Development of Social Indicators of

Fishing Community Vulnerability and Resilience

in the U.S. Southeast and Northeast Regions

Michael Jepson and Lisa L. Colburn

U.S. Department of Commerce

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

NOAA Technical Memorandum NMFS-F/SPO-129

April 2013

Development of Social Indicators of

Fishing Community Vulnerability and Resilience

in the U.S. Southeast and Northeast Regions

Michael Jepson1 and Lisa L. Colburn2

1 Southeast Regional Office

263 13th Avenue South

St. Petersburg, Florida 33701

2 Northeast Fisheries Science Center

28 Tarzwell Drive

Narragansett, Rhode Island 02882

NOAA Technical Memorandum NMFS-F/SPO-129

April 2013

U.S. Department of Commerce

Rebecca M. Blank, Acting Secretary

National Oceanic and Atmospheric Administration

Kathryn D. Sullivan, Acting Administrator

National Marine Fisheries Service

Samuel D. Rauch III, Acting Assistant Administrator for Fisheries
ii

Recommended citation:

Jepson, Michael and Lisa L. Colburn 2013. Development of Social Indicators of Fishing 

Community Vulnerability and Resilience in the U.S. Southeast and Northeast Regions. 

U.S. Dept. of Commerce., NOAA Technical Memorandum NMFS-F/SPO-129, 64 p. 

Copies of this report may be obtained from: 

Southeast Regional Office 

263 13th Avenue South

St. Petersburg, Florida 33701

michael.jepson@noaa.gov 

Northeast Fisheries Science Center

28 Tarzwell Drive

Narragansett, Rhode Island 02882 

lisa.l.colburn@noaa.gov 

Or online at:

http://spo.nmfs.noaa.gov/tm/ and

http://www.st.nmfs.noaa.gov/humandimensions/social-indicators/index

 

Cover photo by Lisa Colburn

Other photos by Michael Jepson and Lisa Colburn
iii

I. Table of Contents

Contents

I. Table of Contents...................................................................................................................iii

II. List of Figures and Tables...................................................................................................... iv

III. Abstract................................................................................................................................ v

IV. Acknowledgements............................................................................................................. vi

V. Introduction............................................................................................................................. 1

A. Background ...................................................................................................................... 1

NOAA Strategic Plan.............................................................................................................. 1

Ecosystem Based Management and Community Well-being and Sustainability................... 1

B. Well-being, Vulnerability and Resilience in Fishing Communities ................................ 2

Well-being............................................................................................................................... 2

Vulnerability and Resilience in Fishing Communities........................................................... 4

Social Indicators...................................................................................................................... 7

VI. Methods................................................................................................................................ 9

A. Workshops........................................................................................................................ 9

Miami Workshop .................................................................................................................... 9

Silver Spring Workshop........................................................................................................ 10

B. Analytical Approach ...................................................................................................... 10

Data Collection ..................................................................................................................... 10

Factor Analysis ..................................................................................................................... 11

VII. Results................................................................................................................................ 12

A. The Indices..................................................................................................................... 12

Social Vulnerability .............................................................................................................. 13

Gentrification Indices of Vulnerability................................................................................. 16

Fishing Engagement and Reliance........................................................................................ 18

B. Summary Tables............................................................................................................. 22

VIII. Discussion ...................................................................................................................... 24

IX. Conclusion ......................................................................................................................... 27

X. References Cited ................................................................................................................... 28

XI. Appendices......................................................................................................................... 33

Appendix 1. Index Tables and Maps ....................................................................................... 33

Appendix 2. Variable Codebook............................................................................................... 59

Appendix 3. Index Calculation and Formulas .......................................................................... 63

Shannon Index ...................................................................................................................... 63

Theta Reliability Calculation ................................................................................................ 64
iv

II. List of Figures and Tables

Table 1. Social Vulnerability Indices........................................................................................... 13

Table 2. Gentrification Vulnerability Indices.............................................................................. 17

Table 3. Fishing Engagement and Reliance Indices.................................................................... 19

Table 4. Sum of Social Vulnerability Indices Dichotomous Scale.............................................. 22

Table 5. Sum of Gentrification Vulnerability Indices Dichotomous Scale. ................................ 23

Table 6. Sum of Fishing Dependence Indices: Commercial and Recreational Fishing 

Engagement and Reliance Dichotomous Scales............................................................ 24

Figure 1. Well-being and Fisheries SIA Conceptual Model.......................................................... 3

Figure 2. The Vulnerability and Resilience Time Series Model ................................................... 4

Figure 3. Social Vulnerability Indices by Community. ............................................................... 15

Figure 4. Social Vulnerability Indices by Community (cont)...................................................... 16

Figure 5. Gentrification Vulnerability Indices by Community.................................................... 18

Figure 6. Commercial Fishing Engagement and Reliance Indices by Community..................... 20

Figure 7. Recreational Fishing Engagement and Reliance Indices by Community. ................... 21

Figure 8. Shannon Index of Occupational Diversity and Mean by Community. ........................ 22
v

III. Abstract

Viable measures of social well-being and sustainability, including measures of vulnerability and 

resilience, are needed for coastal fishing communities. Although sustainable development indices 

have been created and implemented at national and regional levels, few are available at the local 

or community level, and even fewer address the social aspects of U.S. fisheries. We developed 

a suite of social indicators for use in fisheries social impact assessment (SIA). Data from more

than 2,900 coastal communities in 19 states from Maine to Texas were used to create 14 social 

vulnerability and fishing dependence indices. Each index was developed using a factor analysis

of secondary data obtained primarily from government sources, supplemented by a few private 

sources. The availability of these secondary data ensure replicability and feasibility under the 

time constraints usually available for completing social impact assessments for fishery 

management plans. Using cluster analysis, we selected a group of 20 communities to evaluate 

all 14 indices of social vulnerability. These indices can be used for cross-community and crossregional comparisons, and will eventually be incorporated into social impact assessments of all 

U.S. marine fisheries.
vi

IV. Acknowledgements

We have many people to thank for their help and support with this research and producing this 

technical memorandum. We would like to especially thank Rita Curtis, NOAA Fisheries, ST5 

for her support of this work and continuing interest in our endeavors. With Rita’s assistance and 

offer of a rotational detail, our work was greatly enhanced. We would like to thank the 

reviewers of this memorandum for their conscientious and thorough review of this document and 

to Trish Clay for her voluntary informal review that was so helpful. For her countless hours of 

data manipulation,we would like to thank Changhua Weng who turned those data files around in 

quick fashion. We would also like to thank Tarsila Seara for her extensive literature review that 

helped us frame the concepts. Also, for their contributions we would like to thank Julie Olson, 

Christina Package, Kari MacLauchlin, Ava Lasseter, and Brent Stoffle. Certainly, we appreciate 

the thoughtful comments and assistance from all of our workshop participants in both venues. 

And finally, for their continuous expert advice we would like to say a special thanks to Steve 

Jacob and Richard Pollnac; especially to Steve for his patient tutoring in factor analysis and 

willingness to be on call for anything statistical from the beginning.
1

V. Introduction

A. Background

With the growing emphasis on 

ecosystem-based management, there is 

an expanding need for measures of 

social well-being and sustainability, 

including resilience and vulnerability, 

for coastal fisheries and fishing 

communities. Because primary data 

collection is time consuming and 

costly, use of secondary data is a 

practical alternative that can provide 

substantial cost savings in developing 

these measures. The use of secondary 

data in the human dimensions of 

fisheries management is rapidly 

expanding. Community profiles, social 

impact assessments, determination of fishing dependence, and assessments of natural disasters 

are a few of the growing number of areas using secondary data. Here we explore the use of 

secondary data in the development of social indicators to measure fishing community 

vulnerability. 

NOAA Strategic Plan

In the United States (US), the National Oceanic and Atmospheric Administration (NOAA)’s 

vision of the future is one of healthy ecosystems, communities, and economies that are resilient 

in the face of change. Ecosystems, communities, and economies can maintain and improve their 

resilience, health and vitality over time by anticipating, absorbing, and diffusing change—

whether sudden or prolonged. This vision of resilience will guide NOAA and its partners in a 

collective effort to reduce the vulnerability of communities and ecological systems in the short 

term, while helping society avoid or adapt to long-term environmental, social, and economic 

changes. To this end, NOAA will focus on four long-term goals within its primary mission. The 

goals of Healthy Oceans and Resilient Coastal Communities (NOAA Strategic Plan, 2010) are

the primary focus of this analysis, especially with regard to providing coastal decision-makers 

with accurate and reliable tools to apply toward reducing the vulnerability of their communities.

Ecosystem Based Management and Community Well-being and Sustainability

The use of indicators to monitor sustainability and other measures of well-being for all 

components of marine fisheries has long been promoted within international fisheries 

management (FAO 2008). Currently, US Fishery Management Plans (FMPs) have numerous 

measures of well-being and sustainability of fish stocks, but fewer of fishermen and their 

communities, though see GMFMC (2004; 2005) and PFMC (2006). Since the addition of 

National Standard 8 (16 U.S.C. § 1851(a)(8)) with the 1996 reauthorization of the MagnusonStevens Fishery Conservation and Management Act (16 U.S.C. § 1801 et seq.), all NOAA 

Fisheries regions have developed community profiles which required census and fisheries data to 
2

be assembled at the place level. Yet, there have been only a small number of attempts to utilize 

these data in an empirical approach to quantify aspects of well-being and sustainability of fishing 

communities. Two examples are a vulnerability index for fishing communities (Jepson and 

Jacob, 2007) and a Local Fishery Stock Status Index (LFSSI) (Jacob and Jepson, 2009). Cutter 

et al. (2010) created a related index of social vulnerability to coastal hazards that connects to 

geographic place at the county level. While that index has proved beneficial in relating exposure 

to natural hazards to geographic locale, it does not meet the requirements of National Standard 8 

for community-level assessments. Thus, a more focused approach on specific aspects of wellbeing for fishing communities is warranted. Viable standardized social indicators at the local or 

community level for U.S fisheries are needed to allow for comparison, both regionally and 

nationally. The work outlined here is an initial attempt to do so for NOAA Fisheries’ Northeast 

and Southeast Regions.

B. Well-being, Vulnerability and Resilience in Fishing Communities

There are several ways to describe the relationship between the key concepts of well-being, 

vulnerability, and resilience. Because these concepts have no singular objective meaning but 

resonate with a wide range of viewpoints, many authors in diverse fields have attempted to 

define them. For our purpose, it was important to assign objective metrics to the concepts and 

accept an operational definition of the relationship. We defined the relationship as vulnerability 

being the immediate pre-disturbance state and resilience as constituting the ability to cope postdisturbance over time. This working definition allows collection of measures of inherent 

vulnerability and applies them in a model that will allow us to track vulnerability over time and 

document post-event impacts to evaluate resilience in response to fisheries management actions.

We used the Pollnac et al. (2006[2008]) fisheries SIA conceptual model of well-being as an 

organizing framework for the development of quantitative measures of vulnerability. We begin 

with a brief overview of these concepts and their relationship to the social fabric of individuals 

and communities especially those dependent on fishing.

Well-being

The concept of well-being, well established in the literature as a measure of quality of life 

(Schneider, 1976; Stiglitz et al. 2009), has been operationalized through both social and 

economic constructs many times with mixed results (Cobb and Rixford, 1998; Johnston and 

Carley, 1981). However, considerable research has demonstrated that secondary measures of 

well-being and its correlates e.g., vulnerability and resilience, can inform us regarding the quality 

of life of individuals and their communities (Hooghe and Vanhoutte, 2010; Porter, 2011; Smith 

and Clay, 2010). Nevertheless, developing an adequate and easily replicable quantitative 

construct of well-being for coastal communities, and more specifically fishing communities, has

been difficult (Charles et al., 2009). 

 

The Pollnac et al. (2006[2008]) model (Figure 1) illustrates the relationship between multiple 

attributes that directly or indirectly influence well-being at individual and community levels. 

Here we examine the social-community attribute with a direct link to well-being. While we 

recognize the importance of individual well-being in fisheries management (Marshall and 

Marshall, 2007) relatively little data exists at this level. In contrast, a considerable amount of 

secondary data is available at the community level. Further, requirements of both National 

Standard 8 on communities and the National Environmental Policy Act of 1969 (NEPA; 42 

U.S.C. 4332(2)) for attention to cumulative impacts at multiple levels, including communities, 

necessitate more research on communities than has been available. Given the time constraints 
3

that are common in conducting SIAs, and the fact that costs are comparatively small for 

gathering secondary data on communities, as opposed to the (largely primary) data needed to

assess individual well-being, the focus on place based communities allows statistically valid 

analyses that can be replicated for large numbers of communities and in multiple regions within 

a relatively short time frame.

Figure 1. Well-being and Fisheries SIA Conceptual Model.

The concepts of vulnerability and resilience and their relationship to change are important to our 

understanding of well-being and a community’s adaptation to a disruptive event such as a change 

in fishery management regulations. Research has consistently shown strong correlations 

between vulnerability and resilience and socioeconomic status (Cutter et al. 2008, 2003; McLeod

and Kessler, 1990; Sherrieb et al. 2010). The ability to adapt to change is dependent on the 

interrelationship between individuals, families, and external conditions that corresponds to the 

well-being of all (Mederer, 1999). These external conditions include job satisfaction, physical 

and mental health and functional inter-personal relationships (Pollnac et al. 2006[2008]). Social 

change can affect all of these, directly or indirectly. Regulation-related changes in work 

conditions (e.g., ability to choose timing of fishing, level of financial remuneration, time spent at 

sea) can decrease job satisfaction resulting in negative effects on mental health (e.g., anxiety, low 
4

self-esteem, worry, and tension), physical health (e.g., stress-related illness), and impaired 

personal relationships (e.g., divorce) (Pollnac et al. 2006[2008]; Smith et al., 2003). We apply 

these concepts to fishing communities using secondary data to derive social indicators that 

provide some measure of these attributes of well-being.

