OCS Study 

BOEM 2013-0111 

Coastal Marine Institute 

Socioeconomic Responses to Coastal 

Land Loss and Hurricanes: Measuring 

Resilience among Outer Continental ShelfRelated Coastal Communities in Louisiana 

U.S. Department of the Interior

Bureau of Ocean Energy Management 

Gulf of Mexico OCS Region 

Cooperative Agreement

Coastal Marine Institute 

Louisiana State University 
U.S. Department of the Interior Cooperative Agreement

Bureau of Ocean Energy Management Coastal Marine Institute 

Gulf of Mexico OCS Region Louisiana State University 

OCS Study 

BOEM 2013-0111 

Coastal Marine Institute 

Socioeconomic Responses to Coastal 

Land Loss and Hurricanes: Measuring 

Resilience among Outer Continental ShelfRelated Coastal Communities in Louisiana 

Authors: 

Margaret A. Reams 

Nina S.N. Lam 

April 2013 

Prepared under BOEM Cooperative Agreement 

M07AC12941 

by 

Louisiana State University 

Department of Environmental Sciences 

School of the Coast and Environment 

Baton Rouge, Louisiana 70803 

Published by 
iii 

DISCLAIMER 

This report was prepared under contract between the Bureau of Ocean Energy Management 

(BOEM) and Louisiana State University’s Department of Environmental Sciences, School of 

Coast and the Environment. This report has been technically reviewed by BOEM, and it has 

been approved for publication. Approval does not signify that the contents necessarily reflect the 

views and policies of BOEM, nor does mention of trade names or commercial products 

constitute endorsement or recommendation for use. It is, however, exempt from review and 

compliance with BOEM editorial standards. 

REPORT AVAILABILITY 

This report is available on CD from the Bureau of Ocean Energy Management, Gulf of Mexico 

OCS Region, for $15.00, and free of charge as a pdf file downloaded from the BOEM Web site. 

Copies can also be viewed at selected Federal Depository Libraries. The addresses are listed 

below. 

To order a CD, use the Gulf of Mexico OCS Region contact information below and reference 

OCS Study BOEM 2013-0111. To download a pdf copy, use the Environmental Studies Program 

Information System (ESPIS) and search on the study report number. In the near future, you will 

also be able to get this report also from the National Technical Information Service. 

U.S. Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico OCS Region

Public Information Office (GM 217G) 

1201 Elmwood Park Blvd. 

New Orleans, Louisiana 70123-2394 

Phone: (504) 736-2519, 1-800-200-GULF 

Fax: (504) 736-2620 

U.S. Department of Commerce 

National Technical Information Service 

5301 Shawnee Rd. 

Springfield, Virginia 22312 

Phone: (703) 605-6000, 1-800-553-6847 

Fax: (703) 605-6900 

Web site: http://www.ntis.gov/

CITATION 

Reams, M.A. and N.S.N. Lam. 2013. Socioeconomic responses to coastal land loss and 

hurricanes: Measuring resilience among Outer Continental Shelf-related coastal 

communities in Louisiana. U.S. Dept. of the Interior, Bureau of Ocean Energy 

Management, Gulf of Mexico OCS Region, New Orleans, LA. OCS Study BOEM 2013-

0111. 50 p. 
v 

TABLE OF CONTENTS 

LIST OF FIGURES ...................................................................................................................... vii

LIST OF TABLES........................................................................................................................ vii

1. INTRODUCTION ...................................................................................................................... 1

2. RELATED RESEARCH ............................................................................................................ 3

2.1. Definition of Social-Ecological Resilience........................................................................ 3

2.2. Measurement of Community Resilience............................................................................ 4

3. CALCULATION OF THE RESILIENCE INDEX FOR GULF OF MEXICO COASTAL 

COUNTIES AND PARISHES USING PRINCIPLE COMPONENTS ANALYSIS (PCA) 

METHOD ................................................................................................................................. 7

3.1. Study Area and Data .......................................................................................................... 7

Gulf of Mexico Region................................................................................................... 7

Texas ........................................................................................................................... 7

Louisiana ........................................................................................................................ 7

Mississippi...................................................................................................................... 9

Alabama.......................................................................................................................... 9

Florida ......................................................................................................................... 10

Data ......................................................................................................................... 13

3.2. Method One: Construction of the Resilience Index Using Factor Analysis Cutter’s Social 

Vulnerability Index....................................................................................................... 13

3.2.1. Factor Analytical Method................................................................................... 14

4. DEVELOPING AN ALTERNATIVE METHODOLOGY TO EXAMINE SOURCES OF 

RESILIENCE: APPLICATION OF DISCRIMINANT ANALYSIS (DA) TO THE 

COASTAL COUNTIES OF THE GULF OF MEXICO STATES ........................................ 23

5. COMPARISON OF THE TWO METHODS TO CALCULATE COUNTY RESILIENCE .. 31

5.1. Method One: County Resilience Levels Calculated by Factor Analysis......................... 31

5.2. Method Two: County Resilience Levels Calculated by Discriminant Analysis.............. 32

6. EXPLORING ASSOCIATIONS BETWEEN THE RESILIENCE SCORES OF COASTAL 

COUNTIES AND PARISHES AND THE OIL AND GAS INDUSTRY ............................. 35

6.1. Correlation Analyses and Results .................................................................................... 38

6.2. Summary.......................................................................................................................... 40

REFERENCES ............................................................................................................................. 43

APPENDIX................................................................................................................................... 49

ABSTRACTS OF SELECTED THESES ..................................................................................... 49
vii 

LIST OF FIGURES 

Figure 1. Reference map of Texas coastal counties................................................................... 8

Figure 2. Reference map of Louisiana coastal counties............................................................. 9

Figure 3. Reference map of Mississippi and Alabama coastal counties. ................................. 11

Figure 4. Reference map of Florida’s Gulf of Mexico coastal counties. ................................. 12

Figure 5. Results of the factor analysis method for Texas and Louisiana. .............................. 21

Figure 6. Results of the factor analysis method for Mississippi, Alabama, and Florida. ........ 22

Figure 7. Results of the discriminant analysis method for Texas and Louisiana..................... 29

Figure 8. Results of the discriminant analysis method for Mississippi, Alabama, and 

Florida....................................................................................................................... 30

Figure 9. Oil and Gas Employment among Counties within the Gulf of Mexico Region....... 36

LIST OF TABLES 

Table 1. Original Variables Used to Empirically Derive Factor Loadings ........................... 16

Table 2. Rotated Factor Matrixa............................................................................................. 18

Table 3. Variables and Eigenvalues Used to Construct Weighted Community Resilience 

Index ......................................................................................................................... 19

Table 4. Community Resilience Index Values for All Gulf of Mexico Counties ................. 20

Table 5. Variables Used in the Discriminant Analysis Method ............................................ 25

Table 6. Rankings Used to Create A Priori Groupings ......................................................... 27

Table 6. Rankings Used to Create A Priori Groupings (continued)...................................... 28

Table 7. Classification of the Groups by the Discriminant Analysis Methoda...................... 28

Table 8. Comparing Results of the Factor Analysis and Discriminant Analysis Methods ... 33

Table 9. Data Used in the Pearson Correlation Analyses ...................................................... 37

Table 10. Pearson Correlation Analysis between Factor Analysis- Method Resilience 

Scores and Oil and Gas Employment; Oil and Gas Establishments......................... 39

Table 11. Pearson Correlation Analysis between Discriminant Analysis-Method 

Resilience Scores and Oil and Gas Employment; Oil and Gas Establishments....... 40
1 

1. INTRODUCTION 

Since Hurricanes Katrina and Rita in 2005, scholars, policy makers, residents, and other 

stakeholders have raised questions about the long-term survival of coastal communities in the 

face of increasingly intense storms, sea-level rise and other natural hazards. Several Outer 

Continental Shelf (OCS) management questions relate to the overall, long-term resilience of 

communities in coastal Louisiana. 

This research addresses the following questions. 

1) How can community resilience best be measured? 

2) How resilient are OCS communities to threats posed by coastal land loss 

and hurricanes? 

3) Are OCS communities more resilient to these natural threats than coastal 

communities less involved in OCS activities? 

These topics are relevant to BOEM and to Louisiana, given the high levels of OCS activity 

within the coastal areas of the State and the large-scale destruction to property, natural assets, 

and human life that have resulted from major hurricanes during the last few years. 

Recent studies by the BOEM and others have called for more refined examinations of the 

social and economic impacts of OCS activities and for more innovative approaches to estimate 

the resilience of communities facing natural disturbances. For example, Luton and Cluck (2005) 

identified several limitations of the traditional Social Impact Assessment (SIA) framework in 

identifying the socioeconomic effects of OCS activities, given the diffuse and indirect nature of 

these impacts. In January 2007, the NSTC Joint Subcommittee on Ocean Science and 

Technology recommended giving the highest priority over the next ten years to research seeking 

better understanding of resilience to natural hazards and greater insight into patterns of human 

activity that affect the stability and sustainable use of ocean resources (NSTC, 2007). 

