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A Double-Hurdle Probit Analysis of Preferences for a Proposed Buyback Program in the Gulf of Mexico and South Atlantic Shark Fishery

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Title:
A Double-Hurdle Probit Analysis of Preferences for a Proposed Buyback Program in the Gulf of Mexico and South Atlantic Shark Fishery
Creator:
MUSENGEZI, JESSICA D.
Copyright Date:
2008

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Subjects / Keywords:
Assets ( jstor )
Fisheries ( jstor )
Fishers ( jstor )
Fishing ( jstor )
Household income ( jstor )
Mathematical variables ( jstor )
Revenue sharing ( jstor )
Sharks ( jstor )
Taxes ( jstor )
Total revenue ( jstor )
Gulf of Mexico ( local )

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Source Institution:
University of Florida
Holding Location:
University of Florida
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Copyright Jessica D. Musengezi. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Embargo Date:
12/31/2008
Resource Identifier:
649814568 ( OCLC )

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A DOUBLE-HURDLE PROBIT ANALYSIS OF PREFERENCES FOR A PROPOSED BUYBACK PROGRAM IN THE GULF OF MEXICO AND SOUTH ATLANTIC SHARK FISHERY By JESSICA D. MUSENGEZI A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2006

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ACKNOWLEDGMENTS Firstly I would like to thank my committee members, Dr. Sherry Larkin for her patience and encouragement and Dr. Charles Adams whose support has helped me to complete this thesis. I would also like to express my gratitude to Dr. Ronald Ward and Carlos Jauregi for sharing their time and knowle dge with me. I would like to thank the students of the Food and Resource Economics Department for their support. Finally I would like to thank my mother Chiedza Mu sengezi for her endless encouragement and support. ii

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TABLE OF CONTENTS page ACKNOWLEDGMENTS..................................................................................................ii LIST OF TABLES...............................................................................................................v LIST OF FIGURES...........................................................................................................vi ABSTRACT......viii CHAPTER 1 INTRODUCTION OVERCAPACITY AND POSSIBLE SOLUTIONS....................1 Introduction................................................................................................................... 1 Nature of the Problem of Overcapacity........................................................................2 Proposed Solutions for Overcapacity...........................................................................3 Vessel and Permit Buyb acks: The Process...................................................................6 Review of Buyback Programs in the United States......................................................7 Overcapacity in the Gulf of Mexico and South Atlantic Shark Fishery.......................9 Plan of Analysis..........................................................................................................11 2 COMMERCIAL ATLANTIC SHARK FISHERY....................................................13 Introduction.................................................................................................................13 Permit Own ership.......................................................................................................13 Landings Data.............................................................................................................15 Total Revenues in 2003.......................................................................................16 Landings by Species Group in 2003....................................................................17 Shark Landings in 2003.......................................................................................17 Summary.....................................................................................................................19 3 THEORY AND METHODOLOGY..........................................................................21 Introduction.................................................................................................................21 Overview of Contingent Valuation.............................................................................21 Development of Contingent Valuation Bid for this Study.........................................25 Double Hurdle Models...............................................................................................26 Application to the Atlantic Shark Fishery..................................................................27 First Hurdle: Probit Model..................................................................................27 iii

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Second Hurdle: Ordered Probit Model................................................................31 Empirical Model Variables and Hypotheses..............................................................33 Fishing Business and Vessel Variables................................................................33 Permit Owner Information...................................................................................35 Household Information........................................................................................37 Model Specifications..................................................................................................38 Potential Estimation Problems....................................................................................41 4 SURVEY RESULTS..................................................................................................41 Introduction.................................................................................................................41 Household and Permit Owner Information.................................................................42 Fishermen Goals and Perceptions...............................................................................44 Shark Business and Buyback Questions.....................................................................46 Summary.....................................................................................................................50 5 EMPIRICAL RESULTS............................................................................................52 Introduction.................................................................................................................52 Hurdle 1: WTS Shark Permit......................................................................................53 Hurdle 1: WTS Fishing Vessel and All Permits.........................................................56 Hurdle 2: LTA Shark Permit Bid................................................................................60 Hurdle 2: LTA Bid for Vessel and All Permits..........................................................66 Summary.....................................................................................................................73 6 CONCLUSIONS AND IMPLICATIONS.................................................................75 APPENDIX: TSP PROGRAMS........................................................................................83 LIST OF REFERENCES.................................................................................................105 BIOGRAPHICAL SKETCH...........................................................................................108 iv

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LIST OF TABLES Table page 2.1 Number and type of permits held by active commercial shark permit holders, 2004..........................................................................................................................14 3.1 Description of model variables................................................................................38 4.1 Geographic distribution of population and survey response....................................42 5.1 Descriptive statistics of model.................................................................................52 5.2 Willingness to sell shark permit probit model results..............................................54 5.3 Marginal effect of significant variable s on the probability of selling the shark permit.......................................................................................................................55 5.4 Willingness to sell vess el probit model results........................................................57 5.5 Marginal effect of significant variables on the probability of selling the vessel.....58 5.6 Factors affecting likelihood to accep t permit bid: probit model results...................62 5.7 Likelihood to accept permit bid probabilities..........................................................63 5.8 Marginal effects for significant vari ables on likelihood to accept permit bid probabilities..............................................................................................................63 5.9 Likelihood to accept vessel probit model results.....................................................68 5.10 Likelihood to accept vessel bid probabilities...........................................................69 5.11 Marginal effects for significant va riables o likelihood to accept vessel bid probabilities..............................................................................................................69 v

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LIST OF FIGURES Figure page 2.1 Frequency of revenues per vessel by type of shark permit, 2003............................16 2.2 Average vessel revenue and species composition in total and by shark permit type, 2003.................................................................................................................17 2.3 Average value of shark landings 2001-2003............................................................18 2.4 Shark share of total revenue by re venue group and shark permit type, 2003..........18 2.5 Average shark revenue per vessel by to tal revenue group and permit type, 2003...19 4.1 Highest degree level attained by respondent............................................................43 4.2 Distribution of Reported 2004 Taxable Household Income....................................43 4.3 Distribution of the percentage of 2004 taxable household income derived from commercial fishing...................................................................................................44 4.4 Plans for the future of the fish ing business within three years.................................45 4.5 Distribution of responses regarding the importance of each species to the fishing enterprise..................................................................................................................45 4.6 “Do you support or oppose (regardless of the fishery) each of the following measures that are designed to reduce fishing effort?”..............................................46 4.7 Awareness of a potential shark buyback program...................................................47 4.8 Willingness to pay a tax on your shark landings for up to 20 years to fund a program that would buyback vessels and/or shark permits......................................48 4.9 Willingness to sell shark permit) and willingness to sell the vessel and all permits......................................................................................................................48 4.10 Percentage likelihood to accep t (LTA) shark permit bid.........................................49 4.11 Percentage likelihood to accept bid for vessel and all federal permits....................50 vi

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5.1 Impact of change in shark share of re venue on probability of being willing to sell shark permit.......................................................................................................55 5.3 Impact of commercial fishing experien ce on probability of being WTS fishing vessel........................................................................................................................59 5.4 Impact of changes in the percentage of income from fishing on the probability of selling the fishing vessel..........................................................................................60 5.5 Impact of owner age on the likeli hood to accept permit bid probabilities...............64 5.6 Impact of significant binary variables on probability LTA permit bid is 0%..........64 5.7 Impact of significant variables on probability LTA permit bid is 25%-50..............65 5.8 Impact of significant variables on probability LTA permit bid is 75%-100%.........65 5.9 Effect of owner age on the likeli hood to accept vessel bid probability...................70 5.10 Effect of fishing experience on likel ihood to accept vessel bid probabilities..........71 5.11 Effect of significant variables on the probability LTA vessel bid is 0%.................71 5.12 Effect of significant variable on probability LTA vessel bid is 25%-100%............72 5.13 Effect of significant variables on the probability LTA vessel bid is 75%-100%.....72 6.1 Number of vessels represented by likelihood to accept bid categories....................78 6.2 Share of shark total landings represented by likelihood to accept shark permit bid.............................................................................................................................79 6.3 Total cost of a potential sh ark permit buyout in bid dollars.....................................79 6.4 Shark share of total revenue represen ted by likelihood to accept bid for vessel and all permits..........................................................................................................80 6.5 Total cost of potential vessel and permit buyout in bid dollars...............................80 6.6 Total shark revenue by likeli hood to accept shark permit bid, 2003.......................81 6.7 Total revenue (from all species) by lik elihood to accept bid for vessel and all permits, 2003............................................................................................................81 vii

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Abstract of Thesis Presented to the Graduate School of th e University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science A DOUBLE-HURDLE PROBIT ANALYSIS OF PREFERENCES FOR A PROPOSED BUYBACK PROGRAM IN THE GULF OF MEXICO AND SOUTH ATLANTIC SHARK FISHERY By Jessica D. Musengezi Decermber 2006 Chair: Sherry L. Larkin Major Department: Food and Resource Economics The Gulf of Mexico and Atlantic shark fish ery is faced with overcapacity that leads to depleted fish stocks and reduced profits for those in the industr y. Sectors of the shark fishery have proposed to remedy the pr oblem through the purchase and permanent retirement of shark permits and/or fishing vessels in a buyback program. The objectives of the study were: (i) to determine whether shark permit holders were willing to sell their fishing assets in a voluntary buyback program, and (ii) to determine whether it is possible to use transact ion prices from similar programs to predict the value of fishing assets a nd to entice participation. The st udy used data collected from a 2005 mail survey of 551 shark permit holders. In particular, a contingent valuation approach was applied in which respondents we re asked two key questions. First, whether they were willing to sell their fishing assets for a reasonable price. Second if they were willing to sell, how likely they would be to accept a spec ified dollar “bid” amount to surrender these assets. The bid amount o ffered to respondents was unique for each viii

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individual permit holder and was based on previous landings associated with that particular permit using bid ratios from th e Pacific Northwest groundfish program. These two decisions were modeled using a double hurdle approach. In the first stage their willingness to sell was modeled using a binary probit model and the second stage uses an ordered probit to model the likelihood to accept the unique bid, which was solicited with a mutually exclusive closed ended question. Survey results showed that the majority of respondents were willing to sell their fishing vessels and their shark permits for a reasonable price in a buyback program (75% indicated willingness to sell shark permits while 67% indi cated willingness to sell the vessel and all associated permits). Regarding asset valuation, survey results showed that 25% of respondents were at least 50% likel y to accept the bid amount for the shark permit, while 75% were at least 50% likely to accept the bid amount for vessels and all associated permits. Econometric analysis also revealed that willingness to sell fishing assets (vessels and all permits) and the accepta bility of asset values are influenced by a combination of owner, fishing busin ess and household ch aracteristics. Understanding how fishermen perceive such programs and how they value their fishing assets will allow planne rs to anticipate potential part icipation, extent of capacity reduction, and implementation costs that togeth er determine the potential effectiveness and feasibility of conducting a buyback program. ix

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CHAPTER 1 INTRODUCTION OVERCAPACITY AND POSSIBLE SOLUTIONS Introduction An excessive increase of fishing capacity in the world’s oceans can cause the depletion of fish stocks as well as a reduction in the profitability of vessels participating in a given fishery. This increase in capaci ty (as measured by, the number, size, and efficiency of available vessels and crew) can result in excess capacity or overcapacity, both of which are conditions th at lead to over fishing. Excess capacity is a short run effect that results when a firm produces less than it could under normal operating conditions. This is generally due to a change in the market conditions for an input or output. Overcapacity on the other hand is a l ong run effect that exists when potential output that could exist under normal operating co nditions is different from a target level of production in a fishery defined by the re gulatory agency (i.e., such as maximum economic yield or maximum sustainable yield). Firms can change their production levels in response to market conditions to eliminat e excess capacity over the short run; however eliminating overcapacity requires a change in the management environment of a fishery (United Nations Food and Agriculture Organization (FAO1999). In its 1999 International Plan of Action for the Management of Fishing Capacity, the FAO states that ‘‘excessive fishing cap acity . . . contributes substantially to overfishing . . . and causes significant economic waste’’ (p19). Following the subsequent FAO Code of Conduct for Responsible Fisherie s, the United States developed a National Plan of Action (NPOA) with regards to fish ing capacity with the goal to “eliminate or 1

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2 substantially reduce overcapaci ty in 25% of the U.S federally managed fisheries by 2009” (U.S. Department of Commerce, 2003a, p.1). The NPOA identified several measures to manage overcapacity including restricting the number of permits through permit management programs, controlling harvest through quota programs, and the purchase and permanent retirement of fish ing vessels and/or permits. The latter programs, dubbed “buybacks” in this paper, are quickly becoming the preferred method of fishing effort reduction, primarily because they can be implemented relatively quickly and they can more easily gain industry support since they involve compensation (Larkin et al. 2004). Nature of the Problem of Overcapacity Fish are a renewable resour ce but the ability to be productive depends on their ability to reproduce and grow and the extent of fishing effort. Removing too many fish or changing the reproductive cap acity of the population can de plete stocks below levels of a viable population biologically and a financially prof itable fishery commercially. When a fishery has more capacity than it can sustain and fishermen are forced to compete for their share of total al lowable catch (i.e., open access type management systems), fishermen have no assurance that they will profit from taking conservation measures to keep socks around in the long run. Competition fo r fishery resources at sea can result in habitat degradation, mortality of non-targeted species and so cioeconomic hardship. Thus over fishing is ineffi cient (NFCC 1999). The socioeconomic health of a commer cial fishery depends on how fish are captured. Although capital investment in fish ing boats and equipment may increase catch in the short term, which may be needed to compete under an open-access type management system, profits are eroded over l ong-term as fish become more difficult to

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3 find and more costly to capture. During the cl osed seasons, this exce ss capital sits idle. Losses may be passed onto consumers in form of higher priced fish at the retail market level and also lead to social problems associ ated with unstable income. Overcapacity also increases the costs of devel oping and enforcing fishery regu lations and allocating scarce resources among a large and diverse number of user groups (NFCC 1999). Proposed Solutions for Overcapacity There are a variety of ways to tackle ove rcapacity. The FAO (1999) has classified strategies to manage capacity as incentive blocking or incentive adjusting strategies. Incentive blocking strategies are generally s hort run solutions that work by facilitating quick market adjustment of excess capac ity by stopping or slowing the growth of harvesting capacity. On the other hand, in centive adjusting measures are long run solutions designed to eliminate over capacity by changing the regulatory environment to create incentives to reduc e capacity levels in a fish ery. Unlike incentive blocking measures, incentive adjusting measures correct fo r market externalities that are root cause of excess capacity in fisheries. Incentive blocking strategies include lim iting entry, gear and vessel restrictions, fishing quotas and buyback programs. Lim iting entry involves limiting (reducing) the number of licenses in a fishery, but this by it self is not effective against increase capacity since fishermen can still increase capacity by capital stuffing or improving the effectiveness of fishing gear. License lim itation programs can be designed to reduce effects of capital stuffing by including transferability, which allows fishermen to enter the market only when another exits and fractionalization of licenses th at requires the owner of fractional license to buy a nother to create whole license. Vessel and gear restrictions control capacity by controlling the use of fixe d and variable inputs in the production of

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4 fishing effort. In some cases the fisheries may regulate vessel physical characteristics (seemingly fixed inputs) such as vessel length or horsepower. Variable inputs such as the size of nets, number of hooks, length of line, and the constr uction of traps can also be regulated. The drawback with this appro ach is that fishermen can get around these regulations by substituting other inputs for the restricted ones. Quotas are a common regulatory tool that can be implem ented at the industry or vessel level. Total allowable catches (TAC) dictate a maximum harvest level for a specified period of time for the entire fishery. The TAC is based on an estimate of surplus production that will allow the stock to replenish. Using TAC in isolation of other measures is not recommended. It is an open access type management system that encourages a race to fish which leads to in creased harvesting capacity. Such systems, whereby the rights to the excess harvest are not allocated, are incentive blocking. Individual non-transferable type effort quotas such as restrictions on trawl time, time away from port, or fishing days that the vessel can employ can reduce the total amount of fishing effort. Without costly monitoring (such as onboard observers) such measures can be difficult to enforce since th e restrictions apply to when the vessel is away from port. Restrictions on fishing time also encourage capital stuffing. While days fished or trawl time may remain constant , the fishing power of the vessel can be increased by substituting other factor inputs causing the effective fishing effort of the vessel to increase. Buyback programs have become a popular capacity reduction tool in fisheries around the world including the United States . Buyback programs purchase and remove vessels and/or licenses from a fleet to redu ce capacity and hopefully effective fishing

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5 effort. The goals of these programs are often to conserve fish stocks, improve economic efficiency through fleet rationa lization and/or to provide tr ansfer payments. An early study of buybacks in fisheries around the worl d showed that while goals are generally similar between programs, program design varied widely (Holland et al. 1999). The study concluded that although proper design of a buyback program can improve immediate performance, the programs have not generally been an effective way to achieve stated goals of reducing capacity. This is because if open access incentives remain, improvement in stock abundance will attr act more capacity into the fishery. Incentive adjusting measures that would deter a subsequent increase in fishing effort include individual tran sferable quotas (ITQs), taxe s, group fishing rights and territorial use rights. These regulations adjust incentives so that fishermen behave as rational owners of the resource and as such are willing to invest in the future by conserving the fishery resources as well as ot her resources used to harvest fish. As a result, overcapacity is eliminated in the fishery. ITQ programs in particular allow fishermen to buy and sell quota shares. In this way, a market develops where quota is bought and sold. This system allows quota shares to move to the most efficient fishermen because they value them most highly and are w illing to pay the highest price for them. It provides fishermen the opportunity to sell sh ares and leave the fishery when productivity is low or their costs are too high. Four U. S. fisheries are managed under ITQ programs, including Alaskan halibut, Alaskan sabl efish, surf clams and ocean quahogs, and wreckfish (NFCC 1999). A tax on landings is the theoretical equiva lent to ITQs in reducing capacity. A fee paid per pound of fish landed to the managi ng authority would theoretically reduce the

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6 effective price received by fishermen which w ould slow the rate of growth in harvest capacity in a fishery. The draw back of usi ng taxes is the difficulty associated with determining the optimal tax to apply to the fishery at each point in time and the legality of its use in certain countries such as the United States. Other rights-based approaches include group fishing rights, but these have b een shown to be weak if membership cannot be restricted, or when the ability to en force the rights does not reside within the community. Vessel and Permit Buybacks: The Process The protocol for conducting a federally s ponsored buyback is established in the Magnuson-Stevens Fishery Conservation and Ma nagement Act (section 312 (b)-(e)). The process begins with a business plan that is designed for or by the harvesters in the fishery that details the methodology that will be applied. It specifie s anticipated reductions in fishing effort and demonstrates that any federal loan provided for the purchase of fishing assets can be repaid from a tax on 20 years of future dockside landings at a rate not to exceed 5%. In most cases this loan must be repaid buy those operators remaining in the fishery post buyb ack. Thus the maximum 5% tax rate and the anticipated future landings determine the maxi mum amount of the loan that is available. The plan must also show that the buyback program will not negatively affect other fisheries (i.e., fishing effort is not transferred to other fisheries). A request is then submitted to NOAA that includes the business plan and describes how conservation goals will be achieved and how the removed capacity will be prevented from reentering any fishery. If the request is accepted, an implementation plan is developed by NOAA. A referendum of permit holders is then conduc ted. If two thirds of the vote approves the buyback plan then the process continues; th e implementation plan and regulations are

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7 published in the Federal Register and then bi ds are solicited from the permit holders to relinquish their fishing assets. If the bids conform to the plan specifications they are accepted, the bidders are paid, their permits ar e revoked, and their vessel title with the United States Coast Guard is permanently restri cted. A fee system is then implemented to repay the loan. If the bids do not confor m to the buyback specifications, a second referendum may be held or the prepared buyback program can be suspended. Several previous buyback programs have used a “reverse bid” process to determine the specific owners whose ve ssels and/or permits would be compensated for permanent removal from the fishery (or from all fishing activity) (Kitts et al 2001). The reverse bid process asks owners to submit bids, which are then normalized by a measure of historic fishery participation (e.g., average landings during a control period) . In most programs, such as the recent Northwest ground fish fishery and Alaskan crab fishery buyback programs, owners must also modify their estimated value to account for any costs associated with the proposed method to perm anently destroy the tangible fishing assets (e.g., costs to scrap or net salvage value). The ‘reverse’ refers to th e act of sorting the normalized bids in ascending order such that th e lowest values appear first and represent the least expensive in terms of reducing effort in the fishery on a unit landed basis. The lowest bids are accepted until the amount of the loan, which is based on payback ability, is exhausted. Fishermen remaining in the industry then agree to pay a tax on future landings to fund a loan in the amount of the sum of all accepted bids. Review of Buyback Programs in the United States Ten buyback programs have been implement ed since 1976 with the broad objective being to reduce overcapacity and specific objectives of providing short term economic

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8 assistance to fisherman leaving the indus try, improving the profitability of those remaining, and protecting fish stocks. Buyback programs have targeted permits, vessels or a combination of both. From 1976 to 1999, a total of $160 million in federal grants and loans has been provided to purchase more than 3000 permits and 600 vessels in U.S. fisheries. Roughly $140 million of these costs are for buyback programs im plemented since 1995, an indication of the increasing use of buybacks. The remaining $20 million were incurred during the 1970s and 1980s for programs to assist fishermen in the Northwest Salmon industry. There are proposals for six additional programs in othe r fisheries at an estimated cost of $160 million to $220 million. The Atlantic shar k fishery is among these proposals (GOA 2001). The Pacific Northwest salmon buyback is the longest running from 1976 to 1998. A total of five programs were implemented for the reduction of fishing effort, one of which also sought to purchase vessels as well as permits. Vessels purchased in the process were resold with restrictions. A total of 509 vessels and 1,160 permits were purchased over the period. The Bering sea groundfish program started in 1998 and is the most costly program implemented at a total cost of $90.2 milli on, $75 million of which was provided through a federal loan that is repayable over 30 years through a fee on landings of those remaining in the fishery (GOA 2001). The pr ogram bought out nine vessels and all the groundfish permits associated with them. Ve ssels were scrapped or prohibited from commercial fishing in the U.S.

