RULES AND DECISION MAKING: UNDERSTANDING THE FACTORS THAT
SHAPE REGULATORY COMPLIANCE
by
Saba Naseem Siddiki
B.A, University of Puget Sound, 2005
M.A., University of Denver, 2007
A thesis submitted
to the University of Colorado Denver
in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
School of Public Affairs
2011
© by Saba Naseem Siddiki
All rights reserved.
This thesis for the Doctor of Philosophy
degree by
Saba Naseem Siddiki
has been approved
by
Chris Weible
Tanya Heikkila
Christine Martell
John Brett
Elinor Ostrom
Date
Siddiki, Saba Naseem (Ph.D., Public Affairs)
Rules and Decision Making: Understanding the Factors that Shape Regulatory
Compliance
Thesis directed by Associate Professors Chris Weible and Tanya Heikkila
ABSTRACT
This dissertation presents findings from a study to assess compliance motivations
within the context of aquaculture in two states, Virginia and Florida. This analysis is
based upon scholarship relating to policy design, regulatory compliance, and the
institutional analysis and development (IAD) framework. Data for this study were
collected in four stages toward a comparative case study analysis of compliance
motivations in the two study states: (1) a preliminary study involving interviews
(n=10) and a questionnaire (n=56; response rate = 57%) of members of the National
Association of State Aquaculture Coordinators; (2) a comprehensive coding of all
state level regulations governing aquaculture in each study state; (3) formal semistructured interviews with 30 members of the aquaculture communities of Virginia
and Florida; and (4) a questionnaire of aquaculture producers in Florida (n=415;
response rate = 19%). Primary findings from this study, indicate that (1) individuals
are more likely to comply with regulations when regulatory enforcement personnel
are perceived as being knowledgeable; (2) when farmers have a desire to maintain a
good reputation with other members of the industry; (3) when farmers have a strong
sense of guilt associated with not complying with regulatory directives; and (4) that
the expression of compliance motivations, such as reputational concerns and feelings
of guilt, is contingent upon a variety of factors, including the desire to protect the
natural environment, prevent consumers from becoming ill as a result of eating a
contaminated product, and to prevent conflict with neighbors and other resource
users.
This abstract accurately represents the content of the candidate’s thesis. I recommend
its publication.
Signed
Chris Weible
Tanya Heikkila
I dedicate this dissertation to my parents, Muhammad and Maryam Shaukat, who
gave me an appreciation for learning, always encouraged me in my goals, and taught
me the value of perseverance and hard work. I also dedicate this to my husband, Oran
Day, for his unwavering support and encouragement.
ACKNOWLEDGEMENTS
I want to express my sincere gratitude to the chairs of my dissertation committee,
Chris Weible and Tanya Heikkila for sharing their knowledge with me and for so
generously offering their time and support. I also wish to thank Christine Martell,
John Brett, and Elinor Ostrom for their valuable participation and insights. Finally, I
wish to thank Anu Ramaswami and the UCD IGERT program (Grant #: DGE0654378).
Support for this research was provided by the National Science Foundation (Grant #:
0721067)
TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION........................................................................................ 10
CHAPTER 2: BUNDLING REGULATORY INSTRUMENTS: AN ANALYSIS OF
THE U.S. AQUACULTURE INDUSTRY .............................................. 52
CHAPTER 3: DIAGNOSING REGULATORY STRINGENCY USING THE
INSTITUTIONAL GRAMMAR TOOL: A COMPARATIVE
ANALYSIS OF U.S. AQUACULTURE POLICIES.............................. 84
CHAPTER 4: THE CULTURE OF COMPLIANCE: CONTEXTUALIZING GUILT,
SOCIAL DISAPPROVAL, AND FEAR OF MONETARY
SANCTIONS .............................................................................................. 118
CHAPTER 5: RULES AND DECISION MAKING: UNDERSTANDING THE
FACTORS THAT SHAPE REGULATORY COMPLIANCE ........... 159
CHAPTER 6: CONCLUSION .......................................................................................... 198
APPENDICES……………........…………………………………………………………..209
Appendix A: Summarizing Institutional Grammar Characteristics ............................. 210
Appendix B. NASAC Study Interview Questions ........................................................ 212
Appendix C. NASAC Study Questionnaire .................................................................... 216
Appendix D. Broader Study Interview Questions .......................................................... 223
Appendix E. Broader Study Questionnaire .................................................................... 226
Appendix F. Supplementary Ordered Logistic Regression Analyses......................... 232
Appendix G. Ordinary Least Squares Regression Results ............................................ 235
BIBLIOGRAPHY. ............................................................................................................. 237
vii LIST OF TABLES
Table 1.1: Primary independent variables...........................................................
27
Table 1.2: Additional analytical variables relating to compliance......................
28
Table 1.3: Concept measurement: primary independent and dependent
variables.............................................................................................
30
Table 1.4: Most-similar case study design: case selection variables..................
39
Table 1.5: Summary of data collection steps......................................................
46
Table 2.1: Correlations between regulatory factors and compliance..................
71
Table 2.2: Summary of interview findings.........................................................
76
Table 3.1 Types of data generated for each IGT syntactic component.............. 102
Table 3.2: Summary of coded Deontic and Or else data....................................
105
Table 3.3 Summary of coded Attribute and Deontic data................................... 111
Table 4.1: Sampling of Q-Sort statements -- Florida aquaculture producers.....
138
Table 4.2: Comparative summary of interview findings....................................
145
Table 4.3: Summary of q-sort results: agreement between prescribed and
actual behavior...................................................................................
148
Table 4.4: Contingent compliance motivations..................................................
150
Table 5.1: Propositions for testing compliance motivations............................... 170
Table 5. 2: Analytical variables and related operationalizations in
questionnaire......................................................................................
178
Table 5.3: Breakdown of interview responses regarding compliance
motivations......................................................................................... 182
viii Table 5.4: Kendall’s Tau bivariate correlations between regulatory based
compliance motivations and individual and community based
motivations and compliance............................................................... 185
Table 5.5: Model 1: Ordered logistic regression for regulatory based
compliance motivations and compliance...........................................
187
Table 5.6: Model 2: Ordered logistic regression for individual and community
based motivations and
compliance......................................................................................... 188
Table 5.7: Model 3: Ordered logistic regression for significant predictors
from table 5.5 and table 5.6 and compliance.....................................
ix 190
CHAPTER 1: INTRODUCTION
Establishing effective governance necessitates understanding the relationship
between human behavior and institutions, such as policies, laws, and regulations. This
dynamic relationship is informed primarily by three factors: (1) the design of
institutions; (2) the mechanisms established to enforce these institutions; and (3) the
internal and externally based motivations that influence how individuals interpret and
choose to respond to institutional directives. Institutions structure the behavioral
opportunities and constraints available to individuals according to the objectives of
their designers in an effort to achieve desired outcomes (Ostrom 2005; Pierson, 1993,
598; Kiser and Ostrom, 2000, 66-67; March and Olsen, 2006). Enforcement of these
institutions is context dependent and can be conducted via governmental or
community based mechanisms. Ultimately, whether individuals choose to comply
with institutions is dependent upon their internal valuations regarding the costs and
benefits of compliance considering motivations emerging from institutional,
individual, and community contexts. An examination of compliance thus requires a
concerted analysis of motivations emerging from each of these realms. This
dissertation presents findings from a study to assess compliance motivations within
the context of aquaculture in two states, Virginia and Florida.
Three scholarly lenses are relevant for this study: (1) Policy Design to
examine the structure of policies; (2) Regulatory compliance to understand
discrepancies between regulatory policies and the behavior actually exhibited by
10
individuals; and (3) the Institutional Analysis and Development (IAD) Framework to
identify behavioral motivations that may affect institutional compliance. The IAD
framework also houses the Institutional Grammar Tool (IGT). The IGT is an
approach to policy analysis that offers the ability to systematically parse the
constitutive elements of institutions and illuminate institutional design characteristics.
Each of the three lenses identified above was jointly applied to provide relevant
analytical guidance as well as a model of the individual from which research
questions and propositions were derived. For example, coupling the IAD framework
with the regulatory compliance literatures, the researcher was able to draw upon a
broader stock of empirical research that point to analytical variables that have been
demonstrated to influence compliance behavior. Also identified through the pairing of
these two literatures is a model of the individual that characterizes specific tendencies
of actors in relation to the variables chosen for analysis (Ostrom, 2007, 26).
This dissertation involved a comparative most-similar case study design of
aquaculture communities in two states: Virginia and Florida. Aquaculture is an
expanding and increasingly regulated industry in which community members are
asked to adapt their behaviors to meet the criteria of various state and federally
initiated regulations (Ackefors et al., 1994). Data were collected using a mixedmethod approach in four stages:
Stage 1: A preliminary study involving interviews (n=10) and a questionnaire (n=56;
response rate = 57%) of members of the National Association of State
11 Aquaculture Coordinators;
Stage 2: A comprehensive coding of all state level regulations governing aquaculture
in each study state;
Stage 3: Formal semi-structured interviews with 30 members of the aquaculture
communities of the two states; and
Stage 4: A questionnaire of aquaculture producers in Florida (n=415; response rate =
19%).
In the following sections of this introduction, a literature review will be
presented which discusses key aspects of policy design, regulatory compliance, and
the IAD framework as they relate to one another for the proposed study, followed by
a presentation of the research questions and propositions guiding this study, a detailed
discussion of the data collection and sampling methods employed, a discussion of the
theoretical, methodological, and practical significance of the study, and finally, a
brief overview of the organization of the dissertation. The body of this dissertation
consists of four stand alone chapters that address the research questions posited in this
introduction through an analysis of collected data.
Literature Review
Policy Design
In democratic societies, policies reflect the collective output of multiple actors
as represented in policy debates, voting, and other community engagement
mechanisms through which constituents are asked to express their political values and
goals (Bobrow and Dryzek, 1987, 18-19; Pressman and Wildavsky, 1973). The
12 collective output is articulated in a set of prescribed actions, the performance of
which, serve as a means to achieve desired outcomes. Policies are dynamic through
periodic adjustment and revision in response to the variable demands from
constituencies (Peters and Pierre, 1998) and other actors involved in the policy
process (Bobrow and Dryzek, 1987, Schneider and Ingram 1997; Schneider and
Sidney, 2009). Bobrow and Dryzek (1987) articulate these points: “Policy designs,
like any type of design, involves the pursuit of valued outcomes through activities
sensitive to the context of time and place…Policy design faces a messier world of
multiple unclear and conflicting values, complex problems, dispersed control, and the
surprises that human agents are capable of springing.” (Bobrow and Dryzek, 1987,
18-19)
Scholars have studied policy design from a variety of angles to explore the
ways in which policies respond to, and affect, political, social, and bio-physical
contexts. Initially led by Dahl and Lindblom (1953), the discussion centered on how
various forms of policies were being used by governments to reach political goals
(Dahl and Lindblom, 1953; Schneider and Ingram, 1997, 69). Others have sought to
identify the different policy instruments applied by policy makers in different policy
contexts (Bardach, 1980; Salamon, 1989; May, 1991, 187; Sidney, 2007).
Lowi (1964; 1972) characterized policies as consisting of one of the following
four types based on the content of the policy and the political organization of actors
involved with its development: Distributive, Regulatory, Redistributive, and
13 Constituent (Lowi, 1964). Lowi’s policy typography has been criticized for
characterizing policy types as being mutually exclusive and also for focusing on the
ways that politics determine policies without also considering how institutions and
political culture shape policies (Heinelt, 2007, 111-112). However, despite criticisms,
Lowi’s typology offers a generally useful categorization. Of particular interest for this
study is regulatory policy. Regulatory policies are generally those that seek to
regulate behavior by stipulating limits or controls on acceptable behavior and/or
activities. Speaking to the foundational aspects of regulatory policy design, Meier
(2000) writes: “Regulatory policies affect policy through normal mechanisms of
policy implementation. Another policymaker, in this case usually Congress, sets
general guidelines on regulatory policy and agencies expand these general guidelines
into specific policy actions” (Meier, 2001, 72).
Regulatory policies are supported by a regulatory system, including law
enforcement personnel and courts, meant to ensure that policy directives are carried
out and responded to in the manner prescribed – that is, that compliance with policies
is achieved. The underlying assumption is that law abiding citizens are expected to
follow the prescriptions embodied within these documents. It is further expected that
disagreement with them be voiced in accordance with formal governmental
procedures that engage policy makers and other citizens of the community. Bardach
and Kagan (1982) summarize this point:
14 “The basic techniques of these regulatory programs have been the legislation of rules
of law specifying protective measures to be instituted by regulated enterprises and the
enforcement of those rules by government inspectors and investigators, who are
instructed to act in accordance with the terms of these regulations, not on the basis of
their own potentially arbitrary judgment. But uniform regulations, even those that are
justifiable in the general run of cases, inevitably appear to be unreasonable in many
particular cases” (Bardach and Kagan, 1982, 3).
As is expressed by Bardach and Kagan, numerous empirical examples exist to
demonstrate that the ideal situation they describe is very rarely achieved (March and
Olsen, 2006; Schneider and Sidney, 2009; Linder and Peters, 1992) and many policy
design scholars have sought to explore the connection between policies and the
contexts in which they are derived from, and for, in instances in which individuals or
the policies themselves fail to meet ideal expectations (Sidney, 2007; May, 1991;
Linder and Peters, 1989; Bobrow and Dryzek, 1987). For example, Steinberger
(1980) argued that manifested in individuals’ behavior is their interpretation and the
meaning they assign to a particular policy (Steinberger, 1980; Schneider and Ingram,
1997). Other scholars look to motivations such as enforcement practices (Burby and
Paterson, 1993; Gray and Scholz, 1993; Helland, 1998; May, 2005), the perceived
technical competence of regulatory agents (Bardach and Kagan, 1982), and the
presence of trust between enforcement personnel and those they are regulating
(Scholz and Lubell, 1998).
This discussion of policy design is meant to display the various ways that
policy design scholars have examined how policies structure, and are structured by,
the behavior of constituents interacting in relation to particular policy area. For the
15 purposes of this study, policy designs are characterized as the institutions that policy
designers use as a vehicle to achieve desired outcomes by prescribing within them a
set of opportunities and constraints available to different community actors. The
degree of congruency between prescribed behavior articulated within policies and
actual behavior exhibited by individuals is assessed in light of the ways in which is it
informed by various internally and externally derived factors. Such compliance
motivations will be discussed more thoroughly in the following section on regulatory
compliance.
Regulatory Compliance
As previously asserted, compliance is one of the primary goals associated with
regulatory policies. As May writes, “The typical policy is a package of policy
instruments aimed at some combination of gaining compliance through the use of
mandates, improving short-term performance through the use of incentives,
enhancing longer term performance through various capacity building measures, or
altering the system for providing goods and services by introducing system changes”
(May, 1991, 199). When significant efforts are made by policy designers to ensure
that compliance is achieved, cases on non-compliance incite inquiry as to what
motivates individuals to not comply with policy directives. Three types of
motivations identified by regulatory scholars as being particularly influential in
shaping compliance that were explored in this study are: a fear of monetary sanctions,
personal guilt or shame, and social disapproval.
16 The classic regulatory deterrence model is premised upon the assumption that
legal sanctions suffice to thwart the desire for non-compliance on the part of
regulated agents. Consistent with the rational actor model of the individual (Hatcher,
2000), regulated actors from this perspective are considered self utility maximizing
agents in which the incentive to maximize benefits, or conversely, to not bear
excessive costs, is the sole motivator guiding individuals’ decision making processes.
As such, sanctions administered through a regulatory entity are viewed as the primary
coercive mechanism for fostering regulatory compliance (Zimring and Hawkins,
1973; Bentham, 1789; Becker, 1968).
Increasingly, empirical research in the regulatory field that draws upon
scholarship from Sociology and Social Psychology (Elster, 1989; Coleman, 1990;
Ajzen, 1988) has shown that a variety of other factors contribute to regulatees’ costbenefit estimations regarding when to comply with regulatory directives (Hatcher et
al., 2000). Additional factors include social sanctions and influence, or social
disapproval (Sutinen and Kueperan, 1999; Braithwaite and Makkai, 1991), and
feelings of personal shame or guilt (Grasmick and Bursik, 1990). Hatcher et al.
(2000), for example, found that social pressures served as an effective deterrent to
non-compliance relating to catch quotas, or individual fishing quotas, in the United
Kingdom. Similarly, Kuperan and Sutinen (1995) have explored the relationship
between compliance and feelings of moral obligation among regulatees regarding
fishery zoning regulations in Malaysia. Research focusing on individual or
17 community based factors in the regulatory scholarship has been limited to date,
however, and many areas remain to be explored regarding socially based compliance
motivations.
Regulatory scholars have also studied a number of political/regulatory
variables in understanding factors that influence compliance, including: enforcement
practices, specifically, frequency of inspections (Burby and Paterson, 1993; Gray and
Scholz, 1993; Helland, 1998; May, 2005), belief congruency between regulators and
regulates regarding the way an industry should be managed (May, 2005; Bardach and
Kagan, 1992), technical competence of the regulatory agency as perceived by
members of the industry (Bardach and Kagan, 1982), and the presence of trust
between the two types of actors (Scholz and Lubell, 1998).
Presented within this brief overview of regulatory literature is a distinction
between regulatory based compliance motivators, such as sanctions administered by
an outside agency, and individual and community based motivators, such as guilt and
social disapproval that, together, inform the behavior exhibited by individuals. The
latter emerge from the experiences and interactions between actors in a given
regulatory domain. These individual and community-derived motivations are a
product of the bio-physical and social context in which actors are housed. An
institutional approach is best suited to understand the institutions that structure the
behavior of various actors, and individuals’ responses to them, as a result of these
18 bio-physical and social contexts (Ostrom, 2005). The institutional lens used in this
study was the institutional analysis and development (IAD) framework.
Institutional Analysis and Development (IAD) Framework
The institutional analysis and development (IAD) framework provides a
structured approach for mapping out the institutions that govern actions and outcomes
within collective action arrangements (Ostrom, 2007). The structure of the action
space in which individuals interact is presumed to be a product of three inter-related
variables, including “the rules [institutions] used by participants to order their
relationships, attributes of the biophysical world, and the structure of the more
general community within which any particular arena is placed” (Ostrom, 2005, 15).
Within this context, individuals’ actions are presumed to be influenced by the types of
participants involved, their relative positions within a community, potential
behavioral outcomes, perceived action-outcome linkages, the amount of control that
participants can exercise in affecting these linkages, information flows, and the costs
and benefits assigned to actions and outcomes (Ostrom, 2005, 14).
Within the IAD framework, individual behavior is examined in relation to the
institutions by which it is informed. Institutions are understood to be contextual in
nature and interactive with the various cultural and biophysical attributes of the
arenas in which they are applied (Ostrom, 1994). Further, institutions are generated
by actors to structure their behaviors and participant roles and responsibilities.
Ostrom (1994) writes that, “Rules [institutions] are the result of implicit or explicit
19 efforts to achieve order and predictability among humans by creating classes of
persons (positions) who are then required, permitted, or forbidden to take classes of
actions in relation to required, permitted, or forbidden states of the world” (Ostrom et
al., 1994, 38). Sometimes these institutions are formalized into policies. Consistent
with the policy design and regulatory literatures it is through this process that
constraints and opportunities are formally structured and supported by state imposed
enforcement and judicial mechanisms.
The IAD framework offers a distinction between “rules-in-form” and “rulesin-use,” which are also referred to as “institutions-in-form” and “institutions-in-use”
in this dissertation. This is because institutions can exist in the form of rules, norms,
and strategies. Thus, the terms “institutions-in-form and “institutions-in-use” are
more encompassing of different types of institutional directives. Institutions-in-form
are those which have been codified in formal documents such as policies, laws, and
regulations. Comparatively, institutions-in-use are articulated in social patterns of
behavior (Ostrom, 2007). Over time, these institutions-in-use are recognized by
individuals so as to structure their interactions with another in daily life to foster
reciprocity, expected action-outcome linkages, and resource management techniques.
A variety of internal and externally based motivations are presumed to animate
individuals’ decision making behavior in relation to institutional directives (Crawford
and Ostrom, 1995).
20 Decision making can occur at multiple nested levels, where decisions and
institutions designed at one level structure the opportunities and constraints available
to actors at other levels (Ostrom, 2005, 57). Levels of decision making are
categorized as metaconstitutional, constitutional, collective choice, and operational.
According to Ostrom (2005):
“Operational rules directly affect day-to-day decisions made by the participants in
any setting. These can change relatively rapidly—from day to day. Collective choice
rules affect operational activities and results through their effects in determining who
is eligible to be a participant and the specific rules to be used in changing operational
rules. These change at a much slower pace. Constitutional choice rules first affect
collective-choice activities by determining who is eligible to be a participant and the
rules to be used in crafting the set of collective choice rules that, in turn, affect the set
of operational rules” (Ostrom, 2005, 58).
Relevant for this study is understanding how institutions-in-form developed at the
collective choice level are interpreted and responded to by individuals at the
operational choice level and how internally and externally derived motivations shape
this relationship. In other words, how do individuals interpret and respond in their
daily behaviors to institutions designed at the state level by regulatory bodies?
Internal and External Motivations and Related Model of the Individual
The discussion of internal and external motivations as it pertains to this study
is centered on their ability to explain the behavior exhibited by individuals.
Specifically, the internal motivations that were studied herein include (1) the personal
shame or guilt that actors may feel from not complying with institutions-in-form, and
the external motivations include (2) social disapproval, which arises from actors’
21 desires to establish a positive reputation with other actors (Ostrom, 2005, 146-147)
and (3) fear of incurring monetary sanctions.
The internal and external motivations regarding compliance studied herein are
consistent between the IAD and regulatory literatures in that both consider the
influence of the fear of monetary sanctions, peer pressure, and feelings of personal
guilt or shame in influencing compliance outcomes (Frey, 1994; Bendor and
Mookherjee, 1990; Crawford and Ostrom, 1995). This list of factors is certainly not
exhaustive in either of these literatures. The researcher has chosen to focus on these
factors in particular as they are explicitly stated in the IAD literature as being
influential in shaping individuals’ response to institutions (Crawford and Ostrom,
1995; Ostrom, 2005; Speer, 2010).
The discussion of these particular motivators in relation to one another from
the perspective of each is cast around a model of the individual that assumes
individuals are boundedly rational and “that the individuals who calculate benefits
and costs are fallible learners who vary in terms of the number of other persons
whose perceived benefits and costs are important to them and in terms of their
personal commitment to keeping promises and honoring forms of reciprocity
extended to them” (Ostrom et al., 1993, 45). From a regulatory perspective this model
of the individual contrasts with the rational actor model upon which the classical
regulatory deterrence approach is premised. From an institutional perspective, this
view of the individual contrasts with rational choice institutional models that view
22 actors as having static preferences and behavior solely as a product of externally
provided constraints, information, and outcome possibilities, while neglecting
socially, or community, derived motivations (McCay, 2002; Shepsle, 2006, 24-25).
It is assumed, thus, that social pressure and material and non-material rewards
all factor into community members’ decision making processes. Additionally, the
types of enforcement and sanctioning mechanisms chosen by an authoritative body
may also influence how individual actors rationalize decisions regarding actions and
outcomes. In other words, this choice may impact the influence of internal and
external motivations at the individual level regarding compliance. Ostrom explains
that “internal and external motivations are added to an individual’s payoff to
represent the perceived costs and rewards of obeying or breaking a prescription”
(Ostrom, 2005, 146). As such, the decision to rely on external or internal sanctions is
largely driven by the costliness associated with each (Ostrom et al., 1994, 48). As in
any traditional cost-benefit analysis estimation, the rewards of sanctioning must be
greater than the costs of their imposition and enforcement. Crawford and Ostrom
(1995) explain:
“If it is costly to monitor the actions of others and/or to impose sanctions on them,
those assigned these tasks may not be motivated to undertake these assignments
unless (1) the monitor or sanctioner face a probability of an Or else [a sanction
associated with not complying with an institution-in-form], (2) social pressure to
monitor and sanction is large and is salient to the monitor and sanctioner , (3) The
monitor and sanctioner hold some strong moral commitment to their responsibilities,
and/or (4) The payment schemes for the monitor and sanctioner create prudent
rewards high enough to offset the costs.” (Crawford and Ostrom, 1995, 589)
23 Thus, in order to gain a comprehensive understanding of the behavior exhibited by
actors within a community, one needs to understand the array of incentives presented
to the individual externally, as with monetary and social sanctions, as well as the
internal incentives which may manifest in personal shame or guilt associated with
non-compliance.
It is important to note that measuring the cost-benefit estimations of
individuals considering internal and external motivations is a challenging endeavor in
non-laboratory settings; that is, in a simplified social setting where the researcher may
introduce a quantifiable measure to reflect individuals’ cost-benefit estimations in
relation to particular activity. In field settings, the measurement of such variables is
necessarily less quantifiable and less precise.
Compliance from an IAD Perspective
Compliance within the setting of the IAD framework as it relates to this study
is characterized as conformance with institutions-in-form, e.g., state level regulations,
and is shaped by both individuals’ normative and material considerations emerging
from biophysical, community, and individual contexts (Ostrom, 2005, 167). In
addition to the three motivations, monetary sanctions, social disapproval, and
personal guilt or shame, other scholars studying compliance in relation to them have
found that following biophysical, community, and individual factors to be influential:
(1) Change in resource availability with use (Olson, 1991; Hirschman, 1985;
Mansbridge, 1994); (2) Involvement of community members in labor unions (Offe
24 and Wisenthal, 1980); (3) Involvement of community members in rule development
(Frey, 1994); and (4) Perceived legitimacy of rules (Ostrom, 2005). For the purposes
of this study, such variables are characterized as “contingent variables,” meaning that
the extent to which internal and external motivations (fear of social disapproval,
feelings of guilt, and fear of monetary sanctioning) will be expressed in individuals’
decision making is contingent upon a variety of community-based, bio-physical, and
political variables, as well as individual situational and endogenous factors. For
example, interviewees in this study expressed that their primary compliance
motivation was guilt associated with non-compliance and that this guilt is rooted in a
desire to protect the natural environment. In other words, these individuals would feel
guilty from not complying with regulations as this could result in negative impact to
the environment. In this case, a desire to protect the environment would be
characterized as a contingent motivation.
The purpose of this discussion of the IAD framework is to demonstrate that it
is an appropriate lens to support an analysis of the relationship between policy
designs, regulatory compliance, and internal and external compliance motivations.
Research questions and propositions arising from the joint application of these the
three literatures – policy design, regulatory compliance, and the IAD framework –
will be discussed in the following section.
Research Questions and Propositions
Research Questions
25 The following discussion provides a synopsis of how the analytical concepts
explored in this study relate to one another. Behavior exhibited by individuals is
reflective of their decision making considering institutional directives and internal
and external motivations. Contextual bio-physical and community attributes are
assumed to shape the extent to which such motivations may influence individuals’
decision making, along with a variety of individual situational characteristics and
endogenous variables such as principles of justice, feelings of responsibility, and a
desire to act appropriately. These factors are characterized as contingent motivations
and are presumed to shape the expression of internal and external motivations.
Finally, the outcome of interest is the extent to which the behavior exhibited by
individuals in is in compliance, or conformance, with institutions-in-form.
In line with this conceptual logic, the following research questions are
explored in this study as part of the overarching question: what motivates regulatory
compliance? The first of these is the primary research questions. The second and third
are secondary research questions.
RQ1: Which internal and external motivators are most influential in guiding the
behavior of community actors in relation to institutions-in-form?
RQ2: Which contingent motivations are most influential in shaping the expression of
internal and external compliance motivations?
RQ3: How can the constitutive elements of institutions be assessed and compared?
Propositions
26 No explicit theory exists to inform the formulation of specific hypotheses that
can articulate directionality of the influence of the independent variables upon the
dependent variables of interest. As such, one generic proposition was offered in this
study to explain the ways and extent to which individuals’ behavior is influenced by
internal and external compliance motivations; specifically, a fear of monetary
sanctioning, reputational concerns, and feelings of guilt associated with noncompliance: Social disapproval and feelings of personal guilt or shame are more
likely to influence individuals’ decision making regarding compliance than is the fear
of monetary sanctions.
Table 1.1 shows the primary independent variables considered for this study
(internal and external motivations) and the perceived influence the presence/absence
of each was expected to have on the dependent variable – compliance.
Table 1.1 Primary independent variables
Internal Motivations
Personal guilt or shame
Effect on Compliance
(where, ‘+’ = compliance
increases with presence and
‘-‘ = compliance decreases
with presence
+
External Motivations
Monetary Sanctions
+
Social Disapproval
+
Variable Type
Variable
In addition to these variables, Table 1.2 provides a list of additional variables
27 that have been demonstrated to influence compliance and that were assessed in
relation to compliance in this study through interviews and questionnaires. The list
presented in Table 1.2 was collated from various applications spanning the policy
design, regulatory compliance, and IAD literatures.
Table 1.2 Additional analytical variables relating to compliance
Variable
 Enforcement practices of regulatory enforcement personnel
 Perceived technical capacity of regulatory enforcement personnel
 Trust of enforcement personnel
 Regular communication between regulatee and regulator regarding regulations
 Perceived regulatory appropriateness
 Extent to which regulations are perceived as being consistent with industry
level best management practices
 Knowledge sharing among industry members regarding the
regulatory/administrative aspects of the industry
 Knowledge sharing among industry members regarding the scientific/technical
aspects of the industry
 Moral obligation to produce a good product
Finally, the analysis of compliance motivations in this study also examined
the types of contingent motivations that influence the expression of primary
compliance motivations. Contingent motivations were identified in an exploratory
28 matter and thus no variables were identified prior to data collection. A
complementary examination of these primary and contingent variables allows the
researcher to capture more analytical dimensions; the former set of variables
informing when compliance is expected to occur (Ex. When an individual fears
monetary sanctioning, she/he is more likely to comply with institutions-in-form), and
the latter offering an explanation as to how or why certain fixed factors interact with
the internal psychological variables of interest (external and internal compliance
motivations) as they do to produce a particular outcome (compliance) (Baron and
Kenny, 1986). In this way, the contingent variables represent the causal mechanisms
that inform the relationship between behavioral motivations and compliance. George
and Bennett (2005) describe causal mechanisms consistent with this logic: “Causal
mechanisms are the independent stable factors that under certain conditions link
causes to effects [and] are central to causal explanation” (George and Bennett, 2005,
8).
Concept Measurement
Table 1.3 provides a list of the primary independent and dependent variables
relevant for the proposed study. Data for each of the independent variables and the
dependent variable was collected through the preliminary NASAC study, coding,
interviews, and/or the online questionnaire.
29 Table 1.3 Concept measurement: primary independent and dependent variables
Variable
Type
Independent
Dependent
Variable
Research
Question1
1, 2
Data Collection
Method
Interviews and
Questionnaire
IV2:
Monetary
Sanctions
1, 2, 3
Interviews,
Questionnaire,
and Coding
IV3:
Personal
Guilt or
Shame
1, 2
Interviews and
Questionnaire
DV1:
Compliance
1, 2
Interviews and
Questionnaire
IV1:
Social
disapproval
1
Variable Measurement
Self-reporting of study participants in interviews and
questionnaire regarding the extent to which social
disapproval influences their decision making regarding
compliance with institutions-in-form in relation to
monetary sanctions and feelings guilt or shame.
Self-reporting of study participants in interviews and
questionnaire regarding the extent to which the fear of
monetary sanctions influences their decision making
regarding compliance with institutions-in-form in
relation to social disapproval and feelings of guilt or
shame. The presence of monetary sanctions within
institutions-in-form was also identified via coding.
Self-reporting of study participants in interviews and
questionnaire regarding the extent to which feelings of
guilt or shame influences their decision making
regarding compliance with institutions-in-form in
relation to social disapproval and monetary sanctions.
Agreement between prescribed and actual behavior.
Measurement around coding using Institutional
Grammar. Ex. Percent agreement between prescribed
and actual Deontics as indicated by study participants
in interviews.
Research Questions: RQ1: Which internal and external motivators are most influential in guiding the behavior of community actors in relation to
institutions-in-form? RQ2: Which contingent motivations are most influential in shaping the expression of internal and external compliance motivations?
RQ3: How can the constitutive elements of institutions be assessed and compared?
30
Research question 3, regarding how the constitutive elements of institutions can be
assessed and compared, was addressed entirely through coding. Activities exhibited
by individuals that diverge from what is prescribed in institutions-in-form was
characterized as non-compliance; in other words, compliance was equated with
conformance with institutions-in-form, and vice-versa (Ostrom, 2005, 167). The
justification for pursuing compliance data through a questionnaire and interviews
rather than on state records is that there may be types of compliance exhibited by
aquaculturists that are not registered or detected by enforcement officials, and also
because some forms of non-compliance may appear subtle from the state’s
perspective but may represent important messages and/or meaning from the
perspective of aquaculturists.
Case Study
The United States currently produces approximately 20% of its seafood
consumed while importing 80%, resulting in a seafood trade deficit that exceeds nine
billion dollars (NOAA 2009). This deficit has prompted federal and state policy
makers to encourage the development of a domestic aquaculture industry. The
production of aquaculture involves consideration of complex interdependencies
among ecological, economic, technical, and social factors (Firestone, Kempton,
Krueger, and Loper 2004), resistance from the public regarding farmed seafood
(Amberg and Hall 2010; Mazur 2006), and numerous concerns about the industry
31
from disease control to degradation of marine ecosystems (Black 2001; Francik 2003;
Naylor et al., 2000; Mazur 2006; Treece 2002).
As the U.S. aquaculture industry grows, so too is the number of state level
regulations designed to govern it, taking into account all of the above factors. Similar
to regulations designed for other natural resource based industries, aquaculture
regulations tend to be fairly technical and decentralization of regulatory governance is
commonly observed (May, 2005). Such decentralization has meant that the types of
regulations and supporting regulatory mechanisms vary widely from state to state.
The receptivity of recent regulatory efforts in different state aquaculture industry
contexts also varies. When new regulations are applied in states that have well
established industries, receptivity to them depends, in part, on how consistent they are
with industry level best management practices and norms. It also depends on how
contextually appropriate regulations are perceived as being.
Given the changing nature of the regulatory environment relating to U.S.
aquaculture, it provides a theoretically interesting context within which to examine
compliance motivations as it is one that is characterized by increasing levels of state
level regulations while simultaneously representing an environment in which industry
members have demonstrated a proclivity to develop community level best practices
and norms. As such, it provides the appropriate setting for analyzing diverse
compliance motivations, including those stemming from features of the regulatory
environment as well as those that are individual and community based.
32 Research Methodology
Case Study Selection
A comparative case study design was used to address the research questions
posed for this research study in which two cases of aquaculture communities in the
United States were examined to understand cross-case and within-case variation
(Gerring, 2007, 28; Miles and Huberman, 1994; Pierson, 1993). The population of
relevance was any U.S. state which houses an aquaculture community, which
currently includes all fifty states. The selection of study cases was conducted based
on data collected through a preliminary study of state aquaculture coordinators. The
study involved a questionnaire of 56 and interviews with 10 members of the National
Association of State Aquaculture Coordinators (NASAC). The questionnaire was
administered in March 2010. Of the 56 individuals to whom the questionnaire was
sent, 32 responded, yielding a 57% response rate. In the questionnaire, NASAC
members were asked to respond to a series of questions regarding the structure of
regulatory mechanisms within their state, levels of compliance with regulatory
policies, perceived contributors to compliance, and aquaculture community
characteristics. The overall purpose of this study was to glean an understanding of the
regulatory landscape within each U.S. state in order to provide a basis upon which to
compare them on the independent and dependent variable dimensions relevant for the
broader dissertation study.
33 George and Bennett (2005) state that one of the requirements for conducting
sound case study research is ensuring that the variables chosen for inclusion in the
analysis be “of theoretical interest for purposes of explanation” (George and Bennett,
2005, 69). Currently, no causal theory of institutional compliance exists to specify
key variables and case parameters with which to guide an analysis. Ostrom (2005)
states, “The development and use of theories enable the analyst to specify which
components of a framework are relevant for certain kinds of questions and to make
broad working assumptions about these elements. Thus, theories focus on parts of a
framework and make specific assumptions that are necessary for an analyst to
diagnose a phenomenon, explain its process, and predict outcomes” (Ostrom, 2005,
28). To remind, the primary independent variables in this study (fear of monetary
sanctions, fear of social disapproval, and feelings of personal guilt or shame) are
identified by Sue Crawford and Elinor Ostrom (1995; 2005) in relation to the
Institutional Grammar Tool and related discussion of internal and external
compliance motivations. Additionally, many of the additional variables examined in
this study are also are identified in the IAD framework as being key variables
supporting an institutional analysis, including: institutional and/or social/community
attributes. The remaining variables were identified based on a review of the
regulatory compliance literature, and include: political/regulatory characteristics,
individual situational characteristics, and endogenous factors.
34 The type of behavior that was singled out for examination in this study was
compliance with state-level aquaculture regulations. Of central interest was
understanding a lack of variation in compliance outcomes factoring variation on one
independent variable. This type of design is characterized as a most-similar case
study design. In such a design, the cases chosen for comparison share similar qualities
on all variables, except one identified as being of theoretical interest (Gerring, 2007,
131). Typically, variation is sought on the dependent variable, though it is also
possible to have variation on the independent variable based upon which explanatory
factors the analyst wants to uncover (Gerring, 2007). A most-similar research design
allows the analyst to hold multiple variables constant to allow for the ability to
consider additional intervening factors that influence the relationship between
independent and dependent variables.
For the study, similarity was sought on all variables but state level regulations,
specifically considering regulatory stringency. That is, those cases were selected that
exhibit varying degrees of regulatory stringency while sharing similar characteristics
in all other relevant respects, including compliance outcomes. Regulatory stringency,
characterized as a measure relating to institutions-in-form, was central to the analysis
and thus selected as the varying factor. For example, two of the primary independent
variables (feelings of shame or guilt associated with non-compliance with
institutions-in-form and monetary sanctioning) as well the primary dependent
35 variable (compliance with institutions-in-form) are all anchored on this factor. In
other words, each of these variables is directly relates to institutions-in-form.
Additionally, due to the nature of the data being used for case selection,
regulatory stringency was deemed an appropriate choice for the varying factor as this
is information that is easily conveyed by state aquaculture coordinators describing
state level characteristics. This contrasts, for example, with individual situational
characteristics and endogenous factors for which aquaculture coordinators may be
able to provide a general sense but not individual level data. Since this type of data
was not obtainable through the questionnaire, these factors were not an appropriate
choice upon which to select cases. However, it was possible to obtain information
from the questionnaire responses that represent characteristics of the bio-physical
setting, general views regarding the political/administrative aspects of the state
regulatory system, as well as industry dynamics.
A first step in the selection of cases involved identifying how the states
represented in the preliminary NASAC study compared. Data were sorted and
analyzed to allow for a comparison on the variables of interest. For each of the
variable categories (e.g. biophysical attributes, political/regulatory characteristics,
etc.), the two cases were compared on at least one dimension. These dimensions were
captured in one of the questionnaire items posed to respondents from the NASAC
study, with each dimension corresponding with one item. Internal and external
36 motivations were not included as case selection variables as this information must be
obtained from individuals in communities.
The selection of cases was conducted in a stratified manner. From the full
sample of 30 cases, those cases were first selected that exhibited the maximum
amount of variation in terms of regulatory stringency; for example, those cases in
which regulations were either reportedly very stringent or very non-stringent. Next,
the states were compared in relation to regulatory compliance. The sample of cases
was then further narrowed by selecting those cases which were reported to have high
to very high compliance with aquaculture regulations. The remaining cases were then
compared across other dimensions such as political regulatory characteristics,
biophysical characteristics, and social, community, and industry characteristics. Two
sets of two states exhibited opposing levels of regulatory stringency, very high
compliance with aquaculture regulations, and similar characteristics on several other
dimensions: Hawaii and Pennsylvania and Virginia and Florida. From these two
options, Virginia and Florida were chosen as the two cases for the proposed study. In
addition to comparability on theoretical variables, these states are comparable in
additional ways, including, the types of aquaculture produced, the presence of both
marine and inland aquaculture, and the relative establishment of the aquaculture
industry. Virginia and Florida were also considered to be comparable due to their
geographic proximity and shared regional characteristics as compared to
37 Pennsylvania and Hawaii. Table 1.4 displays how the two states compare across the
variables of interest.
38 Table 1.4 Most-similar case study design: case selection variables
Case One: Virginia
Case Two: Florida
Non-Stringent Regulations
Very Stringent
Very Clear Regulations
Very Clear Regulations
Inexpensive Permits
Inexpensive Permits
Institutions-in-Form

