An Economic Analysis of Rebuilding Artisanal Fisheries

Permanent Link: http://ufdc.ufl.edu/UFE0041221/00001

Material Information

Title: An Economic Analysis of Rebuilding Artisanal Fisheries The Potential for Fishermen-Based Ecotourism in the Galapagos Marine Reserve
Physical Description: 1 online resource (131 p.)
Language: english
Creator: Alencastro, Liliana
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010


Subjects / Keywords: artisanal, binary, economic, ecotourism, efficiency, fisheries, fishermen, frontier, interdependence, logit, management, mothership, multinomial, preferences, production, rebuilding, stated, stochastic, technical
Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation


Abstract: This study examined the diversification of artisanal fishermen into tourism as a strategy to recover resource stocks and rebuild fishermen livelihoods in the Galapagos Marine Reserve. Incorporating unique interdependence characteristics between individuals and fishing vessels, and using a combination of stated preference and stochastic production frontier methodologies, the study (1) assessed fishermen switching and tour choice behavior and socio-economic determinants, (2) examined the harvesting potential and technical efficiency of the fleet, and (3) assessed the demand potential for fishermen-operated tours. Results indicate that vessel owners were more willing to switch than crew. Switching preferences also differed between boat size and geographical locations. Interestingly, capital malleability issues were compensated by the opportunity to transfer fishing capital outside the fishing sector. If switching, fishermen preferred bay and diving tours followed by the diving cruise option, and choices depended on vessel size, location, and access to bank funding. The findings suggest that a reduction of fleet size would be less effective than policies managing variable inputs to reduce the harvesting potential of the fleet. Also, the fleet can still improve technical efficiency and increase harvests in the short run. As expected, interdependence between boats influenced the production technology but the effect was negative. Smaller vessels, especially fiberglass boats, were less productive if they were towed by larger boats. Standard tours had a higher demand potential in comparison to artisanal fishing trips. Standard tours had higher booking intentions if half-day excursions were offered and tourists preferred full-day fishing trips with a dining option. Expected conservation and local benefits increased the interest for fishermen-based tours, while wealthier tourists were more reluctant to book these trips. Overall, the study found a promising market potential for fishermen-based ecotourism depending on the extent that fishermen are allowed into the standard tour market by the reserve managers. However, the potential for fishing effort reduction is limited and additional policies focusing on vessel efficiency and rights-based management will be needed to rebuild resource stocks. Future diversification strategies need to differentiate between vessel owners and crew and the specific realities of the fishing ports to entice fishermen to quit fishing.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Liliana Alencastro.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Larkin, Sherry L.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041221:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041221/00001

Material Information

Title: An Economic Analysis of Rebuilding Artisanal Fisheries The Potential for Fishermen-Based Ecotourism in the Galapagos Marine Reserve
Physical Description: 1 online resource (131 p.)
Language: english
Creator: Alencastro, Liliana
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010


Subjects / Keywords: artisanal, binary, economic, ecotourism, efficiency, fisheries, fishermen, frontier, interdependence, logit, management, mothership, multinomial, preferences, production, rebuilding, stated, stochastic, technical
Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation


Abstract: This study examined the diversification of artisanal fishermen into tourism as a strategy to recover resource stocks and rebuild fishermen livelihoods in the Galapagos Marine Reserve. Incorporating unique interdependence characteristics between individuals and fishing vessels, and using a combination of stated preference and stochastic production frontier methodologies, the study (1) assessed fishermen switching and tour choice behavior and socio-economic determinants, (2) examined the harvesting potential and technical efficiency of the fleet, and (3) assessed the demand potential for fishermen-operated tours. Results indicate that vessel owners were more willing to switch than crew. Switching preferences also differed between boat size and geographical locations. Interestingly, capital malleability issues were compensated by the opportunity to transfer fishing capital outside the fishing sector. If switching, fishermen preferred bay and diving tours followed by the diving cruise option, and choices depended on vessel size, location, and access to bank funding. The findings suggest that a reduction of fleet size would be less effective than policies managing variable inputs to reduce the harvesting potential of the fleet. Also, the fleet can still improve technical efficiency and increase harvests in the short run. As expected, interdependence between boats influenced the production technology but the effect was negative. Smaller vessels, especially fiberglass boats, were less productive if they were towed by larger boats. Standard tours had a higher demand potential in comparison to artisanal fishing trips. Standard tours had higher booking intentions if half-day excursions were offered and tourists preferred full-day fishing trips with a dining option. Expected conservation and local benefits increased the interest for fishermen-based tours, while wealthier tourists were more reluctant to book these trips. Overall, the study found a promising market potential for fishermen-based ecotourism depending on the extent that fishermen are allowed into the standard tour market by the reserve managers. However, the potential for fishing effort reduction is limited and additional policies focusing on vessel efficiency and rights-based management will be needed to rebuild resource stocks. Future diversification strategies need to differentiate between vessel owners and crew and the specific realities of the fishing ports to entice fishermen to quit fishing.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Liliana Alencastro.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Larkin, Sherry L.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041221:00001

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2 2010 Liliana A Alencastro


3 To Luis Alfredo, Juanita, Guillermo and Guillermo Jr.


4 ACKNOWLEDGMENTS I thank my family and my fianc, Luis Alfre do, for their constant love and encouragement, especially during the last part of my doctoral program. Withou t all their help and support, completing this journey would not have been possible. I want to recognize my committee chair, Dr. Sherry Larkin, for her excellent and continuous guidance during the prog ress of this dissertation, her s upport during the last phase of my degree and her understanding during difficult times I am very thankful to the rest of my committee members, as well, for their valuable advice regarding the me thodological aspects of this study. I give special thanks to the Compton F oundation and the Tropical Conservation and Development program at the University of Florid a for funding this study. I am also grateful to the Galapagos National Park Service and the Mari ne Conservation Research Department at the Charles Darwin Research Station for providing part of the data us ed in this study and for their assistance during field work and later stages of this research Finally, I extend my gratitude to the Food and Resource Economics Department for the financial support that made my graduate educat ion in the U.S. possible. A special recognition goes to Dr. Jeffrey Burkhardt, Graduate Coordina tor, and Jess Herman for all their help and consideration, as well as to Dr. Carlos Jaureg ui for his collaboration with data management, software use and for important suggestions a bout the econometric analysis shown in this document


5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES................................................................................................................ .........8 ABSTRACT....................................................................................................................... ..............9 CHAPTER 1 INTRODUCTION..................................................................................................................11 Artisanal Fisheries............................................................................................................ ......11 Approaches to Manage Artisanal Fisheries............................................................................13 Artisanal Fisheries in the Galapagos Marine Reserve............................................................15 Approaches to Evaluate Pr eferences and Efficiency..............................................................18 Stated Preference Analysis for Fishery Entry and Exit...................................................18 Efficiency Analysis in Fisheries......................................................................................19 Problem Statement.............................................................................................................. ....20 Goal and Objectives............................................................................................................ ....21 Overview of Methods and Study............................................................................................21 2 EXITING PREFERENCES OF SMALL SC ALE FISHERMEN: AN EXPLORATION OF INTERDEPENDECE EFFECTS.....................................................................................23 Introduction................................................................................................................... ..........23 Methodology.................................................................................................................... .......26 Data and Estimation............................................................................................................ ....28 Results and Discussion......................................................................................................... ..30 Exit Choice Model...........................................................................................................30 Profitability of fishing .... .. .......................................................................................31 Interdependence ...... .................................................................................................32 Demographics ...... ....................................................................................................33 Probability simulations............................................................................................. 34 Tour Choice Model..........................................................................................................36 Summary and Conclusions.....................................................................................................38 3 ASSESING PRODUCTION TECHNOLOGY AND TECHNICAL EFFICIENCY IN SMALL SCALE FISHING FLEETS: DOES VESSEL INTERRELATION MATTER?..... 46 Introduction................................................................................................................... ..........46 The Artisanal Spiny Lobster Fishery......................................................................................48 Methodology...........................................................................................................................50 Data and Model Specification................................................................................................ 53


6 Results........................................................................................................................ .............58 Frontier Model.................................................................................................................58 Technical Efficiency Model............................................................................................60 Summary and Conclusions.....................................................................................................62 4 TOURISTS PREFERENCES FOR FISH ERMEN-OPERATED EXCURSIONS................74 Introduction................................................................................................................... ..........74 Methodology.................................................................................................................... .......77 Data........................................................................................................................... ..............80 Results and Discussion......................................................................................................... ..84 Interest Models................................................................................................................85 Willingness to Book M odels ........................................................................................... 87 Conclusions.................................................................................................................... .........91 5 CONCLUSIONS..................................................................................................................104 APPENDIX A FACE TO FACE SURVEY INSTRUMENT FOR FISHERMEN EXITING BEHAVIOR IN SPANISH...................................................................................................109 B ONLINE SURVEY INSTRUMENT OF PREF ERENCES OF U.S. VISITORS FOR FISHERMEN-OPERATED TOURS...................................................................................115 LIST OF REFERENCES.............................................................................................................123 BIOGRAPHICAL SKETCH.......................................................................................................131


7 LIST OF TABLES Table page 2-1 Distribution of observed responses....................................................................................41 2-2 Variable descriptions and statistics....................................................................................42 2-3 Multinomial logit estimates for exit choice.......................................................................43 2-4 Effects of discrete variable s on the probability of switching.............................................43 2-5 Multinomial logit estimates fo r choice of tour operations.................................................44 3-1 Average fleet characteristics..............................................................................................65 3-2 Geographical distri bution of the fleet................................................................................65 3-3 Variable definition a nd descriptive statistics.....................................................................66 3-4 Sample differences based on observation of towing information......................................68 3-5 Sample statistics by towing status......................................................................................69 3-6 Paramete r estimates........................................................................................................ ...71 3-7 Hypotheses testing for parameters.....................................................................................72 4-1 Variable description....................................................................................................... ....93 4-2 Summary statistics......................................................................................................... ....95 4-3 Willingness to book responses by interest level................................................................97 4-4 Comparison of covariates base d on participant self selection...........................................98 4-5 Ordered logit estimates for tour interest............................................................................99 4-6 Effects of significant variables on the in terest probability for standard tours.................100 4-7 Effects of significant variables on the interest probability for fishing tours....................100 4-8 Binary logit estimates for willingness to book a tour......................................................101 4-9 Effects on the probability of booking a tour....................................................................102


8 LIST OF FIGURES Figure page 2-1 Probability of switching.................................................................................................... .45 3-1 Distribution of ef ficiency scores........................................................................................73 4-1 Distribution of in terest responses.....................................................................................103


9 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy AN ECONOMIC ANALYSIS OF REBUIL DING ARTISANAL FISHERIES: THE POTENTIAL FOR FISHERMEN-BASED ECOT OURISM IN THE GALAPAGOS MARINE RESERVE By Liliana A. Alencastro May 2010 Chair: Sherry Larkin Major: Food and Resource Economics This study examined the diversification of artisan al fishermen into tourism as a strategy to recover resource stocks and rebuild fishermen livelihoods in the Galapagos Marine Reserve. Incorporating unique interdepende nce characteristics between i ndividuals and fishing vessels, and using a combination of stated preference and stochastic production frontier methodologies, the study (1) assessed fishermen switching and tour choice behavior and socio-economic determinants, (2) examined the harvesting potential and technical efficiency of the fleet, and (3) assessed the demand potential for fishermen-operated tours. Results indicate that vessel owners were more willing to switch than crew. Switching preferences also differed between boat size a nd geographical locations. Interestingly, capital malleability issues were compensated by the opport unity to transfer fish ing capital outside the fishing sector. If switching, fishermen preferre d bay and diving tours followed by the diving cruise option, and choices depended on vesse l size, location, and access to bank funding. The findings suggest that a re duction of fleet size would be less effective than policies managing variable inputs to reduce the harvesting potential of the fleet. Also, the fleet can still improve technical efficiency and increase harvests in the short run. As expected, interdependence


10 between boats influenced the production technology but the effect was negativ e. Smaller vessels, especially fiberglass boats, were less producti ve if they were towed by larger boats. Standard tours had a higher demand potential in comparison to artisanal fishing trips. Standard tours had higher booking in tentions if half-day excursi ons were offered and tourists preferred full-day fishing trips with a dining option. Expected conserva tion and local benefits increased the interest for fishermen-based tours, while wealthier tourists were more reluctant to book these trips. Overall, the study found a promising market potential for fishermen-based ecotourism depending on the extent that fishermen are allowe d into the standard tour market by the reserve managers. However, the potential for fishing e ffort reduction is limite d and additional policies focusing on vessel efficiency and rights-based management will be needed to rebuild resource stocks. Future diversification stra tegies need to differentiate betw een vessel owners and crew and the specific realities of the fishing po rts to entice fishermen to quit fishing.


11 CHAPTER 1 INTRODUCTION Artisanal Fisheries Artisanal fisheries represent a very complex and dynamic fishing sub-sector such that a universal definition for it does not exist (Berkes et al. 2001). They are usually described by a range of diverse characteris tics (FAO/RAP 2004; Salas et al 2007; Defeo and Castilla 2005, Berkes et al. 2001). First, arti sanal fishing predominates in developing nations where coastal communities and fishing households are dependent on the marine resources for subsistence, in contrast to fisheries that are exploited by fishing firms (i.e., bus iness ventures). Thus, artisanal fisheries are usually small-scale fisheries that rely on a large number of fishermen and vessels relative to indus trial fishing. Second, artisanal fishing fleets are often charact erized by diverse vessel types that target multiple species, usually a large number of small stocks. Because of this, resource use is seasonal and vessels use multiple traditional gear types su ch as spears, lines, traps, or hand harvesting. That is, technological advancement is slower co mpared to industrial fleets since there is sufficient local labor. They operate mostly near shore, across multiple fishing grounds and landing sites, and on short trips of around one to two days. Therefore, th ere is a low level of capital investment. The harvesting, processing and distribution technologies are labor intensive. According to a recent report from FAO/ RAP (2004), artisanal fishing provides employment to more than 35 million people worldw ide, contributes more than 50% of fish food in the world, and represents 19% of animal prot ein intake in developing nations. Therefore, this occupation plays a critical role in poverty alleviation, food secu rity, and income generation for coastal communities in developing countries in Af rica, Asia and Latin America. However, since these fisheries feed so many in local communities, what harvesters may receive from selling their


12 catch is minimal compared to the size of the overall economy of their nations. Even the highestvalue species in export markets do not translate into an important comp onent of national product as profits are often earned by middlemen. Hence, fi shermen have very limited power to influence the markets, and commercializat ion and credit access is highlydependant on middlemen groups. Despite the recognized importance of artisanal fisheries worldwide1, management efforts for this sector have been met with limited su ccess (where any exist) and many fishery resources have become overexploited. Most artisanal fish eries have operated under open access conditions, mainly to maximize employment and food supplie s to local communities (Salas et al. 2007). Also, the prohibitive costs of monitoring and enforcement in extensive geographical areas prevent the collection of data that might be required to develop and manage a fishery successfully. Lastly, an increas ing demand for high-valued speci es (e.g., lobster, scallops, shrimp) in international market s and the lack of alternativ e employment opportunities that characterizes many artisanal fisheries has often promoted the uncontrolled increase in fishing effort, which has reduced resource stocks, fish ing income and food availability and generally worsened fishermen livelihoods in the long run (Defeo and Castilla, 2005). Artisanal fisheries can be very diverse and so require unique approaches to manage them successfully. There are, however, a few different t ypes of management that have been shown to work for this sector. The following section review s those approaches, which include the use of a coor jointmanagement structure and the use of territorial use rights (the two most common). Following this review, the unique case of managi ng overfished fisheries in the Galapagos Marine Reserve (GMR) is discussed. The final secti on outlines how this study will investigate the 1 Although the terms artisanal and small scale fisheries are often used interchangeably in the literature, in this study artisanal fisheries refer to fishing communities that are he avily dependent on the marine resources for subsistence or sustainability of a localized culture or economy in addition to being small-scale.


13 potential success of a plan to manage an overf ished artisanal fishery a nd to rebuild fishermen livelihoods using the GMR as a case study. Approaches to Manage Artisanal Fisheries Traditional approaches used in developed nati ons and industrial fisheries are recognized as not appropriate for the complexities of artis anal fisheries (Defeo and Castilla 2005). More suitable mechanisms need to consider fishing communities in decision making, emphasize participation of all stakeholde rs, and retain traditional knowledge in the resource management (Berkes et al. 2001). Such approaches include ec osystem-based management, integrated coastal management (fishing as part of a comprehensiv e coastal development policy), community-based and co-management (direct participation of lo cal communities and shared responsibility for resource management between authorities a nd users), and spatiallybased management (e.g., territorial use rights (TURFs), marine protected areas, and no-take zones) (FAO/RAP 2004). Many communities in developing countries have adopted these management schemes. For instance, community quotas, TURF s and exclusive fishing zones have complemented regulated open access controls in some counties in Latin Amer ica, especially for be nthic resources (Salas et al. 2007). No take zones and ban areas have performed successf ully in reef fisheries in the Phillipines, the Caribbean and some countries in Africa (Martin-Smith et al. 2004) and on the demersal resources in the Gulf of Castella mare (Sicily) (Whitmarsh et al. 2003). The most promising results in management of fisheries for community sustainability have been observed under appropriately designed and implemented co-management schemes, particularly when combined with spatial tools and access rights. For instance, the allocation of near shore management areas to local comm unities through well-established fishermen unions, associations or cooperatives has proven to be a stable and strong management system for some artisanal fisheries in Chile, Japan and Mexico. As documented by Cancino et. al. (2007) and


14 Defeo and Castilla (2005), in th ese initiatives the gove rnment sets general regulations such as quota and size limits for the species, gear rest rictions and season le ngth but transfers the authority and responsibility to manage, monitor a nd enforce the management areas to the users. In Chile, the TURF system has been app lied to benthic resources only. The federal government allocates Areas for Management and Exploitation of Benthic Resources (AMERBs) for a given period subject to renewal based on performance and status of stocks. Fishermen associations are requ ired to submit management a nd exploitation projects for evaluation prior to receive an exclusive manageme nt area, as well as periodic monitoring reports for renewal (Gonzalez 1996). In Japan, the allocati on of areas with exclusive access rights is the prevalent management system and applies to a variety of fisheries (e .g., shellfish, shrimp, and mobile fish). Access rights have been managed and coor dinated by Fishery Cooperative Associations traditionally attached to the coas tal areas rather than based on management projects. The cooperatives in tu rn sub-allocate the ar ea to groups of indi viduals, especially families, based on fishing tradition. Interestingly, in the two countries, fishermen as managers integrate efforts with biological consultants or ex tension scientists to monitor the status of the fisheries. Similarly, in the case of the spiny l obster of Punta Allen in Mexico, the government grants exclusive territorial access rights to coop eratives, which in turn sub-allocate the areas and rights to families based on trad ition. Here, the system builds str ongly on the family component of the organization by allowing the ar eas to be inheritable and tr ansferable. In this way, the decentralization of management and the availabil ity of exclusive access rights have transformed production areas and the resour ces into family assets. The success of these TURF-based co-managem ent approaches in artisanal fisheries has relied on the following main aspects:


15 The existence of a clear and appropriate legal framework formalizing (a) the decentralization of management and decision making, (b) the implementation of exclusive territorial use rights and (c) the authority of local communities and cooperatives to manage and coordinate the access rights and area allocation among subgroups. The level of organization of communities and management c ooperatives or associations, such that enforcement of internal ru les and exclusive access may be ensured. The role of fishing tradition and family as productive units in the allocation of management responsibilities. Despite the potential positive outcomes of co-management in artisanal fisheries, a sustainable use of resources and livelihood supp ort has been difficult to accomplish or not realized at all in other cases (Defeo and Castilla 2005, Nasuchon and Charles 2010, Hearn 2008). A perfect example of limited su ccess of a co-management structur e is found in the case of the Galapagos Marine Reserve (GMR) in Ecuador. Artisanal Fisheries in the Galapagos Marine Reserve The GMR is one of the largest marine reserves in the world, covering th e interior waters of the Galapagos Islands and those within 40 nauti cal miles measured from the baseline of the Archipelago. It is approximately 133,000 km2 in si ze and is managed as part of the Galapagos National Park (GNP). Fishing activities are restri cted to small-scale levels by national law. The main income source for the fishing sector, wh ich accounts for around 20% of the economy of the Islands (Taylor et. al. 2006), is provided by two fisheries: sea cucumber (Isotichopus fuscus) and spiny lobster (Panulirus penicillatus and P. gracilis). Both pro ducts have been primarily destined for exports, especially to Asian markets as delicacies. A third and smaller source of income comes from whitefish fisheries, which are harveste d in the area all year long. These are destined mainly to local and mainland markets (Murillo 2002). A co-management framework was formally established in the Special Law for the Conservation of the Province of Galapagos (SLG) in 2000. Under this legal framework the


16 management of natural resources and the decision making process integrated for the first time all local user groups (i.e., scientists, small-scale fishermen, the governmental management agency, and tourism sector), changing from a top-down to a bottom up structure (Congreso Nacional del Ecuador 1998). Three instances of participator y democratic decision making and implementation define the co-management system (Heylings and Bravo, 2007). At the local level, stakeholders are represented on the Participatory Management Board (PMB). Participants of the PMB are representatives of the fishing, t ourism (including naturalists guide s), scientific and management sectors, which propose management actions and decide based on a consensus. At the national level, the Inter-institutional Management Authority (IMA) is the entity in charge of the final decisions about policies based on local-level prop osals, including those that could not reach a consensus at the PMB. Decision makers at this level are representatives of national ministries and the environmental community, as well as re presentatives of the lo cal user groups (i.e., fishing and tourism sectors). At this level, the sc ientific sector acts just as an advisor while the management agency, the Galapagos National Park Service (GNPS), serves as technical secretary. Finally, the GNPS is responsible for the impl ementation and enforcement of all resulting policies. Under co-management the fisheries are regulat ed by limited access, total allowable quotas and traditional input-output contro ls such as gear restrictions, closed seasons and minimum sizes on landed product. Despite an explicit legal framework, management decentralization, and a clear institutional structure for decision making, th e stocks of the most important and valuable species have continued declining and the profitab ility of fishing has wo rsened (Hearn 2008). For example, in 2006 the sea-cucumber fishery was c onsidered to be severe ly overexploited and had to be closed temporarily, and the spiny lobster fishery showed strong in dications of decreasing


17 population abundance (Hearn et al. 2006). Also, ill egal fishing from artisanal fishermen continues to be a concer n (Viteri and Chavez 2007). As noted by Heylings and Bravo (2007), one of the main challenges of the participatory decision making system is the low level of or ganization and fishermen representation. Fishing cooperatives are recently st arting to be active as fishermen a ssociations, and participants from the fishing sector at the PMB are not always considered good re presentatives of the whole group. In addition, the agreements reache d at the local level are not alwa ys perceived as honored by the IAM which creates an issue of cr edibility. Lastly, but not least im portant, a critical limitation is the lack of a clear definition of access rights among users. Al though a legal framework exists formalizing the co-management system, the re source is still managed as property of the government hindering the incentives for sustai nable harvesting among fishermen (Hearn 2008). Given the above mentioned challenges and fish ery problems, alternative strategies to rebuild the fisheries are needed. The manage ment authority (i.e., GNPS) has proposed the creation of alternative livelihood op tions for the sector in order to reduce the fishing effort (i.e., fishermen and fishing vessels). Specifically, th e program proposes a change of activity from fishing to tourism through the concession of new tour operation permits to fishermen in permanent exchange for their fishing and vesse l licenses (Servicio Par que Nacional Galapagos 2006, Murillo et al. 2006). The program is volunt ary and competitive as the number of tour permits is strictly controlled. As such, this ne w program is expected to permanently reduce the size of the fishing fleet. If successful, fisherme n leaving the fishing sector may engage in economically sustainable non extractive activity and the remaining fishermen should also anticipate more profitable fishing as spiny lobster and sea cucumber stocks rebuild in response to a smaller fleet size.