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9 The second most costly was the New England grounfish . Two programs were implemented beginning in 1995 purchasing both vessels and permits. A total of $24 million was spent to remove 79 fishing vesse ls, groundfish permits, and all other permits associated with the vessel. Vessels were scrapped or transferred to other activities outside commercial fishing industry. The remaining programs include the Texa s shrimp program to buy shrimp permits and the Glacier Bay Dungeness Crab program designed to eliminate fishing in parts of Glacier Bay National Park and Preserve in Alaska. The latter program targets permits and vessel gear (GOA 2001). While buyback programs are generally effective in removing some proportion of capacity from the fishery in the short term, the program design ultimately determines the effectiveness of the program as a long-run capacity reduction tool Overcapacity in the Gulf of Mexico and South Atlantic Shark Fishery Concern over the level of fishing capacity in the Gulf of Mexico and South Atlantic Fishery shark fishery has prompted some sectors of the industry to propose a buyback program as a possible solution. Shar ks as a species group are particularly susceptible to over fishing because of thei r relatively low reproductive rate. Several individual shark species are cl assified as over fished, however , the large coastal pelagics are listed as the most affected shark species group. Recent stock assessments have shown large coastal sharks to be overfished while small coastal and pela gic species are fully fished (NMFS 2001). The regulatory agency has so far responded to the capacity problem by tightening regulatory measures and fishermen are faced with declining quotas. Quotas for overfished large coastal species have been successively reduced in recent years. In 2002 annual

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10 TACs were reduced to 1997 levels for all species groups of sharks. For example, the large coastal TAC was reduced from 1717 mt dw in 2003 to 1014 mt dw in 2004 (NMFS 2005). While there are a variety of methods for cap acity reduction, the lite rature points to the use of incentive adjusting strategies because they address the root causes of overcapacity. But such strategies involve inst itutional management changes that can take a considerable amount of time to implement. In the meantime, it is unlikely that shark stocks can continue to withstand the current level of fishing pressure. Thus immediate action is needed to start rebuilding shark stocks. A buyback program is a relatively quick solution to reduce effort and such programs also have socioeconomic benefits. Buybacks are politica lly acceptable policies that may improve biological and economic conditions in th e fishery in the short run. Unlike most regulatory measures, compensatio n to the fishermen who leave the fishery make buyback schemes acceptable to the industr y. Some coastal communities are heavily dependent on an active fishing fleet, and rapid movements to a market-rationalized fleet would certainly cause economic hardship. A buyback program can facilitate the tran sition to a more rationalized fishery. When fisheries are faced with low vessel pr ofits and resource rents, cooperation is difficult to achieve among fishermen. A successful buyback may restore profitability of a fishery. As an example, the industry initiated and financed buyback in the Pacific coast groundfish fishery improved attitudes and incenti ves in the fishery and is helping to lay the foundation for a planned program of ITQs (Squires et al 2006). Simply having fewer

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11 operators can contribute to increased coopera tion and those remaining fishermen tend to be the most committed to the long term economic viability of the fishery. Plan of Analysis The objective of this study is to determine if there is interest in a capacity reduction program on the Atlantic shark fishery and to determine what factors affect the level of interest and the estimated value of their fishing rights and assets. The specific objectives of this study are: i. to determine whether shark permit holders are willing to sell their fishing assets (i.e., permits and / or vessels) in a buyback program; ii. to determine the factor s affecting their willi ngness to sell; and iii. to determine whether transactions prices from similar programs can be used to (a) accurately predict the value of fishin g assets and (b) entice participation. Understanding how fishermen perceive such programs and how they value their fishing rights and assets will allow planners to anticipate the potential participation, extent of capacity reducti on, and implementation costs th at together determine the potential effectiveness and feasibilit y of conducting a buyback program. Such information is the first step in assisting in the design of an eff ective buy-back program that would have the greatest likelihood of being endorsed by the commercial shark fishermen in the Gulf of Mexico and Atlantic Regions. The study is divided into si x sections. This chapter has presented the problem of overcapacity and buyback programs, the latter a so lution to be consider ed for the Atlantic shark fishery. Chapter two describes the shark fishery in the Gulf of Mexico and South Atlantic Region, summarizing data on holders of shark permits over the period 2001 to 2003. Chapter three outlines the theoretical background for the analysis and the specific methodology used for empirical estimation. Ch apter four summarizes the key findings

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12 relating from a mail survey of shark fisher men conducted in April 2005. The fifth chapter presents the results of the empirical results . The final chapter disc usses the key findings of the study in relation to the research objectives, the implications of these findings for the shark fishery at large, and suggestions for the development of buyback programs for other fisheries.

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CHAPTER 2 COMMERCIAL ATLANTIC SHARK FISHERY Introduction For the purposes of this study, the Atlan tic shark fishery is comprised of all “active” shark permit holders, that is those w hose annual permit fee has been paid. Shark permits are classified as “directed” if the vessel owner targets shark species or “incidental” for owners that do not target shark. The permit number is the vessel number (i.e., U.S. Coast Guard number). This chapter includes information on shark permit holders; the species they landed, their de pendence on shark, and the revenue earned. Permit Ownership In April of 2004 there was a total of 605 active shark permit, 249 of which were directed and 356 were incidental. Excludi ng shark, a total of 3801 permits for other species were held. Shark permits holders also held permits for a variety of other species. The species with the largest number of permits held were tuna (304), swordfish (302), king mackerel (244) and Spanish mackerel (243). Other species were represented by fewer than 200 permits each (Table 2.1). The number and variety of permits shows that this is a multi-species fishery and fishermen regularly target other species in addition to shark. 13

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14 Table 2.1. Number and type of permits held by active commercial shark permit holders, 2004 Fishery Directed Incidental Total SERO Permits (April 2004):a Shark 249 356 605 Swordfish 118 186 304 Directed (89) (109) (198) Incidental (27) (66) (93) Handline (2) (11) (13) King Mackerel 108 136 244 Spanish Mackerel 110 133 243 G.O.M. Reef Fish (with, without traps) 81 104 185 S.A. Snapper/Grouper (with, without pots) 64 64 128 Unlimited Grouper (57) (54) (111) Trip Limit Grouper (7) (10) (17) Red Snapper 48 65 113 Lobster (commercial and tailing) 20 31 51 Rock Shrimp (open access) 1 9 10 Charter/Headboat 36 82 118 S.A. Snappe r/Grouper (15) (25) (40) Spanish Mackerel (13) (25) (38) G.O.M. Reef Fish (6) (15) (21) Coastal Migratory Pelagic (2) (17) (19) NERO Permits (July 2004): Atlantic Tunas 122 179 302 Longline (87) (129) (216) General (34) (44) (78) Charter (1) (6) (7) Bluefish 56 94 150 Spiny Dogfish 45 79 124 Scallop (open, limited access) 38 71 109 Monkfish (open, limited access) 35 73 108 Skates 34 68 102 Tilefish (open, limited access) 38 64 102 Groundfish (open, limited access) 26 69 95 Squid, Mackerel, Butterfish (open, limited access) 36 55 91 Herring 28 62 90 Black Sea Bass 20 54 74 American Lobster 12 47 59 Surfclam 16 35 51 Scup 10 39 49 Ocean Quahog 16 30 46 Red Crab (open, limited access) 13 33 46

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15 Table 2.1. Continued Fishery Directed Incidental Total Summer Flounder 5 38 43 Charter/Party 12 32 44 Squid, Mackerel, Butterfish (3) (7) (10) Scup (1) (5) (6) Groundfish (1) (3) (4) Bluefish (2) (7) (9) Black Sea Bass (2) (4) (6) Flounder (3) (6) (9) Total 1397 2288 3686 a The sample of permit holders included those with act ive directed or incidental permits obtained on two days approximately one month apart. The first and second lists contained 594 and 599 permits. b Information on 24 of the 605 was missing such that the number of permits may be underestimated. Landings Data In order to determine the annual revenues associated with each federal fishing permit listed in Table 2.1 for each of the 605 vessels identified in section, total annual revenues were obtained by fishery for the mo st recent 3-year period( 2001 to 2003) in order to determine the extent of, and vari ability in, economic dependence on the various fisheries. Calculation of total revenues by vessel and species requires use of multiple NMFS data sources. This is because landings ar e reported on distinct logbooks. We began with data from the pelagic longline data program for highly migratory species (HMS) and the coastal fisheries data program for snapper/gr oupers, coastal sharks, and mackerel. Ideally, the annual revenues would be calculated us ing intra-annual (monthl y or quarterly) and regional data (to the extent possible) in or der to account for seas onal and regional fish prices and individual fish weights (and yields) that can affect revenue estimates. Given that such precision is beyond the scope of this project, common fish prices and weights are used in this analysis to calculate total dockside revenues landed by each vessel.

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16 Total Revenues in 2003 A profile of the recent shark fishery was created by summarizing information on landings and associated total revenues for all vessels that landed any species in 2003, the most recent year in which a full set of logbook data were available. The sample was composed of 474 vessels that are holders of shark permits. The sample contained 197 directed shark permits and 317 incidental sh ark permits. The data on the 474 permits was sorted by total revenue and divided into nine groups. The total dockside fish revenue in 2003 for all vessels ranged from $0 (131 vesse ls did not land any species) to $1.6 million. Revenue groups of $25,000 to $74,999 and $150,000 to $249,999 had the highest frequencies with 82 vessels in each of th e groups. The second highest revenue group ($750,000 to $999,999) had just 9 vessels. In most revenue groups the number of vessels with incidental permits exceeded those with di rected permits. In particular for incidental permit holders those with revenue from $0 to $4,999, $150,000 to $249,999, and $250,000 to $499,999 had the largest number of vessels (46, 49, and 52 respectively) (Figure 2.1) 0 10 20 30 40 50 600 to 4.95 to 24.9 25 to 74.9 75 to 149.9 150 to 249.9 250 to 499.9 500to 749.9 750 to 999.9 1mill to 1.6millTotal revenue per vessel (Thousands $)Number of vessels Directed Incidental Figure 2.1.Frequency of Revenues per Ve ssel by Type of Shark Permit, 2003

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17 Landings by Species Group in 2003 The composition of total dockside re venues by species landed supports the hypothesis that the shark fishery is indeed a multi-species fishery. Analysis of the 2003 data showed that shark species contributed a low percentage to overall total revenues at the vessel level. The average total dockside revenue for 2003 was about $160,000. Shark comprised 11% of all directed landings, 1% of incidental landings and only 4% of overall landings by value (Figure 2.2). The species compositions by permit type showed key species in terms of revenue in 2003 were swordf ish, tuna, grouper snapper and flounder. $0 $40,000 $80,000 $120,000 $160,000 $200,000 D and I (n=474)Directed (n=197)Incidental (n=277) Shark Snapper Grouper Tuna Swordfish Other Figure 2.2. Average vessel revenue and species composition in total and by shark permit type, 2003 Shark Landings in 2003 The average value of shark of shark la ndings from 2001 to 2003 showed that the average revenues have not varied greatly with only a slight increase in 2002. Shark revenue was generally around $8,000 per vessel in each year (Figure 2.3).

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18 $0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000 $8,000 $9,000 2001 2002 2003Average shark revenue Figure 2.3. Average value of shark landings 2001-2003 Shark contributes a greater share to tota l revenues for vessels with the lowest reported levels of total dockside fishing revenue in 2003. Those with revenues between $5,000 and $24,000 had the largest share of shark revenue at 38% followed closely by those with revenues below $5,000 and thos e in the $25,000 to $74,999 range at 34% and 31%, respectively (Figure 2.4). 0.50.6 4.5 3.9 8.1 14.5 30.7 34.4 37.8 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 0 to 4.9 5 to 24.9 25 to 74.9 75 to 149.9 150 to 249.9 250 to 499.9 500to 749.9 750 to 999.9 1mill to1.6mill Total revenue per vessel (Thousands $) Share of total revenue Directed Incidental Figure 2. 4. Shark share of total revenue by revenue group and shark permit type, 2003

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19 The majority of shark revenues are genera lly derived from directed shark permits. In 2003, 81% of all shark revenues were derive d from directed permits and the remaining 19% from incidental permits. Average reve nues from shark for 2003 ranged from $ 236 to $4,867 per vessel for incidental permits and $2,412 to $29,057 per vessel for directed permits (Figure 2.5). $0 $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 0 to 4.9 5 to 24.9 25 to 74.9 75 to 149.9 150 to 249.9 250 to 499.9 500to 749.9 750 to 999.9 1mill to 1.6mill Total revenue per vessel (Thousands $)Average revenue Directed Incidental Figure 2.5. Average shark revenue per vesse l by total revenue group and permit type, 2003 Summary The Atlantic shark fishery is a multi-species fishery in which owners of shark permits also hold a variety of other federal commercial fishery permits. An analysis of the total dockside revenue in 2003 indicates that shark co mprises a small proportion of overall earnings from commercial fishing in federal waters. Higher revenue vessels tend to rely more on tuna and swordfish species for their business while the lower revenue vessels are more dependent on species such as groupers and mackerel. Latent capacity is evident in this fishery with a relatively large number of not in use (e.g. 131 of 605; 22%

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20 of vessels with active shark permits in 2003 did not report landing any species). The shark fishery is characterized by diversity in the species landed, th e revenues earned, and in permit use, which has implications fo r the development of a shark permit buyback program.

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CHAPTER 3 THEORY AND METHODOLOGY Introduction This chapter summarizes the theoretical a nd methodological concepts used in this study to examine the decision making process of an owner of fishing assets with respect to a potential buyback program fo r the Atlantic shark fishery. The contingent valuation methodology, which forms the basis for obtaining information on how shark fishermen value their fishing assets, is discussed. The double hurdle approach is used to describe the encompassing decision process in which the contingent valuation is embedded. The chapter concludes by showing how these con cepts are combined and adapted to the researchable problem. Overview of Contingent Valuation Contingent valuation (CV) is a survey approach that is used to indirectly value a non-market good (or a good for which there is currently no active ma rket). With this approach, a hypothetical market is descri bed by defining the good itself (e.g. fishing assets), the context in whic h it would be provided (i.e., buyback program) and the way it would be financed (government loan). Each respondent is asked about how much they would be willing to pay for the good or conve rsely how much compensation they would demand to give up the good. In early app lications of this methodology, respondents would be asked to express their maxi mum willingness-to-pay (WTP) or minimum willingness-to-accept (WTA) fo r a hypothetical (e.g., proposed) change in the level of provision of the good or service. Theoretically, contingent valu ation is rooted in the neo21

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22 classical concept of economic value base d on individual utility maximization. This assumes stated WTP amounts are related to respondents underlyi ng preferences in a consistent manner. The technique derives its name from the fact that the value estimates are contingent upon a hypothetical scenario th at is presented to each respondent for valuing. While CV is an accepted method for va luing non-market goods, there are some weaknesses associated with this method. Questio nnaires designed to as k an individual for payment to acquire a good (WTP) should provide similar results as questionnaires designed to ask an individual how much comp ensation is required to give up the same good (WTA). However, empirical studies have shown large disparities between WTP and WTA values obtained using CV (Cummings et al ., 1986). Generally, WTA values tend to be greater than WTP. While the precise cause of this disparity betw een the two measures is unclear, one possible explanation is c ognitive dissonance. The difference between WTP and WTA depends not only on income effe cts (as the approaches make individuals poorer and richer, respectively) but also substitution eff ects associated with small increments in the provision of a good. Substitu tion effects refer to the ease with which other privately marketed commodities can be substituted for the non-market good while maintaining the individual at a fixed level of utility. If large empirical divergences between WTP and WTA are an indication of a general percepti on on the part of individuals surveyed that the private market goods in their choice set are co llectively an imperfect substitute for the non-market good under consideration, then the divergence may be due to substitution effects and are not reflective of a failure in survey design or methodology (Haneman 1991).

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23 While the general CV appro ach has its drawbacks, designing the questions based on the unique characteristics of a particular decision problem can help to alleviate some of the potential weaknesses of the approach. To that end, there are three primary formats for asking CV-type questions: (i) open-ended, where respondents are asked to specify a sum; (ii) sequential bids, where respondents are asked whether or not they would pay or accept some specified sum, then the question is repeated using a higher or lower amount depending on the initial response; and (iii ) closed-ended, where respondents are asked only whether they would pay or accept a si ngle specific sum that is varied across respondents. The choice of question format, in part, will depend on the type of survey being considered. For example, the sequential bi d process (approach ii) is ineffective with a mail survey. A mail survey is, however, of ten preferred due to the difficulty of obtaining a representative sample from random digit dialing or email address lists. Mail surveys are also cost effective. The comple xity of the non-market goods in terms of the amount of background information that needs to be provided can also preclude the use of a telephone survey outright. The closed-ended CV (approach iii) ha s grown in popularity as the preferred method for a number of reasons. This approach is preferred because it presents respondents with a task that is similar to that which they en counter in their usual market transactions; a price is quoted and the re spondent simply decides whether to accept or ‘purchase’, which relieves the respondent of the unfamiliar task of calculating a dollar value as with the open-ended question format (a pproach i). Due to the nature of this task, the closed-ended approach is also referred to as a “dichotomous choi ce” or “referendum,” such as if the respondent were being asked to vote. This approach to CV modeling is also

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24 free of starting point bias that is inherent in iterative bidd ing games (approach ii), and it reduces the opportunity for strategic behavior such as extreme over pledging that is characteristic of open-ended questions (approach i). The closed-ended CV format may also encourage participation a nd reduce protest res ponses relative to ot her formats due to the relative ease and familiarity of the task. While dichotomous choice CV has emerged as the preferred method, its main criticism is that it is subject to ‘yeah-saying’ bias (Desvouges et al., 1993). This occurs if respondents misrepresent valuation responses in an attempt to comply with a presumed expectation of a social ly desirable affirmative response. In the case of the Atlantic shark fishery, a buyout program was proposed by and, thus, supported by a group of harvesters. If a buyout program presents an economically desirable alternativ e in the eyes of fishermen, and particularly in the eyes of a vocal industry group, this may result in ‘yeah saying’ associated with establishing the program. Recall that with the closed-ended CV form at the researcher must provide a value to which the respondent can react, which is usually randomly selected from a range of realistic values. This is necessary since respondent characteristics are usually unknown prior to implementation of the survey. Studies suggest that values ar e not neutral stimuli since respondents WTA and/or WTP is to some extent an outcome of the bid they are presented with (Alberini, 1995; Boyle et al ., 1998, Cooper and Loomis, 1992). In order to reduce biases associated with this phenome non, Boyle et al. (1998) s uggests that values should not be randomly assigned to respondents, but rather they s hould be assigned based on individual information. A prediction of values can, thus, help to improve the efficiency of estimates. This can be done with pre-testing usin g on open ended question

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25 that allows the researcher to see what th e respondents’ values might be. Given that information, a routine can be designed to assign predicted values that might be more amenable to each individual. Development of Contingent Valuation Bid for this Study The hypothetical market in this study is a time-limited buyback program whereby each shark permit holder will be presented with a value that they can accept in exchange for their fishing assets. In developing the va lues (bids) for this study, information on the vessels past fishing activities as well as information on bid formulation from accepted bids in past buyback programs was combined to generate and assign bids. Thus, an important part of this study considers whether it is possible to formulate a bid based on a vessel’s past performance that will elicit a positive response from owners. Would these values accurately reflect the value of fishi ng assets as perceived by shark permit owners? The first step to developing unique bids for each vessel owner was to estimate a relationship between bid ra tios ( bid value divided by respective landings amount) associated with the successful bids and landings from the recent Pacific Northwest ground fish buyback program: Bid Ratio = 2.935 – 0.0000043 * Landings (3.1) (8.84) (3.23) The equation, although simplistic, explained 91 % of the variation in the bid ratios associated with average landings in value from approximately $5,000 to $450,000. Equation (3.1) predicts a declining bid ratio to total landings (as measured in dollars) as total landings increases. The ratio is then multiplied by average landings to get the actual bid. In this way, vessels were assigned unique bid amounts based on their own past fishing behavior.