Regulatory Stringency
Political and Regulatory Characteristics

Regulatory Clarity

Permitting Costs

Industry involvement in Reporting NonCompliance
Moderate Involvement
Moderate Involvement

Regulatory Clarity as a Contributor to
Compliance
Significant Contributor
Significant Contributor

Strong Penalties as a Contributor to
Compliance
Mild Contributor
Mild Contributor

Industry Trust of Monitoring and Enforcement
Officials as a Contributor to Compliance
Moderate Contributor
Moderate Contributor
Significant Barrier
Significant Barrier
Social, Community, and Industry Characteristics

Start Up Costs as a Barrier to Aquaculture
Development
39

Stringent Environmental Protection
Regulations and Safeguards as a Barrier to
Aquaculture Development

Complicated Regulatory Process Associated
with Obtaining Permits, Licenses, Etc. as
Barrier to Aquaculture Development

Domestic Competition as a Barrier to
Aquaculture Development

Local User Conflicts as a Barrier to
Aquaculture Development
Moderate Barrier
Moderate Barrier
Minor Barrier
Minor Barrier
Moderate Barrier
Moderate Barrier
Minor Barrier
Minor Barrier
Significant Barrier
Moderate Barrier
Very High Compliance with
Aquaculture Regulations
Very High Compliance with
Aquaculture Regulations
Bio-physical Attributes

Resource Constraints as a Barrier to
Aquaculture Development
Compliance

Compliance with Aquaculture Regulations
40 As stated by George and Bennett, selecting cases that are identical in all
characteristics, especially at the state level, is challenging and is rarely possible. As
such, many researchers must choose cases that are identical on some variables and
similar on others. This is the method chosen in the case selection procedure for this
study. As may be noted in Table 1.4, all of the variables, except that relating to biophysical attributes, are reportedly identical. For the latter, the criterion was that the
variables be characteristically similar. Resource constraints are a barrier to
aquaculture development in both cases, but more so in Virginia than Florida.
To corroborate findings from the NASAC data, informal interviews with three
NASAC members were conducted to ensure that the cases were appropriate
selections given the researcher’s analytical objectives. In these interviews, the
research objectives of this study were described, including explaining the desire to
compare two U.S. states that share similar characteristics but differ with regard to
regulatory stringency. Discussions with these individuals revealed that these cases
were well suited for the proposed analysis.
Methods of Data Acquisition and Sampling
Once the case study states were selected, data for the study were collected in
the following steps and for the following purposes:
First, all state level aquaculture regulations for each of the selected study
states, Virginia and Florida, were coded in their entirety in accordance with the IAD’s
Institutional Grammar Tool (IGT). In addition to providing a systematic means
41
through which to understand the content of these regulations, the IGT was applied to
demonstrate the Tool's applicability in conducting comparative institutional analyses
and as an explanatory exercise to see if it may be applied as a diagnostic tool to
discern the regulatory stringency of institutions.
The IAD’s Institutional Grammar Tool was first proposed by Crawford and
Ostrom (1995) as a tool with which individuals conducting analysis using the IAD
framework can systematically identify and code institutions. The Tool is applied to
examine institutional documents, such as policies and laws, by dividing phrases from
the document into individual statements and dissecting these statements in accordance
with six syntactic elements (please see Appendix A for a summary on institutional
grammar characteristics):






Attribute [A], the actor to whom the statement applies;
oBject1[B]¸ the animate or inanimate receiver of action within the statement;
Deontic [D], the prescriptive operator that indicates whether the focal action
of the statement may, must, or must not be performed;
aIm [I], the action of the statement;
Condition [C], the temporal, spatial, or procedural boundaries in which the
action of the statement is or is not to be performed; and
Or else [O], the punitive sanction associated with not carrying out the
statement directive as prescribed.
In the Grammar, there are three necessary conditions for a phrase to constitute an
institutional statement. Each institutional statement must contain at minimum an
1
The original grammar did not include the Object as an institutional statement component. The Object
was introduced by Siddiki et al. (2011) in an effort to clarify coding guidelines and enhance the
applicability of the institutional grammar tool.
42 Attribute, an AIm, and a Condition. The Deontic and Or else component may be
present but are not necessary to qualify a phrase as an institutional statement. Those
statements which contain each of the aforementioned components are characterized as
rules (ABDICO), while statements containing the first five components (ABDIC) are
characterized as norms, and statements only containing an Attribute, AIm, Condition,
and/or oBject (AIC/ABIC) are considered to be shared strategies.
Once an analyst has coded all of the institutional statements within a given
document, data for each syntactic element can be aggregated so as to portray a
complete depiction of the target audiences of the institution, the actions associated
with them, conditions specifying the performance of these actions, and sanctions
associated with non-compliance. Doing so gives a more general illustration of how
institutional statements are configured within an institution to reflect the roles and
responsibilities of various actors as intended by the institutional designer(s).
Next, formal semi-structured interviews were conducted with members of the
aquaculture communities of Virginia and Florida (n= 30 or 15 per state). Crawford
and Ostrom (2005) argue that qualitative methods such as in-depth interviews are
required in order to undercover the institutional bases that inform behavior (Crawford
and Ostrom, 2005, 171). Interviews for this study consisted of two parts: In the first
part, interview participants were asked to respond to a series of questions included in
a pre-designed interview protocol meant primarily to capture their compliance
motivations. In the second part, participants were asked to participate in a Q-Sort
43 exercise. The Q-Sort exercise was designed to capture data pertaining to compliance
behavior and compliance motivations.
Twenty-two of the 30 interviews were conducted in-person and 8 were
conducted via telephone. In Florida, a regulatory official provided a list of 50 names
of aquaculture producers to the researcher to contact for participation in the study,
from which 15 were randomly selected and agreed to participate. In contacting
individuals from this list it was evident that the regulator randomly selected these
individuals from a list of Florida aquaculture producers as those contacted expressed
varying degrees of familiarity with the state regulators. For Virginia, the researcher
randomly selected participants from a directory of Virginia aquaculture producers.
The final sample of interview participants across the two states consisted of 18
shellfish producers, seven regulatory officials, two ornamental fish producers, two
aquaculture processor/handler and ornamental fish producers, and one shellfish and
finfish producer. In Virginia, two of the 15 individuals interviewed were regulators
while the rest were aquaculture producers or processors/handlers. In Florida, four of
the 15 individuals interviewed were regulators while the rest were aquaculture
producers or processors/handlers.
Finally, an online questionnaire was administered to aquaculture producers in
Florida (n=78; response rate = 19%)2. Aside from offering an additional data
2
An online questionnaire was also administered to aquaculture producers in Virginia. However, very
few individuals responded, precluding the inclusion of findings from the questionnaire in this study.
44 collection technique, the observed value and limitations of using a questionnaire to
capture data regarding behavior, a non-traditional data collection method for this
purpose within past IAD applications, was examined. Questionnaires are a useful
methodological strategy for testing causal relationships identified in three-variable
hypotheses (Shoemaker et al., 2004, 70, 83) and may be used to support analytic
generalizability across the selected cases (Yin, 2003, 32-33). However, questionnaires
alone are not sufficient to comprehensively capture the information desired. As such,
the questionnaire in this case was used primarily to triangulate data collected through
interviews.
The online questionnaire was administered in the spring and summer of 2011
to 415 aquaculture producers in Florida State. These 415 producers represent the
entire population of aquaculture farmers in Florida registered and licensed with
current email addresses under the FDACS to practice aquaculture. The email
addresses of these individuals were provided to the author by a regulatory official at
the agency. Of the 415 aquaculture producers to whom the questionnaire was sent, 78
responded yielding a 19% response rate. The respondent sample included 23 finfish
producers, 17 ornamental fish producers, 12 aquaculture processor or handlers, 10
shellfish aquaculture producers, 3 individuals who are both aquaculture processor or
The researcher contacted the State Aquaculture Coordinator of Virginia to inquire about possible
reasons for the low response rate. She was informed that multiple surveys were administered to VA
aquaculture producers around the same time, and asking similar types of questions, possibly leading to
confusion or fatigue among the producers.
45 handlers and shellfish producers, 2 individuals who are both aquaculture processor or
handlers and finfish producers, 1 ornamental and finfish producer, 1 aquaculture
processor or handler and alligator producer, and 9 individuals involved in
miscellaneous aspects of aquaculture (e.g. live rock, experimental aquaculture, etc.).
Though the sample was limited, the respondents appropriately reflect the range of
aquaculture participants in the Florida industry which is comprised predominantly of
shellfish, finfish, and ornamental fish producers (UDSA, 2006).
Table 1.5 provides a summary of the data collection steps employed in this
study.
Table 1.5 Summary of data collection steps
Data Collection Step
Procedural Detail
Step 1: Preliminary study to
provide a broad depiction of
the regulatory landscape for
U.S. aquaculture and to
identify an appropriate sample
of cases for the broader
dissertation study.
Interviews with 10 and
questionnaire of all 56
coordinator members of
the National Association
of State Aquaculture
Coordinators (NASAC)
(questionnaire response
rate = 57%).
Step 2: Coding of regulatory
Coding of all state level
documents to dissect and
aquaculture regulations in
compare institutions of
Virginia and Florida in
stringent and non-stringent
accordance with IAD
states.
Institutional Grammar
Tool.
Step 3: Formal semi-structured Interviews with 30
interviews with members of the members of the
aquaculture community to
aquaculture communities
understand motivations of
of Virginia and Floirda.
compliance.
46 Schedule
Fall 2009 and Winter
2010
Summer 2010
Fall 2010
Step 4: Administration of
online questionnaire to
aquaculture producers in
Florida to understand
motivations for compliance
from a larger population.
Administered survey to
415 aquaculture
producers in Florida
(response rate = 19%).
Spring and Summer
2011
Theoretical, Methodological, and Practical Significance of Study
This research brings together a macro and microscopic lens for understanding
the institutions and motivations that govern the behavior of individuals in aquaculture
communities. At the microscopic level, IGT was used to understand the constitutive
elements that comprise institutional statements presented within policies and compare
institutional design characteristics across the two study states. The IGT was coupled
with other data collection techniques, including interviews and a questionnaire, to
understand behavior at a macroscopic level, including the ways in which participants
interact with one another, and how the roles and relationships between them lead to
certain actions and outcomes. Theoretically, by complementarily drawing upon
research relating to policy design, regulatory compliance, and the IAD framework,
the research shows how regulatory, individual, and community based factors
complementarily influence the behavior of community members.
Methodologically, the analytical capacity of the IGT toward comparative
institutional analysis was examined. This was done by demonstrating its utility as
diagnostic tool for assessing regulatory stringency across institutional contexts.
Further, the study combined data collection methods that have rarely been used in
47 similar applications of the IGT. Typically, the IGT is used exclusively to identify
institutional statements present in formal documents, such as policies and laws, upon
which qualitative cluster analyses are subsequently conducted to summarize coding
results (Basurto et al, 2010). For example, Basurto et al. (2010) used this method to
code and analyze U.S. transportation Policy and abortion policy in the State of
Georgia. In this dissertation, the IGT was applied alongside other data collection
forms, such as a questionnaire and interviews, to understand compliance behavior.
Such an application builds off of the work of Speer (2009), in which she used a
similar approach to study participatory governance legislation in Guatemalan
municipalities.
Practically, the study offers insights regarding how aquaculture community
members are responding to aquaculture directives. Such insights are useful for local
and non-local policy makers who are designing policies that meet that needs of
aquaculture constituencies in a way that is appropriate and effective. For example,
they provide an indication of the extent to which different types of regulatory
mechanisms will be effective in promoting compliance based on an assessment of the
compliance factors found to be most influential to aquaculture producers.
Organization of Dissertation
Four analytical chapters are included herein in which different sets of data
collected throughout this study are examined. Each of these chapters is presented as a
stand-alone chapter that relates to the overarching research objectives and questions
48 of the study. In Chapter 2, interview and questionnaire data collected through the
NASAC study are analyzed to assess the perceived effectiveness of aquaculture
regulations in fostering compliance and to identify factors correlating with
compliance. In Chapter 3, data coded using the IGT is analyzed to gain a
comprehensive understanding of the state regulations governing the practice of
aquaculture in Virginia and Florida and to explore the Tool's utility in a comparative
examination of institutional designs. In Chapter 4, interview data is analyzed to
examine the compliance motivations of members of the aquaculture communities of
Virginia and Florida. In Chapter 5, questionnaire data collected among aquaculture
producers in Florida is analyzed, along with interview data, to assess their compliance
motivations. Abstracts for each of these analytical chapters are provided below. Each
of these chapters has or will be submitted to peer-reviewed outlets for consideration
of publication. Following the analytical chapters, a concluding chapter is offered
which summarizes the key findings from each of these, discusses the broader
implications of this research, identifies limitations of this study, and provides a future
research agenda that extends the research conducted herein.
Chapter 2: Bundling Regulatory Instruments: An Analysis of the U.S.
Aquaculture Industry
This chapter reports original mixed-method data from a national study to assess the
perceived effectiveness of aquaculture regulations in fostering compliance. Data were
collected through interviews and a survey of state aquaculture coordinators.
Qualitative data obtained through interviews provide insight on the interdependencies between regulatory factors, including perceptions of peer pressure,
trust, and knowledge sharing and regulatory compliance. Analysis of survey data
49 showed that perceptions of compliance correlates with five factors: (1) Fair and
consistent enforcement of regulations; (2) Belief of regulated persons that regulations
are scientifically and technically appropriate; (3) Trust of regulatory agents; (4) Trust
between industry members; and (5) Knowledge sharing between industry members on
scientific/technical and regulatory/administrative issues. The paper concludes with a
discussion for policy makers for designing effective policy instruments to govern the
aquaculture industry.
Chapter 3: Diagnosing Regulatory Stringency Using the Institutional Grammar
Tool: A Comparative Analysis of U.S. Aquaculture Policies
Advances in comparative institutional analysis necessitate the development of tools
that allow analysts the ability to understand systematically the constitutive elements
that comprise institutions, such as policies, laws, and regulations. One such tool is the
institutional grammar tool (IGT). Housed within the institutional analysis and
development (IAD) framework, the IGT offers the ability to systematically dissect
institutions to gain a comprehensive understanding of the actors being governed by
them, activities that they are allowed, forbidden, and required to perform, the spatial,
temporal, and procedural boundaries of these activities, and gradations of sanctions
for non-compliance. The objectives of this chapter are two-fold: First, to apply the
IGT to understand systematically the content of regulatory policies governing the
practice of aquaculture in Florida and Virginia, United States. Second, to demonstrate
how regulatory stringency may be operationalized using the IGT as a diagnostic tool
to assess any discernable differences between policies that are reportedly stringent
and non-stringent. In pursuing these objectives, this discussion demonstrates the
IGT's applicability toward comparative institutional analysis.
Chapter 4: The Culture of Compliance: Contextualizing Guilt, Social
Disapproval, and Fear of Monetary Sanctions
What motivates regulatory compliance? Drawing from regulatory and institutional
scholarship, this question is explored in this chapter in the context of aquaculture
communities. Findings come from a comparative case study analysis of two U.S.
states, involving a systematic coding of regulatory documents and interviews with
thirty members of the study states’ aquaculture communities. The findings indicate
that: (i) varying levels of compliance among individual farmers depending on the
type of institutional directive; (ii) feelings of personal guilt or shame and fear of
social disapproval, together, are more influential in shaping individuals’ decision
making regarding compliance than fear of monetary sanctioning; and (iii) the
expression of compliance motivations is contingent upon a variety of factors,
including the desire to protect the natural environment, prevent consumers from
50 becoming ill as a result of eating a contaminated product, and to prevent conflict with
neighbors and other resource users.
Chapter 5: Rules and Decision Making: Understanding the Factors that Shape
Regulatory Compliance
What motivates regulatory compliance? This question is examined through the logic
of regulatory scholarship and the Institutional Analysis and Development (IAD)
framework using questionnaire and interview data collected among members of the
aquaculture community in Florida State. The findings indicate that individuals are
more likely to comply with regulations (1) when regulatory enforcement personnel
are perceived as being knowledgeable about aquaculture; (2) when farmers have a
desire to maintain a good reputation with other members of the industry; and (3)
when farmers have a strong sense of guilt associated with not complying with
regulatory directives. In demonstrating the influence of such factors on compliance,
this paper supports past IAD research that emphasizes the influence of community
based factors in shaping compliance while drawing attention to individual behavioral
motivations, such as feelings of guilt. The findings add to the regulatory scholarship
by validating past studies that posit that individuals are more likely to comply with
regulations when they perceive enforcement personnel as being knowledgeable.
51 CHAPTER 2:
BUNDLING REGULATORY INSTRUMENTS: AN ANALYSIS OF THE U.S.
AQUACULTURE INDUSTRY
Abstract
This chapter reports original mixed-method data from a national study to assess the
perceived effectiveness of aquaculture regulations in fostering compliance. Data were
collected through interviews and a survey of state aquaculture coordinators.
Qualitative data obtained through interviews provide insight on the interdependencies between regulatory factors, including perceptions of peer pressure,
trust, and knowledge sharing and regulatory compliance. Analysis of survey data
showed that perceptions of compliance correlates with five factors: (1) Fair and
consistent enforcement of regulations; (2) Belief of regulated persons that regulations
are scientifically and technically appropriate; (3) Trust of regulatory agents; (4) Trust
between industry members; and (5) Knowledge sharing between industry members on
scientific/technical and regulatory/administrative issues. The paper concludes with a
discussion for policy makers for designing effective policy instruments to govern the
aquaculture industry.
Keywords
Aquaculture, Compliance, Environmental Policy, Regulation
Introduction
One of the central pursuits of regulatory scholars has been exploring the
factors that most influence regulatory compliance in order to gain insight for crafting
effective regulatory instruments. Some scholars direct their attention at understanding
how characteristics of the regulatory context, or regulators, affect compliance
outcomes (Gunningham et al. 2005; May and Wood 2003, Burby and Patterson 1993;
Braithwaite and Makkai 1991), while others have sought to examine the affect of
52
characteristics of regulated communities (Grafton 2005; Berkes 1987; Sutinen and
Kueperan 1999). What this research makes abundantly clear is that crafting and
implementing regulatory instruments that appropriately reflect the complexities of
distinct industries, as well as the contexts in which they are applied, remains a
challenging task. Not surprisingly, varying factors have been found to affect
compliance across cases leaving questions regarding the most appropriate choice of
regulatory instruments. Identifying the correct bundle of regulatory instruments to
ensure high levels of compliance is particularly challenging in emerging industries in
which knowledge of broader scale impacts associated with development remain
uncertain, including the effects of development on environmental and health
outcomes (May 2005). U.S. aquaculture represents one such industry. In this paper,
the aquaculture regulatory contexts of thirty states are examined, including factors
reported as being related to compliance, to decipher the types of regulatory
instruments that would be effective for governing this industry. Data come from
interviews with 10 and a survey of 56 members of the National Association of State
Aquaculture Coordinators (NASAC).
Aquaculture, defined as “the propagation and rearing of aquatic species in
controlled or selected environments” (NOAA 1980), provides one example of an
emerging natural resource based industry that is an increasingly important state and
national level policy issue. Aquaculture has the potential to pose significant
economic, environmental, and health impacts to local communities. The United States
53 currently produces approximately 20% of its seafood consumed while importing
80%, resulting in a seafood trade deficit that exceeds nine billion dollars (NOAA
2009). Aquaculture development in the United States faces a number of barriers,
including: an uncertain regulatory landscape (Firestone 2004; Wirth 1999), complex
interdependencies among ecological, economic, technical, and social factors
(Firestone 2004), resistance from the general public regarding farmed seafood (Mazur
2006; Amberg and Hall 2010), conflict about aquaculture development (Kaiser and
Stead 2002), and numerous concerns about the industry from disease control to
degradation of marine ecosystems (Black 2001; Francik 2003; Treece 2002; Naylor
2000; Mazur 2006). The challenges faced by the U.S. aquaculture industry resemble
those faced by other natural resource based industries where regulations tend to be
fairly technical and where decentralization of regulatory governance is commonly
observed (May 2005). The aquaculture industry has been vastly understudied from a
social science perspective. Given, however, the growing importance of the industry in
a national context, more attention must be devoted to analyzing the policy and
regulatory considerations of the industry.
Regulatory scholars have studied compliance in similarly characterized policy
environments, including the agro-environmental industry in Denmark (May 2005),
boatyard industries in the states of Washington and California (May 2005), and the
concentrated animal feeding operation (CAFO) industry (Koski 2007). Like these
industries, aquaculture is an example of a private good that is both maintained and
54 constrained by the availability of natural resources, is governed by environmental
regulations targeted at reducing negative externalities, and is immersed in socioeconomic considerations, including the need to respond to consumer preferences and
resource user conflicts.
As of yet, no single theory exists to explain why individuals comply with
regulations. Increasingly, empirical research in the regulatory field has demonstrated
that a variety of factors contribute to regultees’ decisions to comply with regulatory
directives. Some of these factors relate to characteristics of regulating entities or
relationships between regulators and industry members, including, the enforcement
practices of regulatory agents (E. Ostrom 1994) and the presence of trust between
regulatory and regulated actors (Gunnignham et al. 2003; 2005; Bardach and Kagan
1982; May 2005; Murphy 2004). Others relate to characteristics of industry members.
For example, industry members' confidence in the regulating agency and its
personnel’s technical capacity in administering regulatory policies (Gunningham et
al. 2005; Bardach and Kagan 1982), the presence of trust between a regulated
industry’s members (Ostrom 2005), and social sanctions and influence (Berkes 1987;
Sutinen and Kueperan 1999; Braithwaite and Makkai 1991; May 2005a).
Additionally, knowledge sharing among regulatees regarding scientific/technical and
regulatory/administrative issues may also influence compliance (Grafton 2005).
In examining such factors, this study adds to a body of knowledge that
challenges presumptions posed within classical models of regulatory deterrence. The
55 classic regulatory deterrence model is premised upon the assumption that legal
sanctions will sufficiently thwart the desire for non-compliance on the part of
regulated actors. In this view, regulatees are considered self utility maximizing agents
and, thus, costly sanctions administered through regulatory agencies are viewed as the
primary coercive mechanism in fostering regulatory compliance (Zimring and
Hawkins 1973; Bentham 1789; Becker 1968).
As the aquaculture industry continues to develop in the United States, the
regulatory instruments used to govern the industry are also likely to become
commensurately more comprehensive and complex. As such, it is timely and
important to identify the varying types of regulatory instruments currently in place
across aquaculture producing states as well as gain a nationwide understanding of the
factors perceived as being most important in influencing compliance with regulatory
directives. In doing so, one can begin to discern how they may be crafted to most
effectively foster regulatory compliance in the aquaculture industry, in addition to
other similarly characterized natural resource based industries.
Factors Shaping Regulatory Compliance
Compliance is one of the primary goals associated with regulatory policies. As
May (1991) writes, “The typical policy is a package of policy instruments aimed at
some combination of gaining compliance through the use of mandates, improving
short-term performance through the use of incentives, enhancing longer-term
performance through various capacity building measures, or altering the system for
56 providing goods and services by introducing system changes” (May 1991, 199;
Elmore 1987). Within this discussion, instruments are broadly synonymous with
factors that relate to the ways in which regulations are implemented and enforced; for
example, factors relating to regulatory enforcement personnel. Included within this
broad definition of instruments are factors emerging from regulating entities as well
as those emerging from the community Where significant efforts are made by policy
designers to ensure that compliance is achieved, cases of non-compliance incite
inquiry as to what motivates individuals to comply, or not comply.
Enforcement Practices
Regulatory scholars have found that the enforcement practices of regulatory
agents may be a key motivating factor in fostering regulatory compliance. For
example, where inspections are conducted more frequently, higher compliance has
been observed (Gunningham et al. 2005; May and Wood 2003, Burby and Patterson
1993). However, while regular inspections have been found to increase the capacity
of regulators to detect non-compliant behavior, findings remain unclear as to how
reliably or consistently sanctions are administered when a violation is found
(Braithwaite and Makkai 1991). Reinforcing the notion that regulatory agents are
somewhat autonomous legal decision makers, Kagan (1989) and May and Wood
(2003, 117) posit that regulatory personnel possess a great deal of discretion when it
comes to monitoring and enforcing regulatory directives and the ways in which they
interact with regulatees. Regulatory enforcement relating to the environment, in
57 particular, Zinn (2002, 1) argues, has been demonstrably more favorable to alternative
enforcement techniques, including informal negotiation and compromise with
regulatees. Irrespective of the particular enforcement techniques employed by
regulatory personnel, research has shown that perceived fairness and consistency of
regulatory enforcement can have positive compliance effects (Levi 1998; Ostrom
2005). Thus, more explicit attention must be paid to capturing the reliability with
which sanctions are administered, particularly because this may relate to additional
regulatory factors to influence compliance outcomes. The expectation is that
compliance with regulatory directives will be higher when it is perceived that
industry members view regulations as being fairly and consistently enforced.
Trust and Knowledge Sharing
The concept of trust has been defined variably by scholars to represent
different dimensions that relate to regulatory processes and compliance (Braithwaite
and Makkai 1994; May 2004; Levi 1988). Levi (1998) defines trust generally as “a
holding word for a variety of phenomena that enable individuals to take risks in
dealing with others, solve collective action problems, and/or act in ways that seem
contrary to standard definitions of self-interest” (Levi 1998, 78). More specifically,
trust may be defined in terms of the expectation by regulated agents that regulatory
commitments will be dependably fulfilled by regulatory agents (May 2004; Levi
1988; Murphy 2004). By this definition, trust is thus a by-product of positively
viewed enforcement practices that can result in favorable compliance outcomes.
58 Presumably, where monitoring and enforcement is reliably conducted, trust may be
established between regulatory and regulated agents, which in turn can foster cession
by the latter of self-interest in favor of compliance with regulatory directives (Levi
1998; Scholz and Lubell 1998). In a similar vein, it has been demonstrated that
regulated actors may be more willing to forfeit self-interest for a collective good, in
this case being collective compliance, where they exhibit a high level of trust and
cooperation amongst each other (Putnam 1993). Thus, it is expected that compliance
with regulatory directives will be higher when it is perceived that industry members
trust those monitoring and enforcing regulations as well amongst each other.
An additional possible outcome of trust is a decrease in attempted deceit by
either party as trusting actors are more likely to share information and resources
regarding industry and management matters (Grafton 2005). In the context of
fisheries, Grafton (2005) asserts that where regulatory actors harbor feelings of trust,
they are more likely to share information with one another thus fostering positive
compliance outcomes (Grafton 2005, 755). While Grafton was referring specifically
to the relationship between fishermen and the management authority, it is further
plausible that the presence of trust among industry members themselves may foster
intra-industry knowledge sharing that could similarly result in positive compliance
outcomes. Pomeroy and Berkes (1997) find this to be true in their study of comanagement systems relating to common-pool resources. As such, these findings
suggest that compliance with regulatory directives will be higher when industry
59 members are perceived to share knowledge with one another regarding
scientific/technical and regulatory/administrative aspects of the industry.
Rule Appropriateness
The perceived appropriateness of regulations is another important quality
characterizing the relationship between regulating and regulated agents in influencing
compliance with regulatory directives. Appropriateness of regulations in this case
refers to the applicability of regulations in relation to local resource, political, and
social conditions (Ostrom 1990; 2005). Where regulating and regulated actors possess
disparate beliefs regarding how an industry should be managed, scholars argue that
regulated agents may question the legitimacy of regulatory agents as well as the
legitimacy and fairness of the directives themselves (May 2005; Ostrom 1990). This
in turn, may negatively impact compliance levels (May 2005, 321; Bardach and
Kagan 1982; Levi 1988). Referring to governance rules of common pool resources
more broadly, Ostrom (1990) proffers that rules, or regulations, well tailored to the
context in which they are being applied contribute to the long-term sustainability of
such resources (Ostrom 1990, 92). In exploring this issue within a fisheries context,
Jentoft (2004) asserts that when fishers lose the ability to feel morally committed to
“values such as honesty and respect for rules (Jentoft 2004, 144),” the ascendancy of
regulatory over regulated agents begins to diminish, thereby increasing chances of
non-compliance by the latter. It is thus expected that compliance with regulatory
directives will be higher when it is perceived that industry members feel they are
60 contextually appropriate.
Technical Capacity
Confidence in a regulatory agency and its personnel’s technical capacity, or
competence, as perceived by regulated agents is yet an additional factor that may
influence compliance outcomes. When this quality of the relationship between the
former and the latter is lacking, regulated actors may feel that regulatory agents are
unfairly or incompetently administering regulatory directives (Gunningham et al.
2005). Regulatees may also view those whom they feel are technically incapable as
being overly stringent in their administration of directives due to an overly strict
interpretation of regulatory policies stemming from an inability to reason in varying
regulatory situations from an experiential and knowledge deficiency (Bardach and
Kagan 1982). Based on empirical findings, it is expected that compliance with
regulatory directives will be higher when it is perceived that industry members feel
those enforcing regulations are competent.
Peer Monitoring and Enforcement
Increasingly, empirical research in the regulatory field that draws upon
scholarship from sociology and social psychology (Elster 1989; Coleman 1990; Ajzen
1988) has shown that a variety of other factors contribute to regulatees’ decisions
regarding when to comply with regulatory directives (Hatcher et al. 2000). Additional
factors include social sanctions and influence, or social disapproval (Berkes 1987;
Sutinen and Kueperan 1999; Braithwaite and Makkai 1991), and personal shame or
61 guilt (Grasmick and Bursik 1990). Hatcher et al. (2000), for example, found that
social pressures served as an effective deterrent to non-compliance relating to catch
quotas, or individual fishing quotas, in the United Kingdom. Similarly, Kuperan and
Sutinen (1995) have explored the relationship between compliance and feelings of
moral obligation among regulatees regarding fishery zoning regulations in Malaysia.
However, because regulatory scholars have traditionally focused on top-down
influences on compliance, research focusing on community based or “bottom-up”
factors has been limited to date and many areas remain to be explored regarding
socially based compliance motivations. Based on the research that has been
conducted to date, it is expected that compliance with regulatory directives will be
higher when it is perceived that industry members engage in peer monitoring and
enforcement. Methods of Data Acquisition
Given the expectations from years of research on regulatory compliance
across industries, the next challenge is with sampling and measurement. Data
collection for the discussed study consisted of two parts: In the first part, interviews
were conducted with 10 members of the National Association of State Aquaculture
Coordinators (NASAC). In the second part, an online survey was administered to the
56 individuals listed as members of the NASAC in 2009. Through the interviews and
survey, NASAC members were asked to comment on their perceptions regarding a
variety of regulatory factors in their respective states as well as the relationship
62 between these factors and perceived levels of compliance. NASAC members are
highly knowledgeable about the aquaculture industry, particularly with respect to
regulatory/management and/or technical matters. In many cases, these individuals are
the State Aquaculture Coordinators from the different States. Where there is no
official State Coordinator, these individuals are selected to serve as representatives to
NASAC either due to their professional position or influence in the respective
aquaculture communities. Most states have one representative, though some states
have more.
Given their specialized knowledge regarding various aspects pertaining to
their respective state aquaculture industries, NASAC members were selected as an
appropriate study population in place of industry members whose knowledge may be
limited to issues relevant to their operation. Further, as the intention of this study was
to glean insight into the national context relating to the regulation of the aquaculture
industry, the researcher was interested in a data collection design that would allow her
to select one knowledgeable representative of the industry from each state in the
interest of breadth, rather than interviewing and surveying multiple industry members
within a few select states for a more in-depth understanding of the aquaculture
regulatory landscape. Follow-up studies to the one discussed here will involve the
latter, wherein the researcher will be able to gather data representing farmer's
perceptions of various regulatory instruments and how they influence and are
influenced by the contexts in which they are embedded.
63 Data Collection Instrument: Interviews
Ten telephone interviews were conducted with NASAC members.
Interviewees were asked a series of questions regarding regulatory characteristics
pertaining to aquaculture in their respective state. The interview protocol included
questions that relate specifically to the regulatory factors being examined in this
study, including, enforcement practices, perceived technical capacity of monitoring
and enforcement personnel, peer monitoring and enforcement, knowledge sharing,
and compliance. Questions relating to rule appropriateness and trust, both of
regulatory agents and between industry members, were only posed in the online
survey. The following questions were posed to interviewees for each remaining
relevant regulatory factor.