This research involves the application of concepts from the social-ecological resilience 

literature to coastal communities in Louisiana and the four other northern Gulf of Mexico states: 

Texas, Mississippi, Alabama and Florida. The research objectives are to: 1) develop quantitative 

indicators of community resilience to coastal land loss and to hurricanes; 2) compile these 

indicators into a “resilience index”; 3) create GIS-supported maps depicting vulnerability and 

resilience based on these indicators; and 4) make comparisons among communities of coastal 

Louisiana more and less involved with OCS activities. Specifically, we investigated whether 

communities with more of the workforce employed in oil and gas and supporting industries tend 

to be more resilient to natural disasters than coastal communities with less oil and gas industry 

activity. Computation of the resilience index provides useful baseline information concerning 

the hazards facing OCS communities, the extent to which they have been able to withstand and 

recover from natural disturbances, and whether OCS activities may tend to enhance the overall 

resilience of these communities. 
2 

The residents in the study area, the coastal communities of the Gulf of Mexico region, face 

increasing threats to their health and safety and to the longer-term social and economic stability 

of their communities. Coastal hazards and threats include both large-scale, rapid-moving 

disturbances, such as hurricanes and storm surges, and slower-moving disturbances, such as 

coastal land loss, sea-level rise, and the gradual diminishment of ecosystem services over time. 

While many coastal threats result from or are associated with natural processes, most are 

exacerbated by human activities, such as rapid population growth, inadequate infrastructure 

planning and investment, and unwise land-use decisions. Theorists and community stakeholders 

would benefit from a resilience assessment instrument that would provide them with information 

about their exposure to coastal hazards, their vulnerabilities to those threats, and their capacity 

for taking adaptive steps to make their communities safer and more likely to recover following 

large-scale disturbances. 

Despite abundant research examining aspects of social-ecological resilience, vulnerability, 

hazards and risk assessment, there is yet to be a convincing approach to quantifying and 

measuring community resilience. There is a need to devise a quantifiable resilience index that 

can capture the sources and consequences of resilience and vulnerability, especially for coastal 

communities with significant oil and gas industry activity. 

The challenges involved in developing useful resilience indices are significant (Carpenter et 

al., 2009; Walker et al., 2006a & b; Barnett et al., 2008). Some appear to arise, in part, due to the

various definitions of resilience used by researchers. The definitions are often confused or used 

interchangeably with similar concepts such as vulnerability, sustainability, and adaptability. 

Also, the problem is exacerbated by a lack of empirical validation and evidence for the indices 

derived (Lam et al., 2009). Moreover, most of the literature on resilience tends to be conceptual 

and somewhat abstract. A straight-forward model for measuring resilience that is grounded in 

sound theoretical principles will be very useful for sustainable planning and management and 

may help speed economic assistance and recovery of communities after major disaster events. 

The research seeks to quantify theoretical concepts concerning community resilience and to 

test associations between oil and gas industry activity levels and measures of resilience. Part of 

the project’s theoretical foundation comes from the growing body of scholarly research 

concerning social-ecological resilience, hazard and vulnerability, and coupled natural and human 

systems (Adger et al., 2005; Cutter et al., 2003; Liu et al., 2007). In summarizing the related 

research in the literature below, our discussion focuses on two topics: what is community 

resilience and how can it best be measured? 

The report is organized into the following sections: 1) related research concerning how to 

quantify social-ecological resilience; 2) calculation of the resilience index for the coastal 

Louisiana Parishes and all coastal counties in the northern Gulf of Mexico region; 3) calculation 

of a resilience index for the coastal parishes and counties in the GOMA region using an 

alternative methodology; 4) comparisons of the calculated resilience scores of Louisiana parishes 

most involved in OCS oil and gas activities with other parishes and counties in the Gulf of 

Mexico region; and 5) abstracts of theses research funded by the project. 
3 

2. RELATED RESEARCH 

2.1. DEFINITION OF SOCIAL-ECOLOGICAL RESILIENCE

Useful insight into community resilience can be derived from the large body of literature on 

social-ecological resilience. Some researchers define resilience as how fast a system can return 

to the original state after an external disturbance, while others use the term to refer to how far the

system could be perturbed without shifting to a different state. The former definition is often 

called “engineering resilience” because it concerns the return time, whereas the latter is 

commonly referred to as “ecological resilience” (Holling, 2001; Walker et al., 2006a). Adger 

and others (2005) further stated that “Resilience reflects the degree to which a complex adaptive 

system is capable of self-organization (emphasis added), and the degree to which the system can 

build capacity for learning and adaptation (emphasis added).” They further defined resilience as 

“the capacity of linked social-ecological systems to absorb recurrent disturbances such as 

hurricanes or floods so as to retain essential structures, processes, and feedbacks”. Considering 

the literature, we can summarize that the best working definition of resilience would include 

three characteristics: (a) the magnitude of shock a system can absorb and remain within a given 

state; (b) the degree to which the system is capable of self organization, and (c) the degree to 

which the given system can build capacity for learning and adaptation (Folke et al., 2002; 

Ahmed, 2006; Baker, 2009). 

Closely related to resilience is the concept of vulnerability. In fact, the two terms have been 

used interchangeably by some researchers, while others expanded the definition of vulnerability 

to include resilience. For example, Folke and others (2002) defined vulnerability in an 

ecological sense as “the propensity of an ecological system to suffer harm from exposure to 

external stresses and shocks”, while Cutter and Finch (2008) whose work is found in the hazard 

literature focused on vulnerability in a social system. They defined social vulnerability as “a 

measure of both the sensitivity of a population to natural hazards and its ability to respond and 

recover from the impacts of hazards.” Adger (2006) considered vulnerability in a system to 

include both social and ecological elements, and referred to it as the susceptibility to risk and its 

inability to cope with or absorb a shock. Turner and others (2003) stressed that vulnerability is 

not just exposure to hazards; it includes three elements: exposure, sensitivity, and resilience. 

They further suggested that their expanded framework of vulnerability and vulnerability analysis 

can be used for the assessment of coupled human and natural systems and is a key element of 

“sustainability science” (Turner et al., 2003; Liu et al., 2007). A similar conceptual framework is 

found in the work of Kasperson et al. 2005, and Cutter et al. 2003, as well. 

Various factors may increase social vulnerability, including exclusion of stakeholders from 

the public policy arena, an incorrect understanding of ecosystem processes and risks associated 

with natural hazard, and inadequate plans for disaster management and response. Further, lowerincome residents may tend to live in riskier areas in urban settlements, making them more 

vulnerable to flooding, disease, and chronic stresses. Also, women have been found to be at 

increased risks associated with environmental hazards, often including a disproportionate share 

of the work related to the recovery of home and livelihood after an event (Fordham, 2003; 
4 

Adger, 2006). Thus, potential influences on social vulnerability include age, gender, race, and 

socioeconomic status, special needs population or those that lack normal social safety nets during 

disaster recovery, and the quality and density of the built environment (Cutter et al., 2003). 

2.2. MEASUREMENT OF COMMUNITY RESILIENCE

While there is voluminous literature on the conceptual frameworks, definitions, and case 

studies related to community resilience, very few attempts have been made to quantify resilience 

and/or vulnerability. There are major challenges associated with quantifying resilience. First, 

the numerous definitions of the terms, as discussed above, reflect how different researchers may 

consider and select indicators differently for measuring resilience. Thus, the selection of 

appropriate indicators for measurement becomes a major task. Mathematical and statistical 

methods will be needed to identify and evaluate the key factors iteratively, and the resultant 

measurement model will need to be tested and verified. 

Second, there is a need to consider both social and natural aspects of resilience, and how 

these two systems are linked. A system that has high social resilience may have low ecological 

resilience and vice versa. Moreover, the interaction effects between the two systems may be 

exhibited at different points in time, making them difficult to measure. For example, building a 

levee along the Mississippi River would prevent flooding to the populated areas, hence 

decreasing the vulnerability of the people living in low-lying areas. However, such action may 

lead to increased vulnerability of the wetland ecosystem because of long-term reduction in 

sediment load, leading to an increase in wetland erosion. This consequence can in turn become a 

threat to the human system, because the loss of wetlands would reduce the buffer zone that 

protects the populated areas and increase the vulnerability of communities to hurricanes. This 

type of coupling mechanism is easy to describe but is hard to quantify, especially when useful 

longitudinal data are not available. 

Third, of those who have attempted to measure vulnerability and resilience, the results have 

seldom been validated. Often, the model specification and the weights assigned to different 

variables were arbitrarily determined, making the resultant model difficult to apply and 

generalize. We present below three studies of measuring vulnerability and resilience, which will 

further illustrate the difficulties in measuring resilience and/or vulnerability. 

Yusuf and Francisco (2009) developed a model to assess the vulnerability of sub-national 

areas in Southeast Asia to climate change. They followed the definition developed by the United 

Nations’ Inter-governmental Panel on Climate Change (MaCarthy et al, 2001, p. 995), which 

defined vulnerability (V) as a function of exposure (E), sensitivity (S), and adaptive capacity (C): 

V = f(E, S, C).

“Exposure” refers to the nature and degree to which a system is exposed to significant 

climatic variations, “sensitivity” means the degree to which a system is affected by climaterelated events, and “adaptive capacity” is the ability of a system to adjust to climate change or to 

cope with its consequences. Their model is an additive weighted-average model. The weight of 
5 

each variable was assigned arbitrarily according to the literature. The results from their study are 

the composite vulnerability index of each sub-national region in the Southeast Asia. Their 

approach is straight-forward and the resultant maps are impressive, but the study suffers from the 

pitfalls discussed above, namely, the lack of empirical verification of model results and the 

arbitrariness of weight assignments. 