It is important to note that we are viewing community vulnerability irrespective of it relationship 

to fishing culture. There are other forces of change-global economy, recession, local and state 

regulations-that affect communities and the fishing culture. Here we are placing fishing 

dependency within the context of community vulnerability and resilience understanding that 

there are feedbacks from one to the other.

Vulnerability and Resilience in Fishing Communities

The use of indicators of vulnerability and resilience in the context of evaluating the response of 

fishing communities to change is grounded in a broader social scientific effort to gauge the 

ability of social groups to adapt to change. Social vulnerability and resilience highlight the 

importance of the interrelationship between both people and the environment (Clay and Olson, 

2008). Because the terms have a wide variety of meanings and interpretations, we have 

developed a practical framework with specific definitions to place the concepts in a model of 

community response to management actions (Figure 2). This conceptual model allows testing of 

the social indicators and provides a framework for interpretation of results and development of 

predictive tools and mitigation measures. 

Figure 2. The Vulnerability and Resilience Time Series Model

For example, we might envision a fishing community that is highly dependent upon a particular 

species of fish “X” and has a robust sense of community spirit as evidenced by participation in 

community events and institution [Pre-event]. A new stock assessment indicates that overfishing 

of X is occurring and that X is overfished. The council chooses to reduce the ACL by 100,000 

pounds [Event]. This measure causes several vessels to reduce crew. One person sells his vessel 

and one fish house closes [Immediate adaptive response]. Because of these changes, the 
5

community no longer holds fish frys and not enough people are available to work the annual fish 

festival. This translates into less money raised for lobbying by the local fishing organization. 

Several families are forced to move from the community and overall social networks are 

weakened as key people are no longer available for support work and leadership within the 

community [Post-event Impact]. All of these factors have affected the community’s inherent 

resilience capacity which in turn will feed back into the community’s vulnerability or its ability 

to address future disruptions 

While we use a fishing community as an example, it is purely a result of our primary focus being 

fishery regulatory policy. The social disruption could come from outside of fishing and the 

social networks related to other aspects of community, not necessarily the fishing economy. We 

believe this model could apply to any coastal community and accommodate other types of 

disruptions.

Vulnerability

Vulnerability has multiple definitions, depending on the context such as climate change, natural 

hazards, poverty and food limitation (Klein and Nicholls,1999; Kelly and Adger, 2000; Barnett, 

2003; Moser, 2010;White and Haas, 1975; O’Keefe et al., 1976; Cutter, 1996; Oliver-Smith, 

1996; Turner et al. 2003; Chambers, 1989; Moser, 1998; Watts and Bohle, 1993; Davis 1996). 

Vulnerability research is often used to identify the characteristics of a population (or community) 

that influence the social burden of risk and “susceptibility of a given population, system, or place 

to harm from exposure to the hazard...” (Cutter et al. 2009:2). Further, social vulnerability is 

centered in both demographic and socioeconomic characteristics of local populations that 

increase or attenuate the impacts of hazard events (Cutter et al. 2009). 

For development of social indicators of vulnerability of fishing communities, we chose to 

identify pre-event existing social conditions that are likely to affect the impact of disruptive 

events (Figure 2). The social conditions can lead to adverse or positive responses to these 

events. Our use of vulnerability includes the following characteristics:

• Pre-event, characteristics of the community that may create or negate the potential for 

harm 

• States of susceptibility to harm, powerlessness, and marginality of physical, natural and 

social systems

• Patterns of differential access to resources 

Resilience

Both the natural and social sciences emphasize that a system can have multiple stable states and 

that disturbances can force communities to shift from one state to another and still maintain their 

functional characteristics or be resilient (e.g., Peterson et al., 1998; Folke, 2006). Social 

scientists usually emphasize a system’s ability to cope and adapt to change, but social systems 

cannot be easily separated from ecological systems. The concept of “social-ecological 

resilience” attempts to capture this interaction (Walker et al., 2004). What is clear is that the 
6

interactions between the human and non-human environment have synergistic aspects and may 

adapt or transform over time (Folke, 2006). 

In developing an operational concept of resilience, we have utilized a set of characteristics that 

include some aspects of the social-ecological resilience concept but are primarily about social 

resilience (Pollnac et al., 2006[2008]). We intend to use resilience in the analysis of the response 

of communities after disruptive events in contrast to vulnerability, which we consider the preevent condition. In this use, the concept explicitly includes a dimension of time. Our use of 

resilience includes the following characteristics:

• The ability of a social system to respond to and recover from a disturbance

• The ability of a social system to absorb impacts or cope with stress

• The social system’s inherent ability to function well during non-crisis and adapt/be 

flexible in response to a disturbance event

While we discuss the concept of resilience and believe it is an important aspect within the model 

of well-being (Figure 1), we do not attempt to operationalize it at the community level. Some of 

that difficulty resides with what we consider one of the more important components of 

resilience:social capital. Social capital is often discussed as a key element of resilience, both for 

individuals and communities, and is often linked to participation in civic groups. While we do 

not have secondary measures of group participation at the community level at this time, we hope 

to be able to test this aspect of social capital in the future and further develop a viable measure of 

resilience.

 

Social Response of Fishing Communities to External Factors

In order to develop the model of the social response of fishing communities to management 

actions (Figure 2), it was necessary to place the social indicators of vulnerability described below 

in relation to concepts of community vulnerability and resilience. Although the relationship 

between the concepts of vulnerability and resilience is complex and dependent on disciplinary 

focus and personal preference, it is possible to develop a pragmatic approach to evaluation.

For our model, we have separated the concepts and developed a linear evaluation of pre- and 

post-event social and fishing conditions (Figure 2). The event in our model is a management 

action, such as a new regulation of fishing effort. The availability of secondary data means that 

these events can be in the past, present or future. 

The social response model (Figure 2) first considers that coastal communities are comprised of 

social systems, the built environment, and natural systems. Every community has inherent 

vulnerability and resilience in each of these systems however our focus is on the measurement of 

vulnerability. A highly vulnerable community may have multiple social stressors such as 

significant crime, poverty, and unemployment while a less vulnerable community may have 

fewer or less significant stressors. A community can also have a degraded housing stock and

infrastructure or in contrast, a highly heterogeneous built environment with substantial and 

recent investment. The natural systems can also be vulnerable to storm surge, temperature 

change or overharvesting or be relatively invulnerable. Our social indicators focus on the social 

systems and are measures of the pre-event social and fishing conditions. We consider fishing 
7

community vulnerability as the pre-event 

and post-event existing social and fishing 

conditions and resilience as the capacity to 

cope with change over time. In order to 

understand community resilience, 

vulnerability must be tracked temporally 

and in relationship to successive 

disturbances. 

We posit that each management action 

triggers a variety of responses within the 

fishing community. When new 

management regulations are imposed 

individuals may fish with fewer crew or 

change target species or gear, or any number of responses – including, potentially, seeking 

employment outside the fishing industry. We do not attempt to measure these responses at 

present, but simply assume that they may result in some type of impact on the social indicators. 

The immediate and cumulative effects of current and past changes combined with the inherent 

vulnerability (low to high) of the community will determine the extent to which the community 

can cope with the impact of the regulation. If coping capacity is exceeded, then the capability of 

the community to respond and recover from the event will be low (less resilient). In contrast, if 

community coping capacity is not exceeded, then the capability of the community to respond and 

recover will be greater (more resilient). In this respect, we see the concepts of vulnerability and 

resilience as operating on separate but related continua1

. This, we propose, means that changes in 

vulnerability over time can be an indicator of resilience. Although our present research is a static 

picture of vulnerability reflected as Time 1 in Figure 2, it is our intention to create future 

vulnerability measures to compare over time as indicated by Time 2 in the model, thereby giving 

an indication of resilience until we are able to create viable measures of the concept. 

Social Indicators

As we have noted, there has been considerable interest in social indicators through the decades, 

beginning in the early 1960s with a focus on describing the well-being of society and at times 

addressing the effectiveness and efficiency of government (Cobb and Rixford, 1998; Fox, 1986; 

Johnston and Carley, 1981; Porter, 2011; Schneider, 1976). With recent attention focused on 

ecosystem-based management and ecosystem goods and services, there has been a resurgent 

interest in indicators, both biological and social (Degnbol 2005; DeYoung et al. 2008; Livingston 

2005; MEA 2005a, 2005b). While there has been substantial progress in the creation and 

implementation of sustainable development indicators for fisheries, as Boyd and Charles (2006) 

point out, there have been few attempts to develop such measures at the community level. The 

potential for the development of indicators within an ecosystem-based fishery management 

regime that includes social and economic variables has been widely promoted (Charles et al. 

2009; FAO 1999; Patterson et al. 2010). Only recently have the types of indicators that are 

easily replicable and applicable to coastal communities appeared in an operationalized form that 

 1 This is an adaptation from our previous view that vulnerability and resilience operate on opposite ends of the same 

continuum (Colburn and Jepson, 2012).
8

is practical. Cutter’s social vulnerability index (SoVI) to coastal hazards at the county level 

(Cutter et al. 2000; Cutter et al. 2003; Cutter et al., 2010) has been analyzed for sensitivity to 

scalar change from aggregation at different units of analysis e.g., census tract, county 

(Schmidtlein et al. 2008) and seems to remain fairly stable. Cox et al. (2006) developed a similar 

social vulnerability index applied at the block level in the Northeast to assist communities in 

understanding the vulnerabilities that may affect community action. These examples 

demonstrate that a comprehensive measure of social vulnerability can be developed and applied 

at various geographic contexts and for a variety of uses. 

In an early attempt to explore social indicators for fishing communities in the U.S., Jepson and 

Jacob (2007) created an index of vulnerability for Gulf Coast fishing communities that used both 

census data and a modified shift-share employment analysis. In that assessment, Gulf coast 

fishing communities received a vulnerability score used to identify those within specific fisheries 

that may exhibit vulnerabilities to future regulatory change. However, the shift share component 

was impractical when faced with shortened timeframes for conducting social impact analyses, as 

it takes considerable time to construct and data are not always available. In the Pacific region, 

groundfish fishing communities were classified according to their vulnerability using a Shannon 

Index and other census variables plus a fishing engagement measure all combined into a rank 

order scale (PFMC, 2006). Jacob and Jepson (2009) further explored an index of reliance on 

particular fish species for Gulf of Mexico communities that has implications for sustainability. 

Using landings at the community level and the NOAA Fishery Stock Status Index2

, they 

developed a measure of a community’s reliance upon particular species in comparison to 

regional landings, allowing evaluation of potential community vulnerabilities related to 

sustainability that come from reliance on overfished species. Most recently, Jacob et al. (2010, 

2012) compiled a series of social indices that gauge a variety of community vulnerability 

constructs, including reliance upon either commercial or recreational fishing, for a select group 

of Gulf coast fishing communities. These indices were comprised of U.S. Census, NOAA 

Fisheries and other secondary data then explored through factor analysis to determine the ability 

of the index to measure a particular aspect of community well-being.

Although the straightforwardness of Cutter’s SoVI (Cutter et al., 2010) is appealing, the concept 

of well-being is complex. Some aspects of the SoVI for coastal hazards are not essential to 

assessing vulnerability to regulatory change and its impacts. Furthermore, there are other 

components of social vulnerability for coastal communities that would be difficult to deduce 

from a coastal hazards SoVI. A single vulnerability index may not be discrete enough to 

measure how each component expresses the multiple differing vulnerabilities a community may 

have. Therefore, we have chosen to divide social vulnerability into several different components 

by creating individual indices that contribute to the larger concept of well-being while each 

smaller constituent component provides a more refined portrayal of each factor, similar to Jacob 

et al. (2010, 2012). 

 2 NMFS measures the sustainability of our Nation’s fisheries through the Fish Stock Sustainability Index (FSSI). 

The FSSI measures the performance of 230 key stocks and was calculated by assigning a score for each fish stock or 

complex based on a set of rules concerning overfishing and other stock status measures. For a detailed explanation, 

see: http://www.nmfs.noaa.gov/sfa/statusoffisheries/SOSmain.htm.
9

VI. Methods

This project was developed in two phases (Figure 3). Prior to undertaking our research, a group 

of regional fisheries experts and social scientists familiar with social indicators and fishing 

communities were convened in 2010 to assist in the development of an approach to create 

measures of fishing community well-being. A second workshop held in 2011, shortly after the 

initial development of indicators, was to review and suggest revisions to the indicators. 

Figure 3. Model for Development of Social Indicators for Fishing Communities.

A. Workshops

Miami Workshop

The first three-day workshop held in May 2010 at the Southeast Fisheries Science Center

(SEFSC) in Miami, Florida identified key variables for constructing social indices of fishing 

community vulnerability. Attendees included social scientists from NMFS Headquarters and the 

Northwest, Northeast, Pacific Islands and Southeast Fisheries Science Centers, and the Southeast 

Regional Office. Several individuals outside of NOAA with expertise in the construction of 

these indices also attended and provided advice and potential data warehousing solutions. 