[Enforcement practices] How reliably do you feel sanctions are imposed?

[Technical capacity] What do you feel is the level of understanding among
those enforcing permit requirements regarding activities that are allowed and
forbidden?

[Peer monitoring and enforcement] Do you think peer pressure among
aquaculturists helps to enforce compliance with regulations?

[Knowledge sharing] On whom do members of the aquaculture community
tend to rely to obtain information and/or resources on various aquaculture
related issues?

[Compliance] What is the level of regulatory compliance in your state?
64 Data Collection Instrument: Online Survey
An online survey was crafted to supplement findings from the interview. As
the survey was designed subsequent to the interviews, these questions were more
sharply crafted in accordance with the analytical objectives of this paper. For the
online survey, several questions from the interview protocol were modified and/or
expanded to capture more dimensions on the issues of interest. For example, an
additional dimension of peer monitoring and enforcement was included in the survey:
peer reporting of non-compliance to governmental agencies. In addition, questions
relating to rule appropriateness and trust were added (please refer to Appendix C for a
list of relevant survey questions). In the survey, these factors were examined in
relation to perceived regulatory compliance.
Survey participants included those individuals listed as state aquaculture
representatives in the NASAC database in 2009 (n=56). Prior to receiving the survey,
potential respondents were sent an invitation to participate explaining the purpose and
procedures of the study, possible risks and benefits associated with participation, and
details relating to the confidentiality of respondents’ answers. Following the
administration of the survey, three reminders were sent to non- respondents
requesting their participation in the survey. Two of these reminders were sent by the
author and one was sent by an administrative representative of the NASAC.
65 Survey Responses
Of the 56 individuals to whom the survey was sent, 32 individuals responded,
yielding a 57% response rate. The states represented in the respondent sample were
grouped according to the U.S. Census Bureau’s regional distinctions to determine the
percentage of states from each region that were represented. In accordance with this
regional categorization, 46% of states from the West, 75% from the Midwest, 56% of
states from the northeast, and 59% of southern states were represented in the
respondent sample. Each state had one respondent except for two states which has
two respondents. So that these states were not overrepresented in the analysis, the
mean was calculated between each of these states two respondents’ responses to
produce a combined response. This mean calculation was conducted for
questions/responses that represent state level variables, versus individual level
variables. For example, the responses of the two respondents from Alaska and Ohio
were not combined for questions such as those asking survey respondents to indicate
their gender, years employed in each professional position listed, and educational
background. They were combined for questions such as those asking survey
respondents to provide details about the level of compliance and characteristics of the
regulatory processes in their respective states.
To determine the response bias associated with the sample of respondents,
one-way ANOVA tests were conducted to determine if statistically significant
differences were present between respondent and non-respondent states with respect
66 to a variety of industry demographics; namely, industry size and type of aquaculture
produced. Industry demographics for all states were obtained from the United States
Department of Agriculture (USDA) 2005 Census of Aquaculture (USDA, 2006).
Respondent and non-respondent states were compared on (1) total number of farms;
(2) total farm sales; (3) number of food fish farms; (4) number of sport
fish/recreational farms; (5) number of baitfish farms; (6) number of ornamental fish
farms; (7) number of crustacean farms; and (8) number of mollusk farms. ANOVA
results indicate that respondent and non-respondent states differed statistically only in
terms of the number of sport fish/recreational aquaculture farms (p < .05), with
respondent states having more than non-respondent states.
Data Analyses
Data from the interviews and survey were analyzed separately as the data
collection obtained through these different means speak to different analytical
objectives. Surveys were used to determine if there was a relationship between the
perceived presence of various regulatory characteristics and compliance. Interviews,
on the other hand, were used to gain a contextual understanding of regulatory
characteristics in ten study states.
Data from the interviews were summarized by collating all of the responses
across the ten interviewees around the regulatory factors under consideration.
Interview data were analyzed in this manner to display trends in the responses across
the different factors to supplement findings from the online survey. Analysis of
67 survey data was conducted using SPSS analytical software. First, descriptive statistics
were performed on the data aggregated across the thirty respondent states to assess
the survey participants’ responses for each regulatory factor. Specifically, the
descriptive analyses were applied to determine the breakdown of survey participants’
responses for each of the regulatory compliance factors under consideration. Next, to
identify relationships between each regulatory factor and compliance, correlation
analyses were conducted.
Results
Results: Online Survey
Respondents were first asked to indicate their general perceptions regarding
the regulatory factors under consideration. For this set of questions, respondents were
asked about (for complete questions, please refer to Appendix C):

The extent to which (i) enforcement practices; (ii) rule appropriateness; and
(iii) trust of regulatory personnel and of fellow industry members were
contributors to compliance (Scale: 0 to 4, where 0 = Not a Contributor; 1 =
Mild Contributor; 2 = Moderate Contributor; 3 = Significant Contributor; and
4 = Biggest Contributor).

The extent to which community members conduct monitoring and
enforcement in their respective state (Scale: 0 to 4, where 0 = None; 1 =
Little; 2 = Moderate; 3 = Significant; and 4 = Heavy);

The extent to which they disagree/agree with the statement “most noncompliance is reported to government agencies by other members of the
industry” (Scale: -2 to 2, where -2 = Strongly Disagree; -1 = Mildly Disagree;
0 = Neutral; 1 = Mildly Agree; and 2 = Strongly Agree);

The extent to which they disagree/agree with the statement “industry members
often share knowledge of the scientific/technical aspects of aquaculture with
68 one another” (Scale: -2 to 2, where -2 = Strongly Disagree; -1 = Mildly
Disagree; 0 = Neutral; 1 = Mildly Agree; and 2 = Strongly Agree);

The extent to which they disagree/agree with the statement “industry members
often share knowledge of the regulatory/administrative aspects of aquaculture
with one another” (Scale: -2 to 2, where -2 = Strongly Disagree; -1 = Mildly
Disagree; 0 = Neutral; 1 = Mildly Agree; and 2 = Strongly Agree); and

The extent to which they disagree/agree with the statement “compliance with
aquaculture regulations is very high” (Scale: -2 to 2, where -2 = Strongly
Disagree; -1 = Mildly Disagree; 0 = Neutral; 1 = Mildly Agree; and 2 =
Strongly Agree).
Findings from the descriptive analyses indicate that, overall, respondents had
mixed perceptions regarding enforcement practices (mean response = 1.7), rule
appropriateness (mean response = 1.9), technical capacity (mean response = 1.6), and
trust of regulatory personnel (mean response = 1.5) and fellow industry members
(mean response = 2.2) as contributors to compliance , nearly all of the respondents
indicated that peer monitoring and enforcement was minimal to moderate in their
respective state (mean response = 1.1), only 11% of respondents indicated that most
non-compliance in their state is reported by peers (mean response = -.5), more than
60% perceived frequent sharing of information (scientific/technical issues mean
response = .8; regulatory/administrative issues mean response = .9), and 75%
reported high compliance with regulatory directives (mean response = 1). The
variability between states on the above regulatory factors, evidenced by mixed overall
perceptions on several factors, indicates that a variety of contextual factors are
responsible for determining how regulatory instruments are crafted, implemented, and
69 perceived by regulatees in individual states. Contextual nuances are not made evident
through basic descriptive analyses.
Responses to the above questions were then analyzed using correlation
analyses to see if there is a relationship between perceptions of regulatory factors and
compliance. The results from the correlation analysis are provided in Table 2.1.
Largely consistent with the literature, the following factors were found to be
significantly correlated with compliance with regulatory directives: (1) Fair and
consistent enforcement of regulations by regulatory agents; (2) belief of regulated
persons that regulations are scientifically and technically appropriate; (3) trust of
regulatory agents; (4) trust between members of industry; and (5) sharing of
knowledge between industry members regarding the scientific/technical and
regulatory/administrative aspects of aquaculture. Conversely, the following factors
were not found to be significantly correlated with regulatory directives: (1) Perceived
technical capacity of regulatory agents; and (2) peer monitoring and enforcement.
Evident from these findings is that both types of factors, those relating to
characteristics of regulatory entities as well as those relating to characteristics of the
industry are related to compliance with regulatory directives.
70 Table 2.1 Correlations between regulatory factors and compliance (n=28)
Regulatory Factor
Pearson
Correlation
Enforcement Practices
.391*
Rule Appropriateness
.553**
Technical Capacity
.354
Trust 1: Enforcement Personnel
.466*
Trust 2: Industry Members
.594**
Peer Monitoring and Enforcement 1: Peer
Monitoring and Enforcement
-.228
Peer Monitoring and Enforcement 2: Peer
Reporting
.033
Knowledge Sharing 1: Scientific/Technical
.564**
Knowledge Sharing 2: Regulatory/Administrative
.556**
* = Correlation is significant at the .05 level (2-tailed)
** = Correlation is significant at the .01 level
The findings from the survey provide only a first step in unpacking how
various characteristics of state regulatory instruments impact levels of compliance,
particularly in this study given the small survey sample size. The descriptive results
presented show that there is a fair amount of variability between the states regarding
the different regulatory factors under consideration. For example, the respondents
indicated mixed results for enforcement practices, rule appropriateness, trust of
71 regulatory agents, and perceived technical capacity as being contributors to
compliance in their respective states.
As there has been little federal legislation governing aquaculture in past years,
states have had a great deal of discretion in how they choose to manage their
respective aquaculture industries. As such, the regulatory landscapes concerning
aquaculture differ markedly across states reinforcing the need to heed distinct
regulatory contexts when exploring factors such as those studied herein. The results
from the interviews, while not showing direct relationships between regulatory
factors and compliance, provide further contextual elaborations regarding the
regulatory factors under consideration in ten states.
Results: Interviews
A review of interview data provides useful insight relating to the regulatory
context of aquaculture in ten U.S. States, which, together with survey data, offers a
more illustrative depiction of the national aquaculture regulatory landscape. Interview
findings, do not, however, provide evidence for the relationship between regulatory
factors and compliance. Descriptive findings from the interviews are provided below
and reveal inter-dependencies between regulatory factors.
Enforcement Practices (n=7 out of 10)
Of the seven individuals that responded to this question, five indicated that
sanctions were reliably enforced. Interviewees provided varying responses as to why
they felt this was the case, including, small industry size (Interviewee ID: 004), strict
72 legislative mandates that require monitoring and enforcement personnel to reliably
administer sanctions (Interviewee ID: 002), and the types of activities that are being
enforced; for example, reliable enforcement regarding compliance with paperwork
requirements is relatively easy (Interviewee ID: 003). Another interviewee stated that
enforcement is more reliable when the activity under consideration is “hot at the
time” (Interviewee ID: 001). Finally, consistent with the literature (Kagan, 1989; May
and Wood, 2003), the one interviewee who responded negatively stated that,
“Enforcers have a lot of flexibility in enforcing regulations and so there can be some
variability in how sanctions are administered” (Interviewee ID: 005).
Technical Capacity (n=9 out of 10)
Of the nine interviewees that responded to this question, six stated that those
enforcing regulatory requirements do not possess a good level of understanding
regarding activities that are allowed and forbidden, while three stated that they do.
Two individuals stated that oft changing regulatory requirements is the primary
reason for lack of knowledge among enforcement personnel (Interwiewee IDs: 005
and 006). Another interviewee stated that the regulatory agency tasked with
regulating aquaculture activities lacks a sound knowledge regarding how to manage
the industry, saying, “Their regulations are not based on sound management, but
based on interpretations and whims of employees [enforcement personnel] that don’t
know what they are doing” (Interviewee ID: 009). Other reasons cited as contributing
factors, include personnel turnover (010), lack of state-level monitoring and
73 enforcement capacity (Interviewee ID: 004), and enforcement personnel who are
motivated by their private motives (Interviewee ID: 001).
Peer Monitoring and Enforcement (n=10 out of 10)
Of the ten individuals that responded to this question, six responded that peer
pressure among aquaculturists helps to enforce compliance with permits and
regulations, two responded that peer pressure is becoming increasingly common as
state aquaculture industries continue to grow and develop, and two responded that
peer pressure regarding compliance with aquaculture regulations does not exist. Of
those who responded that peer pressure does play a role, three stated that industry
members applied positive peer pressure on one another to comply with aquaculture
regulations out of a mutual trust of one another that manifests in the sharing of
information and resources. For example, one interviewee stated, “Growers do help to
keep each other accountable – trust and reciprocity in the community – not a cutthroat
industry – very respectful of one another and so they don’t want to not comply with
regulations and risk losing the trust of other industry members” (Interviewee ID:
006). Another interviewee stated, “They share information about what they know –
friends helping friends and monitoring each other” (Interviewee ID: 009). One of the
interviewees who indicated that peer pressure among industry members is growing
linked this trend with changing environmental conditions, stating, “…this will
probably increase as time goes on. The state is entering a new era of dealing with
water issues and as water shortages continue, the community will start applying more
74 peer pressure to make sure that people are complying with the regulations”
(Interviewee ID: 001)
Knowledge Sharing (n=10 out of 10)
Of the ten individuals that responded to this question, nine responded that they
obtain information from other industry members via their respective state aquaculture
association or industry trade publications. A number of these interviewees indicated
that information sharing and coordination was most frequently observed between
farmers conducting similar types of aquaculture (Interviewee IDs: 001, 002, 010).
Compliance (n=8 out of 10)
Six of the eight individuals that responded to this question indicated that
compliance with aquaculture regulations in their state was high to very high, with one
individual stating that compliance was “pretty decent” (Interviewee ID: 006) and one
individual providing a compliance rate at “around 70%” (Interviewee ID: 007). A
variety of factors were cited as contributors to high levels of regulatory compliance,
including non-stringent regulations (Interviewee ID: 001), fear of social disapproval
(Interviewee ID: 001), and adherence to best management practices articulated within
regulatory directives (Interviewee ID: 010). In describing instances of noncompliance, one interviewee stated that sometimes the monetary costs associated with
meeting regulatory requirements can thwart compliance (Interviewee ID: 007).
Another interviewee stated that, most of the time, cases of non-compliance are
75 attributable to oversight on the part of the regulatee as opposed to malicious intent
(Interviewee ID: 006).
Table 2.2 provides a summary of interview results, including general trends
and specific findings in responses for each regulatory factor under consideration.
Table 2.2 Summary of interview findings
Regulatory
Factor
Enforcement
Practices
(n=7/10)
Five out of seven interviewees
indicated that sanctions are
reliably enforced.
Reasons Explaining General
Trends
Small industry size and strict
mandates, and easy to monitor
activities.
Technical
Capacity
(n=9/10)
Six out of nine interviewees
indicated that they do not feel
those enforcing regulations
possess a good level of
understanding of regulations.
Personnel turnover, oft
changing regulatory
requirements, lack of state
level monitoring and
enforcement capacity.
Peer Monitoring
and Enforcement
(n= 10/10)
Eight out of ten interviewees
indicated that peer monitoring
and enforcement is or is
becoming increasingly
prevalent in maintaining
compliance with regulatory
directives.
Desire to look after one
another, industry competition,
increasing resource constraints.
Knowledge
Sharing
(n=10/10)
Nine out of ten interviewees
stated that they share
information with one another.
Information sharing through
state aquaculture association
and/or through industry trade
publications.
Compliance
(n=8/10)
Six out of eight interviewees
reported high compliance.
Non-stringent regulations, fear
of social disapproval,
adherence to best management
practices in regulations.
General Results
76 The responses obtained through the interviews illuminate interdependencies
between regulatory factors. In particular, several interviewees described peer
monitoring and sanctioning and knowledge sharing in relation to the presence of trust
between members of the aquaculture industry. Interestingly, the presence or lack of
trust reported within a community tended to shape how interviewees interpreted the
notion of peer monitoring and enforcement and peer pressure. Where interviewees
felt that trust between industry members is a prominent feature of the aquaculture
industry, they tended to speak about peer involvement in regulatory affairs in a
positive light – industry members trust each other and so they want to look after one
another (Interviewee IDs: 006 and 009). In contrast, where there was an apparent lack
of trust among industry members, interviewees tended to interpret peer monitoring
and enforcement and peer pressure more negatively – industry members’ distrust of
one another leads them to engage in “policing” of one another’s activities
(Interviewee ID: 007).
Responses from the interviews also provide qualitative elaborations as to why
compliance is reported as being relatively high across the majority of aquaculture
producing states, a finding reflected in the responses to both interview and survey
questions. Higher levels of compliance were attributed to non-stringent regulations,
fear of social disapproval, and adherence to best management practices articulated in
regulatory directives. Two of the interviewees also commented that non-compliance
is not grounded in malicious intent, but rather has more often to do with other factors,
77 such as mere oversight or the costliness of compliance (Interviewee IDs: 006 and
007). Such insight is useful as it addresses another dimension of compliance not
addressed in the online survey. Non-Compliance has varying degrees; one end of the
spectrum representing that which is unintentional and relatively innocuous and the
other end of the spectrum representing non-compliance that is based on malicious
intent and poses significantly harmful implications. In future studies, both data
collection instruments should be modified to capture this dimension of
compliance/non-compliance.
Discussion and Conclusions
One of the challenges in governing natural resource based industries remains
crafting bundles of regulatory instruments that appropriately reflect the complexities
of the industries themselves, considering the dynamic relationship between
environmental, social, and political factors. In designing regulatory instruments in
such arenas, policy designers should heed the contexts in which they will be applied
so that they speak to the motivations that expressly influence compliance among
industry members. This study contributes to such an endeavor by articulating factors
that influence compliance in the context of an increasingly salient natural resource
based industry, U.S. aquaculture. In doing so, the findings and discussion offer
answers to the following questions:
What factors relate to compliance with regulations in the aquaculture industry?
78 Of the factors considered in the online survey, those found to be significantly
correlated to compliance include: enforcement practices of regulatory personnel, rule
appropriateness, trust between regulating and regulated agents, trust between industry
members, and knowledge sharing between industry members regarding the
scientific/technical and regulatory/administrative aspects of aquaculture. Factors that
were found not to be significantly related to compliance include: the perceived
technical capacity of regulatory agents and the presence of peer monitoring and
enforcement.
Interviews were used to complement findings from the online survey. From
the interviews, additional insight was garnered to depict contextual differences with
respect to the regulatory factors under consideration in ten states. In general,
interviewees believe that sanctions are reliably enforced, express skepticism
regarding the technical capacity of monitoring and enforcement personnel, tend to
engage in peer monitoring and enforcement, frequently share knowledge with one
another regarding scientific/technical and regulatory/administrative aspects of the
industry, and are largely compliant with aquaculture regulations.
Findings from this study validate previous research that contest the classical
regulatory deterrence model and provide further evidence of the role of an alternative
set of factors in affecting compliance, including those that pertain to characteristics of
the interactions between regulating and regulated actors and characteristics and/or
dynamics of the regulated industry. Should regulatory scholars attempt to construct a
79 theory of regulatory compliance, this study makes clear that incorporation of both sets
of factors is fundamental. The next research step associated with this study is to
explore how compliance motivations differ across the study states considering
contextual factors, including such factors as industry demographics and regulatory
histories.
How should policy makers bundle regulatory instruments to promote
compliance?
The extent to which policy designers and other administrators involved in the
governance of aquaculture can influence community based factors, such as the
establishment of trust between industry members, may be limited. However, they do
possess the capacity to influence other critical factors demonstrated to positively
influence levels of compliance in past regulatory scholarship. For example, they can
foster consistent enforcement of regulations, which in turn can promote trust of
regulatory agents (Scholz and Lubell 1998), develop or maintain collaborative
processes that encourage industry members to provide input on aquaculture
regulations and policies to ensure that they are viewed as being as appropriate
(Ostrom 1990; 2005), and support venues in which industry members can exchange
knowledge regarding the scientific/technical and regulatory/administrative matters of
the industry (Grafton 2005; Pomeroy and Berkes 1997). As several interviewees
stated that they lacked confidence in the technical capacity of regulatory entities to
80 enforce directives, administrative officials should pay careful attention to properly
training agency personnel in such matters.
How does the regulatory governance of aquaculture relate to other natural
resource based industries?
The findings presented in relation to aquaculture may also be useful to
scholars studying factors contributing to regulatory compliance in other natural
resource based industries. The complex regulatory governance systems developed to
manage such industries must mimic the complexity of the industries themselves that
are entangled with ecological, economic, technical, and social concerns. The question
then arises: How can regulatory instruments be both crafted and implemented to deal
with the inherent complexities of the industries they are intended to manage? Any
research that contributes to describing how such industries are managed as well as the
critical factors that contribute to the effectiveness of regulatory instruments is both
useful and necessary. The findings from this study punctuate the need to develop
policy instruments that are geared toward enhancing the efficacy of regulatory
personnel and processes toward achieving compliance in addition to instruments that
foster norms of reciprocity between industry members (Ostrom 2005). Where
industry members exhibit such tendencies, outcomes positively related to compliance,
such as trust and knowledge sharing, were observed to emerge.
This study is not without limitations. Some contradiction was observed
between findings from the online survey and interviews. For example, peer
81 monitoring and enforcement was not reported as being prevalent by survey
respondents, nor was it found to be significantly correlated with compliance. In
contrast, interviewees indicated that peer monitoring and enforcement was observed
and that it does contribute to compliance with regulatory directives. This discrepancy
remains curious to the researcher, though there are reasons that may help to explain
these contradictory findings. A foremost reason lies in differences in data collection
instruments and the types of information they are suited to capture. The wording of
the question in the online survey as opposed to in the interviews may have shaped the
responses received. Further, interviews provided the researcher with the opportunity
to probe for further elaboration regarding the question.
Another notable limitation concerning the study sample is that those
interviewed and surveyed are primarily representatives from the administrative and/or
regulatory agencies dealing with aquaculture in the respective states. This may have
influenced the results to more positively reflect levels of compliance. Leach (2002)
found in his study of multi-stakeholder resource management groups that surveying
only a single stakeholder category can result in a more favorable depiction of the
processes in which these actors are involved (Leach 2002, 641). Additionally,
regulatory and administrative agents may not be privy to the full extent of noncompliance with directives. They also may lack knowledge about peer monitoring
and enforcement. It is difficult to ascertain the level of bias associated with these
results as aquaculture farmers were less represented in the study sample. However,
82 while there may be limitations associated with the selected study sample, state
aquaculture coordinators are some of the most knowledgeable individuals in the
aquaculture community with respect to the regulatory aspects of aquaculture and thus
are poised to offer an informed assessment of the current state of aquaculture
development and associated issues.
Understanding the factors that influence compliance is central to crafting
effective regulatory instruments. In this study, a variety of regulatory factors are
examined in an effort to discern the relative influence of those that relate to
characteristics of regulatory agents as well as those that relate to regulatees as
contributing to compliance. The findings show that an effective bundle of regulatory
instruments will recognize the influence of both types of factors.
83 CHAPTER 3: DIAGNOSING REGULATORY STRINGENCY USING THE
INSTITUTIONAL GRAMMAR TOOL: A COMPARATIVE ANALYSIS OF
U.S. AQUACULTURE POLICIES
Abstract
Advances in comparative institutional analysis necessitate the development of tools
that allow analysts the ability to understand systematically the constitutive elements
that comprise institutions, such as policies, laws, and regulations. One such tool is the
institutional grammar tool (IGT). Housed within the institutional analysis and
development (IAD) framework, the IGT offers the ability to systematically dissect
institutions to gain a comprehensive understanding of the actors being governed by
them, activities that they are allowed, forbidden, and required to perform, the spatial,
temporal, and procedural boundaries of these activities, and gradations of sanctions
for non-compliance. The objectives of this chapter are two-fold: First, to apply the
IGT to understand systematically the content of regulatory policies governing the
practice of aquaculture in Florida and Virginia, United States. Second, to demonstrate
how regulatory stringency may be operationalized using the IGT as a diagnostic tool
to assess any discernable differences between policies that are reportedly stringent
and non-stringent. In pursuing these objectives, this discussion demonstrates the
IGT's applicability toward comparative institutional analysis.
Keywords
institutional grammar tool, institutional analysis and development framework,
aquaculture
Introduction
The crux of comparative institutional analysis is better understanding of the
formation and design of institutions to govern social behavior across contexts such as
different political jurisdictions (Majone, 1999). Institutions are the prescriptions, or
rules, that humans use to structure their interactions (Ostrom, 2005, 3). A
comparative understanding of institutions is an important endeavor for, as Cyr and
deLeon state, “comparative policy analysis raises the possibility of much richer
84 insights concerning the influence of cultural milieu, political competition, and
governmental structures themselves on the characteristics of public policy” (Cyr and
deLeon, 1975, 378). For descriptive exercises, comparative institutional analysis can
elucidate differences in institutional design characteristics. For explanatory exercises,
comparative institutional analysis aids in an examination of the factors that influence
alternative choices of institutional design within seemingly similar political and social
settings (Vining and Weimer, 1999, 39). As a first step in comparatively examining
institutions, however, it is first necessary to gain a thorough understanding of their
design and content. Such a task requires the aid of tools that allow for the systematic
dissection of institutions to decipher their various constitutive elements.
Institutions may be embodied in the form of policies, laws, or regulations or in
the form of cultural and social habits and/or patterns of behavior. Those who study
the former have demonstrated how the design of policies, regulations, and laws
provides insight into the intended objectives of policymakers within a particular
policy domain. Either implicitly or explicitly, institutions reflect the provisions of
power in society and distributions of benefits and burdens (Schneider and Ingram,
1997). Institutions can also outline opportunities and constraints for target audiences
(Ostrom, 2005) and identify the combination of tools or instruments available to
policy makers to achieve policy objectives (Bardach, 1980; Salamon, 1989; May,
1991, 187; Sidney, 2007).
85 Policy scholars have developed several typologies for understanding
institutional or policy design to identify ways in which policies shape, and are shaped
by political, social, and behavioral environments (Lowi, 1964; 1972; Ostrom, 1993;
2005; Schneider and Ingram, 1997; Wilson, 1995). Some of these typologies focus on
how the participation of different types of actors involved in the policy process
affects policy design (Lowi, 1964; 1972, Wilson, 1995). Others shed light on
normative implications of policy designs (Schneider and Ingram, 1997). For example,
in their discussion of the theory of social construction and policy design, Ingram et al.
(2007) posit that "policy designs structure opportunities and send varying messages to
differently constructed target groups about how government behaves and how they
are likely to be treated by government" (Ingram et al., 2007, 98).
Policy typologies have a number of strengths and weaknesses. For example,
while highlighting important factors that affect policy design, they offer little
guidance for gaining a systematic understanding of the elements of policy design that
profoundly shape individual behavior within a policy context. Needed is an approach
that provides meticulous analysis of the constitutive elements of policy designs: Who
exactly are the target audiences of policies? How do policies structure the behavioral
opportunities and constraints available to these different audiences by delineating
policy activities as being allowed, prescribed and forbidden? What types of sanctions
are identified for cases of non-compliance? Understanding these kinds of constitutive
86 elements is fundamental to gaining a comprehensive understanding of policy design
and, thus, is necessary for a comparative examination of them.
To organize diagnostic and prescriptive inquiry regarding institutional design
(Ostrom, 2011), the Institutional Analysis and Development (IAD) framework offers
policy scholars the ability to systematically understand the substantive elements that
constitute institutions. Indeed, Polski and Ostrom (1999) state that one of the
appropriate applications of the framework is “as a diagnostic tool to understand the
information and incentive structure of a policy” (Polski and Ostrom, 1999, 7). This
systematic approach is captured in the Institutional Grammar Tool (IGT) (Crawford
and Ostrom, 1995; Basurto et al., 2010, Siddiki et al., 2011). The IGT allows for a
comprehensive and methodical dissection of institutions and their constitutive
elements. Using the IGT to analyze institutions, one can gain a more complete
understanding of their target audiences, the activities these individuals are required,
allowed, and forbidden to perform, the conditions under which activities are/are not to
occur, and sanctions associated with non-compliance. By conceptualizing institutions
as placing boundaries on acceptable behavior, institutions are viewed as structuring
interactions between actors in relation to specified activities.
In this paper, the IGT is applied to compare state level regulations governing
the practice of aquaculture in Virginia and Florida, United States. Aquaculture is
defined as "the propagation and rearing of aquatic species in controlled or selected
environments” (NOAA, 1980). In the U.S., aquaculture has become an increasingly
87 important policy issue as governments at all levels (i.e. federal and state) seek to
expand the industry to compensate for depleting wild fish stocks and the associated
degradation of marine ecosystems (Naylor et al., 2000) and reduce a seafood trade
deficit that exceeds nine billion dollars (NOAA, 2009). As the industry in the U.S.
continues to expand, so too has the number of regulatory policies designed to govern
it. Regulatory concerns relating to aquaculture include water pollution from farm
effluent, competitive feed pricing, and siting issues in public waters (Ackefors et al.,
1994). The development of aquaculture regulations has been largely decentralized
with individual states being given the autonomy to develop rules for governing the
industry. As such, regulations from state to state differ considerably to account for
differences in the local contexts surrounding the aquaculture industry.
One approach to comparing institutional designs in the study of aquaculture is
to find cases with similar industry and biophysical characteristics but whose
regulations are markedly different. Virginia and Florida represent an example of two
such cases. These two states are notably similar in their industry and biophysical
characteristics. Florida, however, has developed relatively more stringent regulations
to govern its industry. The aim of this paper is two-fold: First, to apply the IGT to
systematically understand the content of the aquaculture regulatory policies of
Virginia and Florida. Second, to demonstrate how regulatory stringency may be
operationalized using the IGT to assess whether there are discernable differences
between policies that are reportedly stringent and non-stringent. In pursuing this
88 analysis, this paper demonstrates the IGT's applicability toward comparatively
examining institutional designs.
The Institutional Analysis and Development Framework, the Institutional
Grammar Tool, and Regulatory Stringency
Institutional Analysis and Development Framework and the Institutional
Grammar Tool
The Institutional Analysis and Development (IAD) framework has been
applied extensively by scholars to study institutions that individuals use to structure
their behavior within collective action settings. Institutions within the framework “are
the result of implicit or explicit efforts to achieve order and predictability among
humans by creating classes of persons (positions) who are then required, permitted, or
forbidden to take classes of actions in relation to required, permitted, or forbidden
outcomes or face the likelihood of being monitored and sanctioned in a predictable
fashion” (Ostrom, 2005, 18). In other words, institutions are the rules, norms, and
strategies that govern human interaction. Institutions can be codified into formal
documents such as policies, laws, or regulations, or may be reflected in social
behavior and cultural practices. Sometimes institutions as formal documents are
congruent with social behavior and cultural practices and sometimes there is no
congruence at all. In other situations, there might be a mix of congruence and
incongruence. The focus of this paper is on these formal documents or “institutionsin-form”.
89 The IAD framework offers a foundation to guide scholars in conducting
institutional analyses for unpacking the different working parts that comprise
institutions, such as, the actors being governed by institutions, how their activities are
to be conducted, and gradations of sanctions for reprimanding behavior agreed upon
to be socially unacceptable (Ostrom, 2005). These guidelines are captured within the
Institutional Grammar Tool, originally developed by Sue Crawford and Elinor
Ostrom (1995; 2005). The purpose of the IGT is to (1) differentiate between different
types of institutional statements (i.e. rules vs. norms vs. strategies)3, where
institutional statements are the individual clauses within institutions that outline
specific actions and outcomes relating to different actors (Crawford and Ostrom,
1995); and (2) to allow analysts the ability to study how the inclusion of different
linguistic elements within institutional statements affects how individuals interpret
regulations and resultant behavioral choices.
Coding institutions, such as regulations, using the IGT involves a two-step
process. First, the institution under examination (e.g. law, statute, regulation, policy,
etc.) is divided into individual institutional statements, and second, those statements
are further dissected into syntactic categories reflecting different grammatical or
3
The Institutional Grammar Tool can be applied to understand institutional statements that are written
down in documents such as policies, laws, and regulations, as well as those that are reflected in social
patterns of behavior. However, to date, it has only been applied to code institutions captured within
policies and regulations (Basurto et al., 2010; Siddiki et al., 2011). 90 syntactic elements. According to the IGT, institutional statements are
compartmentalized along the following six syntactic elements:






Attribute [A], the actor to whom the statement applies;
oBject4[B]¸ the animate or inanimate receiver of action within the statement;
Deontic [D], the prescriptive operator that indicates whether the focal action
of the statement may, must, or must not be performed;
aIm [I], the action of the statement;
Condition [C], the temporal, spatial, or procedural boundaries in which the
action of the statement is or is not to be performed; and
Or else [O], the punitive sanction associated with not carrying out the
statement directive as prescribed.
At a minimum, institutional statements must contain an Attribute, aIm, and Condition.
That is, at a minimum, a statement must identify an activity, an actor associated with
it, and it’s temporal and/or spatial boundaries. One statement ends and another begins
when a new configuration of syntactic elements is observed; when the same or a new
actor is described in relation to the same or different activity within certain temporal,
spatial, and/or procedural boundaries. Many times institutional statements will
correspond to individual sentences within an institution, though it is also commonly
observed that multiple institutional statements may be present within sentences. Thus,
the analyst must take great care to appropriately dissect the institution in accordance
the rules of the Tool. Below is an example of a statement from the Florida Best
4
The original grammar did not include the Object as an institutional statement component. The Object
was introduced by Siddiki et al. (2011) in an effort to clarify coding guidelines and enhance the
applicability of the institutional grammar tool.
91 Management Practices Rule containing an Attribute, oBject, Deontic, aIm, and
Condition.
Example statement: “Farmers must conduct systematic reviews of their operations
annually.”
Attribute: “farmers”
oBject: “systematic reviews of their operations”
Deontic: “must”
aIm: “conduct”
Condition: “annually”
Or else: N/A
When applying the IGT it is sometimes necessary to imply information
corresponding to different syntactic elements to satisfy the minimum requirements for
a phrase to constitute an institutional statement or to provide additional information
beyond just the required information. For example, sometimes statements are written
passively so that there is no explicitly stated Attribute but it is clear who the actor is
that is required to carry out a particular action based on the other passages in the
document and the situation described. For instance, a passively written version of the
aforementioned example would be: “Systematic reviews of operations must be
conducted annually.” There is no clearly stated Attribute in this statement, however, it
may be clear to the coder based on the context of the statement within the regulation
that farmers are those required to perform this activity. Thus, the Attribute, farmers,
would be implied for this particular statement. As another example, where there is no
92 explicit Condition given within a statement, the default Condition is “at all times”
(Basurto et al., 2010; Crawford and Ostrom, 2005, 149). All syntactic elements can
be implied in certain cases based on the structure and arrangement of institutional
statements within a particular institution. In many cases, information from preceding
statements is carried over to subsequent statements. While a coder using the IGT will
frequently find her/himself implying information for different syntactic elements, it is
important to stress that this be done carefully with strict adherence to the institutional
context so as to not introduce innovations to the institution.
Once an analyst has coded all of the institutional statements within a given
document, data for each syntactic element can be aggregated to represent a complete
depiction of the target audiences of the institution, the actions associated with them,
conditions specifying the performance of these actions, and sanctions associated with
non-compliance. Doing so provides a general illustration of how statements are
configured within an institution to reflect the roles and responsibilities of various
actors as intended by institutional designers.
In addition to just providing a descriptive summarization, however,
aggregating coded data across syntactic elements can also reflect additional design
characteristics of institutions. For example, aggregating Deontic data from an
institution not only tells one descriptively the types and frequency of prescriptive
operators associated with the activities outlined therein, it also gives some indication
of the level of stringency of the institution. As Bucciarelli and Johnson-Laird note,
93 “Deontic principles vary in the rigor that they are enforced, and in their
consequences, both for abiding by them and for violating them” (Bucciarelli and
Johnson-Laird, 2005, 160). Within a shared linguistic context, individuals share some
understanding of the relative meaning and stringency of different types of Deontics.
The most often seen Deontics are “must,” “must not,” “may,” “may not,” and
“should.” “Must” and “must not” Deontics imply a greater sense of stringency than
“may,” “may not,” and “should” Deontics, as the word “must” implies that one is
required or forbidden to perform a particular activity, while the word “may” implies a
degree of allowance. As such, an institution that contains a relatively higher portion
of Deontics containing the word “must” may be characterized as being more stringent
than an institution in which the majority of statements are associated with Deontics
containing the word “may.”
Similarly, an aggregation of data in the Or else coding category does not only
tell one how many activities are linked to a sanction and/or the types of sanctions
included within a particular institution. An analysis of Or else data can also be
reflective of the degree of stringency of a particular institution. Institutions contain
various types of sanctions for non-compliance. Common examples of sanctions found
in regulations include the revocation of privileges, monetary penalties, and
incarceration. Numerous studies within the regulatory scholarship have demonstrated
that these types of sanctions are effective in promoting institutional compliance
(Becker, 1968; Zimring and Hawkins, 1973). Designers of policies, regulations, and
94 laws appear to share an understanding of this fact and, thus, the inclusion of sanctions
within such documents may indeed be thought of as a tool used by them to indicate a
higher level of institutional stringency.
To date, the IGT has been applied by few scholars (Basurto et al., 2010;
Siddiki et al., 2011; Speer, 2011; Schluter and Theesfeld, 2010). As such, its
analytical and empirical utility is underdeveloped. Those who have applied it have
either demonstrated or foreshadowed its capacity as a tool for analyzing institutions
cross-sectionally (Basurto et al., 2010; Siddiki et al., 2011) or over-time, as well as its
applicability in relation to policy processes theories and framework outside of the
IAD framework (Basurto et al., 2010). This paper seeks to test its usefulness in
comparative institutional analysis by offering an additional application of the Tool
within the context of U.S. aquaculture and demonstrating how it can be applied as a
diagnostic tool in operationalizing the concept of regulatory stringency. In other
words, can the regulatory stringency of a particular institution, in this case regulatory
policy, be deciphered using the IGT?
Measuring Regulatory Stringency
The concept of regulatory stringency has been operationalized variably by
those studying it. For example, in their examination of the relationship between
housing prices and stringency of state level regulations, Quigley and Raphael (2005)
measured stringency of state regulations based on the number of growth control
measures adopted and included therein. In their international study of the relationship
95 between housing prices and stringency of country level land use policies, Mayo and
Shepard (1996) measured stringency in terms of the number of development controls
included within a country’s land use policies. As another example, in their study of
the relationship between foreign direct investment and stringency of environmental
regulations, List and Co (2000) measured stringency in terms of the actual amount of
monies paid by state agencies and firms as part of pollution control, prevention, and
abatement and a state’s ranking based on the Environmental Protection Index, which
produces a dollar ranking for each state based on a calculation of a combination of
“local, state, and federal government pollution abatement efforts with firm-level
abatement expenditures” (List and Co, 2000, 6).
One lesson gleaned from this sample of the literature is that measurement of
the stringency concept is largely context dependent. The study of regulatory
stringency would benefit from the ability to assess generically the stringency of a
given institution. By focusing on a comparison of syntactic elements within a given
institution as directed by the IGT, rather than case specific measures, the analyst is
better situated to conduct comparative assessments around regulatory stringency.
Based on the syntactic elements highlighted in the IGT, stringency may be thought of
as containing two operational dimensions; the first relates to the types of prescriptive
operators, or Deontics, present within an institution. The second relates to the
frequency and severity of sanctions, or Or elses, relating to non-compliance specified
within an institution. Because the IGT focuses on syntactic elements, in accordance
96 with which institutions may be more objectively coded, it has the potential for being a
useful comparative institutional analysis tool.
Below is a list of four indicators drawn from the IGT that will be examined in
this paper for comparing one “stringent” and one “non-stringent” state to see if there
are discernable differences between the two states’ regulations:
Diagnostic Indicator1: The regulations of stringent states will contain a greater
number of total institutional statements than regulations of non-stringent states.
Diagnostic Indicator2. The regulations of stringent states will have proportionally
more must/must not Deontics and less may/may not/should Deontics than regulations
of non-stringent states.
Diagnostic Indicator3: The regulations of stringent states will contain similarly
stringent Deontics across all types of Attributes.
Diagnostic Indicator4. The regulations of stringent states will contain more
statements with Or else codes that are greater in severity than regulations of nonstringent states.
It is also important to note that the concept of stringency in this case is not
dichotomized so as to represent two opposite dimensions (i.e. stringency vs. nonstringency). Rather, these two dimensions of the concept are treated as two varying
points on a continuum of stringency, assuming that there will be cases that will have
more or less stringent regulations than those of the two selected study states. The IGT
is applied as a diagnostic tool to ascertain the presence of certain institutional
characteristics. And, as with any diagnostic procedure, it is presumed that different
cases will present varying degrees of these characteristics (i.e. extreme nonstringency vs. mild non-stringency or moderate vs. extreme stringency).
97 Methods of Data Acquisition
Case Selection
Two states were chosen for this study based on a comparison of national level
data obtained though an online survey of the National Association of State
Aquaculture Coordinators (NASAC) administered in 2010. The NASAC membership
is comprised of state aquaculture coordinators and knowledgeable members of the
aquaculture industry5. Thirty states were represented in the final survey respondent
sample (n=32 out of 56 NASAC members or 57% response rate). In the survey,
NASAC members were asked to provide information regarding the regulatory context
relating to aquaculture in their respective states, including levels of regulatory
compliance and factors perceived as being most influential in affecting compliance as
well as characteristics of state level aquaculture regulations. In questions pertaining to
the latter, survey respondents were asked to indicate the level of stringency of
regulations in their respective states by expressing their level of agreement with the
following statement: “State regulations are very stringent in requirement and control.”
Using data collected through the survey of NASAC members, Virginia and
Florida were chosen for comparison because their regulations were reported to vary in
5
These individuals are typically employed by a state agency charged with the regulation and/or
development of the aquaculture industry or are prominent members of the aquaculture community.
They are highly knowledgeable about the regulatory, administrative, and/or scientific aspects of
aquaculture production. Most states have one aquaculture coordinator or representative, though there a
few states that have more than one. At the time the survey was administered, there were 56 individuals
included in the NASAC membership database, each representing one U.S. aquaculture producing state. 98 terms of regulatory stringency. But the two states were reportedly similar on a variety
of regulatory and industry characteristics including levels of levels of compliance.
Table 1.4 provides a list of characteristics upon which the two states were compared
demonstrating similarity between the two cases on all variables except for regulatory
stringency in which Florida was reported as being having more stringent regulations
than Virginia. For example, pertaining to political and regulatory characteristics, both
states were reported as having very clear regulations, inexpensive aquaculture
permits, and moderate involvement of the industry in reporting non-compliance. With
respect to social, community, and industry characteristics, both sates were reported to
have significant start-up costs associated with entering the aquaculture industry,
moderate amounts of domestic competition, and low levels of user conflicts (i.e.
between aquaculture producers, commercial fishermen, recreational fishermen, etc.).
In addition to data provided in the NASAC survey, background research
conducted on the two states indicated that they were comparable on several other
dimensions that may serve as control measures. For example, both states were
reported as being supportive of aquaculture development. As such, the design of
aquaculture regulations would not be expected to be influenced by varying levels of
state support of the industry. Second, in terms of aquaculture product, Virginia and
Florida were both regarded as leading shellfish producers in the U.S. Finally, these
states were deemed to be suitable for comparison given their geographic proximity to
one another. Choosing two states within the same geographical region may
99 potentially control for differences in regional characteristics that could influence the
design of regulations and supporting regulatory mechanisms.
Regulatory Sample
As a secondary step in the data collection process, all of the state level
regulations directly pertaining to aquaculture in Florida and Virginia were identified
and coded (a complete list of these regulations is provided in Table 3.2 and listed
below). Following the coding of all regulations, a test of inter-coder reliability was
conducted in which an additional person other than the researcher coded the Virginia
Enclosures Rule. The Enclosures Rule contains 63, or five percent, of the total
statements coded between Virginia and Florida. The coding for each syntactic
element per institutional statement between the researcher and this person’s coding
was compared to assess the degree of agreement. The goal was greater than 80%
agreement among coders across syntactic components. For each of the components,
the following percentage agreement was observed between the two coders: Attribute
(95%), oBject (83%), Deontic (94%), aIm (95%), Condition (79%), and Or else
(97%). The lowest agreement was observed for oBjects and Conditions. These results
from the inter-coder reliability test are consistent with Siddiki et al. (2011) and
Basurto et al.’s (2010) in which the authors observed lowest agreement on Conditions
(Siddiki et al. agreement on Conditions = 80%; Basurto et al. = 67%). After
100 Conditions, Siddiki et al. observed lowest agreement on oBjects (Siddiki et al.
agreement on oBjects = 86%; Basurto et al. = N/A).6
The primary document governing the practice of aquaculture in Florida is a
comprehensive rule known as the, “Florida Aquaculture Best Management Practices
Rule.” Both the development and implementation of this Rule is conducted by the
Florida Department of Agriculture and Consumer Services (FDACS) Division of
Aquaculture. In addition, aquaculture in Florida is governed by Florida Statute Ch.
597, which generally describes the powers and duties of the FDACS, and by parts of
the Florida Submerged Lands Statute and Rule concerning issues of aquaculture
leasing and siting. Aquaculture in Virginia is governed by two documents within the
Virginia State Code and six rules specifically addressing different aspects of
aquaculture conduct, as well as rules specific to particular species. These six rules are
implemented by the Virginia Marine Resources Commission (VMRC) and include
the Aquaculture Structures/On-Bottom Shellfish Structures, Harvest Reporting,
Enclosures, Striped Bass, Cobia, and Shellfish Restrictions Rules.
To ascertain the accuracy of the both the NASAC survey responses and final
regulatory sample, informal interviews were conducted with one individual from the
6
At the time that Basurto et al. conducted their coding exercise, the oBject had not yet been introduced
into the IGT coding framework. The oBject was introduced by Siddiki et al. (2011).
The results from the inter-coder reliability test indicate that the coding guidelines within the framework
require further clarification in differentiating what should be included under both syntactic
components. Still, the results from the inter-coder reliability test for this study are considered
acceptable even though the agreement observed on Conditions, 79%, is slightly less than the 80% goal. 101 primary aquaculture regulating agency in each state and one member of the NASAC
outside of the study states. These individuals confirmed that Virginia and Florida are
similar with respect to a variety of industry characteristics, but differ in terms of
regulatory stringency. These discussions also provided contextual insight on the
aquaculture industry in both states which helped the research better understand the
study context. Once all of the relevant regulations were identified, each was coded in
its entirety in accordance with the IGT.
Data Analyses
The analysis of coded data involved, first, the calculation of the total number
of statements per document per state. Second, data from the coding exercise were
descriptively analyzed in which the data for each syntactic element were summarized.
For each of the six syntactic elements of the IGT, the following types of data were
generated through this descriptive summarization for each piece of regulation
examined:
Table 3.1 Types of data generated for each IGT syntactic component
Attributes:
Complete list of target audiences for each regulation. Examples of
Attributes include aquaculturists, regulating agencies, the
Commissioner of Marine Resources, etc.
oBjects:
Scope of regulatory subjects associated with different types of
Attributes. Examples of regulatory subjects related to aquaculturists
include aquaculture facilities, harvest reporting forms, aquaculture
permits, etc
Deontics:
The types of Deontics associated with the activities assigned to
different types of Attributes. Examples of Deontics include “must,”
102 “must not,” “may,” “may not,” and “should.”
The full array of activities that different types of Attributes are
associated with. Examples of aIms for aquaculturists, along with
their associated oBjects, include, “renew [aIm] aquaculture permits
[oBject]” and “submit [aIm] monthly harvest reports [oBject].”
aIms:
Conditions: The full array of spatial, temporal, and/or procedural boundaries
associated with regulatory subjects and activities for different types
of Attributes. For example, Conditions associated with harvest
reporting forms for aquaculturists in Virginia specify that such
forms “must [Deontic] be submitted [aIm] no later than the fifth of
each month [Condition]” and that they “must [Deontic] be mailed
or delivered [aIm] to the Virginia Marine Resources Commission or
designated locations [Condition].”
Or elses:
The full array of sanctions that different types of Attributes may be
subject to in instances of non-compliance. For example, in Virginia,
failure to report harvest activity may result in “in a minimum of one
year of suspension of all commercial licenses and permits” or a
“class 3 misdemeanor,” depending on how many times the violation
has been made.
Given the analytical objectives of this paper, a discussion of results from these
analyses will focus on examining the total number of regulatory institutional
statements per state and summarized Attribute, Deontic and Or else data in relation to
the posited diagnostic indicators.
Results
The Grammar coded regulations from each state were examined to see if there
was a discernable difference between the institutional designs of the two states’
regulations with respect to stringency. A preliminary overview of the descriptive
analysis of coded regulatory data in each state, displayed in Table 3.2, indicates that
103 there are at least basic differences in the institutional designs of the regulations with
respect to stringency in relation to the diagnostic indicators posited at the beginning
of this paper. The discussion of results in this section is organized around these.
104 Table 3.2 Summary of coded Deontic and Or else data7
Total
Statements
Regulation
No. of Or Else
Statements
(% total)
Deontics (% total for each Deontic)
May
May
Not
Must
Must
Not
Should
Implicit
Explicit
Florida
Florida Best Management Practices
Rule
Florida Statute Chapter 597
Florida Submerged Lands Rule
Florida Submerged Lands Statute
Virginia
Virginia State Code/Statute Ch. 28
Enclosures Rule
Striped Bass
Harvest Reporting Rule
Shellfish Restrictions
Cobia
VA Code Chapter 150
Aquaculture Structures/On-Bottom
Shellfish Structures Rule
544
7
1
281
172
14
17
6
29
1
0
0
100
implicit;
53
explicit
100
implicit;
70
explicit
76
71
82
63
55
32
32
16
13
16
16
13
7
13
25
8
0
0
2
0
0
0
0
66
54
67
90
72
56
13
12
21
16
10
16
13
8
-
-
<1
<1
<1
28
22
13
0
10
10
10
50
30
-
-
0
7
7
100
<1
70
7
0
7
-
100
-
<1
<1
0
7
* These documents contained clauses that indicated that a violation of any of the institutional statements contained therein was subject to legal
penalties. Thus, in the interpretation of Deontics was that institutions are required, though the actual statements contain different Deontics.
105 Diagnostic Indicator1: The regulations of stringent states will contain a greater
number of total institutional statements than regulations of non-stringent states.
In terms of the sheer volume of statements pertaining to the conduct of
aquaculture in both states, as expected, Florida has notably more regulations with a
total of 1,011 statements pertaining directly to aquaculture across four documents and
Virginia having a total of 303 statements across eight documents supporting this first
indicator. Florida’s comprehensive BMP contains 544 institutional statements
addressing all aspects of aquaculture production from aquaculture feed to the design
of aquaculture infrastructure. The Florida Statute Ch. 597 contains 281 statements,
the Florida Submerged Lands Statute contains 14 statements, and the Sumberged
Lands Rule contains 172 statements. In contrast, each of the regulations guiding the
practice of aquaculture in Virginia contains relatively fewer statements: Virginia
State Code Ch. 28 (n=828) Virginia State Code Ch. 150 (n= 13), Aquaculture
Structures/On-Bottom Shellfish Structures Rule (n=10), Harvest Reporting Rule
(n=32), Enclosures Rule (n=63), Striped Bass (n=55), Cobia (n=16), and Shellfish
Restrictions (n=32). Most of these Rules contain guidelines just for aquaculturists,
except for the Harvest Reporting Rule which also contains guidelines for commercial
fishermen.
Diagnostic Indicator2: The regulations of stringent states will have proportionally
more must/must not Deontics and less may/may not/should Deontics than regulations
of non-stringent states.
8
N refers to the total number of institutional statements per document. 106
The second diagnostic indicator offered in this paper suggests that stringent
regulations will contain a higher percentage of total statements containing stronger
Deontics, such as “must” and “must not” than non-stringent regulations. Indeed, the
Florida regulations contain a higher percentage of “must” statements per regulatory
document than the Virginia regulations. Across the regulations for both states, the
“must” statements occurred more frequently than statements containing any other
Deontic. For each of the Florida regulations at least 70% of the total number of
statements contain “must” Deontics. Interestingly, Florida’s primary aquaculture
regulating rule is referred as being “best management practices,” which may imply a
lesser degree of stringency. The preamble of this document, however, clearly
specifies otherwise by outlining austere measures for non-compliance with the rules
established therein:
“Any person who violates any provision of Chapter 597, F.S. [Florida Statute] or
Rule 5L-3 F.A.C. [Best Management Practices Rule], commits a misdemeanor of the
first degree and is subject to a suspension or revocation of his or her certificate of
registration. The Department may, in lieu of, or in addition to the suspension or
revocation, impose on the violator an administrative fine in an amount not to exceed
$1,000 per violation per day. First time offenders will receive written notice of the
BMP deficiencies and given 60 days to comply. Operators not in compliance with
BMPs after 60 days will be fined $100 - $500 per day per occurrence depending upon
the type of violation and circumstances contributing to the violation.” (Florida
Aquaculture Best Management Practices Rule)
Given the nature of this directive, all institutional statements from both the Ch. 597
Statute as well as the Best Management Practices Rule were implicitly characterized
as “must” statements; though within these documents, statements were explicitly
107
assigned “may,” “may not,” “must”, and “must not” Deontics. Table 3.2 displays the
Deontic breakdown for both of these documents, considering those implied and those
explicitly stated. Each statement within these documents was also coded as having an
implicit Or else.
The percentage of “must” statements in the Virginia regulations varies widely
from 13% to 90% (VA Code Ch. 150 = 13%; Aquaculture Structures Rule = 50%;
Enclosures Rule = 54%; Cobia Rule = 56%; VA Code Ch. 28 = 66%; Striped Bass
Rule = 67%; Shellfish Restrictions Rule = 72%; Harvest Reporting Rule = 90%). The
highest number of “must” statements was observed in the Harvest Reporting and
Shellfish Restrictions Rule. Both of these rules pertain to health and sanitation aspects
of aquaculture production. Given the potential gravity of violating these types of
regulations, it is unsurprising that these regulations contain stringent Deontics.
Interestingly, while “must not” statements did not represent a significant portion of
the total statements in either state, there were markedly more “must not” statements
across the Virginia regulations than across the Florida regulations. In Virginia, the
percentage of “must not” statements per document ranged from 8% to 30% (VA Code
Ch. 150 = 8%; Harvest Reporting Rule = 10%; VA Code Ch. 28 = 12%; Cobia Rule
= 13%; Shellfish Restrictions Rule = 16%; Striped Bass Rule = 16%; Enclosures Rule
= 21%; Aquaculture Structures Rule = 30%), whereas in Florida the percentage of
“must not” statements ranged from 0% to 7% (FL Submerged Lands Statute = 0%;
FL Statute Ch. 597 = 7%; FL BMP Rule = 7%; FL Submerged Lands Rule = 7%).
108
Given, however, that “must not” statements did not compromise a significant
proportion of overall statements, Florida is still considered to have more stringent
regulations based on the high presence of “must” statements.
Diagnostic Indicator3: The regulations of stringent states will contain similarly
stringent Deontics across all types of Attributes.
In the case of this study, it is expected that stringent regulations would require
or forbid certain behaviors for both the regulatee and the regulator. An analysis of
Attribute and Deontic data revealed that there were no marked differences in either
state in Deontic use depending on who is the modal Attribute across the two states’
regulations. That is, a higher or lesser degree of stringency is not evident for
documents aimed at different types of actors (e.g., regulating agency versus
aquaculturists). Table 3.3 provides a breakdown of the modal Attributes and Deontic
data for each of coded regulatory documents. The coding results presented in this
table demonstrate that across all of the regulations, for both states, the majority of
statements contained therein were associated with “must” Deontics regardless of the
Attribute occurring most frequently within them. In Florida, for example, “must”
Deontics were applied just as frequently to the FDACS in Ch. 597 of the State’s
Statute as they were to aquaculturists in the BMP Rule. If one considers the explicit
use of “must” Deontics in these two documents, statements pertaining to the FDACS
contained more Deontics. Similarly, in Virginia, there were no major differences
between type of Deontic used and primary Attribute. The only regulation from
109
Virginia in which aquaculturists are not the primary Attribute is the Virginia State
Code. In this document, the VMRC is the primary Attribute, appearing in 26% of the
total statements, and aquaculturists are the second most frequently occurring
Attribute, appearing in 24% of the total statements. In the entire document, 66% of
the statements contain must Deontics; 26% of linked to the VMRC and 16% of which
are linked to aquaculturists. In all of the other regulations from Virginia,
aquaculturists are the primary Attribute and the majority of statements contain "must"
Deontics. These results indicate that regulations of both stringent and non-stringent
states can apply Deontics similarly across all types of Attributes. Thus, this diagnostic
indicator does not indicate any differences in regulatory stringency between Florida
and Virginia
110
Table 3.3 Summary of coded Attribute and Deontic data9
Regulation
Florida Statute Chapter 597
(281)
Florida Best Management
Practices Rule
(544)
Florida Submerged Lands
Statute
(14)
Florida Submerged Lands Rule
(172)
Virginia State Code/Statute
(82)
VA Code Chapter 150
(13)
Top 3 Attributes
(Percent of Total)
1. Florida Department of Agriculture and Consumer Services (49)
2. Aquaculturists (23)
3. Florida State Legislature (10)
4. Florida Aquaculture Coordinating Council (5)
1. Aquaculturists (88)
2. Florida Department of Agriculture and Consumer Services (11)
3. Florida State Legislature (1)
4. Florida Fish and Wildlife Conservation Commission (<1)
1. Board of Trustees of the Internal Improvement Trust Fund (50)
2. Florida Fish and Wildlife Conservation Commission (21)
3. Florida Department of Agriculture and Consumer Services (14)
1. Florida Department of Agriculture and Consumer Services(53)
2. Aquaculturists (35)
3. Board of Trustees of the Internal Improvement Trust Fund (8)
1. Virginia Marine Resources Commission (26)
2. Aquaculturists (24)
3. Virginia State Legislature (21)
4. Graduate Marine Science Consortium (12)
1. Aquaculturists (54)
2. State Health Commissioner (23)
3. Virginia State Legislature (23)
Deontics
(Percent of Total)
May (17)
Must (100) Implicit*
May Not (1)
Must (70) Explicit
Must Not (7)
May (7)
May Not (1)
May (29)
May Not (0)
Must (100) Implicit*
Must (53) Explicit
Must Not (7)
Should (7)
Must (71)
Must Not (0)
May (6)
May Not (0)
Must (76)
Must Not (7)
May (16)
May Not (0)
Must (66)
Must Not (12)
May (8)
May Not (0)
Must (13)
Must Not (8)
9
Where “state legislature” is listed as a modal Attribute, the next most frequently occurring Attribute is listed. Due to the way the coding system
operates, the entity that is prescribing a particular attribute can appear in the Attribute field, but does not necessarily represent an actor that is
significant in terms of performing aquaculture specific activities. In the Deontic category, the percentages will not add up to 100% because there
can be statements in the document that don’t have a Deontic at all. In the Harvest Reporting Rule, “commercial fishermen” and “seafood licensees”
show up as primary Attributes because this rule pertains to both commercial fishermen and aquaculturists. 111
Aquaculture Structures/OnBottom Shellfish Structures
Rule (10)
Harvest Reporting Rule
(32)
Enclosures Rule
(63)
Striped Bass
(55)
Cobia
(16)
Shellfish Restrictions
(32)
1. Aquaculturists (90)
2. Virginia Marine Resources Commission (10)
May (10)
May Not (10)
Must (50)
Must Not (30)
1. Clam Aquaculturists/Oyster Aquaculturists (53)
2. Registered Commercial Fishermen (31)
3. Seafood Landing Licensees (31)
4. Virginia Marine Resources Commission (31)
1. Aquaculturists (36/57)
2. Commissioner of Marine Resources (13/21)
3. Virginia Marine Resources Commission (13/21)
1. Aquaculturists (80)
2. Virginia Marine Resources Commission (18)
3. Aquaculture Purchaser (2)
1. Aquaculturists (56)
2. Virginia Marine Resources Commission (38)
3. Commissioner of Marine Resources (6)
1. Aquaculturists (81)
2. Virginia Marine Resources Commission (19)
May (7)
May Not (0)
Must (90)
Must Not (10)
May (16)
May Not (0)
Must (54)
Must Not (21)
May (13)
May Not (2)
Must (67)
Must Not (16)
May (25)
May Not (0)
Must (56)
Must Not (13)
May (13)
May Not (0)
Must (72)
Must Not (16)
112
Diagnostic Indicator4: The regulations of stringent states will contain more
statements with Or else codes that are greater in severity than regulations of nonstringent states.
Relating to this fourth indicator, the results from the Or else analysis
demonstrate that Florida regulations contain a substantially higher proportion of
statements containing Or else codes than the Virginia regulations. Again, given the
regulatory preamble concerning the violation of Florida’s Ch. 597 Statute or Best
Management Practices, by which it was determined that all statements within these
documents contain an implicit “must” Deontic, all statements within them also
contain implicit “Or elses” as described in the preamble. In Virginia, the regulations
containing the highest number of Or else codes, ranging from 13-28% of the total
statements, were the Harvest Reporting Rule, the Shellfish Restrictions Rule, and the
Cobia Rule. The former two of these also were the regulations containing the highest
number of “must” Deontics among the Virginia regulations. The Or elses in the
regulations for both states were severe in nature, ranging from temporary suspension
and/or revocation of aquaculture licenses or permits to the assignment of Class 1
misdemeanors.
Discussion and Conclusions
This paper employs the IAD’s institutional grammar tool to conduct a
comparative analysis of the designs of the state level regulations guiding the practice
of aquaculture in Virginia and Florida States. In addition to offering an additional
application of the Tool, the capacity of it for operationalizing the concept of
113
regulatory stringency was tested. Toward this aim, four diagnostic indicators were
offered to assess if the regulations of the two states were distinguishable with respect
to stringency based on elements from the:
Diagnostic Indicator1: The regulations of stringent states will contain a greater
number of total institutional statements than regulations of non-stringent states.
Diagnostic Indicator2: The regulations of stringent states will have proportionally
more must/must not Deontics and less may/may not/should Deontics than regulations
of non-stringent states.
Diagnostic Indicator3: The regulations of stringent states will contain similarly
stringent Deontics across all types of Attributes.
Diagnostic Indicator4: The regulations of stringent states will contain more
statements with Or else codes that are greater in severity than regulations of nonstringent states.
To assess the aquaculture regulations in relation to these indicators, regulatory
statements were first parsed using the IGT to uncover the syntactic elements of
statements that, when aggregated, revealed their intended target audiences, the actions
they are permitted, required, and forbidden to perform, the spatial, temporal, and
procedural boundaries under which these actions are/are not to be performed, and
sanctions associated with non-compliance. Grammar coded data was then
descriptively analyzed to compare institutional design characteristics between the two
states' aquaculture regulations focusing specifically on data relating to actors
(Attributes), prescriptive operators (Deontics), and sanctions specified for noncompliance (Or elses).
114
The results showed that, in accordance with three out of the four proposed
diagnostic indicators, Florida’s regulations are more stringent than Virginia’s as
operationalized using the IGT. This stringency was evidenced by the fact that (1)
Florida has a substantially greater number of institutional statements governing the
practice of aquaculture than Virginia, with the former having a total of 1,011
statements pertaining directly to aquaculture across four documents and Virginia
having a total of 303 statements across eight documents; (2) Florida's regulations
contain a higher percentage of total statements with stringent Deontics such as "must"
and "must not" as compared to Virginia; and (3) Florida's regulations contain
significantly more statements with Or else codes than Virginia's regulations.
However, the Or elses identified in both states' regulations were severe in nature,
ranging from the temporary or permanent revocation of aquaculture permits and
licenses to the assignment of Class 1 misdemeanors. The third diagnostic indicator
offered herein presumed that regulations that are more stringent will contain similarly
stringent Deontics across all Attribute types, whereas less stringent regulations may
contain more and less stringent Deontics for different types of Attributes. The results
from the analysis demonstrated that there were no discernable differences between
types of Deontics used and Attribute type in either Virginia or Florida. In other
words, this diagnostic indicator is not appropriate for identifying differing levels of
regulatory stringency.
115
By demonstrating the IGT's utility in assessing regulatory stringency based on
syntactic elements of institutional statements versus case specific measures, this study
contributes complementarily to both the study of regulatory stringency as well as
comparative institutional analysis. While considering context specific details is
indeed fundamental to case study analyses, the IGT does offer the benefit of creating
uniform measures of the concept of regulatory stringency across institutional cases. In
the absence of such uniform measures, the institutional analyst is faced with the
challenge of comparing apples to oranges when seeking to compare the institutions
governing two unique contexts.
Despite the affirmations of the analytical capacities of the Tool toward
understanding different dimensions of the content of institutional design, however, it
is important to note that an examination of institutional designs alone says little about
actual behavior because the latter is informed by a variety of other factors that are
individually based. These are the personal motivations that govern behavior.
According to Ostrom (2005) there are at least three sets of factors that individuals
consider when making decisions about different outcome scenarios: “…there are
three components to what individuals value as outcomes: (1) the physical results
obtained as a result of a chain of actions by participants; (2) the material rewards or
costs assigned to actions and results by payoff rules, and; (3) the valuation placed on
the combination of the first and second components” (Ostrom, 2005, 43). Internal
valuations regarding behavioral choices must be considered to understand how
116
individuals will actually behave in response to institutions. In order to pursue such
information, the analyst must employ additional forms of data collection, such as indepth interviews.
Regardless of these limitations, however, the analytical methods and tools
offered herein to study institutional design offer the ability to gain a comprehensive
understanding of institutions, such as policies, laws, and regulations which may then
may be used as a platform upon which to conduct comparative institutional analyses.
Additionally, such information can then be used in conjunction with other types of
data to get an increasingly thorough understanding how institutional designers seek to
structure human behavior through institutional directives.
117
CHAPTER 4: THE CULTURE OF COMPLIANCE: CONTEXTUALIZING
GUILT, SOCIAL DISAPPROVAL, AND FEAR OF MONETARY
SANCTIONS
Abstract
What motivates regulatory compliance? Drawing from regulatory and institutional
scholarship, this question is explored in this chapter in the context of aquaculture
communities. Findings come from a comparative case study analysis of two U.S.
states, involving a systematic coding of regulatory documents and interviews with
thirty members of the study states’ aquaculture communities. The findings indicate
that: (i) varying levels of compliance among individual farmers depending on the
type of institutional directive; (ii) feelings of personal guilt or shame and fear of
social disapproval, together, are more influential in shaping individuals’ decision
making regarding compliance than fear of monetary sanctioning; and (iii) the
expression of compliance motivations is contingent upon a variety of factors,
including the desire to protect the natural environment, prevent consumers from
becoming ill as a result of eating a contaminated product, and to prevent conflict with
neighbors and other resource users.
Keywords
institutional analysis and development framework, regulatory compliance,
aquaculture
Introduction
Regulatory scholars point to a variety of factors that influence compliance
behavior with regulatory directives. A fundamental consideration in understanding
compliance involves examining the ways in which different types of institutions,
defined herein as "the prescriptions that humans use to organize all forms of repetitive
and structured interactions including those within families, neighborhoods,
118
markets, firms, sports leagues, churches, private associations, and governments at all
scales" (Ostrom, 2005, 3), influence individuals' decision making regarding
compliance. Understanding the role of institutions in governing social behavior is of
central concern to scholars working with the Institutional Analysis and Development
(IAD) framework. The IAD framework is the primary theoretical and analytical lens
applied in this paper.
Within the IAD framework, it is presumed that institutions exert explicit
and/or implicit pressures on individuals to behave in a certain manner. In doing so,
they elicit certain external and internal psychological motivations regarding
behavioral decisions. The behavioral decision of interest in this paper is compliance
with state level regulations. Examples of internal motivations that can influence this
behavioral outcome include feelings of guilt or shame from not complying with
regulations (Grasmick and Bursik, 1990) and perceived institutional appropriateness
(Crawford and Ostrom, 1995; 2005; Ostrom, 1990). Examples of externally derived
motivations include fear of monetary sanctioning administered through a regulatory
authority and individuals' desire to maintain a positive reputation with fellow
community members (Crawford and Ostrom, 1995; 2005; Sutinen and Kuperan,
1999). Given the psychological nature of these factors, it is further assumed that the
expression of internal and external motivations is shaped by a variety of contingent
factors that help to explain how these motivations are considered in individuals'
decision making calculus. In other words, contingent factors are individual-level
119
motivations that temper the expression of internal and external motivations. For
example, in a farming context, an individual may state s/he would feel guilty for not
complying with regulations (internal motivation) and that this guilt is rooted in a fear
of producing and selling a contaminated product as a result of not following
prescribed health testing procedures (contingent motivation).
This paper examines the relative influence of internal and external motivations
in shaping compliance decision making in the context of aquaculture communities in
two U.S. states. Toward this effort, this paper is guided by three research questions:
(i) What is the perceived appropriateness of current state-level aquaculture
regulations by members of the aquaculture community? (ii) What motivating factors
are most influential to individual decision makers regarding compliance with state
level aquaculture regulations? (iii) Which contingent factors are most influential in
shaping the expression of these motivations?
Aquaculture is an increasingly salient natural resource based industry in the
U.S. that few scholars have examined from a social science perspective (Amberg and
Hall, 2010; Mazur, 2006). Aquaculture is defined as "the propagation and rearing of
aquatic species in controlled or selected environments" (NOAA, 1980). It provides an
appropriate setting to study the role of internal and external compliance motivations
as it is one that recently underwent significant regulatory transformations. Most U.S.
aquaculture producing states have implemented aquaculture specific regulations as
recently as in the last 10 to 15 years. Aquaculture production, however, has been
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occurring in some states for several decades, allowing for the development of
community norms and industry level best management practices that may or may not
be articulated in current state level regulations. Coupled with individual
characteristics and circumstances, such community norms exert pressures on
individuals to behave in certain ways, ultimately affecting their decisions to comply,
or not, with state level regulations (Ostrom, 2005).
Compliance motivations have been studied extensively by scholars in the
regulatory field (Burby and Paterson, 1993; Gray and Scholz, 1993; Helland, 1998;
May, 2005). The IAD framework provides a well-packaged analytical lens for
examining the role of institutions as presenting complementary compliance
motivations because it is explicitly geared toward understanding the institutions that
govern social behavior. Despite its appropriateness toward this objective, however,
few IAD scholars have examined the relative influence of internal and external
motivations in shaping compliance (Speer, 2011). The core analytical objective of the
IAD framework is to uncover the institutions that individuals use in social settings to
structure their interactions, relationships, and management of contextual features
(Ostrom, 1990; 2005). The focus of this paper is to examine how individuals within
aquaculture communities comply with state level aquaculture regulations and what
factors relating to institutional design and emerging from individual and community
contexts motivates their decisions to do so. Through this effort, empirical findings
presented in this paper offer insight regarding an industry that has been vastly
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understudied from a social science perspective and explores variables within the IAD
framework that have received little attention in recent scholarship.
The Institutional Analysis and Development Framework and Compliance
Motivations
The IAD framework outlines a structured approach for analyzing the
institutions that individuals use to govern their behavior within collective social
arrangements (Ostrom, 2005). Within the framework, classes of variables are
highlighted that are presumed to impact the design and implementation of institutions,
including attributes of the community such as the bio-physical and social features of
the context in which social arrangements are situated. In addition to shaping the
design of institutions, these contextual factors also influence how individuals interpret
and respond to them. A complementary facet of this presumed relationship,
highlighted in studies of common pool governance (Ostrom, 1990), is that institutions
designed with consideration of contextual attributes are also considered to be more
appropriate and thereby more likely to be accepted by community members (Ostrom,
1990).
Individuals from an IAD perspective are considered boundedly rational. That
is, they are goal oriented and cognitively limited in their ability to internalize and
process information and other sources of external stimuli (Ostrom et al., 1993); for
example, information and pressures emerging from institutional directives. They also
vary in terms of the value they assign to different behavioral motivations. Where
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some individuals assign utmost consideration to avoiding financial penalties, others
are primarily motivated by their desire to maintain social norms of reciprocity
(Crawford and Ostrom, 1995; McCay, 2002; Shepsle, 2006, 24-25). As a result,
individuals’ responses to institutional directives relates to their personal motivations
in different situations.
The discussion of internal and external motivations in this paper is centered on
their ability to explain the behavior exhibited by individuals on a daily basis in
response to institutional directives. Three types of compliance motivations, in
particular, are highlighted by IAD scholars, though few have subjected them in
conjunction to empirical testing (Crawford and Ostrom, 1995). One of these is an
internal motivation, and includes the personal shame or guilt that individuals
experience from not complying with institutional directives. Two are external
motivations, and include fear of social disapproval and fear of incurring monetary
sanctioning resulting from non-compliance. Though all three types of motivations are
psychological considerations processed internally by individual actors, the latter two
are characterized as external as they are derived from factors external to one's self. In
contrast, feelings of guilt or shame are deemed internal, as such emotions may be less
affected by contextual factors and, instead, reflect personal characteristics that
transcend the physical boundaries of the social space in which one resides. Given the
significance of perceived institutional appropriateness on shaping social behavior
within the IAD, this factor was also treated as an internal motivator in this study.
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When analyzing individuals’ decision making behavior in relation to policies
or regulations, the extent to which behavior is aligned with policy directives is
considered one measure of policy compliance. As such, compliance within the IAD
framework is characterized as conformance with institutions and is shaped by both
individuals’ normative and material considerations (Ostrom, 2005, 167) emerging
from biophysical, community, and individual contexts. It is assumed, thus, that
material and non-material rewards all factor into community members’ decision
making processes.
The appropriate setting in which to study individual and community based
compliance motivations with institutions is one in which such they have been
established to govern the behavior of at least a sub-set of members of a community,
and where there is evidence of strong norms among this sub-set of individuals to
behave in certain ways that either complement or contradict formal directives. Given
this criterion, aquaculture communities serve as appropriate cases studies in which to
pursue such a research endeavor. Aquaculture is an example of an emerging natural
resource based industry facing a shifting institutional landscape, marked by both an
increasing number of state level regulations and a strong sense of industry cohesion
and cooperation (Weible and Siddiki, 2011). Industry cohesion is often manifested in
the development of industry norms and/or best management practices. Contextually,
it is an example of a private good that is both maintained and constrained by the
availability of natural resources, is governed by environmental regulations targeted at
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reducing negative externalities, and is immersed in socio-economic considerations,
including consumer preferences and resource user conflicts.
Methods
Case Selection
The study under consideration in this paper involved a two state most similar
case study design. A preliminary study was first conducted to select an appropriate
sample of study states. This preliminary study involved interviews with 10 and a
survey of 56 state aquaculture coordinator members of the National Association of
State Aquaculture Coordinators (NASAC) (response rate = 57%).10 Using data from
this preliminary study, two states were chosen for the analysis presented herein that
were reportedly similar on multiple political and regulatory characteristics, social,
community, and industry characteristics, bio-physical attributes, and overall levels of
regulatory compliance, but differed with regard to the level of stringency of state
aquaculture regulations.11 The selection of cases based on this arrangement of similar
10
NASAC is an affiliate of the National Association of State Departments of Agriculture. NASAC’s
primary mission is to assist in the development of the U.S. aquaculture industry by providing resources
to state aquaculture representatives. NASAC members are highly knowledgeable about regulatory
and/or technical matters relating to the aquaculture industry. These individuals are either state
aquaculture coordinators or selected to serve as representatives to NASAC either due to their
professional position or influence in the respective aquaculture communities. Some states have one
representative, while others have more. This preliminary study yielded both qualitative and
quantitative data, describing perceptions of state regulatory and community characteristics pertaining
to regulatory mechanisms and compliance with state level aquaculture regulations in 30 states. 11
Regulatory stringency was selected as the varying factor as it is considered to be of central analytical
import. The primary variables under consideration are all anchored on state level regulations including
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and different variables characterizes this study as a "most-similar comparative case
study," in which two or more cases are similar on specified variables other than one
independent and/or dependent variable (Gerring, 2007, 90).
The selection of cases was conducted in a stratified manner. From the full
sample of 30 cases, the researcher first selected those cases that exhibited the
maximum amount of variation in terms of regulatory stringency; for example, those
cases in which regulations were either reportedly very stringent or very non-stringent.
Next, the level of regulatory compliance among the remaining set of cases was
examined. The sample was further narrowed by selecting those cases which were
reported to have high to very high compliance with aquaculture regulations. The
remaining cases were then compared across a variety of bio-physical, regulatory, and
social dimensions (please see Table 1.4 for the complete list of factors across which
study states were compared). Two sets of two states exhibited opposing levels of
regulatory stringency, very high compliance with aquaculture regulations, and similar
characteristics on several other dimensions: Hawaii and Pennsylvania, and Virginia
feelings of shame or guilt associated with non-compliance with regulations, fear of social disapproval
associated with non-compliance with regulations, fear of monetary sanctions, and compliance with
regulations. As such, a factor pertaining to regulation characteristics was deemed appropriate for
variation between cases. Additionally, due to the nature of the data being used for case selection,
regulatory stringency was deemed an appropriate choice for the varying factor as this is information
that is easily conveyed by NASAC members describing state level characteristics. While NASAC
members are highly knowledgeable about the regulatory and/or scientific aspects of aquaculture, they
may not be privy to certain industry level characteristics; such as the level of peer pressure to comply
with regulations. Additionally, they are not equipped to respond on aquaculture producers’ emotional
considerations regarding compliance.
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and Florida. From these two options, Virginia and Florida were chosen. In addition
to comparability on theoretical variables, these states are comparable in additional
ways, including the types of aquaculture produced, the presence of both marine and
inland aquaculture, and the relative establishment of the aquaculture industry.
Virginia and Florida were also considered to be comparable due to their geographical
proximity and shared regional characteristics as compared to Pennsylvania and
Hawaii.
To corroborate findings from the preliminary study, informal interviews with
three state aquaculture coordinators were conducted to ensure that the cases were
appropriate selections given the analytical objectives of the researcher. In these
interviews, the researcher described the research objectives, including explaining the
objective of comparing two U.S. states that share similar characteristics but differ
with regard to regulatory stringency. Discussions with these individuals revealed that
these cases were appropriate selections.
Case Studies: Aquaculture in Virginia and Florida
The U.S. currently produces approximately 20% of its seafood consumed
while importing 80%, resulting in a seafood trade deficit that exceeds nine billion
dollars (NOAA, 2009). This deficit has prompted federal and state policy makers to
encourage the development of a domestic aquaculture industry. The production of
aquaculture involves consideration of complex interdependencies among ecological,
economic, technical, and social factors (Firestone et al., 2004), resistance from the
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public regarding farmed seafood (Amberg and Hall, 2010; Mazur, 2006), and
numerous concerns about the industry from disease control to degradation of marine
ecosystems (Black, 2001; Francik, 2003; Naylor et al., 2000; Mazur, 2006; Treece,
2002).
As the U.S. aquaculture industry grows, so too is the number of state level
regulations designed to govern it, taking into account all of the above factors. Similar
to regulations designed for other natural resource based industries, aquaculture
regulations tend to be fairly technical and decentralization of regulatory governance is
commonly observed (May, 2005). Such decentralization has meant that the types of
regulations and supporting regulatory mechanisms vary widely from state to state.
The receptivity of recent regulatory efforts in different state aquaculture industry
contexts also varies. When new regulations are applied in states that have well
established industries, receptivity to them depends, in part, on how consistent they are
with industry level best management practices and norms. It also depends on how
contextually appropriate regulations are perceived as being.
In recent decades, both Virginia and Florida have supported active
aquaculture industries, producing both finfish and shellfish. Both states are generally
better known for their shellfish production; though, as of late, Florida has also housed
a thriving ornamental fish industry. Virginia and Florida share bio-physical
characteristics making the states amenable to broad scale shellfish production, though
state leasing and siting policies may limit the availability and/or access to such
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resources. Both states, for example, have abundant water sources for supporting
shellfish aquaculture. In addition to the Chesapeake Bay in Virginia, the state
contains a number of estuaries along the Atlantic coast (Luckenbach et al., 1999). In
Florida, both the Atlantic and Gulf Coasts provide many suitable locations for
shellfish production.
Both Florida and Virginia have expressed state level support of aquaculture,
touting economic and environmental benefits of shellfish production. In both states,
aquaculture is a multi-million dollar industry that provides employment opportunities
in addition to supporting the state economy. The states support aquaculture from an
environmental standpoint as shellfish production improves water quality in the areas
where it is being conducted, supports local ecosystem diversity, and preserves
wildstock (Virginia Marine Resources Commission, 2011). In fact, the preservation
of wildstock was a primary impetus in both states to grow the aquaculture industry. In
order to facilitate the development of the industry, both states implemented work
transition programs for commercial shellfishermen who were encouraged to seek
careers in aquaculture. They were provided basic aquaculture training, and, in some
cases, subsidies to establish shellfish aquaculture operations. In addition to such
programs, both states have created aquaculture opportunity zones in which
individuals interested in entering the aquaculture industry may do so with the aid of
state subsidies in an effort to reduce the input costs of entering into the industry. By
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inviting newcomers to the industry such state level efforts have contributed to more
heterogeneity within traditional aquaculture communities.
Altogether, state support of aquaculture has resulted in the growth of the
industry, both in terms of sales and the number of farmers, punctuated positive and
negative impacts associated with aquaculture, and increased attention by policy
makers and the general public. To respond to these developments, a variety of
regulations have been established in Virginia and Florida to manage the industry. In
Virginia, the Virginia Marine Resources Commission is the primary regulating
agency charged with implementing and enforcing state level aquaculture regulations.
In Florida, the Division of Aquaculture was established in 1999 within the
Department of Agriculture and Consumer Services for this purpose. A combination of
new regulations and industry entrants with diverse professional backgrounds has
significantly impacted the landscapes of the aquaculture industries in both states.
Such changes exert diverse regulatory and social pressures on the respective state
industries. This paper considers how factors pertaining to each influence decision
making regarding compliance among members of the two states' aquaculture
communities.
Data Collection and Analysis
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Data for this study was obtained in two steps. First, the researcher coded all
state level aquaculture regulations in Virginia and Florida, and second, conducted
interviews with 15 members of the aquaculture communities in each state, for a total
of 30 interviews. The interviews consisted of two parts. In the first part, the
researcher conducted a semi-structured interview using a predesigned protocol. In the
second part, study participants were asked to participate in a modified, structured QSort.
Coding
State aquaculture regulations were coded using a coding tool within the IAD
framework, called the Institutional Grammar Tool (IGT). The IGT was first
developed by Sue Crawford and Elinor Ostrom (Crawford and Ostrom, 1995) as a
tool with which to systematically dissect the content of institutions by parsing the
individual components that comprise them. This is achieved first by breaking
institutions, such as formal policies, into institutional statements. Crawford and
Ostrom (1995, 583) define institutional statements as "the shared linguistic constraint
or opportunity that prescribes, permits, or advises actions or outcomes for actors (both
individual and corporate)." Institutional statements are often captured within
individual sentences in a formal document, and are treated as individual units of
observations. Pertinent to this study, they are treated as individual regulatory
directives describing activities that a particular actor is required, permitted, or
forbidden to perform within certain conditions and penalties associated with not
131
carrying out the activity as prescribed. This information is systematically identified
within the IAD framework by parsing institutional statements into words or phrases
based upon the following coding syntax. Words or phrases are assigned to a syntactic
category based upon the part of the institutional statement they represent.