18 However, much remains unknown about the efficien cy of this strategy as an instrument for fishing effort reduction and for future improvement of the fishing livelihoo ds. This informational gap becomes more relevant when considering the heterogeneity across vessels and individual fishermen (i.e., vessel owners and crew). Approaches to Evaluate Preferences and Efficiency Stated Preference Analysis for Fishery Entry and Exit One useful approach to assess the entry-exit de cisions of fishermen and to identify factors that may affect that choice is the stated preference method, particularly before the implementation of management strategies. With this technique, indivi duals (i.e., fishermen) indicate what their preference would be (i.e., stay or exit) based on a hypothetical management scenario. The preferred choices are assumed to be those that maximize their utility (Bennett and Blamey 2001). The use of this approach has recently in creased in comparison to other approaches commonly employed but which are based on past d ecisions and are not ade quate to predict new behavior. For instance, Ikiara a nd Odink (2000) assessed the stated willingness to accept to quit fishing among small-scale Kenyan fishermen. They found that the main reason to stay was the lack of alternative fishing options, and that socioeconomic factors lik e opportunity costs, profitability of fishing, having more fishing ex perience and owning a fishing vessel increased the resistance to exit. Similarly, Ci nner et al. (2008) also examined the willingness to quit fishing among nine Kenyan communities between Mombas a and Malindi and found that expected reduction in catch levels, and ha ving additional occupations and higher income increased their likelihood to exit. They concluded that poorer households would be less likely to exit even when faced with declining fisheries and that the ge neration of employment opportunities directed to that sector may help reduce fishing effort.


19 Efficiency Analysis in Fisheries Efficiency analysis has been widely applie d in fisheries to ev aluate the production technology of fleets under the inhere nt stochastic nature of fish ing using stochastic frontier methods. With this approach, an economic produc tion function is specifie d in terms of fixed (e.g., vessel size, engine power) and variable inputs (e.g., days at sea, fishing time, number of fishermen on board), measures of fish stock, en vironmental and technological factors that may affect the technology. Variations in catch are assumed to depe nd not only on random events but also on an inefficiency component for each vessel, which in turn is also dependent on fishermen and vessel characteristic s (Pascoe et al. 2003). The fisheries economics literature has used th is type of analysis for a variety of productionrelated purposes, rangin g from development implications to the effect of input controls on vessel economic performance. For instance, Squires et al. 2003 evaluated if development assistance directed to improve artisanal fishing ve ssels is effectively improving technical efficiency and the harvest potential of the fleet in the Malaysian gill net fishery. They concluded that focusing on developing human capital factors would yield more efficiency improvements than technological and capital e xpansions of the fleet, and that development assistance for fishing communities would be be tter focused on aspects other than affecting harvesting capacity. On the other hand, Fousekis and Klonaris (2003) found that variations on harvest depended mostly on random effects as opposed to technical e fficiency, and that vessel characteristic were more important determinants of efficiency than skipper skills for trammel netters in Greece Their results allowed them to examine how existing and proposed structural poli cies might affect the harvesting potential of the fleet given specific input controls.


20 Problem Statement The need to incorporate divers ification options to rebuild ar tisanal livelihoods and stocks in artisanal fisheries management is recognized in the literature (K uperan and Abdullah 1994; Teh et al. (2008), but it is scarce ly documented (Salas et al. 2 007). Salayo et al. (2008) and Teh et al. (2008) found that fisher men show strong support for th e development of alternative livelihoods inside and outside the fishing sector as a management tool, especially in the tourism sector. As noted by Carvalho (2008), marine tour ism jobs seem a natural option for relocated fishermen given their skills in boating and fishing. To the authors best knowledge, only two st udies have addressed the implications of ecotourism as an altern ative employment opportunity for fishermen. Alban and Boncoeur (2004) assessed the potential demand, supply, institutiona l constraints and expected profitability of developing a pluri-activity for fish ermen operating in the Iroise Sea. They indicated that despite a promising interest from tourists and fisherme n, the fiscal and administrative framework and the low profitability of the new activity threatened to limit the potential of the initiative. More recently, Sarr et al. (2008) proposed a two-sector theoretical bioeconomic model to analyze the interaction between artisanal fi shing and ecotourism and the eff ect on social welfare in the Saloum Delta, Senegal, in the context of an existing Marine Na tional Park and a growing tourism sector. They concluded that cr eating a non-extractive activity in ecotourism may help integrate the need for long-term marine c onservation and the immediate need of additional income in poor fishing communities. Understanding fishermen behavior and potential demand is critical to the development and introduction of alternative live lihoods, especially wh en switching activities are considered as permanent decisions. However, it is also important to understand the implications of capital (i.e., vessel) reduction on the production performance of the remaining fleet to accurately evaluate the


21 viability of such alternatives as a fishing effo rt reduction strategy. Moreover, in the context of artisanal fisheries, it is also re levant to consider the role of social ties in the dynamics of harvesting and fishermen behavior as such soci al networks are increas ingly recognized as key components of economic processes a nd desirable outcomes (Sekhar 2007). Goal and Objectives The present study seeks to contribute to the artisanal fisheries management literature (especially when stocks need to be rebuilt) by expanding the assessment of alternative livelihood options as viable management tools by incorpor ating the role of produc tion technology and of social ties that may be especially important in artisanal fisheries. Hopefully, knowledge of this information will help decision makers in the design of management strategies seeking to reduce extractive pressure on severely e xploited resources, particularly when participatory management has had limited results. The goal of this study is to assess the viability of fishing effort divers ification into tourism as an alternative livelihood and effort reduction strategy for the ar tisanal fishing sector of the Galapagos Marine Reserve. Th e specific objectives are: To analyze fishermen stay/exit behavior and identify relevant socio-economic determinants. To assess the production frontier and technical efficiency of the ar tisanal fishing fleet. To ascertain the role of produc tion interrelations proper of sm all scale fishing sectors on fishermen behavior and fleet production. To evaluate the market potential of fish ermen-based operations in the local tourism industry. Overview of Methods and Study The objectives of this study are explored in each of the followi ng chapters. Chapter 2 shows the assessment of stated pr eferences of fishermen to conti nue fishing or to exit using a


22 discrete choice modeling framework. The stated behavior is based on th e findings of personal interview surveys of individual fishermen. Th is primary data collection allowed for the examination of the effect of any interdepe ndency in preferences ba sed on social linkages between fishermen. Chapter 3 implements a stocha stic production frontier m odel using trip-level catch data to determine the production structur e of the sector and th e role of technical inefficiency on harvesting potential and its de terminants (including an examination of the linkages among harvesters). Chapte r 4 explores the potential mark et demand for tour services provided by newcomers from the fishing sector using a probability modeling approach. The modeling is possible based on the primary data collec tion of stated preferences from past visitors to the islands. Finally, Chapter 5 summarizes th e most salient findings of the analysis and presents the general conclusions and implicat ions for artisanal fisheries management.


23 CHAPTER 2 EXITING PREFERENCES OF SMALL SCA LE FISHERMEN: AN EXPLORATION OF INTERDEPENDECE EFFECTS Introduction Overcapacity in fisheries has been recognized as a leading cause of overexploitation of marine resources worldwide (Food and Agricu lture Organization, FAO, 1998). Since the FAO published its Declaration of the Code of conduct for Responsible Fisheries in 1995, the managing of the extractive capacity of fishing fleets has received increas ed attention (Asche 2007). Many studies have explored the use of industry or government sponsored fleet size reduction programs (Guyader et al. 2004; Larkin et al. 2004; Funk et al. 2003; Kitts et al. 2001; Sun 1999) and recent efforts have focused specif ically on the issue of stranded capital as it relates to changing management regimes (Wile n 2009). As noted by Ikiada and Odink (2000), the understanding of the behavior of individuals is as necessary as the understanding of the dynamics of the resource for any management pla n. In the context of fish ing fleet reduction, the identification and understanding of fishermen in centives to exit fisheries is crucial. To date, the majority of behavioral analysis in fisheries has focused on spatial fishing effort allocation and fisheries selection (Smith and Zhang 2007; Smith 2005; Salas and Gaertner 2004; Smith and Wilen 2004; Smith and Wilen 2003; Wilen et al. 2002). This literature concludes that the behavior of individuals and the factors that drive decision making need to be incorporated into management decisions in or der to avoid unwanted or unintended fishery outcomes. By comparison, less work has been conducted regarding the decision making process to exit fisheries. Given the state of many fish eries worldwide (Worm et al. 2006), the need for such information could not be more timely. The majority of studies that have investigat ed fishery switching d ecisions, including the decision to forgo fishing in a ny given fishery, have utilized the profit maximizing assumption


24 (either implicitly or explicitly) (Pascoe and Mardle 2005). Ward and Sutinen (1994) introduced the modeling of entry-exit behavior for vessels in the shrimp fishery of the Gulf of Mexico. They predicted the probability of a vessel to enter, and then stay or exit using revealed preference data. Their main findings are the expected positive eff ect of crowding externalities on the probability of exiting and supporting evidence that exit behavi or is less sensitive to profitability variation than entry decisions. No evidence was found to support the role of opportunity costs, market variability, or stock variability on exiting behavior. More recently, Pradhan and Leung (2004) exte nded the enter-stay-exit choice modeling approach to Hawaiian longline fisheries by also using revealed preference data. The study provides supporting evidence fo r many of Ward and Sutinens (1994) findings namely regarding the opportunity cost of fishing stock abundance, and crow ding externalities and they also found that individual charact eristics such as residency and cap tainship also affected exiting preferences. These findings are impor tant in identifying factors that have affected participation in the short run but they are based on past behavior and, therefore, cannot be used to accurately predict a new choice. Nor do they consider factors that might be unique to small-scale fisheries, especially in developing countries. As vessel decommission schemes receive incr eased attention, so too has the modeling approaches to assess their outcomes. For ex ample, Ikiara and Odink (2000) employed a contingent valuation approach to assess fisher mens resistance to exit small-scale Kenyan fisheries as measured by the stated willingness to accept to quit fishing. In this small scalecontext, fishermen did not have alternative fishin g options but were more interested in securing non-fishing sources of income to support their livelihoods Drawing from the work of Ward and Sutinen (1994) and early models of fishing e ffort allocation, they explored the role of


25 opportunity cost of exiting, imperfect capital ma lleability and indicators of opportunities outside the fishing sector. The effects of other variables describing indivi duals utility function were also addressed such as alternative income sources family tradition and vessel ownership. Their analysis supported the theoreti cal importance of opportunity co sts and profitability in the resistance to exit, but no evid ence was found to support the expect ed effect of imperfect capital malleability. Having more fishing experience an d owning the vessel were, however, found to be significant factors in the resistance to exit. Although previous studies have explored many and varied factors, most research has ignored specific transitions to alternative occupations and a ll models have assumed that individuals make decisions in a completely i ndependent way. However, interdependencies of individual exiting decisions coul d be reasonably expected esp ecially in highly interactive harvesting systems such as small-scale artisanal fi sheries. In these types of fisheries, informal and fraternal linkages might be re levant to the decision making process of members of the same or interrelated production units (FAO 2004). The importance of interdependent preferences has been recognized in the fields of consumption economics, marke ting and transportation research where it has been noted that ignoring such effects when they are likely to exist can yield biased estimates (Yang et al. 2006; Bhat and Pendyala 2005). In fisheries, the issue of interdependence in spatial fishing decisions has been recognized (Hicks et al. 2004). Extending that idea, the relationships among individuals (captain, crew) and between vessels (changing captains, multi-vessel companies, mother boat arrangements) are likely to affect how any one participant will resp ond to an opportunity to forgo fishing rights in perpetuity. Excluding this information from th e exit choice decision can, therefore, lead to inaccurate predictions of future fishing effort.


26 The objective of this chapter is to identify th e factors that help to explain fishermens stated preferences regarding whet her they plan to switch from fi shing to tourism and if they do, which type of tour activities they plan to provi de. This information can help fishery managers facilitate and assess the success of a management program designe d to reduce fleet size, fishing capacity, and fishing effort. In addition, the stud y will contribute to the existing literature by exploring the potential role of interdependence indicators in unders tanding exiting preferences at the individual (disaggregated) level. The analysis illustrates the case of the Galapagos smallscale fishing sector using a st ated preference approach in the wake of an unprecedented management situation. The next section discu sses the modeling methodology, which is followed by a section with details about th e data. The last two sections di scuss the estimation results and conclusions, respectively. Methodology Fishermen are assumed to maximize their utility when deciding whether to switch professions from fishing to tour operator and, if so, what type of tours they would prefer. An alternative methodology is to model choices by a ssuming decisions are based on the objective of maximizing profits. The more general utility appro ach is retained for this analysis since it is more appropriate within an artisanal fishing context. In this study, fishermen are faced with multiple choices for each deci sion. The total utility for alternative j in the universal choice set C held by individual n can be specified by the construct: jn jn jnV U C j (2-1) where Vjn is the systematic, observabl e component of utility and jn represents the random, unobservable part.


27 The observable component Vjn can be defined by the expression I i i n i j jnX V1 (2-2) In Equation 2-2, i identifies the type of characteristics th at are assumed to affect the choice (i.e., Xi variable vectors) and thei r measureable correlation with V (i.e., i parameters). In particular, the variable groups in clude those directly or indirec tly reflecting the benefits of fishing ( i = 1), interdependence indicators ( i = 2), and demographic characteristics ( i = 3). Fishermen opting to switch to tourism are also assumed to maximize th eir perceived utility when deciding among the new tour operations. Following the same theoretical framework, the systematic component of utility for the tour alte rnatives is just an e xpansion of the previous model to include variables that reflect an indi viduals ability to fund a switch into tourism ( i = 4). In addition to the new X4 variable, the set of regressors in X1, X2, and X3 differs between tour choices since their nature are distinctly different. While utility is unobservable, the stat ed preferences of each individual ( Yn) can be used to infer information about utility. Economically ra tional individuals are expected to prefer the alternative with the highest utility, so that the probabi lity of choosing alternative j over all other available options l can be expressed as: P(Yn = j| j,l) = P [(Vjn+ jn) > (Vln+ ln)] l j C l (2-3) Given the computational complexity of multidimensional integrals related to the probability density functions of normally distributed error terms, the jn are assumed to be identically and independently distributed Gumbell random variables. As such, choice probabilities for each individual n can be calculated as follows: C l n Vl Vjn ne e j Y P 1 ) ( (2-4)


28 Since there are more than two choice s, there is a reference category (e.g., j = 1) with the associated probability of: C l n Vl ne Y P 1 1 ) 1 ( (2-5) Thus, there are J -1 equations to be estimated, one fo r each choice relative to the reference or base choice. Data and Estimation Small scale fishermen were interviewed from November 2007 through March 2008 about their preferred choices for switching permanently to tourism. If they indi cated an intention to switch, they were then asked to identify their preferred type of tour permit. A total of 355 randomly selected fishery participants were interviewed (156 vessel owners and 199 crew members). This represents an overall response rate of 34.7% given there were 1,022 registered participants during the 2 007-08 fishing season. Participants were selected across three strata each representing one of the three main islands. In addition to their choices, fishermen were asked about tourism-related factors a nd concerns, operational characteris tics in the fishing sector and general socio-demographic questions. Secondary data include vessel ownership record s and trip-level landings records for the lobster and sea cucumber fisheries for the pe riod 1997-2005. This information was obtained from the Galapagos National Park Service and th e Charles Darwin Research Station. Vessel ownership records provided information on the number and type of vessels linked to the corresponding owner while the land ing records allowed small vessel s to be linked with mother boats in multi day trips. With these secondary data it was also possible to relate fishermen to specific vessels and to iden tify historic inter-vessel harvesting relationships.


29 After excluding non-responses for the independent variables, the final dataset consisted of two samples, one for switching intentions (N = 29 9) and the other for the subsequent choice of tour activities only for those i ndividuals planning to switch (N = 103). Exiting behavior is modeled in terms of the individuals stated respon se to a question asking them if they planned to relinquish their fishing rights (permits) in exch ange for a tour operator permit. The response categories included no, do not know and yes. Those res ponding yes were then asked about their preferences for offering three distinct types of tours including: (1) standard cruises, (2) bay and diving tours, and (3) multi-day diving cruises. Table 2-1 shows the distribution of observed choice responses. Exiting choices are even ly distributed between the options to stay and switch with a slight prefer ence for switching. Most responde nts would prefer to offer bay and diving tours, followed by multiday diving cruise s. This indicates a stronger preference for options involving some type of diving activities (85.4%) than for any other type of operation. The Xi explanatory variables hypothesized to influe nce exit decisions are defined in Table 2-2. Two interdependence variables were created to control for relationships between captain and crew (i.e., intravessel linkages) and operations that involve mothership relationships. The mothership system consists of smaller vess els offloading at sea ont o larger vessels for multiday, multi-zone fishing trips, and it is not uncommon to observe a mother ship working with vessels related to the same family or social group. These variables provided the best approximations to account for the effect of soci al ties in fishermen exit behavior given the available information. Choice in terdependence is addressed by introducing the variables into both models. In the absence of monetary vari ables for economic fact ors such as capital investment and alternative la bor opportunities, this study is not able to include capital malleability or traditi onal opportunity cost measures. Howeve r, we were able to include six


30 variables that related to the pr ofitability of fishing and, thus, serve as proxies for opportunity costs. The capital malleability issu e is less of a concern in this study since it is widely recognized that vessels operating in the Galapagos need to be moved into other uses or they will continue fishing, which is why they are being offered th e chance to join the growing tourism sector. Given the difference between the sets of explan atory factors assumed to affect the stated preference of each choice, the exiting and subsequent tour choices are modeled separately according to equation (4) using an unordered multi nomial logit. The models are estimated using maximum likelihood techniques in NLOGIT. This so ftware allows for differences in utility function specifications that can prove useful wh en analyzing limited sample sizes and distinct alternatives that are likely influenced by differe nt set of factors, such as for our tour choice model. For comparison, a nested multinomial specifi cation was used to correlate the two sets of choices since the choice responses were sequential However, testing for correlation of the errors between tour choices and switching responses indicated a theoretical ly inconsistent structure and the model was rejected. Results and Discussion Exit Choice Model Estimates are reported in Table 2-3, where th e option to continue fishing (i.e., no response to switching alternative) was chosen as the base because identifying the factors contributing to the decision to switch to tour ism is an objective of this study. Additional interaction variables were also evaluated using likelihood ratio test s, but they were statistically insignificant and are not presented in the fina l results. The overall model was statistically significant (p < 0.0001; LogL = -170.8) and the Adju sted R-squared indicated that the model was able to explain 41% of the vari ation in responses. In summary, only five of the 15 explanatory variables were significant at the 10% level or better in the equation explaining whether a


31 fisherman was undecided with respect to switchi ng from fishing to tourism versus someone who was planning to continue fishing. However, a tota l of 11 of the 15 variable s were significant in explaining a fishermens stated intention to sw itch into tourism versus someone who was not. The explanatory variables are discussed by type for each choice decision. Profitability of fishing Of the six variables represen ting the opportunity cost of fishing to a respondent, none explained undecided fishermen but nearly all were correlated with the decision to switch into tourism. Being a crew member ( CREW ) or using a fiberglass or small wooden boat to fish ( SBOAT ) reduced the likelihood that they were pla nning to exchange thei r fishing rights for tourism permits or, equivalently, they were more likely to want to continue fishing (the omitted alternative). Conversely, vessel ow ners or those working on larger boats were more likely to indicate they planned to switch th eir fishing effort from the fish ing to the tourism sector. This result contradicts previous findings supporti ng the hypothesis that ve ssel owners are more resistant to exit than crew under the argument of a career effect in fisheries (Ikiara and Odink 2000). Our result could reflect th e perfect capital malleability in this case study since vessel owners will transfer their boat to the tourism sector and continue operating. Crew members, however, lack the capital asset required to immediat ely begin participating or they may lack the skills that could be required w ith the new tours. The finding that smaller boats are less likely to switch was expected since the requirements to en ter the tourism sector are more stringent for vessels in the smallest size cl ass (Servicio Parque Nacional Ga lapagos 2006). In addition, its possible that this variable is al so accounting for some of the con cern over the need to meet new, more stringent, safety guidelines ( SAFEREG which was not significan t) that would require significant investments in vessel upgrades.


32 Participation in high-value fisheries ( HIGHVAL ), having more years of fishing experience ( FISHYRS ), or being a diver ( DIVER ) also increased the probability that a fishermen has planned to exit the fishing sector and move into tour ism. This result implies that fishermen who rely mostly on whitefish or open water stocks (versus the relatively high-valued spiny lobster or sea cucumber), have less fishing experience, or who do not dive are more likely to want to continue fishing. This result could reflect the decline in stock abundance of the traditional high-valued stocks, which is likely more noticeable by those who have been fishing longer. The reduced abundance of the high-valued specie s also increases the re latively profitability of alternative species that do not require dive skills. Divers have an advantage in switching to marine tours in that diving tour s in particular have been expand ed creating more opportunities in the industry (Servicio Parque Nacional Galapagos 2009). Thus, training programs targeted to replace traditional fishing methods (i.e., free diving to dangerous depths) with more specialized scuba diving methods could be e ffective at enticing hu man capital from fishi ng into tourism. Interdependence An owner-crew relations hip within a vessel ( ICLINK ) was not found to have any effect in explaining exiting intentions, however, a mother boat relationship between small and large vessels ( IMLINK ) increased the probability that the fi shermen were undecided in their decision to switch. This implies that fishermen that were not associated with a mother boat were more likely to indicate they planned to continue fishi ng. This result may reflect the complex and subtle social and economic linkages that characterize sma ll-scale fisheries, especi ally in the Galapagos (Baine et al. 2007). In this c ontext, it is not uncommon to observe a mother boat providing storage and towing service to other vessels.