The purpose of the workshop was to identify needed data and data sources, and a methodology 

for assembly of the social indicators. A review of social indicators from Jacob et al. (2010) and 

work on gentrification in the Northeast were presented to assist in selecting pertinent data for

constructing social indicators that would be both meaningful and adequate for social impact 

assessment. In addition, participants suggested ways to warehouse collected and analyzed data

and provided an overview of needed data types. 
10

Silver Spring Workshop 

A second workshop held in September 2011 

in Silver Spring, Maryland, reviewed the set 

of indicators developed and applied to 

coastal communities along the Eastern and 

Gulf coasts since the previous workshop3

. 

We presented a group of 12 community 

vulnerability indices that measured aspects 

such as labor force, housing characteristics,

poverty, gentrification, and fishing 

dependence. Participants reviewed and 

discussed the concepts of resilience and 

vulnerability at length and reviewed each 

index and recommended revisions. In 

addition, the discussions included new indices that might be developed and other variables 

adding to or replacing current variables in the indices. 

B. Analytical Approach

The approach selected for constructing our indices of community vulnerability closely follows 

that developed by Jacob et al. (2010, 2012). Building on work by Cutter et al. (2003) and Jepson

and Jacob (2007), this approach utilizes several indices to examine different aspects of social 

vulnerability and over time resilience for each location, concentrating on those relevant to the 

coastal economy and fishing communities. 

Data Collection

We collected data from both public and private sources for over 2,900 communities in coastal 

counties in 19 states from Maine to Texas. Estimate data at the place level had recently become 

available through the Census Bureau’s American Community Survey (ACS 2006-2009). Census 

data collected on line through the American Factfinder constituted the bulk of the community 

demographic component. Community data for crime and hazardous weather variables collected 

from nongovernmental websites augmented our demographic profile. We collected fisheries 

data, such as number of permits and volume and value of landings, from both the Northeast and 

Southeast Science Centers. The entire list of 120 variables selected for the analysis is available 

in Appendix 2.

We assembled the data for every Census Designated Place (CDP) from a set of predetermined 

coastal counties along the Eastern and Gulf coasts. The criterion for coastal county designation 

was that it has some connection with the ocean, through a coastline, river, bay or estuary. This

criterion was chosen as we envisioned communities within these counties to have comparable 

economies and experiencing similar vulnerabilities that come from having ocean front property 

or beaches, inlets and bays with access to the ocean and many of the amenities that make coastal 

living such a desirable destination to so many. Communities were not chosen based upon their 

 3 http://www.st.nmfs.noaa.gov/st5/social/workshop.html
11

fishing activity, but once selected fishing activity was placed within the context of that 

community. Thus, communities included in this analysis are coastal communities with some 

NS8 fishing communities included. Once we had verified that datasets for both the Northeast 

and Southeast were complete and verified that all variables and calculations corresponded, we 

began to factor analyze our data.

Factor Analysis

Factor analysis is a data reduction technique that allows for the construction of indices that 

represent the latent structure of a conceptual variable. Latent concepts are unobservable, but 

variables taken together can represent the concept, much as the concept of inflation is measured 

by the consumer price index. Here, our constructs relate to several concepts of well-being and 

fishing activity and correspond to vulnerability to social change at the community level.

We initiated our index construction using the variables originally chosen by Jacob et al. (2010, 

2012). The factor analysis process consisted of a principal component analysis with a varimax 

rotation. Using a varimax rotation allows one to determine which variables are loading the 

highest onto the factor and would more likely result in a one-factor solution if included when a 

single factor is not achieved. When we were unable to achieve a one-factor solution with a 

particular set of variables for an index, we substituted comparable variables that had high factor 

loadings within the overall principal component analysis until we found a satisfactory one-factor

solution. We also used substitution of the mean for missing data to ensure each community 

would receive a scale score and also because we had relatively few missing data.

At the outset, we created indices for the Southeast Region and then attempted to duplicate them 

for the Northeast Region. Where regional indices differed with regard to multiple factors or 

differential availability of specific variables, we again chose comparable substitute variables 

until we obtained corresponding results, meaning each index contained the same variables and a 

one-factor solution. Once we achieved agreement on all indices, we combined data from both 

regions and repeated the factor analyses. We retained an index if it remained a single factor 

solution and met all criteria thresholds and significance levels. The criteria of significance for all 

indices included total variance explained above .450. Although this may be low, for exploratory 

analysis and selecting a single factor 

solution, this criterion should be 

acceptable as we are looking for as few 

factors as possible. The Kaiser-MeyerOlkin measure of sampling adequacy 

above .500 was chosen to compare the 

magnitudes of the observed correlation 

coefficients in relation to the magnitudes 

of the partial correlation coefficients, 

higher values are better. Factor loadings 

were all to be above .350 and Bartlett’s

test of sphericity significance above .05

was adopted to test the hypothesis that the 

correlation matrix is an identity matrix. 

And finally, Armour’s Theta reliability 
12

was used as it does not assume that all items are weighted equally, only coefficients above .500

were retained. In the final analysis, we created 14 original indices used in conjunction with a 

Shannon index of occupational diversity. The results follow with a detailed description of each 

index. 

VII. Results 

A. The Indices

We placed the indices under one of three categories within the larger context of vulnerability. 

The three main categories are social, gentrification, and fishing dependence vulnerability. While 

gentrification is both social and economic in its effect, the process itself has special significance 

as it creates different vulnerabilities and is thought to deserve consideration as a discrete process 

from other social and economic vulnerabilities (GSAFFI, 2010; Colburn and Jepson, 2012). For 

each category, we present the index, the variables included, the factor loadings for each variable,

and the percent of total variance explained. For the labor force structure and housing 

characteristics indices, it was necessary to reverse the scores so that all indices had a common 

directional tendency with regard to vulnerability. For instance, a high score on the original labor 

force index means a strong labor force with more overall participation including more females 

and fewer self employed. Reversing the scores on this index means that low scores are now high 

and higher scores are associated with greater vulnerability. These indices are marked with an 

asterisk (see Table 1d and 1c). 

Appendix 1 includes a table for each index that displays factor scores4 for a select group of 

communities. The value of each variable within an index for a particular community is included 

in the table along with the corresponding factor loading which represent each variable’s 

contribution to the index. The index factor scores in the last column represent each community’s 

rank within each index. Each index table contains the variance explained, highest eigenvalue 

and theta reliability score. These communities also appear in the radar graphs below. 

We selected communities for this discussion using a methodology for classifying coastal 

communities for sampling purposes (Smith et al. 2011). All communities showed some level of 

involvement in either commercial or recreational fishing. Using a community’s factor scores for 

each discrete index, a K-means cluster analysis was used to create a taxonomy of fishing 

communities (Pollnac 2012). This resulted in a typology of 35 communities clusters. Each 

cluster of communities has a unique set of shared characteristics for each index included in the 

analysis. For example, scores for one cluster may indicate a high degree of involvement in 

commercial fishing and be characterized by a strong labor force and low risk of gentrification 

while another cluster may show the opposite. This method made it possible to select

communities with a wide range of involvement in commercial and/or recreational fishing and

exhibiting varying degrees of social vulnerability. 

 4 The factor scores for each index and community are a product of the factor analysis process. Within SPSS, these factor scores are saved and 

become an additional variable within the dataset for each community and are labeled as such.
13

Social Vulnerability

The six indices created under the larger descriptor of social vulnerability are presented in Table 

1. Each of these indices corresponds to one of the many components identified throughout the 

literature as corresponding to social vulnerabilities that may affect communities. Variables 

chosen for each index have appeared in existing indices (Cutter et al., 2010; Jacob et al., 2012) or 

are recognized as an important marker of that particular vulnerability (Table 1).

Personal disruptions (Table 1.a.) includes variables associated with the kinds of changes and 

circumstances that might affect a person’s ability to find work, propensity to be affected by 

crime, exposure to poverty, or personal circumstances affecting family life or educational level. 

Therefore, the community as a whole will exhibit vulnerabilities when these factors are 

combined. Higher factor scores equal higher levels of vulnerability for this index.

Table 1. Social Vulnerability Indices.

Index Variable

Factor 

Loadings

Percentage 

Variance 

Explained

a. Personal Disruption Index

Percent unemployed 0.628

Crime index 0.477

Percent with no diploma 0.786 45.00

Percent in poverty 0.811

Percent females separated 0.600

b. Population Composition Index

Percent white alone -0.898

Percent female single headed households 0.719

Percent population age 0-5 0.675 58.12

Percent that speak English less than well 0.739

c. Poverty Index

Percent receiving assistance 0.544

Percent of families below poverty level 0.915

Percent over 65 in poverty 0.716 59.72

Percent under 18 in poverty 0.862

d. Labor Force Structure Index*

Percent females employed 0.905

Percent population in the labor force 0.951

Percent of class of worker self employed -0.355 65.25

Percent population receiving social security -0.872

e. Housing Characteristics Index*

Median rent in dollars 0.814

Median mortgage in dollars 0.882

Median number of rooms 0.751 60.60

Percent mobile homes -0.648

f. Housing Disruptions Index

Percent change in mortgage 0.801

Percent change in home values 0.810 53.00

Percent of owners monthly costs 35% of income 0.540

* Scores reversed to ensure directional continuity with other scales.
14

Population composition (Table 1.b.) is comprised of variables that correspond to the 

demographic makeup of the population. These variables, which measure the percentage of 

minorities, the percent of young children and female-headed households and the ability to speak 

English well are all common components identified as indicators of socially vulnerable 

populations. Higher factor scores equal higher levels of vulnerability for this index.

Our poverty index (Table 1.c.) contains several different poverty variables that cover all facets of 

the concept including the elderly, young and families in poverty along with the general percent 

of population receiving assistance. Higher factor scores equal higher levels of vulnerability for 

this index, as well.

Labor force structure (Table 1.d.) includes variables that are indicative of the types of 

engagement within the labor force by examining the percent of the total population and the 

number of females that are in the labor force, the percent of those who may be retired and those 

who are self-employed. These variables combined lend themselves to a characterization that 

provides an indication of the strength and stability of the labor force. Factor scores were 

reversed so higher factor scores would equal higher levels of vulnerability for this index.

The housing characteristics index (Table 1.e.) has several variables that relate to the character of 

housing available within a community by measuring the average rent and mortgages and median 

number of rooms. The percentage of mobile homes within a community adds to that 

characterization as an indication of either temporary or seasonal housing and an indication of 

socio-economic status and has a negative loading in contrast to the other variables. Again for 

this index, factor scores were reversed so higher factor scores would equal higher levels of 

vulnerability.

For our measure of housing disruption (Table 1.f.), we settled on a three-item index. It includes 

the changes in mortgages and home values from 2000 to 2010 as an indication of fluctuating 

housing markets, along with the percent of owner monthly costs that may indicate a higher 

number of owners struggling as their mortgages consume a large part of their income. This 

index provides an overall depiction of disruptions in the housing market that may be due to 

changing home values. Higher factor scores equal higher levels of vulnerability for this index.

To demonstrate how one might utilize these indices, we plotted the individual index factor scores 

for a set of communities onto radar graphs to help visualize the interrelatedness of each index 

and to compare communities. The selected set of communities is the same as that in tables 

presented in Appendix 1. There you can view each community’s factor score for a particular 

index and see a map of the entire index plotted by factor scores standard deviation. 

A black circular line on each index chart below represents our threshold of 1 standard deviation 

above the mean. We suggest that scores beyond this threshold would indicate a community is 

experiencing vulnerabilities regarding a particular index or set of indices. We chose one standard 

deviation as it has been used in previous research. Norman et al. (2007) used one standard 

deviation as their threshold for profiling fishing communities analyzed through Data 

Envelopment Analysis. Cutter (2003) also used a range from -1 to 1 standard deviations with 1⁄2 
15

standard deviation increments with counties where scores were greater than 1 standard deviation 

being the most vulnerable. One standard deviation may be conservative and a lower threshold 

might be more appropriate, however groundtruthing of this methodology will inform our 

understanding of the appropriateness of this threshold and whether revision is necessary.

Figure 3. Social Vulnerability Indices by Community.

Several communities demonstrate vulnerabilities with regard to the three indices of social 

vulnerability plotted in Figure 3. The communities of Bayou La Batre, AL, New Bedford, MA, 

and Port Arthur, TX exceed the threshold of one standard deviation on all three indices. The 

community of Gulfport, MS exceeds the threshold for personal disruption and is close to the 

threshold on the other two indices. We suggest that these communities are exhibiting social 

vulnerability as demonstrated by their factor scores and the directionality of each indicator.

-2.5

-1.5

-0.5

0.5

1.5

2.5

Bayou La Batre, AL (32)

Orange Beach, AL (20)

Marathon, FL (14)

St. Marks, FL (33)

Cameron, LA (32)

Houma, LA (10)

Stonington, ME (10)

Cushing, ME (4)

Ocean City, MD (14))

New Bedford, MA (34)

Biloxi, MS (14)

Gulfport, MS (13)

Barnegat Light, NJ (22)

Newport, RI (21)

Point Judith, RI (22)

Seabrook, NH (25)

Wanchese, NC (32)

Port Aransas, TX (25)

Port Arthur, TX (4)

Virginia Beach, VA (18)

Poverty Index Population Composition Index

Personal Disruption Index 1 Std Dev
16

Figure 4. Social Vulnerability Indices by Community (cont).