Attribute [A], the actor to whom the statement applies;
oBject12[B]¸ the animate or inanimate receiver of action within the statement;
Deontic [D], the prescriptive operator that indicates whether the focal action
of the statement may, must, or must not be performed;
aIm [I], the action of the statement;
Condition [C], the temporal, spatial, or procedural boundaries in which the
action of the statement is or is not to be performed; and
Or else [O], the punitive sanction associated with not carrying out the
statement directive as prescribed.
At a minimum, institutional statements must contain an Attribute, an aIm, and
a Condition. The following statement, from the Virginia Shellfish Restrictions Rule,
is dissected using the IGT to demonstrate how the coding syntax is applied: “Any
person violating any provision of this chapter [pertaining to restrictions on shellfish
harvesting] shall destroy all shellfish in his possession in the presence of a Marine
Police Officer.”
Attribute = “any person violating any provision of this chapter”
oBject = “all shellfish in his possession”
12
The original grammar did not include the Object as an institutional statement component. The Object
was introduced by Siddiki et al. (2011) in an effort to clarify coding guidelines and enhance the
applicability of the institutional grammar tool.
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Deontic = “shall”
aIm = “destroy”
Condition = “in the presence of a Marine Police Officer”
Or else = N/A
By aggregating coded data for each individual grammar component the coder
can then begin to identify a macro-level understanding of the document being
analyzed. By aggregating Attribute data, for example, one can identify the primary
target audiences of the document. By aggregating Or Else data, the coder can gain an
understanding of the level of stringency associated with a particular document.
Further, a more complete understanding of the document may be ascertained by
linking data across grammar components. For example, by linking Attribute and
oBject data, one can get a sense of the scope of policy activities assigned to particular
actors. Where an actor is linked with relatively few oBjects, it can be assumed that an
actor’s role in the context being discussed may be limited, and vice versa (Siddiki et
al., 2011). In linking aggregated Attribute, oBject, Deontic, and aIm data, one can
understand the complete array of activities associated with particular actors as well as
whether or not they are required, permitted, or forbidden to perform those activities.
By further linking this with Condition data, one can gain a detailed understanding of
the boundaries under which actors are required, forbidden, or permitted to perform
certain activities.
Therefore, institutional coding using the IGT yields data analyzable at the
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micro level when analyzing syntactic components within individual statements, as
well as at a relatively macro level when aggregating syntactic components to depict
the focal audience of a policy document, the roles and responsibilities of these actors
in performing specified actions, and the relationships between actors and policy
processes (Siddiki et al., 2011). To assess individuals’ actual versus prescribed
behavior, however, one must couple an institutional coding with other means of data
collection, such as interviews, to determine the extent to which it coincides with that
prescribed.
Interviews
Interviews for this study consisted of two parts. Twenty-two of the 30
interviews were conducted in-person and eight were conducted via telephone. A
purposive, snow-ball sampling technique was used to select interview participants in
Florida. A regulatory official provided a list of 50 names to the researcher to contact
for participation in the study, from which 15 were randomly selected and agreed to
participate. In contacting individuals from this list it was evident that the regulator
randomly selected these individuals from a list of Florida aquaculture producers as
those contacted expressed varying degrees of familiarity with the state regulators. For
Virginia, the researcher randomly selected participants from a directory of Virginia
aquaculture producers. The final sample of interview participants across the two
states consisted of 18 shellfish producers, seven regulatory officials, two ornamental
fish producers, two aquaculture processor/handler and ornamental fish producers, and
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one shellfish and finfish producer. In Virginia, two of the 15 individuals interviewed
were regulators while the rest were aquaculture producers or processors/handlers. In
Florida, four of the 15 individuals interviewed were regulators while the rest were
aquaculture producers or processors/handlers.
In the first part of the interviews, interviewees were asked to respond to a
series of questions based on a pre-designed interview protocol. Questions in this
portion of the interview related to perceived appropriateness of state aquaculture
regulations and interviewees' personal motivations for compliance. To link interview
responses with components from the institutional grammar, the following questions
were asked of interviewees:

[Attribute] You are one of the people most often referred to in this legislation.
Does this accurately reflect your level of involvement in the aquaculture
industry?

[Object] You are/are not listed is relation to many “items.” For example
[object 1, object 2, etc.]. How do you think this reflects the scope of activities
that you are involved in on a daily basis?

[Deontic] Some of the prescribed processes assigned to you in the legislation
include [X]. How do you interpret different prescriptive operators in relation
to these [may/may not/must/must not]?

[Condition] How do prescribed conditions influence how you interpret
prescriptive operators?

[Or else] I noticed there [are/are not] a lot of sanctions described in the
legislation for instances in which compliance is not achieved. Why do you
think this is the case? How do you feel about the current level of stringency of
state aquaculture regulations?
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The Deontic question was also used to assess compliance motivations.
Additionally, toward this objective, interviewees were also asked to answer the
following the questions: "Who holds you accountable [people, organizations, etc.] for
performing duties as prescribed in this regulation?" Along with this question,
interviewees were asked to comment on whether fear of monetary sanctions, personal
feelings of guilt or shame, or fear of social disapproval were most influential in
shaping their decisions to comply with regulatory directives.
A second facet of compliance motivations explored using this set of questions
was to identify the contingent factors upon which internal and external motivations
were based. This was done by examining, first, how interviewees weighted the
internal and external motivations provided by the researcher (fear of monetary
sanctions, fear of social disapproval, and feelings of guilt or shame), and, second,
interviewees' elaborations as to why these motivations were influential to them,
where such elaborations were provided. For example, one aquaculture producer stated
that social disapproval is his primary compliance motivation, and then further
elaborated that this stems from a desire to maintain a positive image of his family
business which has been in operation for a long time in the community and has a
good reputation among members of the industry. In analyzing a response such as this
one, social disapproval would be identified as the primary compliance motivation,
and the desire to maintain a positive business image would be classified as the
contingent motivation upon which fear of social disapproval is anchored.
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In the second part of the formal interviews, study participants were asked to
participate in a Q-Sort exercise. The Q-Sort is a methodological technique that allows
study participants to subjectively sort a pre-selected set of statements into a set of
categories designated by the researcher (McKeown and Thomas, 1988). Sample
statements can be chosen following an unstructured or structured approach. In the
latter approach, the researcher chooses the statements that will be sorted based upon
prior collected information, such as through preliminary interviews or from the
examination of existing documents.
A modified, structured Q-Sort was used in the discussed study in which each
participant was given a set of 20 cards containing statements that describe activities
that relate to her/his position in relation to aquaculture as prescribed in the regulatory
documents analyzed in this study. The participant was asked if s/he “must,” “must
not,” “may,” or “may not” perform the activity described on the card based on what
s/he actually does. Once the sorting exercise was completed, the researcher asked the
participant to explain the placement of statements. Follow-up questions were
structured around pro-factual and counter-factual prompts as well as probes relating
to the interviewees’ motivations in performing or not performing prescribed
activities. Additionally, interviewees were also asked to articulate how they
interpreted and weighted different prescriptive operators.
As both Virginia and Florida had multiple policies from which Q-Sort
statements were drawn, the amount of statements chosen in the Q-Sort sample from
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each document was proportionate to the number of statements in a particular
document relative to the total number of statements across all policy documents for a
particular Attribute. Table 4.1 displays how the sample of Q-Sort statements was
selected for Florida aquaculture producers. For example, the aquaculture Best
Management Practices (BMPs) Rule contained 480 statements in which aquaculture
producers was the statement Attribute, accounting for 79% of the total statements
pertaining to aquaculture producers across all Florida aquiculture regulations. As
such, the number of statements to be included from the BMPs in the Q-Sort sample
was 20 multiplied by 79%, or 16 cards.
Table 4.1. Sampling of Q-Sort statements -- Florida aquaculture producers
Total No. of
Statements in
Document
Percentage of Total
Statements
No. of Statements
out of 20
Statute
66
11%
2
BMPs
480
79%
16
Sub Lands Rule
61
10%
2
Sub Lands Statute
N/A
N/A
N/A
Total
607
100%
20
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Results
Coding
The results here only discuss the primary Attributes from the regulatory
documents from both states. Attributes represent the primary target audiences of the
regulations; that is, it is these individuals who are most directly impacted or governed
by the regulations. Identifying the modal Attributes from the regulations, or those
Attributes appearing most frequently in the regulations, was necessary for identifying
an appropriate sample of interview participants.
Eight regulatory documents were coded for Virginia (n = the number of
institutional statements per document): Virginia State Code Ch. 28 relating to
aquaculture (n= 82), Virginia State Code Ch. 150 relating to shellfish sanitation
(n=13), Aquaculture Structures Rule (n=10), Harvest Reporting Rules (n=32),
Enclosures Rule (n=63), Striped Bass Rule (n=55), Cobia Rule (n=16), and the
Shellfish Restrictions Rule (n=32). The modal Attributes from these regulations
included the VMRC, aquaculture producers, the Virginia State Legislature, the
Graduate Marine Science Consortium, registered commercial fishermen, seafood
landing licensees, the Commissioner of Marine Resources, and aquaculture
purchasers.
Four regulatory documents were coded for Florida: Florida Statute Ch. 597
relating to aquaculture (n=281), Florida BMPs Rules (n=544), Florida Submerged
Lands Statute relating to aquaculture (n=14), and the Florida Submerged Lands Rule
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(n=172). The modal Attributes in the legislation included FDACS, aquaculture
producers, the Florida state legislature, the Florida Aquaculture Coordinating
Council, the Florida Fish and Wildlife Conservation Commission, and the Board of
Trustees of the Internal Improvement Trust Fund.
By and large, the majority of statements in both states were directed at
aquaculture producers and their respective aquaculture regulating state agencies
(Virginia = 82% of statements; Florida = 68%). As such, members of these two
Attribute groups were recruited for participation in interviews.
Interviews: Protocol-Based Interviews around Institutional Grammar
Components -- Measuring Perceived Institutional Appropriateness
Virginia
In Virginia, interview participants indicated that the modal Attributes from the
legislation were also those that are most influential in the governance of the
aquaculture industry in Virginia. In addition to those listed in legislation, the State
Health Department (n=6) was cited as being an influential entity. Nine out of 13
interviewees stated that regulations are comprehensive or broad in scope. Two
interviewees stated that regulations were too broad and lacked attention to species
specific considerations, while two interviewees stated that regulations were broad in
scope while addressing context specific considerations. Evident in all of the
interviewees’ responses is that regulatory scope is important for capturing the
nuances of aquaculture production. Aquaculturists cited that the following issues
140
were inappropriately dealt with in regulations: those pertaining to temperature control
(n=3), leasing (n=1), Aquaculture Opportunity Zones (n =2), taxes (n=1), and
transportation of aquaculture products (n=1).
Five out of 14 interviewees stated that they have a strict interpretation of
prescriptive operators within institutional directives. The rest of interviewees stated
that they follow those operators that “make sense to them” (Interviewee ID: 001), are
applicable to their aquaculture operation, and/or are good for their product. Two
interviewees stated that they interpret some operators more strictly than others. For
example, those pertaining to health and sanitation/temperature controls (n=2) and
product tracking (n=1).
Four out of eight interviewees stated that conditions are important for
specifying how rules are important within different contexts. With specific reference
to the industry, one interviewee commented "there are a lot of site specific details to
aquaculture production" (Interviewee ID: 004). Two interviewees stated that
conditions within the regulations are continually being revised and re-evaluated based
on the experiences of the agency and producers. One regulatory representative
commented that conditions are important in preventing regulatory loopholes.
Finally, with regard to perceived regulatory stringency, the responses varied
greatly. Two out of 10 interviewees commented that regulations are becoming more
stringent over time, two interviewees stated that regulations are not stringent, and two
stated that they were appropriately stringent (with two of these individuals stating that
141
they should be stricter). In a similar vein, another interviewee commented that strict
penalties are necessary to protect the industry. A regulatory representative stated that
misdemeanors and felonies are commonly administered in cases of non-compliance.
Florida
As in Virginia, interviewees indicated that the modal Attributes from state
level policies are largely those most influential in reality. In addition to those listed in
the legislation, the following actors were listed as being influential: State water
management districts (n= 7) and the Department of Environmental Protection (n = 3).
Thirteen out of 15 interviewees stated that regulations are broad in scope. According
to regulatory representatives, regulations "are developed with input from an interagency council to make sure that the regulations cover the various regulatory
dimensions associated with aquaculture development (Interviewee ID: 12 and 13).
Two interviewees stated that there are too many regulations, making it difficult to
keep track of them, and one stated that that they are too technical to understand.
Five out of 13 interviewees stated that the regulations are rigid and/or they
have a strict interpretation of Deontics. Five interviewees stated that there is
flexibility in interpretation on some issues but not others. Rules seen as less flexible
include those pertaining to water impacts (n=2), medications and health (n=1),
wetlands, (n=1), and non-native species (n=1). Three interviewees stated that the
Division of Aquaculture purposefully allowed for flexibility in the regulations by
designing them to be “goal oriented rather than process oriented” (Interviewee IDs:
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019, 021, and 022). One of these interviewees, who is an aquaculture producer,
commented, "DACS [Division of Aquaculture and Consumer Services] has an end
result that they want to achieve and there is some wiggle room for producers in
getting to these end results" (Interviewee ID: 019). Finally, one regulatory
representative commented that while the regulations themselves are rigid, the
flexibility comes into the process at the enforcement level.
Similar to the Virginia responses, a regulatory representative in Florida
indicated that conditions relate to context specificity and are frequently re-evaluated
by regulatory designers. With regard to sanctions and penalties in cases on noncompliance, interviewees indicated that enforcement of regulations is flexible (n=2)
and that producers are given a warning before being administered a penalty (n=4).
Two interviewees stated that more severe penalties are needed, while one interviewee
stated that the current level of sanctions is appropriate for the safety of the industry.
Table 4.2 summarizes interview findings for both states. In both states
regulations were perceived as being broad in scope. In other words, aquaculturists
view regulations as addressing the array of activities they are involved with on a daily
basis. In both states, less than fifty percent of interviewees reported a strict
interpretation of Deontics, though in both cases, they reported that there is less
flexibility on some issues than others (e.g. health and sanitation rules and rules
pertaining to water impacts). Interviewees in both states reported that temporal and
spatial conditions are important for specifying the applicability of regulatory
143
directives in different contexts/situations. When asked to describe if they felt
regulations were appropriately stringent, interviewees in Virginia varied widely in
their responses. In Florida, interviewees tended to report that while regulations were
stringent on paper, enforcement tended to be flexible.
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Table 4.2 Comparative summary of interview findings
Grammar Component
Virginia
Florida
Attributes (Those Not
Included in Regulations)
State Health Department (n=6)
oBjects
Regulations are broad in scope (n=9/13)
State water management districts (n= 7)
and the Department of Environmental
Protection (n=3)
Regulations are broad in scope (n=13/15)
Deontics
Issues cited as being inappropriately dealt
with in regulations: temperature control
(n=3) and aquaculture opportunity zones
(n=2)
5/14 reported a strict interpretation
5/13 reported a strict interpretation
Less flexibility in interpretation of
prescriptive operators on certain types of
rules: ex. health and sanitation/temperature
controls (n=2)
Less flexibility in interpretation of
prescriptive operators on certain types of
rules: ex. water impacts (n=2)
9 /14 stated that they follow those rules that
“make sense to them,” are applicable to their
aquaculture operation, and/or good for their
product
3/13 producers and 1 regulatory official
stated regulations designed to be “goal
oriented rather than process oriented”
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Conditions
4/8 said Conditions important for context
specificity
Or elses
Responses varied greatly regarding level of
regulatory stringency
A regulatory representative stated that
misdemeanors and felonies are commonly
administered in cases of non-compliance
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Conditions important for context
specificity according to one regulatory
representative
Enforcement of regulations is flexible
(n=2) and producers are given a warning
before being administered a penalty
(n=4)
2 stated that more severe penalties are
needed, while 1 stated that the current
level of sanctions is appropriate for the
safety of the industry
Interviews: Q-Sort -- Measuring Congruencies and Discrepancies Between
Prescribed and Actual Behavior
Table 3 summarizes the results from the Q-sort showing average agreement
on Deontics in and between Virginia and Florida, as well as the issues on which most
discrepancy was observed between prescribed and actual behavior. The findings from
the Q-Sort exercise show that discrepancies between prescribed and actual activities
are lowest for "must" statements (average agreement = 79%) and highest for "may"
(average agreement = 40%) and "may not" statements (average agreement = 38%).
Such findings indicate that producers are more likely to comply with activities that
are required or forbidden, but have a more lenient interpretation regarding compliance
with directives that are less forcibly stated. Virginia and Florida differed in levels of
discrepancy relating to "must not" statements, with much higher discrepancies being
observed in Virginia. In Virginia, the most discrepancy between prescribed and
actual behavior was observed on issues pertaining to the use of hydraulic dredges
(must not), infrastructure design (must not), placement of temporary protective
enclosures (must not and may), and navigation (may not). In Florida, most
discrepancy was observed, across all four Deontic categories, regarding treatment and
discharge of effluent. In the may not category, the sale and transfer of Atlantic
sturgeon and use of medications for extra label purposes were also issues on which
high discrepancy was noted.
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Table 4.3 Summary of Q-Sort results: agreement between prescribed and actual
behavior
Virginia
Florida
Total
Must
80%
77%
79%
Must
Not
34%
69%
50%
May
39%
41%
40%
May Not
25%
52%
38%
Issues with Most
Discrepancy with
Policies (VA)
Few issues with
high disagreement
Use of hydraulic
dredge, extending
structures higher
than 12”,
placement of temp
protective
enclosures
Placement of
temporary
enclosures
Effects on
navigation from
aquaculture
activities
Issues with Most
Discrepancy with
Policies (FL)
Treatment and
retaining of
effluent
Discharge of
effluent into
wetlands
Treatment and
discharge of
effluent
Discharge of
effluent, sale and
transfer of Atlantic
Sturgeon,
medication use for
extra-label
purposes
Table 4.3 displays discrepancies between prescribed Deontics associated with
regulatory statements and Deontics selected by interview participants associated with
regulatory statements. A discrepancy simply means that an interviewee placed the
statement in a Deontic category that differed from that in the regulatory document.
What this general discrepancy calculation does not show is whether the interview was
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reported to be “over-complying” or “under-complying” with the regulations. For
example, “over-complying” would be noted when an interviewee placed a prescribed
“may” or "may not" statement into the “must” or "must not" category. “Undercomplying” would be noted when an interviewee placed a prescribed “must”
statement into the “may” category or a "must not" statement into a "may not"
category. There were a few instances in which interviewees were reported to be overcomplying with regulations, though most discrepancies were not of this sort. For
example, in Florida, for at least three statements relating to effluent discharge and/or
treatment that are prescribed “may” statements, at least half of the aquaculture
producers that responded to this question stated they “must” do this. Further, on a
discharge related statement in which producers were directed that they "may not add
feed to discharging systems," at least half of the interviewees that indicated that this
constituted at "must not" statement from their perspective.
Interviews: Compliance Motivations
A central focus of this study relating to compliance motivations was
understanding the relative influence of fear of monetary fines, fear of social
disapproval, and feelings of guilt and/or shame in shaping individuals' decisions to
comply. While many interviewees indicated that all three factors are influential, four
out of 25 individuals cited fear of monetary sanctioning as their primary compliance
motivation, 11 out of 25 producers cited fear of social disapproval as their primary
compliance motivation, and 10 out of 25 individuals cited feelings of guilt or shame
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as being their primary compliance motivation. Further, while not asked about this
factor directly, six out of 25 producers cited producing a quality product as their
primary compliance motivation.
A second dimension of understanding compliance motivations was to assess
the underlying factors upon which the expression of internal and external motivations
is based. These underlying factors, or contingent motivations, provide further insight
regarding individuals' compliance decision making. Table 4.4 displays the factors
listed as being most influential in shaping the expression of the primary internal and
external compliance motivations studied here. Of all the contingent factors cited by
interviewees, these were cited most frequently.
Table 4.4 Contingent compliance motivations
Primary Motivation
Fear or Monetary
Sanctioning
Fear of Social
Disapproval
Feelings of Guilt or
Shame
Contingent Motivations

