Legitimacy, local participation, and compliance in the ... · Ocean & Coastal Management 50 (2007)...

ARTICLE IN PRESS

Ocean & Coastal Management 50 (2007) 253–274

0964-5691/$ -

doi:10.1016/j

�CorrespoE-mail ad

www.elsevier.com/locate/ocecoaman

Legitimacy, local participation, and compliance inthe Galapagos Marine Reserve

Cesar Viteria, Carlos Chavezb,�

aNatural Resources Program, Mentefactura, P.O. Box 17-22-20503, Quito, EcuadorbDepartment of Economics, Universidad de Concepcion, P.O. Box 1987, Concepcion, Chile

Available online 11 September 2006

Abstract

We analyze the compliance behavior of artisanal fishermen in the Galapagos Marine Reserve. Our

empirical analysis explores the role of the reserve’s participatory management system as a

determinant factor in decisions to violate regulations. The results indicate that, along with traditional

enforcement tools (detection and penalties), the perception of legitimacy that regulations and local

organizations have among the boat-owners, their individual sense of belonging, as well as their

participation levels in their related organizations are also relevant to the compliance/violation

behavior. Policy implications to improve compliance with fisheries regulations in the reserve

are discussed.

r 2006 Elsevier Ltd. All rights reserved.

1. Introduction

The ‘‘Law of the Special Regime for the Conservation and Sustainable Development ofthe Province of Galapagos’’, enacted in 1998 provides a new frame for fishery activitymanagement within the Galapagos Marine Reserve. When the special regime was enacted,its designers expected the local-based decision-making process to foster the commitment oflocal users to abide by the decisions and regulations designed to improve the managementand conservation of natural resources. However, the results of fisheries monitoring andpopulation analysis are far from being optimistic. Sea Cucumber densities in some zoneshave declined for three consecutive years, as has the catch per unit of effort of SpinyLobster [1,2]. The reasons for this decreased performance have not yet been identified;

see front matter r 2006 Elsevier Ltd. All rights reserved.

.ocecoaman.2006.05.002

nding author. Tel.: +5641 203 067; fax: +56 41 254 591.

dresses: [email protected] (C. Viteri), [email protected] (C. Chavez).

www.elsevier.com/locate/ocecoaman

dx.doi.org/10.1016/j.ocecoaman.2006.05.002

mailto:[email protected]

ARTICLE IN PRESSC. Viteri, C. Chavez / Ocean & Coastal Management 50 (2007) 253–274254

however, enforcement and compliance problems might have contributed to these undesiredoutcomes.In a peer review of the strategies and activities to promote compliance with regulations

among the boat-owners of the reserve’s artisanal fleet, preliminary factors have beenidentified that impact an individual fisherman’s decision to comply with the regulations [3].Although this descriptive study concludes that surveillance and patrolling activities detect6% of the fleet committing transgressions of fishery regulations, the incidence of violationscould be larger. According to responses obtained from a field survey directed at artisanalboat-owners operating in the reserve, the self-declared rate of regulation transgressions isclose to 30% of all boat-owners. This suggests that the objective of reducing non-compliance with the agreed regulations is not limited to improved logistical or technicalaspects for increased monitoring, but also requires addressing social and economicmatters.The objective of this paper is to develop an empirical study of the boat-owners’ violation

behavior regarding management regulations in the context of the artisanal fishery of theGalapagos Marine Reserve. The analysis is devoted to identifying the determinant factorsmotivating decisions to violate regulations, and to estimating the effects these factors haveon this decision. This study places special emphasis on estimating how violation decisionsare impacted by the following: the participation of boat-owners in the institutions thatcomprise the reserve’s participatory management system, the perception of boat-ownersregarding the legitimacy of regulations, and the traditional enforcement instrumentsavailable to the authorities (detection and penalties).We found indications that, along with traditional enforcement tools (detection and

penalties), the boat-owners’ perceptions of the legitimacy of regulations and localorganizations, the sense of belonging of individual boat-owners, and their participationlevels in their organizations are relevant to compliance/violation behavior. Two of theproblems faced by the regulatory and enforcement authorities responsible for fisheriesmanagement in developing countries are the limited availability of specialized staff andrestricted budgets for monitoring and regulation enforcement. Learning what determines afisherman’s compliance/violation behavior might help reserve authorities refine theircurrent enforcement strategies to induce adequate, affordable compliance levels.The conceptual analysis of enforcement and compliance behavior is well established in

the existing fisheries economics literature [4–8]. Following the development of compliancebehavior theories in the fisheries regulation context, interest in empirically analyzing thedeterminant factors explaining compliance decisions of individual fishermen has beengrowing. Unfortunately, most of the existing literature has been conducted in the contextof developed countries [9–12].1 The study of an individual fisherman’s compliance behaviorin the context of developing countries or within protected marine areas is significantly lessdocumented. Interestingly, to our knowledge, only one contribution among the existingliterature is devoted to the analysis of artisanal fishermen’s non-compliance in the contextof a developing country [16].The empirical analysis of the present paper is based on theoretical contributions to an

individual fisherman’s compliance decision [7]. In that work, an extended compliance/violation model is developed. The authors found that the decision to violate a regulation is

1Other recent analyses of enforcement and compliance behavior in fisheries relate to Danish [13], Swedish [14],

and North American [15] fisheries.

ARTICLE IN PRESSC. Viteri, C. Chavez / Ocean & Coastal Management 50 (2007) 253–274 255

not only motivated by pure traditional economic incentives and disincentives, but also byadditional factors, currently emphasized by other social sciences’ disciplines. Similarly,recent literature analyzing governing systems for common pool resources and theperformance of regulatory management systems for fisheries suggests that individualcompliance/violation decisions do not depend only on variables such as the expectedearnings of illegal activity or monitoring efforts and penalties faced by the offender, butalso on social variables such as legitimacy of the regulations, local control, communitymembership, participation levels, degree of individual moral development, and social orpeer pressure [11–13,16–19].

The paper is organized in five sections. In Section 2, we briefly describe the reserve’scurrent management system and artisanal fishery. Section 3 contains a description of thetheoretical framework used in our empirical analysis on fisheries regulation violationdecisions in Galapagos. This section also includes a description of the data used in theempirical analysis. In Section 4, we present the results of the econometric estimations ofthe boat-owners’ violation decision model. Conclusions and implications from our work toimprove compliance with fishery regulations in the reserve are offered in Section 5.

2. Management system in the Galapagos Marine Reserve

The management system in Galapagos is an innovative experience in the management ofnatural resources and one of the few examples—in Ecuador and the world—in which themanagement scheme incorporates the rights and responsibilities of local users in thedecision-making process of the administration of a protected area [20]. The managementsystem was established as part of the Law of the Special Regime for the Conservation andSustainable Development of the Province of Galapagos (Special Regime for Galapagos)2

in 1998, to deal with the existing conflicts over the use of natural resources amongantagonistic groups of the marine reserve and as a strategy for obtaining commitmentsfrom local users regarding the decisions and regulations designed to improve themanagement and conservation of natural resources, as well as to promote complianceof these regulations.3 The Special Regime for Galapagos contains a series of instructions that seek to delegate decision-making of natural-resource management, especiallyfishery resources, to a local coordination body constituted by representatives of thetourism sector, the artisanal fishery sector, and the conservation sector. In addition, thisregime grants the Galapagos National Park the authority and instruments required topromote the implementation of regulations and decisions agreed to by the mentionedsectors.