In Figure 4, we plotted the additional three social vulnerability indices with no community 

exceeding the threshold for all three indices. However, Ocean City MD, Stonington ME, and 

Barnegat Light, NJ all have two indices at or exceeding the threshold. Again, we would suggest 

that these communities are exhibiting vulnerabilities or have the potential to become vulnerable 

because of these social factors, especially those communities that are also exhibiting scores 

beyond the threshold in Figure 3 have compounded vulnerabilities.

Gentrification Indices of Vulnerability

The three components of gentrification we created are retiree migration, urban sprawl and natural

amenities (see Table 2). We selected the variables for each index based on previous research and 

literature on gentrification trends and the potential threat to fishing communities (GSAFFI, 2010; 

Colburn and Jepson, 2012; Gale, 1991; Hall-Arber et al. 2001; Coastal Enterprises Inc. 2002). It 

is has been frequently noted that with the influx of retirees into coastal communities, who are 

often from much different places (the Midwest or an inland part of a coastal state), there comes a 

change in social networks, local power structures, and property tax base (Gale, 1991; Lamarque, 

2009). These impacts often appear through changes in population growth, an increase in the cost 

of living and a rise in home values, all variables included in our urban sprawl index. 

Furthermore, much of the in-migration to coastal communities is driven by proximity to natural 

amenities. Those amenities are commonly associated with a coastal economy, often based upon 

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

Bayou La Batre, AL (32)

Orange Beach, AL (20)

Marathon, FL (14)

St. Marks, FL (33)

Cameron, LA (32)

Houma, LA (10)

Stonington, ME (10)

Cushing, ME (4)

Ocean City, MD (14))

New Bedford, MA (34)

Biloxi, MS (14)

Gulfport, MS (13)

Barnegat Light, NJ (22)

Newport, RI (21)

Point Judith, RI (22)

Seabrook, NH (25)

Wanchese, NC (32)

Port Aransas, TX (25)

Port Arthur, TX (4)

Virginia Beach, VA (18)

Labor Force Index* Housing Characteristics Index*

Housing Disruption Index 1 Std Dev
17

tourism and recreation, which we measure through vacant homes and rentals along with a 

measure of water area and boating infrastructure. 

Table 2. Gentrification Vulnerability Indices

Index Variable

Factor 

Loadings

Percentage 

Variance 

Explained

Retiree Migration Index

Households with one or more over 65 0.950

Percent population receiving social security 0.951

Percent receiving retirement income 0.766 78.59

Percent in labor force -0.866

Urban Sprawl Index

Population Density 0.387

Nearest city w/50k population in miles -0.589

Cost of living index 0.894 49.10

Median home value 0.819

Natural Amenities Index

Rental vacancy rate 0.770

Percent homes vacant 0.824

Boat launches per 1,000 persons 0.605 48.80

Percent water cover 0.493

For our measures of coastal gentrification in Figure 5, we plotted the three indices from Table 2. 

While only one community, Barnegat Light, NJ, has all three gentrification indices exceeding the 

threshold of one standard deviation, we would again suggest that for those communities where 

all three index scores are directionally toward the threshold or two of the indices exceed the 

threshold, gentrification vulnerabilities are present or developing. Due to regional variations in 

population density, it should be noted that urban sprawl is much less important for communities 

in the Southeast than in the Northeast. 
18

Figure 5. Gentrification Vulnerability Indices by Community.

Fishing Engagement and Reliance

To create indicators of community dependence on fishing, we developed a range of 

complementary measures related to fishing activity that cover both engagement and reliance. 

Commercial and recreational fishing engagement are absolute measures of fishing activity as 

measured by the absolute numbers of that activity. For commercial fishing we used permits, 

pounds and value of landings and number of dealers for commercial fishing. For receational 

engagement we used estimated fishing trips from the Marine Recreational Information Program 

(MRIP) site survey for recreational fishing. The MRIP site survey assigns a community name 

associated with each site location selected for interviewing. We used estimates of fishing 

pressure-fishing trips) by fishing mode for each site. The mode refers to the type of recreational 

fishing that is being engaged, whether from a private boat, charter boat, or shore fishing. We 

summed the site estimates for each mode by community identified on the survey. This

summation of each mode was our measure of recreational fishing engagement by community. 

The commercial and recreational reliance indices are relative measures consisting of similar 

variables related to commercial or recreational fishing activity (Table 3). For commercial 

reliance we used value of landings per capita; number of commercial permits per capita; number 

of dealers per capita and percentage employed in agriculture, forestry and fishing. For 

recreatoinal fishing reliance we used the same summation of mode fishing pressure divided by 

populaiton. Each variable is divided by the population and is either multiplied by a constant, e.g.,

1,000 or used as is and reflects the amount of fishing activity in relation to the size of the 

population. 

-5.0

-3.0

-1.0

1.0

3.0

5.0

Bayou La Batre, AL (32)

Orange Beach, AL (20)

Marathon, FL (14)

St. Marks, FL (33)

Cameron, LA (32)

Houma, LA (10)

Stonington, ME (10)

Cushing, ME (4)

Ocean City, MD (14))

New Bedford, MA (34)

Biloxi, MS (14)

Gulfport, MS (13)

Barnegat Light, NJ (22)

Newport, RI (21)

Point Judith, RI (22)

Seabrook, NH (25)

Wanchese, NC (32)

Port Aransas, TX (25)

Port Arthur, TX (4)

Virginia Beach, VA (18)

Retiree Migration Index Urban Sprawl Index

Natural Amenities Index 1 Std Dev
19

Table 3. Fishing Engagement and Reliance Indices

Index Variable

Factor 

Loadings

Percentage 

Variance 

Explained

Recreational Fishing Reliance Index

Recreational fishing mode charter per capita 0.352

Recreational fishing mode private per capita 0.917 58.97

Recreational fishing mode shore per capita 0.897

Recreational Fishing Engagement Index

Recreational Charter Fishing Pressure 0.352

Recreational Private Fishing Pressure 0.815 63.02

Recreational Shore Fishing Pressure 0.761

Commercial Fishing Reliance Index

Value of landings per capita 0.833

Number of commercial fishing permits per capita 0.686

Dealers with landings per capita 0.592 50.30

Percent in agriculture, forestry and fishing 0.705

Commercial Fishing Engagement Index

Value of landings 0.906

Number of commercial fishing permits 0.862

Dealers with Landings 0.580 57.57

Pounds of Landings 0.635

Our two measures of commercial fishing activity for the select group of communities are 

displayed in Figure 6. These two indices are closely related, but a few communities stand out. 

New Bedford, MA has by far the highest engagement score for a commercial fishing community. 

In fact, we truncated the value for the purposes of comparison of scale on this graph. With its 

high value of landings (highest in the nation), the community is highly engaged, but not highly 

reliant upon fisheries. While most communities had values beyond the threshold for both 

indices, a few seem to stand out with higher factor scores on reliance (see Appendix A - Table 

12). Stonington, ME; Barnegat Light, NJ; Point Judith, RI; Bayou La Batre, AL; Cameron, LA;

Wanchese, NC; St. Marks, FL and Port Arthur; TX have index scores that exceed the threshold 

by a much larger margin than other communities do. These communities are likely to be more 

dependent upon commercial fishing as we see both high engagement and reliance. 
20

Figure 6. Commercial Fishing Engagement and Reliance Indices by Community.

In Figure 7 below, we have plotted our measures for recreational fishing reliance and 

engagement for these same communities. Several communities show a similar dependence upon 

this type of fishing economy with its many components. Our measures of recreational reliance 

and engagement are closely aligned, although some communities do exhibit distinct differences. 

The communities of Virginia Beach, VA; Orange Beach, AL; Marathon, FL; St. Marks, FL;

Ocean City, MD; Biloxi, MS; Gulfport, MS; Barnegat Light, NJ; Newport, RI and Point Judith,

RI all exceed the threshold for both recreational engagement and reliance, which would suggest 

these communities have an economy that is at least somewhat dependent on recreational fishing.

-20.0

-10.0

0.0

10.0

20.0

30.0

Bayou La Batre, AL (32)

Orange Beach, AL (20)

Marathon, FL (14)

St. Marks, FL (33)

Cameron, LA (32)

Houma, LA (10)

Stonington, ME (10)

Cushing, ME (4)

Ocean City, MD (14))

New Bedford, MA (34)

Biloxi, MS (14)

Gulfport, MS (13)

Barnegat Light, NJ (22)

Newport, RI (21)

Point Judith, RI (22)

Seabrook, NH (25)

Wanchese, NC (32)

Port Aransas, TX (25)

Port Arthur, TX (4)

Virginia Beach, VA (18)

Commercial Engagement Index Commercial Reliance Index 1 Std Dev
21

Figure 7. Recreational Fishing Engagement and Reliance Indices by Community.

Finally, we calculated a Shannon Diversity Index (Figure 8) to provide a measure of the 

occupational diversity within a community (See Appendix 3 for calculation information). The 

Shannon Index is commonly used to calculate biodiversity (though was originally created to 

measure linguistic diversity), but has been adapted by many other disciplines. We used the six 

occupation categories within the Census ACS dataset for this calculation. 

The closer a community is to zero, the less occupational diversity it exhibits. The index mean

for all communities, represented by the black line in Figure 8, shows several communities 

scoring below the mean5. Gulfport and Biloxi, MS, however, exhibit the least amount of 

diversity with index scores of 0.66 and 0.68 respectively. This indicator, in combination with 

other measures provides a sense of those communities that may be more reliant upon one 

occupation. If that occupation were fishing, as demonstrated through other indices such as the 

commercial and recreational reliance and engagement indices, then we would conclude that this 

particular community might be vulnerable to fishery management actions where job losses are

anticipated impacts. With little occupational diversity, community members may be forced to 

look for work elsewhere. However, because occupations related to fishing show up in several 

different census occupation categories, one could wrongly conclude that occupational diversity is 

present when there is still an over-reliance upon fishing related employment. Thus, care must be 

taken in evaluation of an index alone; more in-depth study may be necessary to clarify these 

results.

 5 Here is the mean is used rather than one standard deviation. This index score does not correspond to the other 

indices and the mean is presented only for demonstration purposes. The key is how close to zero is the index score.

-10.0

-5.0

0.0

5.0

10.0

15.0

Bayou La Batre, AL (32)

Orange Beach, AL (20)

Marathon, FL (14)

St. Marks, FL (33)

Cameron, LA (32)

Houma, LA (10)

Stonington, ME (10)

Cushing, ME (4)

Ocean City, MD (14))

New Bedford, MA (34)

Biloxi, MS (14)

Gulfport, MS (13)

Barnegat Light, NJ (22)

Newport, RI (21)

Point Judith, RI (22)

Seabrook, NH (25)

Wanchese, NC (32)

Port Aransas, TX (25)

Port Arthur, TX (4)

Virginia Beach, VA (18)

Recreational Engagement Index Recreational Reliance Index 1 Std Dev
22

Figure 8. Shannon Index of Occupational Diversity and Mean by Community.

B. Summary Tables

To provide a summary of the data in the graphs above, the results are presented in three different 

tables below using a dichotomous scale of 1 and 0 for a selected group of indices. A community 

receives a 1 if each factor score for a particular index is at or over one standard deviation above 

the mean and 0 if below. 

Table 4. Sum of Social Vulnerability Indices Dichotomous Scale. 

Community Poverty

Population 

Composition

Personal 

Disruption

Housing 

Disruption

Housing 

Characteristics

Labor Force 

Characteristics

Social Vulnerability 

Sum

Port Arthur, TX 1 1 1 0 1 0 4

Bayou La Batre, AL 1 1 1 0 1 0 4

New Bedford, MA 1 1 1 0 0 0 3

Cushing, ME 1 0 0 0 1 0 2

Stonington, ME 0 0 0 0 1 1 2

Ocean City, MD 0 0 0 0 1 1 2

Cameron, LA 1 0 0 0 1 0 2

Gulfport, MS 0 0 1 0 0 0 1

Marathon, FL 0 0 0 1 0 0 1

Orange Beach AL 0 0 0 1 0 0 1

Biloxi, MS 0 0 1 0 0 0 1

Barnegat Light, NJ 0 0 0 0 0 1 1

Wanchese, NC 1 0 0 0 0 0 1

St. Marks, FL 0 0 0 0 1 0 1

Houma, LA 0 0 0 0 0 0 0

Virginia Beach, VA 0 0 0 0 0 0 0

Point Judith, RI 0 0 0 0 0 0 0

Newport, RI 0 0 0 0 0 0 0

Port Aransas, TX 0 0 0 0 0 0 0

Seabrook, NH 0 0 0 0 0 0 0

0.0

0.5

1.0

1.5

2.0

Bayou La Batre, AL...

Orange Beach, AL...

Marathon, FL (14)

St. Marks, FL (33)

Cameron, LA (32)

Houma, LA (10)

Stonington, ME (10)

Cushing, ME (4)

Ocean City, MD (14))

New Bedford, MA...

Biloxi, MS (14)

Gulfport, MS (13)

Barnegat Light, NJ...

Newport, RI (21)

Point Judith, RI (22)

Seabrook, NH (25)

Wanchese, NC (32)

Port Aransas, TX (25)

Port Arthur, TX (4)

Virginia Beach, VA...

Shannon Index Shannon Index Mean
23

Scores for all six indices were summed for each community with above the threshold of one 

standard deviation for at least three of the indices highlighted in Table 4; communities where at 

least 2 of the indices are above the threshold for individual fishing sectors and 3 and above for 

the fishing sector total are highlighted in Tables 5 and 6. 