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26 A technique to capture additional information on the value of the proposed nonmarket good in contingent choice surveys is to solicit the resp ondents’ strength of preference regarding their choice to accept or de cline a bid offer or to select a preferred alternative among a list. If selecting from a list, strength of preference elic itation allows respondents to first choose th eir preferred policy option then choose th eir strength of preference for that option. Stre ngth of preference indicato rs may take the form of ‘strongly preferred’, ‘moderately preferred’, or ‘slightly pref erred’ for a chosen option or ‘strongly rejected’, ‘moderately rejected’, or ‘slightly rejected’ for a rejected option. Comparisons with typical contingent choice formats suggest that ordered strength of preference models (such as estimated by ordered logit or probit) may provide additional efficiency to model estimations (Johnson and Swallow 1999). Thus, a strength of preference measure will be incorporat ed into the design of this study. Double Hurdle Models The double hurdle approach has been widely used in consumer literature to model two sequential decisions such as whether to participate in a hy pothetical (proposed) market and then if so, how much to partic ipate. In the case of a consumer good, a double hurdle model involves the estimation of two equations: an initial dichotomous choice model to explain participati on and a model to explain the consumption level. The double hurdle model is unique in that it allows for the two decisions to be affected by different sets of variables. Also explanatory variable s may have differential and opposite effects in the two stages (hurdles). In this way the model is more flexible. The double-hurdle model was first develope d by Cragg (1971) as an extension to the model developed by Tobin (1958) to analyze censored data (such as when the first stage information is not explic itly known so the data has a large share of zero values).

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27 Subsequently, the double-hurdle regression model has become a general framework employed in many different consumer-choice prob lems; and, because of its structure, it also lends itself well to CVM studies. Recent work by Martinez-Espineira (2004) and Mabiso (2005) are such examples whereby th e first hurdle determines willingness to pay (i.e., participate), and the s econd hurdle establishes the amount of payment (bid value) contingent upon “clearing” the first hurdle. Application to the Atlantic Shark Fishery Given the past precedent of the so-called “reverse bid au ction” process in buyback implementation in U.S. fisheries, the availa bility of permit and landings information on owners of Atlantic commercial shark permit hol ders, and the need to use the mail survey format, a closed-ended question format is best suited for this survey design. The closed ended approach presents the respondent with a question that requires a simple yes or no answer. By using a dichotomous choice CV appr oach to create a hypothetical market we can elicit potential pa rticipation. And by incorporating st rength of preference techniques in the approach for those willing to particip ate in the program we can better determine the level of expected participation. By co mbining dichotomous choice and strength of preference methodology of CV it is possible to use the double hurdle modeling approach to (1) obtain an estimate of the overall pa rticipation rate and (2) an estimate of the likelihood of bid acceptance contingent upon the unique bid. Each step is discussed in application to the Atlantic commercial shark fishery below. First Hurdle: Probit Model The first stage of this double-hurdle regres sion incorporates fact ors influencing an owner’s willingness to sell (WTS) their fishi ng assets. Respondents were asked whether or not they would be willing to sell their fi shing assets (shark permit only and then the

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28 vessel with all permits) for a reasonable pri ce. The question required a simple ‘yes’ ‘no’ or response. Given this binary format respons e, linear regression an alysis cannot be used for estimation since it assumes a continuous dependent variable. A more suitable approach is probit analysis. The probit mode l is a probability mode l with two categories in the dependent variable (Liao, 1994). The binary dependent variable, takes on a value of zero and one. The outcomes of the dependent variable (WTS) are mutually exclusive and exhaustive. WTS, depends on k observable variables Xk, where k= 1,,k (Aldrich and Nelson, 1984). The probit model is as follows: ijijk ijXWTS (3.2) where WTSij is a variable which takes on a value of one when the owner is willing to sell, and zero when the owner is not willing to sell. Xijk is a vector of expl anatory variables and is a vector of coefficients. The subscript i denotes the vessel owner, k denotes the type of information (such as permit owner info rmation, fishing business, or household information) and j denotes the type of mode l (either vessel or permit). i is the error term. The probability WTSij takes on a value of one is given by: )(1 ) Pr()1Pr(ijk ijk ij ijk ijx x xWTS (3.3) where is the cumulative normal distribution function of the random error ij . The probit model assumes that the data are generated from a random sample of size n with the sample observation denoted by i , i = 1, , n. Thus, the observations of WTS must be statistically independe nt of each other; this rule s out serial correlation. In addition, the model assumes that the independent variables (the res ponses to the survey

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29 questions) are random variables and that there is no exact linear dependence among the Xk’ s. This implies that n > k , that each Xk has some variation across observations, and that no two or more Xk’s are perfectly correlated. The probit model is estimated usi ng the method of Maximum Likelihood Estimation (MLE). MLE selects parameter estimates that give the highest probability of obtaining the observed sample of the dependent variable (i.e., WTS). The main principle of MLE is to choose as an estimate of the set of k variables that would maximize the likelihood of having observed this particular sample (Aldrich and Nelson, 1984). The parameter estimates in the probit model only provides information on the directional effect of an explanatory variable. They can not tell us the magnitude of the effects. In order to fully explain the impacts of variables, the marg inal effects of the significant variables on the proba bilities were estimated. One way to determine the effect of each variable on the predicte d probability is to use measures of partial change in the probability due to a change in the independent variable, also referred to as the marginal effect. The marginal effect is computed by ta king the partial derivative of the estimated probability equation and taking the partial deri vative with respect to an independent variable. The marginal effect depends on the values of the ’s for all variables and the levels of all X ’s. One method is to compute the ma rginal effect at the mean of the independent variables. This gives the probability for an ‘average’ member of the sample. This approach is suitable for continuous independent variables but not for binary variables (Long 1997). For the latter variables, a measure of change in probability for a discrete change in the independent variable is used.

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30 Marginal effects for the significant con tinuous variables in the probit model are calculated using the following equation evalua ted at the sample means of the data: )( )1(Pr X X WTS (3.9) where is the standard normal density. In case of binary variables, the first derivative result does not apply. The full effect of a binary variable can be interpre ted by the difference in probabilities when the equation is evaluated at both levels of th e binary variable w ith other explanatory variables at their mean values (Greene 2000). Th erefore, the marginal effect of a binary variable is: 0,1Pr1,1Pr bXWTS bXWTS (3.10) where X equals the mean of all other variables and b is the binary explanatory variable. The marginal values for ordered probit models are derived using the same approaches as those for the probit effects; however methodology differs slightly due to the additional number of categories in the depe ndent variable associated with the ordered probit model. Marginal effects for the significant continuous variable s were calculated using the following equations evaluated at the sa mple means of the data (Greene 2000): X X LTA X X X LTA X X LTA 3Pr )2(Pr( )( )1(Pr (3.11)

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31 where is the standard normal density, X is a vector of expl anatory variables, and is the vector of coefficients. Second Hurdle: Ordered Probit Model The second hurdle differs from other d ouble hurdle models by using an orderedprobit analysis instead of a tobit analysis. In this study, the second hurdle models, for those willing to sell, how likely they are to accept the specific dollar bid amount offered in the questionnaire. Each respondent was pr esented with a unique bid amount generated using equation 3.1 and the average value of their past landings. Two unique amounts were presented, one for the surrender for the shark permit alone and another for the vessel and all permits. Respondents were asked for their likelihood of acceptance (LTA) of each bid on an ordinal scale of 0% to 100%; spec ifically respondents were asked to identify the likelihood that was best reflected by per centages within this range that varied in 25% increments (i.e., a closed-ended question with five possible mutually -exclusive answers). The model categories were combined to form three categories that reflect the three respondent groups that are of most interest for purposes of the study, these are: those who rejected the bid offer (LTA is 0%), those who are most likely to accept the bid (LTA is 75% or 100%) and all others (LTA is 25% or 50%). As such, the dependent variable of the second hurdle is categorically ordered as either 0%, 25-50%, 75-100%. With this type of data on the dependent variable, ordinary least squares (or tobit) regression analysis is not appropriate because it does not account for the discrete ordered nature of the outcomes. Instead the ordered probit model is preferred because it allows for the estimation of a discrete ordered depe ndent variable. The resulting ordered probit model is used to explain each re spondents LTA their unique bid.

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32 ijij ijk ijIMRXLTA (3.4) %100%75 3 %50%25 2 %0 1 orisLTAwhen orisLTAwhen isLTAwhen LTAij where Xijk is a vector of explanatory variables and is a vector of coefficients. The subscript i denotes the vessel owner, k denotes the type of information (permit owner information, fishing business, or household information) and j denotes the type of model (either vessel or permit). i is the error term and IMRi is the Inverse Mills ratio which, is the ratio of the probability density function over the cumulative density function: X X IMR 1 (3.5) This ratio calculated in the first hurdl e probit equation (3.2) is included as a independent variable in the ordered prob it estimation. Including the IMR produces consistent estimation of the parameters in the WTA bid equation and it also provides a test for the presence of sample selection bias (Long, 1997). This type of bias can arise when there are unobservable factors (e.g. expe ctations on the future of the shark fishery) that increase both the probability of partic ipation and the likelihood to accept (i.e. WTS and LTA). Therefore, it could happen that the choice of participating in the buyout may coincide with increased likelihood to accept. If the IMR coefficient is not statistically different from zero then the estimation is not biased by unobservable variables. The probabilities for the three LTA categories are given by X LTAprob X X LTAprob X LTAprob 1)3( )(2 11 (3.6)

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33 where is an unknown threshold or cut point parameter estimated with . For binary variables in the ordered prob it, the effect of th e variable can be interpreted using the difference in probabiliti es when the equation is evaluated at both levels of the binary variable with other explanatory variables at their mean values (Greene 2000) for each of the three categories. Therefore, the marginal effect of a binary variable is: 0,3Pr1,3Pr 0,2Pr1,2Pr 0,1Pr1,1Pr bXLTA bXLTA bXLTA bXLTA bXLTA bXLTA (3.12) where X equals the mean of all other variables and b is the binary explanatory variable. Empirical Model Variables and Hypotheses The explanatory variables for equations (3 .2) and (3.4) can be divided into three groups: fishing business and vessel information, permit owner information, and household information. A detailed description of each variable is contained in table 3.1. Fishing Business and Vessel Variables This set of variable includes descriptors of the business that include ownership and earnings. These variables describe key aspects of the business that influence the decision making process. Plans for the future of the business affect the decision making process in terms of the planning horizon of the business. An individual who plans to expand the business behaves differently to one who plans to exit (LEXIT=1). He has a longer plan ning horizon and considers potential future earnings and is expected to be more reluctan t to surrender assets, at least not for a price

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34 that does not at least reflect these future ea rnings. These variables allow us to assess what effect future plans for the business have on the probability of selling the fishing assets. We expect that those planning to leave the industry have a greater probability of being willing to sell their fishing assets. They are also more likely to accept the bid offer than those who do not plan to exit. The ownership structure of the business affects the ease with which any decisions can be made. A corporately owned vessel (C ORPN =1) has a more complex decision structure in that any decision is likely to i nvolve multiple persons compared to a vessel owned as a sole proprietorship. Therefore, th e coefficient for CORPN is expected to be negative, indicating a lower probability of being willing to sell fishing assets. Fishing revenues are hypothesized to be influential in explaining whether a permit owner is willing to sell fishing assets of the decision process. A commercial fishing enterprise is in fact a business with profit ma ximization as the assumed objective. In this study, past earnings are taken as an indicator of future ea rnings potential and so higher average fishing revenues (AVETR) are expected to decrease the probability of being willing to sell fishing assets. The average revenue from shark (AVESK) is expected to affect the probability of selling the shark permit and the likelihood of accepting the bid to surrender the permit. Increasing shark revenues are hypothesized to decrease the probability of selling the permit and the probability of accepting the bid. The type of fishing permits owned is belie ved to have an impact on the decision making process. It is expected that havi ng a directed shark permit (DIREC) reduces the probability of being willing to sell the shark permit and the probability of being likely to

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35 accept the permit bid. This is because shark is targeted species for these permit owners. Whether or not the shark permit holder owns a tuna or swordfish permit is also expected to influence the decision process. Federal fi shing regulations require that holders of directed swordfish or tuna permits must also hold an incidental shark permit. It is hypothesized that swordfish and tuna permit hol ders will be reluctant to surrender their shark permits since they are a necessary ope rating requirement. Therefore owning a tuna or swordfish permit is expected to reduce the probability of being willing to sell the fishing assets and the probability of bei ng likely to accepting the bid offer for these assets. In some cases vessels did not report la nding any species (NOL AN). The absence of any landings is expected to increase the prob ability of being willing to sell the fishing assets since the fishing assets are not be ing used. When the owner is not landing any species, no fishing revenues are being generated and so we expect that the absence of landings will increase the probability of being likely to accept the bid offer. Vessel characteristics affect the fishi ng capacity. However information such as horsepower, length, age, and gear for each vessel was not available. Only vessel age (VAGE) was available. The expected sign for VAGE is unclear. This is because while, intuitively, one might expect an increased pr obability of selling when the vessel is older, this may not be the case as old vessels ma y be upgraded. Therefore the coefficient may be either positive or negative. Permit Owner Information This group of variables describes owner de mographics, attitudes and perceptions of the shark fishery. The shark fishery is a h eavily regulated industry that has been subject to quotas and permit moratoriums among ot her management measures. Variables are

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36 included that capture attitude toward additional proposed measures. The main variables of interest capture owner attitudes toward buyback programs as proposed measures to address overcapacity in the industry. It is expected that those who support buyback of permits and all fishing assets (DBYPER =1 ; and DBYALL=1 respectively) will have a greater probability of being willing to sell their fishing assets an d also have a higher probability of being likely to accept the bid offered to surrender their assets. Since an implementation plan for a sh ark buyback program has not yet been drafted, we can assume that any detailed info rmation about such a pl an is mostly in the form of rumors, including information on th e prices owners would receive to surrender their assets. The DAWARE variable identifi es owners that are aware of a potential buyback in the industry. The impact this knowledge has on the probabi lity of being likely to accept the bid amount offered in the survey is unknown. However, if owners are expecting artificially high pr ices (as anticipated based on focus group conversations with industry cooperators with the project), then the probability of their LTA will be lower. The variable VALUE captures those that have ever tried to calculate the value their vessels and/or permits. Those who are aware of the market value of their fishing assets since they have tried to calculate them are expected to have a higher LTA probability depending on their values relative to the bids. A willingness to pay a tax on future landin gs to fund a buyback program variable (DSHTAX=1) is also included in the estimati ons. This variable is used to account for ‘yeah saying’ behavior of respondents. If t hose who are willing to pay the tax have a higher WTS or LTA probability, then this wi ll indicate yeah saying since those people

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37 are also the same who would participate in a buyback and as result they would not be the ones who would actually have to pay the tax. Commercial fishing experience measured in years (YRSEXP) is also included. It is expected that the greater th e experience in fishing, the lower the probability the individual will be willing to sell their fishing assets or to be likely to accept the bid. They are assumed to have gained experience to perform more efficiently over time. Demographic variables include owner age in years (OWAGE), poor general health (DGHLTH=1), and an education level va riable for education above high school (DDEGREE=1). All of these variables are expected to have positive coefficients, indicating a higher WTS and LTA bid probabilities. Household Information This includes two dummy variables to id entify higher levels of total annual household income (DHHINC2 and DHHINC3) and the proportion of household income derived from commercial fishi ng (FSHINC). It is expected that higher income households and those less dependent on fishing for income will have a greater probability of being willing to sell their fishing assets and likely to accept the bid. The presence of dependents in the hous ehold (DDEPEN=1) may also influence buyback decisions, particularly if it is a fam ily business and fishing is the major source of household income. Surrendering this income represents a greater risk in terms of maintaining financial and livelihood security of the family and having dependents is expected to reduce the probability of being wi lling to sell fishing assets and of being likely to accept the bid offer.

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38 Table 3.1. Description of model variables Variable Variable description and definition Dependent variables WTS1 WTS vessel and all permits (1 if yes , 0 if no) WTS2 WTS shark permit (1 if yes , 0 if no) LTA1 LTA bid for vessel and a ll permits (1= 0%, 2=25%, 50%, 3=75%, 100%) LTA2 LTA bid for shark permit (1= 0%, 2=25%, 50%, 3=75%, 100%) Fishing business and vessel AVESK Average annual shark revenue (Thousands $) AVETR Average annual total revenue (Thousands $) CORPN vessel ownership (1 if owned by corporation or part nership, 0 otherwise) DIREC shark permit type (1 if directed , 0 if incidental) LEXIT Plan to exit the industry in 3 years (1 if yes, 0 otherwise) NOLAN No landings from 2001 to03 (1 if no, 0 otherwise) SKSHR Shark share of total revenue (%) TUNSWO Tuna and/or swordfish permit (1 if vessel has a swordfish and/or tuna permit, 0 otherwise) VALUE Aware of vessel and permits value (1 if they have tried to calculate value of vessel and permits, 0 otherwise) VDEBT Debt on the vessel (1 if yes, 0 otherwise VSAGE Vessel age (years) VINSR Insured vessel (1 if the vessel is insured , 0 otherwise) Owner Information DAWARE Aware of potential shark b uyback program (1 if yes, 0 if no) DBYALL Support vessel buyback progr ams in general (1 if yes, 0 if no) DBYPER Support permit buyback programs in general (1 if yes, 0 if no) DEGREE education level (1 if responde nt has more than high school, 0 otherwise) DGHLTH Poor health (1 if yes, 0 if no) DSHTAX Willing to pay tax on shark landings (1 if yes, 0 if no) FSEXP Commerci al fishing experience (years) OWAGE Owner age (years) Household information DEPEN Dependents in the household (1 if yes, 0 if no) DHHINC1 Annual household income (1 if income < $50,000, 0 if no) DHHINC2 Annual household income (1 if income $50,000 to $99,999, 0 if no) DHHINC3 Annual household income (1 if income > $100,000, 0 if no) FSINC Proportion of household income from fishing (%) IMR1 Inverse mills ratio for WTS1 model variables IMR2 Inverse mills ratio for WTS2 model variable Threshold parameter Model Specifications A total of four models were estimated. Two binary probit models to explain the first hurdle of willingness to sell fishing assets (one for the permit and one for the vessel

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39 and all federal permits) and two ordered prob it models for the second hurdle to explain the likelihood to accept the bid amount offered (one for the permit and one for the vessel and all federal permits). The empirical model contains variables from each of the four categories discussed in the previous section and are explicitly defined in turn below. The probit model to explain the willingne ss of a shark permit owner to sell their shark permit (WTS1 =1 if yes, 0 if no) is given by: FSINC DHHINC3 DHHINC2 OWAGE FSEXP LEXIT DSHTAX DGHLTH DEPEN DDEGREE DBYPER VSAGE VDEBT TUNSWO SKSHR NOLAN DIREC CORPN AVESK19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 101 WTS (3.13) This model differs from the other first hurdle model, which explains the WTS their vessels and all federal permits (WTS2) in that it contains variables specific to the shark permit. These are average annual shark revenue (AVESK) and shark share of total revenue (SKSHR). These variable s show the importance of shark species to the business. The “vessel model” (WTS2) considers the vessel and all species permits, and as such the average total revenu e of all species is used (AVETR) to replace AVESK. A vessel insurance (VINSR) variable was also included. Having an insured vessel is believed to have a greater impact on the wi llingness to sell the vessel and all permits, than that of selling the permit only. The probit model to explain WTS2 (1 if yes, 0 if no) is thus given by: FSINC DHHINC3 DHHINC2 DEPEN OWAGE LEXIT FSEXP DSHTAX DGHLTH DDEGREE DBYALL VSAGE VINSR VDEBT TUNSWO NOLAN DIREC CORPN AVETR19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 102 WTS (3.14)

