in (11) can be re-written as    Z PX 0 ð1expqET ÞTot_Costs  1 X0 1 ; e2 de2 ¼ F½W  f T ln j½Y; ð12Þ S s qE 1 1 where   3 2 s2 1 X0 PX 0 ð1expqET ÞTot_Costsr T ln 7 6 s S qE 1 7; W ¼6 5 4 s22 ð1r2 Þ1=2  1 1 X0 T ln B qE S C C; B Y ¼@ A s1 0

and F and j denote the univariate standard normal distribution and density functions, respectively. The resulting loglikelihood function is defined as    2 X n n 1 X 1 X0 ln Lðq; KÞE ¼ E ¼  E ¼ E lnð2pÞ E ¼ E lnðs21 Þ 2 T ln þ ln FðWÞ 2 2 S qE 2s1 E¼E

for the set of censored observations, and X mnE o E n 1X ln Lðq; KÞE o E ¼  lnð2pÞ þ lnðjdetðJÞjÞ E o E lnðdet½KÞ hðe1 ; e2 ÞK1 h0 ðe1 ; e2 Þ 2 2 2 EoE

ð13Þ

E¼E

ð14Þ

EoE

for the set of uncensored observations, where n is the number of observations in the respective set, m is the number of endogenous variables (2), J is the m  m matrix of derivatives of (9) and (10) with respect to the two endogenous variables T and E (the Jacobian factor), and hðe1 ; e2 Þ is the matrix of residuals from (9) and (10) [29]. The loglikelihood function for the complete model is obtained by summing over (13) and (14). Standard errors of the parameters q and K along with 95% confidence intervals were obtained through bootstrapping (1000 resamples). Computer codes were written in R 2.6.0 [32]. 4. Results and discussion Table 2 presents the results of the Simultaneous Tobit model for each river system. For comparison purposes, asymptotic intervals (95% confidence level) derived from the maximum likelihood (ML) estimates are also presented. Very precise estimates were generated for the catchability coefficient q across river systems. Estimates were similar with the exception of Togiak river, for which a substantially higher coefficient was estimated. Togiak is a minor system in Bristol Bay which tends to attract a much lower level of effort as compared to other locations. The higher catchability coefficient probably indicates that vessel overcrowding is much less prevalent in this river system. In intuitive terms, q indicates the number of fish harvested by one unit of effort during one unit of time. The statistical significance of this parameter indicates that the effort levels and season lengths observed in the fishery during 2001–2006

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Table 2 Results from the Simultaneous Tobit model of the drift gillnet fishery in Bristol Bay, Alaska, 1980–2006. Standard errors and 95% confidence intervals were obtained through bootstrapping iterations (1000 resamples). Coefficient

River System Naknek

Egegik

Ugashik

Nushagak

Togiak

2.89 0.51 [1.98-4.02] [2.39–3.33]

7.77 0.35 [7.04–8.44] [6.91–8.56]

10.42 1.94 [8.03–16.25] [7.91–12.13]

6.03 0.35 [5.40–6.90] [5.19–6.68]

37.73 5.55 [30.05–53.25] [31.04–42.48]

0.30 0.11 [0.15–0.46] [0.21–0.37]

0.16 0.02 [0.12–0.20] [0.12–0.21]

0.24 0.03 [0.18–0.30] [0.18–0.30]

0.22 0.03 [0.16–0.29] [0.16–0.28]

0.61 0.11 [0.42–0.70] [0.46–0.80]

Estimate Standard error 95% C.I. 95% C.I. - Asymptotic

2.14 2.16 [0.01-5.90] [1.21–3.76]

3.14 0.50 [2.16–4.22] [1.54–4.77]

2.02 0.37 [1.31–2.70] [1.00–3.05]

3.31 0.72 [1.76–4.59] [1.82–4.84]

1.60 0.21 [1.25–2.10] [0.91–2.27]