Concern over an inability to pay fines
 A desire to maintain good relations with other
members of the aquaculture community
 An interest in maintaining the long term sustainability
of the state aquaculture industry
 A moral commitment to conduct aquaculture
appropriately
 A regard for the rules
 A desire to have a prosperous business
 A desire to protect the natural environment
 An adherence to religious values to abide by the law
 A desire to prevent consumer illness from eating a
contaminated product
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There were no marked differences between either the primary or contingent
motivations between regulators and aquaculture producers, except that one regulator
commented that the way he administered regulatory directives was based on a desire
to enforce them consistently.
Discussion
The following three research questions are posed in this study: (i) What is the
perceived appropriateness of current state-level aquaculture regulations? (ii) What
motivating factors are most influential to individual decision makers regarding
compliance with state level aquaculture regulations: fear of monetary sanctioning,
fear of social disapproval, or feelings of personal guilt or shame?; and (iii) Which
contingent factors are most influential in shaping the expression of these motivations?
Following is a discussion pertaining to each of these research questions based on
empirical findings.
What is the perceived appropriateness of current state-level aquaculture
regulations by members of the aquaculture community?
With regard to the level of perceived regulatory appropriateness, results from
the analysis indicate that regulations appropriately reflect the main actors involved in
the aquaculture industries of the two study states, with the exception of the State
Health Department in Virginia and state water management districts and the
Department of Environmental Quality in Florida. In both states, regulations are
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viewed as accurately reflecting the types of activities that aquaculturists are involved
with on a daily basis, i.e., are broad in scope. However, several issues were raised in
the Virginia context that interviewees did not feel were appropriately addressed in
state regulations, including, temperature controls during harvest and transporting of
aquaculture products, leasing, aquaculture opportunity zones, taxes, and
transportation of aquaculture products.
Interviewees expressed mixed interpretations of regulatory Deontics, or
prescriptive operators, based on their perceived appropriateness in relation to
regulatory activities. The majority of interviewees indicated that they observed
Deontics when they felt that they “made sense to them,” were applicable to their
operation, and/or when they perceived that adhering to the Deontic would produce a
better product. Further relating to compliance, many interviewees indicated that the
rigidity with which they interpreted Deontics depends in large part on the substantive
topic of the activity they relate to. For example, in Virginia, interviewees stated that
they are more likely to observe prescribed Deontics for directives relating to health
and sanitation and/or product tracking. In Florida, interviewees cited strict
interpretation of Deontics for regulatory directives relating to water impacts,
medications and health, wetlands, and non-native species.
Across the two study states a disparity was observed between the level of
regulatory stringency on paper and stringency of enforcement in practice. In Virginia,
responses varied greatly regarding the perceived appropriateness of regulatory
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stringency. Generally, while Virginia has less stringent regulations on paper than
Florida, the findings indicate that, in actuality, enforcement of regulations is quite
stringent. One prominent regulatory official bolstered this finding by stating that
misdemeanors and felonies are commonly administered in cases of non-compliance.
In contrast, while Florida is known for its stringent aquaculture regulations, the
interview findings indicate that enforcement of them is relatively lenient. Leniency in
this case refers to allowing aquaculturists to use a variety of means to reach
regulatory goals. Both interviewees and a regulatory official commented that
regulations are interpreted as being more goal oriented than process oriented. In other
words, aquaculture producers are given some leeway with which to interpret
regulatory directives. The regulatory representative further commented that penalties
are infrequently administered in instances of non-compliance. Instead, the regulators
have espoused a culture of seeking to work with aquaculture producers when noncompliance is observed instead of administering a penalty outright.
What motivating factors are most influential to individual decision makers
regarding compliance with state level aquaculture regulations?
With regard to compliance, the most notable finding that emerged from the QSort analysis was that, again, compliance is linked to particular issues. In Virginia
most disagreement between prescribed and actual practices concerned the placement
of temporary protective enclosures. In Florida, most disagreement concerned the
treatment and discharge of effluent, particularly relating to wetlands. This
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discrepancy is informed by comments from producers and regulatory officials that
there has been considerable confusion regarding what exactly constitutes a wetland.
In addition, the Q-Sort results show least disagreement among must statements, and
most disagreement among may and may not statements. Such findings indicate that
aquaculture community members know and comply with directives that are required
and are less informed or have a deliberately looser interpretation of other types of
Deontics (e.g., may and may not Deontics).
With respect to compliance motivations, as supported by findings in the IAD
literature (Ostrom, 2005), the results indicate that fear of monetary sanctions is the
least influential motivator to comply with regulations. Rather, the most influential
motivator was found to be fear of social disapproval, followed by feelings of guilt or
shame. In addition to these motivations, which were specifically probed by the
researcher, the desire to maintain a quality product was also cited by several
interviewees as an influential factor shaping compliance decisions. Given that this
factor was cited by multiple interviewees as being their most important farming
consideration, it was treated as a primary motivation instead of a contingent
motivation.
Which contingent factors are most influential in shaping the expression of these
motivations?
The results from the interviews also indicated that the expression of these
primary compliance motivations is contingent upon a variety of other factors, such as
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a desire to protect the natural environment, prevent consumers from becoming sick as
a result of consuming a contaminated product, and to prevent conflict with neighbors
and other resource users.
The contribution of this paper is two-fold. First, the findings from this study
explore an issue that has received little attention by IAD scholars in past years. Few
IAD scholars have studied motivations to comply with institutional directives,
specifically looking at the relative influence of perceived institutional appropriateness
alongside community and individually based factors such as the psychological
motivations studied herein. Despite a lack of research in this area, however, the
findings suggesting that a fear of social disapproval is more influential than a fear of
monetary sanctioning in shaping compliance decision making are consistent with the
IAD logic and empirical research conducted using the framework (Crawford and
Ostrom, 1995; Ostrom, 2005). IAD scholars have long supported the notion that
community norms profoundly shape social behavior, and, relevant to this study, the
psychological factors associated with behavioral decisions. Given their enduring
nature and resiliency in the face of changing contextual conditions, community norms
are likely to remain influential even in the midst of a transforming regulatory
environment. Further, given that community norms are enforced by neighbors and
other proximate actors within an individual’s community, they have the potential to
be more influential than institutions enforced by regulatory agents with which s/he
may have very limited interaction.
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Findings presented herein regarding compliance motivations are also
consistent with past regulatory scholarship that has also demonstrated the ascendency
of community and individually based motivations in shaping compliance (Braithwaite
and Makkai, 1991; Grasmick and Bursik, 1990; Sutinen and Kueperan, 1999).
Hatcher et al. (2000), for example, found that social pressures served as an effective
deterrent to non-compliance relating to catch quotas, or individual fishing quotas, in
the United Kingdom. Similarly, Sutinen and Kuperan (1995) have explored the
relationship between compliance and feelings of moral obligation among regulatees
regarding fishery zoning regulations in Malaysia. This study differs from past
research, however, by explicitly asking individuals to weight internal and external
compliance motivations in relation to one another.
Another way in which past IAD research lends support to this study’s findings
pertains to the importance of perceived rule appropriateness. Both IAD scholarship
(Ostrom, 1990) and the findings herein demonstrate that individuals are more likely
to comply with institutional directives when they are perceived as being contextually
appropriate. Several interviewees expressed this sentiment when describing their
interpretation of regulatory Deontics, stating that they maintain a strict interpretation
of Deontics when regulatory directives make sense to them or are perceived to be
applicable to their aquaculture operations. This finding also links this research to the
policy design (Bobrow and Dryzek, 1987; Linder and Peters, 1989; May, 1991;
Sidney, 2007) and regulatory compliance (Bardach and Kagan, 1982) literatures, in
156
which the relationship between institutional design and compliance has been
explored. This study differs from past research in these two research traditions by
applying the IGT to systematically dissect institutions and then linking coded data
with other forms of data collection to gain a comprehensive understanding of
perceived institutional appropriateness. Both the interview protocol and Q-Sort
exercise employed in this study were designed around the precept that the IGT coded
data reflect institutional design characteristics.
A second contribution of this paper is the analysis of the aquaculture industry
and related regulations from a social science perspective. Much of the research that
has been conducted on aquaculture to date has focused on the physiological or
infrastructural aspects of the industry. However, social considerations abound in this
growing industry from resistance from the general public (Amberg and Hall 2010;
Mazur 2006) to the role of collaborative decision making in aquaculture policy
development (Calanni et al., 2010). Understanding the social aspects of the industry is
critical for informing its long-term sustainability. Evident in the findings from this
study is that aquaculture producers have a strong internal ethic to produce a good
quality product that will not pose threats to the industry at large or to aquaculture
consumers. This industry level consciousness is also reflected in the finding that fear
of social disapproval is the primary motivator driving producers’ compliance
decisions. The extent to which the findings from this study are generablizable may be
157
ascertained by exploring similar variables within different natural resource based
industry contexts.
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CHAPTER 5: RULES AND DECISION MAKING: UNDERSTANDING THE
FACTORS THAT SHAPE REGULATORY COMPLIANCE
Abstract
What motivates regulatory compliance? This question is examined through the logic
of regulatory scholarship and the Institutional Analysis and Development (IAD)
framework using questionnaire and interview data collected among members of the
aquaculture community in Florida State. The findings indicate that individuals are
more likely to comply with regulations (1) when regulatory enforcement personnel
are perceived as being knowledgeable about aquaculture; (2) when farmers have a
desire to maintain a good reputation with other members of the industry; and (3)
when farmers have a strong sense of guilt associated with not complying with
regulatory directives. In demonstrating the influence of such factors on compliance,
this paper supports past IAD research that emphasizes the influence of community
based factors in shaping compliance while drawing attention to individual behavioral
motivations, such as feelings of guilt. The findings add to the regulatory scholarship
by validating past studies that posit that individuals are more likely to comply with
regulations when they perceive enforcement personnel as being knowledgeable.
Keywords
institutional analysis and development framework, aquaculture, compliance
motivations
Introduction
Over the past several decades, public policy scholars have sought to
understand what motivates regulatory compliance. Empirical research has made
abundantly clear that understanding compliance necessitates a concerted analysis of
features of the regulatory environment (Bardach and Kagan, 1982;
159
Gunningham et al., 2005; May, 2005), regulatory design (Ostrom, 1990; 2005), and
behavioral assumptions and norms of individuals (Crawford and Ostrom, 1995;
Ostrom, 2005; Sutinen and Kueperan, 1999; Grasmick and Bursik, 1990). However,
few studies have examined such factors in conjunction to ascertain their relative
influence on shaping individual compliance (May, 2004; 2005; Gunningham et al.,
2005). Such an analysis is conducted in this paper using findings from regulatory
scholarship and the Institutional Analysis and Development (IAD) framework as
analytical and empirical guides.
The examination of compliance motivations is conducted in the context of
aquaculture in Florida State using interview and questionnaire data. Aquaculture is
defined as “the propagation and rearing of aquatic species in controlled or selected
environments” (NOAA, 1980). Aquaculture is an increasingly salient state and
national level policy issue as the industry continues to expand in response to
depleting wild fish stocks (Naylor et al., 2000) and a seafood trade deficit that
exceeds nine billion dollars (NOAA, 2009). The growth of the industry has been
accompanied by new regulations, supporting regulatory structures, and industry
entrants, as well as heightened public attention and scrutiny (Mazur and Curtis, 2006;
Amberg and Hall, 2010). Aquaculture also represents a theoretically interesting
context within which to examine compliance motivations; it is characterized by
increasing levels of state regulations while industry members have demonstrated a
proclivity to develop community level best practices and norms. As such, it provides
160
the appropriate setting for analyzing diverse compliance motivations, including those
stemming from features of the regulatory environment as well as those that are
individual and community based.
This analysis contributes to the study of regulatory compliance in three ways:
First, the IAD framework is applied to examine a set of analytical variables that both
complement and elaborate upon those studied within the regulatory scholarship. The
IAD framework highlights sets of variables that are presumed to be important within
a particular institutional, or rule governed, context. It is limited, however, in the
extent to which it provides theoretical guidance regarding the relative influence of
certain variables pertaining to institutional, in this case, regulatory, compliance.
However, some theoretical insight on compliance motivations may be gleaned from
drawing upon research applying the IAD’s Theory of Common Pool Resources (CPR
Theory), which supports analyses of the rules that individuals within collective action
settings develop to manage common pool resources. Further, outside of the purview
of a particular theory, some IAD scholars have pointed to variables that may be of
analytical interest to those interested in examining behavioral motivations relating to
compliance, such as socially based reputational concerns and personal feelings of
guilt associated with non-compliance (Crawford and Ostrom, 1995; Speer, 2010). In
response to a lack of clear theoretical guidance, this paper draws upon
complementary findings from both the regulatory scholarship and applications of the
IAD framework to develop propositions regarding compliance motivations.
161
Second, the findings contribute empirical support to the assumption within the
IAD framework that individual and community based motivations are more likely to
be influential in shaping individual compliance than a fear of monetary sanctions.
This is because individual and community norms are likely to be more enduring in
nature than dynamic state level regulations (Ostrom, 2005, 138). Third, the findings
from this paper further support regulatory studies that suggest that compliance with
regulations will be higher when regulatees perceive that those actors enforcing
regulations are knowledgeable (Bardach and Kagan, 1982; Gunningham et al., 2005).
Regulatory Compliance and the Institutional Analysis and Development (IAD)
Framework
Regulatory scholarship, as it pertains to this study, is a body of empirical
research that highlights variables important in shaping compliance behavior. The IAD
framework provides a “conceptual map” for analysts interested in “how institutions
[both those codified in formal documents, such as policies, and those that are
reflected in social norms] affect the incentives confronting individuals and their
resultant behavior” (Ostrom, 2005, 8-9). At the framework level13, it is thus
appropriate for identifying variables of interest when pursuing a systematic
investigation of the influences that undergird individuals’ behavioral choices,
13
IAD scholars distinguish between frameworks, theories, and models. Frameworks “help to identify
the elements (and the relationships among these elements) that one needs to consider for institutional
analysis.” Theories “enable the analyst to specify which components of a framework are relevant for
certain kinds of questions and to make broad working assumptions about these elements.” Models
“make precise assumptions about a limited set of parameters and variables” (Ostrom, 2005, 28). 162
pointing specifically to those emerging from individual and community contexts.
Because the IAD framework offers limited theoretical guidance about the relationship
between these variables, however, these two literatures are applied in conjunction to
gain a comprehensive depiction of factors that shape decision making regarding
compliance, focusing on those relating to regulatory characteristics (e.g.
characteristics of enforcement personnel and regulatory design) as well as those that
are individual and community based (e.g. feelings of guilt associated with noncompliance or compliance based on a desire to maintain a good reputation with
others). The following discussion highlights the specific variables that will be
examined in this study based on past empirical research and offers propositions
relating each to compliance.
Regulatory Scholarship
Early research on compliance was steeped in the long held belief that a fear of
penalty or punishment was a prime compliance motivator (Bentham, 1789) and relied
heavily on the regulatory deterrence model (Becker, 1968). This model is premised
upon the assumption that legal sanctions suffice to thwart the desire for noncompliance on the part of regulated agents. Consistent with the rational actor model
of the individual (Hatcher et al., 2000), regulated actors from this perspective are
considered self utility maximizing agents in which the incentive to accumulate profit,
or conversely, to not bear excessive costs, is the sole motivator guiding individuals’
decision making processes. As such, monetary sanctions administered through
163
regulatory agencies are viewed as the primary coercive mechanism for fostering
regulatory compliance (Becker, 1968; Zimring and Hawkins, 1973).
Increasingly, empirical research in the regulatory field has drawn upon
scholarship from sociology and social psychology (Elster, 1989a; Elster, 1989b;
Coleman, 1990; Ajzen, 1988) to highlight additional variables that may influence
individual decision making regarding compliance. Research in this vein has shown
that a variety of other factors stemming from individual and community contexts
contribute to regulatees’ decisions regarding when to comply with regulatory
directives apart from monetary considerations (Hatcher et al., 2000), including, social
sanctions and influence, or social disapproval (Sutinen and Kueperan, 1999;
Braithwaite and Makkai, 1991), and personal shame or guilt (Grasmick and Bursik,
1990). Hatcher et al. (2000), for example, found that social pressures served as an
effective deterrent to non-compliance relating to catch quotas, or individual fishing
quotas, in the United Kingdom. Similarly, Kuperan and Sutinen (1995) have explored
the relationship between compliance and feelings of moral obligation among
regulatees regarding fishery zoning regulations in Malaysia. However, because
regulatory scholars have traditionally focused on top-down influences, such as the
influence of monetary sanctions (Becker, 1968; Zimring and Hawkins, 1973) or the
behavior of enforcement personnel (Burby and Paterson, 1993; Gray and Scholz,
1993; Helland, 1998; May, 2005) on compliance, research focusing on individual
and/or community based factors has been somewhat limited to date.
164
A number of political/regulatory variables have also been studied by
regulatory scholars in understanding factors that influence compliance that extend
beyond the classical regulatory deterrence model, including: enforcement practices,
specifically frequency of inspections (Burby and Paterson, 1993; Gray and Scholz,
1993; Helland, 1998; May, 2005), belief congruency between regulators and
regulatees regarding the way the industry should be managed (May, 2005; Bardach
and Kagan, 1982), technical competence of the regulatory agency as perceived by
members of the industry (Bardach and Kagan, 1982); and the presence of trust
between the two actors (Scholz and Lubell, 1998). Of these, an important factor that
will be analyzed in this paper is the extent to which regulatees feel that those
enforcing regulations are competent, or knowledgeable (Bardach and Kagan, 1982) as
this has not been studied as widely as the other aforementioned factors. Where they
are not, regulatees may question the legitimacy of rules and/or the ability of
enforcement personnel to administer those rules (Gunningham et al. 2005).
Institutional Analysis and Development Framework
To reiterate, as an analytical lens, the IAD framework provides a structured
approach for mapping out the rules that govern actions and outcomes within
collective action arrangements and points to important variables to consider when
conducting institutional analyses; however, at the framework level, it does not
identify specific propositions regarding the relationships between them. As such, the
propositions offered in this paper regarding compliance motivations draw upon
165
applications of the framework and Common Pool Resource (CPR) theory, as well as
upon regulatory studies. Under the IAD framework, rules are understood to be
generated by actors within a specific context to structure their behaviors and
participant roles and responsibilities. Ostrom (1994) writes that, “Rules are the result
of implicit or explicit efforts to achieve order and predictability among humans by
creating classes of persons (positions) who are then required, permitted, or forbidden
to take classes of actions in relation to required, permitted, or forbidden states of the
world” (Ostrom et al., 1994, 38). Compliance within the setting of the IAD
framework is characterized as conformance with rules and is shaped by both
individuals’ normative and material considerations (Ostrom, 2005, 167) emerging
from biophysical, community, and individual contexts. Compliance has been an
important consideration for IAD scholars, particularly those applying CPR theory to
understand how communities develop rules so as to promote the successful
management of common pool resources (Ostrom, 1990).
Sometimes rules are codified into written documents such as policies, laws,
or regulations. These are characterized as "rules-in-form," as compared to "rules-inuse," which are codified in social norms and customs. Rules-in-use may or may not
be embodied in rules-in-form14. The extent to which they are, however, may enhance
14
“Rules-in-form” and “rules-in-use” may also be characterized as “institutions-in-form” and
“institutions-in-use.” At the broadest level, prescriptions for governing behavior within the IAD
framework are characterized as “institutions,” where these are defined as the “prescriptions that
humans use to organize all forms of repetitive and structured interactions” (Ostrom, 2005, 3)
Institutions are then characterized as rules, norms, or strategies based on the degree of enforcement and
166
their perceived legitimacy (Ostrom, 2005) as found by scholars applying the IAD’s
CPR theory. In other words, rules-in-form, in the case of this paper, state level
regulations, that reflect community or industry norms are more likely to be better
received by regulatees than those that are not. Consistency between rules-in-form and
rules-in-use may be partly informed by the degree to which those being governed by a
set of rules are participants in the rule development process (Ostrom, 1990) or the
extent to which they regularly communicate with those charged with developing
rules.
Another factor highlighted within studies applying the IAD’s CPR theory as
potentially influencing compliance, which is also consistent with findings from
regulatory scholarship, is the perceived appropriateness of rules. Appropriateness of
regulations broadly refers to the applicability of regulations in relation to local
resource, political, and social conditions (Ostrom, 1990; 2005). Where regulating and
regulated actors possess disparate beliefs regarding how an industry should be
managed, scholars argue that regulated agents may question the legitimacy of
regulatory agents as well as the legitimacy and fairness of the directives themselves
(May, 2005; Ostrom, 1990). This, in turn, may negatively impact compliance levels
(May, 2005, 321; Bardach and Kagan, 1982; Levi, 1988). Referring to governance
sanctioning specified within them for non-compliant behavior (Ostrom, 2005; Crawford and Ostrom,
1995). Irrespective of this distinction, however, IAD scholars often use the phrases “rules-in-form” and
“rules-in-use” in discussing what would be perhaps more technically appropriate to refer to as
“institutions-in-form” and “institutions-in-use.” 167
rules of common pool resources more broadly, Ostrom (1990) proffers that rules, or
regulations, well tailored to the context in which they are being applied contribute to
the long term sustainability of such resources (Ostrom, 1990, 92). In exploring this
issue within a fisheries context, Jentoft (2004) asserts that when fishers lose the
ability to feel morally committed to “values such as honesty and respect for rules"
(Jentoft, 2004, 144), the ascendancy of regulatory over regulated agents begins to
diminish, thereby increasing chances of non-compliance by the latter. In the case of
this paper, rule appropriateness is understood specifically in terms of the extent to
which regulations accurately represent the array of activities that regulatees are
involved in on a daily basis.
Finally, outside the purview of CPR theory but still in relation to the IAD
framework, Crawford and Ostrom (1995; 2005) explicitly recognized the influence of
individual and community based motivations in shaping individuals' response to
rules, though the study of such motivations is certainly not confined to IAD studies.
Feelings of guilt associated with non-compliance and a sense of moral obligation to
abide by rules are examples of individually based motivations (Grasmick and Bursik,
1990; Posner and Rasmusen, 1999). An example of a community based motivation
would be the desire to maintain a good reputation with fellow industry members
(Crawford and Ostrom, 1995; Elster, 1989a; Ostrom, 2005, 146-147; Speer, 2010). In
furthering the analysis of these variables within the analytical context of the IAD
framework, Speer (2010) examined the relative influence of a fear of financial
168
penalties, fear of social disapproval, and feelings of guilt in influencing compliance
among local government actors within the case of participatory governance
arrangements in Guatemalan municipalities. She found that social enforcement of the
law by members of civil society was necessary for local government compliance.
Individual and community based compliance motivations such as those discussed
here are consistent between the IAD and regulatory literatures as both consider the
influence of the fear of monetary sanctions, peer pressure, and feelings of personal
guilt or shame in influencing compliance outcomes (Frey, 1994; Bendor and
Mookherjee, 1990; Crawford and Ostrom, 1995; Sutinen and Kueperan, 1999;
Grasmick and Bursik, 1990).
Propositions
Building on past regulatory and IAD scholarship, the following eight
compliance motivations will be examined in this paper: (1) the extent to which
individuals feel that those enforcing regulations are knowledgeable; (2) the extent to
which individuals feel that that regulations accurately reflect the scope of activities
that they are engaged in on a daily basis; (3) whether or not regulations are perceived
as being consistent with industry best practices; (4) whether or not individuals
regularly communicate with members of regulating agencies concerning regulatory
matters; (5) a fear of financial penalties; (6) a desire to maintain a good reputation
with other industry members; (7) feelings of guilt associated with non-compliance;
and (8) a strong moral obligation to produce a good product. The first four of these
169
motivations are characterized as "regulatory based compliance motivations" as they
pertain to features of the regulatory context, such as regulatory enforcement personnel
and regulatory design. The latter four variables are characterized as "individual and
community based motivations" as they are grounded in individuals' unique
compliance considerations based on their individual and the community context.
Table 5.1 summarizes propositions that are posited in this paper in relation to each of
the aforementioned motivations and also specifies how these are characterized.
General propositions are offered in place of formal hypotheses given that it is unclear
how these varying motivations will be animated in the aquaculture context.
Table 5.1 Propositions for testing compliance motivations
Related Proposition Vis-à-vis
Compliance
Compliance Motivation
Knowledgeable
enforcement personnel
Appropriate regulatory
scope
Regulations consistent
with industry best
practices
Farmer regularly
communicates with
regulating agencies
Compliance with regulatory
directives will be higher
when individuals…
…perceive that those
enforcing them are
knowledgeable.
…perceive that regulations
accurately represent the
scope of their daily activities.
…perceive them to be
consistent with industry level
best practices.
…regularly communicate
with members of the
regulating agency concerning
regulatory matters.
170
Type of Motivation
Regulatory based
Regulatory based
Regulatory based
Regulatory based
Fear of facing financial
penalties
Desire to maintain a
good reputation with
industry members
Guilt
Moral obligation to
produce a good product
…fear financial penalties
from not complying.
…fear the not complying
will result in a negative
reputation with fellow
industry members.
…feel a strong sense of guilt
from not complying.
…possess a strong moral
obligation to produce a good
product.
Individual and/or
community based
Individual and/or
community based
Individual and/or
community based
Individual and/or
community based
Study Setting: Aquaculture in Florida
This assessment of factors contributing to compliance with state level
aquaculture regulations was conducted in the State of Florida. The selection of
Florida as an appropriate case study to pursue this endeavor was based on data
collected through a national study of members of the National Association of State
Aquaculture Coordinators (NASAC) (survey n=56; interview n=10; survey response
rate = 57%; states represented in study sample = 30). The purpose of the NASAC
study was to collect information on the regulatory landscapes of aquaculture
producing states, including, characteristics of state regulations (e.g. regulatory
stringency), state regulatory mechanisms (e.g., arrangements for enforcement),
compliance behavior (e.g., levels of regulatory compliance with state level
regulations), and industry dynamics (e.g., extent of peer monitoring and enforcement
among industry members).
Using data from the NASAC study, Florida was selected to assess compliance
motivations because Florida aquaculture producers were reported as being very
171
compliant with aquaculture regulations.15 In relation to the overall findings of the
NASAC study, this makes Florida a typical case (Gerring, 2007) and thus a useful
setting within which to assess the factors are most influential in guiding compliance
decisions. At least with respect to compliance, the fact that it represents a typical case
makes the findings from this single case study more amenable to generalization than
if an outlier case study were examined. More than 70% of study respondents agreed
that compliance with aquaculture regulations in their respective state is very high
(Siddiki and Weible, 2010). In a broader context, Florida has supported an active
finfish, shellfish, and ornamental fish industry, though, until recently, it was best
known for shellfish production. The state has abundant water sources between the
Atlantic and Gulf Coasts to support the development of aquaculture, though current
leasing and citing policies may limit the availability and access to such resources. The
15
Florida was selected as part of a comparative most-similar case study design in which two states
were to be compared in a follow-up study assessing compliance factors (the results of which are
presented in this paper). The two states selected were Florida and Virginia. Based on the NASAC data,
these two states were reportedly similar in a number of regulatory and industry characteristics, but
differed in terms of levels of regulatory stringency. Differentiating Florida and Virginia was that
Florida was reported as having very stringent regulations while Virginia was reported to have nonstringent regulations. In pursuing this type of most-similar design, the author was interested in
determining what factors contribute to reportedly high levels of regulatory compliance when regulatory
designs apparently offer diverse incentives to comply. In states with stringent regulations and high
compliance, i.e. in which regulations contain severe penalties for cases of non-compliance, the
regulations themselves may provide sufficient incentive to comply. However, in states with nonstringent regulations and high compliance, there may be additional incentives motivating individuals’
decision to comply. Further, both regulatory and IAD scholarship suggest that additional factors
beyond those relating to policy design are influential in shaping individuals’ compliance behavior.
Given data limitations, only Florida is explored in this paper. Survey response rates for Virginia in the
follow-up regulatory compliance study were too low to pursue a comparative analysis. Further inquiry
into the reasons behind the low response rate revealed that the author’s survey was administered in
near temporal proximity to others possibly contributing to surveyee fatigue and/or confusion about the
intent of this survey in comparison to others administered around the same time. Nevertheless, Florida
offers a theoretically useful appropriate case in which to study regulatory compliance. 172
state has actively expressed support for the development of aquaculture, touting
benefits to ecosystem diversity and preservation of wild fish stocks. Manifestations of
this support include the implementation of work transition programs, which provide
training for commercial fishermen interested in entering the aquaculture industry, as
well as aquaculture subsidy programs which provide individuals the opportunity to
start up aquaculture operations at a reduced cost. In such programs, new industry
entrants may obtain aquaculture leases within designated Aquaculture Opportunity
Zones (AOZs) at subsidized costs.
The state has well-developed regulations for governing aquaculture. In
Florida, the Division of Aquaculture (FDACS) was established in 1999 within the
Department of Agriculture and Consumer Services for the purposes of developing,
implementing, and enforcing aquaculture regulations. The primary document
governing the practice of aquaculture in Florida is a comprehensive rule known as
the, “Florida Aquaculture Best Management Practices (BMPs) Rule.” This Rule
contains regulations pertaining to all aspects of aquaculture production, including
facility design specifications, water use and conservation and techniques,
administrative reporting requirements, and allowable and restricted species.
As a case study, Florida is theoretically appropriate given the objective of
studying compliance motivations considering that it has a well-developed regulatory
system and that the industry has been around long enough to develop social and
industry norms surrounding the practice of aquaculture. As such, it provides an
173
appropriate case within with to analyze in juxtaposition the effect of motivations
stemming from the regulatory and social environment.
Methods of Data Acquisition
This analysis is based on original interview and questionnaire data. As a first
step in the data acquisition process, semi-structured interviews were conducted with
15 members of the Florida aquaculture community. This sample of interview
participants was identified through a modified snowball sampling technique and
included five shellfish aquaculture producers, two ornamental fish producers, two
ornamental fish processor and handlers, one producer of both shellfish and finfish,
and five regulatory officials and/or enforcement personnel. A list of potential
interview participants was identified based upon conversations with aquaculture
stakeholders (e.g. aquaculture producers and representatives from the FDACS) as
well as through a list provided by a regulatory official from the FDACS. This
regulatory official selected names of potential participants randomly based on
geographic region; that is, it was evident that individuals were not selected based on
their regulatory standing (i.e. compliance behavior). This was ascertained by the
author as aquaculture producers expressed varying degrees of familiarity with
FDACS regulatory personnel. While representing only a small percentage of the total
population of aquaculture producers in the State (15 out of 415 registered producers),
this sample was comprised of individuals reflecting each of the major actor categories
within the aquaculture community.
174
Interview participants were asked to comment specifically on (1) the extent to
which they feel that regulations accurately represent the scope of activities that they
are involved with on a daily basis; and (2) to comment on whether a fear of financial
penalties, a desire to maintain a good reputation with other members of the industry,
or personal feelings of guilt associated with non-compliance was most influential in
shaping their decisions to comply with regulatory directives. Additional questions
posed in the interviews were meant to capture a broader understanding of
interviewees' perceptions relating to the aquaculture regulatory context in Florida,
including whether they feel regulations should be more or less stringent, the general
level of compliance with regulations, and overall perceptions of the strengths and
weaknesses of current state level regulations.
Following the completion of the interviews, an online questionnaire was
developed using data obtained through the interviews. This was done foremost as an
attempt to ensure that the survey appropriately reflected the regulatory context
surrounding aquaculture in Florida. The questionnaire was more sharply crafted than
the interview protocol to capture data pertaining to each of the compliance
motivations under consideration in this paper, in addition to containing questions
relating to the broader regulatory context.
The online questionnaire was administered in the spring and summer of 2011
to 415 aquaculture producers in Florida State. These 415 producers represent the
entire population of aquaculture farmers in Florida registered and licensed with
175
current email addresses under the FDACS to practice aquaculture. The email
addresses of these individuals were provided to the author by a regulatory official at
the agency. Of the 415 aquaculture producers to whom the survey was sent, 78
responded yielding a 19% response rate. The respondent sample included 23 finfish
producers, 17 ornamental fish producers, 12 aquaculture processor or handlers, 10
shellfish aquaculture producers, 3 individuals who are both aquaculture processor or
handlers and shellfish producers, 2 individuals who are both aquaculture processor or
handlers and finfish producers, 1 ornamental and finfish producer, 1 aquaculture
processor or handler and alligator producer, and 9 individuals involved in
miscellaneous aspects of aquaculture (e.g. live rock, experimental aquaculture, etc.).
Though the sample was limited, the respondents appropriately reflect the range of
aquaculture participants in the Florida industry which is comprised predominantly of
shellfish, finfish, and ornamental fish producers (UDSA, 2006).
Data Analyses
Questionnaire and interview data were analyzed in conjunction to gain a more
comprehensive understanding of compliance motivations, unattainable from
analyzing one type of data alone. Whereas the qualitative analysis allowed for the
opportunity to discern the nuances informing the relationship between compliance
motivations among individual producers, the quantitative analysis allowed for a more
direct assessment of the relationship between regulatory based and individual and
community based motivations and compliance.
176
Interview data were analyzed by collating responses to the relevant questions
from the interview protocol and identifying trends in responses. Survey data were
analyzed using bivariate and multivariate analyses based on responses to questions
asking survey respondents to identify the extent to which they feel regulatory
enforcement personnel are knowledgeable about aquaculture (Independent Variable
or IV1); perceived regulatory appropriateness (IV2); the extent to which regulations
are perceived as being consistent with industry level best business practices (IV3);
whether or not respondents regularly communicate with regulating agencies to
discuss aquaculture regulations(IV4); the extent to which a fear of financial penalties
(IV5); a desire to maintain a good reputation with other industry members (IV6);
personal feelings of guilt associated with non-compliance (IV7); and a moral
obligation to produce a good product (IV8) are important to respondents in making
compliance decisions, and levels of compliance (DV1). Each of these variables was
treated as independent variables, except for level of compliance, which was the
dependent variable. Each of these independent and dependent variables was
operationalized in the questionnaire as described in Table 5.2:
177
Table 5. 2 Analytical variables and related operationalizations in questionnaire
Variable
DV: Compliance
Corresponding Question or Statement in
Questionnaire
“I always comply with aquaculture
regulations.”
IV1:
“Those enforcing aquaculture regulations
Knowledgeable are knowledgeable about aquaculture.”
enforcement
personnel
IV2: Appropriate
“Aquaculture regulations reflect the full
regulatory
scope of activities that I am involved
scope
with at my facility on a daily basis.”
IV3: Regulations
“State regulations reflect the best
consistent with business practices of the industry.”
industry best
practices
IV4: Farmer
“In the last five years, I have regularly