Port Arthur, TX and Bayou La Batre, AL stand out as exhibiting vulnerabilities to social 

disruptions as they score above the threshold on four indices. We assume that these two 

communities may have difficulties in rebounding from any disruption to the local economy 

because of fishery management regulation. Therefore, within any social impact assessment these 

communities should be highlighted as vulnerable. Communities scoring above the threshold for 

three of the indices, e.g., New Bedford, MA, might also be considered vulnerable. 

Table 5. Sum of Gentrification Vulnerability Indices Dichotomous Scale.

Community Retiree Migration Urban Sprawl Natural Amenities

Gentrification 

Sum 

Barnegat Light, NJ (22) 1 1 1 3

Ocean City, MD (14)) 1 0 1 2

Orange Beach, AL (20) 0 0 1 1

St. Marks, FL (33) 0 0 1 1

Stonington, ME (10) 0 0 0 1

Biloxi, MS (14) 0 0 1 1

Point Judith, RI (22) 0 0 1 1

Port Aransas, TX (25) 0 0 1 1

Bayou La Batre, AL (32) 0 0 0 0

Marathon, FL (14) 0 0 0 0

Cameron, LA (32) 0 0 0 0

Houma, LA (10) 0 0 0 0

Cushing, ME (4) 0 0 0 0

New Bedford, MA (34) 0 0 0 0

Gulfport, MS (13) 0 0 0 0

Newport, RI (21) 0 0 0 0

Seabrook, NH (25) 0 0 0 0

Wanchese, NC (32) 0 0 0 0

Port Arthur, TX (4) 0 0 0 0

Virginia Beach, VA (18) 0 0 0 0

The summation of our gentrification measure is found in Table 5. Both Barnegat Light, NJ and 

Ocean City, MD exceed the threshold on at least two of the three gentrification indices. We 

would expect these communities to be vulnerable to gentrification. This predisposition toward 

gentrification can be problematic for those employed in fishing as it may signal a transition 

toward an economy no longer based on fishing or water related occupations.

Our summary for fishing dependence (Table 6) has summations of both commercial and 

recreational fishing and a total fishing summary. Seven communities score above our threshold 

on both commercial engagement and reliance. We would expect these communities to be more 

dependent on commercial fishing than the others. Similarly, the ten communities that exceed the 

threshold for the two measures of recreational fishing activity would be expected to demonstrate 

some dependence upon recreational fishing. Finally, eight communities in Table 6 exceed the 

threshold on three or more of the fishing indices as reflected in the Total Fishing Sum. These 
24

communities show an overall dependence upon a fishing economy in general and could be 

affected by management action on either the commercial or the recreational sector.

Table 6. Sum of Fishing Dependence Indices: Commercial and Recreational Fishing Engagement 

and Reliance Dichotomous Scales.

Community

Commercial 

Engagement

Commercial 

Reliance

Commercial 

Sum

Recreational 

Engagement

Recreational 

Reliance

Recreational 

Sum

Total 

Fishing 

Sum

Barnegat Light, NJ (22) 1 1 2 1 1 2 4

Point Judith, RI (22) 1 1 2 1 1 2 4

Marathon, FL (14) 1 0 1 1 1 2 3

St. Marks, FL (33) 0 1 1 1 1 2 3

Ocean City, MD (14)) 1 0 1 1 1 2 3

Biloxi, MS (14) 1 0 1 1 1 2 3

Newport, RI (21) 1 0 1 1 1 2 3

Wanchese, NC (32) 1 1 2 1 0 1 3

Bayou La Batre, AL (32) 1 1 2 0 0 0 2

Orange Beach, AL (20) 0 0 0 1 1 2 2

Cameron, LA (32) 1 1 2 0 0 0 2

Stonington, ME (10) 1 1 2 0 0 0 2

New Bedford, MA (34) 1 1 2 0 0 0 2

Gulfport, MS (13) 0 0 0 1 1 2 2

Virginia Beach, VA (18) 0 0 0 1 1 2 2

Houma, LA (10) 1 0 1 0 0 0 1

Cushing, ME (4) 0 1 1 0 0 0 1

Seabrook, NH (25) 0 0 0 1 0 1 1

Port Arthur, TX (4) 1 0 1 0 0 0 1

Port Aransas, TX (25) 0 0 0 0 0 0 0

The summary tables are another way to demonstrate overall fishing dependence as reflected in 

both community engagement and reliance. The community comparison is one way to use these 

indices in a social impact assessment to identify those places where vulnerabilities might exist

for a specific management action. It may also be possible to measure fishing dependence in 

relation to species or species groups. A more detailed description of community involvement in 

a particular fishery and possible effects from management actions that is placed into the context 

of these vulnerabilities provides a more empirical measure of vulnerability and, over time, 

resilience that can be assembled in a relatively short amount of time. These indicators 

contribute to improving social impact assessment of fishery management actions, especially 

when combined with other types of quantitative and qualitative analyses.

VIII. Discussion

There are a few limitations to this approach, the first being the relative availability of data for 

different geographic units of analysis. Most of the data collected here are at the Census 

Designated Place (CDP) level. These data are not always available for communities that have 

fishing related businesses, in that not all of such communities are identified CDPs. Databases 

built around different levels of geography, like zip codes, census blocks, or Minor Civil 

Divisions (MCDs), can be created, but do make assembling data for meaningful community 

descriptions more difficult. 
25

As NMFS began profiling fishing communities it became obvious that decisions had to be made 

regarding the geographical boundaries for data collection purposes. The decision to use a 

particular geographic boundary was a regional decision based upon the characteristics 

surrounding a particular community and its participation within its region’s fisheries. In most 

cases, CDP6 was chosen to represent community boundaries. Although CDP does not always 

correspond to an incorporated place, it is used by the Census Bureau for the purposes of data 

collection and does include incorporated cities, towns and villages. For these analyses, while 

CDP level data were used primarily, in some cases where CDP data were not available other 

geographies were used to delineate a community that would otherwise be excluded. MCD level 

data, for instance, were used when CDP level data were not available for the Northeast and in 

one instance, Census Tract level data were used because MCD data did not truly represent the 

consensus boundaries for the community as recognized by agency social scientists. These 

geographical boundaries can include zipcode and Census Block data in other regions. The use of 

these other boundaries is consistent with research conducted in many different regions that 

demonstrated any single geographic definition of fishing community would be difficult, as 

fishermen can live either in or near ports where they dock and/or unload their catch. Associated 

businesses and other support activity may also take place within or outside a narrow boundary of 

community. Where extensive ethnographic research is not available, these boundaries are the 

best estimates available for data collection purposes.

Not all Census data came from the 

ACS. The percent water coverage 

variable was extracted from the 

Census Bureau’s Tigerline files found 

online at the Topologically Integrated 

Geographic Encoding and 

Referencing (TIGER) system 

(census.gov/geo/www/tiger/). Other 

data from the 2000 decennial census 

were also collected for those variables 

where demographic change over time 

needed to be calculated.

Several types of data were not as 

readily accessible and downloadable 

as census data. Variables for crime and weather hazards were available but not for download, 

requiring data to be cut and pasted by hand on a community-by-community basis into the 

database. The crime index variable was collected from CLRsearch.com and weather hazards 

from Moving.Com. The entire process was extremely tedious and time-consuming. The 

Environmental Protection Agency (EPA) registered facilities7 variable, drawn from the EPA 

Environofacts website, was calculated based on the frequency of registered facilities in each 

community. Data on the number of boat launches were drawn from the Census County Business 

 6 The community name used in the establishment of a CDP is to “be one that is recognized and used in daily 

communication by the residents of the community.” Census Designated Place (CDP) Program for the 2010 Census 

- Final Criteria, Federal Register, February 13, 2008 (Volume 73, Number 30), accessed March 9, 2012. 7 A registered facility is facilities, sites or places subject to environmental regulations or of environmental interest.
26

Patterns database using the North American Industry Classification System (NAICS) code for 

marinas. Each data source underwent an evaluation process in which we attempted to establish 

the authenticity of the data by identifying the primary source. Other types of data relating to 

climate change and sea level rise were also researched, but were not found in a format at the 

community level easily adapted to our needs. This remains an area for further research.

NMFS fisheries data were available for the east coast from both the Northeast and the Southeast 

Regional Science Centers and Southeast Regional Office. Although the commercial fisheries 

data (permits, pounds and value landed) were comparable, the NMFS recreational data were

more variable. The SERO generates numerous recreational data (vessel designation as 

commercial or recreational; permits by fishery; and charter and recreational permits by home 

port and owner address). The Northeast recreational data are limited to federally permitted forhire charter activity, making regionally comparable recreational indices impossible with NEFSC, 

SERO and SEFSC data. Neither region has quantitative data on subsistence fishing. However, as 

described above, a regionally comparable customized database was generated from the Marine 

Recreational Fishing Information Program (MRIP) site surveys8

, using a summation of all 

estimated fishing trips for sites near a particular community. It includes community level data on 

recreational fishing pressure (as estimated by the number of trips) representing shore, charter 

boat (federal and state), and private recreational fishing activity. Further, not all NMFS regions 

have MRIP. These regional differences in data collection for the recreational fishery mean this 

measure will need to be calculated on a region-by-region basis until a uniform measure can be 

developed for all regions.

While these indices are sufficiently robust for the assessment of fishing community vulnerability,

additional indices and data on social capital, critical for understanding social networks and social 

cohesion, would strengthen the analysis. Data on fishing community infrastructure would 

improve our assessment of community dependence on fishing, both recreational and commercial. 

Additional variables and indices will also be needed to assess well-being more broadly. 

Currently, databases for most of these variables are inadequate at the community level. With 

regard to sea level rise, community level measures of coastal hazards are an area to be explored,. 

However, as the level of analysis progresses from community, to county, and to state there is 

increasingly more secondary data; measures of health and well-being, for instance, are more 

attainable at county and state levels than at the community level and should be researched.. This 

may allow addition of new variables and indices at different geographic levels that could 

complement and contextualize the community indicators. 

This discussion highlighted issues encountered in the compilation of a cross regional database. 

Further, such issues to resolve are anticipated to arise as this data collection effort is expanded to 

include all communities in coastal counties in the U.S. Creating comparable national data will 

have some challenges, as not all fisheries data is comparable. However, regional analyses will 

reflect the relative richness of data in each region.

 8 Unfortunately, the state of Texas does not participate in the MRIP program, so there are no recreational fishing 

engagement or reliance measures for the state. This makes our common recreational measure less robust than is 

ideal.
27

IX. Conclusion

We have developed a set of social indicators using secondary data that can improve the 

analytical rigor of fisheries social impact assessment. The majority of these data are readily 

accessible and can be compiled quickly to create measures of social vulnerability and to update 

community profiles. Because we know these communities exist within a larger coastal economy, 

the ability to profile the context of vulnerability to social factors outside of fishing is critical to 

understanding how regulatory change will be absorbed into these multifaceted places. Creating 

social indicators of vulnerability for fishing communities provides a pragmatic approach toward 

standardization of data and analysis for assessment of some of the long term effects of 

management actions. 

The advantage of this approach is the ability to use secondary data rather than rely on primary 

data collection. It provides a significant timesaving compared with conducting fieldwork or 

implementing a survey, an important consideration given the sometimes-short timeframe in 

which social impact assessments are conducted. These types of measures can be created for 

communities and other geographic scales and offer the ability to compare across regions. The 

analysis is comparatively straightforward and can be modified to adapt to changing 

circumstances and differential regional availability of specific variables within topical areas 

without compromising the results. Because they are based on existing standard time series data, 

these measures can also be developed retroactively to allow valuable analyses of change within 

a community over time as proxies of resilience. 

This research forms the initial step in developing more empirical measures to enhance NMFS’ 

ability to understand the dynamics of fishing communities and their ability to recover from 

disruptive events, whether they are man-made, such as regulatory change, or natural. Lessons 

learned from this assessment can guide further research and assist in the development of social 

impact assessment best practices for management actions.
28

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33

XI. Appendices

Appendix 1. Index Tables and Maps

Table A-1. Personal Disruptions Index.

Community

Percentage 

unemployed

Crime 

index

Percentage 

with no 

diploma

Percent 

in 

Poverty

Percentage 

females 

separated

Personal 

Disruptions 

Index

Score

Gulfport, MS (13) 6.0 137 11.6 18.2 16.1 2.122

Bayou La Batre, AL (32) 7.5 82 15.8 22.8 9.7 1.918

New Bedford, MA (34) 6.0 151 15.5 22.2 4.7 1.483

Port Arthur, TX (4) 6.2 124 14.4 23.4 5.3 1.455

Biloxi, MS (14) 4.4 99 9.8 12.6 13.5 1.280

Houma, LA (10) 2.8 87 15.8 16.4 2.9 0.578

Ocean City, MD (14)) 2.4 383 7.0 9.0 1.3 0.567

Cushing, ME (4) 5.7 48 12.6 20.9 0.0 0.472

Orange Beach, AL (20) 2.7 150 1.1 5.3 12.7 0.428

Cameron, LA (32) 0.0 143 18.2 18.6 0.0 0.420

Marathon, FL (14) 2.3 165 7.2 10.7 4.2 0.197

Stonington, ME (10) 2.4 59 15.3 6.9 3.4 0.106

Newport, RI (21) 2.6 202 4.7 10.6 1.5 -0.048

Port Aransas, TX (25) 0.9 151 8.7 14.0 0.9 -0.089

St. Marks, FL (33) 0.0 135 11.5 4.8 4.3 -0.107

Point Judith, RI (22) 4.1 2 4.7 19.6 0.6 -0.283

Virginia Beach, VA (18) 3.1 62 5.5 6.9 2.9 -0.413

Seabrook, NH (25) 3.1 6 9.6 7.2 0.5 -0.572

Wanchese, NC (32) 0.0 115 1.8 9.0 3.2 -0.664

Barnegat Light, NJ (22) 1.8 53 2.0 4.5 0.4 -1.096

Factor Loading 0.628 0.477 0.786 0.811 0.600

Percentage Explained Variation 45.00

Index scores in bold are at or above threshold of one standard 

deviation

Theta Reliability 0.640

Eigenvalue 2.231
34

Figure A-1. Personal Disruptions Index.
35

Table A-2. Population Composition Vulnerability Index.