A similar but improved approach to the above IPCC vulnerability model was used to measure 

the vulnerability of Australian rural communities to climate variability and change (Nelson et al., 

2009a & b). The research group recognized that the concept of vulnerability is rarely converted 

to quantitative measures that can be used to prioritize policy interventions and evaluate their 

impacts. In developing their vulnerability index, they emphasized the need to include some 

measures of adaptive capacity to complement the existing hazard-impact modeling. An 

adaptive-capacity index was created using the rural livelihood analysis framework proposed by 

Ellis (2000) that includes indicators from the five categories of resources or “capitals”: human, 

social, natural, physical, and financial. 

The third example is the approach used and pioneered by Cutter and her research group to 

quantify the social aspects of vulnerability into the Social Vulnerability Index (Cutter et al., 

2003; Cutter and Emrich, 2006; Cutter and Finch, 2008). The group has used the index to 

evaluate the social vulnerability of the entire United States, the coastal counties of the United 

States, and the relative impacts of Hurricane Katrina on the Gulf Coast. To develop the index, 

Cutter and others selected 42 socioeconomic variables from the U.S. Census that demonstrated 

aspects of social vulnerability as identified by the literature. They conducted a factor analysis in 

the form of principal component analysis to derive 11 factors that accounted for 76.4% of 

variance of all the variables. The relative index of vulnerability for each county was derived by 

adding their factor scores, and the final index was mapped using standard deviations from the 

mean score to determine level of vulnerability. Those counties with the highest standard 

deviations from the mean were described as the most vulnerable while those with the lowest 

standard deviations were described as the least vulnerable. 

To verify the accuracy of the index, Cutter and the group correlated the number of 

presidential disaster declarations with the vulnerability score given to each county. The result 

was disappointing; they found literally no correlation (r=-0.099) between the derived 

vulnerability index and the political designations. Nevertheless, Cutter’s approach has advanced 

two important concepts regarding the measurement of resilience or vulnerability, which are, the 

need to derive the index through statistical modeling and the need to validate the index through 

empirical comparisons with outcomes such as the number of disaster declarations. 

There are various ways in which Cutter’s method could be improved, such as applying 

different weighting of factor scores and better selection of factors and variables. Moreover, 

different statistical techniques, especially an inferential statistical modeling technique such as 

discriminant analysis, could be utilized to develop a mathematical model that can be inferred to 

other study regions. We have conducted several studies during the course of this project that 

have helped to refine the methodology we applied to this research. Two completed 

Environmental Sciences Master’s theses resulted from the project and their abstracts are included 

in this final report. 
6 

The following sections present the results of two statistical approaches we used to create 

resilience measures for the coastal parishes of Louisiana and coastal counties of the five northern 

Gulf of Mexico states of the United States. 
7 

3. CALCULATION OF THE RESILIENCE INDEX FOR GULF OF MEXICO 

COASTAL COUNTIES AND PARISHES USING PRINCIPLE 

COMPONENTS ANALYSIS (PCA) METHOD 

3.1. STUDY AREA AND DATA

Gulf of Mexico Region 

The focus of this study was the northern Gulf of Mexico region, specifically the coastal 

counties of Texas, Louisiana, Mississippi, Alabama and Florida. Counties selected for this study 

had some part of their land mass bordering the Gulf of Mexico. A total of 51 counties met this 

selection criterion and were used in this analysis. 

Texas 

From 1990 to 2000, Texas’s population grew by 22.8 % (U.S. Census Bureau, 2008). The 

coastal counties in Texas grew on average 11% between 1990 and 2000. The county with the 

highest population growth was Cameron County and the county with the lowest growth was 

Kleberg County with a population loss of 10%. The 14 Texas coastal counties included in the 

study area were: Orange County, Jefferson County, Chambers County, Galveston County, 

Brazoria County, Matagorda County, Calhoun County, Aransas County, San Patricio County, 

Nueces County, Kleberg County, Kenedy County, Willacy County, and Cameron County. 

These counties are shown in Figure 1. 

Louisiana 

From 1990 to 2000 Louisiana’s population grew by 5.9 %. The coastal counties of Cameron 

Parish, Iberia Parish, Terrebonne Parish, and Vermillion Parish all grew between 7% and 8%, 

while Orleans Parish had a population decline of 2.5% and St. Mary Parish had a population loss 

of 7.9%. The 10 Louisiana coastal counties included in study area were: Cameron Parish, Iberia 

Parish, Jefferson Parish, Lafourche Parish, Orleans Parish, Plaquemines Parish, St. Bernard 

Parish, St. Mary Parish, Terrebonne Parish, and Vermillion Parish. These counties are shown in 

Figure 2. 
8 

Figure 1. Reference map of Texas coastal counties. 
9 

Figure 2. Reference map of Louisiana coastal counties. 

Mississippi 

Mississippi experienced a population growth of 10.5% during the decade between 1990 and 

2000. All three coastal counties experienced larger than average growth. Rates of change were 

35.3% for Hancock County, 14.7% for Harrison County, and 14.7% for Jackson County. These 

three counties are shown in Figure 3. 

Alabama 

Alabama grew 10.1% from 1990 to 2000. The coastal county of Baldwin, a suburb of 

Mobile, grew by 42.9%, while Mobile County grew by 5.6%. These two counties are shown in 

Figure 3. 
10 

Florida 

The Florida Gulf Coast contains a mix of rural and urban areas. Florida grew by 23.5% 

from 1990 to 2000 (U.S. Census Bureau, 2008). Areas with large growth were the panhandle 

area, where Santa Rosa County experienced a 44.3 % increase in population. In the same area, 

Walton County grew by 46.3% and Wakulla County grew by 61%. In the more southern part of 

Florida Collier County grew by 65.3%, while its neighboring counties Lee and Charlotte grew 

31.6% and 27.6%, respectively. Monroe County, which is largely uninhabited except for the 

Florida Keys, grew the least at 2%. The twenty three Florida counties included in this analysis 

were all counties bordering the Gulf of Mexico. These were: Bay County, Charlotte County, 

Citrus County, Collier County, Dixie County, Escambia County, Franklin County, Gulf County, 

Hernando County, Hillsborough County, Jefferson County, Lee County, Levy County, Manatee 

County, Monroe County, Okaloosa County, Pasco County, Pinellas County, Santa Rosa County 

Sarasota County, Taylor County, Wakulla County, and Walton County. These counties are 

shown in Figure 4. 
11 

Figure 3. Reference map of Mississippi and Alabama coastal counties. 
12 

Figure 4. Reference map of Florida’s Gulf of Mexico coastal counties. 
13 

Data 

Demographic and economic data used in this study were gathered from the U.S. Census 

Bureau 2000 Census of Population and Housing, and the 1997 and 2002 Economic Census. The 

data were obtained from the agency’s website which contains county-level information collected 

and reported for multiple years. 

Environmental data were obtained in two different ways that will be discussed in more detail 

in the next section. The Toxic Release Inventory (TRI) data were obtained from the U.S. 

Environmental Protection Agency’s website. The annual TRI tracks over 650 toxic chemicals 

used in manufacturing processes in the U.S. Each year, regulated facilities permitted to 

discharge any of these chemicals are required to report their estimated discharge amounts. 

Digital elevation measures were obtained from the USGS National Elevation Dataset (NED). 

The NED is available to the public and provides elevation values for all areas throughout the 

U.S. The final group of variables were taken from the coastal hazards data set made available by 

the University of South Carolina’s Coastal Hazard’s Lab. This information is available through 

the Spatial Hazard Events and Losses Database for the United States (SHELDUS) website and 

 includes county-level information concerning storms and other natural hazard events from 1960-

2000. 

3.2. METHOD ONE: CONSTRUCTION OF THE RESILIENCE INDEX USING FACTOR 

ANALYSIS CUTTER’S SOCIAL VULNERABILITY INDEX 

As introduced above, the social aspects of vulnerability were first quantified by Cutter et al. 

(2003). They developed the Social Vulnerability Index by selecting 42 socioeconomic indicators 

of social vulnerability and conducting a factor analysis in the form of principal component 

analysis (PCA) to create an index of these variables to measure social vulnerability. In order to 

verify the accuracy of their index, Cutter et al. (2003) correlated the number of presidential 

disaster declarations with the vulnerability score given to each county with their index. They 

found literally no correlation (r=-0.099, s=0.000) between the vulnerability index and these 

political designations. 

 

There are a few ways in which Cutter’s method (2003) could be improved. First, the factor 

analysis method might be changed. This would presumably give different results. Instead of 

using a principal component analysis to create the factors a principal axis factoring method could 

be used. A principal component analysis seeks to explain all the common and unique variance of 

the variables while a principal axis factoring method seeks only to explain the common 

variances. Second, a principal component analysis is a variance-based approach while principal 

axis factoring is a correlation-focused approach. This means that in a principal axis factoring 

method while every variable is included in the analysis not every variable is deemed important. 

In other words, a principal-axis factoring method acts as a filter while a principal component 

analysis includes all the variables (Norusis, 2003). 

Third, factor scores are the sum of positive and negative values of variables around an axis 

for a case. They are in themselves an index of the relationship of indicators to each other. 
14 

Therefore, to create an index of factor scores is to include all variables into the index, and create 

an index of an index. This is neither practical nor manageable. Could the factor analysis provide 

a methodology to discern which variables are most important to each dimension or factor instead 

of using factor scores? 

Fourth, Cutter et al. (2003) made no a priori assumptions about importance. They used an 

additive model that did not weight the variance explained by each factor. Each factor explains a 

percent of the variance (i.e. eigenvalue) within the data matrix and this varies based on the 

relationship of the variables to each within each factor. Therefore, each factor should be 

weighted to its relative importance, and this is statistically determined when the factors are 

calculated. 