33 Demographics The majority of demographic variables we re significant in explaining the undecided responses, and only one of the se ven variables did not have any explanatory power with respect to the yes responses, which indicated a plan to switch into tourism. Geographic differences in plans were evident as residents of San Cristobal and Isabela we re less likely to switch into tourism (i.e., more likely to c ontinue fishing) compared to re sidents of Santa Cruz who were more likely to switch to tourism. Residents of San Cristobal were also more likely to be undecided versus those who pl an to continue fishing. The probability that a fishermen intends to sw itch to tourism versus continue fishing was found to be directly corr elated with the number of children in the house ( CHILD ) and total household income ( INC2 or INC3 ). Having an alternativ e source of income ( ALTINC ) in the household in addition to fishing income was also a factor that increase d the probability of a switch versus remaining in the fishing sector. This result likely reflects the ability to pay for costs associated with switching into the tourism sector, or it coul d reflect the ability to access to formal funding sources. Issues related to access to investment (start-up) capital for small-scale food production in developing countries are not ne w (FAO 2005), especially as it relates to inherently risky resource-based products. For fisheries managers and policy makers who hope fishermen decide to abandon fishing and enter th e tourism sector, it may be more important to identify those individuals who are undecided. In this study, it is fishermen that live in San Cristobal, have no (or fewer) children, or those with a monthly income of US$501-$1,000. Lastly, having a college degree ( EDUC ) was not found to affect e ither the decision to exit or whether a fisherman was undecided1. Although a higher education level may represent more 1 A college level education was examined in the model because the majority of participants had high school education.


34 opportunities outside fishing, there are not many othe r alternatives besides fishing and tourism in the Galapagos. Given that both fishing and touris m rely on skills associated with marine boating, the extra education was not a factor. Probability simulations Using the yes equation from Table 2-3, we calculated a base or benchmark probability level to explain how the decision to continue fishing or to switch into tourism varied between the vessels owners and crew members ( CREW ). The benchmarks were calculated with the sample means for the continuous variables ( FISHYRS, CHILD ) and assuming a value of zero for the discrete variables. The distinct effects of each significant variable are summarized in Table 2-4. This is a critical simulation as the decision to allocate equal and independent rights for crew is topic of much debate. Are they equally likely to want to switch professions? Do certain characteristics explain their reluctance to swit ch as opposed to vessel owners? The answers to these questions can help fishery managers devi se the appropriate social programs that would have the best chance of achieving th eir fishing effort reduction goals. The benchmark probabilities indicated that vesse l owners had a much hi gher probability of switching from fishing to tourism compared to crew members (i.e., 55% versus 9%). The simulations show that both vessel owners and cr ew react in a similar way to all socioeconomic variables; however crew members are more sensitive to changes in those factors. This is best illustrated by the relative change in probability (percent change) rather than the nominal change (percentage points). Owners and crew that had the highest level of income ( INC3 ) or alternative sources of income ( ALTINC ) had the highest probability of switching from fishing to tourism (i.e., switching probabilities increased from 55.3% to 83.9% or 81.9% for owners and from 9.1% to 26.8% or 29.6% for crew, respectivel y), but crew were dramatically more sensitive to changes in


35 these variables. Earning the highest income leve l increased the switching probability of crew in 233% as opposed to only a 51.6% increase for ve ssel owners. Similarly, crew members with access to alternative income were 193% more likel y to switch, while vessel owners were just 48% more likely to do so. The probability of switching from fishing to tourism was also found to be much more sensitive to the type of target fishery and divi ng practices for the crew than for vessel owners. Owners participating in high value fisheries had a probabi lity of switching that was 40.6% lower than those targeting other fisheries compared to a 142% lower probability for crew. Being a diver increased the probability of a crew member switching in 188% wh ile that of owners increased only in 47.3% in comparison to non-divers. Finally, while the probabilities of switching are directly re lated to fishing experience ( FISHYRS ) and number of dependents ( CHILD ) for both owners and crew, the size and magnitude of these effects differed greatly (figure 1a and 1b). The number of dependents increased the probability that a crew member would switch at an incr easing rate versus a constant rate for owners. The more years of fi shing experience by the crew also increased the probability they would switch at an increasing ra te. However, the probability of switching for vessel owners increases at a decreasing rate. These results underscore the importance of accounting for human capital in fish ing since the crew are less lik ely to switch, especially those with less fishing experience. Although this result may seem counterintuitiv e (as we might expect the more experienced fishermen to have the comp arative advantage to con tinue fishing), it is likely that those with more experience have a better understanding of the extent of overfishing and the potential for recovery. Training or fundi ng programs to entice fishermen to leave the fishing sector would be more effective if they target recently active owne rs and crew (e.g., recent


36 beneficiaries of parental fishing rights or boat investors who entered the sector during the fishing expansion in the late 1990s). Tour Choice Model Results for the multinomial logit models of t our choice operations are reported in Table 25 where the standard cruise ope ration was chosen as the base category since it is the most common type of tour that is already being offered. While the models include an additional set of variables related to the tour choices ( X4), they include fewer explanatory opportunity cost interdependence and demographic variables compared to the exit choice model. The reduction in explanatory variable s increased the degrees of freedom available to the model but was also n ecessary to better match the distinct nature of each choice. For example, the bay and diving tour option is a single-day op eration restricted to in-shore visits such that the size of boats and investment required are modest. Variables reflecting vessels size ( SBOAT ) and traditional financing options ( FBANK, FFAM ) are included in that model only. On the other hand, diving cr uises are multi-day, off shore diving excursions which require high levels of capital and sp ecialization. Hence, high investment needs ( INVHIGH ) and large funding sources ( FINV ) are hypothesized to affect this option only. Additional relevant factors such as owner-crew and owner-owner interdependence, diving skills ( DIVER ), geographical location and concern with safety regulations are included in both specifications. The overall model was significant (p < 0.00001; LogL = -60.81) and the adjusted R-squared indicated that the model explaine d 35.0% of the varia tion in the responses. Five out of the nine explanatory variables significantly explained th e choice of bay and diving tours; while only three of the eight covari ates explained the divi ng cruise option at the 10% level at least. The probability of choosing ba y and diving tours is higher for individuals working or owning small fishing vessels, or w ho are concerned with safety regulations in


37 comparison to standard cruises. This reflects the f act that fishermen are not used to strict safety procedures and the costly impli cations of adapting small boats fo r traditional cruise operations. Safety aspects in tourism are critical and safe ty procedures, equipment and vessel upgrades for single-day excursions may seem more manageable than for other types of trips. Interestingly, there is no evidence supporting the significance of diving skills ( DIVER ) to explain the choice of tour options by fishermen in the st udy site. For this specific case, abilities to perform alternative occupations in recreational excursions are perhaps more relevant in their tour choice behavior. Out of the two possible funding sour ces, only access to bank credits ( FBANK ) increases the probability of individuals enga ging in bay and diving tours. Th is is expected as investment requirements for standard cruise activities may be more prohibitive for this type of funding. Fishermen from San Cristobal ( LOC_CR ) are also more likely to want to engage in bay and diving excursions, while i ndividuals from Isabela ( LOC_IS ) are more reluctant to do so and also to engage in diving cruises. This is, individuals from this latter community have a higher probability of preferring standa rd cruise operations overall. Fishermen with an identif ied ownercrew relation ( ICLINK ) are less willing to want a multi-day diving cruise permit in comparison to standard cruises. The result may reflect the highly specialized nature of a di ving cruise and the informal ties among individuals working in the same vessel. A fishing vessel may be the wor kplace for members of the same family or social network, and the relocation of fi shing crew into tourism may s eem more difficult the higher the specialization and complexity of the tour excursion. However, interrelations between vessel owners ( IMLINK ) were not found to affect tour choice behavior. Fishermen have a higher probability to choose multiday diving cruises when faced with higher investment than standard cruises ( INVHIGH ). Access to informal credit institutions


38 ( FFAM ) and third party investors ( FINV ) was not found to explain tour choice, which may reflect the lack of these types of investment opportunities in small-scale fishing communities. Summary and Conclusions The two main marine stocks that support the local small-scale artisanal fishery in the Galapagos Marine Reserve are near extinction. In an effort to re duce fishing effort, the managers have proposed an ambitious program to exchange permits to fish for tour operator permits permanently. The tour permits would be freely tr ansferrable in the current market where the numbers of permits are tightly-controlled and have a relatively high market value. To assess the potential effectiveness of this program in term s of an effort reduction in the fishery, and to identify any factors that can be used to help the authorities devi se training programs to increase the effort reduction, a stated pref erence survey was implemented. The data was used to estimate multinomial logit models of the exit decisions of both vessels owners and the crew, who would be eligible to obtain equal and autonomous rights. Those indicat ing they planned to exchange their fishing rights for tourism pe rmits were then asked to indicat e the type of tour they would prefer to offer. Results support the relevance of accounting for interdependence between fishing participants in addition to traditional economic and demographic factors to explain exit choices and preferences for alternative tour occupations in the context of sm all scale fisheries. Consistent with economic theory and previous findings, opportunity cost indicat ors are significant explanatory factors of stated or revealed exit behavior. Vessel ow nership, participation in highvalue fisheries, longer fishing experience and possessing diving skills were found to increase the likelihood of a fisherman to switch to tourism, while working with small fishing boats reduced that probability. In contrast to previous studies, these results mode l the transition to a particular alternative occupation where capita l malleability is not an issu e. Geographical location, total


39 household income, additional non-fishing occupations and household size were also statistically significant demographic variables that helped to explain differences in switch choices. As hypothesized, interdependent relationships affected a fish ermans stated behavior. A mother boat relationship increase d the probability that a fisherman remained undecided in their decision to exit the fishing industry. Thus, it is important to r ecognize that production relationships can prohibit inde pendent decision making. Any pr ogram designed to transfer fishing effort may need to redefine a produc tion unit to account for such relationships. Among those fishermen planning to switch into tourism, interdependence within a production unit with respect to human capital (i.e., ow ner and crew) was found to redu ce the probability of planning to offer multi-day diving cruises. Because this variable will capture relatives, it could be that offering such a trip would not be possible due to the need to have some of these family member stay at home. It is important for fisheries manage rs that are devising effort reduction strategies to recognize the constraints on households in thei r ability to particip ate in every option. The analysis found different react ions of crew and vessel owners especially to differences in income level, availability of alternative income, target fishery, diving practice, number of children and fishing experience, which could also be a proxy for age. Both groups, who often lack access to formal financial assistance, s howed the highest dependency on their own income resources in order to exit the fisheries. Improve d access to external funding or training is likely to play a role to facilitate the relocation of fi shermen outside the fisheries. Furthermore, funding aspects such as access to bank loans and investment needs are critical determ inants of the type of tour options fishermen choose besi des geographical, safety and infras tructure considerations. The findings of this study have important management implications. Future fishing capacity programs need to be tailored to th e characteristic of e ach port, which can be


40 accomplished through the local cooperative organi zations. For instance, fishing cooperatives could institute savings and cred it programs for their active members (to improve access to funding in the absence of formal credit offering for the sector). Second, plans aiming to entice fishermen to exit should include an effective descri ption of the status of the stocks sea cucumber and lobster fisheries to emphasize the durati on and difficulty of the rebuild. Traditional instruments such as closed seasons, taxes to landing values or innovative policies such as allocations of quotas to mother boat structures in multiday fishing trips may prove effective in reducing fleet size while protecting endangered stocks. The scope of these findings regarding stated fishermen exiting behavior is limited to the context of a fishing-tourism transition where th e issue of capital malleability is overcome. However, the results provide further evidence ab out expected rational behavior and social aspects relevant to small-scale fisheries. Give n data availability and sample size considerations, this study used simple linkage indicators among i ndividuals to identify possible interdependency in behavior. Further research that explores more refined scales of relationships, such as actual correlations between related fish ermen choices, is needed. Anothe r area beyond the scope of this paper is the role of vessel effi ciency on exiting behavior. Furthe r exploration of this topic is important to better understand observed differences among individuals related to different fleet segments and to help management authorities design fleet-targeted, and perhaps more efficient capacity reduction plans.


41 Table 2-1. Distribution of observed responses Response No. observations Percent Exit choice: No 120 40.10% Do not know 51 17.10% Yes 128 42.80% Total 299 100.0% Tour choice: Standard cruise 15 14.60% Bay and diving tour 51 49.50% Diving cruise 37 35.90% Total 103 100.0% Tour options with less than 10 observations were excluded from the model.


42Table 2-2. Variable descriptions and statistics Exit choice (N = 299) Tour choice (N = 103) Variable Definition Mean Std dev. Mean Std dev. Profitability of fishing (X1): CREW 1 if crew; 0 if owner 0.552 0.497 0.165 0.372 SBOAT 1 if works with or is a small boat, 0 otherwise 0.866 0.341 0.864 0.343 HIGHVAL 1 if fishes lobster and sea cucumber, 0 ot herwise 0.856 0.351 0.845 0.363 FISHYRS Fishing experience (years, range: 0 60) 18.07 9.321 21.068 9.743 DIVER 1 if worked as diver, 0 otherwise 0.411 0.492 0.544 0.499 SAFEREG 1 if concerned with safety regulations, 0 otherwise 0.198 0.395 0.265 0.439 Interdependence (X2): ICLINK 1 if crew-owner relation observed, 0 ot herwise 0.528 0.499 0.417 0.494 IMLINK 1 if 'mother boat relation observed, 0 otherwise 0.147 0.354 0.281 0.450 Demographics (X3): LOC_CR 1 if resides in San Cristobal, 0 ot herwise 0.475 0.499 0.330 0.471 LOC_IS 1 if resides in Isabela, 0 otherwise 0.298 0.448 0.233 0.423 CHILD Children living in household (number, range: 0-4) 0.916 0.956 1.107 1.006 INC2 1 if monthly income: USD$5011,000, 0 otherwise 0.405 0.491 0.447 0.498 INC3 1 if monthly income >USD$1000, 0 otherwise 0.237 0.426 0.379 0.486 ALTINC 1 if have alternative income, 0 othe rwise 0.472 0.499 0.612 0.488 EDUC 1 if college graduate, 0 otherwise 0.055 0.225 0.064 0.254 Funding to enter tourism (X4): FBANK 1 if bank funding available, 0 otherwise N/A N/A 0.670 0.464 FFAM 1 if family funding available, 0 otherwise N/A N/A 0.304 0.458 FINV 1 if funding from third-party investors, 0 otherwise N/A N/A 0.320 0.467 INVHIGH 1 if high investment level needed, 0 otherwise N/A N/A 0.592 0.492 N/A indicates the variable was not applicable to the choice set.


43 Table 2-3. Multinomial logit estimates for exit choice Do not know Yes Variable Estimate t-value Estimate t-value Constant -4.732 -2.742*** -1.125 -0.940 CREW 0.841 1.122 -2.501 -5.012*** SBOAT 0.559 0.936 -1.013 -1.726* HIGHVAL 0.143 0.249 1.039 1.746* FISHYRS -0.049 -1.497 0.051 2.121** DIVER -0.346 -0.684 1.269 2.961*** SAFEREG 0.596 0.966 0.535 1.118 ICLINK 0.631 1.307 -0.068 -0.170 IMLINK 1.922 2.075** 0.634 0.944 LOC_CR 3.103 2.897*** -1.169 -2.291** LOC_IS -0.685 -0.459 -1.074 -1.927* CHILD -0.518 -1.911* 0.425 1.938* INC2 1.147 2.135** 0.845 1.828* INC3 1.243 1.830* 1.442 2.538** ALTINC 0.402 0.873 1.296 3.022*** EDUC 1.387 1.377 -0.135 -0.151 LogL -170.828 Adj. R-Sq 0.415 N 299 *Significant at the 0.10 level; **Significant at th e 0.05 level; ***Significant at the 0.01 level. Table 2-4. Effects of discrete variables on the probability of sw itching by type of fishermen Variable Owner Crew (Base= 0.553) (Base = 0.091) SBOAT -0.240 (-43.9%) -0.050 (-61.6%) HIGHVAL 0.225 ( 40.6%) 0.130 (142.0%) DIVER 0.262 ( 47.3%) 0.173 (188.2%) LOC_CR -0.284 (-51.4%) -0.064 (-69.9%) LOC_IS -0.256 (-46.2%) -0.058 (-63.5%) INC2 0.188 ( 34.0%) 0.097 (105.7%) INC3 0.286 ( 51.6%) 0.205 (223.4%) ALTINC 0.266 ( 48.0%) 0.177 (193.2%) Note: Results calculated using th e Yes model from Table 2-3.


44 Table 2-5. Multinomial logit estimates for choice of tour operations Bay and diving tour Diving cruise Variable Estimate t-value Estimate t-value Constant 0.520 0.365 2.349 2.034** SBOAT 1.364 1.645* N/A N/A DIVER -0.058 -0.066 0.989 1.123 SAFEREG 1.646 1.653* 0.490 0.481 ICLINK -0.953 -0.989 -2.028 -2.096** IMLINK -0.993 -1.076 -1.075 -1.149 LOC_CR 3.114 3.856*** N/A N/A LOC_IS -2.895 -2.799*** -3.940 -3.755*** FBANK 1.194 1.698* N/A N/A FFAM -0.038 -0.063 N/A N/A FINV N/A N/A 0.936 1.419 INVHIGH N/A N/A 1.316 2.122** LogL -60.811 Adj. R-sq 0.350 N 103 *Significant at the 0.10 level; **Significant at th e 0.05 level; ***Significant at the 0.01 level. N/A indicates the variable was not estimated. LOC_CR is not included in the diving cruise option because of lack of observation. INVHIGH is included in one option only given the limited to variation of the variable in the base category.


45 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 051015202530354045505560 Fishing experienceProbabilityA 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 01234 No. Children Probability B Figure 2-1. Probability of switc hing. (A) Probability by number of children. (B) Probability by fishing experience. Crew Owner Crew Owner


46 CHAPTER 3 ASSESING PRODUCTION TECHNOLOGY A ND TECHNICAL EFFICIENCY IN SMALL SCALE FISHING FLEETS: DOES VESSEL INTERRELATION MATTER? Introduction The literature on production and te chnical efficiency analysis in large commercial fisheries has increasingly expanded in the last decade. Most empirical analyses usually focus on evaluating the effectiveness of capacity manageme nt programs, the effect s of traditional input controls on harvest levels and ve ssel performance, and on the asse ssment of the role of fishing skills and vessel characteristic s on explaining technical effici ency (Sharma and Leung 1999; Greenville et al. 2006; Pascoe and Coglan 2002; Tingley et al. 2005; Fe lthoven 2002; Kirkley et al. 1998). However, fewer studies have addressed th e specific production dynamics and technical efficiency of artisanal fishing fleets. In comparison to the indu strial large commercial sector, artisanal fisheries are strongly in fluenced by socio-economic, technol ogical and political factors. These distinctive characteristics in turn affect the appropriate type of management strategies (Berkes et al. 2001). Some studies explicitly mentioning the effect of production factors on efficient output levels indicate that harvests ar e more sensitive to variable inputs than to measures of vessel capital (e .g., size and capacity) (Fousekis and Klonaris 2003; Lokina 2008; Esmaeili 2006). Other studies have analyzed in more detail the technical efficiency of small scale fisheries in developing countries. There is evidence that the level of efficiency in input use is more relevant than random events or luck in explaining output variability (Esmaeli 2006, Jeon et al. 2006, Madau et al. 2009), althoug h for the particular cases of artisanal trammel netters in Greece and gill netters in the east coast of Malaysia inefficiency was not critical in determining production performance (Fousekis and Klonaris 2 003, Squires et al. 2003). In average, small scale fishing fleets seem to operate with modera te technical efficiency, suggesting a significant


47 scope for output increases given the same input le vels and resource conditions or input and costs reductions for the existing output levels. The previous literature has addressed the producti vity and efficiency of fishing vessels as independent production units, which decide auto nomously how many inputs to use and what fishing grounds to operate in. On ly Schnier et al. ( 2006) presented a descriptive analysis of paired trawling within and between producer segm ents using a latent class production model to explore the issue of unobserved heterogeneity in the Northeas t Atlantic herring fishery. However, paired trawlers were excluded from the production modeling and efficiency estimation. In the context of artisanal fishing fleets and their limited infrastructure, vessel interaction in the production pro cess may be a usual and customary practice to access different fishing grounds, especially those far from the home ports. This interaction is likely to be facilitated by the social ties prevalent on community-based pr oduction systems, where it is not uncommon to observe family and friends forming composite production units. Hence, accounting for vessel interdependence will help de pict an appropriate production technology for small scale fleets that operate under that sy stem and obtain accurate estimates of their productivity and technical efficiency. From a capac ity management perspective for instance, this information would be helpful to better evaluate the potential impacts of fleet reduction programs that affect multi-vessel production units. The objective of this chapter is to evaluate the producti on possibilities and technical efficiency of individual vessels and also the role of mothershipcatcher vessel interrelations on boat performance with an application to the ar tisanal spiny lobster fishery in the Galapagos Marine Reserve. The next section describes th e characteristic of th e fishery, section three explains the stochastic frontier methodology em ployed, followed by the data description and


48 model specification in section f our. Finally, results and conclusi ons with recommendations about strategies to influence techni cal efficiency in the fleet ar e provided in section five. The Artisanal Spiny Lobster Fishery The spiny lobster fishery in the Galapagos Islands targets two species, Panulirus penicillatus and Panulirus gracili s, commonly known as red and green lobster respectively. It started in 1960 for subsistence purposes and expanded throughout the years with the introduction of fishermen and vessels from the mainland a nd the adoption of improved harvesting methods (Toral et al. 2002). Since 1998, with the establishment of the spec ial Law for the Galapagos and the creation of the Marine Reserve, the fishing fleet has b een constrained to limited entry and small scale operations only. The fishery has become the s econd more important inco me-generation activity for the fishing sector ( after s ea cucumber) with landings of 31.40 MT and an ex-vessel value of USD$900,000 approximately in 2006 (Hearn et al. 2007). The fishing fleet is comprise d by a total of 446 registered vessels grouped in three fleet segments. Small wooden boats (WB) have a leng th between 3-8 mts. and the most limited storage capacity and propulsion power, medium fi berglass (FG) vessels are 5 to 9.5 mts. long and large boats (LB) are vessels of up to 18 mts ., with the highest registered tonnage and engine power measures (Table 3-1). Overal l, fiberglass vessels account for th e majority of the fleet (i.e., 51%), while large boats repres ent only 14% (Table 3-2). The fleet is distributed among the three main i nhabited islands: San Cristobal, Santa Cruz and Isabela. Most vessels are lo cated in San Cristobal and, cont rary to the other ports; wooden boats represent most of the island fleet. By 2005, only sixty percent of the overall fleet was active, with 88% of fiberglass boats, 46% of wooden boats and 43% of large boats actually fishing (Hearn et al. 2006). Vesse ls usually operate independently for single-day fishing trips,


49 which are mostly near shore. For multi-day offsho re fishing trips, however, vessels work within the mother boat system. In this system, larger or mother boats tow sma ller vessels to distant fishing grounds and provide storag e for their catches until landing at the home port (Baine et al. 2007). Compensation is given as a fixed fee or share of total ha rvests. The mother boat system can be considered as a joint production unit where the harvesti ng performance of subunits (i.e., smaller vessels) is re lated to the corresponding mother boat. Only fiberglass and wooden boats are considered fishing units in this fishery (Castrejon 2007)1. The fishery is managed by limited entry a nd traditional input and output controls. The fishing season is limited to 4-5 months a year, generally from September to December. The product is collected exclusively by diving (i .e., scuba, hookah and snorkel), using Hawaiian fishing spears with a trident le ngth not exceeding 40 cm or just hand picking. Output controls include minimum sizes of 26 and 15 cm for whole individuals and tails re spectively, restrictions to spawning stocks and total allowable catch m easures per year. For 2009, the total catch quota was set in 30 MT, which will be adjusted to th e stock condition every season (Comisin Tcnica Pesquera de la Junta de Ma nejo Participativo 2009). Despite the innovative participatory management framework and fishing controls, lobster stock abundance has decreased during the last year s raising concerns about the sustainability of the resource and the local fishing community. From 2000 to 2005, total harvests decreased in about 59% from an estimate of 85 MT to less than 35 MT per year with total revenues declining in 53% (from almost USD$ 2 million to $900.000) (Hearn et al. 2006). As with other open access fisheries worlwide, the need to rebuild thes e stocks and to limit the fishing capacity of the fleet has become a priority for this resource. 1 Large boats are considered fishing units only for year round pelagic and deme rsal fisheries, specifically, whitefish.