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40 The second hurdle models contain additiona l explanatory variables. These variables are hypothesized to have a greater bearing on the LTA decision (which require an assessment of asset values) than the WTS d ecision. Whether the permit holder has ever tried to calculate the fair market value of their fishing assets (VALUE) or whether they were aware of the potential for a buyout pr ogram in the shark fishery (DAWARE). Since rumors of a program and values existed, th ese two variables were believed to uniquely affect their stated LTA decision. The ordered probit model explaining th e LTA the unique bid to surrender their shark fishing permit is given by: IMR1 FSINC DHHINC3 DHHINC2 DEPEN VALUE OWAGE LEXIT FSEXP DSHTAX DGHLTH DDEGREE DBYALL DAWARE VSAGE VDEBT TUNSWO SKSHR NOLAN DIREC CORPN AVESK21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 101 LTA (3.15) where LTA1=1 if the respondent indicates that their likelihood of accepting the proposed bid was 0%; LTA1=2 if respondent indicates a 25% or 50% LTA the unique bid; and LTA1=3 if respondent indicates a 75% or 100% LTA the bid. As with the probit models, the permit ordered probit model includes permit specific information for revenue, average shark re venue (AVESK) and shark share of total revenue (SKSHR), while the vessel ordered probit considers all species. The vessel model also includes a vessel insurance variable (VINSR) which is believed to have a greater influence on the likelihood to accept a spec ified bid. It is expected that if the vessel is insured then the owne r will prefer a higher price that accounts for the cost of

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41 insurance premiums, thus the vessel insu rance coefficient should have a negative coefficient. The ordered probit model to explain a respo ndent’s likelihood to accept the unique bid derived to surrender their vessel and all federal permits is given by: IMR2 FSINC DHHINC3 DHHINC2 DEPEN VALUE OWAGE LEXIT FSEXP DSHTAX DGHLTH DDEGREE DBYALL DAWARE VSAGE VDEBT TUNSWO SKSHR NOLAN DIREC CORPN AVESK21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 102 iLTA (3.16) where LTA2i is either 1, 2, or 3 depending on the indicated LTA, which is defined the same as LTA1i. Potential Estimation Problems Probit models are sensitive to missp ecification. Model estimators will be inconsistent if an explanatory variable is om itted or if there is heteroskedasticity. Testing for an omitted explanatory variable can be u ndertaken using a likelihood ratio (LR) test. The LR test determines if the slope of the log likelihood function evaluated at the restriction of zero slopes is significantly diffe rent from zero. If the restriction holds true then the slope of the likelihood function at th is point should not be significantly different from zero, in which case the set of explanat ory variables is concluded to adequately explain the variation in the dependent variable (Maddala 2001). Multicollinearity between variables was a concern in estimation since variables appear to be associated with each other. For example it’s expected that people with directed shark permits (DIREC) have highe r average shark revenue (AVESK) and these two variables may have high correlation coefficients. Simple correlations detect

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42 collinearity between two specific variables but it does not detect collinearity for cases in which three or more variables are collinear. In order to detect this type of collinearity the conditional index numbers were calculat ed for each set of econometric model explanatory variables. If the conditional num ber is greater than 30 then there is high degree of multicollinearity in the model (Kennedy 1992).

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CHAPTER 4 SURVEY RESULTS Introduction The primary objective of the survey wa s to determine the willingness of shark permit holders to sell their fishing assets and how they value those a ssets. The survey also aimed to collect some general demographic income information to better understand the factors motivating shark permit holders. The first section of the survey was designed to elicit information on permit holders’ fishing goals and management preferences. The second section collected specific information on the respondent’s fishing op eration, including specific buyback questions asking whether the respondent was willing to sell (WTS) the fishing enterprise (i.e., shark permit only or all permits and the vessel). It also included corresponding willingness to accept (WTA) compensation questions. The WTAtype question, sought to elicit the likelihood that the permit owner would be to accept a given bid amount (LTA), which was generated for each vessel based on past land ings using a predicted bid ratio from the successful bids in a recent buyback pr ogram. The LTA was elicited by asking respondents to identify the likelihood among five mutually exclusive choices from 0% (not at all likely) to 100% (absolutely sure) that they would accept the bid amount offered to forfeit their fishing assets. The specific choice set was 0%, 25%, 50%, 75% or 100%. The final section gathered socio-demographic information. Information on the 605 permit owners a nd vessels (including landings histories from 2001 through 2003, the most recent comple te years of available data) were obtained 41

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42 from various NMFS databases. This populati on was reduced to an effective population of 551 unique owners in early 2005 that continue d to have a permit, a valid mailing address, and ownership of multiple permits. The mail survey was sent to all 551 permit owners regardless of type of shark permit held or wh ether or not they repo rted any landings from 2001 through 2003. A total of 322 responses were received for an overall response rate of 59%. Details of the survey are contained in a report submitted to the Gulf and South Atlantic Fisheries Foundation(2005). Household and Permit Owner Information Vessels landing shark in 2003 were primarily located in Florida, but also included the gulf coast and up the east coast as far as Maine (Table 4.1). The survey was able to capture responses for all the main shark fishery areas. Table 4.1.Geographic distribution of population and survey response Homeport Region Number of Permits onse egion Resp Rate by R TX, LA, MS, A 82 (14%) L 22% FL 30 (51%) GA, SC, NC, V 78 (13%) MD – Maine 45% A 136 (22%) 54% 51% Approximf (50.8%) of the shark permit holders were over 50 years of age. The average age of vessels owners was 51.6 y ears. The age of vessel owners ranged from 28 years to 82 years. The survey results showed that most respondents had attained a high school education. A high school degree was the highest degree level achieved for 56.4% of respondents. About fourteen percent of respondent had not attained high school level degree (Figure 4.1). ately hal

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43 14% 1 1% 14% 4% none high school 56% associate bachelor graduateFigure 4.1. Highest de gree level attained by respondent (n=319) es of less $100,000 income of $50,000 ile 26% had an annual household income of both fishing and non-fishing Overall 74% of respondents had taxa ble household incom (Figure 4.2). A total of 41% of respondent s had a taxable household to $99,999, 33% had less than $50,000, wh $100,000 or greater. Household income was ge nerated from sources. 0% 10% 15% 20% 25% 5% 30% 35% 45% less than $50,000$50,000 -$99,999$100,000 and over Annual household income 40% Figure 4.2. Distribution of Reported 2004 Taxable Household Income (n=299) A large proportion of shark permit holders are heavily dependent on commercial fishing as their main source of income. The su rvey results showed that an average of 78%

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44 of taxable household income in 2004 was deri ved from commercial fi respondents deriving 75% to 100% of th eir income from fi shing (Figure 4.3). shing, with 68% of 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Response 0% less than 25%25% to 49%50% to 74%75% to 100% Household income from fishing from commercial fishing (N = 292) Fishermen Goals and Perceptions Figure 4.3. Distribution of the percentage of 2004 taxable hous ehold income derived The majority of respondents stated that they plan to remain in the commercial fishing business. A total of 70% of respondents indicated that they plan to remain within the fishery or expand their business, while 30 % indicated that they planned to leave the commercial fishing industry within the next 3 three years (Figure 4. 4). This corresponds with household income information that sh ows that most shark permit holders have a high dependence on commercial fishing as a source of income.

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45 0% 20% 30% 40% 50% 60% 10% 70% 80% Plan to stay in or expand Plan to exit Figure 4.4. Plans for the future of the fishing business within three years (n=319) Given the species composition of total revenues, we hypothesized that fishermen who land shark also land a wide range of other species that contribute significantly to their revenue. The survey showed that there are several species that fishermen considered to be very important to their business; 45 % considered shark very important to their business (Figure 4.5). 0% % Shark Swespo 20% 40% 60 100% (n=308) ordfish (n=257) Tuna (n=263) Snapper (n=233) Grouper (n=244) Other (n=224)Rnse 80% 4= Very important 3 2= somewhat 1 0= Not at all important Figure 4.5. Distribution of responses rega rding the importance of each species to the fishing enterprise.

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46 Shark Business and Buyback Questions Attitudes towards measures to reduce fish ing effort varied, however respondents indicated support for buybacks. Results s howed that 71% of respondents supported buying back permits and 72% supported buyi ng back vessels and permits (Figure 4.6). 0% 20% 60% 80% 100%ponse 40%Res Revoke permit (n=292) Buyback permit (n=303) Buyback vessel & permits (n=303) Tighten regulations (n=293) Tranferable quotas (n=294) oppose no opinion support Figure 4.6.“Do you support or oppose (regardless of the fishery) each of the following To d back program for the shark fishery, respondents were asked if they were aware of the possibility of such a measures that are designed to reduce fishing effort?” These results most likely reflect the current regulatory status of the industry. The shark fishery is highly regulated, and regulatio ns such as quotas that are currently in place have been tightened over the years. The preferences indicated by the respondents are an indication of the desire for a solution that is economica lly favorable in the eyes of the fisherman. Most of the respondents had previously attempted to value their vessels and permits; 60% indicated that they had tried to value their vessels and /or permits. The remaining 40% had never tried to assign a value to their vessels and permits. etermine the respondents’ awareness of a potential buy

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47 progr ram ith the program (Figure 4.7). am in the Atlantic shark fishery. Results showed that 47% of respondents were not aware of the potential program, (i.e., they ha d either never heard of the buyback prog before receiving the survey or were unsure) and 53% were aware of the program. This latter group consisted of respondents who had heard of the program and those who were very familiar w 30% 60% 40% 50% 0% 10% 20% Not aware of program Aware of program Figure 4.7. Awareness of a potentia l shark buyback program (n=318) If a buyback program was implemented in the shark fishery, a tax on landings to be paid by the remaining fishermen would be a means of generating the funds to cover ine whether lity of paying tiveness of the buyback. rk aining the cost of buying back vessels and/or perm its. It was important to determ fishermen who might wish to stay in the fish ery, were open to the possibi taxes on future landings, as this would likel y influence the effec Respondents where asked whether they woul d be willing to pay a tax on their sha landings for up to twenty years. A total of 48% of respondents were not willing to pay tax on their future shark landings to fund the pr ogram (Figure 4.8). Only 24% of respondents indicated that they would be willing to pay a tax on shark landings while the rem 28% were undecided.

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48 48% 24% 28% No Yes don't knowprogram that would buyback vessels and/or shark permits (N = 317) A key question in this study was whether or not fishermen we re open to selling their shark permits. A total of 75% of respondents were willing to sell their shark permit and 25% were not willing to sell for a reason able price. When asked about whether they ted permits, 67% were willing to Figure 4. 8. Willingness to pay a tax on your shark landings for up to 20 years to fund a would consider selling their vessel with all the associa sell while 33% were not (Figure 4.9). 25% 33% No 67% 75% Yes Figure 4.9 . Willingness to sell shark permit (n=307) and willingness to sell the vessel and ling to sell were then asked if they were willing to ccept the bid amount offered to them. Respond ents were presented with a dollar value Willingness to sell vessel Willingness to sell shark permit all permits (n=310) Those respondents who were wil a

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49 based on their average revenue for the previo us three years as disc ussed earlier (equation e to surrender their shark permits only and also ho to accept the unique dollar bid amount to surrender their vessel an as presented in the form of a closed ende A total of 69% of respondents ind e 4.10). This indicated th at the bid offer for shark permits did not corres 3.1). Specifically, they were asked how likel y they would be to accept this valu w likely they would be d associated permits. The “likeliness” w d question with five response levels icated they were 0% likely to accept the unique b id offer to surrender their permit while 15% indicated that they were 100% likely to accept the bid (Figur pond to what permit owners consider ed to be the value of these permit. 6% 15% 7% 3% 0% LTA 25% LTA 50% LTA 75% LTA 100% LTA 69% Figure 4.10. Percentage likelihood to accept (LTA) shark permit bid (n=219) Results showed that a large proportion of respondents would be highly likely to accept the unique bid offered for their vessel and all federal permits. A total of 41% of respondents indicated that they would be 100% likely to accept the bid and 19% indicated that they would be 0% likely to accept th e bid. The bid offers for the vessel and all

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50 federal permits appeared to be more reflec tive of respondents valuat than those fro shark permits. ion of their assets 19% 6% 0% LTA 25% LTA 41% 50% LTA 75% LTA 19% 15% 100% LTA3) t g in a buyback program; respondents indicated a willingness to sell shark permits and fishing vessels with all thei r associated permits. However when offered a specific dollar bid am ount to surrendered these assets, results suggested that using past revenues to pred ict the value of fishing assets was more appropriate for valuing the fishing vessel and associated permits, than for valuing shark permits alone. Respondents indicated a higher likelihood of accepting the bid amount offered for the vessel and all associated pe rmits and a lower likelihood of accepting the bid amount for the shark permit. In terms of pa ying for the cost of such a program a large Figure 4.11. Percentage likelihood to accept bid for vessel and all federal permits (n=20 Summary The responses collected in the survey showed that permit owners support a buyback program for shark as a capacity ma naging measure. The responses showed tha there is interest in participatin

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51 proportion of respondents were unwilling to pay a tax on future landings to fund a program for the shark fishery.

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CHAPTER 5 EMPIRICAL RESULTS Introduction This chapter presents the empirical resu lts estimated using the econometric models described in chapter 3. The summary statisti cs for the variables included in the four empirical models are listed in table 5.1. Table 5 1.Descriptive statistics of model Variable Name Observations Mean Standard Deviation Minimum Maximum Dependent variables WTS1i 307 0.749 0.434 0 1 WTS2i 310 0.671 0.471 0 1 LTA1i 219 1.484 0.774 1 3 LTA2i 203 2.369 0.788 1 3 Fishing business and vessel AVESK 319 0.836 1.75 0 13.182 AVETR 319 59.389 105.899 0 870.856 CORPN 308 0.506 0.501 0 1 DIREC 319 0.451 0.498 0 1 NOLAN 319 0.245 0.43 0 1 SKSHR 319 0.0685 0.185 0 1 TUNSWO 319 0.429 0.496 0 1 VALUE 301 0.601 0.49 0 1 VINSR 312 0.497 0.501 0 1 VSAGE 302 23.424 9.938 1 77 Owner information DAWARE 318 0.525 0.5 0 1 DBYALL 300 0.717 0.451 0 1 DBYPER 300 0.703 0.458 0 1 DDEGREE 317 0.287 0.453 0 1 DGHLTH 316 0.0728 0.26 0 1 DSHTAX 317 0.243 0.43 0 1 FSEXP 314 29.452 10.792 1 72 LEXIT 319 0.301 0.459 0 1 OWAGE 315 51.663 10.094 28 82 52

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53 Table 5.1. Continued. Variable Name Observations Mean Standard Deviation Minimum Maximum Household Information DEPEN 319 0.404 0.492 0 1 DHHINC1 299 0.334 0.473 0 1 DHHINC2 299 0.411 0.493 0 1 DHHINC3 299 0.254 0.436 0 1 FSINC 292 77.534 28.601 0 100 Hurdle 1: WTS Shark Permit Maximum likelihood estimates of parameters for the probit equation are shown in table 5.2. A total of 226 observations were used in estimation of the model. A likelihood ratio test that coefficients on all explanatory variables in the equation are zero is rejected at the 1% level of significance based on a ch i-squared value of 62.24 with 19 degrees of freedom (df) indicating that overall the set of explanatory variables explains the variation in the dependent variable. Further validation of the model can be obtained from the prediction accuracy of the model. Overall the model performs well predicting 84% of the sample points correctly. Support for permit buyback programs in gene ral (BYPER) had a positive impact on the WTS at the 5% level of significance. Respondents who generally support buyback programs have greater probability of sell ing relative to those who do not support permit buyback programs as a fishing capacity reduction tool. Shark share of total revenue (SKSHR) was al so significant at the 5% level and had a negative impact on the owners WTS their shar k permit. Higher shark share of revenue implies a greater dependence on shark as a s ource of revenue, and the owner has a lower probability of being WTS the shark permit. The level of household income also influenced the WTS. A Household income between $50,000 and $100,000 (DHHINC2)

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54 versus a lower level of income had a significant positive impact, increasing the probability of being WTS the shark permit. Higher household may mean the household is less dependent on shark revenue to generate th is income and as such are more willing to surrender the permit. The health status of the respondent also in fluenced the decision to sell the permit, with the poor health (DGHLTH) variable si gnificant at the 10% level. Poor health increases the probability of being willing to sell the permit. An individual in poor health is likely to have grater difficulty with the day to day operation of the business. Table 5.2. Willingness to sell sh ark permit probit model results Parameter Estimate t-statistic P-value Fishing business and vessel AVESK -0.050 -0.682 0.495 CORPN -0.214 -0.874 0.382 DIREC -0.052 -0.221 0.825 NOLAN -0.226 -0.844 0.399 SKSHR** -1.313 -2.154 0.031 TUNSWO 0.055 0.213 0.831 VDEBT 0.211 0.853 0.394 VSAGE 0.001 0.089 0.929 Owner Information DBYPER** 1.150 4.909 0.000 DDEGREE 0.110 0.440 0.660 DEPEN -0.273 -1.169 0.242 DGHLTH* 1.196 1.959 0.050 DSHTAX 0.204 0.734 0.463 LEXIT 0.401 1.528 0.127 FSEXP 0.001 0.095 0.925 OWAGE 0.007 0.442 0.658 Household information DHHINC2** 0.689 2.608 0.009 DHHINC3 0.369 1.283 0.199 FSINC 0.001 0.191 0.849 Intercept -0.810 -0.904 0.366 **significant at the 5 % level *significant at the 10% level

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55 Marginal effects were calculated for the significant variables explaining willingness to sell the shark permit (Table 5.3). The pr obability of being WTS the shark permit (WTS1=1) was calculated at the mean values of all variables. The probability of being willing to sell the permit was 0.28. Table 5.3. Marginal effect of significant vari ables on the probability of selling the shark permit Variable Change in variable Change in probability of selling permit Change from 0 to 1 BYPER Do not support permit buyback to support 0.316 DGHLTH Not poor health to poor health 0.449 DHHINC2 Household income increase from <$50,000 to $50,000 to $99,999 0.232 Marginal change SKSHR Shark share of total revenue increases by 10% points -1.057 The shark share of revenue had a largest imp act on the probability of selling the permit, reducing probability by 105.7% for a percentage increase in share. 0 0.1 0.2 0.3 0.4 Base00.10.20.30.40.50.60.70.80.91 Share of revenueProbability Figure 5.1 . Impact of change in shark share of revenue on probability of being willing to sell shark permit However even for those with no shark re venue the probability of selling was still low (Figure 5.1). Poor health had the larges t positive impact on probability, increasing

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56 the probability of selling by 44.9%. Suppor ting permit buyback as management tool increases the probability of selling by 31.6%. Being in household income category of $50,000 to $99,999 increased the probability of selling by 23.2%. Hurdle 1: WTS Fishing Vessel and All Permits A total of 225 observations were used in the estimation. A likelihood ratio test that coefficients on all explanatory variables in the equation are zero is rejected at the 1% level of significance based on a chi-squared value of 70.13 with 19 df indicating that as a set the explanatory variables explain the variation in the dependent variable WTS2i. Overall the model performs well predicting 76% of the sample points correctly. Estimates of parameters for the probit equa tion are shown in Table 5.4. Estimates show that the probability of bein g willing to sell the vessel and all federal permits for a reasonable price is influenced by a combination of the owners’ goals and perceptions as well as household characteristics. Support for vessel buyback program (BYALL) had a significant positive impact on th e probability of being willing to sell. Planning to exit the industry within the ne xt three years (LEXIT) has a significant positive impact, increasing the probability of being willing to sell. The type of permits owned also influenced the probability of being willing to sell. The model shows that owning a tuna and/or swordfish permit (TUN SWO) increases the probability of being willing to sell. The level of household income had a signifi cant impact on the pr obability of being WTS. A household income greater than $100,000 (DHHINC3) had a positive impact on the probability of being willing to sell. The proportion of household income derived from fishing (FSINC) was also significant and had a positive impact on the probability of being willing to sell.