We suggest that after these changes are made there may be a stronger positive association 

between the index and some measure of recovery, like population change or presidential disaster 

declarations. 

3.2.1. Factor Analytical Method 

The methods used by Cutter et al. (2003) to create the Social Vulnerability Index were 

modified to create an index of community resilience. Forty-three socioeconomic variables were 

obtained from the 2000 Census, 36 of these variables were taken from the research of Cutter et 

al. (2003) and seven variables were added that measured additional aspects of vulnerability and 

resilience, including voting rates among residents, capacity of local government, and 

environmental factors. All variables are shown in Table 1. 

The variables taken from Cutter et al. (2003) were selected because they measure generally 

accepted aspects of social vulnerability. These include: lack of access to resources, limited 

access to political power and representation, social networks and collections, beliefs and 

customs, building types and age and physically limited individuals (Cutter et al., 2003). 

Specific variables that identify these measures of vulnerability are age, gender, race and 

socioeconomic status. Other measures of the social capital of an area are housing type and 

abundance, rental properties, and housing values. Measures of the economic conditions of the 

area include commercial development, manufacturing density, earning density, and primary 

employments in an area. Supplemental Security Income (SSI) recipients were added as an 

additional measure of vulnerability. Measures of resilience in the form of local government 

spending were added. These are listed in the government section of Table 1. 

Additional variables that measured environmental aspects were added to determine if they 

had any significant influence on community resilience. These variables included Toxic Release 

Inventory (TRI) release rates and mean elevation of the county. The TRI reflects the estimated 

discharges of TRI-listed chemicals in any given area and were included because they offer 

insight into local environmental conditions. Elevation was used because it indicates 

susceptibility to flooding, a relevant consideration in coastal, hurricane-prone areas. The Toxic 

Release Inventory (TRI) data was obtained from the EPA website using the TRI Explorer tool. 
15 

Release reports were selected for 2000. The data was selected by county, and total on site or 

offsite disposal or other releases with chemical name was used to obtain a measure of toxic 

pollution per county for all chemicals across all industries. These numbers were listed in of 

pounds. Data in the year 2000 ranged from: 0 releases of any chemical in Kleberg, Texas, to 

55,247,688 lbs in Escambia County, Florida. The median value of TRI release in the Gulf of 

Mexico region in the year 2000 was 283,910 pounds of toxic releases. 
16 

Table 1. 

Original Variables Used to Empirically Derive Factor Loadings 

Demographic Variables 

PCTBLACK90 Percent African American 

PCTINDIAN90 Percent Indian 

PCTASIAN90 Percent Asian 

PCTKIDS90 Percent of population under 5years of age

PCTOLD90 Percent of population over 65 

PCTFEM90 Percent of population that is female 

PCTHISPANIC90 Percent Hispanic 

MEDAGE90 Median age 

AVGPERHH Average number of people per families 

BRATE90 Birth rate 

Social Capital Variables 

PCTF_HH90 Percent female-headed household 

CTRFRM90 Percent rural farm population 

PCTMOBL90 Percent of housing units that are mobile homes 

PCTRENTER90 Percent of housing units that are renter occupied 

PCTNOHS90 Percent of population over 25 with no high school diploma 

FEMLBR90 Percent of civilian labor force that is female 

PCTVLUM91 Percent civilian labor force that is unemployed 

TOTCVLBF91 Percent of population participating in the labor force 

PCTPOV90 Percent of population below the poverty level 

HOSPCT03 Hospitals per capita, 2003 

NRRESPC90 Number of nursing home residents per capita 

HOUDENUT90 Housing density per square mile 

Economic Variables 

MVALOO90 Median value of owner occupied housing 

MEDINCOME Median income 

RPROPDEN90 Total value of all farm products sold per square mile 

EARNDEN90 Earnings ($1,000) of all establishments per square mile 

AGRIPC90 Percent employed in primary extractive industries 

TRANPC90 

Percent employed in transportation, communications and other 

public utilities 

SERVPC90 Percent employed in service occupations 

PCTHH7590 Percent of households earning over $75,000 per year 

SSBENPC90 Per capita Social Security recipients 

MEDRENT90 Median rent 

MAESDEN92 Number of manufacturing establishments per square mile 

PCTFARM92 Percent farm land as a percent of total land 

SSIREC89 

Percent of the population that received Supplemental Security 

Insurance benefits 

COMDEVDN92 Number of commercial establishments per square mile, 

Government Variables 

EXPED Local expenditures for education 

PERVOTE92 Percent of population that voted in presidential election 

LGFREVPERCAP Local government finance, revenue per capita 

PROPTACPC Property tax, per capita 

GENEXPPC92 Direct general expenditures per capita, 

Environmental Variables 

MELE County mean elevation above sea level 

TRI lbs. of toxic release per county 
17 

The variable “mean elevation” was obtained through a multistep process. First, data was 

downloaded from the USGS Seamless Map Server Program in a digital elevation model format 

(DEM) for the all coastal counties bordering the Gulf of Mexico, and the entire state of 

Louisiana in a NED 1 arc second data format. This data was added to a GIS using ARCMap 9.2 

as a layer file and then exported into a raster file .img. Once in a raster file format this data was

able to be combined into one seamless digital elevation data set. This procedure was followed 

for each coastal state. Once the DEMs were seamlessly processed they were added as a layer file 

to a GIS. Over this layer a coastal county shape file was overlayed. Coastal county data was 

obtained from the Census Bureau: Counties 2000 shapefile option for all coastal states. Then 

using the Spatial Analyst Tool, digital elevation for each county was calculated. Mean elevation 

for each county was selected. Mean elevation data ranged from 0 feet above sea level in Orleans 

Parish the lowest county in the Gulf of Mexico region to 34.3 feet above sea level in Mobile 

County, Alabama. 

All variables were normalized by conversion into densities per square mile, per capita, or 

percents. The 42 socioeconomic variables were placed in a Principal Factor Analysis using the 

Varimax rotation option and seven factors explaining 69% of the variance were derived. From 

each factor the variable that had the highest loading was selected. The rotated factor matrix is 

shown in Table 2. 
18 

Table 2. 

Rotated Factor Matrixa

.095 -.188 .860 .053 -.005 .036 .074

.045 -.005 -.132 .033 .896 -.064 .039

-.074 -.474 .116 -.600 -.275 .313 -.337

.370 .285 -.253 .063 .120 -.333 -.112

-.171 -.133 -.160 -.153 -.558 .336 -.546

-.250 .218 .096 .782 .224 .147 -.042

-.628 -.327 -.092 -.304 -.387 .084 -.048

-.710 -.145 -.288 -.053 -.177 .188 .141

.691 -.052 .346 -.174 .262 -.449 .191

.706 .344 .118 -.156 -.035 .023 .490

.303 .021 .793 -.011 -.028 .020 -.033

.497 .569 -.379 -.030 .231 .198 .311

-.090 -.699 .603 -.213 .218 .106 -.050

.363 .639 -.185 .489 -.045 .031 .271

.599 .540 -.364 .262 .040 -.096 .241

.775 .308 -.053 .166 .056 .058 .110

.112 .015 .137 .040 .853 .013 -.004

-.196 -.147 .362 -.006 .158 .304 .253

-.206 -.753 -.084 .145 -.052 .093 .093

.337 -.076 -.292 .467 -.075 .086 .159

-.066 .045 .067 .186 -.074 .853 -.070

.072 .053 .091 .477 -.123 .006 .049

.830 .031 .088 .093 -.058 -.064 -.054

.198 .745 -.370 .461 -.059 .024 .139

.902 .246 -.020 .045 .033 -.048 .022

-.177 .002 .118 -.675 -.226 -.130 .221

.294 .415 .009 .208 .093 .110 -.037

PCTBLACK

PCTKIDS

PCTOLD

PCTHISPANIC

MEDAGE

AVGPERHH

FARMPOP

PCTMOBL

PCTRENTER

PCTNOHS

FEMLBR

TOTCVLBF

PCTPOV

MVALOO

MEDRENT

BLDPER

BRATE

RPROPDEN

AGRIPercent

TRANPercent

PERVOTE92

GENEXPPC

HOUDEN

MEDINCOME

EXPED

MELE

TRI

1 2 3 4 5 6 7

Factor

Extraction Method: Principal Axis Factoring. 

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 34 iterations.
19 

The variables extracted from this factor matrix based on their importance within the factor 

are shown along with their eigenvalue in Table 3. Because these 6 variables only explained 69% 

of the variance, the variance was rescaled to equal 100% of the total explained by these 

variables. These values are shown in Table 3. Each variable and their rescaled variances were 

then placed in a weighted- average model to derive resilience. The formula used was: 

a) Vi

 =[(X-Xmin)/(Xmax-Xmin)], 

b) I=∑=

6

i 1

Viλi .

The normalized raw data of the variable X was scaled from 0 to 1. This was renamed V,

where V= normalized variable. V was then multiplied by the rescaled variance to create a 

weighted value for that variable per county. These new, weighted values were then summed to 

give an index value that ranged from 0 to 1. Thus, the resilience index had a possible range of 0 

to 1, where 0 was the least resilient; 1 was the most resilient. Table 4 shows these values. 

