Economic Resilience and Well-being of
Gulf Communities:
Environmental Disaster Events and Changes in
Economic Diversity
Jason Chun Yu Wong
University of Maryland
Environmental Science and Policy – Environmental Economics
Resilient Coastal Communities and Economies
National Centers for Coastal Ocean Science - Hollings Marine Laboratory, Charleston, SC
Dr. Susan Lovelace, Maria Dillard
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Outline
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•
•
•
•
•
•
•
Motivation
Questions
Methodology
Results & Analysis
Future Research
My Next Step
Acknowledgement
Sources
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Motivation
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Gulf of Mexico Timeline
Hurricane Rita- $108,755
decrease in Revenue at
Sea Rim State Park
(Jefferson, TX)
Hurricane Dennis$3.4b in property
damages
2003
2004
Hurricane Ivan$6.6b in property
damages (AL, FL,
LA, MS)
2005
2006
2007
2010
Deepwater Horizon Oil
Spill approx. 205.8
million gallons
Hurricane Katrina- Total
Resident Population declined
from 455,188 on July 1, 2005 to
208,548 on July 1, 2006 in
Orleans, LA
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Why do we care about
economic diversity?
• Conventional wisdom:
portfolio and risk
diversification – shock
resistance
• Don’t put all your eggs in
one basket!
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Research Questions
• Was there a significant difference in economic
diversity over time, from 2003-2007, in Gulf
States counties?
• What socioeconomic factors could help
explain economic diversity? Does the number
of environmental disaster events affect
economic diversity?
• How could we interpret economic resilience
from diversity indicators?
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Measuring Economic Diversity
By Employment, or Earnings
Secondary Data Needs:
U.S. Census, Bureau of Economic Analysis
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Measuring Economic Diversity
• This study uses four
static measures:
Ogive, National
Average, Herfindahl,
and Entropy/Log
Share
• T-Tests, Wilcoxon
Rank Sum,
Correlation and
Regression Analysis
National Average
𝑆𝑖
𝑁𝑖 =
𝑠=1
Ogive
𝑆𝑖
𝑂𝑖 =
𝑠=1
𝑒𝑠𝑖 𝑒𝑠
−
𝑒𝑖
𝑒
𝑒𝑠
𝑒
2
𝑒𝑠𝑖 1
−
𝑒𝑖 𝑠𝑖
1
𝑠𝑖
2
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Results: Difference in Diversity?
Distribution of 𝐷03−07 (𝑁𝑖), by employment
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Wilcoxon Signed-Rank Results
Non-Coastal, n=441
Coastal, n=139
Paired Years
Indicator
2003-2007
0.0131
2003-2007
0.0346*
2003-2007
0.0624*
2003-2007
-0.0647*
• Coastal counties
experienced less
significant changes
in industrial
composition
compared to noncoastal counties
from 2003-2007
* Pr > |S| < 0.05
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Results: What can help explain
economic diversity?
• Correlation: using existing socioeconomic
dataset on coastal counties, selected variables
based on literature and data availability
• Count of Environmental Disaster Events in
2005 included
• Ran multiple regressions (standard, Max R,
Stepwise); checked for multicollinearity with
Variance Inflation Factor
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Correlation
2007
2007
Pearson R
Pearson R
% Working age
-0.01360
-0.10700
% Female
-0.35496*
-0.11879
% Hispanic/Latino
0.26036*
0.21240*
Income per capita
-0.24315*
-0.25622*
Poverty rate
0.09405
0.05459
Unemployment
-0.06750
0.10999
Environmental disaster events,
2005
-0.23995*
-0.21269*
* Pr > |r| < 0.05
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Regression
Variance Inflation
Factor
2007
β
β
R2
0.2640
0.1512
-
Intercept
12.70078
1.66464
0
% Working age
-0.05408
-0.00268
1.95517
% Female
-0.13964*
-0.00409
1.82239
% Hispanic/Latino
0.01037*
0.00253*
1.43765
Income per capita
-0.00002434
-0.00001154*
1.91350
Poverty rate
-0.00597
-0.01204*
2.10565
Unemployment
-0.19043*
0.01005
1.49754
Environmental disaster events,
2005
-0.03147
-0.00765
1.09909
* Pr > |t| < 0.05
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Limitations
• Time frame & boundary problem
• Population may have a threshold
effect
• Factors requiring population-adjusted
measures: Education, Resource
availability
• Specialization may be stable
• Diversity measures sensitive to
number of industries
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Economic Resilience
• Employment diversity = resilience?