50 Methodology Production frontier methods provide a framewor k to identify the bes t practices in an industry, creating boundaries or fron tiers against which the inefficiency of firms is assessed. Firms on the frontier operate efficiently wh ile those below the boundary are considered inefficient. From an output perspective, the best practice frontier refers to the maximum possible output attainable by a firm at obser ved input combinations and given technology (Kumbhakar and Lovell 2000).This paper uses the Stochastic Produc tion Frontier (SPF) methodology to assess the production technology and technical efficiency of the Galapagos artisanal fishing fleet. This method is considered particularly appropriate in fisheries production analysis, given the stochasticity related to th e environmental and biol ogical aspects of the activity (Kirkley, Squi res and Strand 1995). Following the Battese and Coelli (1995) approa ch for panel data, a stochastic frontier production function is defined as jt jt jtX f Y ) ; (ln ln with jt jt jtu v (3-1) Yjt represents the level of output for firm j in period t, Xjt is the vector of production factors2 used by firm j in period t, is a vector of coefficients to be estimated and jt is a composite error term where vjt represents a traditional random noise and ujt is an additional nonnegative one-sided e rror component capturing technica l inefficiency of firm j3. To obtain estimates of technical efficiency it is necessary to make distributional assumptions regarding the error components vjt and ujt. Both terms are assumed to be 2 In fisheries, production factors are usually represented by a vector of capital stock or fixed inputs, variable inputs, non-discretionary stocks out of the producers control, and a vector of temporal, environmental or technological conditions (Kirkley et al 2002). 3 This composite error definition allows impacts of random events or unmeasured inputs on final output not to be confused or combined with inefficien cy deviations (Kirkley et al 2002).


51 independently distributed from each other, with vjt ~iid N(0, v 2) and ujt ~id N*( u 2) where N* indicates the normal distri bution truncated at zero4. In order to estimate inefficiency effects, ujt is specified as a function of relevant variables, zjt, hypothesized to influence firm s efficiency such that ujt = 0+ zjt + wjt ; jit ~ N*(0, u 2 ) and ujt ~id N*( zjt, u 2) (3-2) where zjt is a vector of exogenous firm and operato r specific factors. These factors differ from those in the production frontier as they are mo re related to influencing the ability of vessels to locate and catch fish than to determining the production technology. is the set of parameters to be estimated and wjt is a random variable not required to be independently and identically distributed, assumed to follow a nor mal distribution truncated at zjt to satisfy the nonnegativity condition of ujt. Equations 3-1 and 3-2 are estimated jointly using maximum likelihood techniques5. Details on the likelihood functi on and variance parameters and transformations are provided by Battese and Coelli 1993. The technical efficiency (TE) score for the i ndividual firm j in period t is defined in a logged-based functional form as: ) exp( ) exp(jt jt jt jtw z u TE (3-3) As the SPF model (Equations 3-1 and 3-2) is es timated given the joint density function of the composite error term jt, the estimator of technical efficiency, TE, is based on the conditional 4 Additional distributional assumptions for uit used in the literature include (a) a half-normal, (b) exponential and (c) 2-parameter Gamma distributions. The truncated normal distribution allows a more flexible specification for the one-sided efficiency error (Kumbhakar and Lovell 2000). 5 Joint estimation of the frontier and t echnical efficiency effect s avoids inconsistencies between the distributional assumptions of the inefficiency term arising when Equati ons 3-1 and 3-2 are estimated using a 2-step approach. In that case, estimation of the stochastic frontier in the fi rst step assumes identically and independently distributed inefficiencies (uit). In the second stage, the predicted TE scores are assumed to be a function of firm specific variables, meaning that the uits are not identically distributed (Battese and Coelli 1995).


52 expectation of ujt and TEjt. As shown in Kumbhakar and L ovell (2000), the TE for individual firm j is predicted by the following expression: *)} / ( / *] *) / [( { *]} 2 / 1 {exp[ ] | ) [exp(2 j j j j ju E (3-4) where i* and are transformations of the va riances of the error components uj and vj Estimation of technical efficiency effects (E quation 3-2) is plausible provided that technical efficiency exists and that it is stoc hastic. The validity of these assumptions can be tested using standard generalized likelihood ratio tests. To test whet her or not inefficiency effects are absent, the null hypothesis is expressed by = =0, where = u 2 / ( v 2+ u 2). If technical inefficiency indeed applies to the model, it is n ecessary to test if they are stochastic through the null hypothesis =0.6. Finally, the null hypothesis =0 is used to test if technical inefficiency is a linear function of the hypothesized Zjt factors. As noted by Battese and Coelli (1995), if the hypothesis that all are zero can not be rejected (excluding the intercept 0), the uit would have a truncated normal distribution with mean 0. A flexible functional form such as the tr anslog production specification is recommended for econometric estimation since it avoids imposi ng too restrictive assumptions on returns to scale and elasticities of substitutio ns in the technology. The tran slog production function is given by: jt jt mjt nnk m m m kjt njt nk njt n jtu v d X X X Y 2 0ln ln 2 / 1 ln ln (3-5) 6 If technical ineffici ency is absent, the production frontier would reduce to the standard OLS mean production function with only the input variables Xjt included. In this case the degrees of freedom (number of parameter restrictions) equal 1 for and the number of coefficients, including o. If technical ef ficiency is not stochastic (i.e., deterministic), the frontier reduces to a mean production function with the zjt variables added to the input (Xjt) variables in the specification. In this case, th e number of degrees if freedom equals 1 for plus the number of nonidentified parameters, in some cases being just o. For these two hypotheses, the likelihood ratio statistic has an asymptotic mixed chi-squared distribution as =0 is on the boundary of the parameter space (Battese and Broca 1997). The appropriate critical values can be found in Table 1 of Kodde and Palm 1986.


53 where Yjt and Xnjt rpresent output and producti on inputs respectively, dmjt are additional technology indicators, such as time resource stocks, etc. However, this approach is likely to create problems of multicollinearity and degr ees of freedom (Coelli 1995). An alternative functional form such as the Cobb-Douglas produc tion function can also be tested as a special case of the translog f unction where all the nk equal zero in order to validate the assumption of a more restrictive production t echnology (Pascoe et al. 2003)7. Data and Model Specification The data used in this study are pooled cross se ctions of trip-level catch and fishing effort from a total of 316 individual vessels during th e period 1997-2006. The total dataset consisted of 8746 trip observations and it was obtained from th e Charles Darwin Research Station monitoring program8. The information included landing volume, fish ing effort, fishing sites, vessel names, departure and landing ports and an indicator of whether the ve ssel was towed or not in the specific trip. Data on physical boat characteristics such as length, engine power, gr oss registered tonnage and construction year was provided by the Galapagos National Park Service. The estimation sample was limited to fibergla ss and wooden vessels only because reported output from large boats (i.e., motherships) corres ponds to the catch of the smaller vessels they tow (Castrejon 2007). Vessels with less than 5 trip s were excluded in order to reduce the number of missing time series. Also, catc h data with values of zero or more than 500 MT and gross registered tonnage (GRT) of more than 5.68 tons were considered outliers and were not included 7 Specifically, the Cobb-Douglas specifica tion restricts output elasticities to be constant at all input levels and returns to scale to be equal across all firms. 8 Information was collected from almo st all active fishing vessels during each season though either on-board observers or field assistants filling reco rd sheets at the docks (Hearn et al 2 006). Vessels were required to report their landings in order to obtain a monitoring certificate from the Galapagos National Park to be able to sell their product. Landings that were not monitored are considered illegal catch.


54 in the final sample9. Missing information for towing accounted for 21% of the sample and it was dropped from the sample to avoid bias from a ny missing data adjustment method as this is a fundamental variable in the an alysis. Therefore, the analys is was conditional on towing information. Most missing data for control variable s such as capital characteristics and fishing effort accounted for less than 6% of the sample and were replaced using the dummy adjustment method10,11. The final sample included 201 fishing boats and 6381 observations. A detailed description of the variable s, summary statistics and a priori expected effects are presented in Table 3-3. In comparison to a sample that is not conditional on observing towing data, boats in the final sample had higher averag e catch and storage capacity levels (Table 3-4). The absence of towing information caused only a moderate underre presentation of fishing units departing from or fishing in San Cristobal, of those operating during the first two years in the analysis (19971998) and those fishing by hand picking. Convers ely, boats operating ar ound the other fishing ports, during the period 1999-2001 and which used mechanized gear were overrepresented. The rest of the variables remained considerably similar. The analysis follows the approach suggested by Battesse and Coelli (1995) given its ability to identify factors influencing te chnical inefficiency in a panel data context. A full Translog specification was not allowed by the data as several interaction terms had a linear correlation 9 GRT outliers represented only 1% of the sample and consisted of 60 observations from 3 fiberglass boats. They differed substantially from the rest of the sample as a measur e of capital, and were likely to introduce biases in the model, as indicated by preliminary analysis. 10 The dummy adjustment method repl aces missing values with the mean of the corresponding variable and additionally creates a dummy variable indicating that the observation was missing, which must be included in the model (Allison 2002).This approach allows retaining non missing information from the same cross sectional unit with missing data, and it is better justified if applied to a small percentage of the sample. 11 Preliminary regression analysis was used to compare the effect of listwise deletion vs. dummy adjustment for missing data. Coefficients remained relatively stable for most factors across methods, but listwise deletion of missing data on boat age (14% sample) created stability and converging problems. For that reason dummy adjustment was considered a justified approach.


55 greater than 0.99 and caused stab ility and convergence problems. Hence, th e frontier model for the Galapagos spiny lobster fishery is speci fied as a Cobb-Douglas production function12: jt jt j jt jt m j m m k jt k k jt jt j jtu v DWBOAT DTOW DTOW DWBOAT DYEAR DZONE NDIVERS SEADAYS GRT WEIGHT ln ln ln ln13 12 10 2 11 19 1 3 2 1 0 (3-6) WEIGHTjt represents catch volume in kilograms for vessel j in trip t13; GRTj is the gross registered tonnage of vessel j capturing the capital stock measure14, and variable inputs are denoted by SEADAYSjt and NDIVERSjt the number of days at s ea and number of divers observed for vessel j in trip t respectively. DZONEkjt is a vector of dummy variables indicati ng if vessel j fished in zone k and it is intended to control for high and low productivity areas capturing the effe ct of resource stock availability15. The next variables are dummy indicator s used to control for temporal and technological conditions. DYEARm represents a vector of M-1 y ear binary variables which are intended to capture non linear te chnical change in time, and DWBOATj indicates if vessel j is a wooden boat. The term DTOWjt is introduced to control for vessel interrelations in the production process, taking the valu e of one if vessel j was towed in trip t. This variable was 12 Generalized likelihood ratio tests of joint significance did not support the inclusion of cross product input terms in a translog specification context. 13 Catch is expressed in kilograms to avoid negative values after logarithmic transforma tions and it is treated as a single-output measure since there are not price differentials between the two ta rget species. In addition, lobster output is modeled separately from other fisheries also open during lobster season since participation in different fisheries requires completely separate fishing trips given the differences in gear use, technology and the spatial distribution among resources. 14 An additional fixed input, horse power, was also consider ed in preliminary models, but the inclusion of a second factor did not improve the performance of the model as indicated by likelihood ratio tests. The inclusion of GRT over horse power was supported by AIC and BIC criteria. 15In the absence of independent measures of stock biomass, using area-based dummy variables as proxies for stock abundance allows to control for spatial differences in resource abundan ce, fishing practices and socio-economic conditions (Squires et. al. 2003). In this case, all fishing zones are included in the model because they are not mutually exclusive; vessels can fish in more than one site in the same trip, especially during multi-day trips.


56 introduced in the frontier function as opposed to the inefficiency model because the towing system reflects a technological component likely to affect input use and maximum possible output. It allows vessels to spend more days at sea and to go to more and different fishing sites increasing their exposure to differe nt levels of resource abundance. Finally, the interaction term DTOWjt*DWBOATj captures possible differential effects of the towing system between fiberglass and wooden boats16. The inefficiency measure for vessel j in trip t, ujt, is specified as: jt jt jt jt jt jtDGEAR DFDIVE DPORT DPORT DPORT u5 4 3 2 1 04 3 1 jt jt ju v BAGE 6 (3-7) The first five explanatory variables are dummy indicators taking the value of one if the vessel homeport is Puerto Baqueri zo Moreno (San Cristobal) ( DPORT1jt), Puerto Villamil (Isabela) ( DPORT 3jt) or others ( DPORT4jt), if divers practiced free diving as opposed to hookah methods ( DFDIVEj), and hand-picked the catch ( DGEARjt); while BAGEj measures the age of the boat in years. These variables intend to captu re the effect of soci al and vessel-specific practices and characteristics on the ob served efficiency of input use. According to economic theory, fixed and variable inputs ( GRT, SEADAYS and NDIVERS ) are expected to have a positive effect on the ca tch frontier and the year indicators for 2005 and 2006 ( DYEAR9 and DYEAR10 ) are hypothesized to show a nega tive effect given the reduction in resource biomass observed in those years. The coefficients for DPORT1 DPORT3 and DPORT4 are expected to have a negative effect on t echnical inefficiency as they represent ports with higher fishing activ ity in comparison to the tourismbased economy of the base port 16 A model with interactions between WBOAT and DTOW with inputs was also explored to check for differential impacts on inputs main effects but they were jointly non-si gnificant as indicated by likelihood ratio test statistics.


57 ( DPORT2 ) and they may presumably be more effici ent in their input use given their fishing specialization. The pr actice of free diving ( DFDIVE ) and the use of non-mechanized gear ( DGEAR ) are likely to limit the full use of th e existing technology increasing technical inefficiency (reducing technical efficiency). Older vessels may be in worse maintenance conditions or constructed with out dated materials and designs, whic h is also expected to limit the optimal use of available inputs a nd boat efficiency (Pascoe and C oglan 2002; Squires et al 2003). Vessel interrelation ( DTOW ) may have different effects on the production frontier. On one hand, it could show a positive effect on output as being towed increases the number of days that individual vessels can spend fi shing and the number of sites th ey can reach, simulating an expansion of fishing technology. On the other ha nd, towed boats function just as a part of a bigger fishing unit (i.e., mothership ) and they may need to adjust their catch considering the rest of vessels in the mothership and the available storage space. Consequently, their catch per day may be more limited than for independent boats. Additionally, controlling explicitly for the towing factor in the frontier mode l might capture part of what mi ght be considered inefficiency in the absence of that control. These are empiri cal questions to be answered by the analysis. The rest of the variables have unknown a priori expectations. Characteristics of the sample differed betw een towed and independent boats in a given fishing trip (Table 3-5). On average, towed vessels were smaller (i.e., lower GRT ), spent more days at sea and had higher catches per trip than autonom ous boats (25,980 vs. 11,448 kgs. respectively).The majority of towed boats in th e sample (68%) operated in the north and west section of Isabela Island and around two other zones father north in the archipelago in comparison to 91% of independent boats which fish ed closer to the main ports (Santa Cruz, San Cristobal and the southern part of Isabela Island). In order to assess the implications of


58 controlling for the effect of ve ssel interrelation (i.e., towing eff ects) on production and technical efficiency, the complete (base) model presented in Equation 3-6 and Equati on 3-7 is compared to a restricted version without thos e controls (i.e., restricted mode l). The production frontier and the inefficiency effects models ar e estimated simultaneously using maximum likelihood routines in FRONTIER 4.1 (Coelli 1996). Results Frontier Model The coefficient estimates for the complete (base) and restricted models are reported in Table 3-6. Generalized likelihood ra tio tests for the validity of a stochastic fr ontier specification, inefficiency determinants and interrelation eff ects for both models are reported in Table 3-7. Results show that the stochastic frontier fr amework is more appropriate than the mean production function for the sample data ( = =0 strongly rejected), t echnical inefficiency is stochastic ( =0 strongly rejected), and that ineffi ciency effects are a linear function of the proposed social and vess el specific factors ( 1= 5=0 strongly rejected). The inclusion of vessel interrelation (DTOW and interaction term) significantly improved the final model specification as supported by the result of the first hypothesis for the complete model. Result interpretations will be based on the complete model and comparisons with the restricted specification will be introduced later to assess any differences. Input variables were statistical ly significant, had the expect ed signs, and since the model was expressed in logarithmic form the significant input coefficients represent out put elasticities. Given the assumption of a Cobb-Douglas technol ogy, trip productivity depended mostly on the number of days spent at sea as a proxy of fish ing time (0.97 elasticity), followed by labor (0.50 elasticity) and cap ital (0.017), although only th e variable inputs had si gnificant effects. This finding is not surprising since the fiberglass and wooden ve ssel segments are relatively


59 homogenous in terms of capital and by nature, th e lobster fishery is a diving intensive activity. The fishing technology exhibited increasing returns to scale even if excluding the non-significant elasticity of capital (i.e., an expa nsion of 1% in all inputs yielde d more than 1% output increase). In fisheries, there are a priori reasons to expect vari able returns to scal e (Pascoe et al. 2003), specially decreasing returns, gi ven the incentives for overcapitaliz ation in open access fisheries. However, this result reflects the artisanal natu re of the Galapagos fi shing fleet, and it is consistent to the findings of pr evious studies. For instance, retu rns to scale for the trammel net inshore fleet in Northeastern Greece and for the gillnet artisanal fleet operating in the Hormozgan Province (Iran) were estimated at 1.26 and 1.42 respectively (Fousekis and Klonaris 2003; Esmaeili 2006). Similarly to our study, higher harvest responses for these artisanal fleets were associated primarily to changes in variab le inputs such as the amount of gear used or number of days at sea. Interes tingly, in theses cases, capital inputs significantly affected output levels. Some of the disaggregated zone indicators ca pturing the spatial dist ribution of stocks are statistically significant. Vesse ls fishing around San Cristobal South and East, Isabela West, Marchena and Genovesa Islands had significantly la rger catches, while harvests were lower in Santa Cruz grounds. This finding in dicates particularly unfavorab le stock conditions in Santa Cruz, which is consistent with the historical lower abundance levels repo rted for that area in comparison to other zones (Hearn et al. 2006). A dditionally, trip productivity was significantly lower for vessels operating during the 2004 and 2006 seasons. These temporal effects are most likely attributed to the observed drop in resour ce stocks after 2004 rather than to any technical change effect in the fleet. As hypothesi zed, the vessel interre lation variable ( DTOW ) and the corresponding inte raction term ( DWBOAT*DTOW ) were highly significant. Results showed that


60 in fact, towed vessels were less productive than autonomous boats despite the expa nsion of days at sea and fishing zones availabl e. This indicates that the towi ng system (mothership) limits the harvesting potential of small vessels, but the nega tive effect was lower for small wooden boats in comparison to fiberglass units as indicated by the positive sign of the interaction term. The finding supports the relevance of accounting for vessel interr elations in the production technology of small scale fisheries. Technical Efficiency Model Port of origin, gear type and vessel age were significant determinants of the fleets technical efficiency (Table 3-6). The positive signs indicate an increase in technical inefficiency or alternatively, a decrease in technical efficiency. Vessels departing from Isabela ( DPORT3 ) were more inefficient than vessels from Sant a Cruz, the main tourism hub, and there was no evidence of higher efficiency for vessels origin ating in the main fishing port, San Cristobal ( DPORT1 ). Only vessels related to other, occasion al, departure ports s howed higher technical efficiency (i.e., lower technical inefficiency) re lative to the base. A possible explanation may arise from the lack of active cooperative orga nization in the fishing communities, which limits the development of fishermen attributes that may be applied to increase th eir fishing efficiency (e.g., training and specializati on options). Although these resu lts did not support the hypothesis of better performance for vessels from fishing-ba sed communities relative to the tourism-based port, they suggested that technical efficiency varies across geographi cally distributed vessel groups. Hand picking ( DGEAR ) and free diving ( DFDIVE ) increased technical inefficiency (i.e., reduced efficiency) relative to the use of Hawaiian spears or h ooks and more advanced methods like hookah diving. Surprisi ngly, older vessels ( BAGE ) were less inefficient (more efficient) than


61 younger boats17. However, a similar effect was also obser ved by Squires et al (2003) in artisanal fisheries. They argued that ve ssel performance may respond to a le arning period, and that older vessels are more likely to be mastered to the fu llest, increasing their efficiency. Alternatively, older vessels may have taken corrective measur es such as updating their propulsion power in order to compensate for the limita tions of older capital vintage (e .g., dated construction materials and hull design), actually increasing their effi ciency in reaching fishing grounds faster. As indicated by the (Gamma) coefficient, deviations from the output frontier depended mostly on technical inefficiency rather than on random shocks as the variance of ujt ( 2 u) represented 96.6% of total error variance, 2 u + 2 v. There is also considerable scope for technical efficiency improvement in the fishery. Fleet performance varied widely, with scores ranging from a lowest of 0.01 to a maximum of 0.92 a nd a mean technical efficiency of 0.65. The distribution of the scores was sli ghtly skewed to the right (Figure 3-1A), the majority of trips (57%) were between 60%-80% effi cient, and only around 15% were more than 80% efficient. These results support the findings of previous studies in small scal e fishing fleets; however, the average performance and scores distribution are somewhat lower than those previously reported in the literature. For instance, Fousekis and Klonaris (2003) reported and average technical efficiency of about 72% for the inshore fleet of artisanal trammel netters in Greece with most vessel units performing above 80% efficiency. Similarly, the small scale gillnet fleet of the Hormozgan Province in Iran, which targets deme rsal and pelagic multispecies like tuna and mackerel, was found to operate at around an average of 78% efficiency (Esmaeili 2006). Using the estimated coefficients from the restri cted model (Table 3-6), technical efficiency scores for the fleet were also estimated for comp arison with the complete model. Interestingly, 17 Non linear effects for boat age (BAGE) were also explored but results did not show any difference from the linear specification.