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57 The importance of owner charact eristics are reflected in the significance of the owner age (OWAGE) and commercial fishing experience (FSEXP) variables. Fishing experience had negative impact on the probability of being WTS while age had a positive impact increasing the probability of being WTS. Table 5.4.Willingness to sell vessel probit model results Parameter Estimate t-statistic P-value Fishing business and vessel AVETR 0.001 0.578 0.563 CORPN -0.269 -1.087 0.277 DIREC 0.341 1.642 0.101 NOLAN -0.132 -0.544 0.586 TUNSWO* 0.446 1.804 0.071 VDEBT 0.005 0.021 0.984 VINSR 0.401 1.625 0.104 VSAGE -0.008 -0.846 0.398 Owner Information DBYALL** 0.921 3.821 0.000 DDEGREE -0.069 -0.299 0.765 DGHLTH 0.344 0.656 0.512 DSHTAX 0.213 0.761 0.447 FSEXP* -0.022 -1.851 0.064 LEXIT** 0.809 3.127 0.002 OWAGE** 0.034 2.391 0.017 Household information DEPEN 0.069 0.304 0.761 DHHINC2 0.406 1.625 0.104 DHHINC3* 0.654 1.953 0.051 FSINC* 0.006 1.716 0.086 Intercept ** -2.591 -3.126 0.002 **significant at the 5 % level *significant at the 10% level

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58 The probability of being willing to sell the vessel (i.e., the probability WTS2=1) was calculated using equation 3.3 at mean values of all variables. The probability of being willing to sell vessel and all federal permits for the average permit owner is 0.72. Marginal effects show the change in this base probability when the binary variable goes from zero to one (i.e., the binary characteristic is observed). Marginal effects of the significant variables show that support for vessel buybacks had the largest impact on the base probability, increasing the probability of being willing to sell the vessel by 33.4%. Individuals who plan to exit the industry have a 24.3% greater probability of being willing to sell than those who do not plan to exit. Owning a tuna and/or swordfish permit increased the probability of being willing to sell the vessel by 21.8%. The proportion of household income derived from commercial fishing had the least impact on probability, increasing the probability of selling by 0.5% (Table 5.5). Table 5.5. Marginal effect of significant variables on the probability of selling the vessel Variable Change in variable Change on probability of selling vessel Change from 0 to 1 BYALL Do not support to support vessel buyback 0.334 DHHINC3 Household income increases from < $50,000 to > $100,000 0.196 LEXIT Plan to exit the industry 0.243 TUNSWO Own tuna and/or swordfish permit 0.218 Marginal change FSEXP Commercial fishing experience (1 year) -0.016 FSINC Proportion of household income from fishing (1% point) 0.005 OWAGE Owner age (1 year) 0.025 Figures 5.2 to 5.4 show the impact of ch anges in the continuous variables on the probability of being willing to sell the fishing vessel and all permits. Permit owner age from 28 years to 78 years. Figure 5.2 shows the effect of 10 year increments in age on

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59 the probability. As age increases, the prob ability of selling the vessel increases as expected. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Base283848586878 AgeProbability Figure 5.2. Impact of owner age on the proba bility of being WTS fishing vessel and all permits Figure 5.3 shows that as the individual’s commercial fishing experience increases, the probability of selling the fishing vessels decreases. However the probabilities are relatively high. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Base110203040506070 Commercial fishing experience (years)Probability Figure 5.3. Impact of commercial fishing ex perience on probability of being WTS fishing vessel

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60 Increasing percentage of income from fishing increased the probability of being willing to sell the vessel and all permit. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Base0102030405060708090100 Percentage of household income from fishingProbability Figure 5.4. Impact of changes in the percenta ge of income from fishing on the probability of selling the fishing vessel Hurdle 2: LTA Shark Permit Bid A total of 166 observations were used in the estimation of the ordered probit model. A likelihood ratio test that coefficients on all explanatory variables in the equation are zero is rejected at the 5% leve l of significance based on a ch i-squared value of 34.23 with 22 df, indicating that the set of explanatory variable explain th e variation in the dependent variable (LTA1). The Inverse Mills Ratio (IMR1) was not significant showing that there was no sample selection bias. The coefficients indicate the effect on the first category (likelihood to accept bid is 0%) an d the third category (likelihood to accept bid of 75% and 100%). A positive coefficient increases the probability of being 75 to 100% likely to accept the bid and reduces the pr obability of being 0% likely to accept. However the effects on the second category (2 5% and 50% likely to accept bid) are ambiguous and since they are of lesser intere st they will not be discussed explicitly. The

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61 fishing business variable had largest number of significant variables influencing the likelihood to accept the bid for their shark permit. The results are reported in Table 5.6. The estimated coefficient for vessels ow ned as a corporation or partnership (CORPN) variable indicates that owning a ve ssel as a corporation or partnership reduces the probability of being 75% to 100% likely to accept the bid amount and increases the probability of not accepting the bid. Another vessel characteristic, having debt on the vessel (VDEBT), had a positive impact on the probability of being 75% to 100% likely to accept. Awareness of the value of their fish ing assets (VALUE) has a negative impact on the probability of being 75% to 100% likely to accept. No landings from 2001 to 2003 (NOLAN) also had a significant negative impact. Not having any landings reduces the probability of being in the 75% to100% lik ely to accept category, indicating that those who are not actively using their shark perm its are not very wiling to accept the bid amount offered for their permits. This may be because this group of was presented with a threshold value for the bid and this amount may not have reflected the true value of the permit. The willingness to pay a tax on shar k landings (DSHATAX) had significant positive impact on the probability of being 75%to 100% likely to accept the bid amount. This again shows some strategic behavior by respondents thos e who are the most likely to accept the bid amount to exit the industry leaving those who re main to make the actual payments Likelihood to accept is also influen ced by characteristics of the owner. Results showed that owner age (OWAGE) and educat ion above high school level (DEGREE) had a significant impact on the likelihood to accept the bid. Owner age had a positive impact, increasing the probability of being highly likel y (75% to 100%) to accept the bid offer.

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62 Results showed that having an education level exceeding high school had a negative impact on the probability of being 75% to 100% likely to accept the permit bid. Table 5.6. Factors affecting likelihood to accept permit bid: probit model results Parameter Estimate t-statistic P-value Fishing business and vessel AVESK 0.010 0.149 0.881 CORPN** -0.852 -3.108 0.002 DIREC -0.055 -0.220 0.826 NOLAN** -0.841 -2.693 0.007 SKSHR -0.984 -0.765 0.444 TUNSWO 0.137 0.537 0.591 VDEBT** 0.699 2.433 0.015 VSAGE 0.009 0.712 0.477 Owner Information DAWARE 0.069 0.309 0.757 DBYPER 0.321 0.496 0.620 DDEGREE* -0.444 -1.724 0.085 DGHLTH 0.018 0.032 0.975 DSHTAX* 0.530 1.946 0.052 FSEXP -0.009 -0.666 0.506 LEXIT -0.124 -0.424 0.672 OWAGE* 0.028 1.766 0.077 VALUE** -0.731 -3.066 0.002 Household information DEPEN 0.149 0.531 0.596 DHHINC2 -0.325 -0.816 0.414 DHHINC3 0.043 0.114 0.910 FSINC -0.002 -0.378 0.706 Intercept -1.379 -0.881 0.378 IMR1 0.923 0.762 0.446 ** 0.589 5.545 0.000 **significant at the 5 % level *significant at the 10% level The probabilities for each of the th ree likelihood to accep t categories were calculated using mean values of the model variables. The calculated probability represents the probability of the average respondent falling into each of the three likelihood categories. The resulting probabi lities are shown in table 5.7. The results showed that the average respondent had a grea ter probability of being 0% likely to accept

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63 the permit bid, than the probability of being 75%100% likely to accept. The probability of being 0% likely to accept was the highest at 0.79 followed by the probability of being 25%-50% likely to accept at 0.13, and probabi lity of being 75%-100% likely to accept was 0.08 (Table 5.7). Table 5.7. Likelihood to accept permit bid probabilities Probability Prob (LTA= 0%) 0.79 Prob (LTA= 25% to 50%) 0.13 Prob (LTA= 75% to 100%) 0.08 Willingness to pay a tax on future landings (SHTAX) had the largest positive impact on increasing the probability of be ing 75-100% likely to accept the permit bid by 9.5% relative to those who were not willing to pay a tax. Presence of debt on the vessel also had a significant positive impact, increasing the probability of being 75-100% likely to accept the permit bid by 9.4%. Corporate ownership of the vessel had the largest negative impact reducing the probability of be ing 75-100% likely to accept the permit bid by 13.1% (Table 5.8). Table 5.8. Marginal Effects for significant variables on likelihood to accept permit bid probabilities Variable Change in variable Likelihood to accept categories 0% 25% 50% 75% 100% Change from 0 to 1 VALUE Not aware to aware of vessel and permits value 0.217 -0.097 -0.120 SHTAX Not WTP to WTP on shark landings -1.672 0.0716 0.095 CORPN Not corporately owned to corporately owned vessel 0.242 -0.111 -0.131 VDEBT No debt to debt on the vessel 1.966 -0.103 0.094 DEGREE No degree to degree above high school level 0.177 -0.589 -0.058 NOLAN No landings for 2001-03 0.079 -0.0438 -0.035 Marginal change OWAGE Owner age (1 year) -0.004 -0.012 0.021

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64 The impact of age on the probability of each likelihood category is shown in figure 5.5. As age of the individual increases th e probability of being 75-100% likely to accept the permit bid increases while that probability of being 0% likely to accept decreases. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 283848586878 AgeProbability 0% LTA 25-50% LTA 75-100% LTA Figure 5.5. Impact of owner age on the likelihood to accept permit bid probabilities Ranking of the significant variables affec ting probability for each of the likelihood to accept categories from the most negative to the most positive are shown in Figures 5.65.8. -0.2-0.100.10.20.3 Changes in probability Corporately owned Aware of vesse/permit value Debt on vessel Education >Highschool No landings WTP tax on landings Figure 5.6. Impact of significant binary variables on probability the likelihood to accept the skark permit bid is 0%

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65 Willingness to pay tax on future landings had the largest negative impact on the probability of being 0% likely to accept the permit bid, reducing the probability by 1.670. Corporate ownership of the vessel had the largest positive impact increasing the probability by 0.242 (Figure 5.6). -0.2 -0.1 0 0.1 Change in probability WTP tax on landings No landings Education > High school Aware of vessel/permit value Debt on vessel Corporately owned Figure 5.7. Impact of significant variab les on probability the likelihood to accept the shark permit bid is 25%-50 -0.2 -0.1 0 0.1 Change in probability WTP tax on landings No landings Education> High school Debt on vessel Aware of vessel/permit value Corporately owned Figure 5.8. Impact of significant variab les on probability LTA permit bid is 75%-100% The impact of significant variables on th e probability of be ing 25%-50% LTA the permit bid was relatively smaller than the ot her two likely to accept categories. Corporate ownership of the vessel had th e largest negative impact reducing the probability 0.111.

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66 Willingness to pay tax on landings had th e largest positive impact increasing the probability by 0.072 (Figure 5.7). Ranking of the impacts on probability of being 75%-100% likely to accept were similar to the 25%-50% LTA category. Corpor ate ownership of the vessel had the largest negative impact on the probab ility of being 75%-100% likely to accept the permit bid reducing the probability by 0.131.Willingne ss to pay tax on future landings had the largest positive impact increasing the probability by 0.096 (Figure 5.8). Hurdle 2: LTA Bid for Vessel and All Permits A total of 149 observations were used in the estimation of the ordered probit model. A likelihood ratio test show that the hypot hesis that coefficients on all explanatory variables in the equation are ze ro is rejected at the 1% le vel of significance based on a chi-squared value of 66.59 with 22 df, indi cating that the set of explanatory variable explain the variation in the dependent variable LTA2. The Inverse Mills Ratio (IMR2) was not significant indicating that there was no sample selection bias and that there are no unobservable factors that significantly im pact the likelihood to accept probability. Estimates of parameters for the probit equation are shown in Table 5.9. Seven variables had a significant impact on the likelihood to accept bid amount presented to respondents in the survey. Re sults show that fishing business and permit owner characteristics are among the significan t factors that influence the probability of accepting the vessel bid amount. Previous U.S. buyback programs have reli ed on taxing future landings as a means of generating the funds to pay for the pr ogram. Willingness to pay a tax on landings (DZSHTAX) was significant w ith a positive impact on the probability of being 75% to 100% likely to accept the bid mount offered. Th is indicates that those permit owners who

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67 are willing to pay a tax on shark landings are more likely to accept the bid offer. This is an interesting result since if they accept the offer, they will not be paying the tax since they will no longer be fishing (at least with the same permits and gear). This could be a case of strategic response even though the ta x question was asked early in the survey and the willingness questions were asked toward the end. Not having landings (NOLAN) negatively impacted the probability of being 75% to 100% likely to accept. Those owners wh o had not landed any catch over a period of 2001 to 2003 were less likely to accept the bid amount offered. The result that shark permit holders without landings of any species across a recent three year period have a lower probability of accepting the bid offered for their vessel should be qualified by the fact that these owners were presented with a th reshold bid value of $10,000. This, to some extent, reflects the problem of latent permits (i.e., unused fishing capacity) and speculative behavior in the industry whereby ve ssels with active permits (i.e., those who have paid the annual fee) do not fish. Vessel ownership is also important in deciding to accept the bid offer. Vessels owned as corporations or partnerships (COR PN) had a negative impact on the probability of being 75%-100% LTA relative to those owne d as sole proprietorships. Insured vessels (VINSR) had a positive impact on the probabi lity of being 75-100% likely to accept. Commercial fishing experience (FSEXP) had a significant negative impact on the probability of being 75%-100% likely to accept. Respondents with more commercial fishing experience were less likely to accept th e bid, which may reflect their intention to continue fishing (i.e., work in the career wh ere they have the most experience). Owner age (OWAGE) was significant and has a positive impact on the probability of being in

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68 75%-100% likely to accept category. This would be expected since as the individual gets older the earning horizon would be shorter, ceteris paribus. The level of education also influences probabilities, with the educati on above high school level (DEGREE) having a significant negative impact, decreasing the pr obability of being 75%-100% LTA the bid for the vessel and all permits. Table 5.9. Likelihood to accept vessel probit model results Parameter Estimate t-statistic P-value Fishing business and vessel AVETR 0.001 1.096 0.273 CORPN** -0.823 -2.730 0.006 NOLAN** -1.386 -4.370 0.000 TUNSWO 0.501 1.436 0.151 VDEBT -0.037 -0.133 0.894 VINSR* 0.587 1.791 0.073 VSAGE 0.012 1.015 0.310 Owner Information DAWARE 0.318 1.324 0.186 DBYALL 0.495 0.757 0.449 DDEGREE* -0.486 -1.906 0.057 DGHLTH 0.470 0.870 0.385 DSHTAX** 0.777 2.504 0.012 FSEXP** -0.064 -3.273 0.001 LEXIT 0.308 0.766 0.444 OWAGE** 0.058 2.286 0.022 VALUE -0.067 -0.265 0.791 Household information DEPEN 0.392 1.550 0.121 DHHINC2 0.076 0.211 0.833 DHHINC3 -0.405 -0.900 0.368 FSINC 0.009 1.543 0.123 Intercept -2.152 -0.843 0.399 IMR2 0.880 0.806 0.420 ** 1.086 6.840 0.000 **significant at the 5 % level *significant at the 10% level Marginal effects for significant variables are reported in table 5.10. The marginal effects for continuous variables can be in terpreted as the incr eased or decreased probability the individual would be in each likelihood to accept category ( 0%, 25%-50%,

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69 or 75%-100%), given one more unit of the expl anatory variable, with other variables held at their mean. For the binary variables the inte rpretation is the increase or decrease in the probability if the binary variable is equal to 1. For purposes of the study the first and third categories are of the most interest since these tell the probability of rejecting the bid and the probability of being highly likely to accept the bid, respectively. The probabilities for each of the three likelihood to accept categories were calculated using mean values of the model variables. Table 5.10. Likelihood to accept vessel bid probabilities Probability Prob (LTA= 0%) 0.15 Prob (LTA= 25% to 50%) 0.37 Prob (LTA= 75% to 100%) 0.49 The results showed that the average resp ondent had a greater probability of being in 75%100% likely to accept vessel bid category than of being in the other two categories. The probability of being 75%100% likely to accept was the highest at 0.49 followed by the probability of being 25%-50% likely to accept at 0.37, and probability of being 0% likely to accept was 0.15 (Table 5.9). Table 5.11. Marginal Effects for significan t variables o likelihood to accept vessel bid probabilities Likelihood to accept categories 0% 25% and 50% 75% and 100% Change from 0 to 1 NOLAN No landings for 2001-03 0.503 -0.135 -0.367 CORPN Insured vessel 0.274 0.008 -0.282 DEGREE Corporate/ partnership ow ned vessel 0.173 -0.014 -0.159 VINSR Degree above high school level -0.197 -0.005 0.203 SHTAX Willing to pay on shark landings -0.229 -0.058 0.287 Marginal change OWAGE Owner age -0.007 -0.016 0.023 FSEXP Commercial fishing experience 0.008 0.020 -0.029

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70 The results showed that no landings had the greatest negative impact on the likelihood to accept with a marginal value of -0.367 for the 75-100% category. This means that fishermen who have not landed any species decrease their probability of being 75%100% likely to accept the bid by 36.7% (Table 5.10). This was followed by corporate ownership of the vessel which reduced the probability of being highly to accept the bid by 28.2%. Willingness to pay a tax on shark landings had the largest positive effect, increasing the probability of being highly likely to accept the bid by 28.7%. Owner age and commercial fishing experience had the least impact on probability with age increasing the probability by 2.3% a nd experience reducing it by 2.9%. An additional year in age of experience does not have a large effect on probability, examining the effect of probability of larger increments in age and experience may be more meaningful. The figures below show th e how probabilities change with 10 year increments in age and experience. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 283848586878 A ge Probability 0% LTA 25-50% LTA 75-100% LTA Figure 5.9.Effect of owner age on the likelihood to accept ve ssel bid probability The figure shows that individuals have a greater probability of not accepting the bid up to age 40 years but after this point they have an increasing probability being highly

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71 likely to accept the bid. This is expected since as a person gets older and approaches retirement they may want to exit the industry. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 110203040506070 Experince in yearsProbability 0% LTA 25-50% LTA 75-100% LTA Figure 5.10. Effect of fishing experience on likelihood to accept vessel bid probabilities When experience approaches 40 years, subsequent increase adds increasingly to the probability of being 0 % likely to accept. Ranking of significant factors impacting probabilities for the three cat egories of likely to accept from most negative to most positive are shown in figures 5.11 to 5.13. -0.3-0.2-0.100.10.20.30.40.50.6 Change in probability Nolandings Corporately owned Education >High school Insured vessel WTP tax on landings Figure 5.11. Effect of significant variable s on the probability LTA vessel bid is 0%

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72 Willingness to pay a tax on future landings had the largest negative impact on the probability of being 0% likely to accept the bid for the vessel and all permits, decreasing the probability by 0.229. No landings had th e largest positive impact increasing the probability by 0.503 (Figure 5.11). -0.2 -0.1 0 0.1 Change in Probability Corporatley owned Insured vessel Education> High school WTP tax on landings No landings Figure 5.12. Effect of signifi cant variable on probability likely to accept vessel bid is 25%-100% -0.4-0.3-0.2-0.100.10.20.3 Change in probability WTP tax on landings Insured vessel Education> High school Corporately owned No Landings Figure 5.13. Effect of significant variables on the probability likely to accept vessel bid is 75%-100%. The impact of significant variables on th e probability of bein g 25%-50% likely to accept the vessel bid is relatively smaller than on the other two likely to accept

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73 categories. No landings had the largest negativ e impact on the proba bility of being 25%50% LTA decreasing the probability by 1.3, while corporate ownership of the vessel had the largest positive impact increasing th e probability by 0.008 (Figure 5.13). No landings had the largest negative imp act on the probability of being 75%-100% likely to accept the bid for the vessel and all permits, decreasing the probability by 0.367. Willingness to pay a tax on future landings had the largest positive impact on the probability increasing it by 0.287 (Figure 5.21). Summary Empirical results showed that being in poo r health as well as the shark share of total dockside revenues were the two factors th at had the largest impact on the probability of being willing to sell th e shark permit for a reasonable price. Support for vessel buyback by permit holders and planning to exit the industry were the variables that had the greatest impact on the probability of be ing willing to sell the vessel and all federal permits for a reasonable price. When considering the response to the uniq ue bid amounts offered, results showed that not landing any species had the greates t impact on the probability of being 75%100% likely to accept the bid for vessel and all permits. Corporate ownership of the fishing vessel and willingness to pay a tax on future landings had the greatest impact on the probability of being 75%-100% likely to accept the shark permit bid. The significance of the willingness to pay a tax on future shark landings variable (SHTAX) indicated some strategic behavior by respondents. Those who in dicated they were willing to pay the tax were more likely to accept the bid offer for their assets, but those who would accept the bids would remove them from the group liable to the pay a tax if the program were to be implemented.