As for the artisanal fishery, the Special Regime for Galapagos establishes two mainaspects aimed at regulating the use of fishery resources in the marine reserve. First, theLaw considers a local management regime of common property resources with an

2Congreso Nacional del Ecuador, Plenario de las Comisiones Legislativas. Ley de Regimen Especial para la

Conservacion y Desarrollo Sustentable de la Provincia de Galapagos, Registro Oficial No. 278. Quito, Ecuador,

1998.3These aims are reflected in the vision of the new management regime elaborated by the Grupo Nucleo (group

made up of representatives of the fishery and tourism sectors in the Galapagos, the Charles Darwin Foundation,

the National Park and the Fisheries Bureau) in June 1997. The vision highlights effective participation of local

sectors in decision-making as a key element to promote local commitment towards the compliance of the

regulations [21].


identified and limited group of users. In this sense, the regime restricts fishery activities inthe area of the reserve to small-scale (artisanal) fisherman, as defined in the ManagementPlan and applies entrance barriers to the fishery through the imposition of a series ofrequirements that affect both fishermen and boats.4

Second, the Law establishes a decision-making scheme to control artisanal fisheryactivities.5 The scheme considers the involvement of local fishermen in the decision-makingprocess through the Participatory Management Board. The Board analyzes several aspectsrelated to the development of fishery activities and subsequently makes a proposal to theInter-institutional Management Authority. The decisions made in this regard aresummarized in the fishery calendar, an instrument that contains information about whichkind of species are allowed to be caught, their quotas, and fishing seasons. Once approvedby the management authorities, the fishery calendar becomes the ‘‘rules of the game’’which the artisanal fishermen must obey.Third, the Special Regime for Galapagos designates the Galapagos National Park

Direction as the organism in charge of the administration and control of the reserve. Thedirection of the park should keep records of and regulate the number of individualsinvolved in the artisanal fishery, control the activities that are carried out in the reservethrough a surveillance and patrol program, and establish administrative proceduresagainst offenders and apply the penalties described in the Special Regime for Galapagos,should an infraction be detected.The artisanal fishery, along with tourism, is among the most important economic

activities developed in the reserve. Currently, fishing generates approximately US$4–6million annually (2002) and sustains a population of about 1000 fishermen and theirfamilies living on the islands of the Galapagos archipelago [22]. Fishing activity is focusedon three main fisheries:

(i)

4Se

Mari

Smal

proh

perm5T

estab

Natio

mana

comp

mana

reser

users

the P

Boar

Natio

decis

Sea cucumber (Isostichopus fuscus), the most important source of income for localfishermen; in 2002, this fishery generated a gross income close to 1.7 million USD(larger than the income generated by the Spiny lobster or whitefish fisheries), with atotal catch of 8.3 million individuals [22]. The sea cucumber is harvested to a depth ofapproximately 20m in eight specific macro-zones of five different islands. Thefishermen cook, salt, and dry the harvested sea cucumbers. The most important

rvicio Parque Nacional Galapagos. Plan de Manejo de Conservacion y Usos Sustentable para la Reserva

na de Galapagos. Parque Nacional Reserva Marina de Galapagos Ecuador. Puerto Ayora, Ecuador, 1998.

l-scale fishing is characterized by restrictions imposed on boats, including banning certain fishing gear and

ibitions against using mechanical equipment. Entrance barriers include a moratorium for issuing new boat

its and a restriction only granting new fishing permits to fishermen’s kin.

he decisions agreed upon to regulate the small-scale fishery stem from the participatory management system

lished by the Special Regime for Galapagos and the Management Plan, which works at three levels: (1) the

nal Park, which is responsible for the administration of the area and the control of natural-resource

gement; (2) the Inter-Institutional Management authority, which is comprised of institutions which have

etence and control over the area, and is also the top level agency for policy making regarding reserve

gement; and (3) the Participatory Management Board, which channels the responsible participation of local

ve users, including small-scale fishermen. Decision-making begins with a participatory process in which local

of the reserve submit their proposals to the board to be discussed. The decisions agreed upon by consensus in

articipatory Management Board are sent to the management authority for approval by voting. Within the

d, the National Park behaves as any other stakeholder seeking a consensus; in the management authority, the

nal Park serves as a Technical Secretariat. Then, the National Park is responsible for implementing the

ions made, with the support of the co-responsible stakeholders in the system [20].


management tools applied in this fishery include no-take zones, restricted fishingseasons (2 months, April–May), size restrictions, and a total allowable catch (TAC)quota.

(ii)
Spiny Lobster, the second largest in terms of economic relevance; in 2002, the fisheryyielded revenues of 1.2 million USD with a catch of 51.4metric tons (mt) [22]. Threelobster species are caught: Spiny Red Lobsters (Panulirus penicillatus), Green Lobsters(Panulirus gracilis), and Slipper Lobsters (Scyllarides astori). Lobsters are hand-harvested by divers; only the tails of the lobsters are marketed and most of them arefrozen and exported. The management of this fishery comprises a fishing season fromSeptember to December, a minimum legal size of 15 cm tail length, gear restrictions(lance- and pistol-type harpoons are prohibited), and a ban on the harvest of egg-bearing females.
(iii)
Whitefish, less important in economic terms than the previous fisheries; in 2000 thetotal catch was 374.2mt, in economic terms, approx. 0.7 US million [22]. Landingscomprise a wide range of species and are year-round. Management instrumentsinclude gear restrictions, such as a longline ban and shark catch prohibition.
Most potential fishing violations include infringement of one or several of themanagement regulations mentioned for the three fisheries. According to the NationalPark patrolling reports, the more frequent violations include fishing in no-take zones andillegal catching and trading of shark. Another violation is the employment of individualswho are not in the park records. Fishing activity in the reserve has generated greatinternational attention, primarily for its potential impact on the biodiversity of the marinereserve.

3. Behavior of the fishery boat-owner

In this section, we describe the theoretical model, on which our empirical analysis isbased, for the individual decisions to infringe the regulations. We briefly discuss adeterrence model, where the infraction decision is motivated by purely economic variables(expected costs and benefits of violating regulations) as well as social variables, which weconsider to be decisive for the violation decision. Then, we specify an econometric model tocarry out the empirical analysis.

3.1. Individual regulation violation decisions

Our conceptual understanding of the individual decision to violate a regulation is basedprimarily on the contribution of Sutinen and Kuperan [7]. The model attempts to explainan individual fisherman’s behavior facing the non-compliance decision, and it merges thebasic deterrence model with intrinsic and extrinsic motivations that influence theindividual’s decision of whether to obey the law.

Specifically, to evaluate the influence of social factors on the decision of whether or notto comply, this model extends the classical deterrence model of Becker [23] by explicitlyconsidering two additional factors: the moral and social reputation of the individual.The first factor describes how the compliance decision is influenced by individual’smoral development, personal values, and perception of the legitimacy of the norms.


The second factor attempts to reflect how social pressures inside the community affect theindividual’s decision.6

Likewise, it has been suggested [24] that the compliance with social norms will dependon, among other aspects, the level of legitimacy of the norm, the institutions whichpromote that norm, and the level of membership the individual feels to the community. Ithas also been underlined [11] that current fishery management literature with a local focuson ‘‘co-management’’ frequently suggests that broader participation of fishermen in themanagement process results in an increment of compliance levels due to the regulationsbeing agreed upon and having legitimacy. This can be understood as the individual feelinga moral commitment towards the compliance and success of the regulations, under thepremise that when an individual identifies with a regulation and does not feel it to be anobstacle to his/her freedoms imposed by some external body, this regulation is more likelyto be successful.7

Including an individual’s social reputation and commitment to the institutionalframework has two effects [7]. First, the number of offenders decreases because thecondition for the decision to violate is stricter than in the simple case of the classicdeterrence model. Second, since the marginal costs of infringing are larger in the extendedmodel than the marginal costs of infringing in the context of the classic deterrence model,the magnitude of the infraction decreases because fishermen allocate less effort to illegalactivities.Thus, following the analysis of the fisherman’s compliance decision in other studies

[7,11,12,16,18], and considering the participatory management system of the reserve and itspotential effects on the compliance decision, we consider a set of social factors that wepresume influence the individual’s behavior: legitimacy of regulations and institutionsrelated to fishery management; the boat-owner’s sense of belonging to the community; andthe boat-owner’s participation in local organizations.