Estimate Standard error 95% C.I. 95% C.I. - Asymptotic

0.40 0.39 ½-0:6020:98 ½-0:1120:99

 0.35 0.23 ½-0:8620:07 ½-0:8520:22

0.23 0.42 ½-0:9020:75 ½-0:4621:02

0.11 0.46 ½-0:8920:81 ½-0:4320:82

 0.95 0.09 ½-0:982-0:89 ½-1:002-0:89

q Estimate Standard error 95% C.I. 95% C.I. - Asymptotic

s1 Estimate Standard error 95% C.I. 95% C.I. - Asymptotic

s2

r

For comparison purposes, the asymptotic intervals derived from the maximum likelihood estimates are also shown.

largely conformed to the standard regulated open-access scenario outlined by HW (Eqs. (9)–(10)). The data suggests that the regulating agency (ADF&G) scheduled fishery openings based on the strength of runs and the level of effort committed by the industry. In turn, committed fishing effort in 2001–2006 was largely conditioned by the low salmon prices, which drastically reduced the rent-generating capacity of the fishery. The number of participating vessels during this period, even if lower than the total number of permitted units, was large enough to dissipate resource rents. The model also indicates that the relatively high prices prevailing during 1980–2000 would have attracted large levels of effort had access to the resource not been restricted.10 Estimates for most other model parameters (in particular s1 and s2 ) were also statistically significant, as signaled by their small standard errors. The 95% confidence intervals obtained through bootstrapping largely overlapped the asymptotic intervals from the ML estimates. These results suggest that the Simultaneous Tobit formulation satisfactorily explains the joint equilibrium outcomes (season length T and effort level E) reached annually by the regulatory and commercial harvesting sectors in Bristol Bay. Fig. 6 illustrates the annual equilibrium levels of effort that would emerge in the four major river systems if the binding limited-entry constraints were removed, as suggested by the model estimation and the CFEC cost equation [35]. These effort levels correspond to the regulated open-access equilibrium in the HW model (eROA in Fig. 4). It is clear that limitedentry regulations were instrumental in preventing a much greater level of effort (triangles in Fig. 6) from entering the fishery during 1980–2000. By the late 1990s, however, the underlying levels of effort under ROA declined as ex-vessel prices fell. From 2001 through 2006, a regulated open-access equilibrium emerged in the fishery as participation rates fell below 100%. The predicted levels of effort for 2001–2006 correspond to the intersection eB in Fig. 5, matching approximately the actual effort levels observed in the fishery (Fig. 6).11 The model also suggests that, in some of the most critical years, the fishery has actually incurred financial losses. For example, the Naknek-Kvichak system experienced a very poor salmon run in 1997 (only 3.3 million fish returned for spawning, which was around 20% of the historical average for 1980–2006). As a result, the model predicted complete dissipation of rents at very low effort levels (less than 100 vessels; see Fig. 6(a)), despite a relatively high ex-vessel price (Fig. 1). The actual standardized level of effort was 394 vessels [33], nearly four times higher than the effort required for rent dissipation.

10 In fact, it can be argued that the statistical significance of the model stems primarily from the drastic differences in price levels between 1980– 2000 and 2001–2006. The fishery was clearly profitable up to the year 2000 and the model easily captures that. Even the moderate price levels observed during 1990–1996 were compensated by above-average harvests (Fig. 1). The opposite was observed during 2001–2003, when collapsing prices were accompanied by poor salmon runs. 11 Fig. 6 illustrates the rent-dissipating levels of effort based on the CFEC cost equation, which does not explicitly account for crowding externalities. These effects can be important as effort levels increase. However, for the purposes of this study, the useful insight comes from examining the temporal evolution of ROA equilibrium values. The observed trend is consistent with the theoretical expectation of falling rents in the fishery.

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10

12

Effort (Thousand Vessels)

Full Participation. LE constraint is binding.

Participation below 100%.

8 6 4

Effort (Thousand Vessels)

9 10

2

8

Full Participation. LE constraint is binding.

Participation below 100%.

7 6 5 4 3 2 1

0

0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

7

6 Participation below 100%.

6 Effort (Thousand Vessels)

Effort (Thousand Vessels)

5

Full Participation. LE constraint is binding.

4 3 2 1

Full Participation. LE constraint is binding.

Participation below 100%.