62 there is no evidence of overrepresentation of t echnical efficiency when failing to control for vessel interrelation ( DTOW ) as the extent of technical ine fficiency and the distribution of efficiency scores between both models are similar (Figure 3-1A). The result may reflect the particular fleet under study and further explorat ion of this effect on technical efficiency estimation in other empirical applications is needed to test the validity of this conclusion. Finally, technical efficiency of towed vessels was lower than for autonomous boats (Figure 3-1B).18 Fishing trips from towed units tended to concentrate more in the lower efficiency ranges, with only 67% of towed trips operating at more than 80% efficiency in comparison to 72% of independent units performing at the same e fficiency level. It is important to note that in order to assess technical e fficiency differences between the towing and autonomous technologies, it will be necessary to analyze th e performance of the motherships and towed boats in a trip as a whole and compar ing it to that of independent tr ips, which is beyond the scope of this study. Summary and Conclusions This paper analyzed the produc tion technology and technical e fficiency of the artisanal lobster fishing fleet in the Galapagos Marine Reserve duri ng the period 1997-2006, accounting for vessel interrelations into the production framework. Given the assumed production technology, ha rvests were not signi ficantly affected by vessel capital. Instead, they were more sensitive to variable fishing effort. The fleet operated at increasing returns to scale, whic h corresponds to the findi ngs of previous studi es also applied to the small scale fisheries. 18Frontier and inefficiency effects models were explored for the towed and autonomous subsamples separately, but the increased lack of balance in the panel for the towed group created model instability and the pooled model was preferred.


63 The analysis revealed that considering ve ssel interrelation structures between large motherships and smaller catcher boats in the modeling of the production technology matters, but it did not affect the extent of the technical efficiency found in the fleet. Being towed limited the harvesting frontier of individual catcher vessels, but the extent of the impact varied between wooden and fiberglass boats. In addition, trips wh ere individual vessels are towed also tended to be less technically efficient than when they operate autonomously. Results indicated a considerable potential for te chnical efficiency improvement in the fleet. Technical inefficiency accounted for most of the variation in cat ches and, on average, vessels were only 65% efficient in their input use. Th ese findings contribute to the evidence of moderate-to-high technical efficiency observed in small scale fishing fleets. Geographical location, diving methods, gear ty pe and vessel age had significant impacts on technical efficiency levels. Policies promoting and enforcing the use of more advanced diving methods and collection gear, as well as targeting the crew of younger ve ssels with training programs will likely increase the technical effi ciency of the boats. Locally-based governance efforts to strengthen fishermen cooperatives might also prove useful to improve the technical efficiency of vessels in San Cristobal and Isabel a. Active fishermen organizations are likely to have the incentives to conduct a more organized and monitored fishing activity. They may also facilitate appropriate training of members through partnerships w ith the management authority or development NGOs working in the area. Technical efficiency increase in fisheries may be an undesirabl e objective in th e absence of appropriately defined property rights, especially in stock declining fish eries (Jeon et al. 2006). However, policies that promote input savings wh ile maintaining the observed output levels will


64 help increase the profitability of the fleet, improving their economic performance without expanding the fishing pressure on th e stocks in the short run. From a capacity management point of view the results suggest that capital reduction policies (e.g., vessel decommissioning programs) in the small scale fiberglass and small wooden fleet segments will have little effect on the effi cient harvesting potential in the Galapagos spiny lobster fishery. Instead, controls targeting variable fishing effo rt (e.g., fishing time and labor) will be more useful to affect catch at the trip level19. However, the potential for technical efficiency improvements in the fleet are a late nt limitation for the success of capacity reduction policies in the absence of strategies that comp lementarily address the lack of property rights. Given multicollinearity limitations to estimat e a flexible functional form of technology, this paper assumed a Cobb-Dougl as production function and focu sed on the effect of vessel interrelation on fiberglass and small wooden vessels as individual production units. Additional research addressing multi-vessel structures as co mposite fishing units and the influence of vessel interrelation in technical effici ency measurement in other empiri cal applications is recommended to complement the present analysis. Finally, the results of this study are conditional on observing towing information. Future work needs to addres s the issue of sample selection due to missing information for towing status. 19 Limitations on days at sea will likely be compensated w ith increases in labor effort but the output effect of increased labor will be lower than that caused by change s in days at sea as indicated by the estimated output elasticities.


65 Table 3-1. Average fleet characteristics Boat Fiberglass Wooden boat Length (mts) 11.14 6.94 4.81 GRT (tonnes) 20.62 2.85 1.71 Engine power (hp) 109.35 75.68 46.78 Crew capacity (individuals) 6.60 3.00 3.00 Source: Fishing Registry 2008, Ga lapagos National Park Service. Table 3-2. Geographical distribution of the fleet Type Total San Cristobal Santa Cruz Isabela Boat 14.35% 16.51% 18.75% 6.61% Fiberglass 51.57% 36.79% 64.29% 66.12% Wooden boat 34.08% 46.70% 16.96% 27.27% Total Fleet 100.00% 47.53% 25.34% 27.13% Source: Fishing Registry 2008, Ga lapagos National Park Service.


66Table 3-3. Variable defini tion and descriptive statistics Variable Description Mean Std. Dev. Min Max Expected effect WEIGHT Catch weight in kilograms per trip 13165.83 21664.99 40 343637 Frontier: GRT Gross registered tonnage in tons 2.195 0.822 1 5.68 (+) SEADAYS Number of days at sea per trip 1.740 2.000 1 23 (+) NDIVERS Mean number of divers per trip 1.300 0.468 1 4 (+) DZONE1 1 if fishing zone Isabela Sur, 0 otherwise 0.486 0.499 0 1 (/ +) DZONE2 1 if fishing zone Santa Cruz, 0 otherwise 0.245 0.429 0 1 (/ +) DZONE3 1 if fishing zone San Cristobal South and East, 0 otherwise 0.114 0.317 0 1 (/ +) DZONE4 1 if fishing zone Isabela West, 0 otherwise 0.042 0.199 0 1 (/ +) DZONE5 1 if fishing zone Isabela North 0 otherwise 0.029 0.168 0 1 (/ +) DZONE6 1 if fishing zone Sa n Cristobal North and West, 0 otherwise 0.023 0.148 0 1 (/ +) DZONE7 1 if fishing zone Santiago, 0 otherwise 0.019 0.136 0 1 (/ +) DZONE8 1 if fishing zone Floreana, 0 otherwise 0.011 0.103 0 1 (/ +) DZONE9 1 if fishing zone Pinta, 0 otherwise 0.013 0.112 0 1 (/ +) DZONE10 1 if fishing zone Marchena, 0 otherwise 0.013 0.111 0 1 (/ +) DZONE11 1 if fishing zone Espanola, 0 otherwise 0.004 0.059 0 1 (/ +) DZONE12 1 if fishing zone Wolf, 0 othe rwise 0.006 0.075 0 1 (/ +) DZONE13 1 if fishing zone Santa Fe, 0 otherwise 0.005 0.072 0 1 (/ +) DZONE14 1 if fishing zone Darwin, 0 otherwise 0.005 0.067 0 1 (/ +) DZONE15 1 if fishing zone Genovesa, 0 otherwise 0.006 0.076 0 1 (/ +) DZONE17 1 if fishing zone Rabida, 0 ot herwise 0.002 0.043 0 1 (/ +) DZONE18 1 if fishing zone Pinzon, 0 othe rwise 0.002 0.042 0 1 (/ +) DYEAR2 1 if 1998, 0 otherwise 0.003 0.052 0 1 (/ +) DYEAR3 1 if 1999, 0 otherwise 0.136 0.343 0 1 (/ +) DYEAR4 1 if 2000, 0 otherwise 0.175 0.380 0 1 (/ +) DYEAR5 1 if 2001, 0 otherwise 0.267 0.442 0 1 (/ +) DYEAR6 1 if 2002, 0 otherwise 0.169 0.375 0 1 (/ +) DYEAR7 1 if 2003, 0 otherwise 0.139 0.346 0 1 (/ +) DYEAR8 1 if 2004, 0 otherwise 0.059 0.236 0 1 (/ +)


67Table 3-3. Continued DYEAR9 1 if 2005, 0 otherwise 0.049 0.215 0 1 (-) DYEAR10 1 if 2006, 0 otherwise 0.003 0.048 0 1 (-) DWBOAT 1 if wooden boat, 0 otherwise 0.400 0.490 0 1 (/ +) DTOW 1 if towed, 0 otherwise 0.118 0.323 0 1 (/ +) Inefficiency DPORT1 1 if home port Baquerizo Moreno, 0 otherwise 0.185 0.388 0 1 (-) DPORT3 1 if home port Puerto Villamil, 0 otherwise 0.519 0.499 0 1 (-) DPORT4 1 if home port Other, 0 otherwise 0.003 0.053 0 1 (-) DFDIVE 1 if free diving, 0 otherwise 0.014 0.119 0 1 (-) DGEAR 1 if hand gear 0 otherwise 0.167 0.373 0 1 (+) BAGE Vessel age in years 4.763 4.465 0 21 (+) Dummy base categories: 1997, hookah diving, fi berglass, autonomous vessels, mechanized gear ( spear and hook), and Puerto Ayora home port (Santa Cuz Island, tourism hub).


68 Table 3-4. Sample differences based on observation of towing information (DTOW) Final sample (N=6381) Not based on observing DTOW (N=8101) Variable Mean Std. Dev. Mean Std. Dev. WEIGHT 13165.83 21664.99 12615.3920290.47 Frontier: GRT 2.195 0.822 2.136 0.808 SEADAYS 1.740 2.000 1.677 1.852 NDIVERS 1.300 0.468 1.328 0.476 DZONE1 0.486 0.499 0.454 0.497 DZONE2 0.245 0.429 0.199 0.398 DZONE3 0.114 0.317 0.182 0.385 DZONE4 0.042 0.199 0.036 0.185 DZONE5 0.029 0.168 0.024 0.153 DZONE6 0.023 0.148 0.036 0.186 DZONE7 0.019 0.136 0.017 0.129 DZONE8 0.011 0.103 0.017 0.131 DZONE9 0.013 0.112 0.011 0.105 DZONE10 0.013 0.111 0.012 0.107 DZONE11 0.004 0.059 0.006 0.008 DZONE12 0.006 0.075 0.005 0.067 DZONE13 0.005 0.072 0.005 0.072 DZONE14 0.005 0.067 0.004 0.062 DZONE15 0.006 0.076 0.005 0.070 DZONE17 0.002 0.043 0.002 0.044 DZONE18 0.002 0.042 0.002 0.044 DYEAR2 0.003 0.052 0.068 0.252 DYEAR3 0.136 0.343 0.108 0.310 DYEAR4 0.175 0.380 0.139 0.347 DYEAR5 0.267 0.442 0.212 0.409 DYEAR6 0.169 0.375 0.168 0.374 DYEAR7 0.139 0.346 0.134 0.341 DYEAR8 0.059 0.236 0.059 0.236 DYEAR9 0.049 0.215 0.046 0.210 DYEAR10 0.003 0.048 0.002 0.046 DWBOAT 0.400 0.490 0.441 0.496 DTOW 0.118 0.323 0.118 0.323 Inefficiency DPORT1 0.185 0.388 0.278 0.448 DPORT3 0.519 0.499 0.482 0.499 DPORT4 0.003 0.053 0.005 0.067 DFDIVE 0.014 0.119 0.016 0.126 DGEAR 0.167 0.373 0.240 0.427 BAGE 4.763 4.465 5.003 4.439


69Table 3-5. Sample st atistics by towing status DTOW=0 (N=5627) DTOW=1 (N=754) Variable Mean Std. Dev. MinMax Mean Std. Dev. MinMax WEIGHT 11448.72 15225.21 200 268409 25980.35 45370.50 40 343636.10 Frontier GRT 2.230 0.821 1 5.68 1.940 0.785 1 4.35 SEADAYS 1.549 1.287 1 17 3.167 4.383 1 23 NDIVERS 1.270 0.454 1 4 1.548 0.498 1 2 DZONE1 0.525 0.499 0 1 0.203 0.399 0 1 DZONE2 0.271 0.444 0 1 0.051 0.217 0 1 DZONE3 0.127 0.332 0 1 0.015 0.116 0 1 DZONE4 0.016 0.126 0 1 0.231 0.421 0 1 DZONE5 0.009 0.096 0 1 0.178 0.382 0 1 DZONE6 0.026 0.157 0 1 0.000 0.000 0 0 DZONE7 0.007 0.084 0 1 0.108 0.310 0 1 DZONE8 0.007 0.082 0 1 0.041 0.199 0 1 DZONE9 0.002 0.044 0 1 0.093 0.290 0 1 DZONE10 0.002 0.042 0 1 0.093 0.290 0 1 DZONE11 0.004 0.064 0 1 0.000 0.000 0 DZONE12 0.000 0.013 0 1 0.046 0.211 0 1 DZONE13 0.004 0.062 0 1 0.015 0.120 0 1 DZONE14 0.000 0.019 0 1 0.036 0.186 0 1 DZONE15 0.003 0.050 0 1 0.031 0.172 0 1 DZONE17 0.000 0.013 0 1 0.015 0.120 0 1 DZONE18 0.001 0.030 0 1 0.008 0.089 0 1 DYEAR2 0.003 0.055 0 1 0.000 0.000 0 0 DYEAR3 0.154 0.361 0 1 0.004 0.063 0 1 DYEAR4 0.176 0.381 0 1 0.167 0.373 0 1 DYEAR5 0.242 0.428 0 1 0.454 0.498 0 1 DYEAR6 0.164 0.370 0 1 0.211 0.408 0 1 DYEAR7 0.150 0.357 0 1 0.058 0.235 0 1 DYEAR8 0.063 0.243 0 1 0.032 0.176 0 1 DYEAR9 0.045 0.208 0 1 0.074 0.262 0 1


70Table 3-5. Continued DYEAR10 0.003 0.052 0 1 0.000 0.000 0 0 DWBOAT 0.383 0.486 0 1 0.529 0.499 0 1 Inefficiency DPORT1 0.167 0.373 0 1 0.317 0.466 0 1 DPORT3 0.544 0.498 0 1 0.338 0.473 0 1 DPORT4 0.002 0.044 0 1 0.009 0.096 0 1 DFDIVE 0.016 0.126 0 1 0.000 0.000 0 0 DGEAR 0.156 0.363 0 0.267 0.442 0 1 BAGE 4.797 4.436 0 21 4.513 4.664 0 20


71 Table 3-6 Parameter estimates Restricted model Complete model Variable Estimate t-value Estimate t-value Stochastic Frontier Model Constant 9.336 29.621*** 9.373 29.906*** GRT 0.014 0.531 0.016 0.581 SEADAYS 0.970 57.577*** 0.972 57.239*** NDIVERS 0.497 17.484*** 0.499 17.457*** DZONE1 -0.035 -0.646 -0.066 -1.223 DZONE2 -0.184 -3.367*** -0.224 -3.868*** DZONE3 0.229 3.830*** 0.194 3.179*** DZONE4 0.128 2.097** 0.162 2.625** DZONE5 -0.051 -0.824 -0.019 -0.294 DZONE6 0.139 1.776* 0.103 1.294 DZONE7 -0.083 -1.138 -0.065 -0.872 DZONE8 0.161 1.700* 0.141 1.469 DZONE9 -0.079 -0.920 -0.041 -0.459 DZONE10 0.250 2.850*** 0.277 3.054*** DZONE11 0.150 1.051 0.112 0.783 DZONE12 0.045 0.325 0.127 0.919 DZONE13 -0.071 -0.539 -0.101 -0.752 DZONE14 0.100 0.634 0.143 0.925 DZONE15 0.355 3.071*** 0.353 3.023*** DZONE17 0.001 0.000 0.038 0.193 DZONE18 -0.051 -0.238 -0.073 -0.348 DYEAR2 -0.025 -0.073 -0.021 -0.061 DYEAR3 -0.069 -0.224 -0.075 -0.244 DYEAR4 -0.061 -0.198 -0.065 -0.209 DYEAR5 -0.240 -0.776 -0.236 -0.766 DYEAR6 -0.336 -1.086 -0.329 -1.064 DYEAR7 -0.297 -0.959 -0.301 -0.975 DYEAR8 -0.629 -2.024** -0.633 -2.039** DYEAR9 -0.427 -1.375 -0.424 -1.368 DYEAR10 -0.921 -2.612*** -0.921 -2.602*** DTOW -0.181 -3.911*** DWBOAT -0.018 -0.833 -0.028 -1.195 DTOW*DWBOAT 0.135 2.459** Inefficiency Model Constant -16.510 -2.829*** -14.437 -3.316*** DPORT1 -0.282 -2.058** -0.143 -1.022 DPORT3 0.766 3.391*** 0.853 3.628*** DPORT4 -6.786 -2.421** -7.266 -4.240*** DFDIVE 5.288 3.918*** 4.818 4.119*** DGEAR 3.502 3.072*** 3.113 3.403*** BAGE -0.079 -6.647*** -0.069 -8.132*** Sigma-squared 9.146 3.135*** 7.959 3.738*** Gamma 0.970 97.967*** 0.966 102.283*** Log likelihood -6869.523 -6862.642 N 6381 6381 *Significant at the 0.10 level; **Sig nificant at the 0.05 level; ***Si gnificant at the 0.01 level.


72 Table 3-7. Hypotheses testing for parameters of the frontier and inefficiency models Null Hypothesis Loglikelihood value Likelihood ratio statistic Criticala value No. restrictions Restricted model 1. No TE ( = = 0) -7089.982 440.92 19.05 11 2. Deterministic TE ( = 0) -7065.588 392.13 5.14 2 3. Z factors do not affect TE ( 1 = = 9=0) -6890.976 42.91 16.92 9 Complete model 1. No tow effects ( DTOW= DTOW*DWBOAT=0) -6869.523 14.68 5.99 2 2. No TE ( = = 0) -7075.244 425.20 19.05 11 3. Deterministic TE ( = 0) -7049.550 373.82 5.14 2 4. Z factors do not affect TE ( 1 = = 9=0) -6884.505 43.73 16.92 9 a Critical values for hypotheses about correspond to a mixed chi-s quared distribution and were obtained from Table 1 in Kodde and Palm(1986).


73 0 5 10 15 20 25 30 35 40 [0.00 0.10) [0.10 0.20) [0.20 0.30) [0.30 0.40) [0.40 0.50) [0.50 0.60) [0.60 0.70) [0.70 0.80) [0.80 0.90) [0.90 1] Efficiency Range%Trips0 10 20 30 40 50 60 70 80 90 100Cummulative % Base FullA 0 5 10 15 20 25 30 35 40 [0.00 0.10) [0.10 0.20) [0.20 0.30) [0.30 0.40) [0.40 0.50) [0.50 0.60) [0.60 0.70) [0.70 0.80) [0.80 0.90) [0.90 1] Efficiency Range% Trip s 0 10 20 30 40 50 60 70 80 90 100Cummulative % Towed AutonomousB Figure 3-1. Distribution of efficiency scores. (A) Comparis on between restricted and complete models. (B) Comparison between towed and autonomous boats.


74 CHAPTER 4 TOURISTS PREFERENCES FOR FISH ERMEN-OPERATED EXCURSIONS Introduction In the wake of collapsing fisheries worldwide, fisheries managers have begun to consider new and creative ways to reduce fishing effort while at the sa me time continuing to support fishing communities. Recent strategies include vessel decommissioning programs or a change in management system such as toward rights-base d program. These programs can be effective, but are costly to the public and time consuming and may not be possible in isolated areas. For example, artisanal fishing communities do not have alternative sources of employment or the infrastructure to disable fishing vessels. One creative alternative to reduce commercial fishing effort but take advantage of the available capital (vessels and fishing expertise) is to transition fishermen into marine-based recreation (Kusakawa 1992; DiarioC 2003; Estrategia and Negocios 2009). In order for this type of strategy to work, managers need to consider the potential demand fo r marine or boat-based recreational activities offered by these new operators prior to promoting and implementing a strategy that is new, different and, therefore, risky. In order to assess the poten tial for new products, research ers have relied on stated preference analysis. With respect to new marine -based tours, and fishing-related tours in particular, the existing literature has focused on su ch factors as changes in catch rates and fishing conditions to understand consum er behavior (Alberini et al 2007; Rolfe and Prayaga 2007; Paulrud 2006; Prayaga et al. 2010 ; Stoll and Ditton 2006). Much of this literature has relied on the contingent valuation approach to estimate the non-market valu e of regulatory changes that affect recreational fishing.


75 To our knowledge, the only study specifically a ddressing the market potential of new boatbased tours in the context of th e transition of commercial fish ers to tourism was presented by Alban and Boncoeur (2004). They analyzed the general interest and potential demand for guided fishing and non-fishing t ours offered by commercial fishermen in the Iroise Sea, France. Using personal interviews, they explored the role of demographics and tour -related attitudes on preferences for the new trips. Their results s uggested considerable ge neral interest on tours offered in commercial fishing boats, especially for non-fishing activities in comparison to fishing-only tours. Preferences significantly differed between ge nder, age and social origin. Females, participants between 20 to 55 years old and seniors or white coll ar workers were more likely to prefer these tours. In summary, the tr ansition of fishermen to the tourism industry was expected to be a promising alterative due to the potential demand for the new tours. The Galapagos Marine Reserve (GMR) provides a recent example of a similar initiative to transfer existing fishing effort to local tourism in an attempt to rebuild declining fish stocks and ensure sustainability of the local community. After appropriate training and vessel renovations, selected fishermen will be allowed to provide th e same type of tours provided by traditional operators (standard tours like bay and diving trips, interisland and diving cruises), as well as new artisanal fishing trips where consumers will be e xposed to the local fishi ng culture and practices. This latter option will offer fishermen the opport unity to continue applying their fishing and boating skills and generate revenue for the local economy1. To assess the potential success of the program, information about the market potential for fishermen as tour operators and for the proposed new services is of critical importance. 1 While all tours generate revenue, those booked through large foreign tour-operators can often result in leakage of funds outside of the local community (either to mainland Ecuador or to foreign companies).


76 The objective of this chapter is to assess the interest in and potential demand for standard tours and new artisanal fishing tr ips provided by fishermen in the GM R. The analysis uses stated preference methods and probability models as the tour services are new options in the market. Identifying the level of interest in the new operations provides a firs t indicator of market potential. The level of interest is expected to depend primarily on consumer s attitudes regarding the characteristics of the tour providers, the qu ality of services, and th e appeal of new tour experiences (e.g., fishing in small boats) compar ed to current operations. This information will help identify the type of consumers to target a nd the most relevant factors to increase the demand for the new fishermen-based operations. This is especially important since fishermen have to compete with well-established traditional operators. In addition, the potential demand can be assessed for specific tours by examining what ch aracteristics would incr ease the likelihood of tourists including them in their it ineraries (Roehl et al. 1993). To examine the potential for new marine-bas ed recreational boating tours offered by the new tour operators (i.e., ex-com mercial fishermen), I conducted a survey of past visitors. The selection of past visitors as the sample frame was considered necessary due to the uniqueness of the destination and, thus, the relatively small target population. In addi tion to standard sociodemographic information, past visitors were aske d about their trip, the benefits and concerns of new tours, their general level of support for the program and level of inte rest for two different types of tours, and the characteri stics of each type of trip they would most prefer. The following section describes the methodology that was used to quantitatively analyze interest and willingness to book two specific types of new tours. Then, we describe the data and empirical specifications of the models. Following a discussion of the results, we conclude with a brief summary of the potential success of this program based on the re ported consumer preferences.