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74 These results show that the WTS fishing assets and the likelihood to accept the unique bid are influenced not just by vesse l characteristics, but by a combination of owner, fishing business, and household characteristics. The key variables in particular (i.e., those that were statistically significant in any one of the models) include, shark share of revenue (SKSHR), support for permit (DBYPER) and vessel buybacks (DBYALL), poor health (DGHLTH), owner ag e (OWAGE), education above high school level (DDEGREE), commercial fishing experi ence (FSEXP), annual household income (DHHINC2 and DHHINC3), proportion of a nnual household income from fishing (FSINC), ownership of tuna or swordfish permit (TUNSWO), plan to exit industry, willingness to pay tax on future shark landings (DSHTAX), aware of vessel and/or permit value (VALUE), corporate vessel ownershi p (CORPN), vessel debt (VDEBT), and no reported landings(NOLAN).

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CHAPTER 6 CONCLUSIONS AND IMPLICATIONS The first objective of this study was to dete rmine if there is interest in a vessel or permit buyback program. The research shows that there is interest in a potential buyback program and a large proportion of respondents we re willing to sell their fishing assets as 75% of respondents were willing to sell thei r shark permits and 67% were willing to sell their vessels and all federal pe rmits for a reasonable price. There are likely numerous possible explanations for why fishermen do what they do, all of which may not be easily captured. However, this analysis also showed that contrary to common expectations, vessel characteristics were not the most important factors in determining will ingness to sell fishing asse ts. Many buyback programs are designed using vessel characteristics as a tool to target participants. This is done because this type of information is readily availabl e whereas socioeconomic data is not. However, previous research on the New Engl and ground fish buyback by Kitts et al. (2000) suggests that collecting demographic and so cio-economic data on members of the fishery prior to buyback implementation would enhance buyback program design. The results from this study show that there other factors that influence willingness to sell, such as education, business goals and attitudes and opinions regarding buybacks that support their assertions. The final objective of this study was to determine if is possible to use price information from similar buyback programs to predict the value of fishing assets and to encourage participation. In regard to asset va luation, the survey results showed that using 75

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76 past landings as an indication of future revenues is an acceptable method for valuing vessels. However this method was not appropriate when valuing permits. A total of 41% of respondents indicated they were 100% lik ely to accept the bid amount offered for the vessel and all permits, compared to 15% for th e shark permit bid offer. Further analysis into the factors influencing the acceptability of the values revealed a number of factors behind the results. Support fo r buyback programs, health stat us of the owner, dependence on fishing and shark species (i.e., share of fishing revenues for shark species), vessel ownership and willingness to pay a tax on la ndings to fund the program had the largest impact on the decisions to sell fishing asse ts and the acceptability of the bid offers. The results showed that attitudes towa rds a buyback program were the most important factor influencing willingness to sell both the permit and the vessel with all permits. Permit owners that generally support buyback programs as a tool for reducing capacity had a significantly higher probability of being likely to sell their fishing assets in a buyback. This has specific implications on targeting of participants; if members of the fishery are not in favor of buybacks this can significantly reduce the willingness to sell and subsequently the level of voluntary part icipation. Encouraging support of buybacks by members of the fishery and extension personnel would be an effective way of increasing participation. The dependence on shark has a significant e ffect on the willingness to sell shark permits. Those with a larger share of shark revenue relative to total fishing revenues had a lower probability of being willing to sell their permits. Thus, a buyout would most likely capture those who do not depend heavily on shark, which affects the amount of capacity that would be removed from the fisher y. A large number of participants may not

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77 necessarily correspond to the rem oval of a large amount of capaci ty as used in the Gulf of Mexico and South Atlantic shark fishery. The research also showed that permit ow ners who were not actively using their permits were less willing to sell their assets in a buyback. Not having any landings was the main factor increasing in the probability of not accepting the bid offer for vessels and all permits. The reasons for this apparently speculative behavior are unclear; perhaps, fishermen have expectations of increased future revenues if they believe current conservation efforts will be successful. However, what is apparent, is that there is a considerable amount of latent capacity in the shark fisher y (e.g., 22% of shark permits were unused in 2003) and this must be accounted for in buyback design. These results suggest that special care has to be taken to address this group in the buyout process (as required under the Magnuson-Stevens act) sinc e the prospect of financial compensation does not appear to be sufficient to entice speculators to forgo their current fishing rights. The results of the study indica te the potential to remove a number of fishing vessels and shark permits from the Atlantic shar k fishery. Based on the likelihood of accepting the bid offer, results show that potentially more vessels would be removed under a buyback of vessels and all permits th an a buyback of shark permits only. When considering a shark permit buyout, those that indicated a 75% or greater likelihood of accepting the shark permit bid re presented 38 shark permit holders (Figure 6.1). Those that indicated a 75% or greater likelihood of accepting the bid offer for vessels and all permits represent 82 vessels.

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78 0 20 40 60 80 100 120 140 160 100%75%50%25%0% likelihood to accept bidnumber of individuals permit only vessels & permits Figure 6.1. Number of vessels represented by likelihood to accept bid categories Assuming that those who indicated a likelihood to accept of 75% or greater would sell their assets it is possible to estimate the potential for reduction of landings that is represented by this group, should a buyout of permits or vessels and all permits be implemented. If a permit buyout is implemented, those who indicated a 75% or greater likelihood to accept the shark permit bid represent a removal of 8.5% (in volume) of all shark species landed in 2003 (Figure 6.2). Using the shark permit bid value as the cost of purchase for each permit, such a program would cost just $280, 217 (Figure 6.3).

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79 0 2 4 6 8 10 12 14 16 18 20 100%75%50%25%0% Likelihood to accept shark permit bidShare or total shark landings (%) Figure 6.2. Share of shark total landings (in volume) represented by likelihood to accept shark permit bid $0 $100,000 $200,000 $300,000 $400,000 $500,000 $600,000 $700,000 100%75%50%25%0% Likelihood to accept shark permit bid Figure 6.3. Total cost of a potent ial shark permit buyout in bid dollars Those who indicated 75% or greater like lihood to accept the vessel and all permits bid represent 16% of all shar k species landed (by volume) in the Gulf of Mexico and South Atlantic fishery in 2003 (Figure 6.4). Us ing the bid values offered as cost for each vessel, the cost of removing this share of shark landings would be $39.7 million (Figure 6.5).

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80 0 2 4 6 8 10 12 14 100%75%50%25%0%Likelihood to accept vessel bid Share of total shark landings (%) Figure 6.4. Share of shark landings (in volume) represented by likelihood to accept bid for vessel and all permits $0 $5,000,000 $10,000,000 $15,000,000 $20,000,000 $25,000,000 $30,000,000 $35,000,000 100%75%50%25%0% Likelihood to vessel bid accept Figure 6.5. Total cost of potential vessel and permit buyout in bid dollars In terms of capacity reduction as repres ented by landings of shark species, a buyback of vessels and all federal permits w ould present the opport unity to reduce shark landings by a larger share th an a shark permit buyback. A vessel buyout appears to be preferred by members of the shark fishery and would reduce shark landings by almost twice as much as a permit buyout. However, it would represent a considerably greater cost to the industry in terms of funding the program, as well as in earnings forgone by the industry. Shark earnings forgone would amo unt to $32,975 (figure 6.6), while earnings

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81 forgone when the vessels and all permits are purchased amount to $ 9.7 million (Figure 6.7) $0 $10 $20 $30 $40 $50 $60 $70 $80 $90 $100 100%75%50%25%0% Likelihood to accept shark permit bidTotal shark revenue (Thousands) Figure 6.6. Total shark revenue by lik elihood to accept shark permit bid, 2003 $0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000 $8,000 100%75%50%25%0% Likelihood to accept vessel bidTotal Revenue (Thousands) Figure 6.7. Total revenue (from all species ) by likelihood to accept bid for vessel and all permits, 2003 The study shows that there is interest in a shark buyback and the results allow fishery managers to better understand the beha vior of members of the shark fishery. This

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82 information can be used to improve the desi gn of an effective buyback program for the shark fishery with a better chance of being accepted by members of the South Atlantic and Gulf of Mexico shark fishery.

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APPENDIX TSP PROGRAMS OPTIONS MEMORY=175 NWIDTH=10 SIGNIF=3 LIMPRN=95 LINLIM=1500 LEFTMG=0; READ (FILE='E:\THESIS MODEL DATA\Tsp data1' FORMAT=EXCEL); ? Shark Permit Probit Model and Simulation; ? List of All 51 variables; LIST ALLVAR EXPAN LEXIT IMPSH IMPSW IMPTN REVOK BYPER BYALL REGUL QUOTA VALUE AWARE SHTAX LNGHT VSAGE PPORT OWNER CORPN VDEBT VINSR WTSSH WTASH WTSALL WTALL OWAGE FSEXP COMPU GHLTH MARIT DEGREE HARR ADULT CHILD DEPEN HHINC FSINC HARVEST WSALE RETAILE JOBFT JBOPT PRTFL BIDSK BIDTR AVESK AVETR SKSHR NOLAN DIREC INCID TUNSWO; ? Transform continuous variables to combine categories; DIMPSH = (IMPSH=0 | IMPSH=1 | IMPSH=2)*0 + (IMPSH=3 | IMPSH=4)*1; DIMPSW = (IMPSW=0 | IMPSW=1 | IMPSW=2)*0 + (IMPSW=3 | IMPSW=4)*1; DIMPTN = (IMPTN=2 | IMPTN=3 | IMPTN=4)*0 + (IMPTN=0 | IMPTN=1)*1; DREVOK = (REVOK=-1 | REVOK=0)*0 + (REVOK=1)*1; DBYPER = (BYPER=-1 | BYPER=0)*0 + (BYPER=1)*1; DBYALL = (BYALL=-1 | BYALL=0)*0 + (BYALL=1)*1; DREGUL = (REGUL=-1 | REGUL=0)*0 + (REGUL=1)*1; DQUOTA = (QUOTA=-1 | QUOTA=0)*0 + (QUOTA=1)*1; DSHTAX = (SHTAX=0 | SHTAX=2)*0 + (SHTAX=1)*1; DAWARE = (AWARE=0 | AWARE=1)*0 +(AWARE=2 | AWARE=3)*1; DGHLTH = (GHLTH=3 | GHLTH=4 | GHLTH=5)*0 + (GHLTH=0 | GHLTH=1)*1; DUMMY DGHLTH; DMARIT = (MARIT=0 | MARIT=3 | MARIT=4 | MARIT=5)*0 + (MARIT=1 | MARIT=2 )*1; DHARR = (HARR=1 | HARR=2)*0 + (HARR=0)*1; DDEGREE = (DEGREE=0 | DEGREE=1)*0 + (DEGREE=2 | DEGREE=3 | DEGREE=4)*1; DHHINC = (HHINC=0)*1 +(HHINC=1 )*2 + (HHINC=2 | HHINC=3 | HHINC=4 | HHINC=5)*3; DUMMY DHHINC; ? Transform dependent variable WTASH and WTALL for ordered probit model into 3 groups; 83

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84 ZWTASH = (WTASH=0 )*1 + (WTASH=25 | WTASH=50)*2 +(WTASH=75 | WTASH=100)*3; ZWTALL = (WTALL=0 )*1 + (WTALL=25 | WTALL=50)*2 +(WTALL=75 | WTALL=100)*3; ?list of all model variables with transformed/dummy variables; LIST MODELVAR EXPAN LEXIT DIMPSH DIMPSW DIMPTN DREVOK DBYPER DBYALL DREGUL DQUOTA VALUE DAWARE DSHTAX LNGHT VSAGE PPORT CORPN VDEBT VINSR WTSSH ZWTASH WTSALL ZWTALL OWAGE FSEXP COMPU DGHLTH DMARIT DDEGREE DHARR ADULT CHILD DEPEN DHHINC1 DHHINC2 DHHINC3 FSINC HARVEST WSALE RETAILE JOBFT JBOPT PRTFL BIDSK BIDTR AVESK AVETR SKSHR NOLAN DIREC TUNSWO; ? MSD (BYVAR, CORR, TERSE) MODELVAR ; ?Summary statistics of the variables for variable selection; Title 'WTS permit model'; LIST XVAR LEXIT DBYPER DSHTAX CORPN VDEBT DGHLTH DDEGREE DEPEN DHHINC2 DHHINC3 NOLAN DIREC TUNSWO VSAGE OWAGE FSEXP SKSHR AVESK FSINC ; ? Eliminating missing obs from RH vars used in Ord Prob. PROC OPNOMIS; SELECT .NOT.MISS(LEXIT).AND..NOT.MISS(DBYPER).AND..NOT.MISS(VALUE) .AND..NOT.MISS(DAWARE).AND..NOT.MISS(DSHTAX).AND..NOT.MISS(CORPN) .AND..NOT.MISS(VDEBT).AND..NOT.MISS(DGHLTH).AND..NOT.MISS(DDEGREE) .AND..NOT.MISS(DEPEN).AND..NOT.MISS(DHHINC2).AND..NOT.MISS(DHHINC3) .AND..NOT.MISS(NOLAN).AND..NOT.MISS(DIREC).AND..NOT.MISS(TUNSWO) .AND..NOT.MISS(VSAGE).AND..NOT.MISS(OWAGE).AND..NOT.MISS(FSEXP) .AND..NOT.MISS(SKSHR).AND..NOT.MISS(AVESK).AND..NOT.MISS(FSINC) ; ENDPROC OPNOMIS; OPNOMIS; ?WTS Permit Probit Model; ?=====================================================================; ? List of Explanatory Variables for WTS shark permit model the dependent var is WTSSH; ? 19 var LIST XVAR LEXIT DBYPER DSHTAX CORPN VDEBT DGHLTH DDEGREE DEPEN DHHINC2 DHHINC3 NOLAN DIREC TUNSWO VSAGE OWAGE FSEXP SKSHR AVESK FSINC ; ?13 Dummy variables; LIST XDUMVAR LEXIT DBYPER DSHTAX CORPN VDEBT DGHLTH DDEGREE DEPEN DHHINC2 DHHINC3 NOLAN DIREC TUNSWO ; ?6 Continuous variables LIST XCONVAR VSAGE OWAGE FSEXP SKSHR AVESK FSINC ; MSD (TERSE) XDUMVAR XCONVAR;

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85 ?mark significant variables with STARS; REGOPT (STARS,STAR1=.10,STAR2=.05,) T; PROBIT (MILLS= IMR1) WTSSH C XDUMVAR XCONVAR; MAT PR=@COEF; ? Variables 2-14 are dummies & 15-20 are continuous; ? Mean Minimum Maximum ?1 C ------------------------------------?2 LEXIT | 0.310 0.000 1.000 ?3 DBYPER | 0.724 0.000 1.000 ?4 DSHTAX | 0.254 0.000 1.000 ?5 CORPN | 0.500 0.000 1.000 ?6 VDEBT | 0.323 0.000 1.000 ?7 DGHLTH | 0.0690 0.000 1.000 ?8 DDEGREE | 0.293 0.000 1.000 ?9 DEPEN | 0.422 0.000 1.000 ?10 DHHINC2 | 0.461 0.000 1.000 ?11 DHHINC3 | 0.250 0.000 1.000 ?12 NOLAN | 0.237 0.000 1.000 ?13 DIREC | 0.457 0.000 1.000 ?14 TUNSWO | 0.422 0.000 1.000 ?15 VSAGE | 23.254 1.000 76.000 ?16 OWAGE | 51.457 28.000 82.000 ?17 FSEXP | 29.108 1.000 72.000 ?18 SKSHR | 0.0617 0.000 1.000 ?19 AVESK | 0.864 0.000 13.182 ?20 FSINC | 76.806 0.000 100.000 ? ============================================================================ ? Simulator: 1 C + 113 Dummies + 6 continuous = 20 coeff. ? ============================================================================ ? Creates a name that starts with A. for the 20 coefficients DOT(VALUE=J) 1-20; SET A.=PR(J,1); ENDDOT; MMAKE AA A1-A20; ? Matrix with prob coefficients ? Initializing the variables SET ZZVAR1 = 1; SET ZZVAR2 = 0.310; SET ZZVAR3 = 0.724; SET ZZVAR4 = 0.254; SET ZZVAR5 = 0.500; SET ZZVAR6 = 0.323; SET ZZVAR7 = 0.0690; SET ZZVAR8 = 0.293; SET ZZVAR9 = 0.422; SET ZZVAR10 = 0.461; SET ZZVAR11 = 0.250; SET ZZVAR12 = 0.237; SET ZZVAR13 = 0.457; SET ZZVAR14 = 0.422; SET ZZVAR15 = 23.254; SET ZZVAR16 = 51.457;

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86 SET ZZVAR17 = 29.108; SET ZZVAR18 = 0.0617; SET ZZVAR19 = 29.551; SET ZZVAR20 = 76.806; PROC INIT; SET ZZVAR1 = 1; SET ZZVAR2 = 0.310; SET ZZVAR3 = 0.724; SET ZZVAR4 = 0.254; SET ZZVAR5 = 0.500; SET ZZVAR6 = 0.323; SET ZZVAR7 = 0.0690; SET ZZVAR8 = 0.293; SET ZZVAR9 = 0.422; SET ZZVAR10 = 0.461; SET ZZVAR11 = 0.250; SET ZZVAR12 = 0.237; SET ZZVAR13 = 0.457; SET ZZVAR14 = 0.422; SET ZZVAR15 = 23.254; SET ZZVAR16 = 51.457; SET ZZVAR17 = 29.108; SET ZZVAR18 = 0.0617; SET ZZVAR19 = 29.551; SET ZZVAR20 = 76.806; ENDPROC INIT; ? Matrix for simulated probabilities MFORM(TYPE=GEN , NCOL=4, NROW=500) SIMAT=0; SET I = 0; SET VARVAL = 0; PROC ZSIMZ; SET I = I + 1; MMAKE ZZ ZZVAR1-ZZVAR20; MAT XA = ZZ'AA; SET XXA = XA; ? Calculating the probabilities SET PROB1 = CNORM(XXA); ? Specifying the components for the matrix with simulated probabilities SET SIMAT(I,1) = SIMNUM; SET SIMAT(I,2) = VARVAL; SET SIMAT(I,3) = PROB1; ENDPROC ZSIMZ; ? =================================================== ? Sim = 1 Initial values ? =================================================== SET SIMNUM = 1; INIT; ZSIMZ; ?===================================================== ? Sim = 2 ZZVAR3 (BYPER Support permit buyback) ?===================================================== SET SIMNUM = 2;

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87 INIT; SET ZZVAR3 =1; ?support; ZSIMZ; INIT; SET ZZVAR3 =0; ?do not support; ZSIMZ; ?================================================================= ? Sim = 3 ZZVAR7 (DGHLTH Poor health) ?================================================================= SET SIMNUM = 3; INIT; SET ZZVAR7 =1; ?poor health; ZSIMZ; INIT; SET ZZVAR7 =0; ?no poor health; ZSIMZ; ?================================================================= ? Sim = 4 ZZVAR10 (DHHINC2 household income $50k to $999,99k) ?================================================================= SET SIMNUM = 4; INIT; SET ZZVAR10 =1; ?DHHINC2; ZSIMZ; INIT; SET ZZVAR10 =0; ? not DHHINC2; ZSIMZ; ? ===================================================================== ? Sim = 5: DO loop for ZZVAR18 (SKSHR shark share of revenue): 0.0-1.0 ? ===================================================================== DO J=0 TO 1 BY 0.1; SET SIMNUM = 5; INIT; SET VARVAL = J; SET ZZVAR18 = J; ZSIMZ; ENDDO; WRITE(FORMAT=EXCEL,FILE='C:\Sims\PERMITPRMEANS.XLS') SIMAT; END;