5 4 3 2 1 0

0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Actual Number of Vessels

Actual Number of Vessels

Regulated Open Access Model -Predicted Effort -Rents = Zero

Regulated Open Access Model -Predicted Effort -Rents = Zero

Fig. 6. Historical levels of effort (1980–2006) in the drift gillnet sockeye fishery in Bristol Bay, and predictions from the Simultaneous Tobit model. The limited-entry constraint was binding during 1980–2000: (a) Naknek, (b) Egegik, (c) Ugashik, and (d) Nushagak.

A similar situation was observed during 2001–2002 across all river systems. The combination of below-average salmon runs and low prices led to a substantial decrease in effort (Fig. 2). The model, however, predicted even lower participation rates. This result is consistent with recent financial analyses from CFEC [35] suggesting that the drift gillnet fishery has been earning negative economic profits in recent years, at least during the period 2001–2003. Fig. 6 also suggests that actual fishing effort adjusts sluggishly to changes in production and economic parameters, which adds to the potential for financial losses in years of low productivity. The nature of market interactions between the aquaculture industry and the salmon fishery is better understood by illustrating how the industry curve in the regulated open-access model has shifted overtime as a result of the steady decline in ex-vessel prices (see Fig. 5). To this end, Fig. 7 displays the industry and agency curves estimated for the Naknek River for the years 1991 and 2006. Salmon runs in these two years shared similar characteristics (similar run size and escapement levels), meaning that the agency curves (T91 and T06 ) are relatively close to each other in Effort (E) : Season Length (T) space. The intersection of the E91 and T91 curves in Fig. 7 indicates the ROA equilibrium that would have emerged if limitedentry regulations had not been in place. The intersection of the E91LE and T91 curves (eLE91 ) represents the predicted outcome of the model given the limited-entry constraint ELE ¼ 722 vessels. The most remarkable feature in Fig. 7 is the large downward shift of the industry curve by 2006 (E06 ) resulting from a 27% decline in ex-vessel prices (from $1.65 to $1.21/kg). The shift of the industry curve E91 is almost entirely due to falling prices as salmon run and harvest levels were similar in 1991 and 2006. The intersection of the E06 and T06 curves indicates the predicted regulated open-access equilibrium emerging in 2006. The predicted effort in 2006 was 401 vessels while actual effort was 509 (see Fig. 6(a)). The predicted and actual levels of effort in 2006 are lower than the limited-entry restriction observed in 1991, revealing less than 100% participation.

4.1. Productivity growth in aquaculture vs. capture fisheries The implications of aquaculture development for Alaskan salmon fisheries are also revealed by examining how productivity has historically evolved in each of the two sectors. Fig. 8(a) presents export prices and average production costs in Norwegian salmon farming during 1985–2006, while Fig. 8(b) displays ex-vessel prices and average production costs in the Bristol Bay drift gillnet fishery . The latter costs were derived from the CFEC cost Eq. (5) [35], using the actual

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5.0

E91

P91 = $1.65/kg

4.5 Effort: E (Thousand Vessels)

125

P06 = $1.21/kg

4.0 3.5 3.0 T91

2.5 2.0

T06

1.5 1.0

eLE-91

ELE

E91-LE

0.5

eROA-06

E06

0.0 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Season Length: T (Thousand Hours) Fig. 7. Predictions from the econometric model of the drift gillnet sockeye fishery in Naknek River, Bristol Bay, 1991 vs. 2006. The limited-entry constraint was binding in 1991. ROA—regulated open access and LE—limited entry.

effort and season length data observed in the fishery and assuming an homogeneous fleet with average vessel characteristics. It is clear that the success of Norwegian salmon farming has been based on the industry’s ability to lower production costs while expanding total output. Regardless of the overall trend of declining prices, Norwegian farmers have managed to maintain profit margins over the years. In contrast, despite advances and investments in fishing technology, production costs for the Bristol Bay drift gillnet fishery have remained relatively flat (in real terms) over the 1985–2006 time period. While production costs in salmon farming are predominately driven by advances in culture technology, costs in Alaskan fisheries are largely determined by the size of the runs, with production costs per fish increasing markedly during years of poor runs (e.g., years 1997 and 1998 in Fig. 8(b)). Salmon farmers have achieved a large degree of control over the production environment while technological innovation in salmon fishing has been directed at ensuring access to the resource, rather than increasing production efficiency [5,8]. In order to test for potential impacts of increasing vessel horsepower on the harvesting capabilities of the fishing fleet, the econometric model (7)–(10) was reformulated by defining the catchability coefficient q as a time-varying parameter. None of these alternative formulations provided statistical evidence that q increased overtime as average horsepower increased. This clearly demonstrates the level of effort redundancy in Bristol Bay: the fishery has progressively transformed into a collection of oversized vessels with virtually non-existent gains in productivity. This result is a logical consequence of physical characteristics of the fishery (large number of vessels concentrated on narrow locations), the limitations on biological productivity of the salmon runs, and the current structure of economic incentives, which favor a derby-style type of fishery.