77 Methodology This study uses stated preference methods and a probability modelling framework to analyze past visitors interest ratings and w illingness to book fishermen-operated tours (both in general and for a specific type of fishing tour). In summary, a higher level of interest in or willingness to book a tour is assumed to generate a higher level of utility to the respondent. Following Liao (1994), the latent indi rect utility function for individual n generated by fishermen-based tours can be modeled linearly as: n i n I i i nx V 1* 4-1 where i identifies the type of characteristics that are assumed to influence visitors interest and willingness to include tours in their itineraries (i.e., xi variable vectors) and estimated parameters. The types of variables assumed to affect either their interest or willingness to have booked one of these new tours on thei r last trip include traditiona l demographic characteristics, information about their last trip (duration and purpose, for example) the benefits and concerns of booking with new operators, and for a specific tour, the characteristic s of the trip that would be most appealing to them. Since utility is unobservable, and what is observe d is the individuals st ated interest for or likeliness to book a tour, we can use the obser ved information to define a new variable, Yn, to specify the preferences. For a multinomial ordered response model: Yn = 1 If Vn* 1 ( 1=0) Yn = 2 If 1 < Vn* 2 Yn = 3 If 2 < Vn* < 3 Yn = J If J-1 < Vn*


78 where the J are unknown cutoff or threshold paramete rs differentiating each response category and 1 is normalized to zero so that only J-2 threshold values are to be estimated. With this specification, the probabil ity of observing the different ou tcomes (such as successively higher interest ra tings) is given by (Borooah 2002): I i i n i I i i n i n I i n i n i nx F x x Y1 1 1Pr 0 Pr ) 1 Pr( I i i n i n I i i n i I i n i n i nx x x Y1 2 1 1 2Pr 0 Pr ) 2 Pr( I i i n i I i i n ix F x F1 1 2 4-2 I i i n i n I i i n i I i n i n i nx x x Y1 3 1 2 1 3 2Pr Pr ) 3 Pr( I i i n i I i i n ix F x F1 2 1 3 I i i n i J nx F J Y1 21 ) Pr( where F represents the cumulative density function of the error term n. The errors are assumed to follow a standard logistic distribut ion since the outcomes from other distributional assumptions are similar and the results ar e easier to interpret (Liao, 1994). Response probabilities for each individual n can then be calculated with a logit model as follows: I i i n i J I i i n i jx x ne e J Y1 11 ) Pr( 4-3 To facilitate interpretation of the estimated parameters, it is common to try to isolate the effects of each variable by looking at the change in probability at the variable means. The effect


79 of a continuous covariate on th e response variable is given by the partial derivative of a particular probability with respec t to the explanatory variable of interest. In general notation, it can be defined as follows where f represents the probability density function of the logistic distribution: i I i i n i j I i i n i j i nx f x f x j Y 1 1 1) Pr( 4-4 Partial derivatives are not; however appropriate to capture the e ffect of discrete covariates. In this case, the appropriate ma rginal effect is better estimat ed by taking the difference between estimated probabilities when the discrete explanatory variable ix ~ takes the value of 0 and 1 holding all other variables consta nt, usually at their means, using equation 4-3 such that: ) 0 ~ | Pr( ) 1 ~ | Pr( ~ ) Pr( i n i n i nx j Y x j Y x j Y. 4-5 Binomial processes follow the same logic of the general ordered framework described above, without the need of speci fying separating thresholds as there is just one response category. In this case, the binary outcome Y*n (such as whether or not the respondent would have been willing to book a tour) is ge nerated by the following relation: Y*n =1 if Vn*>0 Y*n=0 otherwise The probability of observing Y*n =1 is now given by: I i i n i I i i n i n I i n i n i nx F x x Y1 1 11 Pr 0 Pr ) 1 Pr( 4-6 Again, assuming a logistic di stribution for the error term n this probability may be estimated using the following equation:


80 I i i n i I i i n ix x ne e Y1 11 ) 1 Pr( 4-7 Lastly, following Long (1997), the marginal a nd discrete effects on the probability of binary response variables are computed as: i i n i n i nx Y x Y x Y) | 1 Pr( 1 )[ | 1 Pr( ) 1 Pr( 4-8 ) ~ | 0 Pr( ) ~ | 1 Pr( ~ ) 1 Pr(i n i n i nx Y x Y x Y 4-9 Under this framework the interest ratings and the subsequent willingness to book are modeled independently. This is be cause respondents were directed to identify the characteristics of their ideal tour and their wi llingness to book that tour only if they had an interest in the general concept. Thus, the decision to answer the willingness to book questions were made independently by each respondent and not determined by a pre-specified response to the interest question. As a result, a range of interest ratings could respond to those answering the willingness to book questions but those answer ing the latter were se lf-selected based on their own level of perceived interest. Data The data used for this study was collected from a sample of past visitors to the Galapagos Marine Reserve (GMR). Tourist information wa s obtained from the tour ist arrival/departure record forms collected in 2007 by the tourism m onitoring department at the Galapagos National Park Service. Of the total of 97,390 internationa l visitors recorded in 2006, 46% were from the United States. Given their documented importan ce among the group of international tourists (Kerr 2005) and, in order to re duce survey costs, the focus of the study was limited to U.S. visitors. A random sample of 2,500 households was selected and sent a le tter to respond to an


81 Internet survey in August 2009. A total of 229 letters were returned as undeliverable. By September 15, 2009, 282 completed responses (12.4% response rate) had been submitted and were used for the analysis. The survey instrument had five components. The first section obtai ned information on the visitors last trip. The second s ection discussed the entry fee (e very foreign visitor pays a $100 entry fee of which $5 goes to the GMR) and their willingness to pay an a dditional $5 fee directly to the GMR (which nearly everyone agreed). The third section described the state of the fishery and the plan to move vessels from fishing to tour ism. Participants were asked to individually rate several distinct benefits and c oncerns associated with the pro posal. In the fourth section, respondents were asked to state th eir level of interest in booking two differe nt types of tours: standard excursions and artisanal fishing trips on a five-point quali tative scale ranging from not at all interested to very interested. At this point, re spondents were directed to specify their preferred trip characteristics (usi ng a closed-ended framework) if they had any interest at all in a tour offered by ex-artisanal fisher men. The trip characteristics included trip length, boat size, and activity options. Then, respondents were asked to cons ider their itinerary on thei r last trip and, if their ideal tour would have been offered, would they have adde d the tour to their itinerary, replaced another tour with this one, or would not have taken the t our. The final questions in this section asked respondents to provide their maximu m willingness to pay for the ideal tour they specified as an open-ended que stion; however, a low response to this question and the high variability of responses pr ecluded further analysis.2 In the last section respondents were asked a series of demographic questions. 2 Respondents were not required to answer any question. The high variability of responses was expected following traditional findings of open-ended contingent valuation models, although surveying past visitors was expected to partially solve the anchoring problem. Voluntary comments at the end of the survey indicated that the majority of


82 The dependent and explanatory variables are defined in Table 4-1 and the corresponding summary statistics for each model are presented in Table 4-2. Four de pendent variables are defined for analysis: interest level in tours o ffered by ex-fishermen both traditional tours that are currently being offered and tours that would provide an ar tisanal fishing experience and whether they would have been willing to book one of these tours by ex-fishermen. Interest variables were defined as categoric al ratings on a scale of 1 (not interested at all) to 5 (very interested). Potential demand is based on their stated willingness to have booked (added or replaced) one of the tours during th eir last visit had they been available. Assessing to what extent these new tours would negatively affect the demand for existing t ours is important to evaluate the overall effectiveness of the program to the economy as a whole. Un fortunately, insufficient responses for the replace alternatives pr ecluded the estimation of a multinomial logit specification that would have formally analyzed the options independently3. Thus, these discrete dependent variables reflect the willingness to book one of the t ours regardless of whether the tour would be added to an itinerary or replace an identical tour operated by a traditional tour agency. Figure 4-1 shows the distribution of responses fo r the interest response variables. Interest for standard tours ( INT1 ) is normally distributed with th e majority of respondents being somewhat interested. Of the subset of those re porting some level of interest (i.e., 69% of sample), 59% of those would have been willin g to book the standard tour they constructed ( D1 ). On the other hand, the distribution of res ponses for interest on artisanal fishing ( INT2 ) was skewed to the left, with most of the respondents being not interested at all in this tour option. Of respondents had pre-purchased a set number of tours such that they could not recall pricing of individual components. 3 Of course this is only unfortunate for our analysis, but the fact that the majority of respondents stated they would add a tour instead of replace one is an encouraging result for the managers.


83 those that did have some level of interest in an artisanal fish ing tour (48%), 64% would have been willing to include the fishing excurs ion of their design in their itineraries ( D2 ). A detailed comparison of willingness to book responses with respec t to the level of interest for each type of tour is shown in Table 4-3. Due to the self selection characteristic of the willingness to book questions, the above mentioned perc entages apply to the group of vi sitors who were particularly interested in these tours and not to the overall samp le of visitors. This is especially true of the artisanal fishing tours which are hyp othesized to apply to only a s ubset of visitors. However, the characteristics of this sample were not dramatically different from those of a sample including the self excluded respon dents (Table 4-4). The explanatory variables ( Xi) were classified into four groups. The first group ( X1) represented relevant characteristics of the res pondents last trip to the site, which were hypothesized to influence demand intentions. Thes e were very similar between samples, and indicated that most visitors went to the islands for vacation as opposed to study or business, most arranged their trips exclusively with travel ag encies and stayed for six days on average. The second group of explanatory variables ( X2) included the potential benefits and shortcomings associated with fishermen-operate d tours. Most respondent s, ranging from 78% to 88% across models, considered fi sh conservation as a highly im portant benefit of the switching program; while only 37% to 40% considered the attraction of small boat amenities highly compelling. The most important concern among vis itors was the lack of experience of fishermen as tour operators, which accounted for around half the visitors across samples, and the least important concern was the lack of language ski lls as only 33% to 36% co nsidered this a highly important limitation associated to the new tours.


84 The third group of covariates ( X3) included the trip characteris tics for standard tours and artisanal fishing trips operated by fishermen that will affect whether th ey would be willing to book a tour. Most respondents pref erred standard tour trips in at least medium size boats and boating activities instead of snorkeling and diving specially (only 15% ch ose this last option), but they showed similar preferences for different trip lengths. When deciding for an artisanal fishing tour, half day trips were preferred to full day excursions ( accounted for only 28%), catching finfish using hook and lines or nets was preferred to a di ving-based lobster fishing trip (36%), and more than half respondents were comp elled by the option of eating their catch at the end of the trip. Finally, the fourth group of explanatory variables ( X4)included the demographic characteristics of the respondent that are assumed to affect both whether th ey were interested in or were willing to book either the traditional or new fishing tours offered by the ex-fishermen Across models, respondents were relatively similar by gender, geographic origin and membership to environmental and developmen t organizations. The average respondent was between 48 to 53 years old and had a relatively high household income level4 (USD$100,000USD$150,000).Most participants we re white and highly educated, as 91%of them had at least a college degree and 54% had a graduate education. Results and Discussion All models were estimated in Limdep 4. 0 using maximum likelihood estimation (MLE) techniques. Interest models were estimated us ing ordered logit specif ications and potential 4 Income categories were specified as continuous instead of dummy variables as preliminary model runs suggested a positive and increasing correlation between interests and income across binary indicators. This also retained degrees of freedom, which is important with limited sample sizes. LOGINC was considered as an explanatory variable in order to explore non-linear effects of income. Preliminary model runs, likelihoo d ratio tests and information criteria for model selection did not support the inclusion of the linear income term for any of the artisanal fishing models, and only the logged term was retained for explaining interest and demand of fishing excursions.


85 demand models, as indicated by the willingness to book any type of tour, assumed binary logit specifications. Coefficient estimates and marginal effects are discussed below for each type of tour and policy implications are di scussed at the end of the section. Interest Models Parameter estimates for the ordered multinomial logit models of the level of interest expressed for the standard and artisanal fishing tours are shown in Table 4-5. All models were superior to constant only specifi cations (p < 0.001) and statistical ly significant coefficients are indicated at the 10% level or lower. All threshold parameters ( 1, 2 and 3) were statistically significant confirming that the response categories of both models are indeed ordered. Ten out of the nineteen explanatory variables were significant determinants of intere st for standard tours, while only five out of eighteen covariates significantly explained the in terest for artisanal fishing excursions. For standard tours, of the four benefits considered in the m odel, fish stock conservation ( FISHCONS ), exposure to the local culture ( CULTURE ) and small boat experiences ( SBOAT ) explained interest level, while only one of the four concerns considered, namely the lack of affiliation with a known company ( AFFIL ), was statistically significant. Gender, age, higher education and income were also relevant factor s explaining interest for this type of tour. Expected benefits were less relevant in explaining the interest for artisanal fishing tours in comparison to standard trips, and concerns were not significant at all. Only direct support to the local community ( LSUPPORT ) influenced visitors interest for a fishing excursion. Gender, age, and geographical location also influenced interest for this type of tour, but in contrast to standard trips, education and income were not found to e xplain differences in the general interest for fishing trips.


86 As the sign and magnitude of the estimated co efficients for ordered response models are not directly interpretable, the direction and exte nt of the impact for each variable is better illustrated by their marginal effects. These effect s were computed at the means of the covariates and the marginal effects of binary variable s were calculated as the difference between the predicted probabilities when the va riable take the values of zero (i.e., reflects the base category) and one (i.e., reflects the include d category). Table 4-6 and Table 47 report the marginal effects on the probability of interest for the variables that were found signi ficant in the standard tour and artisanal fishing models. For standard tours, the vari ation in responses for the somewhat interested middle category could not be significantly explained by the variables in the model (Table 4-6). Thus, the trade-offs in probability changes are observed betw een the two lowest and highest interest levels only. Individuals very compelled by fish conservation increased th eir probability of being highly interested by 21 percentage poi nts. High interest for exposure to the local culture and in supporting operators of the smallest boats rais ed the probability of be ing highly interested (providing a 4 or 5 interest rating) by 6 and 25 percentage points, re spectively. Interest in smaller boats had the highest positive impact on the intere st for traditional tours. The lack of affiliation with a known company reduced the probability of being highly interested by 10 percentage points. Gender and age had opposite eff ects on the interest probability for standard tours. Men were about 10 percentage points more likely than women to be highl y interested in this type of tour, while a 10 year increase in average age redu ced the probability of be ing very interested by 4 percentage points. Higher levels of education and income5 also reduced the interest in standard 5 This observation refers to the full effect of income shown in Table 4-6.


87 excursions. College graduates were 19 percentage points less likely to be in the highest response categories and as average income increases on e category, the probability of being highly interested decreases by 23 percentage points. In comparison to standard tours, significant trade-offs in the interest probabilities for artisanal fishing were observed between the lowest ( INT1=1) and the three top response categories ( INT1=3,4,5) with the marginal effects evenly distributed among each of the upper categories (Table 4-7). Being hi ghly compelled by the opportunity to contribute di rectly to the local community reduced the probability of bein g not interested at a ll in fishing tours by 16 percentage points but increased the probability of being at least somewhat interested by 17 percentage points. Men and indivi duals from the southern and north central regions of the U.S. were more likely to be at least somewhat inte rested in fishing tours by 14 and 22 percentage points, respectively, as compared to women and residents of the Pacific coast. Age was the only demographic variable indicating a de crease in interest for artisanal fi shing. A ten-year increase of average age reduced the probability of being at least somewhat interested by 0.7 percentage points. Conversely, the probability of being not interested at all increased by around 0.6 percentage points. Willingness to Book Models The binary logit estimates fo r the willingness to book fisherme n-based standard tours and artisanal fishing trips are presented in Table 48. The models were significantly better than constant-only specifications (p<0.05) and corr ectly predicted 80% and 77 % of the responses, respectively. Seven of the twenty nine explan atory variables for the standard tour model significantly explained the willingness to book such a tour, while five out the twenty five covariates explained the likelihood to book an artisanal fishing excursion.


88 From the characteristics of their last trip only independent trav el arrangements had a significant effect on the intention to book standard tours. Of all benefits, concerns and specific trip characteristics considered, only a high interest for exposure to the local culture, high concern for the lack of affiliation with known tour ope rators, and preferences for multi-day operations played a role in explaining the decision to book a traditional tour. Geographical location and income were the two demographic factors influe ncing the booking intentions for this type of trips. The sign of the estimates in a binary log it model indicates the direction of the effects, suggesting that only individuals highly compelled by the loca l culture and residing in the northeastern region were more likely to book standard trips. Contrary to standard tours, booking preferences for artisanal fishing we re not significantly explained by any benefits or concerns. A combination of inde pendent and agencybased travel arrangements and the length and dining characte ristics of the trip significantly affected the demand intentions for this tour. Individuals looking for full-day trips and those interested in dining their catch at the end of the day were more likely to book a fishing excursion. Interestingly, the type of target species was not a significant factor. Gender, graduate education and income significantly influen ced individuals booking intentions for an artisanal fishing tour and, of the three, only men were more likely to book this option. As in the interest models, the extent of the effects of the significant explanatory variables on the probability of booking standard and artisana l fishing excursions is better illustrated by marginal effects. Marginal effects of significant variables for both models are presented in Table 4-9, and were calculated similarly to the effects shown in the previous section. Regarding the demand for standard tour operatio ns, visitors who arranged their last trip on their own were 30 percentage points less likely to book standard tours than those who had a


89 travel agency arrange their trip. This is not surp rising as visitors arranging their excursions are able to better discriminate among trips than t hose who had to choose from a pre-established set of options offered by travel agencies. The negative effect of the lack of known affiliation concern was higher than the positive effect of the cult ure-associated benefit on the probability of booking a standard tour (i.e., an increase of 26 percenta ge points versus a re duction of 34 percentage points). Visitors who preferred multiday excursions were 52 percentage points less likely to include a traditional tour in their itineraries. This implies that those interested in half day trips were more likely to try this tour. Residents of the northeast increased their likelihood to demand this tour by 23 percentage points in comparison to residents of the pacific coast, while having higher income dramatically reduced the booking probability of standard tours provided by fishermen by 83 percentage points6. For artisanal fishing trips, having a wider scope of tour planning resources (i.e., own planning and agency assistance) reduced the booking probability by 37 percentage points in comparison to those who arranged their trip ex clusively with travel agencies. This outcome suggests that tourists were more lik ely to choose this type of tour if offered as part of their prearranged package rather than to search specifica lly for it, especially when having more resources to design their itineraries. From the trip characte ristics, the opportunity to eat the days catch had the highest positive impact. Individuals preferring full day trips and the dining option were 31 and 35 percentage points more likely to book a fishing trip, respectively. The reduction in booking intentions was higher for individuals with graduate education than for those with higher income (43 versus 27 percentage points), while be ing male raised the probability of including a fishing excursion in their itineraries by 40 percentage points in comparison to women. 6 This observation refers to the full effect of income shown in Table 4-9.


90 These results have important planning implicatio ns. The analysis indicates that of the total number of respondents, 40% and 31% would be willi ng to add or replace a tr aditional trip with a new standard and artisanal fishing tour respectively. Considering the share of U.S. visitors to the Galapagos in 2007 (i.e.44,312 tourists) and the lim ited number of fishing boats that could be included in the tourism sector, an approximate extrapolation of the number of fishermen operated trips that are likely to be booked suggests a promising demand (17,325 and 13,427 tours approximately). Campaigns to inform tourists about the expected conservation benefits, small scale recreational attractions such as unique sm all-boat amenities and access to the local culture, as well as the legitimacy and pr ofessionalism of fishermen-based tour companies will be useful to increase U.S. tourists interest and poten tial demand for fishermen operated excursions, especially for standard tours. At the same ti me, financing and training efforts to ensure the optimal condition of small vessel infrastructure service quality and lear ning of tour-specific skills will be needed to complement advertisi ng campaigns. Marketing efforts must also address the significant differences in preferences for the new fishermen-based tours across gender, education and income, focusing on raising the interest of women, highly educated and wealthier individuals. Also, fishermen interested in providing sta ndard tours will have higher demand potential by focusing on half-day operations rather than on multiday trips. To entice the demand for artisanal fishing tours, operator s should offer a full day of activ ities and the unique experience of dining the catch at the end of the day. A full da y of activities may entail visitations to several fishing sites or different fishing targets during the day. Authorities need to consider the viability of these aspects into the normative for new tour s. Additionally, building relationships with traditional tour operators and travel agencies to negotiate the inclusion of the new standard and


91 artisanal tours as part of pre-arranged packages may prove very useful for the new providers to introduce their tours in the mark et with a level of legitimacy. Conclusions As a strategy to reduce fishing effort in th e Galapagos Marine Reserve, managers have offered small scale fishermen the opportunity to become marine tour operators in permanent exchange of their fishing permits. They will be allowed to offer standard excursions such as those currently offered by traditional operators (single-day bay and diving tours and multiday interisland and diving cruises) as well as new artisanal fishing tours where visitors will be exposed to traditional fish ing culture and practices. Using a stated preference approach, this st udy investigated tourists support for the new program of fishermen-based tour operations and identified relevant factors that explain general interest and booking intentions (i .e., demand) for two types of t our excursions: standard torus and artisanal fishing trips. Similarly to the fi ndings of Alban and Boncoe ur (2004), the potential demand for fishermen-based excursions is promisi ng, especially for standard tours. Conservation benefits, small scale tour attractions and affilia tion concerns associated to the inclusion of fishermen into tourism, trip length, dining attr actions, type of trip arrangement, as well as individual characteristics such as gender, educat ion and income were the most important factors explaining tourists preferences. The scope of these findings is limited to the U.S market in general and past visitors in particular. Visitors from other countries are likely to have di fferent preferences although its reasonable to expect that there would be some po sitive level of demand such that the projections in this paper are conservative. Also, this study us ed simple main effects to explore the role of individual characteristics on tour interest a nd demand and additional research is needed to identify relevant market niches in order to better target marketing efforts. The next step of this


92 analysis is to identify clusters of visitors a nd incorporate that information into the modeling of interests and booking intentions. Additionally, gi ven the self-selection characteristic of the willingness to book questions, the modeling could be extended to consider sample selection issues to identify possible biases in the resu lts. Despite the mentioned imitations, this study provides valuable insights for sm all-scale fisheries managers about the potential market support for diversification of fishermen into tourism and about relevant determinants of potential demand for the new fishermen-based tour operations that may help planning similar fishing diversification programs in other artisanal fishing communities.