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88 OPTIONS MEMORY=175 NWIDTH=10 SIGNIF=3 LIMPRN=95 LINLIM=1500 LEFTMG=0; READ (FILE='E:\THESIS MODEL DATA\Tsp data1' FORMAT=EXCEL); ? Shark Permit Ordered Probit Model and Simulation; ? List of All 51 variables; LIST ALLVAR EXPAN LEXIT IMPSH IMPSW IMPTN REVOK BYPER BYALL REGUL QUOTA VALUE AWARE SHTAX LNGHT VSAGE PPORT OWNER CORPN VDEBT VINSR WTSSH WTASH WTSALL WTALL OWAGE FSEXP COMPU GHLTH MARIT DEGREE HARR ADULT CHILD DEPEN HHINC FSINC HARVEST WSALE RETAILE JOBFT JBOPT PRTFL BIDSK BIDTR AVESK AVETR SKSHR NOLAN DIREC INCID TUNSWO; ? Transform continuous varibles to combine categories; DIMPSH = (IMPSH=0 | IMPSH=1 | IMPSH=2)*0 + (IMPSH=3 | IMPSH=4)*1; DIMPSW = (IMPSW=0 | IMPSW=1 | IMPSW=2)*0 + (IMPSW=3 | IMPSW=4)*1; DIMPTN = (IMPTN=2 | IMPTN=3 | IMPTN=4)*0 + (IMPTN=0 | IMPTN=1)*1; DREVOK = (REVOK=-1 | REVOK=0)*0 + (REVOK=1)*1; DBYPER = (BYPER=-1 | BYPER=0)*0 + (BYPER=1)*1; DBYALL = (BYALL=-1 | BYALL=0)*0 + (BYALL=1)*1; DREGUL = (REGUL=-1 | REGUL=0)*0 + (REGUL=1)*1; DQUOTA = (QUOTA=-1 | QUOTA=0)*0 + (QUOTA=1)*1; DSHTAX = (SHTAX=0 | SHTAX=2)*0 + (SHTAX=1)*1; DAWARE = (AWARE=0 | AWARE=1)*0 +(AWARE=2 | AWARE=3)*1; DGHLTH = (GHLTH=3 | GHLTH=4 | GHLTH=5)*0 + (GHLTH=0 | GHLTH=1)*1; DUMMY DGHLTH; DMARIT = (MARIT=0 | MARIT=3 | MARIT=4 | MARIT=5)*0 + (MARIT=1 | MARIT=2 )*1; DHARR = (HARR=1 | HARR=2)*0 + (HARR=0)*1; DDEGREE = (DEGREE=0 | DEGREE=1)*0 + (DEGREE=2 | DEGREE=3 | DEGREE=4)*1; DHHINC = (HHINC=0)*1 +(HHINC=1 )*2 + (HHINC=2 | HHINC=3 | HHINC=4 | HHINC=5)*3; DUMMY DHHINC; ? Tranform dependent variable WTASH and WTALL for odrered probit model into 3 groups; ZWTASH = (WTASH=0 )*1 + (WTASH=25 | WTASH=50)*2 +(WTASH=75 | WTASH=100)*3; ZWTALL = (WTALL=0 )*1 + (WTALL=25 | WTALL=50)*2 +(WTALL=75 | WTALL=100)*3; ?list of all model variables with tranformed/dummy variables; LIST MODELVAR EXPAN LEXIT DIMPSH DIMPSW DIMPTN DREVOK DBYPER DBYALL

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89 DREGUL DQUOTA VALUE DAWARE DSHTAX LNGHT VSAGE PPORT CORPN VDEBT VINSR WTSSH ZWTASH WTSALL ZWTALL OWAGE FSEXP COMPU DGHLTH DMARIT DDEGREE DHARR ADULT CHILD DEPEN DHHINC1 DHHINC2 DHHINC3 FSINC HARVEST WSALE RETAILE JOBFT JBOPT PRTFL BIDSK BIDTR AVESK AVETR SKSHR NOLAN DIREC TUNSWO; ? MSD (BYVAR, CORR, TERSE) MODELVAR ; ?Summary statistics of the varialbes for variable selection; Title 'WTS permit model'; LIST XVAR LEXIT DBYPER DSHTAX CORPN VDEBT DGHLTH DDEGREE DEPEN DHHINC2 DHHINC3 NOLAN DIREC TUNSWO VSAGE OWAGE FSEXP SKSHR AVESK FSINC ; ? Eliminating missing obs from RH vars used in Ord Prob. PROC OPNOMIS; SELECT .NOT.MISS(LEXIT).AND..NOT.MISS(DBYPER).AND..NOT.MISS(VALUE) .AND..NOT.MISS(DAWARE).AND..NOT.MISS(DSHTAX).AND..NOT.MISS(CORPN) .AND..NOT.MISS(VDEBT).AND..NOT.MISS(DGHLTH).AND..NOT.MISS(DDEGREE) .AND..NOT.MISS(DEPEN).AND..NOT.MISS(DHHINC2).AND..NOT.MISS(DHHINC3) .AND..NOT.MISS(NOLAN).AND..NOT.MISS(DIREC).AND..NOT.MISS(TUNSWO) .AND..NOT.MISS(VSAGE).AND..NOT.MISS(OWAGE).AND..NOT.MISS(FSEXP) .AND..NOT.MISS(SKSHR).AND..NOT.MISS(AVESK).AND..NOT.MISS(FSINC) ; ENDPROC OPNOMIS; OPNOMIS; ?WTS Permit Probit Model; ?=====================================================================; ? List of Explanatory Variables for WTS shark permit model the dependent var is WTSSH; ? 19 var LIST XVAR LEXIT DBYPER DSHTAX CORPN VDEBT DGHLTH DDEGREE DEPEN DHHINC2 DHHINC3 NOLAN DIREC TUNSWO VSAGE OWAGE FSEXP SKSHR AVESK FSINC ; ?13 Dummy variables; LIST XDUMVAR LEXIT DBYPER DSHTAX CORPN VDEBT DGHLTH DDEGREE DEPEN DHHINC2 DHHINC3 NOLAN DIREC TUNSWO ; ?6 Continuous variables LIST XCONVAR VSAGE OWAGE FSEXP SKSHR AVESK FSINC ; MSD (TERSE) XDUMVAR XCONVAR; ?mark significant variables with STARS; REGOPT (STARS,STAR1=.10,STAR2=.05,) T; PROBIT (MILLS= IMR1) WTSSH C XDUMVAR XCONVAR; MAT PR=@COEF;

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90 ? Variables 2-14 are dummies & 15-20 are continuous; ? Mean Minimum Maximum ?1 C ------------------------------------?2 LEXIT | 0.310 0.000 1.000 ?3 DBYPER | 0.724 0.000 1.000 ?4 DSHTAX | 0.254 0.000 1.000 ?5 CORPN | 0.500 0.000 1.000 ?6 VDEBT | 0.323 0.000 1.000 ?7 DGHLTH | 0.0690 0.000 1.000 ?8 DDEGREE | 0.293 0.000 1.000 ?9 DEPEN | 0.422 0.000 1.000 ?10 DHHINC2 | 0.461 0.000 1.000 ?11 DHHINC3 | 0.250 0.000 1.000 ?12 NOLAN | 0.237 0.000 1.000 ?13 DIREC | 0.457 0.000 1.000 ?14 TUNSWO | 0.422 0.000 1.000 ?15 VSAGE | 23.254 1.000 76.000 ?16 OWAGE | 51.457 28.000 82.000 ?17 FSEXP | 29.108 1.000 72.000 ?18 SKSHR | 0.0617 0.000 1.000 ?19 AVESK | 0.864 0.000 13.182 ?20 FSINC | 76.806 0.000 100.000 ? ============================================================================ ? Simulator: 1 C + 113 Dummies + 6 continuous = 20 coeff. ? ============================================================================ ? Creates a name that starts with A. for the 20 coefficients DOT(VALUE=J) 1-20; SET A.=PR(J,1); ENDDOT; MMAKE AA A1-A20; ? Matrix with prob coefficients ? Initializing the variables SET ZZVAR1 = 1; SET ZZVAR2 = 0.310; SET ZZVAR3 = 0.724; SET ZZVAR4 = 0.254; SET ZZVAR5 = 0.500; SET ZZVAR6 = 0.323; SET ZZVAR7 = 0.0690; SET ZZVAR8 = 0.293; SET ZZVAR9 = 0.422; SET ZZVAR10 = 0.461; SET ZZVAR11 = 0.250; SET ZZVAR12 = 0.237; SET ZZVAR13 = 0.457; SET ZZVAR14 = 0.422; SET ZZVAR15 = 23.254; SET ZZVAR16 = 51.457; SET ZZVAR17 = 29.108; SET ZZVAR18 = 0.0617; SET ZZVAR19 = 29.551; SET ZZVAR20 = 76.806; PROC INIT; SET ZZVAR1 = 1; SET ZZVAR2 = 0.310; SET ZZVAR3 = 0.724;

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91 SET ZZVAR4 = 0.254; SET ZZVAR5 = 0.500; SET ZZVAR6 = 0.323; SET ZZVAR7 = 0.0690; SET ZZVAR8 = 0.293; SET ZZVAR9 = 0.422; SET ZZVAR10 = 0.461; SET ZZVAR11 = 0.250; SET ZZVAR12 = 0.237; SET ZZVAR13 = 0.457; SET ZZVAR14 = 0.422; SET ZZVAR15 = 23.254; SET ZZVAR16 = 51.457; SET ZZVAR17 = 29.108; SET ZZVAR18 = 0.0617; SET ZZVAR19 = 29.551; SET ZZVAR20 = 76.806; ENDPROC INIT; ? Matrix for simulated probabilities MFORM(TYPE=GEN , NCOL=4, NROW=500) SIMAT=0; SET I = 0; SET VARVAL = 0; PROC ZSIMZ; SET I = I + 1; MMAKE ZZ ZZVAR1-ZZVAR20; MAT XA = ZZ'AA; SET XXA = XA; ? Calculating the probabilities SET PROB1 = CNORM(XXA); ? Specifying the components for the matrix with simulated probabilities SET SIMAT(I,1) = SIMNUM; SET SIMAT(I,2) = VARVAL; SET SIMAT(I,3) = PROB1; ENDPROC ZSIMZ; ? =================================================== ? Sim = 1 Initial values ? =================================================== SET SIMNUM = 1; INIT; ZSIMZ; ?===================================================== ? Sim = 2 ZZVAR3 (BYPER Support permit buyback) ?===================================================== SET SIMNUM = 2; INIT; SET ZZVAR3 =1; ?support; ZSIMZ; INIT; SET ZZVAR3 =0; ?do not support; ZSIMZ; ?=================================================================

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92 ? Sim = 3 ZZVAR7 (DGHLTH Poor health) ?================================================================= SET SIMNUM = 3; INIT; SET ZZVAR7 =1; ?poor health; ZSIMZ; INIT; SET ZZVAR7 =0; ?no poor health; ZSIMZ; ?================================================================= ? Sim = 4 ZZVAR10 (DHHINC2 household income $50k to $999,99k) ?================================================================= SET SIMNUM = 4; INIT; SET ZZVAR10 =1; ?DHHINC2; ZSIMZ; INIT; SET ZZVAR10 =0; ? not DHHINC2; ZSIMZ; ? ===================================================================== ? Sim = 5: DO loop for ZZVAR18 (SKSHR shark share of revenue): 0.0-1.0 ? ===================================================================== DO J=0 TO 1 BY 0.1; SET SIMNUM = 5; INIT; SET VARVAL = J; SET ZZVAR18 = J; ZSIMZ; ENDDO; WRITE(FORMAT=EXCEL,FILE='C:\Sims\PERMITPRMEANS.XLS') SIMAT; END;

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93 OPTIONS MEMORY=175 NWIDTH=10 SIGNIF=3 LIMPRN=95 LINLIM=1500 LEFTMG=0; READ (FILE='E:\THESIS MODEL DATA\Tsp data1' FORMAT=EXCEL); ? Vessel WTS Probit Model and Simulation; ? List of All Variables 51 variables; LIST ALLVAR EXPAN LEXIT IMPSH IMPSW IMPTN REVOK BYPER BYALL REGUL QUOTA VALUE AWARE SHTAX LNGHT VSAGE PPORT OWNER CORPN VDEBT VINSR WTSSH WTASH WTSALL WTALL OWAGE FSEXP COMPU GHLTH MARIT DEGREE HARR ADULT CHILD DEPEN HHINC FSINC HARVEST WSALE RETAILE JOBFT JBOPT PRTFL BIDSK BIDTR AVESK AVETR SKSHR NOLAN DIREC INCID TUNSWO; ? Transform continuous varibles to combine categories; ? Transform BYPER into 2 categories; DIMPSH = (IMPSH=0 | IMPSH=1 | IMPSH=2)*0 + (IMPSH=3 | IMPSH=4)*1; DIMPSW = (IMPSW=0 | IMPSW=1 | IMPSW=2)*0 + (IMPSW=3 | IMPSW=4)*1; DIMPTN = (IMPTN=2 | IMPTN=3 | IMPTN=4)*0 + (IMPTN=0 | IMPTN=1)*1; DREVOK = (REVOK=-1 | REVOK=0)*0 + (REVOK=1)*1; DBYPER = (BYPER=-1 | BYPER=0)*0 + (BYPER=1)*1; DBYALL = (BYALL=-1 | BYALL=0)*0 + (BYALL=1)*1; DREGUL = (REGUL=-1 | REGUL=0)*0 + (REGUL=1)*1; DQUOTA = (QUOTA=-1 | QUOTA=0)*0 + (QUOTA=1)*1; DSHTAX = (SHTAX=0 | SHTAX=2)*0 + (SHTAX=1)*1; DAWARE = (AWARE=0 | AWARE=1)*0 +(AWARE=2 | AWARE=3)*1; DGHLTH = (GHLTH=3 | GHLTH=4 | GHLTH=5)*0 + (GHLTH=0 | GHLTH=1)*1; DUMMY DGHLTH; DMARIT = (MARIT=0 | MARIT=3 | MARIT=4 | MARIT=5)*0 + (MARIT=1 | MARIT=2 )*1; DHARR = (HARR=1 | HARR=2)*0 + (HARR=0)*1; DDEGREE = (DEGREE=0 |DEGREE=1)*0 + (DEGREE=2 | DEGREE=3 | DEGREE=4)*1; DHHINC = (HHINC=0)*1 + (HHINC=1 )*2 + (HHINC=2 | HHINC=3 | HHINC=4 | HHINC=5)*3 ; DUMMY DHHINC; ? Tranform dependent variable WTASH and WTALL for odrered probit model into 3 groups;

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94 ZWTASH = (WTASH=0 )*1 + (WTASH=25 | WTASH=50)*2 +(WTASH=75 | WTASH=100)*3 ; ZWTALL = (WTALL=0 )*1 + (WTALL=25 | WTALL=50)*2 +(WTALL=75 | WTALL=100)*3 ; ?list of all model variables with tranformed/dummy variables; LIST MODELVAR EXPAN LEXIT DIMPSH DIMPSW DIMPTN DREVOK DBYPER DBYALL DREGUL DQUOTA VALUE DAWARE DSHTAX LNGHT VSAGE PPORT CORPN VDEBT VINSR WTSSH ZWTASH WTSALL ZWTALL OWAGE FSEXP COMPU DGHLTH DMARIT DDEGREE DHARR ADULT CHILD DEPEN DHHINC1 DHHINC2 DHHINC3 FSINC HARVEST WSALE RETAILE JOBFT JBOPT PRTFL BIDSK BIDTR AVESK AVETR SKSHR NOLAN DIREC TUNSWO; ? Eliminating missing obs from RH vars used in Ord Prob; PROC OPNOMIS; SELECT .NOT.MISS(LEXIT).AND..NOT.MISS(DBYALL).AND..NOT.MISS(VALUE).AND..NOT.MISS(DAWAR E).AND..NOT.MISS(DSHTAX) .AND..NOT.MISS(CORPN).AND..NOT.MISS(VDEBT).AND..NOT.MISS(VINSR).AND..NOT.MISS(D GHLTH).AND..NOT.MISS(DDEGREE) .AND..NOT.MISS(DEPEN).AND..NOT.MISS(DHHINC2).AND..NOT.MISS(DHHINC3).AND..NOT.MI SS(NOLAN).AND..NOT.MISS(DIREC) .AND..NOT.MISS(TUNSWO).AND..NOT.MISS(VSAGE).AND..NOT.MISS(OWAGE).AND..NOT.MISS( FSEXP).AND..NOT.MISS(FSINC) .AND..NOT.MISS(AVETR); ENDPROC OPNOMIS; OPNOMIS; ?mark significant variables with STARS; REGOPT (STARS,STAR1=.10,STAR2=.05,) T; ?Probit model ?=================================================================== ? List of explanatory variables for WTS Vessel model LIST YVAR LEXIT DBYALL DSHTAX CORPN VDEBT VINSR DGHLTH DDEGREE DEPEN DHHINC2 DHHINC3 NOLAN DIREC TUNSWO VSAGE OWAGE FSEXP FSINC AVETR ; ?14 duumy variables LIST YDUMVAR LEXIT DBYALL DSHTAX CORPN VDEBT VINSR DGHLTH DDEGREE DEPEN DHHINC2 DHHINC3 NOLAN DIREC TUNSWO ; ?5 continuous variables LIST YCONVAR VSAGE OWAGE FSEXP FSINC AVETR ;

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95 MSD (TERSE) YDUMVAR YCONVAR; ? Variables 2-15 are dummies & 16-20 are continuous ? ? Mean Minimum Maximum ?1 C -----------------------------------?2 LEXIT | 0.303 0.000 1.000 ?3 DBYALL | 0.736 0.000 1.000 ?4 DSHTAX | 0.251 0.000 1.000 ?5 CORPN | 0.502 0.000 1.000 ?6 VDEBT | 0.333 0.000 1.000 ?7 VINSR | 0.498 0.000 1.000 ?8 DGHLTH | 0.0649 0.000 1.000 ?9 DDEGREE | 0.290 0.000 1.000 ?10 DEPEN | 0.433 0.000 1.000 ?11 DHHINC2 | 0.459 0.000 1.000 ?12 DHHINC3 | 0.247 0.000 1.000 ?13 NOLAN | 0.238 0.000 1.000 ?14 DIREC | 0.446 0.000 1.000 ?15 TUNSWO | 0.429 0.000 1.000 ?16 VSAGE | 23.359 1.000 77.000 ?17 OWAGE | 51.165 28.000 82.000 ?18 FSEXP | 29.048 1.000 72.000 ?19 FSINC | 77.398 0.000 100.000 ?20 AVETR | 67.046 0.000 870.856 PROBIT (MILLS= IMR2) WTSALL C YDUMVAR YCONVAR; MAT PR=@COEF; ? ============================================================================ ? Simulator (Probit): 1 C + 14 Dummies + 5 continuous = 20 coeff. ? =========================================================================== ? Creates a name that starts with B. for the 20 coefficients DOT(VALUE=J) 1-20; SET B.=PR(J,1); ENDDOT; MMAKE BB B1-B20; ? Matrix with prob coefficients ? Initializing the variables SET ZZVAR1 =1; ? intercept SET ZZVAR2 =0.303; ? all variables set at the mean value; SET ZZVAR3 =0.736; SET ZZVAR4 =0.251; SET ZZVAR5 =0.502; SET ZZVAR6 =0.333; SET ZZVAR7 =0.498; SET ZZVAR8 =0.0649; SET ZZVAR9 =0.290; SET ZZVAR10 =0.433; SET ZZVAR11 =0.459; SET ZZVAR12 =0.247; SET ZZVAR13 =0.238; SET ZZVAR14 =0.446; SET ZZVAR15 =0.429; SET ZZVAR16 =23.359; SET ZZVAR17 =51.165; SET ZZVAR18 =29.048; SET ZZVAR19 =77.398; SET ZZVAR20 =67.046;

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96 PROC INIT; SET ZZVAR1 =1; ? intercept SET ZZVAR2 =0.303; ? all variables set at the mean value; SET ZZVAR3 =0.736; SET ZZVAR4 =0.251; SET ZZVAR5 =0.502; SET ZZVAR6 =0.333; SET ZZVAR7 =0.498; SET ZZVAR8 =0.0649; SET ZZVAR9 =0.290; SET ZZVAR10 =0.433; SET ZZVAR11 =0.459; SET ZZVAR12 =0.247; SET ZZVAR13 =0.238; SET ZZVAR14 =0.446; SET ZZVAR15 =0.429; SET ZZVAR16 =23.359; SET ZZVAR17 =51.165; SET ZZVAR18 =29.048; SET ZZVAR19 =77.398; SET ZZVAR20 =67.046; ENDPROC INIT; ? Matrix for simulated probabilities MFORM(TYPE=GEN , NCOL=4, NROW=500) SIMAT=0; SET I = 0; SET VARVAL = 0; PROC ZSIMZ; SET I = I + 1; MMAKE ZZ ZZVAR1-ZZVAR20; MAT XB = ZZ'BB; SET XXB = XB; ? Calculating the probabilities SET PROB1 = CNORM(XXB); ? Specifying the components for the matrix with simulated probabilities SET SIMAT(I,1) = SIMNUM; SET SIMAT(I,2) = VARVAL; SET SIMAT(I,3) = PROB1; ENDPROC ZSIMZ; ? =================================================== ? Sim = 1 Initial values ? =================================================== SET SIMNUM = 1; ?base model; INIT; ZSIMZ; ?===================================================== ? Sim = 2 ZZVAR2 (LEXIT Plan to exit) ?===================================================== SET SIMNUM = 2; INIT; SET ZZVAR2 =1; ?Plan to exit; ZSIMZ;