Community

Percent 

white alone

Percent 

female 

single 

headed 

households

Population 

age 0-5 

Percent 

Speak 

English less 

than very 

well 

Population 

Composition 

Vulnerability 

Index Score

Port Arthur, TX (4) 27.8 20.5 7.4 13.9 1.500

Bayou La Batre, AL (32) 59.4 9.9 11.1 18.3 1.063

New Bedford, MA (34) 70.8 21.1 7.2 17.0 0.974

Gulfport, MS (13) 56.5 21.4 8.3 2.6 0.775

Houma, LA (10) 66.9 16.4 7.7 2.6 0.352

Biloxi, MS (14) 67.5 15.1 6.4 4.4 0.238

Virginia Beach, VA (18) 66.6 13.3 7.1 3.8 0.215

Marathon, FL (14) 72.5 6.8 7.4 12.0 0.175

Wanchese, NC (32) 100.0 9.4 9.6 0.0 -0.370

Newport, RI (21) 88.1 10.5 4.4 2.2 -0.527

Cushing, ME (4) 98.1 6.6 6.1 0.0 -0.772

Stonington, ME (10) 97.0 6.9 5.3 0.5 -0.796

Point Judith, RI (22) 96.4 7.5 3.5 3.8 -0.801

Seabrook, NH (25) 96.1 7.0 4.4 0.8 -0.847

Port Aransas, TX (25) 85.7 5.4 2.4 3.1 -0.853

Cameron, LA (32) 86.9 12.1 0.0 0.0 -0.912

St. Marks, FL (33) 92.3 10.0 0.0 0.0 -1.083

Orange Beach, AL (20) 93.2 4.6 1.3 3.0 -1.101

Ocean City, MD (14)) 97.0 2.3 1.9 3.7 -1.178

Barnegat Light, NJ (22) 98.6 2.9 2.2 0.9 -1.252

Factor Loading -0.898 0.719 0.675 0.739

Percentage Explained Variation 58.120

Index scores in bold are at or above threshold of one standard 

deviation

Theta Reliability 0.640

Eigenvalue 2.231
36

Figure A-2. Population Composition Vulnerability Index.
37

Table A-3. Poverty Index.

Community

Percent 

receiving 

assistance

Per cent of 

families 

below 

poverty 

level

Percentage 

65 and over 

in poverty

Percent 

people 

under 18 

in poverty

Poverty 

Index

Score

New Bedford, MA (34) 7.1 18.9 14.3 34.0 1.574

Cameron, LA (32) 0.0 20.4 24.0 36.0 1.417

Port Arthur, TX (4) 2.6 19.5 19.1 31.7 1.332

Cushing, ME (4) 2.1 19.1 10.3 45.5 1.309

Bayou La Batre, AL (32) 1.4 21.4 22.5 23.8 1.240

Wanchese, NC (32) 2.8 5.2 39.5 13.3 0.978

Gulfport, MS (13) 3.5 14.1 12.6 25.6 0.821

Port Aransas, TX (25) 0.9 11.2 8.2 32.4 0.498

Houma, LA (10) 0.8 13.1 12.4 21.8 0.460

Biloxi, MS (14) 3.0 9.6 10.0 15.2 0.260

Newport, RI (21) 1.3 5.1 12.1 13.2 -0.050

Marathon, FL (14) 0.2 8.5 7.9 14.9 -0.094

Seabrook, NH (25) 3.6 3.5 4.7 8.5 -0.281

Virginia Beach, VA (18) 1.0 5.1 4.9 10.0 -0.386

Ocean City, MD (14)) 0.3 6.6 4.7 7.9 -0.435

Point Judith, RI (22) 2.4 3.8 6.0 3.6 -0.439

Orange Beach, AL (20) 0.0 3.4 2.2 10.3 -0.622

St. Marks, FL (33) 0.0 0.0 12.8 0.0 -0.660

Stonington, ME (10) 0.0 0.6 6.9 5.4 -0.701

Barnegat Light, NJ (22) 1.0 2.6 4.8 1.2 -0.701

Factor Loading 0.544 0.915 0.716 0.862

Percentage Explained Variation 59.720

Index scores in bold are at or above threshold of one 

standard deviation if rounded

Theta Reliability 0.939

Eigenvalue 3.397
38

Figure A-3. Poverty Index.
39

Table A-4. Labor Force Structure Index.

Community

Percent of 

Class of 

Worker 

Self 

Employed 

Percent 

Labor 

force 

Percent 

females 

employed

Percent 

people 

receiving 

Social 

Security

Labor 

Force 

Structure 

Index

Score*

Barnegat Light, NJ (22) 9.7 46.5 37.0 61.2 1.716

Ocean City, MD (14)) 6.9 46.3 38.2 49.2 1.332

Stonington, ME (10) 48.9 63.5 52.4 37.2 0.950

Port Arthur, TX (4) 5.9 54.0 40.6 34.1 0.627

Orange Beach, AL (20) 4.5 54.2 45.1 39.9 0.610

Port Aransas, TX (25) 10.8 58.1 54.6 44.5 0.463

Houma, LA (10) 5.1 58.2 46.8 32.7 0.270

Cushing, ME (4) 22.5 65.4 52.2 31.6 0.220

Gulfport, MS (13) 7.0 61.9 48.5 28.0 0.028

Bayou La Batre, AL (32) 4.9 60.7 55.2 34.0 -0.017

Point Judith, RI (22) 8.5 62.6 53.7 31.5 -0.023

Marathon, FL (14) 12.2 63.6 56.8 32.3 -0.046

New Bedford, MA (34) 4.2 61.7 51.8 30.0 -0.066

Seabrook, NH (25) 9.2 64.5 58.8 35.3 -0.117

Biloxi, MS (14) 5.4 67.3 53.6 28.8 -0.296

St. Marks, FL (33) 15.7 74.9 69.1 37.5 -0.544

Newport, RI (21) 8.0 70.1 60.1 25.3 -0.602

Cameron, LA (32) 15.0 70.2 70.0 29.3 -0.642

Virginia Beach, VA (18) 4.7 72.7 60.5 21.1 -0.866

Wanchese, NC (32) 14.4 79.8 81.5 17.3 -1.578

Factor Loading -0.355 0.951 0.905 -0.872

Percentage Explained Variation 65.25 Index scores in bold are at or above threshold of one 

standard deviation if rounded. * Scores reversed to 

ensure directional continuity with other scales.

Theta Reliability 0.821

Eigenvalue 2.601
40

Figure A-4. Labor Force Structure Index.
41

Table A-5. Housing Characteristics Index.

Community

Median 

gross rent 

Median 

mortgage

Median 

number of 

rooms 

Percent 

Mobile 

Homes

Housing 

Structure 

Index

Score*

Cameron, LA (32) . 1,007.0 5.0 38.3 1.365

Stonington, ME (10) 471.0 1,244.0 5.0 15.1 1.315

Bayou La Batre, AL (32) 306.0 928.0 5.0 8.2 1.284

Cushing, ME (4) 688.0 1,047.0 5.0 18.8 1.165

St. Marks, FL (33) 817.0 1,125.0 5.0 23.8 1.149

Port Arthur, TX (4) 550.0 942.0 5.0 1.0 1.008

Ocean City, MD (14)) 838.0 1,559.0 4.0 5.0 0.987

Houma, LA (10) 611.0 1,220.0 5.0 5.5 0.794

Seabrook, NH (25) 959.0 1,856.0 5.0 21.8 0.769

Biloxi, MS (14) 775.0 1,285.0 5.0 8.1 0.758

Wanchese, NC (32) 883.0 1,350.0 6.0 25.5 0.739

Gulfport, MS (13) 821.0 1,076.0 5.0 6.4 0.716

Marathon, FL (14) 923.0 2,387.0 4.0 16.2 0.605

Port Aransas, TX (25) 957.0 1,931.0 4.0 8.6 0.561

New Bedford, MA (34) 717.0 1,663.0 5.0 0.4 0.457

Orange Beach, AL (20) 908.0 2,250.0 4.0 7.1 0.358

Newport, RI (21) 1,021.0 1,964.0 5.0 0.8 0.003

Point Judith, RI (22) 1,161.0 1,848.0 5.0 0.8 -0.135

Virginia Beach, VA (18) 1,101.0 1,666.0 6.0 1.0 -0.170

Barnegat Light, NJ (22) 1,123.0 2,453.0 6.0 1.9 -0.643

Factor Loading 0.814 0.882 0.751 -0.648

Percentage Explained Variation 60.60 Index scores in bold are at or above threshold of one 

standard deviation if rounded. * Scores reversed to ensure 

directional continuity with other scales. Missing data were 

replaced with the mean.

Theta Reliability 0.793

Eigenvalue 2.476
42

Figure A-5. Housing Characteristics Index.
43

Table A-6. Housing Disruptions Index.

Community

Percent 

change in 

mortgage 

2000-2010

Percent change 

in home value

2000-2010 

Owner’s 

monthly cost 

over 35% of 

income

Housing 

Disruptions 

Index Score

Marathon, FL (14) 81.1 100.4 -0.8 1.530

Orange Beach, AL (20) 111.3 97.3 -22.6 1.428

Barnegat Light, NJ (22) 46.0 169.0 -64.5 0.962

Stonington, ME (10) 74.0 105.0 -69.0 0.880

New Bedford, MA (34) 63.0 118.0 -2.0 0.728

Port Aransas, TX (25) 84.1 82.0 19.4 0.476

Newport, RI (21) 44.0 168.0 -10.0 0.453

Ocean City, MD (14)) 41.0 139.0 -41.0 0.279

Point Judith, RI (22) 45.0 154.0 -98.0 0.223

St. Marks, FL (33) 52.0 68.0 -61.0 0.218

Wanchese, NC (32) 53.2 169.6 22.6 0.069

Virginia Beach, VA (18) 44.0 118.0 -3.0 0.007

Seabrook, NH (25) 44.0 48.0 -97.0 -0.339

Port Arthur, TX (4) 50.2 46.8 4.5 -0.378

Biloxi, MS (14) 48.0 62.1 -15.5 -0.545

Bayou La Batre, AL (32) 36.3 47.8 -29.8 -0.548

Houma, LA (10) 49.9 67.9 2.2 -0.582

Cushing, ME (4) 22.0 47.0 -62.0 -0.729

Gulfport, MS (13) 34.7 47.9 -6.8 -0.791

Cameron, LA (32) 56.1 47.0 -100.0 -1.130

Factor Loading 0.801 0.810 0.540

Percentage Explained Variation 53.00

Index scores in bold are at or above threshold of 

one standard deviation if rounded. 

Theta Reliability 0.557

Eigenvalue 1.590
44

Figure A-6. Housing Disruptions Index.
45

Table A-7. Retiree Migration Index.

Community

Percent of 

Households 

with persons 

65 and over

Percent 

people 

receiving 

Social 

Security

Percent 

people 

receiving 

Retirement 

Income

Percent 

in Labor 

Force

Retiree 

Migration 

Index

Score

Barnegat Light, NJ (22) 56.4 61.2 39.3 46.5 2.137

Ocean City, MD (14)) 46.9 49.2 34.3 46.3 1.523

Orange Beach, AL (20) 38.1 39.9 28.1 54.2 0.764

Port Aransas, TX (25) 31.5 44.5 25.5 58.1 0.557

Port Arthur, TX (4) 28.6 34.1 17.0 54.0 0.133

Seabrook, NH (25) 31.3 35.3 22.9 64.5 0.127

Stonington, ME (10) 34.9 37.2 14.6 63.5 0.064

Cushing, ME (4) 32.8 31.6 21.1 65.4 0.011

Point Judith, RI (22) 27.6 31.5 19.6 62.6 -0.084

Houma, LA (10) 26.0 32.7 13.6 58.2 -0.145

Marathon, FL (14) 26.4 32.3 17.9 63.6 -0.161

Biloxi, MS (14) 24.1 28.8 25.6 67.3 -0.182

Gulfport, MS (13) 23.3 28.0 19.6 61.9 -0.246

Bayou La Batre, AL (32) 20.3 34.0 15.9 60.7 -0.247

New Bedford, MA (34) 24.7 30.0 15.1 61.7 -0.279

St. Marks, FL (33) 31.7 37.5 14.2 74.9 -0.282

Newport, RI (21) 23.1 25.3 17.6 70.1 -0.556

Cameron, LA (32) 31.5 29.3 2.5 70.2 -0.662

Virginia Beach, VA (18) 18.9 21.1 22.6 72.7 -0.680

Wanchese, NC (32) 14.5 17.3 16.1 79.8 -1.202

Factor Loading 0.950 0.951 0.766 -0.866

Percentage Explained Variation 78.59

Index scores in bold are at or above threshold of one 

standard deviation if rounded.

Theta Reliability 0.907

Eigenvalue 3.143
46

Figure A-7. Retiree Migration Index.
47

Table A-8. Urban Sprawl Index.