The weighted index values for the Gulf of Mexico region had a low value of .35 in Willacy 

County, Texas, and seven counties had the highest possible value of 1. These counties were: 

Jefferson Parish, Louisiana, Kenedy County, Texas, Okaloosa County, Florida, Hernando 

County, Florida, Sarasota County, Florida, Pinellas County, Florida, and Hillsboro County, 

Florida. Index values for all counties are shown in Table 4. The results of the index were 

mapped using a natural breaks method to visually demonstrate patterns of resilience across the 

Gulf of Mexico region. Figure 5 depicts the results of the factor analysis method for Texas and 

Louisiana while Figure 6 represents the results of the factor analysis method for Mississippi, 

Alabama, and Florida. 

Table 3. 

Variables and Eigenvalues Used to Construct Weighted Community Resilience Index 

Variable Name Resiliency 

% 

Original 

Variance 

Explained 

Rescaled 

Variance 

Expenditures for education positive 20.13 29.18

Median income of the parish positive 13.53 19.61

Percent of the workforce that is female positive 10.4 15.08

Mean elevation of the parish positive 10.2 14.79

Percent of the population below 5 years old positive 9.1 13.1

Percent of the population that voted in the last 

presidential election positive 5.7 8.26

In order to determine the accuracy of our index we correlated it with the percent of 

population change between 1990 and 2000. There was no statistically significant correlation 

between the index values and population change (r= .157, s=0.000). 
20 

Table 4. 

Community Resilience Index Values for All Gulf of Mexico Counties 

County Index County Index 

Hillsboro, FL 1.00 Cameron, LA 0.75 

Pinellas, FL 1.00 Chambers, TX 0.75 

Sarasota, FL 1.00 Jefferson, FL 0.75 

Hernando, FL 1.00 Jackson, MS 0.75 

Okaloosa, FL 1.00 Lafourche, LA 0.74 

Kenedy, TX 1.00 Nueces, TX 0.74 

Jefferson, LA 1.00 Vermilion, LA 0.74 

Santa Rosa, FL 0.95 Wakulla, FL 0.73 

Manatee, FL 0.95 St. Mary, LA 0.73 

Citrus, FL 0.95 Jefferson, TX 0.72 

Charlotte, FL 0.94 Franklin, FL 0.72 

Lee, FL 0.93 Hancock, MS 0.69 

Walton, FL 0.92 Levy, FL 0.66 

Pasco, FL 0.91 Terrebonne, LA 0.65 

Escambia, FL 0.90 Harrison, MS 0.65 

Baldwin, AL 0.90 Orange, TX 0.64 

Mobile, AL 0.87 Taylor, FL 0.62 

Bay, FL 0.85 San Patricio, TX 0.58 

Gulf, FL 0.84 Aransas, TX 0.58 

Galveston, TX 0.84 Matagorda, TX 0.57 

Orleans, LA 0.84 Dixie, FL 0.55 

St. Bernard, LA 0.82 Calhoun, FL 0.55 

Monroe, FL 0.82 Kleberg, TX 0.52 

Collier, FL 0.80 Cameron, TX 0.40 

Iberia, LA 0.79 Willacy, TX 0.35 

Plaquemines, 

LA 

0.77 

Brazoria, TX 0.77 
21 

Figure 5. Results of the factor analysis method for Texas and Louisiana. 
22 

 

Figure 6. Results of the factor analysis method for Mississippi, Alabama, and Florida. 
23 

4. DEVELOPING AN ALTERNATIVE METHODOLOGY TO EXAMINE

SOURCES OF RESILIENCE: APPLICATION OF DISCRIMINANT 

ANALYSIS (DA) TO THE COASTAL COUNTIES OF THE GULF OF 

MEXICO STATES 

An alternative method to construct an index of resilience is one based on the application of 

discriminant analysis. This method was used by Liu and Lam (1985) to construct a zonal index 

of vegetation and to determine the probability of a modern analogue. 

 Discriminant analysis requires an a priori classification of samples (coastal counties in 

this study) into groups. The technique derives linear combinations of variables that are 

independent of each other (Liu and Lam, 1985; Klecka, 1980). This technique also can be used 

to classify groups with unknown membership into the preexisting classifications (Liu and Lam 

1985). In order to run a discriminant analysis five statistical assumptions have to be met. These 

are: 1) The samples in each group are randomly chosen. 2) The probabilities of a sample 

belonging to any one group are equal. 3) The samples used to derive the discriminant functions 

are correctly classified. 4) The variance-covariance matrices of the groups are statistically equal. 

5) The variables are normally distributed within each group. 

A major difference between factor analysis and discriminant analysis is that discriminant 

analysis is an inferential statistical method while factor analysis is a descriptive statistical 

method. In other words, if the statistical assumptions are met the discriminant functions derived 

can be used to ascribe the resilience level of other counties. 

The variables used in the discriminant analysis were a subset of the initial variables shown in 

Table 1 and included several new measures of coastal storm events over time and the associated 

damages. These variables are shown in Table 5. 

Four different resilience groups were utilized for this discriminant analysis. To create these 

groups a continuous statistical surface of disaster and impact was derived. The elements 

included in the measurement of resilience were the number of coastal hazards from 1960 to 

2006, the number in thousands of dollars in property damage from these coastal hazards per 

county, and the population change from 1990 to 2000. The number of coastal hazards per county 

and the amount of property damage in thousands of dollars was obtained from the University of 

South Carolina Hazard’s Lab Coastal Hazard’s Database. The population change data was 

obtained from the U.S. Census Bureau Census of Population and Housing Censtats Databases. 

Coastal hazards included in this study were hurricanes, tropical storms, coastal flooding, 

storm surges, tornados, and thunderstorms. Each of these events was counted as one discrete 

event and was categorized as listed by the Coastal Hazards Database maintained by the 

University of South Carolina Hazards Lab. Other coastal hazards like subsidence, sea level rise 

and coastal erosion were not addressed in this study due to the fact that they are slow insidious 

threats to communities that are not easily measureable. 
24 

The number of coastal hazards ranged from a low of 8 hazards in 46 years in Orange County, 

Texas to a high of 60 coastal hazards in 46 years in Escambia County, Florida. This data was 

then given a rank value of 1–4. These groups were decided by dividing the number of counties 

by 4 to give approximately 12 counties in each group. For the ranking of coastal hazards 4= 

most coastal hazards, while 1 = fewest coastal hazards. 

To create a ranking for property impacts, property damages caused by the storms and floods 

were pooled where 1= least property damage and 4= most property damage. The least property 

damage was found in San Patricio County, Texas. The residents of this county experienced 11 

coastal hazard events in 46 years that totaled $9,022,930 in damages. The most property damage 

was found in Jefferson Parish, Louisiana, an area with 40 coastal hazard events in 46 years that 

resulted in $6,097,123,543 in damages. 

To create the last variable, a ranking of population change, a score of 1 indicated “least 

growth” and 4 designated “most growth”. This data ranged from a growth rate of 65% in Collier 

County, Florida between 1990 and 2000, to population losses of 2.5% in Orleans, Louisiana, 

7.9% in St. Mary Parish, Louisiana, and 10% in Kenedy, Texas. 

Once each county was given a ranking for each category described above, these rankings 

were combined to create a new composite ranking for each county. These values were added 

across the variables, so that areas with high population growth occurring in the context of high 

storm hazards, and high property damage were considered to be the highly resilient while areas 

with low growth, few hazards and lower levels of damage were considered to be least resilient. 

Values ranged from 3 to 12. Santa Rosa, Florida which received a 4 in all three categories 

finished with a rank of 12, or most resilient, while Kenedy, Texas, Kleberg, Texas, and Nueces, 

Texas all received “ones” in each category, resulting in rankings of 3. The rankings for each 

county and parish are shown in Table 6. 
25 

Table 5. 

Variables Used in the Discriminant Analysis Method 

Demographic 

Variables 

PCTBLACK Percent black 

PCTKIDS Percent under 5 years old 

PCTOLD Percent over 65 years old 

PCTHISPANIC Percent Hispanic 

AVGPERHH Average number of people per household 

Social Capital 

Variables 

PCTNOHS 

Percent of the population over 25 with no high school 

diploma 

FEMLBR Percent of the workforce that is female 

PCTCVLBF Percent of the workforce that is employed 

PCTMOBL Percent of homes that are mobile homes 

PCTRENTER Percent of the population that rents 

PCTPOV Percent of the population living below poverty 

HOUDEN Number of houses per square mile 

Economic 

Variables 

MANDEN 

Number of manufacturing establishments per square 

mile 

MEDINCOME Median income of the county 

PCTAG 

Percent of the population employed by agriculture, 

fishing or hunting 

MVALOO Median value of owner occupied housing 

MEDRENT Median rent 

RPROPDEN Value of all farm products sold 

Government 

Variables 

LGFINREVPC Local government finance, revenue per capita 

GENEXPPC 

Local government finance general expenditures per 

capita 

PERVOTE92 Percent of the population that voted in the last election 

EXPED Local government finance expenditures for education 

Environmental 

Variables 

MELE Mean elevation of the county 

TRI Toxic Release Inventory 
26 

An important note is that this analytical approach using these indicators may overlook some 

“resilient” communities that have experience slow growth, would not identify as “resilient” any 

county that experienced low growth, few coastal hazards and little associated property damage 

over the last few decades. As a result, some resilient communities may be overlooked with this 

analysis. The next step in this process was to classify the groups into four categories. To do this,

rankings of 3 through 12 were rescaled into values 1, 2, 3, and 4. Values of 4 were assigned to 

indicate the most resilient counties and parishes, while values of 1 were indicated the least 

resilient. The new rankings of the counties and parishes are shown in Table 6. 