93 Table 4-1. Variable description Variable Description Response variables: INT1 Interest rating for standard tours (1= not to 5=very) INT2 Interest rating for artisanal fishing tours (1= not to 5= very) D1 1 if willing to book standa rd tours, 0 otherwise D2 1 if willing to book artisanal fishing, 0 otherwise Last visit (X1): VACATION 1 if purpose of visit wa s vacation, 0 otherwise ARRANGEI 1 if trip arranged indepe ndently only, 0 otherwise ARRANGEIA 1 if trip arranged independently and with agency, 0 otherwise LENGTH Length of trip (days) Benefits and Concerns (X2): FISHCONS 1 if highly compelled by fish conservation, 0 otherwise LSUPPORT 1 if highly compelled by lo cal support, 0 otherwise CULTURE 1 if highly compelled by lo cal culture, 0 otherwise SBOAT 1 if highly compelled by small boats, 0 otherwise EXP 1 if highly concerned with lim ited experience, 0 otherwise LANG 1 if highly concerned with language skills, 0 otherwise AFFIL 1 if highly concerned by lack of known affiliation, 0 otherwise VSIZE 1 if highly concerned with small vessel amenities, 0 otherwise Trip characteristics (X3): BMED 1 if medium boat size, 0 otherwise BLARGE 1 if large boat size, 0 otherwise FULLD 1 if full day trip, 0 otherwise MULTD 1 if multiday trip, 0 otherwise SNKL 1 if snorkel, 0 otherwise BTOUR 1 if boat tour, 0 otherwise LOBSTER 1 if lobster target, 0 otherwise DINING 1 if dining option, 0 otherwise Demographics (X4): MEMBER 1 if environmental or developm ent organization, 0 otherwise MALE 1 if men, 0 otherwise AGE Visitor's age ( years) SOUTH 1 if resides in Southwest or Southeast, 0 otherwise NCENTRAL 1 if resides in Great Lakes and Mountain Prairie, 0 otherwise NEAST 1 if resides in Northeast, 0 otherwise COLLEGE 1 if college graduate, 0 otherwise GRADS 1 if graduate school, 0 otherwise


94 Table 4-1. Continued INC Household income levels:1=less than USD$50,000; 2=USD$50,000USD$ 99,000; 3=USD$100,000-USD$149,000; 4=USD$150,000USD$199,000; 5=USD$200,000-USD$499,000; 6=more than USD$500,000 LOGINC Natural logarithm of income level WHITE 1 if white race, 0 otherwise Dummy base categories: study/othe r visit purpose, trav el agency exclusive arrangement, small boat, half day trip, diving, finfish target, no di ning, Pacific coast residence, less than college degree.


95Table 4-2. Summary statistics Standard tours Artisanal fishing Interest (N = 218) Demand (N = 196) Interest (N = 220) Demand (N = 100) Variable Mean Std dev. Mean Std dev. Mean Std dev. Mean Std dev. INT1 2.954 1.202 N/A N/A N/A N/A N/A N/A D1 N/A N/A 0.587 0.494 N/A N/A N/A N/A INT2 N/A N/A N/A N/A 2.395 1.365 N/A N/A D2 N/A N/A N/A N/A N/A N/A 0.640 0.482 Last visit (X1): VACATION N/A N/A 0.918 0.274 N/A N/A 0.910 0.287 ARRANGEI N/A N/A 0.163 0.370 N/A N/A 0.170 0.377 ARRANGEIA N/A N/A 0.102 0.303 N/A N/A 0.140 0.348 LENGTH N/A N/A 6.535 1.662 N/A N/A 6.420 1.706 Benefits and Concerns (X2): FISHCONS 0.775 0.418 0.806 0.396 0.782 0.414 0.880 0.326 LSUPPORT 0.660 0.474 0.668 0.472 0.664 0.473 0.780 0.416 CULTURE 0.693 0.462 0.688 0.464 0.690 0.463 0.800 0.402 SBOAT 0.404 0.492 0.387 0.488 0.400 0.491 0.370 0.485 EXP 0.532 0.500 0.500 0.501 0.536 0.499 0.530 0.502 LANG 0.357 0.480 0.331 0.472 0.364 0.482 0.330 0.472 AFFIL 0.417 0.494 0.403 0.492 0.423 0.495 0.400 0.492 VSIZE 0.408 0.492 0.408 0.493 0.409 0.492 0.410 0.494 Trip characteristics (X3): BMED N/A N/A 0.469 0.500 N/A N/A N/A N/A BLARGE N/A N/A 0.438 0.497 N/A N/A N/A N/A FULLD N/A N/A 0.326 0.470 N/A N/A 0.280 0.451 MULTD N/A N/A 0.357 0.480 N/A N/A N/A N/A SNKL N/A N/A 0.367 0.483 N/A N/A N/A N/A BTOUR N/A N/A 0.495 0.501 N/A N/A N/A N/A LOBSTER N/A N/A N/A N/A N/A N/A 0.360 0.482


96Table 4-2. Continued DINING N/A N/A N/A N/A N/A N/A 0.530 0.502 Demographics (X4): MEMBER 0.472 0.500 0.459 0.499 0.468 0.500 0.500 0.502 MALE 0.504 0.501 0.500 0.501 0.504 0.501 0.480 0.502 AGE 53.300 15.740 51.830 15.390 53.540 15.620 48.280 16.240 SOUTH 0.303 0.460 0.311 0.464 0.300 0.459 0.370 0.485 NCENTRAL 0.206 0.405 0.194 0.396 0.204 0.404 0.210 0.409 NEAST 0.234 0.424 0.229 0.422 0.240 0.428 0.180 0.386 COLLEGE 0.376 0.485 0.372 0.485 0.373 0.485 0.370 0.485 GRADS 0.536 0.499 0.540 0.499 0.540 0.499 0.540 0.500 INC 3.261 1.430 N/A N/A 3.277 1.430 N/A N/A LOGINC 1.069 0.503 1.069 0.513 1.074 0.503 1.051 0.539 WHITE 0.922 0.268 0.923 0.266 0.927 0.260 0.900 0.301 N/A indicates the variable was not applicable to the model.


97 Table 4-3. Willingness to book responses by interest level Standard tour Artisanal fishing Interest Add Replace No Add Replace No 1 4 2 20 1 0 15 2 5 4 23 5 2 7 3 28 12 30 13 9 11 4 27 14 3 15 4 3 5 14 4 3 12 3 0


98 Table 4-4. Comparison of covari ates based on participant self selection for booking an artisanal fishing tour Final sample (N=100) Not based on self selection (N=207) Variable Mean Std. Dev Mean Std. dev VACATION 0.910 0.287 0.928 0.259 ARRANGEI 0.170 0.377 0.164 0.371 ARRANGEIA 0.140 0.348 0.101 0.303 LENGTH 6.420 1.706 6.522 1.639 FISHCONS 0.880 0.326 0.783 0.413 LSUPPORT 0.780 0.416 0.657 0.476 CULTURE 0.800 0.402 0.700 0.459 SBOAT 0.370 0.485 0.386 0.488 EXP 0.530 0.502 0.536 0.499 LANG 0.330 0.472 0.357 0.480 AFFIL 0.400 0.492 0.402 0.495 VSIZE 0.410 0.494 0.406 0.492 FULLD 0.280 0.451 0.324 0.469 LOBSTER 0.360 0.482 0.346 0.478 DINING 0.530 0.502 0.479 0.501 MEMBER 0.500 0.502 0.464 0.499 MALE 0.480 0.502 0.502 0.501 AGE 48.280 16.24 52.899 15.631 SOUTH 0.370 0.485 0.309 0.463 NCENTRAL 0.210 0.409 0.198 0.399 NEAST 0.180 0.386 0.237 0.426 COLLEGE 0.370 0.485 0.367 0.483 GRADS 0.540 0.500 0.546 0.499 INC N/A N/A N/A N/A LOGINC 1.051 0.539 1.078 0.508 WHITE 0.900 0.301 0.923 0.267


99 Table 4-5. Ordered logit es timates for tour interest Standard tours Artisanal fishing Variable Estimate t-value Estimate t-value Constant 2.781 3.308*** 0.795 1.001 FISHCONS 1.389 3.779*** 0.171 0.461 LSUPPORT 0.370 1.012 0.679 1.905* CULTURE 0.715 2.031** 0.148 0.428 SBOAT 1.328 4.450*** 0.343 1.191 EXP -0.474 -1.571 0.055 0.181 LANG 0.055 0.162 0.073 0.218 AFFIL -0.590 -1.950* -.354 -1.168 VSIZE 0.217 0.710 0.322 1.031 MEMBER 0.063 0.233 -0.153 -0.572 MALE 0.573 2.084** 0.897 3.270*** AGE -0.024 -2.676*** -0.027 -2.960*** SOUTH -0.102 -0.281 0.602 1.668* NCENTRAL 0.445 1.199 0.819 2.160** NEAST 0.113 0.303 0.205 0.557 COLLEGE -1.339 -2.653*** -0.587 -1.171 GRADS -1.030 -2.069** -0.288 -0.576 INC 0.587 1.662* N/A N/A LOGINC -1.835 -1.829* -0.320 -1.162 WHITE -0.397 -0.783 0.201 0.392 u1 1.359 8.526*** 0.565 5.908*** u2 3.482 19.710*** 1.850 11.785*** u3 5.325 21.328*** 3.029 12.738*** LogL -275.80 -302.85 LogL0 -333.69 -321.25 p-value <0.001 0.005 N 218 220 Significant at 10%; ** Significan t at 5%; *** Significant at 1%.

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100 Table 4-6. Effects of significant variables on the interest probability for standard tours Standard tours Variable 1 2 3 4 5 FISHCONS -0.149a -0.156c 0.093a 0.162c 0.050c LSUPPORT -0.030 -0.044 0.006 0.051 0.017 CULTURE -0.062a -0.084b 0.022 0.094b 0.031b SBOAT -0.096c -0.144c -0.022 0.187c 0.075c AFFIL 0.048a 0.069b -0.008 -0.081a -0.027a MALE -0.044b -0.066b 0.003 0.080b 0.028a AGE 0.002b 0.003c 0.000 -0.003b -0.001b SOUTH 0.008 0.012 -0.001 -0.014 -0.005 NCENTRA -0.031 -0.050 -0.008 0.065 0.024 COLLEGE 0.122b 0.152c -0.044 -0.172c -0.058b GRADS 0.079b 0.116b 0.001 -0.144b -0.053a INC -0.045a -0.069a 0.003 0.082a 0.028a LOGINC 0.141a 0.215a -0.010 -0.257a -0.088a INC* 0.096 0.146 -0.007 -0.175 -0.060 a Significant at 10%; b Significant at 5%; c Significant at 1% INC* denotes the full effect of income. It was calc ulated as the sum of the individual marginal effects of INC and LOGINC. This sum is equivalent to calculating the full marginal effect as the product f (X* ) ( INC + LOGINC) where f represents the probability density function of the logistic distribution, X* is the vector of mean values of explanatory variables, is the complete set of estimated coefficients, INC is the estimated coefficient on linear income and LOGINC is the coefficient on the log of income (Borooah 2002). Table 4-7. Effects of significant variables on the interest probability for fishing tours Artisanal fishing Variable 1 2 3 4 5 FISHCONS -0.041 -0.002 0.016 0.016 0.011 LSUPPORT -0.163a -0.003 0.065a 0.060a 0.042a CULTURE -0.035 -0.002 0.014 0.014 0.010 SBOAT -0.081 -0.005 0.029 0.033 0.024 AFFIL 0.084 0.004 -0.032 -0.033 -0.023 MALE -0.210c -0.011 0.076c 0.083c 0.061c AGE 0.006c 0.000 -0.002c -0.003c -0.002c SOUTH -0.137a -0.012 0.046a 0.059 0.045 NCENTRA -0.179b -0.022 0.051c 0.082b 0.068a COLLEGE 0.140 0.004 -0.055 -0.053 -0.037 GRADS 0.068 0.004 -0.025 -0.027 -0.019 LOGINC 0.076 0.004 -0.029 -0.030 -0.021 a Significant at 10%; b Significant at 5%; c Significant at 1%.

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101 Table 4-8. Binary logit estimat es for willingness to book a tour Standard tours Artisanal fishing Variable Estimate t-value Estimate t-value Constant 1.940 0.954 3.753 1.211 VACATION -0.465 -0.595 -1.622 -1.269 ARRANGEI -1.224 -2.032** -1.247 -1.294 ARRANGEIA -0.962 -1.207 -1.570 -1.689* LENGTH 0.160 1.120 -0.182 -0.944 FISHCONS 0.558 0.916 0.462 0.405 LSUPPORT 0.552 0.979 0.708 0.727 CULTURE 1.101 2049** 0.161 0.172 SBOAT 0.479 1.015 0.702 1.034 EXP 0.464 0.911 -0.237 -0.335 LANG -0.632 -1.213 -0.467 -0.619 AFFIL -1.492 -3.147*** 0.562 0.806 VSIZE -0.046 -0.095 0.217 0.273 BMED -0.352 -0.511 N/A N/A BLARGE -0.315 -0.430 N/A N/A SNKL -0.820 -1.276 N/A N/A BTOUR -0.245 -0.391 N/A N/A MULTD -2.358 -3.650*** N/A N/A FULLD -0.299 -0.549 1.818 2.192** LOBSTER N/A N/A -0.736 -1.026 DINING N/A N/A 1.727 2.534** MEMBER -0.2126 -0.497 0.449 0.716 MALE -0.137 -0.318 2.044 2.804*** AGE 0.004 0.258 0.018 0.759 SOUTH 0.614 1.108 0.076 0.103 NCENTRAL 0.477 0.819 -0.916 -0.947 NEAST 1.102 1.906* -0.214 -0.217 COLLEGE -1.399 -1.365 -1.270 -1.650 GRADS -1.555 -1.524 -1.324 -1.906* INC 2.039 3.303*** N/A N/A LOGINC -5.640 -3.101*** -1.324 -1.906* WHITE -1.137 -1.241 -1.283 -1.073 LogL -89.06 -44.49 p-value <0.001 0.019 % correct predictions 80.10% 77.00% N 196 100 Significant at 10%; ** Significan t at 5%; *** Significant at 1%.

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102 Table 4-9. Effects on the pr obability of booking a tour Variable Standard tours Artisanal fishing ARRANGEI -0.295** -0.288 ARRANGEIA -0.234 -0.366* CULTURE 0.260** 0.034 AFFIL -0.343*** 0.114 MULTD -0.525*** N/A FULLD -0.069 0.310*** DINING N/A 0.355*** MALE -0.032 0.402*** NEAST 0.227** -0.046 GRADS -0.340 -0.434* INC 0.470*** N/A LOGINC -1.303*** -0.276* INC* 0.833 N/A Significant at 10%; ** Significan t at 5%; *** Significant at 1%. INC* denotes the full effect of income on intere st probabilities for standard tours. It was calculated as the sum of the individual margin al effects of INC and LOGINC. This sum is equivalent to calculating the full marginal effect as the product f (X* ) ( INC + LOGINC) where f represents the probabi lity density function of the logistic di stribution, X* is the vector of mean values of explanatory variables, is the complete set of estimated coefficients, INC is the estimated coefficient on linear income and LOGINC is the coefficient on the log of income (Borooah 2002).

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103 0 10 20 30 40 50 60 70 80 90 12345 INT1FrequencyA 0 10 20 30 40 50 60 70 80 90 12345 INT2FrequencyB Figure 4-1. Distribution of intere st responses. (A) Interest for st andard tours. (B) Interest for artisanal fishing tours. N =218 N =220

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104 CHAPTER 5 CONCLUSIONS This study presents a socio-economic analysis of the potential of tran sferring fishing effort (i.e., vessels and fishermen) to th e tourism industry as a successful strategy to rebuild small scale fisheries illustrating the case of the Galapagos Marine Reserve. The evaluation combined three different areas: individual fishing preferences fleet production and efficiency and tourists interest and potential demand. The main findings are summarized below. Fishermen were almost as likely to stay as to exit, and a considerable number of individuals were undecided. Vessel owners we re more likely to switch to tourism than crew and both groups had different sensitivi ties to changes in demographics and fishing related factors. Preferences also differed between geographical lo cations. Fishermen from Santa Cruz, the tourism hub for the Archipelago, were more enticed to switch than other individuals. These findings indi cate the need to devise diversification strategies that differentiate between vessel owne rs and crew and the specific realities of the fishing ports. Participants in high value but depleted fisheries (higher oppo rtunity costs) or who dived were also more likely to exit. Higher availa bility of income (funding) also encouraged fishers to switch. Capital malleability issu es were compensated by the opportunity to transfer fishing capital investme nts outside the fishi ng sector. This particular feature of the diversification strategy provide d vessel owners with stronger incentives to exit the fisheries, in contrast to prev ious findings in the literature suggesting a negative effect of ownership on exiting decisions. From the existing standard tour operations, fish ermen were more likely to engage in bay and diving tours (singleday) followed by the diving cruise op tion. Mostly fishermen from Isabela were interested in standard cruise operations. Individuals pr eferred bay and diving tours if they own small vessels, had concerns about safety implications, were from San Cristobal or had access to bank funding. Indivi duals willing to make great investments preferred diving cruises to standard cruise operations. Vessel lobster catch depended mostly on the leve l of variable inputs us ed, especially days at sea, and not on the fleets capital. Also, th e fleet shows a considerab le level of technical inefficiency (around 35%), which is the prev alent factor influencing per-trip harvest variation. Technical efficiency differed be tween geographical locat ions, and it could be increased by the use of more sophistic ated diving methods and gear types. Interrelations between vessels and fishermen pl ayed a role on fishermen decisions and the production structure of the small scale fishi ng fleet. Results suggested that linkages between owners and crew in the same fishing vessel influenced their choices for diving cruise operations in the local tourism industry. Also, the pe r-trip harvesting potential and technical efficiency of individua l vessels was limited when they participated in mothership

PAGE 105

105 operations (i.e., composite fishing units) in co mparison to independent boats. This limiting effect was higher for fiberglass than for wooden vessels. Results suggested a promising market pot ential for fishermen-based ecotourism considering the limited number of fishing vesse ls in the Islands in contrast to the number of visitors per year. Standard tours ha d a higher interest and demand potential in comparison to artisanal fishing trips. Conservation benefits, exposure to the local culture and specially preference for small boats increa sed the general interest for standard tours, while the direct contribution to the local community made fishing trips more appealing. Only the lack of affiliation with a know n company had a negative effect on tourist preferences, specifically for standard tours. The demand potential for artisanal fishing trips was higher for full-day fishing with a dining option, and standard tours had higher booking intentions if half-day excursions are offered. High income individuals were more reluctant to book any type of tours offered by fishermen. These findings have important implications about the success of the switching program. Fishermen and visitors are willing to engage in st andard tours, especially in half day operations (e.g., bay and diving tours). From the supply and demand perspective, this suggests a promising potential for the program as a viable economic alternative for fishermen outside the fishing sector to the extent that they are allowed to participate in standard tour market by the management authority. However, the level of interest from tourists still needs to be increased to realize a full potential. Management authorit ies or fishermen groups need to develop informational campaigns or advertising for actual and potential visitors about the expected fish conservation and local community benefits related to the transition of fishermen into tourism in order to increase the appealing of the plan. In this sense, the promotion of artisanal fishing trips needs special attention as the activity is comple tely new, visitors are more unfamiliar with its characteristics and it still has limited potential. Efforts to create a legitimate image of fishermen as tour providers are also critical, and these may include the formal organization of the new providers in companies or associations, as well as service quality a nd technical training. Partnerships or collaborations w ith local restaurants to offer the unique option of dining the catch and with travel agencies to make fisher men-based tours known are also needed.

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106 From a production perspective, the program has a more limited scope for controlling the harvesting potential of the fleet in the lobster fi shery. As the results suggest, per-trip harvests depend mostly on variable inputs, participation in mothership structures an d technical efficiency. Since the program is likely to be more successf ul among owners of larger boats, its impact is expected to be higher on the capitalization of the motherboat segment than on the other vessel groups. Provided that fish stocks and other produc tion factors remain stable in the short run, a reduction in the number of moth er ships will also reduce the harvest limitations observed among towed vessels in comparison to independent boats actually improving harv est rates per day of trip. In the long run, and in the absence of altern ative strategies to rebu ild resource populations, stocks will be reduced. In addi tion, the catch potential of the remaining fishing units may also increase if technical efficiency is improved. In order to restore fishery stocks in the GM R, additional policies controlling input use and technical efficiency are needed (e .g., limiting the number of days at sea per trip or restricting the use of mechanized gear and divi ng methods). Contrary to agricultu re, increasing the efficiency of fishing fleets is not a desirable outcome give n the problem of open access in fisheries and declining stocks. On the other hand, inefficien t fishing vessels are not a desirable economic outcome either. In the long run, managers will need to direct their attention to alternative management schemes, such as rights based polic ies. This regime will help control the race for catch while promoting an efficient allocation of fi shing effort in order to achieve more promising outcomes in fisheries conservation. This study contributes to the fi sheries management literature in several ways. First, as emphasized by the modern approach to artisan al fisheries management focused on poverty reduction and livelihood improvement, management policies must include the generation of

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107 alternative employment opportunitie s while promoting reduction of harvesting effort. This study suggests a promising potential for fishermen and fi shing vessels diversifi cation into ecotourism as an alternative livelihood strate gy, but it also illustrates the possi ble limitations of such plan as a mechanism to reduce the producti on capacity of artisanal fishi ng fleets when catch is not dependent on vessel capital. It al so identifies factors that can be useful to the design of similar programs in other artisanal fishing communities. Second, this analysis contributes to the understanding of artisanal fishermen incentives to stay or exit when faced by the opportunity to switch into alternative occupations. Knowledge of this behavior is critical to design better informed fishing management strategies. Las tly, the study demonstrated the relevance of explicitly considering the unique characteristics of artisanal fishing systems, namely vessel interrelations, into the assessment of fleet harvesting potential. This is particularly important for the design of more appropriate capacity manageme nt programs targeted to small scale fishing fleets, which consider the socioeconomic ties influencing the operations of this sector. The conclusions of this study are subject to some limitations. Limited linkage information for all the fishermen in the sample precluded a rich er analysis of the role of interdependence in fishermens switching (exiting) decisions and choice of tour operations. Hence, those results should be interpreted only as preliminary indicat ors of relevance of individual linkages rather than as exact effects. Similarl y, results about linkage effects on the production technology of the fleet are dependent on observati ons of responses regarding vesse ls towing status. Also, findings about input elastic ities and returns to scale are assumed to be the same for all firms regardless of the level of input use as a flex ible functional form of technology could not be analyzed given high collinearity issues in the data. Results about tourists preferences ar e limited to the market of U.S visitors. Therefore, the demand potential suggested by the analysis may be considered as

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108 conservative as it is reasonable to expect some interest for fishermen-operated tours from other visitor markets. Lastly, the an alysis of potential demand is dependent on the assumption of complete independence between interest and de mand preferences for fishermen-based tours. The next steps to address some of the above mentioned limitations in clude the exploration of alternative measures of interdependence am ong fishermen for switching preferences analysis. To the possible extent allowed by the survey data a reduced sample with only vessel owners and crew members from the same fishing units (i.e ., vessels, motherships) may be defined and new indicators will reflect a relations hip with a yes response from another individual in the same production unit .Also, this study wi ll be extended by examining samp le selection implications on the modeling of interdependence effects on harv esting technology and on t ourists preferences. Finally, further research that addresses (1) the ro le of technical efficien cy on exiting behavior, (2) fishermen incentives to stay or exit fishing regarding other diversif ication opportunities, (3) clear market niches for fishermen-based tours, and (4) the demand potential for fishermen-based ecotourism in other markets will be very useful to complement the contributions of the present study to the understanding of ar tisanal fishing and the improvement of its management.