3.2. Hypothesis

The empirical analysis is guided by the theoretical response of a boat-owner’scompliance behavior when changes are perceived in independent variables. We denoteV* as the difference between the maximum expected utility transgressing the regulationsand the maximum utility the individual gains by obeying the regulations [7]; that differenceis likely to depend on variables such as the price of landings (p), the surveillance effortfrom the regulatory authority (probability of detection and conviction) (v), the magnitudeof the fine ðf Þ, the cost structure linked with the characteristics of the vessel (size, power,employees, storage capacity, etc.) ðkÞ, the legitimacy of the current regulations (l), the senseof belonging of the individual in the community (s), the participation level that theindividual has in the institutions involved in the creation of the norm (u), and a set of

6Along the same lines, the non-monetary factors that affect the compliance of a catch quota among fishermen in

the UK were analyzed [11]. This model, as with others [7,18], is an extension of the basic deterrence model to

evaluate the influence of moral obligation, peer attitudes, and perceived legitimacy of the norm on compliance.

The authors find some evidence that a better level of involvement of individuals in the management process, leads

to better compliance levels since the regulations will be accepted with more legitimacy.7A similar proposition has been concluded elsewhere [25]. In addition, the issue of lost freedom due to external

regulations and their effect on an individual’s behavior in the framework of common property resources has been

addressed [17].


characteristics for each individual ðaÞ, i.e.,

V�ðp; v; f ; k; l; s; u; aÞ. (1)

We interpret V* as the magnitude of the incentive that individuals have to not obey therules and to allocate effort to illegal fishing activities; a positive difference indicatesincentives to violate. It can be shown that the magnitude of the incentive to violateincreases with the financial incentive (p) and diminishes as result of an increase in thesurveillance effort from the regulatory authority (detection and conviction probability) aswell as for positive changes in the magnitude of the penalty. The same effect is observedwhen we consider social variables; thus, the incentive to violate decreases with thelegitimacy of the regulations, the sense of belonging of the individual, and the individual’sparticipation levels in the institutions that generate the regulations.8

3.3. Econometric model specification and data description

In this section, we are interested in evaluating the violation probability of an agreedregulation by a boat-owner of the artisanal fishery fleet of Galapagos. The generalequation we use to evaluate the decision to violate regulations in the reserve is9

Violaltion ¼ Violation ½Detection and Penalty; Price of Landings; Costs; Individual

Characteristics;Legitimacy;Belonging to the Community; and Participation�, ð2Þ

where Violation is equal to one if the fisherman violates the regulations; otherwise thevariable has a value of zero. Considering the dichotomous nature of the dependentvariable, we estimated the parameters of interest by using a probit model.

Assuming a normal distribution of errors, the probability of observing Violation ¼ 1 isgiven by the value of the normal distribution function evaluated with the estimatedparameters from the estimation procedures for Eq. (2).

The independent variables considered are classified in seven groups. Detection and

Penalty includes a set of variables intended to capture the individuals’ perception of thelikelihood of being detected in violation and if so, being punished, and the individualfisherman’s perception about the magnitude of the penalty. Price of Landings is a variablethat attempts to capture the financial incentive to violate the regulations. Costs is a set ofvariables through which we control for the influence of the operational cost of fishingactivity on the violation decision. Individual Characteristics includes variables for severalindividual characteristics of each fisherman. Legitimacy includes a set of variables for theperception of the legitimacy of a regulation and the authorities that promote it. Finally,Belonging to the Community and Participation is a group of variables that represents theindividual sense of belonging to the local community as well as the participation of thefisherman in her/his organization (cooperative).

The data used in the estimations comes from a field survey applied to a sample of boat-owners from the artisanal fleet operating in the reserve. The survey was conducted by oneof the co-authors of this work during September 2002, spending about 1 week in eachof the three main inhabited islands. Boat-owners were selected randomly at landing sites.

8Comparative static results [7].9We avoid the use of a sub-index to simplify notation. The list of specific variables and their definitions are

presented in Table 1a.


The survey was carried out thanks to the collaboration of the Galapagos ArtisanalFishermen Cooperatives: COPESPROMAR (all acronyms in this paragraph are inSpanish), Cooperative of Fishery Production ‘‘San Cristobal’’(COPESAN), Cooperativeof Fishery Production Horizons of Isabela, and Cooperative of Galapagos ArtisanalFishery Production (COPROPAG), and the Participatory Management Board. The surveywas confidential; as such, no data that could reveal the identity of the boat-owner wasrequested. In addition, the survey design allowed the boat-owner to fill out the surveyindependently,10 even though most interviewed fishermen preferred a diallog. We obtained155 observations from a population of 426 boat-owners registered by the Unit of MarineResources of the Galapagos National Park.11

Our empirical analysis is based on the fishermen’s responses to the survey questions;therefore, non-compliance status is reported as non-compliance. We acknowledge that thisimplies that we are assuming that fishermen provided truthful answers. Although theassumption is unavoidable, the experience doing field work resulted in the perception thatthe responses are reliable. This is the perception of the person who conducted all thesurveys, using face-to-face interviews/diallog.The survey included several sections addressing different topics. The first section

requested information about the characteristics of the boat-owners and their families. Thesecond section asked for data about technical characteristics of the boat and details of thefishery activity. The third section inquired about the boat-owners’ perception of thesurveillance activities performed by the authority. The fourth section asked the boat-owners about their behavior regarding compliance with regulations; the last section askedboat-owners about their perception regarding the fishery regulations, the managementauthorities, the fishermen cooperatives, and participation in these organizations.In the compliance section, the survey asked boat-owners to choose a sentence that best

described their fishery activity in the last season (2001 was the reference year); it also askedthem directly if, in the past season, the boat-owner had committed some of the mostfrequent infractions according to the records of the National Park’s patrolling reports.Thus, with the information collected, we proceeded to classify the boat-owner by her/hisself-reported compliance status. We defined a violator to be any individual who stated that,in the last season, their fishery activities ‘‘rarely fulfill the regulations, because there are toomany restrictions’’, or who stated directly that they have carried out some activity judgedas an infraction.Table 1a presents the list of variables considered in the econometric model. The table is

divided in two sections. The first section shows the dependent variable (Violation),representing the violation decision. The second section shows, separated in blocks, theexplanatory variables used in the estimations. Each block represents an element of thevector of independent variables specified in the theoretical model: v; f ; k; p; a; l; s; u.Block I ‘‘Probability of Detection and Punishment (v)’’ contains the variables ZONE,

MOTORHP, AGE, SEEN, SEA_INSPECTION, INSPECTED_PUNISHED, andPUNISHED; and attempts to represent the perception of each boat-owner regarding

10To maintain confidentiality, the results of this paper do not refer to any specific island or town.11We employed the stratified sampling technique [26]. The sample was divided into three strata according to

boat type (bote, fibra and panga); to make this division we used the detection variances observed in each boat type

group of boat-owners. Additionally, the selected strata were distributed proportionally among the three towns.

The sample size resulted in 148 observations of a population of 426 boat-owners, with a theoretical error of 3%

and a confidence level of 95%.

ARTICLE IN PRESS

Table 1(a)

Description of the variables used in the econometric model

Name of the variable and descriptiona

Dependent variable

VIOLATION: 1: If the boat-owner acknowledges: to have carried out an activity considered infraction; rarely

fulfilling regulations or having made some infraction. 0: otherwise.

Independent variables

I. Probability of detection and punishment (v)

ZONE: 1: If the boat-owner fishes in North, North West, or West zones or declares to operate in the whole

archipelago. 0: otherwise.

MOTORHP: Defined as the power of the boat ‘s motor; this variable is expressed in horse power (HP).

AGE: Age of the boat-owner in years.

SEEN: 1: If the boat-owner states having seen the surveillance of the GNP or the National Army: always (A),

almost always (AA) or sometimes (ST). 0: otherwise.

SEA_INSPECTION: 1: If the boat-owner states having been boarded for inspection A, AA or ST by personal

of the GNP or the National Army. 0: otherwise.

INSPECTED_PUNISHED: 1: If the boat-owner considers that ships detained for infractions are punished A,

AA or ST. 0: otherwise.

PUNISHED: 1: If the boat-owner indicates having been punished at some time. 0: otherwise.