Community

Population 

Density

Cost of 

Living

Nearest City 

w Population 

50,000

Median 

Home Value

Urban 

Sprawl 

Index

Score

Barnegat Light, NJ (22) 723.0 126.0 0.0 847,550 1.472

New Bedford, MA (34) 4,568.0 135.0 0.0 248,500 0.678

Newport, RI (21) 3,158.0 118.0 16.8 433,600 0.419

Seabrook, NH (25) 955.0 135.0 13.8 270,900 0.415

Point Judith, RI (22) 1,193.0 109.0 19.8 416,700 0.123

Virginia Beach, VA (18) 1,747.0 99.0 0.0 268,600 -0.076

Orange Beach, AL (20) 375.1 89.0 23.3 403,500 -0.376

Biloxi, MS (14) 966.8 89.0 0.0 150,100 -0.506

Gulfport, MS (13) 1,093.4 89.0 0.0 118,800 -0.559

Port Aransas, TX (25) 267.9 89.0 21.0 201,100 -0.716

Marathon, FL (14) 1,045.7 102.0 77.3 446,000 -0.724

Port Arthur, TX (4) 371.5 82.0 0.0 52,700 -0.846

St. Marks, FL (33) 109.2 85.0 20.6 142,300 -0.903

Ocean City, MD (14)) 1,595.0 92.0 75.4 364,100 -1.009

Bayou La Batre, AL (32) 381.9 84.0 18.5 67,700 -1.026

Houma, LA (10) 2,223.8 86.0 40.4 137,700 -1.075

Cushing, ME (4) 73.0 91.0 54.9 170,800 -1.177

Cameron, LA (32) 23.6 81.0 30.1 77,300 -1.219

Wanchese, NC (32) 276.1 83.0 72.8 282,800 -1.349

Stonington, ME (10) 99.0 90.0 87.2 197,900 -1.565

Factor Loading 0.387 0.894 -0.589 0.819

Percentage Explained Variation 49.10

Index scores in bold are at or above threshold of one 

standard deviation if rounded.

Theta Reliability 0.602

Eigenvalue 1.973
48

Figure A-8. Urban Sprawl Index.
49

Table A-9. Natural Amenities Index.

Community

Percent 

of rentals 

vacant

Percent of 

all housing 

vacant

Number of 

boat 

launches per 

1,000 

persons

Percent of 

watercoverage

Natural 

Amenities 

Index

Score

Ocean City, MD (14)) 90.3 85.9 2.1 0.9 4.563

St. Marks, FL (33) 0.0 30.2 24.0 0.4 3.773

Orange Beach, AL (20) 71.6 76.8 0.0 7.8 3.542

Barnegat Light, NJ (22) 58.6 79.4 2.9 0.4 3.490

Port Aransas, TX (25) 45.6 55.6 0.0 36.5 2.828

Marathon, FL (14) 7.8 40.7 2.2 9.0 0.941

Stonington, ME (10) 10.4 48.7 1.0 0.7 0.851

Port Arthur, TX (4) 5.4 14.7 0.0 46.6 0.725

Cameron, LA (32) . 16.5 4.3 9.3 0.714

Biloxi, MS (14) 14.2 18.4 0.1 18.1 0.460

Wanchese, NC (32) 7.2 17.9 2.0 14.9 0.451

Point Judith, RI (22) 7.4 30.3 1.3 0.6 0.324

Gulfport, MS (13) 13.0 16.8 0.1 13.1 0.266

Cushing, ME (4) 5.1 33.8 0.0 0.3 0.126

Seabrook, NH (25) 11.8 15.8 0.4 7.9 0.120

Newport, RI (21) 7.4 22.8 0.4 0.3 -0.011

Bayou La Batre, AL (32) 7.6 21.6 0.0 1.9 -0.065

Houma, LA (10) 5.5 10.8 0.0 1.5 -0.416

New Bedford, MA (34) 5.8 9.3 0.1 0.2 -0.462

Virginia Beach, VA (18) 4.4 6.7 0.0 0.5 -0.577

Factor Loading 0.770 0.824 0.605 0.493

Percentage Explained Variation 48.80 Index scores in bold are at or above threshold of one 

standard deviation if rounded. Missing data were replaced 

with the mean.

Theta Reliability 0.620

Eigenvalue 1.874
50

Figure A-9. Natural Amenities Index.
51

Table A-10. Recreational Fishing Reliance Index.

Community

Recreational 

fishing mode 

charter by 

population

Recreational 

fishing mode 

private by 

population

Recreational 

fishing mode 

shore by 

population

Recreational 

Fishing 

Reliance 

Index Score

Virginia Beach, VA (18) 0.0 974.7 849.0 10.845

St. Marks, FL (33) 21.3 500.8 75.8 5.219

Marathon, FL (14) 1.4 649.1 204.6 4.752

Ocean City, MD (14)) 4.3 260.7 414.2 4.443

Point Judith, RI (22) 0.9 182.3 495.8 4.213

Orange Beach, AL (20) 5.8 243.7 149.0 2.658

Biloxi, MS (14) 0.2 230.2 245.5 2.651

Barnegat Light, NJ (22) 4.0 272.4 148.8 2.618

Gulfport, MS (13) 0.0 161.9 214.2 2.046

Newport, RI (21) 0.0 68.0 187.0 1.355

Seabrook, NH (25) 2.0 68.3 80.9 0.832

Wanchese, NC (32) 4.2 119.2 0.0 0.785

New Bedford, MA (34) 0.0 52.0 76.6 0.498

Cameron, LA (32) 0.0 67.4 23.0 0.205

Bayou La Batre, AL (32) 0.0 40.6 28.5 0.100

Stonington, ME (10) 0.0 0.0 3.4 -0.294

Houma, LA (10) 0.0 0.0 0.0 -0.317

Cushing, ME (4) 0.0 0.0 0.0 -0.317

Port Aransas, TX (25) 0.0 0.0 0.0 -0.317

Port Arthur, TX (4) 0.0 0.0 0.0 -0.317

Factor Loading 0.352 0.917 0.897

Percentage Explained Variation 58.97

Index scores in bold are at or above threshold of 

one standard deviation if rounded.

Theta Reliability 0.653

Eigenvalue 1.769
52

Figure A-10. Recreational Fishing Reliance Index.
53

Table A-11 Recreational Fishing Engagement Index.

Community

Recreational 

charter 

fishing 

pressure

Recreational 

private fishing 

pressure

Recreational 

shore fishing 

pressure

Recreational 

Engagement 

Index Score

Virginia Beach, VA (18) 15,167.7 47,758.3 43,297.5 10.841

Ocean City, MD (14)) 30,495.7 12,487.7 21,580.5 6.856

Marathon, FL (14) 13,739.9 35,049.6 9,411.3 5.641

Orange Beach, AL (20) 33,812.6 12,747.7 7,106.1 5.626

Point Judith, RI (22) 14,288.1 8,477.5 26,526.6 5.153

Barnegat Light, NJ (22) 16,357.1 13,430.1 7,561.1 3.709

Biloxi, MS (14) 8,330.2 11,579.9 12,201.7 3.126

St. Marks, FL (33) 4,432.7 26,494.1 3,570.4 3.093

Seabrook, NH (25) 17,024.4 3,581.2 3,849.9 2.442

Gulfport, MS (13) 0.0 7,951.1 10,901.8 1.668

Newport, RI (21) 574.1 3,286.6 9,666.9 1.157

Wanchese, NC (32) 6,436.7 5,628.6 0.0 0.964

New Bedford, MA (34) 1,698.5 2,448.1 4,053.5 0.573

Cameron, LA (32) 0.0 3,228.1 1,200.2 0.125

Bayou La Batre, AL (32) 0.0 2,001.8 1,447.2 0.038

Stonington, ME (10) 0.0 0.0 151.7 -0.297

Houma, LA (10) 0.0 0.0 0.0 -0.314

Cushing, ME (4) 0.0 0.0 0.0 -0.314

Port Aransas, TX (25) 0.0 0.0 0.0 -0.314

Port Arthur, TX (4) 0.0 0.0 0.0 -0.314

Factor Loading 0.352 0.815 0.761

Percentage Explained Variation 63.02

Index scores in bold are at or above threshold of 

one standard deviation if rounded.

Theta Reliability 0.768

Eigenvalue 2.045
54

Figure A-11. Recreational Fishing Engagement Index.
55

Table A-12. Commercial Fishing Reliance Index

Community

Value of 

landings by 

population

Number of 

commercial 

permits by 

population

Number of 

dealers with 

landings by 

population

Percent in 

agriculture, 

forestry and 

fishing

Commercial 

Reliance 

Index Score

Stonington, ME (10) 46,442,040.0 108.7 7.2 44.1 14.785

Cameron, LA (32) 12,086,479.0 40.4 24.5 7.6 6.701

St. Marks, FL (33) 5,649,861.0 110.6 19.2 5.0 5.494

Barnegat Light, NJ (22) 7,337,200.0 181.8 1.2 7.7 4.525

Bayou La Batre, AL (32) 11,447,615.0 48.8 7.1 9.5 4.387

Wanchese, NC (32) 10,081,620.0 42.2 5.9 11.6 4.020

Cushing, ME (4) * 34.3 0.7 9.5 1.771

Point Judith, RI (22) 1,921,666.0 93.9 0.7 1.4 1.642

New Bedford, MA (34) 3,327,873.0 41.4 0.3 0.9 1.084

Houma, LA (10) 270,150.0 0.6 0.8 11.2 0.728

Ocean City, MD (14)) * 40.1 0.3 0.0 0.579

Marathon, FL (14) 1,006,345.0 8.4 1.4 4.4 0.565

Port Aransas, TX (25) 73,373.0 1.9 2.4 5.4 0.496

Seabrook, NH (25) * 27.1 0.1 0.3 0.191

Port Arthur, TX (4) 714,330.0 0.7 0.2 1.6 0.008

Newport, RI (21) 280,046.0 12.2 0.3 0.2 0.001

Orange Beach, AL (20) * 2.2 0.2 2.9 -0.012

Biloxi, MS (14) 361,776.0 2.1 0.2 0.8 -0.099

Virginia Beach, VA (18) * 0.4 0.0 0.4 -0.249

Gulfport, MS (13) 0.0 0.2 0.0 0.4 -0.253

Factor Loading 0.833 0.686 0.592 0.705

Percentage Explained Variation 50.03 Index scores in bold are at or above threshold of one standard 

deviation if rounded.

* Data suppressed for confidentiality purposes

Theta Reliability 0.664

Eigenvalue 1.995
56

Figure A-12. Commercial Fishing Reliance Index.
57

Table A-13. Commercial Fishing Engagement Index

Community

Value of 

landings 

Number of 

commercial 

permits 

Number of 

dealers 

with 

landings 

Pounds of 

Landings

Commercial 

Engagement 

Index Score

New Bedford, MA (34) 303,964,574.0 3,785.0 31.0 125,049,839.0 36.953

Point Judith, RI (22) 31,857,371.0 1,557.0 11.0 33,363,620.0 8.807

Cameron, LA (32) 8,375,930.0 28.0 17.0 167,109,618.0 6.519

Barnegat Light, NJ (22) 25,782,922.0 745.0 5.0 12,547,401.0 4.480

Bayou La Batre, AL (32) 32,373,854.0 138.0 20.0 6,946,891.0 4.278

Stonington, ME (10) 45,280,989.0 106.0 7.0 16,137,402.0 3.951

Port Arthur, TX (4) 39,742,480.0 38.0 9.0 16,976,794.0 3.484

Houma, LA (10) 8,799,330.0 21.0 26.0 14,848,297.0 2.589

Wanchese, NC (32) 15,283,736.0 64.0 9.0 4,110,964.0 2.117

Biloxi, MS (14) 16,970,532.0 99.0 8.0 15,730,345.0 2.052

Newport, RI (21) 6,786,625.0 295.0 7.0 7,141,975.0 1.979

Ocean City, MD (14)) * 282.0 2.0 * 1.925

Marathon, FL (14) 9,758,531.0 81.0 14.0 2,240,007.0 1.850

Seabrook, NH (25) * 230.0 1.0 * 0.849

Port Aransas, TX (25) 276,688.0 7.0 9.0 67,693.0 0.572

Virginia Beach, VA (18) * 177.0 1.0 * 0.544

Cushing, ME (4) * 48.0 1.0 * 0.427

St. Marks, FL (33) 1,175,171.0 23.0 4.0 463,453.0 0.297

Orange Beach, AL (20) * 13.0 1.0 * -0.057

Gulfport, MS (13) 0.0 11.0 0.0 0.0 -0.142

Factor Loading 0.906 0.862 0.580 0.635

Percentage Explained Variation 57.57 Index scores in bold are at or above threshold of one 

standard deviation if rounded.