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109 APPENDIX A FACE TO FACE SURVEY INSTRUMENT FOR FISHERMEN EXITING BEHAVIOR IN SPANISH PARTE 1 : El primer grupo de preguntas que le har se relacionan con su inters y preocupaciones como armador pesquero respecto al cambio de actividad de pesca hacia turismo que seria posible a travs del programa de concesin de nuevo s cupos de operacin turstica. Q1. Est interesado en cambiar su cupo de pe sca por un nuevo cupo de operacin turstica en la Reserva Marina de Galpagos? Si respuesta es SI: Contine Si respuesta es NO, NO SE, NO RESPONDE: Pase a la pregunta Q2. Q1.A En que modalidad turstica preferira participar? Escoja SOLO UNA opcion por favor. Cualquier respuesta: Contine. Q1.B De las siguientes actividades que le mostrare, por favor seleccione TODAS las que usted ofrecera en su negocio turstico: Cualquier respuesta: Contine. Q1.C Cuenta con la embarcacin y equipos ADECUADOS para iniciar la operacin de esta modalidad turstica? Cualquier respuesta: Contine. Encuestador ENCIERRE solo UNA respuesta (1) Si (0) No (8) NS/talvez (9) NR Encuestador ENCIERRE solo UNA respuesta: (1) baha (2) baha y buceo con puertos (3) baha y buceo sin puertos (4) Puerto a puerto (5) Buceo navegable (8) NS/ Inseguro ( 9 ) N R ( 77 ) Otros Encuestador ENCIERRE TODAS las que apliquen: (1) Snorkel (5) Surf (2) Paseo panga (6) Kayak (3) Caminatas (8) NS (4) Buceo (9) NR Encuestador ENCIERRE solo UNA respuesta (1) Si (0) No (9) NR

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110 Q1.D Su negocio sera SOLO propio o en sociedad? Por favor escoja SOLO UNA opcin. Cualquier respuesta: Contine. Q1.E Cunto dinero aproximadamente necesit a invertir para cambiarse a la actividad turstica?: Cualquier respuesta: Contine. Q1.F Por favor responda Si No, NR a la s siguientes preguntas. Para financiar el cambio de actividad, ud. contara con: a. Con prstamo bancario? b. Con inversin de so cio-operador turstico? c. Con inversin de socioarmador pesquero? d. Con prstamos de familia o amigos? e. Con prstamos de chulqueros? f. Con otra fuente de recursos?_____________ Cualquier respuesta: Contine. Q1.G. Le interesara alquilar su cupo de operacin turstica? Cualquier respuesta: Contine. Q1.H Cuenta con los conocimientos administra tivos para manejar su negocio turstico? Cualquier respuesta: Contine. Encuestador ENCIERRE solo UNA: (1) Si (0) No (8) NS/talvez (9) NR Encuestador ENCIERRE solo UNA opcin: (1) Menos de $10,000 (2) $10,001-$50,000 (3) $50,001-$100,000 (4) $100,001-$200,000 (5) Mas de $200,000 ( 8 ) NS ( 9 ) N R Encuestador ENCIERRE solo UNA respuesta (1) Propio (2) Sociedad (8) NS (9) NR Encuestador ENCIERRE solo UNA: (1) Si (0) No (8) NS/inseguro (9) NR

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111 Q2. Le mencionare 6 aspectos relacionados con el cambio de actividad de pesca a turismo. Por favor dgame que tan preocupado esta ud. por cada aspecto usando una escala del 1 al 3 donde 1 representa NADA PREOCUPADO, 2 significa UN POCO PREOCUPADO y 3 MUY PREOCUPADO. Nada Poco Mucho NS NR Que tan preocupado esta ud 1 2 3 8 9 A) Por el costo de cambiarse al turismo? 1 2 3 8 9 B) Por la cantidad de nuevos cupos a otorgarse? 1 2 3 8 9 C) Por las medidas de seguridad requeridas? 1 2 3 8 9 D) Por el compromiso con horarios fijos? 1 2 3 8 9 E) Por encontrar tripulacin apta para su barco? 1 2 3 8 9 F) Por la necesidad de un traductor de idiomas? 1 2 3 8 9 G) Otra preocupacin? ________________________ Cualquier respuesta: Contine PARTE DOS : Ahora le har unas preguntas sobre su experiencia en el sector pesquero, la necesidad por otras fuentes de empleo y c ondiciones socioeconmicas en general. Q3. Por cuantos aos ha estado involucrado en el sector pesquero de Galpagos? Cualquier respuesta: Contine Q4. Durante el ltimo ao, ha trabajado uste d como patrn o capitn de alguna embarcacin pesquera? Si respuesta es SI: Contine. Si respuesta NO o NO RESPONDE: pase a la pregunta Q5 Q4.A Cuantos aos de experiencia tiene como capitn? Cualquier respuesta: Contine Q4.B Alguna de las embarcaciones pesqueras en las que ha sido capitn tiene sistema de identificacin de coor denadas geogrficas? Cualquier respuesta: Contine Encuestador ESCRIBA NUMERO o NR si no responde: ______ aos Encuestador ENCIERRE respuesta: (1) Si (0) No (9) NR Encuestador ESCRIBA NMERO o NR si no responde: _______ aos Encuestador ENCIERRE respuesta: (1) Si (0) No (8) NS (9) NR

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112 Q4.C. Tomando en cuenta las pesqueras en la s que ud. ha participado durante el ultimo ao, aproximadamente cuantas empresas del co ntinente le compraron su captura en el 2007? Cualquier respuesta: Contine Q4.D Ha obtenido un precio ms alto por los animales ms grandes de la captura de cualquier pesquera? Cualquier respuesta: Contine Q5. Durante los ltimos 12 meses, ha trabajado usted como tripulante de alguna embarcacin pesquera? Cualquier respuesta: Contine Q6. Durante los ltimos 12 meses, ha trabajado usted como buzo? Si respuesta es SI contine. Si respuesta NO, NO RESPONDE pase a la pregunta Q7 Q6.A Que mtodo de buceo prefie re: con hookah o buceo libre? Cualquier respuesta: Contine Q7. En cualquiera de los TRES ltimos aos, ha participado ud. en: 1 0 9 a. Pesquera de langosta? Si No NR b. Pesquera de pepino? Si No NR c. Pesca blanca? Si No NR d. Pesca de altura? Si No NR Cualquier respuesta: Contine ESCRIBA NMERO incluido 0 si respuesta es ninguna o NR si no responde: _____ empresas Encuestador ENCIERRE respuesta: (1) Si (0) No (9) NR Encuestador ENCIERRE respuesta: (1) Si (0) No (9) NR Encuestador ENCIERRE respuesta: (1) Si (0) No (9) NR Encuestador ENCIERRE respuesta: (1) Hookah (2) Libre (9) NR

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113 Q8. Trabaja en otras actividade s adems de la pesca? Si la respuesta es SI: Contine. Si respuesta NO, NO RESPONDE pase a la pregunta Q9 Q8.A Le mostrare una lista de otros sectores econmicos. Por favor dgame en cuales de ellos ud. tambin trabaja. Cualquier respuesta: Contine Q8.B Que le genera mayor ingreso: la pesca o sus otras actividades? Cualquier respuesta: Contine PARTE TRES : Las siguientes preguntas que le har tr atan sobre sus caract ersticas sociales generales: Q9. En que ao naci? Cualquier respuesta: Contine Q10. Termin la escuela primaria? Cualquier respuesta: Contine Q11. Es ud. originario de Galpa gos o de Ecuador continental? Cualquier respuesta: Continu Encuestador ENCIERRE respuesta: (1) Si (0) No (9) NR Encuestador ENCIERRE solo UNA: (1) Pesca (2) Otra (9) NR Encuestador ENCIERRE TODAS las que digan: (1) Turismo (5) Transporte (2) Agricultura (6) ONGs (3) Comercio (77) Otro ___________ (4) Empleo de gobierno (9) NR ESCRIBA ANO (ej: 1954) o NR si no responde: Ao ______ Encuestador ENCIERRE respuesta: (1) Si (0) No (9) NR Encuestador ENCIERRE solo UNA respuesta: (1) Galpagos (2) Continente (9) NR (77) Otro __________

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114 Q12. Cuantas personas viven en su casa, incluido ud.? Cualquier respuesta: Contine Q13. De aquellos que viven con ud., cuan tas personas son menor es de 12 aos? Cualquier respuesta: Contine Q14. Adems de usted, cuantos miembros de su casa ayudan a mantener el hogar? Cualquier respuesta: Contine Q15. Cual de las siguientes categoras qu e le mostrare representa el ingreso mensual aproximado de su hogar proveniente de todas las actividades laborales en su casa durante el 2007?: Esto termina nuestra conversacin. Muchas gr acias por su tiempo y participacin. Tiene preguntas adicionales sobre esta encuesta o s ugerencias sobre los nuevos cupos de turismo u otras estrategias que la autori dad debera considerar para promover una pesca sostenible? ESCRIBA NMERO o NR si no responde: ______ personas ESCRIBA NMERO (incluido 0 =ninguno) o NR si no responde: ____ personas ESCRIBA NMERO (incluido 0 =ninguno) o NR si no res p onde: _____ p ersonas Encuestador ENCIERRE solo UNA opcin: (1) Menor $500 (4) $1501-$2000 (2) $501-$1000 (5) Mayor $2000 (3) $1001-$1500 (9) NR

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115 APPENDIX B ONLINE SURVEY INSTRUMENT OF PREF ERENCES OF U.S. VISITORS FOR FISHERMEN-OPERATED TOURS Section I: Your last Galapagos visit Q1. In your opinion, what was the quality of th e environment in the Ga lapagos Islands at the time of your visit? Very poor (conservation efforts are not working) 1 Poor 2 Okay 3 Good 4 Very good (conservation efforts are working well) 5 Q2. How important was it for you to visit the Galapagos Islands someday in your life? Not at all 1 2 Somewhat 3 4 Very 5 Q3. Which of the following sources of informa tion did you use to learn about the Galapagos prior to your visit? [Please check all that you remember using] a) Ecuadorian Ministry of Tourism webpage b) Galapagos Chamber of Tourism webpage c) Charles Darwin Research Station webpage d) Galapagos National Park Service webpage e) Ecotourism webpage f) Environmental organization webpage g) Specialized travel books/pamphlets/magazines h) AAA i) Past visitors

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116 Q4a. Were the Galapagos Islands the main destination of this trip? Yes No Q4b. What was the main purpose of this visit to the Galapagos Islands? Select one... Q5. In what year was this trip? Select one... Q6. Including you, how many people traveled in your party? Select one...persons Q7. How long did you stay in the Islands? Select one...days Q8. How did you arrange this visit to the Galapagos Islands? Select one... Q9. How did you arrive in the Galapagos Islands? Select one... Q10. What recreational activities did you do durin g this visit? [check all that apply] a) Snorkeling b) Surfing c) Camping d) Scuba diving e) Kayaking f) Horse riding g) Mountain biking h) Trekking/hiking i) Flora/wildlife watching j) Shopping k) Boat tour of islands l) Other Q11. Please complete the table about any excursi ons (either land or wate r-based) you might have taken on this trip. Length Number taken Average cost per person per trip Half-day Select one... USD$ Full-day Select one... USD$ Multi-day Select one... USD$

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117 Section IIA: Park entry fee The Galapagos National Park (GNP) was establis hed in 1959 and includes over 1.7 million acres of the Galapagos Islands or 90% of the total land area. The GNP collects an entry fee from every visitor. The current base fee is USD$100 for a dult foreign tourists. This money goes to the Ecuadorian Government but USD$40 (40%) is re turned to the park for operating expenses. Management of the Galapagos Marine Reserve (GMR) began when it was added to the GNP in 1988. The GMR extended 40 nautical miles from the Islands for an area of nearly 33 million acres; it is the largest marine re serve in a developing country and the second largest in the world. The Ecuadorian Government returns USD$5 (5 %) from each visitor fee to the GMR. Q12a. How did you pay your entry fee on your last trip? Separately Included in package Dont recall Small scale commercial fishing also known as arti sanal fishing since the monies earned stay in the local community and support the sustainability of locals is the second most important economic activity in the GMR after tourism. Howe ver, stocks of the main commercial fisheries (i.e., sea cucumber and spiny lobster) have dramatic ally declined during the past few years due to overharvesting to supply strong demand in overseas markets. To rebuild the populations, fisheries, and ensure sustainability of local co mmunities (as fishing revenue stays in the local economy), the management authority (i.e., Gal apagos National Park Service) plans to permanently exchange fishing licenses for touris m permits, which are tightly controlled. The fishermen selected as new tour operators will be authorized to operate tours similar to those currently offered. They will be subject to the same training and procedural regulations as for traditional tour operators. Likewise, the fish ing boats will have to be appropriately equipped before obtaining final authorization and will be subject to the same itinerary and safety policies. As standard practice in the industry, these boa ts will also have a lic ensed naturalist guide on board. Q12b. If an Artisanal Fishermans Trust Fund were established to s upport the rebuilding of stocks (e.g., cost to determine sustainable yields and increased enforcement) and the transition of fishermen to tour operators, how support would you have been to have to pay an additional $5 on your entry fee for this fund? 0% Not at all (not willing to pay) 50% dont know (somewhat willing to pay) 100% supportive (very willing to pay)

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118 Section IIB: Opinions on new tours and operators Artisanal fishing boats have been classified in the following 3 groups: Small Material: wood Length: 3.8 8.3 meters Engine: 10 85 HP Passengers: 5 Medium Material: fiberglass Length: 5 8.6 meters Engine: 25 200 HP Passengers: 8 Large Material: wood Length: 8 18 meters Engine: 30 219 HP Passengers: 16 Q13. We want to assess whether tourists lik e you would be willing to book excursions with fishermen as new tour operators. The main expect ed benefits associated with the new providers are listed below. How compelling is each aspect to you? [Note: we recognize that a trip to the Galapagos may be a once in a lifetime experience. Please try to consider these questions as if you were planning another trip]. Not 1 2 Somewhat 3 4 Very 5 a) Aids in conservation of marine fisheries stocks b) Direct financial suppo rt to local residents c) Direct exposure to local culture d) Trips on smaller boats Q14. Some areas of concern related to fisherme n as new tour operators are also listed below. How concerned are you with each of these aspects? Not at all 1 2 Somewhat 3 4 Very 5 a) Lack of crew experience with tourism b) Poor English language skills of crew (other than guide)

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119 c) Not affiliated with known company d) Lack of amenities available on larger vessels Section IIC: Evaluating two alternative types of excursions Alternative 1: Q15a. Considering the potential benefits and co ncerns of excursions operated by new providers that you just indicated, how interested are you in booking one of these excursions? [Note: As before, please try to think back to when you were planning your trip when answering the following questions]. Not at all 1 2 Somewhat 3 4 Very 5 Q15b. If you would have been in terested in these new tour oper ators, which type of excursion would you have been most likely to try? Please construct this tour by selecting your preferred boat size, trip length, and main ac tivity of the choices offered. Boat size: Small Medium Large Trip length: Half day Full day Multi-day Main activity: Scuba diving Snorkeling Inter-island Transportation Q15c. Would the availability of such an excursion have ch anged your itinerary during your previous visit? If you booked an all-inclusive package, assume this was one of the options YES I would have added an excursion by one of these new operators YES I would have replaced an excursion I took with one from these new operators NO I would not have taken this excursion

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120 Alternative 2: Besides the same tour activities offered by traditional operators, fishermen will also offer a unique opportunity for fishing trips with th em in medium to large boats. Known as "Artisanal Fishing Tours," this will be the first time that visitors have been allowed the opportunity for recreational fishing. Visitors wi ll experience the local fishing culture and will learn about day-to-day fishing practices. They coul d even end their fishing experience by eating their fresh catch in a local restaurant. Q16a. Considering the same potential benefits and concerns of excursions operated by new providers that you evaluated on th e previous page, but ignoring th e previous alternative (Q15), how interested would you have been (if at all) in booking a new Artisanal Fishing Tour? Not at all 1 2 Somewhat 3 4 Very 5 Q16b. If you would have been in terested in an Artisanal Fishi ng Tour, which type of excursion would you have been most likely to try? Please construct this tour by selecting your preferred trip length, fishing trip, and whethe r you would like the dining option. Trip length: Half Day Full day Fishing options & method: Lobster (scuba diving) Finfish (hook & line, nets) Dining option (eat caught seafood at local restaurant after trip): Yes (retain catch) No (release catch) Q16c. Would the availability of su ch an excursion, if priced re asonably or was available as an option, have changed your itiner ary during your prev ious visit? YES I would have added an excursion by one of these new operators YES I would have replaced an excursion I took with one of these new operators N O I would not have taken this excursion

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121 Q16d. How much would you have been willing to pay for this tour? Please consider your individual selections, the averag e cost of tours during your last visit, and the uniqueness of this opportunity. The most I would pay for this tour is: USD$ Section III: Informat ion about the sample of respondents Q17. Are you or was anyone in your party Yes No a) a member of any conservation organization? b) a member of a development/aid organization? c) employed in any aspect of the tourism industry? Q18. What is your gender? Select one... Q19. In what year were you born? Select one... Q20. In what region do you consider your primary home to be located? [Please select from the pull-down menu to the right of the map] Select one... Q21. What is the highest level of education you have completed? Select one... Q22. In what range approximately was your total household income before taxes in 2008? Select one... Q23. And, just to see if we have a representative sample, wo uld you please share your race? Select one...

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122 Q24. Would you say that you are of Hispanic ancestry? Select one... Section IV: Comments and finish Thank you for your information, which will be used to estimate the potential use and value of this new tourism sector.

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123 LIST OF REFERENCES Alban, F., and J. Boncoeur. 2004. An Assessment of the Potential Intere st of Fishermen to Engage in Boat-Chartering in the Context of a Marine Park: The Case of the Iroise Sea, Western Brittany, France. Contesting the Foreshore: Tourism, Society, and Politics on the Coast J. Boissevain and T. Selwyn, eds., pp. 185-203. Amsterdam, The Netherlands: Amsterdam University Press. Alberini, A., V. Zanatta, and P. Rosato. 2007. Co mbining Actual and Contingent Behavior to Estimate the Value of Sports Fi shing in the Lagoon of Venice. Ecological Economics 61 (2-3):530-541. Allison, P. 2002. Missing Data Sage University Papers Series on Quantitative Applications in the Social Sciences, 07-136. Thousand Oa ks, CA: Sage Publications, Inc. Asche, F. 2007. Capacity Measurement in Fisheries: What Can We Learn? Marine Resource Economics 22 (1):105-108. Baine, M., M. Howard, S. Kerr, G. Edgar, a nd V. Toral. 2007. Coastal and Marine Resource Management in the Galapagos Islands and the Archipelago of San Andres: Issues, Problems and Opportunities. Ocean and Coastal Management 50:148-173. Battese, G.E.,and S.S. Broca. 1997. Functional Forms of Stochastic Production Functions and Models for Technical Efficiency Effects: A Comparative Study for Wheat Farmers in Pakistan. Journal of Productivity Analysis 8:395-414. Battese, G.E.,and T.J. Coelli. 1993. A Stochastic Frontier Production Fu nction Incorporating a Model for Technical Inefficiency Effects. Working Papers in Econometrics and Applied Statistics No. 69. Department of Econometric s, University of New England, Armidale, Australia. ____. 1995. A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data. Empirical Economics 20:325-332. Bennett, J., and R. Blamey. 2001. Introduction. The Choice Modelling Approach to Environmental Valuation J. Bennett and R. Blamey, eds., pp.1-10. Chentelham, U.K.: Edward Elgar. Berkes, F., R. Mahon, P. McConney, R. Pollnac, and R. Pomeroy. 2001. Managing Small-Scale Fisheries: Alternative Directions and Methods Ottawa, ON: International Development Research Center. Bhat, C., and R. Pendyala. 2005. Modeling Intr ahousehold Interactions and Group Decision Making. Transportation 32:443-448. Borooah, V.K. 2002. Logit and Probit: Ordere d and Multinomial Models Thousand Oaks, CA: Sage Publications.

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124 Cancino, J.P., H. Uchida, and J. E. Wilen. 2007. TURFs and ITQs: Collective vs. Individual Decision Making. Marine Resource Economics 22:391-406. Carvalho, A.R. 2008. Profits and Social Performan ce of Small-scale Fishi ng in the Upper Parana River Floodplain (Brazil). Brazilian Journal of Biology 68(1):87-93. Castrejon, M. 2007. Personal Communication. Cinner, J.E., T.Daw and T.R. McClanahan. 2008. Socioeconomic Factors that Affect Artisanal Fishers Readiness to Exit a Declining Fishery. Conservation Biology 23(1):124-130. Coelli, T.J. 1995. Recent Developments in Fron tier Modelling and Efficiency Measurement. American Journal of Agricultural Economics 39 (3): 219-245. ____. 1996. A Guide to Frontier Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function Estimation. Worki ng Paper No. 7/96. Centre for Efficiency and Productivity Analysis, University of New England, Armidale, Australia. Comisin Tcnica Pesquera de la Junta de Manejo Particip ativo (CTPJMP). 2009. Captulo Pesca del Plan de Manejo de la Reserva Marina de Galpagos. http://www.galapagospark.org/documentos/ capitulo_pesca_reserva_marina_galapagos.pdf last accessed October 29, 2009. Congreso Nacional del Ecuador. 1998. Ley de Rgimen Especial para la Conservacin y Desarrollo Sustentable de la Provincia de Galpagos Quito, Ecuador, pp.42. Conrad, J. 1999. Resource Economics New York, NY: Cambridge University Press. Diario C. 2003. Proyecto de Turismo A lternativo de Pescadores Artesanales. http://www.diarioc.com.ar/inf_general/Proy ecto_de_turismo_altern ativo_de_pescadores_a rtesanales/32782 last accessed March 3, 2010. Defeo, O., and J.C. Castilla. 2005. More than On e Bag for the World Fishery Crisis and Keys for Co-management Successes in Selected Artis anal Latin American Shellfisheries. Reviews in Fish Biology and Fisheries 15:265-283. Esmaeili, A. 2006. Technical Efficiency Analysis for the Iranian Fishery in the Persian Gulf. ICES Journal of Marine Science 63:1759-1764. Estrategia and Negocios. 2009. Pe sca Artesanal Atrae Turistas. http://estrategiaynegocios. net/vernoticia.aspx?option=63381 last accessed March 3, 2010. FAO. 1995. Code of Conduct for Responsible Fisheries Rome, FAO, 41 pp. ____. 1998. Report of the Technical Working Gr oup on the Management of Fishing Capacity. FAO Fisheries Report No. 586 Rome, Italy.

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131 BIOGRAPHICAL SKETCH Liliana Alencastro was born in Guayaquil, Ecuador. She obtained her undergraduate degree in Economics with a minor in Finance in 2000 at the Escuela S uperior Politcnica del Litoral (ESPOL) in her home country. After worki ng as a research assist ant in the Center for Economic Research at ESPOL, she continued her graduate education and obtained her Master of Science degree in the Food and Resource Economics Department at the University of Florida in 2004. In the same year, she started the doctoral pr ogram in the same department and received her Ph.D. degree in May of 2010. Her research inte rests are focused on natural resource economics, especially on the human dimensions of artisan al fisheries management. She has studied the preferences of hobbyists for marine ornamental fish, and more recently, the socioeconomic and efficiency implications of fishing capacity mana gement in small scale commercial fisheries.