II. Magnitude of the fine. (f )

FINE: 1: If the boat-owner considers that the fines applied to offenders are high. 0: otherwise.

III. Structure of costs kÞ

STORAGE: Indicates the storage capacity of the boat in metric tons (mt).

DAYCOST: Indicates the daily cost in American dollars of a fishery trip.

CAPITAL: Indicates the commercial value of the ship in American dollars.

INDEBT: 1: If the boat-owner declares having a debt related to fishery activity. 0: otherwise.

IV. Structure of prices (p)

PRICEIND: Price Index based on the market prices of the species the boat-owner caught, weighted by the share

of each species in the total income of boat-owners. The index is expressed in American dollars.

V. Individual characteristics (a)

MARRIED: 1:If the boat-owner is married or cohabiting. 0: otherwise (single, separate, widow).

FAMILY: The number of boat-owner family members.

EDUCA: Number of years of formal education completed.

ISLAND1: 1: If the boat-owner lives on the Islands. 0: otherwise.

VI. Legitimacy of the regulations and authorities that promote them (l).

REGULA_GRL: 1: If the boat-owner strongly agrees (SA), agrees (A) or neither agrees nor disagrees (NA/

ND) with the fishery regulations. 0: otherwise

REGULA_SHARK: 1: If the boat-owner strongly disagrees (SD), disagrees (D) or NA/ND, with allowing

shark fishery. 0: otherwise.

REGULA_SEASON: 1: if the boat-owner strongly agrees (SA), agrees (A) or NA/ND with the statement: ‘‘no-

catch seasons benefits the fisherman’’. 0: otherwise.

REGULA_SEACUCUMBER: 1: If the boat-owner strongly disagrees (SD), disagrees (D) or NA/ND with

allowing unregulated fishery of sea cucumber. 0:otherwise.

OTHER: 1: If the boat-owner declares that other fishermen usually make some infraction of those commonly

detected by the GNP. 0: otherwise.

JUNTA_AUTHORITY: 1: If the boat-owner considers that the JMP/AIM takes into account the sector fishery

little time, most of the time or every time. 0: otherwise.

LEAD_REPRESENT:1: If the boat-owner considers that his leaders represent the interests of the sector

appropriately or at least they represent them fairly. 0: otherwise.

C. Viteri, C. Chavez / Ocean & Coastal Management 50 (2007) 253–274 261

ARTICLE IN PRESS

Table 1(a) (continued )

VII. Sense of membership to the community (s).

NATIVO: 1: The boat-owner was born in the Galapagos. 0: otherwise

MOVECONT: 1: If the boat-owner declares that he could change his residence to the continent. 0: otherwise

YEARSMIGR: 1/Number of years of immigration.

FISH_EXC: 1: If the boat-owner is exclusively devoted to the fishery activity. 0: otherwise

YEARSEXP: Years of experience that the boat-owner has in the fishery activity.

VIII. Participation (or)

COOPDIR:1: If the boat-owner is a representative or occupies some directive position in the Cooperatives. 0:

otherwise.

ATTEND:1: If the boat-owner attends all the meetings of the cooperative. 0: otherwise.

Source: Elaborated by the authors.aThe variables ZONE, SEEN, SEA_INSPECTION, INSPECTED_PUNISHED, MARRIED, ISLAND1,

REGULA_GRL, REGULA_SHARK, REGULA_SEASON, REGULA_SEACUCUMBER, JUN-

TA_AUTHORITY, and LEAD_REPRESENT that originally were of multiple discreet answers have been

transformed to binary discreet variables, with the purpose of increasing the degrees of freedom in the regression

and of obtaining a better behaved model.

C. Viteri, C. Chavez / Ocean & Coastal Management 50 (2007) 253–274262

the probability that an offender is detected and punished. Block II ‘‘Magnitude of the fine

(f )’’ contains the variable FINE, which represents the size of the fine perceived by the boat-owners. Block III ‘‘Structure of Costs (k)’’ includes STORAGE, DAYCOST, CAPITAL,INDEBT; and indicates the cost structure faced by the boat-owners. Block IV ‘‘Structure

of Prices (p)’’ has the variable PRICEIND; an indicator of the market prices of thetargeted species as observed by the boat-owner. We used this variable to indicate thefinancial incentive faced by individual fisherman to violate the regulation.12 Block V‘‘Individual Characteristics (a)’’ contains MARRIED, FAMILY, EDUCA, ISLAND1;these describe the personal features, family characteristics education, and place ofresidence of each boat-owner. Block VI ‘‘Legitimacy of the Regulations and Authorities that

promote them (l)’’ includes: REGULA_GRL, REGULA_SHARK, REGULA_SEASON,REGULA_SEACUCUMBER, OTHER, JUNTA_AUTHORITY, LEAD_REPRE-SENT; which represents the degree of legitimacy that regulations and relevant authoritieshave among the boat-owners. Block VII ‘‘Sense of Ownership to the community (s)’’contains the variables: NATIVO, MOVECONT, YEARSMIGR, FISH_EXC, YEAR-SEXP; and represents the level of membership of the boat-owner towards his town andunion. Finally, block VII ‘‘Participation (u)’’, includes: COOPDIR, ATTEND, attempts toindicate the degree of involvement of the boat-owner in the local organizations(cooperatives).Table 1b presents descriptive statistics (mean, standard deviation, and the coefficient of

variation) for the variables considered in the model.

12The construction of a variable that represents the expected benefit from violating differs among the related

empirical literature according to the type of violation analyzed. For example, the financial incentive to violate

variable is defined as the difference in the value of catch per unit of effort between inshore and offshore areas [18];

as restriction of earning (in percentage) due to quotas [12]; they also consider a binary variable for leasing in extra

quota. Finally, the expected difference in value of catch per crew member between illegal and legal mesh size

regulation has also been considered [16].

ARTICLE IN PRESS

Table 1(b)

Description of the variables used in the econometric model

Variable Average Standard deviation Coefficient of variation

Dependent variable

VIOLATION 0.305 0.462 1.51

Independent variables

I. Probability of detection and punishment (v)

ZONE 0.760 0.429 0.56

MOTORHP 68.554 42.452 0.62

AGE 40.507 10.640 0.26

SEEN 0.409 0.493 1.21

SEA_INSPECTION 0.305 0.462 1.51

INSPECTED_PUNISHED 0.409 0.493 1.21

PUNISHED 0.292 0.456 1.56

II. Magnitude of the fine (f )

FINE 0.481 0.501 1.04

III. Structure of costs (k)

STORAGE 1.841 2.430 1.32

DAYCOST 108.920 115.591 1.06

CAPITAL 43746.200 103634.000 2.37

INDEBT 0.468 0.501 1.07

IV. Structure of prices (p)

PRICEIND 2.412 1.726 0.72

V. Individual characteristics (a)

MARRIED 0.877 0.330 0.38

FAMILIA 3.500 1.958 0.56

EDUCA 10.058 3.869 0.38

ISLAND1 0.286 0.453 1.58

VI. Legitimacy of the regulations and authorities that promote them (l)

REGULA_GRL 0.435 0.497 1.14

REGULA_SHARK 0.305 0.462 1.51

REGULA_SEASON 0.909 0.288 0.32

REG_SEACUCUMBER 0.708 0.456 0.64

OTHER 0.721 0.450 0.62

JUNTA_AUTHORITY 0.552 0.499 0.90

LEAD_REPRESENT 0.273 0.447 1.64

VII. Sense of ownership to the community (s)

NATIVO 0.474 0.501 1.06

MOVECONT 0.084 0.279 3.32

YEARSMIGR 0.023 0.028 1.22

FISH_EXC 0.805 0.397 0.49

YEARSEXP 19.604 10.447 0.53

VIII. Participation (u)

COOPDIR 0.273 0.447 1.64

ATTEND 0.370 0.484 1.31

Source: Elaborated by the authors based on the survey of September 2002.