Once these a priori groupings were assigned, the discriminant analysis was run in SPSS, 

Version 15.0. All of the socioeconomic and environmental variables were grouped together. 

The result was 94.2 percent of the counties were correctly placed into their assigned resilience 

group (from 1 to 4). Among those few counties whose group membership was not correctly 

predicted or assigned, Matagorda, Texas had an a priori classification of “2”, while the 

discriminate analysis placed this county as a “3”. Similarly, Manatee, Florida which was placed 

as a 4, or most resilient should have been placed in 3 or middle resilient, while Sarasota, Florida 

was a priori placed as a 3 or middle resilient, but the discriminant analysis placed it as a 4 or 

most resilient. 

The results of the discriminant analysis were mapped using a natural breaks method. Figure 

7 shows the results of the discriminant analysis for Texas and Louisiana, while Figure 8 shows 

the results of the discriminant analysis across Mississippi, Alabama, and Florida. 
27 

Table 6. 

Rankings Used to Create A Priori Groupings 

County 

Hazard 

Rank 

Damage 

Rank 

Popchange 

Rank 

Resilience 

Rank 

New Resilience 

Rank 

Santa Rosa, FL 4 4 4 12 4

Charlotte, FL 3 4 4 11 4

Lee, FL 3 4 4 11 4

Baldwin, AL 3 3 4 10 4

Collier, FL 2 4 4 10 4

Escambia, FL 4 4 2 10 4

Hancock, MS 2 4 4 10 4

Manatee, FL 3 4 3 10 4

Okaloosa, FL 3 4 3 10 4

Walton, FL 4 2 4 10 4

Bay, FL 4 2 3 9 3

Brazoria, TX 2 3 4 9 3

Gulf, FL 4 2 3 9 3

Jefferson, LA 4 4 1 9 3

Lafourche, LA 4 4 1 9 3

Plaquemines, LA 4 4 1 9 3

Sarasota, FL 2 4 3 9 3

Terrebonne, LA 4 3 2 9 3

Wakulla, FL 3 2 4 9 3

Chambers, TX 1 3 4 8 3

Franklin, FL 3 2 3 8 3

Galveston, TX 2 3 3 8 3

Harrison, MS 2 4 2 8 3

Hillsborough, FL 3 2 3 8 3

Jackson, MS 2 4 2 8 3

Orleans, LA 3 4 1 8 3

St. Bernard, LA 3 4 1 8 3

Citrus, FL 2 1 4 7 2

Dixie, FL 1 2 4 7 2

Hernando, FL 2 1 4 7 2

Jefferson, FL 2 3 2 7 2

Levy, FL 2 1 4 7 2

Monroe, FL 4 2 1 7 2

Taylor, FL 3 2 2 7 2

Cameron, TX 1 1 4 6 2

Cameron. LA 2 2 2 6 2

Iberia, LA 1 3 2 6 2

Mobile, AL 2 3 1 6 2

Pasco, FL 2 1 3 6 2
28 

Table 6. 

Rankings Used to Create A Priori Groupings (continued) 

County 

Hazard 

Rank 

Damage 

Rank 

Popchange 

Rank 

Resilience 

Rank 

New 

Resilience 

Rank 

Pinellas, FL 3 1 2 6 2

St. Mary, LA 2 3 1 6 2

Vermilion, LA 1 3 2 6 2

Aransas, TX 1 1 3 5 2

Matagorda, TX 2 2 1 5 2

Orange, TX 1 3 1 5 2

Calhoun, TX 1 1 2 4 1

Jefferson, TX 1 2 1 4 1

Nueces, TX 1 1 2 4 1

San Patricio, 

TX 1 1 2 4 1

Willacy, TX 1 1 2 4 1

Kenedy, TX 1 1 1 3 1

Kleberg, TX 1 1 1 3 1

Table 7. 

Classification of the Groups by the Discriminant Analysis Methoda

Predicted Group Membership 

Resilience 1.00 2.0 3.0 4.0 Total 

Original Count 1.00 7 0 0 0 7

 2.00 1 17 0 0 18

 3.00 0 0 16 1 17

 4.00 0 0 1 9 10

 % 1.00 100.0 0.0 0.0 0.0 100.0

 2.00 5.6 94.4 0.0 0.0 100.0

 3.00 0.0 0.0 94.1 5.9 100.0

 4.00 0.0 0.0 10.0 90.0 100.0

 Note: a = 94.2% of original grouped cases correctly classified. 
29 

 

Figure 7. Results of the discriminant analysis method for Texas and Louisiana. 
30 

Figure 8. Results of the discriminant analysis method for Mississippi, Alabama, and Florida. 
31 

5. COMPARISON OF THE TWO METHODS TO CALCULATE COUNTY 

RESILIENCE 

5.1. METHOD ONE: COUNTY RESILIENCE LEVELS CALCULATED BY FACTOR 

ANALYSIS 

The counties with the lowest resilience levels according to the first methodology, the 

weighted resilience index derived through the Factor Analysis (FA) were: Willacy County, 

Texas Cameron, Texas Kleberg, Texas and Calhoun, Texas, and Dixie, Florida. With the 

exception of Calhoun, Texas, all these counties had median incomes below $30,000 per year. 

They also had relatively low voter turnout in the 2000 presidential election that ranged from 18 

% in Cameron, Texas to 33 % in Dixie, Florida. Typically they had a higher percentage of the 

population under the age of 5 years. The percentage of children in the population of these 

counties ranged from 8.2% in Willacy, Texas to 5.9 % in Dixie, Florida. 

Those counties estimated to be most resilient counties in the Gulf of Mexico region were 

centered around the suburban areas of New Orleans, and Tampa, along with the growing beach 

communities in Alabama and Okaloosa county in Florida. Surprisingly, Kenedy, Texas also is 

calculated to be among the most resilient counties in the Gulf of Mexico region. These counties 

all had a high percentage of women in the workforce (above 47%) and high voter turnout. 

Kenedy, Texas had the highest voter turnout in the Gulf region with 55%, and other counties that 

exhibited high resilience had above 40% voter turnout. 

In our analysis, expenditures for education were weighted at 29%. Areas with high 

expenditures for education were estimated to be more resilient. These areas included the urban 

communities of Orleans Parish, Louisiana, Hillsborough, Florida and Pinellas, Florida. 

Hillsborough and Pinellas both received a score on the resilience index of 1 or most resilient, 

while Orleans Parish received a score of .84, placing it in the mid range of resilience. 

The next important variable was median income. This was given a weight of 19.6%, while 

percent of the labor force that was female and mean elevation of the county were both weighted 

at 15%. The final two variables were percent of the population under 5 years old which was 

weighted at 13% and percent of the population that voted in the last presidential election was 

weighted at 8.2%. Affluence and education account for roughly 50% of what makes a 

community resilient in this first analysis. 

What is interesting, however, is that a combination of the other factors can easily place a 

county in the highly resilient category. For example, Kenedy, Texas, has the lowest expenditures 

for education in the region, a very low median income, a middle elevation, a high number of 

children, and the highest value of voter participation. Given the weighting method a high level 

of voter participation is enough to push a county into a higher level of estimated resilience, 

despite the presence of other factors that would suggest socioeconomic vulnerability 
32 

5.2. METHOD TWO: COUNTY RESILIENCE LEVELS CALCULATED BY 

DISCRIMINANT ANALYSIS 

The discriminant analysis (DA) resulted in a different pattern of estimated resilience among 

the Gulf of Mexico coastal counties than the first method, the Factor Analysis (FA) This can be 

seen in Table 8, where the FA column represents the results of the first method, the equal 

interval column represents the results of the factor analysis method that were mapped and 

divided into four groups via equal intervals, and the DA column represents the numeric results of 

the Discriminant Analysis (DA) method. 

Table 8 is divided into two sections. The left-hand section shows the counties that are 

grouped in the same resilience category by the two methods. In these counties there is a 

correspondence between the resilience grouping based on risk and the resilience grouping based 

on socioeconomic factors. Twenty-six counties in the Gulf of Mexico region have the same 

resilience categorization by both methods. For example, Willacy, Texas is in the least resilient 

category via both the Factor Analysis and the Discriminant Analyses methods, while Okaloosa, 

Florida is estimated to be in the most resilient category by both methods. 

On the right-hand of Table 8, the counties that have a different resilience categorization as 

the result of FA and DA are shown. Most of these counties are categorized differently by only 

one resilience group. For example, Cameron, Texas is placed in group 1, or the least resilient 

group via the factor analysis method, but by the discriminant analysis method is placed in group 

2, a slightly more resilient group. Another example is Hillsborough, Florida, which by the factor 

analysis method is placed into group 4, the most resilient group, but using the discriminant 

analysis method is placed into group 3, a slightly less resilient group. Nine counties had 

differences that are greater than a one-group difference. These counties are: Jefferson, Texas, 

Nueces, Texas, Kenedy, Texas Jefferson, Louisiana, Mobile, Alabama, Pasco, Florida, Citrus, 

Florida, Hernando, Florida and Pinellas, Florida. For each of these counties, the difference 

between the resilience scores for the two methods was two group categories. 