* Data suppressed for confidentiality purposes

Theta Reliability 0.750

Eigenvalue 2.294
58

Figure A-13. Commercial Fishing Engagement Index.
59

Appendix 2. Variable Codebook

Variable name Variable Description Data source

GEO_ID Geography Identifier Census ACS Summary File

GEO_ID2 Geography Identifier Census ACS Summary File

SUMLEVEL Geographic Summary Level Census ACS Summary File

GEO_NAME Community Name Census ACS Summary File

TOTPOP Total estimated Population Census ACS Demographic Summary File C0

PCTMALES Percent Males Census ACS Demographic Summary File C6

PCTFEMALE Percent Females Census ACS Demographic Summary File C10

POPCHPCT Pop Change (per cent) 

Census ACS Demographic Summary File C0 minus 2000 

Population Divided by 2000 Population

POPDENS Population Density 

Census ACS Demographic Summary File C0 Divided by Area in 

sq miles

POP0_5PCT Population age under 5 Census ACS Demographic Summary File C14

POP85PCT Population 85 plus Census ACS Demographic Summary File C62

MEDAGE Median Age Census ACS Demographic Summary File C64

POPWAPCT

Percent White Alone 

population Census ACS Demographic Summary File C125

POPBAPCT

Percent Black Alone 

population Census ACS Demographic Summary File C129

POPNAPCT

Percent Native Americans 

Alone population Census ACS Demographic Summary File C133

POPAAPCT

Percent Asian Alone 

population Census ACS Demographic Summary File C153

POPPAPCT

Percent Pacific Alone 

population Census ACS Demographic Summary File C185

POPHSPCT Percent Hispanic population Census ACS Demographic Summary File C257

POPWNPCT

Percent White non-Hispanic 

population Census ACS Demographic Summary File C285

TOTHU Total Housing Units Census ACS Housing Summary File C0

PCTVACANT Percent Vacant Housing Units Census ACS Housing Summary File C10

HMOWNVAC Homeowner Vacancy Rate Census ACS Housing Summary File C12

RENTVAC Rental Vacancy Rate Census ACS Housing Summary File C15

PCTMBLHM Percent Mobile Homes Census ACS Housing Summary File C52

PCTBLT2005 Proportion Built 2005 or later Census ACS Housing Summary File C64

PCTBLT2000 Proportion Built 2000-2004 Census ACS Housing Summary File C66

HUMNR Median number of rooms Census ACS Housing Summary File C138

PCTOWNER Percent Owner Census ACS Housing Summary File C175

PCTRENTER Percent Renter Census ACS Housing Summary File C179

MOV2005 Moved in 2005 or later Census ACS Housing Summary File C193

MOV2000TO04 Moved in 2000 to 2004 Census ACS Housing Summary File C197

PCTNOVHCL

Households w/o transportation 

vehicles Census ACS Housing Summary File C221

HOUSHEATFUEL

Percent of households heating 

with fuel oil Census ACS Housing Summary File C253

PCTNOPLBG

Households w/o plumbing 

facilities Census ACS Housing Summary File C281

MEDHOMVAL Median Home Value Census ACS Housing Summary File C343

MED_MTMRG

Median mortgage, monthly 

payment Census ACS Housing Summary File C390

OWNRMTHLYCST

Percent of households with 

monthly owner costs 35.0 

percent or more of household 

income Census ACS Housing Summary File C442

MED_GRRNT Median gross rent Census ACS Housing Summary File C514

RENTRMTHLYCST

Percent of households with 

monthly renter costs 35.0 

percent or more of household Census ACS Housing Summary File C546
60

income

TOTHH Total estimated Households Census ACS Social Summary File C0

PCTHFOFMNOHS

2+ persons HH, other family 

HH, female HHldr, no husband Census ACS Social Summary File C30

PCTHHUNDR18

Households w one or more 

under 18 Census ACS Social Summary File C50

PCTHHUOVER65

Households w one or more 

over 65 Census ACS Social Summary File C54

AVGHHSZE Average HH Size Census ACS Social Summary File C56

PCTMALESEPARATD Percentage males separated Census ACS Social Summary File C104

PCTMALEDIVORCD Percentage males divorced Census ACS Social Summary File C112

PCTFEMALESEPARATD Percentage females separated Census ACS Social Summary File C128

PCTFEMALEDIVORCD Percentage females divorced Census ACS Social Summary File C136

PCTPOP9THGRD Less than 9th grade Census ACS Social Summary File C227

PCTNODIPLOMA 9th to 12th no diploma Census ACS Social Summary File C231

PCTPOPHSGRD High school grad Census ACS Social Summary File C235

PCTBATCHLRS Bachelor's degree Census ACS Social Summary File C247

PCTLIVESMHS Lived in same house 1 yr ago Census ACS Social Summary File C289

PCTLIVEDFCO Lived in different county Census ACS Social Summary File C301

PCTLIVDFST Lived in different state Census ACS Social Summary File C309

PCTFORBRN Percent foreign born Census ACS Social Summary File C341

PCTSPKENGNTWL

Percent speak English less than 

very well Census ACS Social Summary File C425

PCTLABFORCE Percent Labor force Census ACS Economic Summary File C6

PCTEMPLOYED Percent Employed Census ACS Economic Summary File C14

PCTUNEMPLD Percent Unemployed Census ACS Economic Summary File C18

PCTLABFEMALE Percent females in Labor force Census ACS Economic Summary File C45

PCTEMPFEMA Percent females employed Census ACS Economic Summary File C49

MEANTRAVTIM Mean travel time to work Census ACS Economic Summary File C95

PCTSERVOCC Service occupations Census ACS Economic Summary File C108

PCTFRMFSHOCC

Farm, fishing and forestry 

occupations Census ACS Economic Summary File C116

PCTAGRFRFSH

Agriculture, forestry, fishing, 

and hunting industry Census ACS Economic Summary File C132

PCTSERVIND

Arts, Entertainment and 

recreation industry Census ACS Economic Summary File C172

PCTSLFEMP

Percent of Class of Worker 

Self Employed Census ACS Economic Summary File C197

PCTHHHUNDR10K

Households w/ income under 

$10,000 Census ACS Economic Summary File C208

PCTHH200K HH w/ income $200,000+ Census ACS Economic Summary File C244

MEDHHINC Median HH income Census ACS Economic Summary File C246

MEANHHINC Mean HH income Census ACS Economic Summary File C249

PCTRECSOC

Percent people receiving 

Social Security Census ACS Economic Summary File C261

PCTRECRET

Percent with Retirement 

income Census ACS Economic Summary File C268

MEANRETINC Mean Retirement income Census ACS Economic Summary File C270

PCTRECSSI Percent people receiving SSI Census ACS Economic Summary File C275

PUBLICASSIST

Percent households with cash 

public assistance income Census ACS Economic Summary File C282

PERCPHHINC Per Capita Income Census ACS Economic Summary File C341

PCFMINPOV

Per cent of families below 

poverty level Census ACS Economic Summary File C377

PCTPOV

Percentage population in 

poverty Census ACS Economic Summary File C404

PCTCHLDPOV Percentage under 18 in poverty Census ACS Economic Summary File C407

PCT65POV

Percentage 65 and over in 

poverty Census ACS Economic Summary File C425

PCTCHGRNT Percentage change median rent Census ACS data Summary File and Census 2000 Data
61

2000-2010

PCTCHGMRG

Percentage change median 

mortgage 2000-2010 Census ACS data Summary File and Census 2000 Data

PCTCHNGHOMVAL

Percentage change in property 

values 2000-2010 Census ACS data Summary File and Census 2000 Data

PCTCHGRENTER

Percent change in # of renters 

2000-2010 Census ACS data Summary File and Census 2000 Data

PCTCHGUNEM

Percentage change in 

unemployment 2000-2010 Census ACS data Summary File and Census 2000 Data

PCTCHGRAVTIM

Percentage change travel time 

to work 2000-2010 Census ACS data Summary File and Census 2000 Data

MEDDWELLAGE Median dwelling age Census ACS data Detailed Tables B25035_1_EST

MDYRSRESID Median years in residence Census ACS data Detailed Tables B25039_1_EST

HASMORTGAGE

Percent of housing units with a 

mortgage Census ACS Detailed Tables B25081_1_EST

HSMORTGAGELON

Percent of housing units with a 

mortgate and both second 

mortgage and home equity 

loan Census ACS Detailed Tables B25081_2_EST

PCTGRPQRTRS

Percent living in group 

quarters Census ACS Detailed Tables B25081_6_EST

GININDEX Gini Index Census ACS Detailed Tables B19083_6_EST

SHNNINDEX Shannon Index Calculated With ACS Census Economic Summary File

PCTWTRCVRG Percent Water-cover Census Tiger Files Places Shape Files

MARINANUM Number of Marinas in County Census Business Patters

EPAREGFAC EPA Registered facilities EPA http://www.epa.gov/enviro/

NUMBOTRMPS Boat Launches per population MRIP Site Survey

NEARCTYPOP

Nearest city w/ 50k pop in 

miles Neighborhood guides (www.moving.com)

CSTOFLIVNG Cost of living index 2010 CLResearch Website (http://www.clrsearch.com)

CRIMINDEX Total Crime Index CLResearch Website (http://www.clrsearch.com)

HAILRSKAVG Damaging Hail Risk Average Neighborhood guides (www.moving.com)

HURRSKAVG

Damaging Hurricanes Risk 

Average Neighborhood guides (www.moving.com)

TORNRSKAVG

Damaging Tornadoes Risk 

Average Neighborhood guides (www.moving.com)

WNDRSKAVG

Damaging Winds Risk 

Average Neighborhood guides (www.moving.com)

POUNDS Pounds of Landings Regional NOAA Fisheries

PNDSPRPOP

Pounds of Landings per 1,000 

persons Regional NOAA Fisheries

VALUE Value of Landings Regional NOAA Fisheries

VALUPRPOP

Value of Landings Per 

population Regional NOAA Fisheries

COMMPMT

Number of Commercial 

Permits Regional NOAA Fisheries

COMMPRPOP

Commercial Fishing Permits 

per population Regional NOAA Fisheries

CHARTERPMT Number of Charter Permits Regional NOAA Fisheries

CHARTERPMTPOP Charter Permits per population Regional NOAA Fisheries

DEALERNUM Number of Dealers Regional NOAA Fisheries

DEALERNUMPOP

Dealers with Landings per 

population Regional NOAA Fisheries

RECPSSRMODEALL

Recreational fishing pressure 

estimate all modes combined MRIP Site Survey

RECMODEALLPOP

Recreational fishing pressure 

estimate all modes by 

population

RECPSSRMODECH

Recreational fishing pressure 

estimate charter mode MRIP Site Survey
62

RECMODECHRPOP

Recreational fishing pressure 

estimate charter mode by 

population

RECPSSRMODEPR

Recreational fishing pressure 

estimate private recreational 

mode MRIP Site Survey

RECMODEPRPOP

Recreational fishing pressure 

estimate private recreational 

mode by population

RECPSSRMODESH

Recreational fishing pressure 

estimate shore mode MRIP Site Survey

RECMODESHRPOP

Recreational fishing pressure 

estimate shore mode by 

population

PRIMARY_LATITUDE Latitude USGS State and Topical Gazetteer Download Files

PRIMARY_LONGITUDE Longitude USGS State and Topical Gazetteer Download Files
63

Appendix 3. Index Calculation and Formulas

Shannon Index

The Shannon index is a diversity index that is often used in the ecological literature. It has 

numerous other titles such as Shannon's diversity index, the Shannon-Wiener index, the 

Shannon-Weaver index, the Shannon-Weiner index and the Shannon entropy. The idea is that

the more different species (or in this case occupations) there are, and the more equal their 

proportional abundances, the more species diversity exists within the ecosystem. Therefore, the 

presumption is the closer to zero the Shannon index score, the less diversity. In fact, if only one 

species (or occupation) exists within the ecosystem then the Shannon index equals zero.

The calculation used all occupations for each state’s coastal communities. So, the index score is 

calculated on a state by state basis, using only those coastal communities in each state included 

in the research.

Calculation for the Shannon Index of Occupational Diversity:

 s

H = ∑ - (Pi * ln Pi) i=1

where:

H = the Shannon occupational diversity index

Pi = fraction of the entire population made up of occupation i

S = number of occupation categories encountered

∑ = sum from occupation 1 to occupation S

SPSS Syntax for calculating the Shannon Index from Census Occupational data percentages.

DATASET ACTIVATE DataSet1.

COMPUTE Logmanag=LG(ManagePct).

EXECUTE.

COMPUTE Logsrvice=LG (SrvicePct).

EXECUTE.

COMPUTE Logsales=LG(SalesPct).

EXECUTE.

COMPUTE Logfrmfsh=LG10(FrmfshPct).

EXECUTE.

COMPUTE Logcnstrct=LG(CnstrctnPct).

EXECUTE.

COMPUTE Logprodctn=LG(ProdctnPct).

EXECUTE.

RECODE Logmanag Logsales Logsrvice Logfrmfsh Logcnstrct Logprodctn (SYSMIS=0.00).

EXECUTE.

COMPUTE Managpctlog=ManagePct * Logmanag.

EXECUTE.

COMPUTE Srvicepctlog=SrvicePct * Logsrvice.
64

EXECUTE.

COMPUTE Salespctlog=SalesPct * Logsales.

EXECUTE.

COMPUTE Frmfshpctlog=FrmfshPct * Logfrmfsh.

EXECUTE.

COMPUTE Cnstrctnpctlog=CnstrctnPct * Logcnstrct.

EXECUTE.

COMPUTE Prodctnpctlog=ProdctnPct * Logprodctn.

EXECUTE.

COMPUTE Indexcalc=Managpctlog + Srvicepctlog + Salespctlog + Frmfshpctlog + 

Cnstrctnpctlog + Prodctnpctlog.

EXECUTE.

COMPUTE Shannonindx=Indexcalc * -1.

EXECUTE.

Theta Reliability Calculation

Theta reliability was hand calculated using the following formula:

θ = [ρ/ (ρ -1)] x [1-(1/λ)]

ρ = # scale items

λ = largest eigenvalue