4. Econometric results

In this section, we present the econometric results for the violation decision of fisherymanagement regulations in the Galapagos Marine Reserve. The section concludes with thequantification of the impact of relevant independent variables on the individual violationdecision.

4.1. Violation decision model results

Considering the nature of the independent variable, we estimated the parameters ofinterest by using a probit model. Table 2 presents the violation decision model results fortwo alternative specifications. The first model specification (Model 1) is estimatedaccording to the original specification of Eq. (2). The second specification (Model 2) is arestricted version of the previous one. We decided to estimate a more parsimoniousspecification, which excluded certain variables. The restricted model was obtained afterexperimenting with several alternative specifications. Highly correlated variables were usedas exclusion criteria. Furthermore, we also performed tests for omitted variables on thosevariables included in the original specification that were not statistically significant. Inaddition, for the second specification, we also decided to exclude the variablePUNISHED.13 In this way, a simpler specification was obtained.14

From a global perspective, the estimated models are statistically significant. Weevaluated the null hypothesis (all parameters equal to zero) against the alternativehypothesis (all parameters different from zero). For this evaluation, we used the criteria ofthe maximum-likelihood statistics and the percentage of certainty. First, due to the factthat the maximum-likelihood statistic is higher than the critical value for the two estimatedmodels, we reject the null hypothesis that the parameters of the independent variables areall equal to zero; consequently, both models are relevant when explaining the decision toviolate by the artisanal fishermen operating in the reserve. Second, the models predictcorrectly 85.5% and 80.5% of the observations for the first and second estimatedspecification, respectively. This tells us that both models, based on the information of theindependent variables, can correctly forecast the individual’s violation decision in morethan 80% of the observations.

13This variable might not adequately represent the effect that we wish to represent; namely, that the existence of

previous experiences with authorities increase the likelihood of being punished, which would thus have an effect

on the probability of being detected and punished. Furthermore, even if we are able to capture that effect, there

are endogeneity/causality problems with this variable, as being punished in the past depends in part on being a

violator.14The variables with high correlation were: YEARSMGR with NATIVO; and YEARSEXP with AGE. Highly

correlated variables were considered to be those variables with correlation coefficients greater than 0.5.

Furthermore, in order to avoid the omitted variable problem, an omitted variable test was carried out before the

elimination of each variable; here, the evaluated null hypothesis H0:bi ¼ 0, against the alternative hypothesis

H1:bi 6¼0. For this, we used the maximum-likelihood MV ¼ �2:ðln LR � ln LNRÞ, which has a w2 distribution; itscritical value with 1 degree of freedom is 3.84 (at 5% level of significance). As a result, H0 was not rejected.

Furthermore, the Wald-statistic was calculated to evaluate the null hypothesis that the parameters of the 20

variables eliminated as one group are all equal to zero. The value obtained was 22.44, for which the null

hypothesis is not rejected (critical value at the 5% significance level is w2ð20Þ ¼ 31:41); that is to say, the 20

parameters of the variables equal zero as a group.

ARTICLE IN PRESS

Table 2

Estimation results

Block Variables Model 1 Model 2

Coefficient ‘‘t’’ Std. Coefficient ‘‘t’’ Std.

CONSTANT �1.077 �0.608 �0.695 �0.807

I ZONE 0.402 1.014

MOTORHP 0.003 0.494

AGE 0.004 0.158

SEEN �1.003 �2.307�� 0.528 �1.793�

SEA_INSPECTION 0.416 1.022

INSPECTED_PUNISHED �0.055 �0.151

PUNISHED 0.759 1.895�

II FINE �1.154 �2.695�� 0.917 �2.959��

III STORAGE 0.177 2.020�� 0.144 2.590��

DAYCOST �0.004 �1.626

CAPITAL 0.263 1.162

INDEBT 0.356 1.020

IV PRICEIND 0.037 0.416

V MARRIED �0.797 �1.407

FAMILIA 0.009 0.099

EDUCA 0.125 2.359�� 0.127 2.870��

ISLAND1 0.843 1.987�� 1.091 3.253��

VI REGULA_GRL �1.075 �2.548�� 0.710 �2.352��

REGULA_SHARK 0.368 0.822

REGULA_SEASON �0.025 �0.043

REG_SEACUCUMBER �1.174 �2.831�� 0.947 �2.969��

OTHER 0.723 1.686� 0.782 2.281��

JUNTA_AUTHORITY �0.155 �0.418

LEAD_REPRESENT �1.378 �3.174�� 1.276 �3.418��

VII NATIVO 0.856 1.531

MOVECONT 0.115 0.172

YEARSMIGR 21.529 2.184�� 15.040 2.667��

FISH_EXC 0.459 0.954

YEARSEXP �0.047 �1.885� �0.033 �2.122��

VIII COOPDIR 0.299 0.726

ATTEND �1.048 �2.805�� 0.864 �2.954��

No. of observations 154 154

w2 81.11 62.85

Critical value at 5% w(31)2¼ 44.99 w(31)

2¼ 21.03

% correct 84.4 80.5

Source: Elaborated by the authors from the econometric estimation results of the models.�Coefficient significant at 10%, two-tail test.��Coefficient significant at 5%, two-tail test.��Coefficient significant at 1%, two-tail test.



Interestingly, most of the individual parameters were estimated as statistically significantand had the expected signs according to the related literature. If we consider theparameters that turned out to be relevant, taking into account the blocks in which theindependent variables were, it can be noted that all blocks had at least one relevantvariable, except for Block IV ‘‘Structure of prices’’. That is, factors considered in thetheoretical model are relevant to the individual’s decision to violate fisheries regulations inthe reserve. In Block I ‘‘Probability of Detection and Punishment’’, the variable SEEN wasestimated as significant at the usual levels. For Block II ‘‘Magnitude of the fine (f )’’, FINEwas also statistically significant. In principle, it is possible that individuals violate/complywith the management regulations because they think the fine is low/high. However, it isalso possible that individuals respect regulations regardless of the existence of the fine. Infact, our data set reveals that not all violators consider the fine to be low, nor do allcompliers report the fine to be high. The estimation allows us to compare individualperceptions with respect to the fine and test if those perceptions are relevant to theviolation decision. The results indicate that those perceptions are indeed relevant.Additionally, perceptions are important in a person’s decision-making process, as theycontain different information handled by, and characteristics of the, individual.15

In the case of Block III ‘‘Structure of Costs (k)’’, only the variable STORAGE turnedout to be relevant for the violation decision; this can be explained by the correlation thatexists between the variables of this block, since, for example, a large storage capacity isrelated to high costs and a higher capital investment.In the case of Block IV, which contains the PRICEIND, we obtained the expected sign

for the associated estimated parameter; however, we were not able to estimate it asstatistically significant. We think that, due to the way in which the variable wasconstructed, it probably does not adequately reflect the economic incentives for the boat-owner to violate the wide range of regulations considered.16

The results obtained in these first four blocks confirm the relevance of the probability ofdetection, the amount of the penalties, and some of the economic considerations, asdeterminant in the decision to violate.The variables ISLAND1 and EDUCA in Block V, ‘‘Individual Characteristics (a)’’, were

estimated as statistically significant with positive signs. This is to say that individuals thatreside on that specific island or those with a higher level of education are more likely toinfringe than the rest of the boat-owners who do not display these characteristics. ForBlock VI, ‘‘Legitimacy of the Regulations and Authorities that promote them’’, the followingvariables were relevant for the non-compliance decision: REGULA_GRL, REGULA_-SEACUCUMBER, OTHER, and LEAD_REPRESENT. Block VII, ‘‘Sense of Member-

ship of the community(s)’’, contains various relevant parameters (YEARSMIGR and

15We acknowledge; however, that there might be some problems for the comparisons. First, what a high or low

fine means might vary among individuals. Second, it might also be that it is only after experiencing the compliance

decision that individuals form their perception about the extent of the fine. Survey questions asked about

compliance behavior during the past season, therefore, it might be the case that for some individuals the size of the

fine was perceived to be high/low depending on their self-reported compliance behavior.16As noticed before, the variable PRICEIND (price index) is constructed based on market prices of the target

species for boat-owners who operate in the reserve. The prices are weighted by the percentage that each species

represents in the total harvest. As mentioned earlier, other authors who analyze the effects of financial incentives

on compliance behavior focused on one or two types of specific violations and attempted to reflect the economic

incentive for that specific type of violations.