Overall, the Factor Analysis (FA) method led to more counties being categorized in a higher, 

“more resilient” category than their placement as determined by the Discriminant Analysis 

method. This is, in part, because the Discriminant Analysis method reflects levels of relative risk 

present overtime within the counties, while the Factor Analysis method reflects factors such as 

expenditures for education and percent of the population that voted in the last presidential 

election, possible indicators of “adaptive capacity” within the community. Taken together, both 

metrics are useful for managers, and can highlight areas that are vulnerable due to high physical 

risks and because they have less resilient populations. 
33 

Table 8. 

Comparing Results of the Factor Analysis and Discriminant Analysis Methods 

Areas of Similar Resilience 

Groupings 

 Areas of Different Resilience Groupings 

County FA Equal 

Interval 

DA County FA Equal 

Interval 

DA 

Willacy 0.35 1 1 Cameron, TX 0.40 1 2 

Dixie 0.55 2 2 Kleberg 0.52 2 1 

Matagorda 0.57 2 2 Calhoun 0.55 2 1 

Aransas 0.58 2 2 San Patricio 0.58 2 1 

Taylor 0.62 2 2 Harrison 0.65 2 3 

Orange 0.64 2 2 Terrebonne 0.65 2 3 

Levy 0.66 2 2 Jefferson, TX 0.72 3 1 

Franklin 0.72 3 3 Nueces 0.74 3 1 

Wakulla 0.73 3 3 St. Mary 0.73 3 2 

Lafourche 0.74 3 3 Vermilion 0.74 3 2 

Jackson 0.75 3 3 Jefferson, FL 0.75 3 2 

Chambers 0.75 3 3 Cameron, LA 0.75 3 2 

Brazoria 0.77 3 3 Iberia 0.79 3 2 

Plaquemines 0.77 3 3 Monroe, FL 0.82 3 2 

St. Bernard 0.82 3 3 Hancock 0.69 3 4 

Orleans 0.84 3 3 Collier 0.80 3 4 

Galveston 0.84 3 3 Kenedy 1.00 4 1 

Baldwin 0.90 4 4 Mobile 0.87 4 2 

Escambia 0.90 4 4 Pasco 0.91 4 2 

Walton 0.92 4 4 Citrus 0.95 4 2 

Lee 0.93 4 4 Jefferson, LA 1.00 4 2 

Charlotte 0.94 4 4 Hernando 1.03 4 2 

Manatee 0.95 4 4 Pinellas 1.06 4 2 

Santa Rosa 0.95 4 4 Gulf 0.84 4 3 

Okaloosa 1.02 4 4 Bay 0.85 4 3 

 Sarasota 1.06 4 3 

 Hillsborough 1.08 4 3 

 
35 

6. EXPLORING ASSOCIATIONS BETWEEN THE RESILIENCE SCORES 

OF COASTAL COUNTIES AND PARISHES AND THE OIL AND 

GAS INDUSTRY 

Oil and gas activity can affect social-ecological resilience in a number of ways. For 

example, the oil and gas industry provided almost 2 million people with jobs and more than $800 

million in revenue for Louisiana in 2000 (LOGA, 2008). The economic impacts of oil and gas 

industry activities can lead to a higher occurrence of affluence, a variable that theoretically 

strengthens social resilience and recovery from major disturbances (Folke et al., 2005). On the 

other hand, oil and gas production is also suspected to be associated with wetland loss through 

erosion and subsidence, as well as other forms of environmental degradation, problems that, 

theoretically, weaken ecological resilience (Austin et al., 2004). 

Are social-ecological resilience and oil and gas activity associated in any way? Specifically, 

does this study suggest that higher levels of employment within the oil and gas industry may help 

make a community more resilient to natural and man-made disasters? 

To address these questions, it is useful to consider patterns of employment within the oil and 

gas industry across the five states. The map displayed in Figure 9 depicts the county-level 

employment in 12 NAICS categories related to the oil and gas industry (Pulsipher, et al. 2012) 

and includes additional inland counties that are not part of our study of coastal counties. The 

map is included here to illustrate the distribution jobs in the industry throughout the region. 

Areas of higher oil and gas employment are evident among several coastal counties in Louisiana 

and Texas, and also Jackson, Mississippi and Mobile, Alabama. 
36 

Figure 9. Oil and Gas Employment among Counties within the Gulf of Mexico Region. 

To determine whether there are statistically significant associations between the presence of 

the oil and gas industry and the county-level resilience scores, we conducted Pearson Correlation 

Analyses between variables measuring industry activity and the resilience scores for each of the 

coastal counties in the study area. The Pearson r Correlation Coefficient ranges from 0 to 1, with 

values closer to 1 indicating a stronger association between two variables. The directional sign of 

the Pearson r indicates whether the association is inverse or positive. Also, the p value 

determines whether an observed association is statistically significant, with a smaller value (less 

than .10) signaling a more significant association (Antonius, 2003). 

We used 2008 employment data from the WholeData information service, purchased and 

made available for this project by the LSU Center for Energy Studies. WholeData estimated 

employment figures from County Business Patterns (CBP) data, using iterative estimation 

techniques to fill in the gaps where data may have been missing in the original CBP data. The 

employment data reflects five oil and gas employment categories: NAICS codes 211 (Oil and 

Gas Extraction), 213111 (Drilling Oil and Gas Wells), 213112 (Support Activities for Oil and 

Gas), 333132 (Oil and Gas Field Machinery Manufacturing), and 541360 (Geophysical 

Surveying). We used the sum of employment and establishments for these 5 codes for each 

county to create two measures of oil and gas industry presence. 

The data included in the Pearson Correlation Analyses are shown in Table 9. The counties 

and parishes are listed in alphabetical order; the ten with the largest sum of oil and gas 

employment are highlighted. 
37 

Table 9. 

Data Used in the Pearson Correlation Analyses 

County 

FA 

Resilience 

Score 

DA 

Resilience 

Score 

Sum of O&G 

Employment 

(5 NAICS 

codes) 

Sum of O&G 

Establishments 

(5 NAICS 

codes) 

Aransas, TX 0.58 2 18 7 

Baldwin, AL 0.90 4 2 3 

Bay, FL 0.85 3 336 2 

Brazoria, TX 0.77 3 751 37 

Calhoun, TX 0.55 1 73 6 

Cameron, LA 0.75 2 136 14 

Cameron, TX 0.4 2 99 3 

Chambers, TX 0.75 3 184 16 

Charlotte, FL 0.94 4 0 0 

Citrus, FL 0.95 2 0 0 

Collier, FL 0.80 4 17 7 

Dixie, FL 0.55 2 0 0 

Escambia, FL 0.90 4 6 4 

Franklin, FL 0.72 3 0 0 

Galveston, TX 0.84 3 257 22 

Gulf, FL 0.84 3 0 0 

Hancock, MS 0.69 4 67 2 

Harrison, MS 0.65 3 6 3 

Hernando, FL 1 2 1 1 

Hillsborough, FL 1 3 29 7 

Iberia, LA 0.79 2 1395 57 

Jackson, MS 0.75 3 26 2 

Jefferson, FL 0.75 2 0 0 

Jefferson, LA 1 3 2540 54 

Jefferson, TX 0.72 1 517 22 

Kenedy, TX 1 1 0 0 

Kleberg, TX 0.52 1 110 14 

Lafourche, LA 0.74 3 1491 41 

Lee, FL 0.93 4 15 5 

Levy, FL 0.66 2 0 0 

Manatee, FL 0.95 4 3 2 

Matagorda, TX 0.57 2 80 10 

Mobile, AL 0.87 2 436 17 

Monroe, FL 0.82 2 5 3 

Nueces, TX 0.74 1 3420 178 
38 

County 

FA 

Resilience 

Score 

DA 

Resilience 

Score 

Sum of O&G 

Employment 

(5 NAICS 

codes) 

Sum of O&G 

Establishments 

(5 NAICS 

codes) 

Okaloosa, FL 1 4 2 1 

Orange, TX 0.64 2 232 4 

Orleans, LA 0.84 3 1866 48 

Pasco, FL 0.91 2 47 4 

Pinellas, FL 1 2 19 6 

Plaquemines, 

LA 0.77 3 941 41 

San Patricio, TX 0.58 1 435 27 

Santa Rosa, FL 0.95 4 203 6 

Sarasota, FL 1 3 13 4 

St. Bernard, LA 0.82 3 72 7 

St. Mary, LA 0.73 2 2993 38 

Taylor, FL 0.62 2 0 0 

Terrebonne, LA 0.65 3 6078 89 

Vermilion, LA 0.74 2 494 28 

Wakulla, FL 0.73 3 0 0 

Walton, FL 0.92 4 0 2 

Willacy, TX 0.35 1 0 0 

The two counties with the most oil and gas industry employees are Louisiana’s Terrebonne 

Parish, with 6078 employees, and Nueces County in Texas, with 3420 workers. These counties 

are followed by several parishes or counties in Louisiana: St. Mary Parish (2993), Jefferson 

Parish (2540), Orleans Parish (1866), Lafourche Parish (1491), Iberia Parish (1395), Plaquemine 

Parish (941); and two in Texas: Brazoria County (751) and Jefferson County (517). 

6.1. CORRELATION ANALYSES AND RESULTS 

We ran bi-variate correlation analyses between the two measures of oil and gas industry 

activity (total employment of the five NAICS categories and number of establishments) and the 

resilience scores (calculated by the two methods) for the coastal counties within the study area. 

The analyses were conducted in SPSS version 15.0. 