4.2. How to compete successfully against aquaculture: the economic case for rights-based fisheries management In Anderson’s original model of market interactions, the most fundamental implication of growth in the aquaculture sector was concerned with the biological status of the open-access resource as supply from aquaculture could mitigate substantially the economic incentives leading to overfishing. In the context of regulated fisheries, aquaculture growth is mostly felt through impacts on the rent-generating potential of fishing regulations. The limited-entry program in Alaska was initially established with the dual purpose of preserving salmon stocks and generating rents for fishery participants. However, the market shocks created by aquaculture have seriously hampered the ability of the limited-entry program to fulfill the latter purpose. The challenges brought on by aquaculture provide an opportunity to re-examine the effectiveness of current institutional arrangements in Alaskan salmon fisheries. In this regard, it is clear that incentive-based approaches to fisheries management, which depend on stronger forms of property rights, will be necessary to cope successfully with competition from aquaculture. The development of an incentive-based framework for fisheries regulation in Bristol Bay can be guided by experiences in other Alaskan fisheries. In recent years, the federal government has restructured management of several Alaskan fisheries through individual fishing quotas (IFQs) for halibut and sablefish and community development quotas (CDQs) for Bering Sea fisheries. While IFQs are issued to individuals based on their historical catch and other factors, CDQs are allocations of part of the allowable harvest to specific coastal communities [28]. Harvesting cooperatives have also been proposed to reduce overcapacity in Alaska. In a cooperative, either all or some of the permit holders in a fishery agree that

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100 90 Export Price

80

Production Cost

2006 NOK/kg

70 60 50 40 30 20 10 1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

9 8 Ex-vessel Price

7

Production Cost

2006 US$/kg

6 5 4 3 2 1 1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

Fig. 8. Historical comparison of prices and production costs between farm-raised and wild-caught salmon: (a) Farm-raised Norwegian Atlantic salmon (sources: [17,31]) and (b) Bristol Bay sockeye salmon, drift gillnet fishery (sources: [3,35], this study).

only some of the co-op members will fish, but all share in the profits. In 2001, some of the permit holders in the Chignik12 purse seine fishery petitioned the Alaska Board of Fisheries to approve such an arrangement. In a recent survey of permitholder attitudes, the majority of participating fishermen stated that they were better off financially because of the changes brought about by the cooperative [24]. In addition to savings in production costs, surveyed fishermen also mentioned that profitability had increased due to improvements in the quality of harvested salmon as fishing had taken place in a less frenzied environment. Higher prices resulting from improved product quality may represent an important source of rent gains resulting from rationalization [23]. Nevertheless, some of the fishermen who did not join the Chignik co-op felt that they were worse off and challenged the cooperative in court. In early 2006 the Alaska Supreme Court ruled that the co-op violated Alaska’s limited entry law and proceeded to declare it illegal. The legal battles that surrounded the Chignik cooperative during its brief existence reflect the ongoing tension between the constitutional goal of providing open access to Alaska’s marine resources and the economic efficiencies promoted by rights-based fishing [28]. Reduced participation in common-property fisheries caused by competition with a growing aquaculture sector is not a phenomenon restricted to Alaskan salmon fisheries. The shrimp fisheries in the Gulf of Mexico are undergoing a similar

12

The Chignik area is a relatively minor bay system in Southwestern Alaska.