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97 INIT; SET ZZVAR2 =0; ? do not Plan to exit; ZSIMZ; ?================================================================= ? Sim = 3 ZZVAR3 (BYALL support vessel buyback) ?================================================================= SET SIMNUM = 3; INIT; SET ZZVAR3 =1; ?support; ZSIMZ; INIT; SET ZZVAR3 =0; ?Do not support; ZSIMZ; ?===================================================== ? Sim = 4 ZZVAR12 and ZZVAR12 (DHHINC household income) ?===================================================== SET SIMNUM = 4; INIT; SET ZZVAR12 =1; ?DHHINC3; ZSIMZ; INIT; SET ZZVAR12 =0; ? not DHHINC3; ZSIMZ; INIT; SET ZZVAR11 =1; ?DHHINC2; ZSIMZ; INIT; SET ZZVAR11 =0; ? not DHHINC2; ZSIMZ; ?===================================================== ? Sim = 5 ZZVAR15 (TUNSWO= Swordfish or tuna permit) ?===================================================== SET SIMNUM = 5; INIT; SET ZZVAR15 =1; ?TUNSWO ; ZSIMZ; INIT; SET ZZVAR15 =0; ? No TUNSWO ; ZSIMZ; ?====================================================== ?Sim = 6: DO loop for ZZVAR17 (OWAGE owner age): 28-82 ?====================================================== DO J=28 TO 82; SET SIMNUM = 6; INIT; SET VARVAL = J; SET ZZVAR17 = J; ZSIMZ; ENDDO; ?================================================================ ?Sim = 7: DO loop for ZZVAR18 (FSEXP years fishing exper): 1-72 ?================================================================ DO J=1 TO 72; SET SIMNUM = 7; INIT;

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98 SET VARVAL = J; SET ZZVAR18 = J; ZSIMZ; ENDDO; ?===================================================================== ?Sim = 8: DO loop for ZZVAR19(AVETR ave revenue all species): 0-100 ?===================================================================== DO J=0 TO 100 ; SET SIMNUM = 8; INIT; SET VARVAL = J; SET ZZVAR19 = J; ZSIMZ; ENDDO; WRITE(FORMAT=EXCEL,FILE='C:\Sims\VESSELPRMEANS.XLS') SIMAT; END;

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99 OPTIONS MEMORY=175 NWIDTH=10 SIGNIF=3 LIMPRN=95 LINLIM=1500 LEFTMG=0; READ (FILE='C:\THESIS MODEL DATA\Tsp data1' FORMAT=EXCEL); ? Vessel WTS Orderd Probit Model and Simulation; ? List of All Variables 51 variables; LIST ALLVAR EXPAN LEXIT IMPSH IMPSW IMPTN REVOK BYPER BYALL REGUL QUOTA VALUE AWARE SHTAX LNGHT VSAGE PPORT OWNER CORPN VDEBT VINSR WTSSH WTASH WTSALL WTALL OWAGE FSEXP COMPU GHLTH MARIT DEGREE HARR ADULT CHILD DEPEN HHINC FSINC HARVEST WSALE RETAILE JOBFT JBOPT PRTFL BIDSK BIDTR AVESK AVETR SKSHR NOLAN DIREC INCID TUNSWO; ? Transform continuous varibles to combine categories; DIMPSH = (IMPSH=0 | IMPSH=1 | IMPSH=2)*0 + (IMPSH=3 | IMPSH=4)*1; DIMPSW = (IMPSW=0 | IMPSW=1 | IMPSW=2)*0 + (IMPSW=3 | IMPSW=4)*1; DIMPTN = (IMPTN=2 | IMPTN=3 | IMPTN=4)*0 + (IMPTN=0 | IMPTN=1)*1; DREVOK = (REVOK=-1 | REVOK=0)*0 + (REVOK=1)*1; DBYPER = (BYPER=-1 | BYPER=0)*0 + (BYPER=1)*1; DBYALL = (BYALL=-1 | BYALL=0)*0 + (BYALL=1)*1; DREGUL = (REGUL=-1 | REGUL=0)*0 + (REGUL=1)*1; DQUOTA = (QUOTA=-1 | QUOTA=0)*0 + (QUOTA=1)*1; DSHTAX = (SHTAX=0 | SHTAX=2)*0 + (SHTAX=1)*1; DAWARE = (AWARE=0 | AWARE=1)*0 +(AWARE=2 | AWARE=3)*1; DGHLTH = (GHLTH=3 | GHLTH=4 | GHLTH=5)*0 + (GHLTH=0 | GHLTH=1)*1; DUMMY DGHLTH; DMARIT = (MARIT=0 | MARIT=3 | MARIT=4 | MARIT=5)*0 + (MARIT=1 | MARIT=2 )*1; DHARR = (HARR=1 | HARR=2)*0 + (HARR=0)*1; DDEGREE = (DEGREE=0 |DEGREE=1)*0 + (DEGREE=2 | DEGREE=3 | DEGREE=4)*1; DHHINC = (HHINC=0)*1 + (HHINC=1 )*2 + (HHINC=2 | HHINC=3 | HHINC=4 | HHINC=5)*3 ; DUMMY DHHINC; ? Tranform dependent variable WTASH and WTALL for odrered probit model into 3 groups; ZWTASH = (WTASH=0 )*1 + (WTASH=25 | WTASH=50)*2 +(WTASH=75 | WTASH=100)*3 ; ZWTALL = (WTALL=0 )*1 + (WTALL=25 | WTALL=50)*2 +(WTALL=75 | WTALL=100)*3

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100 ; ?list of all model variables with tranformed/dummy variables; LIST MODELVAR EXPAN LEXIT DIMPSH DIMPSW DIMPTN DREVOK DBYPER DBYALL DREGUL DQUOTA VALUE DAWARE DSHTAX LNGHT VSAGE PPORT CORPN VDEBT VINSR WTSSH ZWTASH WTSALL ZWTALL OWAGE FSEXP COMPU DGHLTH DMARIT DDEGREE DHARR ADULT CHILD DEPEN DHHINC1 DHHINC2 DHHINC3 FSINC HARVEST WSALE RETAILE JOBFT JBOPT PRTFL BIDSK BIDTR AVESK AVETR SKSHR NOLAN DIREC TUNSWO; ? Eliminating missing obs from RH vars used in Ord Prob; PROC OPNOMIS; SELECT .NOT.MISS(LEXIT).AND..NOT.MISS(DBYALL).AND..NOT.MISS(VALUE).AND..NOT.MISS(DAWAR E).AND..NOT.MISS(DSHTAX) .AND..NOT.MISS(CORPN).AND..NOT.MISS(VDEBT).AND..NOT.MISS(VINSR).AND..NOT.MISS(D GHLTH).AND..NOT.MISS(DDEGREE) .AND..NOT.MISS(DEPEN).AND..NOT.MISS(DHHINC2).AND..NOT.MISS(DHHINC3).AND..NOT.MI SS(NOLAN).AND..NOT.MISS(DIREC) .AND..NOT.MISS(TUNSWO).AND..NOT.MISS(VSAGE).AND..NOT.MISS(OWAGE).AND..NOT.MISS( FSEXP).AND..NOT.MISS(FSINC) .AND..NOT.MISS(AVETR); ENDPROC OPNOMIS; OPNOMIS; ?mark significant variables with STARS; REGOPT (STARS,STAR1=.10,STAR2=.05,) T; ?Probit model ?=================================================================== ? List of explanatory variables for WTS Vessel model LIST YVAR LEXIT DBYALL DSHTAX CORPN VDEBT VINSR DGHLTH DDEGREE DEPEN DHHINC2 DHHINC3 NOLAN DIREC TUNSWO VSAGE OWAGE FSEXP FSINC AVETR ; ?14 duumy variables LIST YDUMVAR LEXIT DBYALL DSHTAX CORPN VDEBT VINSR DGHLTH DDEGREE DEPEN DHHINC2 DHHINC3 NOLAN DIREC TUNSWO ; ?5 continuous variables LIST YCONVAR VSAGE OWAGE FSEXP FSINC AVETR ; PROBIT (MILLS= IMR2) WTSALL C YDUMVAR YCONVAR; MAT PR=@COEF;

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101 ?Ordered Probit Model WTA Vessel Bid ?==================================================================== ? List of explanatory variables for WTA Vessel model the dependent variable is ZWTALL; ?20 variables no DIREC LIST ZVAR LEXIT DBYALL VALUE DAWARE DSHTAX CORPN VDEBT VINSR DGHLTH DDEGREE DEPEN DHHINC2 DHHINC3 NOLAN TUNSWO VSAGE OWAGE FSEXP FSINC AVETR; ? 15 dummy variables LIST ZDUMVAR LEXIT DBYALL VALUE DAWARE DSHTAX CORPN VDEBT VINSR DGHLTH DDEGREE DEPEN DHHINC2 DHHINC3 NOLAN TUNSWO ; ? 5 continuous variables LIST ZCONVAR VSAGE OWAGE FSEXP FSINC AVETR ; MSD(TERSE) ZDUMVAR ZCONVAR IMR2 ; ? Variables 2-16 are dummies & 17-21 are continuous ? ? Mean Minimum Maximum ? 1 C -----------------------------------? 2 LEXIT | 0.303 0.000 1.000 ? 3 DBYALL | 0.736 0.000 1.000 ? 4 VALUE | 0.589 0.000 1.000 ? 5 DAWARE | 0.502 0.000 1.000 ? 6 SHTAX | 0.251 0.000 1.000 ? 7 CORPN | 0.502 0.000 1.000 ? 8 VDEBT | 0.333 0.000 1.000 ? 9 VINSR | 0.498 0.000 1.000 ? 10 DGHLTH | 0.0649 0.000 1.000 ? 11 DDEGREE | 0.290 0.000 1.000 ? 12 DEPEN | 0.433 0.000 1.000 ? 13 DHHINC2 | 0.459 0.000 1.000 ? 14 DHHINC3 | 0.247 0.000 1.000 ? 15 NOLAN | 0.238 0.000 1.000 ? 16 TUNSWO | 0.429 0.000 1.000 ? 17 VSAGE | 23.359 1.000 77.000 ? 18 OWAGE | 51.165 28.000 82.000 ? 19 FSEXP | 29.048 1.000 72.000 ? 20 FSINC | 77.398 0.000 100.000 ? 21 AVETR | 67.046 0.000 870.856 ? 22 IMR2 | 2.535D-09 -2.008 1.625 ORDPROB ZWTALL C ZDUMVAR ZCONVAR IMR2; MAT ORPR=@COEF; ? ============================================================================ ? Simulator: 1 C + 15 Dummies + 5 continuous + IMR2 + 1 MU = 23 coeff. ? =========================================================================== ? Creates a name that starts with A. for the 23 coefficients DOT(VALUE=J) 1-23; SET A.=ORPR(J,1);

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102 ENDDOT; MMAKE AA A1-A22; ? Matrix with ord prob coefficients (no mu) ? Initializing the variables SET ZZVAR1 =1; ? intercept; SET ZZVAR2 =0.303; ? mean value; SET ZZVAR3 =0.736; SET ZZVAR4 =0.589; SET ZZVAR5 =0.502; SET ZZVAR6 =0.251; SET ZZVAR7 =0.502; SET ZZVAR8 =0.333; SET ZZVAR9 =0.498; SET ZZVAR10 =0.0649; SET ZZVAR11 =0.290; SET ZZVAR12 =0.433; SET ZZVAR13 =0.459; SET ZZVAR14 =0.247; SET ZZVAR15 =0.238; SET ZZVAR16 =0.429; SET ZZVAR17 =23.359; SET ZZVAR18 =51.165; SET ZZVAR19 =29.048; SET ZZVAR20 =77.398; SET ZZVAR21 =67.046; SET ZZVAR22 =2.535D-09; PROC INIT; SET ZZVAR1 =1; ? intercept; SET ZZVAR2 =0.303; ? mean value; SET ZZVAR3 =0.736; SET ZZVAR4 =0.589; SET ZZVAR5 =0.502; SET ZZVAR6 =0.251; SET ZZVAR7 =0.502; SET ZZVAR8 =0.333; SET ZZVAR9 =0.498; SET ZZVAR10 =0.0649; SET ZZVAR11 =0.290; SET ZZVAR12 =0.433; SET ZZVAR13 =0.459; SET ZZVAR14 =0.247; SET ZZVAR15 =0.238; SET ZZVAR16 =0.429; SET ZZVAR17 =23.359; SET ZZVAR18 =51.165; SET ZZVAR19 =29.048; SET ZZVAR20 =77.398; SET ZZVAR21 =67.046; SET ZZVAR22 =2.535D-09 ENDPROC INIT; ? Matrix for simulated probabilities MFORM(TYPE=GEN , NCOL=8, NROW=500) SIMAT=0; SET I = 0; SET VARVAL = 0; PROC ZSIMZ;

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103 SET I = I + 1; MMAKE ZZ ZZVAR1-ZZVAR22; MAT XA = ZZ'AA; SET XXA = XA; ? Calculating the probabilities SET PROB1 = CNORM(-XXA); SET PROB2 = CNORM(A23-XXA) CNORM(-XXA); SET PROB3 = 1 CNORM(A23-XXA); ? Specifying the components for the matrix with simulated probabilities SET SIMAT(I,1) = SIMNUM; SET SIMAT(I,2) = VARVAL; SET SIMAT(I,3) = PROB1; SET SIMAT(I,4) = PROB2; SET SIMAT(I,5) = PROB3; ENDPROC ZSIMZ; ? =================================================== ? Sim = 1 Initial values ? =================================================== SET SIMNUM = 1; INIT; ZSIMZ; ?===================================================== ? Sim = 2 ZZVAR6 (SHTAX WTP Shark tax) ?===================================================== SET SIMNUM = 2; INIT; SET ZZVAR6 =1; ?WTP tax; ZSIMZ; INIT; SET ZZVAR6 =0; ?not WTP tax; ZSIMZ; ?================================================================= ? Sim = 3 ZZVAR7 (CORPN Vessel owned by corporation/partnership) ?================================================================= SET SIMNUM = 3; INIT; SET ZZVAR7 =1; ?corporation; ZSIMZ; INIT; SET ZZVAR7 =0; ?Not corporation; ZSIMZ; ?===================================================== ? Sim = 4 ZZVAR9 (VISR Insured vessel) ?===================================================== SET SIMNUM = 4; INIT; SET ZZVAR9 =1; ?Insured; ZSIMZ; INIT; SET ZZVAR9 =0; ?not Insured; ZSIMZ; ?===================================================== ? Sim = 5 ZZVAR11 (DDEGREE Education> highschool) ?===================================================== SET SIMNUM = 5;

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104 INIT; SET ZZVAR11 =1; ?>highschool ; ZSIMZ; INIT; SET ZZVAR11 =0; ?
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LIST OF REFERENCES Aldrich, J. H. and F. D. Nelson. 1984. Linear Probability, Logit, and Probit Models. Newberry Park, CA: Sage Publications, Inc. Boyle, K. A., M. P. Welsh, and R.C. Bishop . 1998. Validation of Empirical Measures of Welfare Change: Comment. Land Economics 64(Feb): 94-98. Cooper, J., and J. Loomis. 1992. Sensitivity of Willingness-to-Pay Estimates to Bid Design in Dichotomous Choice Contingent Valuation Models. Land Economics 68 (May):211-24. Cragg, G. J. 1971. Some Statistical Mode ls for Limited Dependent Variables with Application to the Demand for Durable Goods. Econometrica, 39 (5):829-844. Cummings, G, D. S. Brookshi re, and W. D., Schulze 1986. Valuing Public Goods: An Assessment of the Conti ngent Valuation Method, Totowa, NJ: Rowman and Allanheld, 1986. Desvouges, W, Grable A, Dunford R, and Hudson S. 1993. Contingent Valuation: The Wrong Tool to Measure Passive Use Values. Choices, 32(2): 9-11. Food an Agricultural Organisatio n of the United Nations (FAO). 1999. International Plan for the Management of Fishing Capacity, Rome. GAO (United States General Accounting Office). 2001.Commer cial Fisheries: Effectiveness of Fishing Buybacks Can be Improved, Testimony before the subcommittee on Fisheries Conservation May 10 2001. Wildlife and Oceans, Committee on Resources, House of Representatives. ____. 1999. Federally Funded Buyback Progra ms for Commercial Fisheries.Briefing for the House Committee on Resources Sept ember 23 1999, Resources, Community and Economic Development Division. Haneman W. M. 1999. Willingness to Pay and Willingness to Accept: How Much Can They Differ? The American Economic Review, 81(3):635-647. Holland, D., E. Gudmunson, and J. Gates. 1999. Do Fishing Vessel Buyback Programs Work: A Survey of the Evidence. Marine Policy. 23(1): 47-69. 105

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106 Johnston R.J., Swallow S.K. 1999. Asymmetr ies in Ordered Strength of Preference Models: Implications of Focus Shift fo r Discrete Choice Preference Estimation. Land Economics, 75 (2): 259-310. Kitts, A., E. Thunberg and J. Robertson. 2001. Willingness to Participate and Bids in a Fishing Vessel Buyout Program: A Ca se Study of New England Groundfish. Marine Resource Economics 15:221-232. Larkin S.L., C. M. Adams, J. Musengezi , and V. De Veau. 2005. “Assessing the Fair Market Value of Commercial Shark Permits and Vessels in the Gulf of Mexico and Atlantic Regions”. A report submitted to the Gulf and South Atlantic Fisheries Foundation Inc. Larkin S.L., W. Kiethly, C .M. Adams, an d R. F. Kazmierczak. 2004. Buyback Programs for Capacity reduction in the U.S. Atlantic Shark Fishery. Journal of Agricultural and Applied Economics 36(2):317-332. Liao, T.F. 1994. Interpreting Probability Models: Logit, Probit, and Other Generalised Linear Models. Thousand Oaks, CA: Sage Publications, Inc. Long, J. S. 1997 Regression Models for Categorical and Limited Dependent Variables . Thousand Oaks, CA: Sage Publications, Inc. Mabiso, A.2005.”Estimating Consumers W illingness-to-Pay for Country-of-Origin Labels in Fresh Apples and Tomatoes: A double Hurdle Probit Analysis of U.S. Data using Factor Scores”. A thesis presente d to the University of Florida in Partial Fulfillment Of the degree of Master of Science, University of Florida. MartinezEspineira, R. “A Box Cox Doubl e-Hurdle Probit Analysis of Wildlife Valuation: The Citizen’s Perspective.” Economics Working Paper Archive EconWPA http://econwpa.wustl.edu/ep s/othr/papers/0410/0410003.pdf Accessed March 2006. Musick, J. A. 1999. Criteria to Define Extinction Risk in Marine Fishes. Fisheries (US) 24(12):6-14. National Fisheries Conservation Center (NFCC). Issue Overview: Overcapacity. http://nfcc-fisheries.org Accessed March, 2006 National Marine Fisheries Service (NMFS) .1996. “Magnuson-Stevens Fishery Conservation and Management Act” Public Law 94-265. ____. 2003. U.S. National Plan of Action for th e Management of Fishing Capacity. U.S. Department of Commerce, Silver Spring, MD. ____. 2001. United Stated National Plan of Action for the Conservation and Management of Sharks. U.S. Department of Commerce , Silver Spring, MD.

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107 Tobin, J. 1958. Estimation of Relationships for Limited Dependent Variables. Econometrica 26: 24-36. U.S. Department of Commerce. 2003. Unite d States National Plan of Action for the Management of Fishing Cap acity. Silver Spring, MD.

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BIOGRAPHICAL SKETCH Jessica Musengezi was born on 22 Ju ly 1981 in Bulawayo, Zimbabwe. She received her Bachelor of Science in Agricu lture with honors from the University of Zimbabwe in August 2003 with specialization of agricultural economics. She went on to receive her Master of Science degree in food and resource economics from the University of Florida in August 2006. 108