The first analysis between the resilience score generated through the Factor Analysis method 

for the 52 counties and the oil and gas activity variables resulted in no statistically significant 

correlations between resilience scores and industry activity. The Factor Analysis Method 

Resilience Score (FASCORE) and EMPLOY yielded a Pearson r coefficient of -.059, p=.680. 

Similarly, the FASCORE and ESTAB resulted in a Pearson r of -.050 and a p value of .756. The 

results of these correlation analyses are shown below in Table 10. 
39 

Table 10. 

Pearson Correlation Analysis between Factor Analysis- Method Resilience Scores and Oil and Gas 

Employment; Oil and Gas Establishments 

Correlations

1 -.059 -.050

. .680 .726

1.342 -534.262 -12.174

.026 -10.476 -.239

52 52 52

-.059 1 .811**

.680 . .000

-534.262 6.2E+07 1350741

-10.476 1216834 26485.116

52 52 52

-.050 .811** 1

.726 .000 .

-12.174 1350741 44677.308

-.239 26485.116 876.026

52 52 52

Pearson Correlation

Sig. (2-tailed)

Sum of Squares and

Cross-products

Covariance

N

Pearson Correlation

Sig. (2-tailed)

Sum of Squares and

Cross-products

Covariance

N

Pearson Correlation

Sig. (2-tailed)

Sum of Squares and

Cross-products

Covariance

N

FASCORE

EMPLOY

ESTAB

FASCORE EMPLOY ESTAB

**. Correlation is significant at the 0.01 level (2-tailed).

Next, we conducted a Pearson Correlation Analysis using the resilience scores calculated 

through the Discriminant Analysis (DA) method. We found no significant correlation between 

these resilience scores and industry employment or the number of oil and gas establishments 

within the county. The DA Method Resilience Score (DASCORE) and number of employees 

yielded a Pearson r coefficient of -.072, p=.611. Similarly, the DASCORE and the number of 

establishments variable resulted in a Pearson r of -.199 and a p value of .157. The results of this 

analysis are shown in Table 11. 
40 

Table 11. 

Pearson Correlation Analysis between Discriminant Analysis-Method Resilience Scores and Oil and Gas 

Employment; Oil and Gas Establishments 

Correlations

1 -.072 -.199

. .611 .157

46.692 -3882.385 -287.462

.916 -76.125 -5.637

52 52 52

-.072 1 .812**

.611 . .000

-3882.385 6.2E+07 1358447

-76.125 1197914 26123.971

52 53 53

-.199 .812** 1

.157 .000 .

-287.462 1358447 44932.113

-5.637 26123.971 864.079

52 53 53

Pearson Correlation

Sig. (2-tailed)

Sum of Squares and

Cross-products

Covariance

N

Pearson Correlation

Sig. (2-tailed)

Sum of Squares and

Cross-products

Covariance

N

Pearson Correlation

Sig. (2-tailed)

Sum of Squares and

Cross-products

Covariance

N

DASCORE

EMPLY

ESTABLIS

DASCORE EMPLY ESTABLIS

**. Correlation is significant at the 0.01 level (2-tailed).

6.2. SUMMARY

The calculation of the resilience scores for the coastal counties and parishes yielded 

information about the relative socioeconomic conditions among coastal jurisdictions and the 

extent to which the coastal communities have held on to populations over time in light of 

exposure to storms and other large-scale disturbances. The two methods employed, the Factor 

Analysis method and the Discriminant Analysis method, produced slightly different rankings or 

scores for parishes and counties. For example, using the FA method to calculate the resilience 

scores resulted in Terrebone Parish, Louisiana being placed in the third quartile of resilience 

among the coastal counties and parishes (Figure 5), while the DA method placed it in the second 

quartile (Figure 6). 

The DA method may be considered a more refined approach in that it includes the exposure 

to hurricanes and other natural disturbances over time in the calculation of the resilience score. 

In that sense, it provides a method to compare areas that have been similarly affected by 

disturbances when evaluating their resilience or ability to maintain and increase their population 

over time. 
41 

The Pearson Correlation Analyses yielded no evidence of statistically significant associations 

between either of the calculated resilience scores and the measures of oil and gas industry 

activity within the counties and parishes. However, even in light of these findings, the study is a 

first step in operationalizing key theoretical concepts from the social-ecological resilience 

literature that are relevant to considerations of how industry presence may influence the 

socioeconomic conditions of communities. We have aggregated a range of socioeconomic and 

environmental variables so that comparisons can be made among coastal communities, 

demonstrating a useful approach for the more systematic examination and comparison of 

exposure, vulnerability and capacity for resilience among these coastal areas. 
43 

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APPENDIX: 

ABSTRACTS OF SELECTED THESES 
49 

A.1. LAUREN DEFRANK, RESILIENCY OF NEW ORLEANS 

FOLLOWING HURRICANE KATRINA: A STUDY OF 

COMMUNITIES THREE YEARS AFTER THE STORM 

In late August 2005, Hurricane Katrina and subsequent levee failures produced 

catastrophic flooding in New Orleans, Louisiana, killing roughly fifteen-hundred residents, and 

forcing the evacuation of most of the local population. Over four years later, many areas of the 

city had not recovered in terms of housing, business operations, infrastructure, or population. 

The authors examine patterns of repopulation within Orleans Parish as an indicator of 

community resilience to large-scale external disturbances. They pose two central research 

questions: To what extent do patterns of recovery observed in natural-systems after large 

disturbances yield insight into the recovery patterns of human communities? Also, what factors 

may account for variation in the resilience of coastal cities like New Orleans? The researchers 

analyzed mail-delivery data at the zip-code and Census-tract levels in Orleans Parish before, and 

at monthly intervals after the storm, as an indicator of returning population. Cluster Analysis 

yielded three distinct recovery patterns–“resilient”, “resistant” and “susceptible”–similar to those 

of natural systems. Then, they used Discriminate Analysis to examine the 181 Census tracts of 

Orleans Parish and to estimate the relative influence of socioeconomic and environmental factors 

of the neighborhoods on the rates of population return. They found that three factors – higher 

Katrina flood depths, greater percentage of African-American residents, and lower educational 

attainment –predicted patterns of population return for over 75% of the 181 Census Tracts within 

Orleans Parish. Of these three factors flood depth was, by far, the most important predictor of 

returning population, indicating that among those who experienced flooding of ten feet or more, 

even residents with sufficient economic resources to rebuild largely have opted not to do so. 

 

A.2. ARIELE BAKER, CREATING AN EMPIRICALLY DERIVED INDEX OF COMMUNITY 

RESILIENCE FOR THE GULF OF MEXICO REGION 

As coastal areas increase in populations there is an increasing need to determine what 

community characteristics are most resilient to coastal disasters. This research proposes two 

methods to quantify community resilience. The factor analysis method results in a weighted 

additive index model of six variables to derive community resilience. The index places every 

community in the Gulf of Mexico on a scale from 0 to 1. The most resilient counties in the Gulf 

of Mexico region were found to be Hillsborough, Florida, Pinellas, Florida, Sarasota, Florida, 

Hernando, Florida, Okaloosa, Florida, Kenedy, Texas, and Jefferson, Louisiana with a resilience 

score of 1. The least resilient counties in the Gulf of Mexico were found to be Cameron, Texas 

and Willacy, Texas with a resilience score of below 0.40. The six key variables used to create the 

resilience index were expenditures for education, median income, percent of the workforce that 

is female, mean elevation of the parish, percent of the population below five years old, and 

percent of the population that voted in the 2000 presidential election. 

The second method is a discriminant analysis method. In this method an a priori

grouping based on the number of coastal hazards, property damage, and population change for 
50 

each county was derived. Twenty-four social, economic, and environmental variables were input 

into the discriminant analysis to determine if they can be used to explain and define resilience. 

The discriminant analysis results in a classification accuracy of 94.2%. Counties found to be in 

the most resilient group were Hancock, Mississippi, Collier, Florida, Baldwin, Alabama, 

Escambia, Florida, Walton, Florida, Lee, Florida, Charlotte, Florida, Manatee, Florida, Santa 

Rosa, Florida, Okaloosa, Florida. Counties found to be in the least resilient group were Kleberg, 

Texas, Calhoun, Texas, San Patricio, Texas, Jefferson, Texas, Nueces, Texas, Kenedy, Texas, 

and Willacy, Texas. 

This study represents a preliminary attempt in quantifying community resilience. It 

outlines the methods that can be used to define resilience and offers a general guideline about the 

variables that might contribute to a communities’ ability to recover from a coastal disaster. 

Further refinements with the variables are necessary in future studies. 
The Department of the Interior Mission

As the Nation’s principal conservation agency, the Department of the Interior has 

responsibility for most of our nationally owned public lands and natural 

resources. This includes fostering the sound use of our land and water 

resources, protecting our fish, wildlife and biological diversity; preserving the 

environmental and cultural values of our national parks and historical places; and 

providing for the enjoyment of life through outdoor recreation. The Department 

assesses our energy and mineral resources and works to ensure that their 

development is in the best interests of all our people by encouraging stewardship 

and citizen participation in their care. The Department also has a major 

responsibility for American Indian reservation communities and for people who 

live in island communities. 

The Bureau of Ocean Energy Management 

The Bureau of Ocean Energy Management (BOEM) works to manage the 

exploration and development of the nation's offshore resources in a way that 

appropriately balances economic development, energy independence, and 

environmental protection through oil and gas leases, renewable energy 

development and environmental reviews and studies.