regularly
communicated with members of
communicates regulating agencies to discuss
with regulating aquaculture regulations.”
agencies
IV5: Fear of facing
“How important is the possibility of
financial
facing financial penalties to you when
penalties
deciding whether or not to comply with
regulations?”
IV6: Desire to
“How important is maintaining a good
maintain a
reputation with other members of the
good
industry to you when deciding whether or
reputation with not to comply with regulations?”
industry
members
IV7: Guilt
“How important are personal feelings of
guilt from not complying with state level
regulations to you when deciding
whether or not to comply with
regulations?”
178
Response Scale in
Questionnaire
(Corresponding
Numeric Scale)
Totally disagree to
totally agree
(-2 to +2)
Totally disagree to
totally agree
(-2 to +2)
Totally disagree to
totally agree
(-2 to +2)
Totally disagree to
totally agree
(-2 to +2)
No/Yes
(0/1)
Not important at
all to very
important
(0 to 4)
Not important at
all to very
important
(0 to 4)
Not important at
all to very
important
(0 to 4)
IV8: Moral
obligation to
produce a good
product
“How important is a moral obligation to
produce a good product to you when
deciding whether or not to comply with
regulations?”
Not important at
all to very
important
(0 to 4)
Given that the variables under consideration are all ordinal, for the bivariate
analysis, Kendall's Tau correlations were conducted to determine if statistically
significant relationships exist between each of the independent variables and
dependent variable of interest. For the multivariate analyses, ordered logistic
regressions were conducted to determine if the independent variables were significant
predictors of compliance.16 Given the small sample size, no more than four predictor
variables were included in each regression analysis, given that the total number of
observations included in the regressions ranged from 54 to 55. The number of
predictors selected for the models was based on the recommendation that the sample
size should be at least 10 times the number of predictors in logistic regression
analyses (Harrel et al. 1985; Peduzzi et al., 1996; Van Belle, 2002). Also motivated
by the small sample size, two were conducted in which two sets of four predictor
variables were considered (one set including regulatory based compliance
16
To determine if the data was normally distributed, skewness statistics were calculated for each of the
independent variables and dependent variables included in the analysis. Results from this analysis
revealed that the following variables exceed the acceptable skewness values of +1/-1 (Leech et al.
2008): Compliance motivation: face financial penalties, (skewness value: -1.41), compliance
motivation (skewness value: -1.37), moral obligation to produce a good product (skewness value: 2.92), compliance (skewness value: -1.90). Because each of the variables under consideration is
ordinal, transformation of the data to correct for non-normality was not conducted. The author opted
instead to conduct non-parametric statistical techniques. 179
motivations and one set including individual and community based motivations)
along with one final model that examined the best predictors from these in relation to
compliance. Each ordered logistical regression was conducted using the robust
command in the statistical software program, Stata 10.1.
Results
A review of results from the interviews will first be discussed, followed by a
discussion of results from the analyses of questionnaire data.
Interview Results
Interview participants were asked to comment specifically on the extent to
which they feel that regulations accurately represent the scope of activities that they
are involved with on a daily basis (Interview Question: “In the regulations, you
are/are not listed in relation to many activities, such as x, y, z. How do you think this
reflects the scope of activities that you are involved in on a daily basis?”). In response
to this question, 13 out of 15 interviewees indicated that regulations are broad in
scope. One aquaculture producer, commenting on both the scope of regulations as
well on the extent to which they are consistent with industry best practices, stated,
"Regulations cover every aspect of what producers are involved with when it comes
to aquaculture; but a lot of it, such as disease management, is stuff that producers
would do anyway" (Interviewee ID: 018). Another producer commented on the scope
of regulations, the knowledge of enforcement personnel, social disapproval of noncompliant behavior, and the relationship between regulations and industry best
180
practices, saying, "The regulations cover the gamut of what producers do on a daily
basis when it comes to their aquaculture operations. DACS representatives are
intimately acquainted with aquaculture. If someone is flagrantly negligent of
regulations, the whole aquaculture community frowns upon that. That which is
included in the BMPs is really the best thing to do for your business and operation"
(Interviewee ID: 019).
Interview participants were also asked to comment on whether a fear of
financial penalties, a desire to maintain a good reputation with other members of the
industry, or personal feelings of guilt was most influential in shaping their decisions
to comply with regulatory directives (Interview Question: “If you had to weight a fear
of monetary sanctions or penalties, a desire to maintain a good reputation with other
industry members, and personal feelings of guilt from not complying with
regulations, which would you say is the most important in influencing your
compliance decisions?”). Twelve out of 15 interviewees responded to this question.
Some interviewees explicitly weighted these motivations in relation to one another,
while others provided varying responses based upon their interpretation of the
question. In the case of the latter, interviewees stated the motivation(s) most
influential for them beyond the three that the author explicitly inquired about. Table
5.3 provides a breakdown of interviewee responses. With respect to the
aforementioned compliance motivations, a desire to maintain a good reputation with
181
industry members and feelings of guilt associated with non-compliance17 were cited
more often than a fear of financial penalties as a primary compliance motivation.
Regarding reputational concerns, one producer commented, "Reputation is
everything. People in the community know each other and there is a concern
regarding integrity and character" (Interviewee ID: 018). This same interviewee said
about guilt being a primary compliance motivator, "Guilt is also important. I have to
live with myself." Another interviewee commented about reputation, "Industry is
kind of like a club. If you know someone is not doing what they are supposed to -others will turn on you. This is because someone's bad practice impacts the whole
industry" (Interviewee ID: 025). Reputation and guilt were cited as primary
compliance motivators by each type of actor included in the interview sample (i.e.
regulators, aquaculture producers, and processor/handlers).
Table 5.3 Breakdown of interview responses regarding compliance motivations
Interviewee ID
Interviewee Type
012
013
Regulator
Regulator
Primary
Motivation(s)
Guilt
Needs the job,
reputation, and
protect natural
environment
Secondary
Motivation(s)
-
17
In many cases, interviewees cited "a desire to do what is right" as a compliance motivation. In Table
2, this was coded as feelings of guilt and this response implies that individuals are morally concerned
with complying with regulations.
182
014
015
Ornamental
processor/handler
Shellfish producer
Reputation and
guilt
Fines and guilt
Reputation
016
Shellfish producer
Guilt
Fines
018
Reputation and
guilt
Reputation and
guilt
Fines
Reputation
Fines
022
Ornamental fish
producer
Ornamental fish
producer and
processor/handler
Ornamental fish
producer
Regulator
-
023
Shellfish producer
Consistency in rule
enforcement
Following
regulations is good
for business
024
Shellfish producer
Guilt
Fines
025
Shellfish producer
Reputation
-
019
020
Fines
-
-
These interview findings provide some qualitative insight into the regulatory
context of aquaculture in Florida. The results indicate that regulations are largely
perceived as appropriately reflecting the scope of activities in which members of the
aquaculture community are engaged. Further, the results indicate that a desire to
maintain a good reputation with fellow industry members and personal feelings of
guilt associated with non-compliance are both important compliance considerations
among members of the aquaculture community. They also show, that when asked to
weight the relative influence of these factors alongside a fear of financial penalties,
the former tend to be more influential.
183
While this qualitative insight is useful in understanding contextual elements of
the regulatory environment in terms of a few variables, a more complete analysis of
each of the compliance motivations examined in this paper was conducted using
quantitative data obtained through the online questionnaire, the results of which are
presented next.
Questionnaire Results: Bivariate Analysis
First, bivariate analyses were conducted to determine whether or not the
independent variables of interest were significantly correlated with the dependent
variable. The results from these analyses are provided in Table 5.4. In support of the
posited propositions, each of the regulatory based motivations (‘knowledgeable
enforcement personnel,’ ‘appropriate regulatory scope,’ ‘regulations consistent with
industry best practices,’ and ‘farmer regularly communicates with regulating
agencies’) and individual and community based motivations (‘fear of facing financial
penalties,’ ‘desire to maintain good reputation with industry members,’ ‘guilt,’ and
‘moral obligation to produce a good product’) under consideration were found to be
significantly and positively correlated with compliance, except for ‘farmer regularly
communicates with regulating agencies’ and ‘moral obligation to produce a good
product.’
184
Table 5.4 Kendall’s Tau bivariate correlations between regulatory based compliance
motivations and individual and community based motivations and compliance
Independent Variables
Dependent Variable:
Compliance
Knowledgeable enforcement personnel
(n=55)
.43**
Appropriate regulatory scope (n=64)
.46**
Regulations consistent with industry best
practices (n=64)
.43**
Farmer regularly communicates with
regulating agencies (n=55)
-.07
Fear of facing financial penalties (n=58)
.33**
Desire to maintain good reputation with
industry members (n=58)
.33**
Guilt (n=58)
.37**
Moral obligation to produce a good
product (n=58)
.18
** = correlation is significant at the 0.01 level (2-tailed)
* = correlation is significant at the 0.05 level (2-tailed)
Questionnaire Results: Multivariate Analyses
Building on the results of the bivariate analysis, ordered logistic regression
analyses were conducted to assess the relationship between the various compliance
motivations and compliance when modeled alongside one another. These results are
displayed in Tables 5.5, 5.6, and 5.7. The first table displays results from an analysis
of regulatory based compliance motivations; the second from an analysis of
185
individual and community based compliance motivations; and the third from an
analysis that examines the significant predictors from the other two models alongside
each other. The tables display the ordered log odds regression coefficients and
associated odds ratios, robust standard errors, number of observations included in the
model, and the model’s McFadden’s Pseudo R2, Wald Chi2 statistic, and overall
model significance (Prob > chi2).
Table 5.5 contains the results of the first model to examine regulatory based
compliance motivations in relation to compliance. The results indicate that the model
as a whole is significant at the .01 level (95% confidence interval) according to the
model’s Wald Chi2 statistic equal to 13.30. The model has a Pseudo R2 equal to 19%.
Of all the independent variables included in the model, the coefficient for
‘knowledgeable enforcement personnel’ was significant at the .01 level (95%
confidence interval). The interpretation of this result is that for a one unit increase in
‘knowledgeable enforcement personnel’ (i.e. going from 0 to 1 or “somewhat agree to
totally agree”), we expect a .68 increase in the log odds of being in a higher level of
comply (i.e. “neither agree nor disagree” to “partially agree” or “partially agree” to
“totally agree”), given all of the other variables in the model are held constant.
However, because log odds are difficult to interpret, the odds ratios relating to each of
these coefficients was also calculated (odds ratios were calculated by exponentiating
the coefficient, ecoef). Odds ratios are easier to interpret as they indicate the
proportional likelihood of being at a higher level in the dependent variable based on a
186
one unit increase in the predictor variable. So, for example, for ‘knowledgeable
enforcement personnel,’ the odds ratio calculation demonstrates that for a one unit
increase in this variable, the odds of a higher level of compliance are 1.98 times
greater, given the other variables are held constant.
Table 5.5 Model 1: ordered logistic regression for regulatory based compliance
motivations and compliance
Independent Variables: Regulatory Based
Compliance Motivations
Dependent Variable:
Compliance
Ordered Log Odds
Regression Coeff.
(Std. Error)
Odds Ratio
(Std.Error)
.68** (.25)
1.98 (.55)
Appropriate regulatory scope
.50 (.37)
1.66 (.53)
Regulations consistent with industry best
practices
.26 (.32)
1.30 (.42)
Farmer regularly communicates with
regulating agencies
-.09 (.65)
.91 (.61)
Knowledgeable enforcement personnel
Number of observations
54
Pseduo R2
19%
Wald Chi2/sig.
13.30/.010
** = correlation is significant at the 0.01 level (2-tailed)
* = correlation is significant at the 0.05 level (2-tailed)
Model 2, the results of which are displayed in Table 5.6, examines individual
and community based motivations in relation to compliance. The results indicate that
187
this model too is significant at the .01 level (95% confidence interval) according to
the model’s Wald Chi2 statistic equal to 15.40. The model has a Pseduo R2 equal to
14%. Of all the independent variables included in the model, the coefficients for
‘desire to maintain a good reputation with industry members’ and ‘guilt’ were found
to be significant at the .05 level (95% confidence interval). For the former, a one unit
increase in ‘desire to maintain a good reputation with industry members,’ is
associated with a .58 increase in the log odds of being in a higher level of compliance,
corresponding to an odds ratio of 1.79, given that all of the other variables in the
model are held constant. For the latter, for a one unit increase in ‘guilt,’ we expect to
see a .44 increase in the log odds of being in a higher level of compliance,
corresponding to an odds ratio of 1.55, holding all other variables constant.
Table 5.6 Model 2: ordered logistic regression for individual and community based
motivations and compliance
Independent Variables: Individual
and Community Based Motivations
Dependent Variable:
Compliance
Ordered Log Odds
Regression Coeff.
(Std. Error)
Odds Ratio
(Std.Error)
Fear of facing financial penalties
.26 (.27)
1.30 (.36)
Desire to maintain good reputation
with industry members
.58* (.58)
1.79 (.55)
Guilt
.44* (.44)
1.55 (.37)
188
Moral obligation to produce a good
product
-.07 (.42)
Number of observations
.93 (.43)
55
Pseduo R2
14%
Wald Chi2/sig.
15.40/.004
** = correlation is significant at the 0.01 level (2-tailed)
* = correlation is significant at the 0.05 level (2-tailed)
A third model assessed only those variables whose coefficients were found to
be significant in Models 1 and 2 in relation to compliance: ‘knowledgeable
enforcement personnel,’ ‘desire to maintain a good reputation with industry members,
and ‘guilt.’ The results from this analysis are provided in Table 5.7. Model 3 was the
strongest of three regression models; significant at the .01 level (95% confidence
interval) according to the model’s Wald Chi2 statistic equal to 25.47. The Model has a
Pseduo R2 equal to 30%. The coefficients of all three independent variables included
in the analysis were found to be significant, with ‘knowledgeable enforcement
personnel’ and ‘desire to maintain a good reputation with industry members’
significant at the .01 level (95% confidence interval) and ‘guilt’ significant at the .05
level (95% confidence interval). Based on the results, for a one unit increase in
‘knowledgeable enforcement personnel,’ we expect to see a 1.09 increase in the log
odds of being in a higher level of compliance, corresponding to an odds ratio of 2.96,
for a one unit increase in ‘desire to maintain a good reputation with industry
189
members,’ we expect to see a .79 increase in the log odds of being in a higher level of
compliance, corresponding to an odds ratio of 2.21, and for a one unit increase in
‘guilt,’ we expect to see a .57 increase in the log odds of being in a higher level of
compliance, corresponding to an odds ratio of 1.77, given all of the other variables in
the model are held constant.
Table 5.7 Model 3: ordered logistic regression for significant predictors from table
5.5 and table 5.6 and compliance
Independent Variables:
Significant Predictors from
Tables 4 and 5
Dependent Variable:
Compliance
Ordered Log Odds
Regression Coeff.
(Std. Error)
Odds Ratio
(Std.Error)
Knowledgeable enforcement
personnel
1.09** (.26)
2.96 (.86)
Desire to maintain good
reputation with industry
members
.79** (.26)
2.20 (.73)
Guilt
.57* (.24)
1.77 (.46)
Number of observations
Pseduo R2
Wald Chi2/sig.
55
30%
25.47/.000
** = correlation is significant at the 0.01 level (2-tailed)
* = correlation is significant at the 0.05 level (2-tailed)
190
To further ascertain the robustness of the significance of these variables in
relation to the all of the independent variables tested, different combinations of
regulatory based and individual and community based motivations were analyzed
together in separate logit models (the results of which can be found in Appendix F).
This was done in an effort to determine if modeling different combinations of
regulatory and individual and community based factors together would impact which
predictors were showing up as significant in relation to compliance. These additional
tests demonstrate that the coefficients for 'knowledgeable aquaculture personnel,’
'desire to maintain a positive reputation with industry members,' and 'guilt' remain
significant even when these variables are modeled with different combinations of
variables.18 As an additional attempt to demonstrate the robustness of the significance
of these variables, two ordinary least square regression models were conducted
assessing the relationship between regulatory based motivations and compliance
18
Combination 1: Desire to maintain a good reputation with industry, appropriate regulatory scope,
regulations consistent with industry best practices, and farmer regularly communicates with regulating
agencies.
Combination 2: Guilt, appropriate regulatory scope, regulations consistent with industry best
practices, and farmer regularly communicates with regulating agencies.
Combination 3: Knowledgeable enforcement personnel, fear of facing financial penalties, and moral
obligation to produce a good product. 191
(multiple regression Model 1) and individual and community based motivations and
compliance (multiple regression Model 2) (results for these analyses can be found in
Appendix G). Both models were significant at the .05 level with adjusted R2 values
of 33% (model 1) and 29% (model 2). In model 1, 'knowledgeable enforcement
personnel' was significant at the .05 level. In model 2, 'desire to maintain a good
reputation with industry members' was the only significant variable. Multicollinearity
diagnostics conducted along with these models demonstrate a lack of muticollinearity
between the predictor variables under consideration.
Discussion and Conclusions
The primary research question guiding this inquiry is: what motivates
individuals to comply with regulations? This question was examined in the context of
aquaculture in the State of Florida. The influence of eight different motivations on
compliance were examined. The first set of motivations analyzed are characterized as
regulatory based, and include: the extent to which individuals feel regulatory
enforcement personnel are knowledgeable about aquaculture; perceived regulatory
appropriateness; the extent to which regulations are perceived as being consistent
with industry level best business practices, and; whether or not individuals regularly
communicate with regulating agencies to discuss aquaculture regulations. The second
set of factors is characterized as being individual or community based, and includes: a
fear of financial penalties; a desire to maintain a good reputation with other industry
members; personal feelings of guilt associated with non-compliance, and; a moral
192
obligation to produce a good product. Overall, the results of the analysis indicate that
the following three factors are significant predictors of compliance: perception by
regulatees that those enforcing regulations are knowledgeable, a desire to maintain a
good reputation with other members of the industry, and feelings of guilt associated
with non-compliance. The results indicated that the following factors are not
significant predictors of compliance for aquaculture producers in Florida: perception
that regulations appropriately reflect the scope of activities producers are involved
with on a daily basis, perception that regulations are consistent with industry level
best management practices, producers' engagement in regular communication with
regulatory entities, fear of financial penalties, and feelings of moral obligation to
produce a good product.
Interview and questionnaire data were analyzed conjunctively to arrive at
these overall conclusions. Interview data provided a contextual understanding of the
regulatory environment surrounding aquaculture in Florida, focusing on four factors,
'appropriateness of regulatory scope,' 'fear of financial penalties,' 'a desire to maintain
a good reputation with industry members,' and 'feelings of guilt associated with noncompliance.' The interview question pertaining to the appropriateness of regulatory
scope was meant primarily to capture a descriptive understanding of interviewees’
perception regarding this factor. The interview question pertaining to 'fear of financial
penalties,' 'a desire to maintain a good reputation with industry members,' and
'feelings of guilt associated with non-compliance,' was designed to understand the
193
relative influence of these factors in shaping interviewees' compliance decisions.
Again, this question specifically asked interview participants to weight these three
factors based on which is most influential to them when making compliance
decisions. Interview findings indicate that the latter two of these motivations are,
indeed, important considerations for aquaculture community members when making
compliance decisions and that both are individually more influential than a fear of
financial penalties. These findings help to bolster the findings of Speer (2010) in her
assessment of compliance motivations in the context of participatory governance in
Guatemala. Speer's and this study are the only two that specifically examine the
relative influence of these factors. As such, the findings offered herein lend empirical
support to an understudied area within the IAD framework. In addition, they further
bolster a core assumption of the framework that community based influences
profoundly shape individual behavior (Ostrom, 2005; 26-27).
Findings relating to the influence of individual and community based
motivations on compliance were further corroborated through the analysis of survey
data in which, of the eight motivations examined, reputation and guilt were found to
be significant predictors of compliance, along with perceptions that enforcement
personnel are knowledgeable about aquaculture. This finding regarding the perceived
competence of enforcement personnel as a significant predictor of compliance offers
a contribution to the regulatory scholarship as few others have subjected this variable
to empirical testing (Bardach and Kagan, 1982; Gunningham et al. 2005).
194
While the findings observed herein are supported by empirical research
relating to the IAD framework and regulatory scholarship, it is difficult to say for
certain why some factors were more important in the context of aquaculture in
Florida than others when the other factors included in the analysis have been
demonstrated to impact compliance. The fact that reputational concerns was a
significant factor in this study context was somewhat expected given that the industry
in Florida has had sufficient time to develop and that aquaculture producers have a
proclivity to develop industry level best management practices. It is also unsurprising
that guilt was a significant predictor given that non-compliance with regulations can
have grave consequences, particularly relating to human health and the environment.
Other findings were more unexpected. For example, why is it that perceived
regulatory appropriateness was not found to be a significant predictor of compliance?
Perhaps this is because Florida regulations are noted by aquaculturists, both in and
outside of the state, as being especially broad in scope and thus there is not enough
variability in study populations' views regarding this factor to assess its influence in
relation to compliance.
The primary limitation associated with the analysis presented herein is the
small sample size. Providing a mixed-method analysis, employing both interview and
questionnaire data, as well as conducting multiple statistical tests to demonstrate the
robustness of significant variables does help to legitimate the findings despite the
sample size. Further, given that the online questionnaire was administered to the
195
entire population of aquaculture producers in Florida, the vast majority of which the
author had no previous contact with, a 19% response rate is considered respectable.
The extent to which the findings offered herein are generalizable to a broader
context is difficult to fully ascertain given that the study was based on the
examination of a single case and the limited sample size. Again, the author sought to
ameliorate the effect of these limitations by selecting a typical case within the
aquaculture context and conducting a mixed-method analysis to demonstrate the
robustness of findings. Clearly, however, such a study should be replicated in
additional states and in the context of different industries. The aquaculture industry is
characteristically similar to other natural resource based industries in which
regulations tend to be fairly technical, where decentralization of regulatory
governance is commonly observed (May, 2005), and where there are complex
interdependencies between among ecological, economic, technical, and social factors
(Firestone et al., 2004). As such, it is expected that the findings of this study
regarding compliance motivations should be at least somewhat generalizable to other
industry contexts, though this supposition must be subject to empirical testing.
This paper lends credence to past research within the regulatory field and the
IAD framework that outline a number of regulatory based and individually and
community based motivations for affecting compliance. It adds to the literature by
examining the relative influence of understudied variables such as reputational
concerns, feelings of guilt, and the perceived competence of regulatory enforcement
196
personnel. Aside from exploring the influence of these variables within other
contexts, future research relating to compliance motivations should incorporate
additional analytical variables from behavioral theories, such as the Theory of
Planned Behavior (Ajzen,1991; Armitage and Connor, 2001), that highlight the ways
in which individuals’ preferences, attitudes, and abilities temper compliance.
197
CHAPTER 6: CONCLUSION
Understanding what motivates regulatory compliance has been an enduring
quest within the field of public policy. Primarily, this is because understanding
compliance necessitates a consideration of two key inter-related elements of
governance: human behavior and the institutions designed to govern it. As such, in
order to examine regulatory compliance, one must conduct a concerted analysis of the
behavioral motivations that influence human decision making and institutions-inform, and the dynamic interactions between these two elements.
This dissertation presents findings from a study to assess compliance
motivations within the context of U.S. aquaculture. The study was guided by three
research questions. The primary research question (RQ) was: RQ1: Which internal
and external motivators are most influential in guiding the behavior of community
actors in relation to institutions-in-form? One general proposition was offered in
relation to this research question based upon scholarship relating to policy design,
regulatory compliance, and the institutional analysis and development (IAD)
framework: Social disapproval and feelings of personal guilt or shame are more
likely to influence individuals’ decision making regarding compliance than is the fear
of monetary sanctions. In addition to this primary question, two secondary questions
were explored: RQ2: Which contingent motivations are most influential in shaping the
expression of internal and external compliance motivations? RQ3: How can the
constitutive elements of institutions be assessed and compared?
198
To respond to these research questions, a comparative case study analysis of
Virginia and Florida was conducted. Data were collected in four stages:
Stage 1: A preliminary study involving interviews (n=10) and a questionnaire (n=56;
response rate = 57%) of members of the National Association of State
Aquaculture Coordinators;
Stage 2: A comprehensive coding of all state level regulations governing aquaculture
in each study state;
Stage 3: Formal semi-structured interviews with 30 members of the aquaculture
communities of the two states; and
Stage 4: A questionnaire of aquaculture producers in Florida (n=415; response rate =
19%).
The two study states were selected in accordance with a most-similar case study
research design. Virginia and Florida are similar on a variety of analytical variables of
interest relating to this study, including levels of regulatory compliance, but differ in
terms of the level of stringency of state aquaculture regulations. Examination of these
data is presented in this dissertation within four stand-alone chapters that respond to
the posited research questions. Types of data obtained through this study are analyzed
individually and in conjunction within each of these chapters based upon the specific
objective of each.
Following is a discussion of the key findings from this study, followed by a
discussion of broader implications and a future agenda relating to this research.
Key Findings
199
The general proposition guiding this study is that individual and community
based motivations (i.e. reputational concerns and feelings of guilt associated with
non-compliance) will be more influential than a fear of monetary sanctions in shaping
individuals’ decision making regarding compliance. This proposition is offered in
relation to research questions 1 and 2 which are addressed in Chapters 4 and 5 of this
dissertation; RQ1 and RQ2 in Chapter 4 and RQ1 in Chapter 5. The findings from
these chapters, in which interview and questionnaire data are analyzed to assess
compliance motivations, lend support to this general proposition. In the interviews,
study participants were asked to explicitly weight the above motivations in relation to
one another to indicate which was/were most influential in shaping their compliance
decisions. The interview findings show that in addition to reputational concerns and
feelings of guilt, a desire to maintain a quality product is also a primary compliance
motivator for members of the aquaculture community in Virginia and Florida.
Through the interviews, data regarding contingent motivations were also obtained.
Contingent motivations are the factors presumed to shape the expression of primary
compliance motivations (i.e. fear of monetary sanctions, reputational concerns, and
feelings of guilt). The results from the interviews indicated that the expression of
primary compliance motivations is contingent upon a variety of factors, such as a
desire to protect the natural environment, to prevent consumers from becoming sick
as a result of consuming a contaminated product, and to prevent conflict with
neighbors and other resource users.
200
In the questionnaire, study participants were asked to indicate the extent to
which a wide variety of factors, beyond just those listed above, were important in
shaping their compliance decisions. Factors included in the questionnaire ranged from
a moral obligation to produce a good product, to maintaining the long-term
sustainability of the industry, to protecting the environment. In addition to this
question, questionnaire participants were asked to provide their perceptions on a wide
range of issues relating to state regulations, enforcement of state aquaculture
regulations, and industry dynamics. They were also asked to comment on the level of
participation they personally exercise in the regulatory process. Their responses to
some of these questions were examined in relation to compliance to determine if they
were significant predictors of the latter. Specifically, those factors examined included
perceived regulatory appropriateness, whether or not the questionnaire participant
regularly communicates with the respective regulating agency(ies) regarding
aquaculture regulations, and the degree to which participants feel that aquaculture
regulations are consistent with industry-level best management practices. The results
from the questionnaire administered to aquaculture producers in Florida demonstrate
that in addition to reputational concerns and feelings of guilt associated with noncompliance, the perception that those enforcing aquaculture regulations are
knowledgeable was also a significant predictor in relation to compliance.
In response to research question 3, the IAD’s Institutional Grammar Tool
(IGT) was applied in Chapter 3 to determine its utility toward comparative
201
institutional analysis and also to examine whether it can be used as a diagnostic tool
to assess regulatory stringency based on the consideration of syntactic elements
within institutional statements comprising institutions-in-form, such as policies, laws,
and regulations. Such an analysis was appropriate given the research design selected
for this study wherein two states were compared that have differing levels of
regulatory stringency. As such, the IGT’s applicability as a diagnostic tool was able
to be assessed as an exploratory exercise to see if this difference in levels of
regulatory stringency between states was determinable within the context of the Tool.
Finally, while the results from the preliminary study of members of the
National Association of State Aquaculture Coordinators (part of which were
presented in Chapter 2) were intended primarily as a means through which to identify
cases for the larger dissertation study, the results also relate to the primary research
question guiding this study by providing qualitative and quantitative insight of the
perceived effectiveness of state aquaculture regulations within a national context as
well as some indication of factors that relate to compliance. Qualitative data obtained
through interviews provided insight on the inter-dependencies between regulatory
factors, such as perceptions of peer pressure, trust, and knowledge sharing and
regulatory compliance. Analysis of questionnaire data showed that perceptions of
compliance correlates with five factors: the perception that regulations are fairly and
consistently enforced, the belief of regulated persons that regulations are scientifically
and technically appropriate, trust of regulatory agents, trust between industry
202
members, and knowledge sharing between industry members on scientific/technical
and regulatory/administrative issues.
Broader Implications
This dissertation contributes to the policy design, regulatory compliance, and
IAD framework literatures by assessing the relative influence of regulatory and
individual and community based motivations for complying with institutions-in-form
and by assessing the utility of the IGT toward comparative policy analyses.
First, this study is one of two that tests the relative influence of a fear of
monetary sanctions, reputational concerns, and feelings of guilt associated with noncompliance. In doing so, this study offers an empirical examination of the factors
identified by Crawford and Ostrom (1995) as being of analytical interest in
influencing institutional compliance. An explicit examination of the relative influence
of such factors also offers a contribution to the regulatory scholarship, in which
individual and community based factors have been highlighted as important
compliance factors (Grasmick and Bursik, 1990; Sutinen and Kueperan, 1999). The
findings help to bolster the findings of Speer (2010) in her assessment of compliance
motivations in the context of participatory governance in Guatemala in which she
found that social enforcement of the law by members of civil society was necessary
for local government compliance. To the author's knowledge, Speer's and this study
are the only two that examine the relative influence of these factors.
Second, this study furthers a discussion of the methodological utility of the
203
IGT by applying it as a diagnostic tool to assess regulatory stringency in a
comparative institutional analysis. As the Tool has only been applied to date by few
scholars, each additional application lends insight into its versatility and compatibility
within varying institutional contexts. Not only does this application highlight its
usefulness toward institutional analysis within the IAD framework, in which it is
housed, it also contributes to the study of policy design more broadly. This study
demonstrates how the IGT can be used to systematically and comprehensively parse
the constitutive elements of policies based on generic measures such as syntactic
elements easing a comparative examination of policy design elements and
characteristics.
Third, findings from this study support a core assumption within the IAD
framework regarding the influence of community based factors in shaping individual
behavior (Ostrom, 2005; 26-27). In the case of this study, the specific behavior of
interest was compliance with institutions-in-form. Ultimately, as policy scholars, one
of our central pursuits must be to understand the interaction between human behavior
and the institutions designed to govern it. Such an understanding is fundamental to
crafting effective institutions.
Study Limitations
The primary limitation of this study was the low questionnaire response rate
for both the preliminary NASAC study (response rate = 57%) and the broader
dissertation study. In Florida, only 19% of individuals to whom the questionnaire was
204
sent responded. Even less responded to a questionnaire sent to Virginia aquaculture
producers, precluding the inclusion of the data in this study. Follow-up inquiry
regarding the low response rate in Virginia revealed that the researcher's survey was
administered at approximately the same time as others asking aquaculture producers
to respond to similar types of questions. These circumstances could have possibly led
to confusion and/or fatigue among the study population. However, despite this
limitation, findings from this study offer both theoretical and practical contributions
as explained in the preceding and following discussion.
Future Research and Achieving Broad Impacts
Future Research
In order to truly assess the generalizability of the findings of this research
study, a similar analysis must be conducted in an additional industry context that
faces similar issues as those faced by the aquaculture industry. The organic farming
industry is one such example. Like aquaculture, organic farming is an example of an
emerging natural resource based industry that is an increasingly important state and
national level policy issue. It is both maintained and constrained by the availability of
natural resources, is governed by environmental regulations targeted at reducing
negative externalities, is an industry facing increasing levels of public attention, and
is one in which industry members harbor a strong sense of industry norms and thus
are inclined to develop industry level best management practices that may or may not
205
be encompassed within state level regulations (Michelsen, 2001; Moynihan, 2005;
Harris, 2005).
The researcher has recently been awarded (along with Co-PIs Chris Weible,
Xavier Basurto, and John Brett) a grant by the National Science Foundation to pursue
a modified version of this dissertation study within the context of organic farming.
The funded project will investigate the linkages between institutions and compliance
in the context of organic farming in six states with the following objectives: (1) To
provide an institutional analysis of organic farming policies, including organic
farming certifications, comparatively across six states for crop and livestock/dairy
farms; (2) to investigate the relationship between institutions and compliance among
all certified organic producers and regulators (n~1,600) in six states; (3) to contribute
to understanding regarding the interaction between institutions-in-form and
institutions-in-use and a variety of individual motivators in shaping human behavior
within the purview of the institutional analysis and development framework (IAD)
(Ostrom, 2005), the institutional grammar tool (Crawford and Ostrom, 1995; 2005;
Basurto, et al., 2010; Siddiki et al., 2011), and other theories and approaches for
understanding regulatory compliance and motivations (Ajzen, 1991; May, 2005); and
(4) to provide a comparison between institutions and compliance in organic farms in
six states to aquaculture farms studied by the researcher is this dissertation, in
addition to Colorado State, in which a pilot study for this dissertation was conducted.
Achieving Broad Impacts
206
An immediate next step concerning this dissertation is to seek publication of
the four stand-alone chapters presented herein in peer-reviewed outlets targeted at
academic audiences. In addition to the findings from this chapter, those from a pilot
study of this dissertation conducted in Colorado have also been submitted for
publication, along with co-authors Xavier Basurto and Chris Weible. In the Colorado
study, the researcher applied the IGT to code all state level aquaculture regulations
and piloted the interview and q-sort protocol used in the broader dissertation study of
compliance motivations.
Significant efforts have been, and will continue to be made to disseminate the
findings from this study to non-academic audiences. This research is based partly on
principles of engaged scholarship (Van de Ven, 2007), which emphasizes the joint
advancement of scientific and practical knowledge. A foundational principle of
engaged scholarship is working closely with individuals from the communities in
which research is being conducted and crafting research designs that are mutually
beneficial. The preliminary NASAC study was designed in consultation with
administrative members of the organization. Both the interview and questionnaire
protocol from the NASAC study were reviewed by these individuals so as to ensure
that information captured through them was relevant and useful to members of the
aquaculture community. Following the completion of the NASAC study, a report was
submitted to the organization summarizing key findings (Siddiki and Weible, 2010).
This report was reviewed and approved by the board members of the organization and
207
made available for distribution to all NASAC members and the broader aquaculture
community. In addition, findings from the preliminary NASAC study were also
published in an international practitioner oriented aquaculture publication called
Global Aquaculture Advocate (Siddiki and Weible, 2011). The researcher will seek
additional publications through this outlet based on the findings from the larger
dissertation study.
208
APPENDICES
209
Appendix A: Summarizing Institutional Grammar Characteristics
1. Attribute Characteristics and Examples
 Must be an animate actor
“A qualified fish health pathologist shall inspect all facilities
annually.”
“Fish health inspections shall be conducted annually [by a
qualified fish health pathologist]*.”