YEARSEXP). For the last block ‘‘Participation (u)’’ the variable ATTEND wasstatistically significant.

The results obtained from blocks VI–VIII are especially relevant because they tend tosupport some of the previously analyzed evidence on the importance of social factors, suchas legitimacy, membership, and participation on the decision to violate fisheriesregulations.

4.2. Effect of the independent variables on the violation decision

Considering the results obtained by the estimation of the violation model, we decided toquantify the impact of the relevant independent variables on the violation probability.Table 3 shows the results of the marginal effects obtained for each independent variable onthe violation decision. Because we did not find significative differences between the resultsobtained for the marginal effects associated with the estimated specifications, we decidedto present results only considering Model 2.17

In Block I ‘‘Probability of Detection and Punishment (v)’’, the variable SEEN has amarginal effect over the probability of infringement of �0.14, which indicates that boat-owners who report having seen, with some frequency, National Park and the NationalNavy surveillance have a 0.14 less probability of being a violator. For Block II ‘‘Magnitude

of the fine (f )’’, the variable FINE has a marginal effect of �0.25, whereas in Block III‘‘Structure of Costs (k)’’, the proxy STORAGE reaches a marginal effect of 0.04. Theformer result suggests that those individuals that consider the penalties to be high have a0.25 lower probability of being a violator; the latter indicates that the probability ofviolating increases by 0.04 for each extra metric-ton increment in boat storage capacity.

Considering Block V, ‘‘Individual Characteristics (a)’’, for variables EDUCA andISLAND1, we estimated marginal effects of 0.04 and 0.30, respectively. This indicates that,for each additional year of schooling, the probability of a fisherman being a violatorincreases by 0.04.18 Also, if the boat-owner is a resident on Island 1, the probability ofbeing a violator increases by 0.30. As for Block VI ‘‘Legitimacy of the Regulations and the

Authorities that promote them (l)’’, we present the marginal effects estimated for thevariables: REGULA_GRL (�0.19), REGULA_SEACUCUMBER (�0.26), LEAD_RE-PRESENT (�0.35), and OTHER (0.21). These results indicate that a boat-owner whodeclares some affinity with the regulations in general (REGULA_GRL), or with the sea-cucumber regulations (REGULA_SEACUCUMBER), has a probability of being aviolator that is 0.19 and 0.26 lower, respectively. The same occurs with a fisherman whoconsiders that the leaders of his/her organization represent his/her interests. In this case,the probability of being a violator was estimated to decrease by 0.35. The opposite occurs

17We calculated the marginal effect of each continuous independent variable on the probability of violation as

the impact of one additional unit of the independent variable with the other explanatory variables held constant

(mean levels). In the case of a binary independent variable, say xj, we calculated the marginal effect on the

violation probability using the expression Prob (Violation ¼ 1jx, xj ¼ 1)�Prob (Violation ¼ 1jx, xj ¼ 0); where x

represents the vector containing the sample mean of the other explanatory variables in the model, and xj has a

value of 1 or 0, for a discrete change, respectively.18Perhaps surprisingly, this result is qualitatively similar to that obtained for the effect of education on the

probability that a fisherman would have been in violation in the Lake Victoria artisanal fisheries [21]. In their

study, they were able to estimate that the probability for a fisherman being a non-violator decreased by 2% for

each additional year of schooling.

ARTICLE IN PRESS

Table 3

Marginal effects of the independent variables

Block Variables Model 2

Marginal effect

I SEEN �0.144�

II FINE �0.250��

III STORAGE 0.039��

V EDUCA 0.035��

ISLAND1 0.297��

VI REGULA_GRL �0.194��

REG_SEACUCUMBER �0.258��

OTHER 0.213��

VII LEAD_REPRESENT �0.348��

YEARSMIGRa 0.084��

YEARSEXP �0.010��

VIII ATTEND �0.236��

Source: Elaborated by the authors with the econometric estimation results.aThe estimated marginal effect for this variable shows the impact on the violation probability of a unitary

reduction in the number of years of immigration from the sample mean (see variable definition in Table 1a).�Coefficient significant at 10%, two-tail test.��Coefficient significant at 5%, two-tail test.��Coefficient significant at 1%, two-tail test.

C. Viteri, C. Chavez / Ocean & Coastal Management 50 (2007) 253–274268

with a fisherman who declares that other fishermen commit infractions (OTHER), whichincreases the probability of being a violator by 0.21.In Block VII ‘‘Sense of membership within the community (s)’’ the variables

YEARSMIGR and YEARSEXP obtained marginal effects of 0.084 and 0.01, respectively.We notice here that YEARSMIGR is an immigration index that is constructed as: 1/(Number of years with residency in the Galapagos). The index adopts the value of zero inthe case of natives and one for recent immigrants with a residency of 1 year. This index isnot very sensitive to the variance of years of residence on the islands. Thus, we see that forthe interval of migration years observed in the sample—1 to 53 years—the averageincrement of the index is very small (0.0185) for each year less of residency. According toour estimations, a unitary change in the number of years of immigration from the samplemean leads to an increase of 8% in the probability of violation. For example, if weconsider the extreme case of a recent arrival, that is to say, an individual with animmigration indicator equal to 1, this individual exhibits a violation probability close to orequal to the upper limit of the probability interval (i.e., 1) which will always be higher thanany other fisherman who has resided on the islands for a longer time. In addition, ourestimation for the marginal effect of YEARSEXP indicates that a unitary increase in theyears of fishery experience reduces the infringement probability by 1%. Finally, in BlockVIII ‘‘Participation (u)’’, the variable ATTEND has a marginal effect of �0.24, whichsuggests that the boat-owners who participate in his/her organization by attending the co-operative meetings reduce the probability of being a violator by about 0.24.


Using the coefficients obtained by the econometric estimation of the violation modelfor the second specification (Model 2, in Table 2) and considering the sample mean ofthe continuous variables reported in Table 1b, it is possible to write the violationprobabilities as

ProbabilityðViolation ¼ 1Þ ¼ F½�0:695� 0:528 � SEEN� 0:917 � FINEþ 0:144 � 1:841þ 0:127 � 10:058

þ 1:091 � ISLAND1� 0:710 �REGULA_GRL� 0:947 �REGULA_SEACUCUMBER

þ 0:782 � 0:721� 1:276 � LEAD_REPRESENTþ 15:04 �YEARSMIGR� 0:033 � 19:604� 0:864 �ATTEND�,

ð3Þ

where F represents the normal standard distribution function. We use expression (3) toevaluate violation probabilities. Table 4 presents the estimated violation probabilities forvarious categories of boat-owners. Initially, the boat-owners were divided in two groups:recent immigrants (7.5 years of residence19) and natives of the Galapagos Islands.20 Thisfirst differentiation between individuals is motivated by the fact that the marginal effect ofYEARSMIG was estimated to be strong. Additionally, within the immigrant and nativegroups, individuals were differentiated according to their perception of the authorities’surveillance efforts, their perception of the size of penalties, their place of residence, theiraffinity to the regulations and leadership of fishery co-operatives, and their participation inco-operative meetings. These divisions generated 12 profiles with the peculiarity that eachindividual within the immigrant group has a counterpart within the native group. Theresults for the estimated probabilities of each individual profile being a violator arepresented in Table 4.