• How to measure the ability of communities
“bounce back”
• Simulation-based modeling
– Using Computable General Equilibrium (CGE)
– Hypothetical, constructed measure
• Retrospective data analysis
– Adapted the resilience method for ex post facto
resilience analysis
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Conclusion
• Framework for economic well-being assessment
• Industrial composition in coastal communities is
“mature” and established
• Environmental disasters were not a strong
explanatory variable for economic diversity
• Time frame may be a significant factor that is
industry-dependent
• Future research – finance and input-output
methods
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My Next Step…
• Honors Thesis – Contingent Valuation Survey – Rain
Gardens
• Germany Study Abroad – International Environmental
Law
• Interdisciplinary PhD in Environment and Resources:
Environmental Economics, Sociology, Law, Policy
• Economics of Climate Change and Valuation of the
Environment; Non-Market Valuation Techniques; Survey
Methodology
• A teacher, a researcher, and a scholar in environmental
economics and policy
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Acknowledgements
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Susan Lovelace
Maria Dillard
Lauren Brown, C of C MES ‘12
Mark Plummer, NMFS, NWFSC
Peter Edwards, NMFS, Habitat
Jeffery Adkins, NOS, CSC
Linwood Pendleton, Acting Chief Economist
HML Human Dimensions Lab Members
HML admin and IT staff; OEd Scholarship Team
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Sources
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•
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US Census, Bureau of Economic Analysis
Wagner, J. E. (2000). "Regional Economic Diversity: Action, Concept, or State of
Confusion." Journal of Regional Analysis & Policy 30(2): 1-22.
Rose, A. L., Shu-Yi (2005). "Modeling Regional Economic Resilience to Disasters: A
Computable General Equilibrium Analysis of Water Service Disruptions." Journal of
Regional Science 45(1): 75-112.
Rose, A. (2004). "Defining and measuring economic resilience to disasters."
Disaster Prevention and Management 13(4): 307-314.
State of Hawaii (2008). Measuring Economic Diversification In Hawaii, Research
and Economic Analysis Division, Department of Business, Economic Development
and Tourism, State of Hawaii.
Manyena, S. B. (2006). "The concept of resilience revisited." Disasters 30(4): 435450.
Quigley, Thomas M.; Haynes, Richard W; Graham, Russell T., tech. eds. 1996.
Integrated scientific assessment for ecosystem management in the interior
Columbia basin and portions of the Klamath and Great Basins. Gen. Tech. Rep.
PNW-GTR-382. Portland, OR: U.S. Department of Agriculture, Forest Service,
Pacific Northwest Research Station
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Thank you!
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ex post facto resilience indicator
• 𝑅𝑖 =
•
•
𝑝𝑖 −𝐸(𝑝𝑖 )
, 𝐸(𝑝𝑖 )
𝐸(𝑝𝑖 )
≠0
Where psi= actual performance of industry s in region i
And E(psi)= expected performance of industry s in region i.
• For 𝐸(𝑝𝑖 ) ≠ 0,
• 𝑅𝑖 = 0 𝑤ℎ𝑒𝑛 𝑝𝑖 = 𝐸(𝑝𝑖 ), the event had no effect on the
ability of the economy to meet its expected performance;
• 𝑅𝑖 < 0 𝑤ℎ𝑒𝑛 𝑝𝑖 < 𝐸(𝑝𝑖 ), the event reduced the ability for
the economy to meet its expected performance;
• 𝑅𝑖 > 0 𝑤ℎ𝑒𝑛 𝑝 > 𝐸(𝑝𝑖 ), the event in fact increased the
ability for the economy to meet its expected performance.
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