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process of effort attrition due in part to competition with farm-raised imports. In the Amendment 13 to the Shrimp Fishery Management Plan document [16, page 8], the Gulf of Mexico Fishery Management Council (GMFMC) reports: yThe shrimp fishery in the Gulf has been experiencing economic losses since approximately 2001 primarily due to reduced prices from competition with imports and high fuel costs. These economic losses have resulted in effort reductions through the exodus of vessels from the fishery, and reductions are expected to continue through approximately 2012. Based on the likelihood that at some point in time the number of vessels in the offshore shrimp fleet will decline to a point where the fishery again becomes profitable for the remaining participants, there is a need to prevent new effort from entering the fishery and thus negating or at least lessening profitability when that time comes. Consequently, the Council is considering the establishment of a moratorium on the issuance of new federal shrimp vessel permits. In October 2006, the GMFMC effectively established a 10-year moratorium on the issuance of new federal shrimp vessel permits. Prior to 2006, shrimp fisheries in the Gulf of Mexico were essentially exploited under open access. The market interactions conceptualized by Anderson [6] have played an instrumental role in moving a common-property shrimp fishery from open access to limited-entry. Continued competition with aquaculture may provide the impetus for further rationalization in this and other fisheries. 5. Conclusions The empirical analysis of restricted-access fisheries poses methodological complexities due to the censored nature of effort data and the economic incentives for capital stuffing. To address these analytical challenges, the HW regulated openaccess model [22] was adapted to the limited-entry case by means of a Simultaneous Tobit formulation, with effort level modeled as a censored endogenous regressor. This methodology allowed us to uncover the underlying regulated open access characteristics of the drift gillnet salmon fishery in Bristol Bay. Capital stuffing, made apparent primarily by increases in average horsepower triggered by restrictions on the number and length of vessels, was addressed by embedding per-vessel cost estimates from CFEC. Anderson’s analysis of market interactions [6] discussed the positive consequences of aquaculture development for open-access fisheries exploited beyond maximum sustainable yield (MSY), in terms of increased harvest and stock levels and lower prices for the consumer. In the case of regulated open-access and limited-entry fisheries, the effects of aquaculture on harvest and stock levels are minimal due to the presence of a regulatory sector that prevents stock biomass from falling below the MSY level. However, aquaculture development may define important economic aspects of limitedentry fisheries. Because lower prices result in less revenue, the economic rents created in a limited-entry program will fall considerably if limited-entry regulations are not revised to address the effects of aquaculture competition. If prices are sufficiently low, the fishery will return to a regulated open-access equilibrium despite entry restrictions. These findings accurately portray the current economic conditions of the Bristol Bay sockeye salmon fishery. The analysis reveals that differences in productivity growth define the nature of market interactions between the salmon aquaculture and fisheries sectors. The salmon farming sector has invested heavily on R&D in order to drive down production costs while increasing overall output. In contrast, fishermen take advantage of innovations in technology to outcompete other participants in the race to fish, which does little to increase productivity of the sector as a whole. Overcapacity has been a long-standing feature of Alaskan salmon fisheries that generated little sense of urgency while exvessel prices remained high. The emergence of aquaculture has dramatically changed this scenario, forcing the fishery sector to take a hard look at itself and address the economic inefficiencies created by overcapacity [26]. Competition from aquaculture also provides a rationale for alternative institutional arrangements based on stronger forms of private rights, such as harvesting cooperatives. Interestingly, the analytical approach adopted in this study can also be used to examine the economic rationale for fishing cooperatives. Further research will explore this theme thoroughly.

Acknowledgments Financial support for this research was provided by the Rhode Island Agricultural Experiment Station (RIAES), Hatch Regional Project W-1004C. The authors are particularly grateful to Frank Asche (University of Stavanger, Norway), who showed great interest for our work and provided valuable comments during the preparation of the manuscript. Gunnar Knapp (University of Alaska Anchorage) offered useful insights on the phenomenon of capital stuffing in Alaska. Kurt Schelle (ADF&G) provided valuable advice for the estimation of total costs of the Alaska fishing fleet. Three anonymous reviewers also provided useful comments. The authors remain responsible for any errors or omissions. References [1] Alaska Commercial Fisheries Entry Commission, Permit Value Reports, Juneau, Alaska, USA, 2009, Online at: /http://www.cfec.state.ak.us/ mnu_permit_values.htmS.

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