Within the group of recent immigrants, a probability of being a violator was estimatedfor individual ‘‘M’’ to be 0.997. The profile for this individual considers someone who hasnot seen National Park or Navy vigilance; does not perceive the penalties applied by thePark to be high; does not agree with the regulations; does not consider the leaders of theco-operatives to be representative; and does not have high attendance at organizationmeetings. In contrast, for M’s counterpart within the group of natives, individual ‘‘N’’, weestimated a probability of non-compliance of 0.78. It is interesting to note that if only thevariables from Blocks I and II are modified—the individual has seen surveillance andconsiders the penalties to be high—for the immigrant (AM), we estimated a violationprobability of 0.90, whereas the native counterpart (AN) experiences a higher decrease inthe violation probability to 0.25. Furthermore, we also explored the effect of modifyingvariables included in Block VI, related to the legitimacy of the regulations (REGU-LA_GRL and REGULA_SEACUCUMBER). In this case, the violation probability ofindividual ‘‘BM’’, from the immigration group, falls to 0.87 and the violation probability ofthe counterpart, in the native group (BN), decreases to 0.19. Thus, blocks that representperception of regulations’ legitimacy have a larger effect on the estimated reduction in theviolation probability than those representing traditional enforcement tools (monitoringand penalty), suggesting that individuals who show a degree of agreement with theregulations are more likely to respect them.

19The immigration index (YEARSMIGR) in this case is 0.133. The number of years residency considered (7.5)

is consistent with the expansion of migration registered in the 1990s.20The immigration index (YEARSMIGR) for natives of the islands is 0.

ARTIC

LEIN

PRES

STable 4

Probability of committing an infraction by type of boat-ownera

Block Variable Characteristics of the boat-

ownerbRecent immigrants Natives

M AM BM CM DM EM N AN BN CN DN EN

I SEEN Boat-owner has observed

surveillance of the GNP or the

National Navy almost always

or sometimes

No Yes No No No NO No Yes No No No No

II FINE Boat-owner considers the fines

to be high

No Yes No No No No No Yes No No No No

V ISLAND1 Boat-owner resides on Island 1 No No No No No Yes No No No No No Yes

VI REGULA_GRL Boat-owner strongly agrees,

agrees or is indifferent with the

regulations of small-scale

fishery in general

No No Yes No Yes Yes No No Yes No Yes Yes

REGULA_SEACUCUMBER Boat-owner strongly agrees,

agrees or is indifferent with the

regulations that are applied to

the capture of sea-cucumber

No No Yes No Yes Yes No No Yes No Yes Yes

LEAD_REPRESENT Boat-owner feels that the

leadership of the fishery sector

represents its interests or at

least moderately represents

them

No No No Yes Yes Yes No No No Yes Yes Yes

VII YEARSMIGR Number of years that the boat-

owner has resided on the islands

in the case of not being a native

7.5 7.5 7.5 7.5 7.5 7.5 Native Native Native Native Native Native

VIII ATTEND The boat-owner attends all of

the co-operative meetings

No No No Yes Yes Yes No No No Yes Yes Yes

Probability of infringementc 0.997 0.908 0.868 0.736 0.153 0.526 0.779 0.249 0.187 0.085 0.001 0.026

Source: Elaboration by the authors with the results obtained from the econometric estimations.aModel 2 is defined as: VIOLATION ¼ VIOLATION(CONSTANT,SEEN,FINE,PUNISHED,STORAGE,EDUCA,ISLAND1,REGULA_GRL,REGULA_

SEACUCUMBER,OTROS,LEAD_REPRESENT,YEARSMIGR,ATTEND).bTo calculate the probability of infraction, the specified variables were considered relevant at 5% and 10%. The most prominent results are presented here.cThe probability of infraction is calculated with the average of the variables (continuous and discrete), except for the variables presented in this table.

C.

Viteri,

C.

Ch

avez

/O

cean

&C

oa

stal

Ma

na

gem

ent

50

(2

00

7)

25

3–

27

4270


We then also considered modifying the variables of Block VI, related to attendance atorganization meetings and the level of representation of the fishery leadership, and BlockVIII, related to attendance at organization meetings. Individual (CM) exhibits a violationprobability of 0.74, lower than that of the previous individuals. Similarly, for thecounterpart (CN), in the native group, we estimate a violation probability of 0.09, far lowerthan the previous native individual. This result indicates that fishermen who feel that theyare represented by the organizations leaders and also get involved in co-operative meetingstend to have a lower probability of infringement than those who do not feel represented bythe leadership and who do not attend the co-operative meetings. Furthermore, the decreasein the probability due to these variables (LEAD_REPRESENT and ATTEND) is higherin magnitude than the decrease in probability due to the variables of Blocks I, II, and thoserelated to the legitimacy of the regulations (see Table 4).

Finally, the probability of infraction for each of the profiles for native boat-owners isinferior to the probability of its counterpart within the group of immigrant profiles. Thisresult supports the relevance of the sense of belonging to a community as a determinantfactor of the individual violation decision. Furthermore, this is an indication that theproblems of fishery regulation transgressions for the recent immigrant group need to bedealt with specifically for this group.

5. Conclusions

The results obtained from the econometric analysis indicate that decisions to violatemanagement regulations in the case of boat-owners of the Galapagos Marine Reserve arenot only determined by deterrence variables, but also by variables related to the perceptionof boat-owners with respect to the legitimacy of regulations, the level of membershipwithin the community, and fishermen’s participation in their organizations.

It is important to emphasize the results we obtained related to variables that representsocial aspects, which we found to influence the violation decision, i.e. the level of legitimacyof the norms and the sense of belonging to the community; as well as the moralcommitment the individual has to the compliance and success of the regulations, reflectedin the level of participation in the institutions that generate the regulations. Specifically,our results indicate that the level of legitimacy that the regulations have within the groupof boat-owners, the individual’s sense of belonging, the legitimacy of local organizations,and the levels of participation of fishermen in their cooperatives positively influencedecisions to violate the agreed regulations within the reserve. Thus, our results contributeempirical evidence supporting recent theoretical developments to explain regulationcompliance/violation for the management of natural resources in fisheries.

Additionally, the results are limited in terms of determining a profile based on particularcharacteristics. Only two characteristics were identified to have a significant positive effecton the violation decision: residency on Island 1 and level of formal education. The result incase of education is perhaps surprising. A more in-depth analysis of the individuals withhigher levels of education within the context of participatory management indicates thatthe majority of these individuals (56%) disagree with the regulations and most of them(81%) consider the representation of the leadership to be inadequate.21 This suggests that

21Those individuals with 12 or more years of schooling were considered to belong to the higher educated group;

they represent 37% of the sample.


there is a relationship between education and adopting a critical position towards theregulations and the leadership and, as we observed in the previous results, these aspects areimportant in the violation decision.The results also have implications for the proper design of enforcement to achieve

adequate levels of compliance with fisheries regulations in the reserve. Monitoring andpenalties, traditional deterrence instruments, are limited in their ability to improve thedetection capacity and increase the financial cost of non-compliance, appear to haveinferior results in terms of deterring violations when compared to those instrumentsorientated towards promoting the legitimacy of the norms, improving the representation ofindividuals by the leadership of local organizations, and increasing the participation ofindividuals within grass roots organizations.22

The results of the role of social variables as determinants of violations are useful for theGalapagos National Park authority as well as for conservation organizations that work onthe islands. They suggest that improving compliance might require reallocating efforts andresources in their annual programming. How to make users feel participatory in theirinstitutions is perhaps beyond the purposes of this work; however, there is a wide range ofpotential areas where the reallocation might have positive impacts on compliance results.These range from actions that provide more information about regulations and theirscientific basis, which can directly contribute to improving the legitimacy of theregulations, to actions such as supporting the operation of the participatory managementsystem of the reserve, improving co-operative leader representation, and strengthening co-operative organizations. According to our results, these actions should have morepaybacks in terms of respecting the regulations than traditional enforcement strategiessuch as increased vigilance or penalties.Our suggestions to reallocate efforts and resources to improve compliance do not mean

abandoning traditional strategies of enforcement, control, monitoring, and the impositionof penalties, since all groups are likely to include the presence of chronic violators, who canonly be controlled by traditional methods such as tougher sanctions and more surveillance.The control of these individuals is important because their illegal behavior could erode thelevel of legitimacy of norms and increase the probability of infractions by their fellowfishermen.23