May be explicit or implicit

Must include all relevant descriptors
“The fish health board shall meet annually.”Descriptors = “fish
health”

Attribute must logically be able to perform
aIm
“The Commissioner shall enforce all rules and
regulations concerning aquaculture except those which
relate to fish health.”
“Exemptions granted by the director shall be valid unless the
applicant fails to comply with the terms of the exemption.”
2. oBject Characteristics and Examples
 The oBject is the inanimate or animate part of
a statement that is the receiver of the action
described in the aim and executed by the
agent in the attribute.

The words coded in the oBject category must
include with it all relevant descriptors.
“All applications for stocking exemptions shall include a Whirling
Disease Management Plan.”

If there are two oBjects for which all other
fields are identical, including the Deontic,
aIm, Condition, etc., then the statement does
not need to be divided up into multiples
statements.
“The fish health board shall review any orders for the destruction
of aquatic organisms or quarantines of aquaculture facilities which
last beyond thirty days…”
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3. Deontic Characteristics and Example
 The prescriptive operator of an institutional
statement that describes what is permitted,
obliged, or forbidden

Usually explicit, but may also be implicit
4. aIm Characteristics and Examples
 Describes the goal or action of the statement,
i.e. usually the verb of the statement.

Any qualifiers of the aIm, including the
identification of temporal and spatial
boundaries, should be included in the
Condition.
5. Condition Characteristics and Examples
 Includes all qualifiers of the aIm, including
when, where, and how the action in the aIm is
to be performed
6. Or else Characteristics and Examples
 The punitive action if the directive is not
followed.
“The Aquaculture Board shall annually select a chairperson.”
“The Board is authorized to recommend rules to the
Commissioner.”
Implied Deontic = may
“Director of the Division may approve destruction orders.”
“The aquaculture board shall annually select a chairperson and
vice chairperson.”
aIm = “select”
Condition = “annually”
"Applications for exemption shall be submitted to the Director at
least 60 days prior to any proposed stocking.”
“Any person that violates the provisions of this article shall be
fined no less than one thousand dollars and no more than five
thousand dollars.”
211
Appendix B. NASAC Study Interview Questions
Section 1: State Aquaculture Characteristics
1. How long has aquaculture been in practice in your state?
Probe: How has the practice of aquaculture changed over time?
2. What is the current state of development?
Probe: Declining, Maintaining, Growing?
Section 2: Formal and Informal Rules
3. What are the state level policies that impact aquaculture in your State?
Probe: Aquaculture Act, Aquaculture Development Plan, etc.
Probe: When were these policies enacted?
Probe: What are the respective roles for Dept Ag/Wildlife/etc.
Section 3: Rule Configuration Questions
4. What are the types of permits that aquaculturists in your state are required to have
for general operating purposes?
5. Is it difficult to obtain aquaculture permits in your State?
Probe: What about specialized permits?
6. What do you feel is the level of understanding among aquaculturists regarding
activities that are allowed and forbidden?
7. What do you feel is the level of understanding among those enforcing permit
requirements regarding activities that are allowed and forbidden?
8. What is the level of community involvement in the permitting process (i.e.
development, means of administration, etc.)?
Probe: Is there community education and outreach that the permitting agency
performs regarding new permits or amendments to existing permits?
212
9. How severe do you think sanctions are in your state when it comes to noncompliance with permit requirements?
10. How reliably do you feel sanctions are imposed?
11. Do you think peer pressure among aquacultursists helps to enforce compliance
with permits, regulations, etc.
Probe: Have there been instances where peer pressure was used to encourage
non-compliance with permits, regulations, etc.?
Section 4: Regulatory Compliance
12. What is the level of regulatory compliance in your State?
Probe: What factors do you think have contributed to the current state of
regulatory compliance (weak or strong sanctions, monitoring, local culture
fosters compliance)?
Section 5: Monitoring and Enforcement
13. What types of monitoring and enforcement systems exist in your State regarding
aquaculture activities?
Probe: One central enforcement agency that performs monitoring of
aquaculture regulations, decentralized monitoring centers (i.e. local agencies)
that perform monitoring of aquaculture regulations, community-based
monitoring systems (i.e. peer pressure, civil dispute resolution without state
involvement, etc.)?
Probe: Have systems been effective? How and why?
14. On what issue(s) has monitoring and enforcement been most difficult?
Section 6: Influential People/Organizations and Partnerships
15. Who do you consider, people and/or organizations, to be particularly influential
when it comes to aquaculture activities in your State?
16. Are there any multi-stakeholder processes in existence?
213
Probe: Partnerships, Councils, etc.
Probe: List some of the APP groups the we studied if we included in their
State in the APP.
Section 7: Role of NASAC
17. What role has/does NASAC play in providing resources and information to State
Aquaculture Coordinators to addresses aquaculture related issues?
18. What role has the NASAC and State Coordinators played in helping the
aquaculture industry achieve its administrative and political goals?
Section 8: Coordination and Networking Questions
19. Which groups in the aquaculture industry do you tend to coordinate with most
frequently?
Probe: Why do you tend to coordinate with those groups and not with others?
Probe: Can you tell me the types of coordination activities that you engage in?
20. On whom do members of the aquaculture community tend to rely to obtain
information and/or resources on various aquaculture-related issues?
Section 9: Broader Political Questions
21. What would you say are currently the biggest barriers to aquaculture development
in your State?
Probe: What have been some of the challenges to the involvement of the
aquaculture industry at the state level?
Probe: What is the level of state governmental support of aquaculture in your
State?
Probe: How supportive is the general public of aquaculture in your State?
Probe: Has there been opposition to aquaculture development by interest
groups?
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22. What strategies has/does the industry use to overcome barriers to
aquaculture development?
215
Appendix C. NASAC Study Questionnaire
1. Please list your professional title and the organization for which you work. If you
have more than one affiliation (ex. State Aquaculture Coordinator and farmer), please
list all.
Professional Title/Organization 1
Professional Title/Organization 2
Professional Title/Organization 3
2. Please indicate how long you have held this position/these positions (in months or
years).
Position 1
Position 2
Position 3
3. Please indicate which of the following organizations are involved in the
governance of aquaculture in your State.
a. US Army Corps of Engineers
b.US Fish and Wildlife Service
c. US Environmental Protection Agency
d.US Department of Agriculture
e. US Food and Drug Administration
f. State Environmental Protection Agency
g.State Department of Agriculture
h.State Wildlife Agency
i. Local Government (County, City, Districts)
j. State Aquaculture Association
k.National Aquaculture Association
l. Regional or National Species-Specific Aquaculture Association (Ex. Trout
Farmers Association, Shellfish\ Growers Association, etc)
m. Industry members
n. Industry labor unions
o. Other, please specify
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4. Please indicate how much you agree or disagree with each of the following
statements (scale: strongly agree, mildly disagree, neutral, mildly agree, strongly
agree).
a. I have a lot of authority to shape the development of the aquaculture industry
in my State.
b. My role is mostly advisory and I have little decision-making authority.
c. My primary role is to provide scientific/technical support on aquaculture
issues in my State.
d. My primary role is to provide administrative support on aquaculture issues in
my State.
e. My primary role is to provide political support on aquaculture issues in my
State.
5. Please indicate how much you agree or disagree with each of the following
statements regarding regulations in your State (scale: strongly agree, mildly disagree,
neutral, mildly agree, strongly agree).
a. State regulations are very stringent in requirement and control.
b. State regulations are very clear and understandable in describing the activities
that are allowed and forbidden.
c. State regulations contain severe penalties for people who do not comply with
them.
d. State regulations are meant to give a lot of discretion to individuals
interpreting them.
e. State regulations are outdated and no longer are appropriate for governing
aquaculture in the State.
f. Do not exist.
6. Please indicate how much you agree or disagree with each of the following
statements regarding permits in your State (scale: strongly agree, mildly disagree,
neutral, mildly agree, strongly agree).
a. It is too expensive to obtain aquaculture permits.
b. There is too much paperwork required to obtain aquaculture permits.
c. The permitting process is very fragmented (i.e. many agencies involved).
d. The permitting process is so complex that it prevents people from entering the
aquaculture industry.
e. The permitting process is so complex that individuals will conduct aquaculture
without obtaining necessary permits.
f. More permits are needed to adequately regulate the aquaculture industry.
217
g. No permits for aquaculture are required in my State.
7. For each of the following people/organizations, please check the box in the
appropriate row/column indicating: (1) On whom you feel industry members tend to
rely on most to obtain information on aquaculture related issues; (2) On whom you
personally tend to rely on most to obtain information on aquaculture related issues;
and (3) The people/organizations you most frequently provide information
to as a State Aquaculture Coordinator.
a. State Aquaculture Association
b. National Aquaculture Association
c. Regional or Species- Specific Association (Ex. Trout Farmers Association,
Shellfish Growers Association)
d. State University Extension Service
e. State Agencies (Ex. Depart of Agriculture, Department of Natural Resources)
f. Members of the industry
g. Coordinators from other states
h. NASAC
8. In the last five years, please indicate how often the industry has used the following
strategies to overcome barriers to aquaculture development (scale: daily, monthly,
yearly, less than yearly, never).
a. Sought legislative support.
b. Engaged in publicity/marketing to change perceptions regarding aquaculture.
c. Coordinated activities among allied individuals/organizations to convince
decision makers to adopt industry position.
d. Used and/or hired experts to refute opponents' claims.
e. Used and/or hired experts to develop defensible positions.
f. Negotiated with opponents to produce consensus.
g. Influenced the composition of decision making or advisory committees to
include individuals/organizations to support your position.
9. Does your state have monitoring and enforcement systems in place for ensuring
compliance with aquaculture related regulations (scale: yes or no)?
10. Please indicate the extent to which the government and community members
conduct monitoring and enforcement in your State (scale: no monitoring and
enforcement, little monitoring and enforcement, moderate monitoring and
enforcement, significant monitoring and enforcement, heavy monitoring and
enforcement).
218
Government (Ex. regulatory agencies and personnel)
Community Members (Ex. industry members)
11. Please indicate how much you agree or disagree with each of the following
statements regarding compliance with aquaculture regulations in your State (scale:
strongly disagree, mildly disagree, neutral, mildly agree, strongly agree).
a. Compliance with aquaculture regulations is very high.
b. Compliance with regulations varies from year to year.
c. Most non-compliance is identified by government agencies.
d. Most non-compliance is reported to governmental agencies by the public.
e. Most non-compliance is reported to governmental agencies by other members
of the industry.
12. Please indicate which of the following you feel are the primary contributors to
compliance with aquaculture regulations in your State (scale: not a contributor, mild
contributor, moderate contributor, significant contributor, biggest contributor).
a. Clear and well-defined regulations.
b. Strong penalties for non-compliance.
c. Trust and cooperation among industry members.
d. Industry members feel regulations are scientifically and technically
appropriate.
e. Industry members feel that those enforcing them are competent.
f. Industry members feel that regulations are fairly and consistently enforced.
g. Industry members trust those monitoring and enforcing regulations.
h. Industry members comply because they don’t want to be perceived negatively
by other community members.
i. Industry members comply because it makes them feel guilty and ashamed not
to.
13. The following questions are meant to capture the dynamics of the aquaculture
industry. Please indicate how much you agree or disagree with each of the following
statements (scale: strongly disagree, mildly disagree, neutral, mildly agree, strongly
agree).
a. There is a lot of tension in the industry between farmers who have been
practicing aquaculture for a long time and those who have newly entered the
industry.
219
b. Industry members often share their knowledge of the scientific/technical aspects
of aquaculture with one another.
c. Industry members often share their knowledge of the regulatory/administrative
aspects of aquaculture with one another.
d. Industry members are very competitive and rarely share knowledge with one
another.
e. Industry members exhibit a high level of trust and cooperation.
f. Due to resource constraints (limited land, water, etc.), industry members are
forced to be competitive and
carefully monitor each others' activities.
g .Due to a competitive economic environment, industry members carefully
monitor each others' activities.
h. Industry members rarely come into contact with one another.
i. The industry works together to develop voluntary programs to help promote the
management and development of the aquaculture industry.
j. There are strong aquaculture industry labor unions in the State.
k. Other (please describe)
14. Regarding the role of the National Association of State Aquaculture Coordinators
(NASAC) in your
State, please indicate how much you agree or disagree with each of the following
statements (scale: strongly disagree, mildly disagree, neutral, mildly agree, strongly
agree).
a. The NASAC helps me coordinate aquaculture programs in my State.
b. The NASAC provides a unified political voice for state industries at the national
level.
c. The NASAC has helped the industry in my State reach administrative and
political goals.
d. The NASAC has effectively promoted, encouraged, and assisted the
development of aquaculture in the United States.
e. The NASAC has effectively addressed the breadth of aquaculture issues facing
the aquaculture industry.
f. Having an official State Aquaculture Coordinator has significantly benefited the
aquaculture industry in my State.
15. Please describe how you think NASAC can better serve the needs of State
Aquaculture Coordinators.
16. Approximately how many aquaculture farms are currently operating within your
state?
220
17. Of the aquaculture farms in your State, what percentage of them fall under the
following annual income categories?
≥ $1,000,000
$500,000 to $999,999
$250,000 to $499,999
$100,000 to $249,999
$50,000 to $99,999
<$49,999
18. Of the aquaculture farms in your State, please indicate what percentage of them
are associated with recreational stocking, commercial business, or personal use.
% of farms for recreational stocking
% of farms for food fish production
% of farms for personal use
% of farms for fee fishing or pay lakes
% of farms for ornamental fish production
19. Please indicate the number of full time equivalents (FTEs) currently working in
the aquaculture industry in your State.
20. Please indicate the extent to which the following factors pose a barrier to
aquaculture development in your State (please check all that apply) (scale: not a
barrier at all, minor barrier, moderate barrier, significant barrier, largest barrier).
a. Input costs (land prices, labor, and material costs)
b. Start up costs (capital investments, application fees, obtaining leases)
c. Resource constraints (water scarcity, land availability, energy)
d. Stringent environmental protection regulations and safeguards
e. Complicated regulatory process associated with obtaining permits, licenses, etc
f. General public resistance to aquaculture development
g. Cohesiveness or cooperation among industry members
h. Foreign competition
i. Domestic competition
j. Current economic downturn
k. Inexperienced farmers
l. Local user conflicts (recreational users, commercial fisheries)
21. Sex (scale: male or female)
221
22. Please indicate the highest level of education you have attained (please select one
choice from the
following list.
a. Not a high school graduate
b. High school graduate
c. Some college
d. Bachelors degree
e. Masters or professional degree
f. Ph.D., MD, or JD
Other, please specify
23. Please rate the level of competence you have in each of the following broad
subject areas (scale: no competence, slightly competent, somewhat competent, fairly
competent, very competent).
a. Physics/Chemistry
b. Ecology/Biology
c. Engineering
d. Economics (e.g. Agriculture/Natural Resource Economics)
e. Business
f. Architecture and Planning
g. Public Policy
h. Law
i. Animal Health/Medicine (e.g. Veterinarian, Fish Health Pathologist, etc.)
222
Appendix D. Broader Study Interview Questions
I. Background Questions
1. Can you please tell me about your current professional position?
2. In what ways are you/your activities connected with the [regulation name]?
Probe: Were you involved with the creation and/or development of this
regulation?
Probe: Activities guided/dictate by the regulation?
II. Regulation, Grammar, and Modal Attribute
3. You are one of the people [in terms of position] most often referred to in this
legislation. Does this accurately reflect your level of involvement in the aquaculture
industry?
Probe: Given your knowledge of the aquaculture community, do you think
[modal attribute] is the appropriate target audience?
Probe: Please describe why or why not.
Probe: If not, please describe who you think should be.
Probe: Who are the other people [in terms of position] that you think are most
involved in aquaculture in the State?
4. [Object] You are/are not listed is relation to many “items.” For example [object 1,
object 2, etc.]. How do you think this reflects the scope of activities that you are
involved in on a daily basis?
5. [Deontic and Aim] Some of the prescribed processes assigned to you in the
legislation include [X]. How do you interpret different prescriptive operators in
relation to these [may/may not/must/must not]?.
Probe: When you see a “may” and “may not” in the legislation, vs. a “must”
or “must not,” what factors influence you decision to perform the directive or
not?
223
6. [Condition] How do prescribed conditions influence how you interpret these?
7. [Or else] I noticed there are not a lot of sanctions described in the legislation for
instances in which compliance is not achieved. Why do you think this is the case?
8. [Or else] Who holds you accountable [people, organizations, etc.] for performing
duties as prescribed in this regulation?
III. Regulation History and Context
9. When was this regulation enacted?
10. Why was the regulation created?
Probe: Was there a particular problem(s) or issue(s) that the regulation was
created to address?
Probe: Was there a person or group of people that were responsible for its
enactment?
Probe: Could you tell me a little more about the political landscape
surrounding aquaculture in the State when this regulation was developed or
enacted?
11. How was the regulation developed?
Probe: Special advisory group to the agency, by a committee mostly made up
of government processes, multi-stakeholder processes, community forum, etc.
Probe: How much were/are industry members involved in the development
and amendments to the regulation?
IV. Regulatory Compliance
12. How well do you think this regulation is understood by [Attribute(s)]?
Note: Adjust the attribute you are referring to depending on interviewee.
13. What would you say is currently the level of compliance with this regulation?
14. On what issues has there been the most resistance from community members?
224
Probe: For industry members, monitoring enforcement officials, etc.
Probe: How is this resistance expressed?
V. Regulatory Effectiveness
15. What do you feel are the strengths of this regulation?
16. What do you feel are the shortcomings of this regulation?
17. Are there important issues that are not currently included in the legislation? 225
Appendix E. Broader Study Questionnaire
Part One. Professional Affiliation
Please specify the type of organization or sector you represent (please select all that
apply).
o
o
o
o
o
Aquaculture Processor or Handler
Finfish Aquaculture Operation – Not ornamental
Finfish Aquaculture Operation – Ornamental
Shellfish Aquaculture Operation
Other Aquaculture (ex. aquatic plants); please specify.
Part Two. Respondent’s Regulatory Involvement
Please specify your role in the development of aquaculture regulations in your state
(please select all that apply).
o
o
o
o
In the last five years, I have been a frequent participant in public meetings
regarding aquaculture regulations.
In the last five years, I have regularly communicated with members of
regulating agencies to discuss aquaculture regulations.
In the last five years, I have served on at least one advisory committee or
regularly participated in processes that provided recommendations to state
agencies on aquaculture regulations.
I contributed to the initial development of state aquaculture regulations.
Part Three. State Regulations
Please indicate how much you disagree or agree with following statements, using a
scale from totally disagree to totally agree.
a. State aquaculture regulations reflect the full scope of activities that I am
involved with at my facility on a daily basis.
b. State aquaculture regulations are based on sound scientific evidence.
c. State aquaculture regulations need to be more specific about what activities
are allowed and forbidden.
226
d. State aquaculture regulations reflect the “best business practices” of the
industry.
e. State aquaculture regulations accurately reflect species specific
considerations.
f. State aquaculture regulations accurately reflect geographic specific
considerations.
g. State aquaculture regulations require too much paperwork.
h. State aquaculture regulations are too technical to understand.
i. State aquaculture regulations should contain more severe penalties for those
who do not comply with them.
j. There are so many state regulations that they are no longer effective for
achieving compliance.
k. State aquaculture regulations are outdated and no longer appropriate for
governing the aquaculture industry.
Please indicate how much you agree or disagree with the following statements
regarding your interpretation of regulations.
a. If the aquaculture regulations say I must do something, then I always and
consistently comply.
b. Before I comply with regulations, I typically assess whether specific rules are
applicable to me and my aquaculture operation.
c. Before I comply with regulations, I typically assess whether specific rules are
consistent with how I like to conduct aquaculture.
d. Before I comply with regulations, I typically communicate with other
members of the aquaculture industry to see if most of them have been
complying with the rules for extended periods of time.
e. I don't comply with state level regulations if they are not consistent with
industry level standards of practice.
Part Four. Regulatory Enforcement
Please indicate how much you agree or disagree with the following statements
regarding enforcement of current aquaculture regulations, using a scale from totally
disagree to totally agree.
a. Those enforcing aquaculture regulations are knowledgeable about
aquaculture.
227
b. Those enforcing aquaculture regulations are knowledgeable about aquaculture
regulations.
c. There has been a lot of turnover in enforcement personnel in the last five years
making enforcement of aquaculture regulations less effective.
d. Enforcement of aquaculture regulations has decreased in the last five years
due to state budget cuts making it less effective.
e. Enforcement of state aquaculture regulations is too lenient.
Part Five. Compliance
For each of the following factors, please indicate how important each is to you when
deciding whether or not to comply with regulations using a scale from not important
at all to very important.
a.
b.
c.
d.
e.
f.
g.
h.
Possibility of facing financial penalties.
Possibility of facing legal penalties.
Moral or ethical obligation to comply with the law.
Maintaining a good reputation with other members of the industry.
Maintaining a good reputation with regulators.
Moral obligation to produce a good product.
Personal feelings of guilt from not complying with state level regulations.
Feelings of shame in front of other members of the industry from not
complying with state level regulations.
i. Long-term sustainability of the industry.
j. Protection of the environment.
k. Have a financially prosperous business.
Please indicate how much you agree or disagree with each of the following
statements regarding your personal level of compliance with aquaculture regulations,
using a scale from totally disagree to totally agree.
a. I always comply with aquaculture regulations.
b. I only comply with regulations when they are consistent with my ideas about
how aquaculture should be practiced.
c. I comply with regulations because doing so is good for my business.
d. I only comply with regulations when I think they are scientifically/technically
appropriate.
e. I only comply with regulations when I think they are financially appropriate.
228
f. I only comply when I feel that regulations are fairly and consistently enforced.
g. I comply with regulations because I want to prevent conflict with my
neighbors.
h. I comply with regulations if doing so is inexpensive.
i. I comply with regulations if doing so does not interfere with my work
commitments.
Part Six. Industry Level Standards of Practice
Has the industry developed a set of agreed upon standards of practice for conducting
aquaculture in addition to state level regulations? Yes/No
If ‘yes,’ please indicate how much you agree or disagree with each of the following
statements regarding industry level standards.
a. Industry level standards of practice are very different from state level
regulations.
b. Industry level standards of practice are more consistent with how I like to
conduct aquaculture than state level regulations.
c. I feel it is more important to comply with industry level standards than state
level regulations.
For each of the following factors, please indicate how important each is to you when
deciding whether or not to comply with industry level standards of practice using a
scale from not important at all to very important.
a.
b.
c.
d.
Possibility of facing penalties from other members of the industry.
Maintaining a good reputation with other members of the industry.
Personal feelings of guilt from not following industry standards of practice.
Feelings of shame in front of other members of the industry from not
following industry standards of practice.
Please indicate how likely you are to follow the following types of rules all of the
time. For those that do not apply to you, please check not applicable.
a. Administrative Rules (ex. filling out daily and monthly product tracking
forms)
b. Boating Rules (ex. Design of aquaculture boats)
c. Disease Management (ex. Use of drugs for controlling disease)
229
d.
e.
f.
g.
h.
Health and Sanitation Rules (ex. Following seasonal harvesting restrictions)
Invasive or Non-Native Species Rules
Import/Export Rules
Leasing, Siting, and/or Navigation Rules
Water Quality Management Rules
Part Seven. Industry Characteristics
Please indicate how much you disagree or agree with each of the following
descriptions of the aquaculture industry in [Virginia or Florida]?
a. In recent years there has been continuous tension in the aquaculture industry
between farmers who have been practicing aquaculture for a long time and
farmers who have newly entered the industry.
b. Industry members regularly share knowledge of the scientific/technical
aspects of aquaculture with one another.
c. Industry members often share their knowledge of the
regulatory/administrative aspects of aquaculture with one another.
d. There has been a high level of trust among aquaculture industry members for
years.
e. There has been a high level of cooperation among aquaculture industry
members for years.
f. Due to resource constraints (limited land, water, etc.), industry members
regularly monitor each others’ activities.
g. Due to a competitive economic environment, industry members carefully
monitor each others’ activities.
h. Aquaculture industry members rarely come into contact with one another.
Part Eight. Demographic Information
Please indicate the number of years that you have practiced aquaculture in [Virginia
or Florida]:
a.
b.
c.
d.
e.
0-4 years
5-9 years
10-14 years
15-19 years
20 + years
230
Are you: Male or Female
Please indicate your age group: 20-29, 30-39, 40-49, 50-59, 60+
How liberal or conservative do you consider yourself to be on domestic policy issues?
Very conservative, conservative, moderate, liberal, very liberal.
Please indicate your annual farm income:
a.
b.
c.
d.
e.
f.
<$49,999
$50,000 to $99,999
$100,000 to $249,999
$250,000 to $499,999
$500,000 to $999,999
>$1,000,000
231
Appendix F. Supplementary Ordered Logistic Regression Analyses
Combination 1
Independent Variables:
Combination 1
Dependent Variable:
Compliance
Ordered Log Odds
Regression Coeff.
(Std. Error)
Odds Ratio
(Std.Error)
.89** (.24)
2.44 (.73)
Appropriate regulatory scope
.71 (.38)
2.03 (.65)
Regulations consistent with
industry best practices
.61 (.37)
1.84 (.59)
Farmer regularly
communicates with
regulating agencies
-.07 (.73)
.93 (.65)
Desire to maintain a good
reputation with industry
members
Number of observations
Pseduo R2
Wald Chi2/sig.
54
22%
17.69/.001
** = correlation is significant at the 0.01 level (2-tailed)
* = correlation is significant at the 0.05 level (2-tailed)
232
Combination 2
Independent Variables:
Combination 2
Dependent Variable:
Compliance
Ordered Log Odds
Regression Coeff.
(Std. Error)
Odds Ratio
(Std.Error)
Guilt
.60* (.25)
1.82 (.44)
Appropriate regulatory scope
.64 (.41)
1.89 (.60)
Regulations consistent with
industry best practices
.41 (.35)
1.51 (.47)
Farmer regularly
communicates with
regulating agencies
.34 (.78)
1.41 (1.00)
Number of observations
Pseduo R2
Wald Chi2/sig.
54
19%
13.93/.008
** = correlation is significant at the 0.01 level (2-tailed)
* = correlation is significant at the 0.05 level (2-tailed)
233
Combination 3
Independent Variables:
Combination 3
Dependent Variable:
Compliance
Ordered Log Odds
Regression Coeff.
(Std. Error)
Odds Ratio
(Std.Error)
.92** (.25)
2.50 (.66)
Fear of facing financial
penalties
.46 (.25)
1.59 (.44)
Moral obligation to produce a
good product
.52 (.40)
1.67 (.70)
Knowledgeable enforcement
personnel
Number of observations
Pseduo R2
Wald Chi2/sig.
55
19%
20.37/.000
** = correlation is significant at the 0.01 level (2-tailed)
* = correlation is significant at the 0.05 level (2-tailed)
234
Appendix G. Ordinary Least Squares Regression Results
Model 1: Multiple regression results for regulatory based compliance motivations and
compliance
Unstandardized
Coefficients
Collinearity Statistics
Tolerance
VIF
Knowledgeable
enforcement personnel
.21*
.70
1.43
Appropriate regulatory
scope
.20
.80
1.25
Regulations consistent
with industry best
practices
.04
.64
1.57
Farmer regularly
communicates with
regulating agencies
.02
.94
1.07
Number of observations
54
Adjusted R2
F-Statistic/sig.
33%
3.43/0.020
** = correlation is significant at the 0.01 level (2-tailed)
* = correlation is significant at the 0.05 level (2-tailed)
235
Model 2: Ordinary least squares regression results for individual and community
based motivations and compliance
Unstandardized
Coefficients
Collinearity Statistics
Tolerance
VIF
Fear of facing
financial penalties
.11
.83
1.20
Desire to maintain
good reputation with
industry members
.23*
.74
1.36
Guilt
.14
.75
1.34
Moral obligation to
produce a good
product
-.03
.79
1.27
Number of
observations
55
Adjusted R2
29%
F-Statistic/sig.
3.02/.030
** = correlation is significant at the 0.01 level (2-tailed)
* = correlation is significant at the 0.05 level (2-tailed)
236
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