Our work can be extended in a number of ways. We would like to mention two of them.First, it would be desirable to develop an investigation that further explores the differencesthat exist between violation decisions of native individuals with deeper island roots andrecent immigrants. Immigration might be associated with more rule-breaking behavior fora number of reasons. For example, the immigrants might have different views on whatrules should be used, different attitudes toward social pressures, or perhaps differentintertemporal preferences. In addition, because we use a cross-section data set oncompliance behavior, the existence of a potential assimilation process of immigrants to the

22This result is observed independently if the boat-owner is a recent immigrant or native, and also

independently if the individual resides on Island 1.23It is important to underline that the surveillance effort of the National Park is not devoted only to the control

of the activities of the artisanal fisherman operating in the reserve, but also to the potential illegal incursions of

continental and foreign vessels. Weakening the control or sanctions of illegal behavior for potential chronic

violators or vessels outside of the community might send two messages to the rest of the boat-owners who

normally comply; namely, that the regulations are unfair, and that the regulations neither adequately protect the

fishery resources nor the local fishermen [18].


native population behavior is difficult to uncover. Second, we also feel that exploringalternative management regulations in the reserve is worth pursuing. It is possible thatboth the type of management tools used in the reserve fisheries as well as the high rate ofself-reported non-compliance by fishermen have contributed to diluting the sense of secureownership of resources in the reserve. Exploring the possibilities for the use of propertyrights-based systems for the management of Galapagos’ fisheries is another possible roadof future research.

Finally, the empirical results of this study provide evidence indicating a positivecontribution of the Participatory Management System implemented in the GalapagosMarine Reserve on compliance with management regulations. Furthermore, the resultsalso underline the potential of the system to increase compliance with the regulationsbetween the boat-owners of the Galapagos’ artisanal fleet. This is a desirable objectiveconsidering that the conservation and sustainable management of this importantecosystem depend upon them. Exploring this and other issues might contribute to abetter design of conservation programs for the protection of the archipelago.

Acknowledgements

Our thanks go out to the Galapagos National Park for the information provided,especially to the Unit of Marine Resources and the Law Department. Also, we thank theGalapagos Fishermen Cooperatives, the Araucaria Project of the Spanish CooperationAgency, and the Participatory Management Board for their support during fieldwork.Additionally, we are grateful to the Marine Conservation and Research Department of theCharles Darwin Research Station for the information provided. The fieldwork waspossible thanks to the financial of the Catherine and John MacArthur Foundation, in theframework of the program for graduate degree theses in protection of biodiversity andconservation of ecosystems (Master Program on Natural Resource and EnvironmentalEconomics, University of Concepcion). The authors gratefully acknowledge detailed andinsightful comments and suggestions on previous versions of this article from the Editorand an anonymous referee. Our colleagues Jorge Dresdner and Hugo Salgado alsoprovided helpful comments and suggestions on early stages of this research.

References

[1] Toral MV, Murillo JC, Piu M, Nicolaides F, Moreno J, Reyes H, et al. La pesquerıa de pepino de mar

(Isostichopus fuscus) en la Reserva Marina de Galapagos en el ano 2005. Fundacion Charles Darwin/Parque

Nacional Galapagos, Santa Cruz, Galapagos, Ecuador, 2005. 41pp.

[2] Hearn A. Evaluacion poblacional de la langosta roja en Galapagos, 2005. Fundacion Charles Darwin, Santa

Cruz, Galapagos, Ecuador, 2005. 17pp.

[3] Viteri C, Chavez C. Fiscalizacion y Cumplimiento de las Regulaciones Pesqueras en la Reserva Marina

Galapagos (RMG): 1998–2001. Cuestiones Economicas 2003;19(33):135–77.

[4] Sutinen J, Andersen P. The economics of fisheries law enforcement. Land Economics 1985;61(4):387–97.

[5] Anderson L, Lee D. Optimal governing instrument, operation level, and enforcement in natural resource

regulation: the case of the fishery. American Journal of Agricultural Economics 1986;68(4):679–90.

[6] Milliman S. Optimal fishery management in the presence of illegal activity. Journal of Environmental

Economics and Management 1986;13(4):363–81.

[7] Sutinen J, Kuperan K. A socioeconomic theory of regulatory compliance in fisheries. International Journal

of Social Economics 1999;26(1/2/3):174–93.


[8] Charles A, Mazani L, Cross M. The economics of illegal fishing: a behavioral model. Marine Resource

Economics 1999;14:95–110.

[9] Sutinen J, Gauvin J. An econometric study of regulatory enforcement and compliance in the commercial

inshore lobster fishery of Massachusetts. In: Neher PA, et al., editors. Rights based fishing. Dordrecht:

Kluwer Academic Publishers; 1989. p. 415–28.

[10] Furlong W. The deterrent effect of regulatory enforcement in the fishery. Land Economics

1991;67(1):116–29.

[11] Hatcher A, Jaffry S, Thebaud O, Bennett E. Normative and social influences affecting compliance with

fishery regulations. Land Economics 2000;76(3):448–61.

[12] Gordon D, Hatcher A. Further investigations into the factors affecting compliance with UK fishing quotas.

Land Economics 2005;81(1):71–86.

[13] Nielsen JR, Mathiesen C. Important factors influencing rule compliance in fisheries: lessons from Denmark.

Marine Policy 2003;27(5):409–16.

[14] Eggert H, Ellegard A. Fishery control and regulation compliance: a case for co-management in Swedish

Commercial Fisheries. Marine Policy 2003;27(6):525–33.

[15] Randall J. Improving compliance in US Federal Fisheries: an enforcement agency perspective. Ocean

Development & International Law 2004;35:287–317.

[16] Eggert H, Lokina R. Regulatory compliance in Lake Victoria Fisheries. Unpublished Working Paper,

Department of Economics, Goteborg University, Goteborg, Sweden, 2005.

[17] Beckenkamp M, Gumbel W. Emotional appraisal of different situations in commons dilemmas depending on

social value orientation. In: Proceedings of the IASCP 2000 Conference, University of Saarland—

Psychology.

[18] Kuperan K, Sutinen J. Blue water crime deterrence, legitimacy and compliance in fisheries. Law and Society

Review 1998;32(2):309–37.

[19] Nielsen J. An analytical framework for studying compliance and legitimacy in fisheries management. Marine

Policy 2003;27(5):425–32.

[20] Heylings P, Bravo M. El Sistema de Manejo Participativo de la Reserva Marina de Galapagos-Principales

Actividades en el ano 2001. In: Informe Galapagos 2001–2002, Fundacion Natura/WWF Quito, Ecuador,

2002. p. 71–6.

[21] Heylings P, Cruz F, Bustamante R, Cruz D, Escarabay M, Granja A, et al. El Grupo Nucleo de la Reserva

Marina de Galapagos. In: Informe Galapagos 1997–98, Fundacion Natura/WWF, Quito, Ecuador, 1998.

p. 14–6.

[22] Murillo JC. ‘‘Indicadores Socio-economicos de la pesca’’, En: ‘‘Reserva Marina de Galapagos, Lınea Base de

la Biodiversidad’’. In: Danulat E, Edgar GJ, editors. ECCD-SPNG, Santa Cruz, Galapagos, Ecuador, 2002.

p. 440–8.

[23] Becker G. Crime and punishment: an economic approach. Journal of Political Economy 1968;76(2):169–217.

[24] Bardhan P. Analytics of the institutions of informal cooperation in rural development. World Development

1993;21(4):633–9.

[25] Cardenas JC, Strandlund J, Willis C. Local environmental control and institutional crowding-out. World

Development 2000;28(10):1719–33.

[26] Scheaffer R, Mendenhall W, Ott L. Elementos de Muestreo. Tercera Edicion. Estados Unidos de America:

PWS Publishers; 1986.

Legitimacy, local participation, and compliance in the ... · Ocean & Coastal Management 50 (2007)...

Documents

Transcript of Legitimacy, local participation, and compliance in the ... · Ocean & Coastal Management 50 (2007)...