Popul Environ (2008) 29:271–291
DOI 10.1007/s11111-008-0072-y
ORIGINAL PAPER
Social vulnerability and migration in the wake of
disaster: the case of Hurricanes Katrina and Rita
Candice A. Myers Æ Tim Slack Æ Joachim Singelmann
Published online: 16 September 2008
Springer Science+Business Media, LLC 2008
Abstract This study explores the relationship between place-based social
vulnerability and post-disaster migration in the U.S. Gulf Coast region following
Hurricanes Katrina and Rita. Using county-level data from the U.S. Census Bureau,
we develop a regional index of social vulnerability and examine how its various
dimensions are related to migration patterns in the wake of the storms. Our results
show that places characterized by greater proportions of disadvantaged populations,
housing damage, and, to a lesser degree, more densely built environments were
significantly more likely to experience outmigration following the hurricanes. Our
results also show that these relationships were not spatially random, but rather
exhibited significant geographic clustering. We conclude with a discussion of the
implications of these findings for future research and public policy.
Keywords
Disaster Migration Vulnerability
Introduction
On August 29, 2005, Hurricane Katrina made landfall as a Category 3 storm near
the Louisiana–Mississippi state border. The hurricane created catastrophic damage
along the coasts of Louisiana, Mississippi, and Alabama, including a storm surge
that breached the levee system protecting New Orleans, leading to widespread
flooding in the city. Katrina was the costliest, and among the deadliest, hurricanes to
ever strike the United States, ranking it among the most devastating disasters in the
nation’s history (Knabb et al. 2005). Less than 1 month later, on September 24,
2005, Hurricane Rita made landfall near the Louisiana–Texas state border. Also a
C. A. Myers (&) T. Slack J. Singelmann
Department of Sociology, Louisiana State University, 126 Stubbs Hall,
Baton Rouge, LA 70803, USA
e-mail: [email protected]
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Category 3 storm, Rita resulted in extensive damage throughout the region,
including a tremendous storm surge that devastated the coastal parishes of
southwestern Louisiana (Knabb et al. 2006). Together these storms prompted one of
the greatest disaster-related migrations in U.S. history, and also provided a vivid
illustration of how the cleavages of social inequality can influence vulnerability to
disasters (Brunsma 2007; Oliver-Smith 2006).
While substantial research attention has been devoted to understanding the
biophysical dimensions of disaster vulnerability, far less attention has been paid to
the social conditions that make people and places more or less susceptible to
environmental hazards (Cutter et al. 2003). This relative neglect is curious given
that social context plays a key role in shaping differentials in disaster-related risks
as well as how people come to conceptualize losses and other impacts incurred in
the wake of a catastrophic event. As the tremendous population shifts in the Gulf
Coast region following Hurricanes Katrina and Rita clearly illustrated, one
important social consequence of disaster-related losses is migration (Hugo 1996;
Hunter 2005). Indeed, as noted by Oliver-Smith (2006), ‘‘migration, whether
permanent or temporary, has always been a traditional response or survival strategy
of people confronting the prospect, impact or aftermath of disasters.’’ To date,
research has examined the factors that contribute to the social vulnerability of places
as well as how disasters influence human migration. However, little research has
joined these bodies of work to provide a macro-level assessment of how social
vulnerability influences migration in the wake of disaster. This study seeks to help
bridge this gap by examining the relationship between place-based vulnerability and
migration in the Gulf Coast region following Hurricanes Katrina and Rita.
Conceptualizing disasters and social vulnerability
The conceptualization of ‘‘disaster’’ has long been a subject of debate among social
scientists (Kreps 1984, 1995; Quarantelli 1987, 1989, 1993, 1998). While no clear
consensus has been reached, there is wide agreement that disasters are inherently
sociological processes (Bolin 1998; Perry and Quarantelli 2005; Quarantelli 1989,
2000; Quarantelli and Dynes 1977; Smith 2006). Quarantelli (2000, p. 682) defines
disasters as occurrences when ‘‘the routines of collective social units are seriously
disrupted and when unplanned courses of action have to be undertaken to cope with
the crisis.’’ Bolin (1998, p. 27) echoes this view, stating ‘‘disasters are fundamentally social phenomena; they involve the intersection of the physical process of a
hazard agent with the local characteristics of everyday life in a place and larger
social and economic forces that structure that realm.’’ In fact, Smith (2006) refutes
the very idea that any disaster is ‘‘natural’’ in a pure sense, stating ‘‘there is no such
thing as a natural disaster. In every phase and aspect of a disaster—causes,
vulnerability, preparedness, results and response, and reconstruction—the contours
of disaster and the difference between who lives and who dies is to a greater or
lesser extent a social calculus.’’
There are, of course, a wide range of agents that can act as catalysts for disasters.
Examples include environmental degradation, such as drought and desertification;
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biological hazards, such as insect infestation and disease epidemics; technological
hazards, such as oil spills and other pollutants; geophysical hazards, such as
hurricanes and tsunamis; and social hazards, such as war, terrorism, and other types
of civil unrest (Dynes and Drabek 1994; McGuire et al. 2002; Picou et al. 2004).
Katrina, in particular, was characterized by almost all of these factors and brought
into clear focus the concept of social vulnerability (Picou and Marshall 2007).
The notion of social vulnerability centers on the influence of social and economic
stratification in relation to disasters (Cutter 1996; Cutter et al. 2003; Hewitt 1998;
Oliver-Smith 1996; Oliver-Smith and Hoffman 1999; Tierney 2006) and puts ‘‘the
main emphasis on the various ways in which social systems operate to generate
disasters by making people vulnerable’’ (Wisner et al. 2004, p. 10). In other words,
social vulnerability refers to ‘‘the characteristics of a person or group in terms of
their capacity to anticipate, cope with, resist, and recover from the impact of a
natural hazard’’ (Wisner et al. 2004, p. 11). The consideration of social vulnerability
encourages the framing of disasters as social phenomena moderated by the existing
social structure. Moreover, place-based conceptualizations of social vulnerability
emphasize the socioeconomic features of a delimited spatial area, such as
community composition and stratification, and how such features influence
susceptibility to disasters (Cutter et al. 2003). In the U.S., the specter of social
vulnerability has been tragically illustrated not only by recent hurricanes, but also
by heat waves (Browning et al. 2006; Klinenberg 2002) and other events.
One method used to empirically quantify social vulnerability is the hazards-ofplace model developed by Cutter et al. (2003). This approach draws on aggregate
(county-level) socioeconomic and demographic data to construct an index of placebased social vulnerability. Using factor analysis, a range of variables evolving from
the vulnerability literature is reduced to a smaller number of independent constructs.
Using 1990 data at the national level, the factors that emerged from this process
included measures tapping county-level wealth, the age structure, the density of the
built environment, housing stock and tenancy, the racial and ethnic composition, the
occupational structure, and infrastructure dependence. Cutter et al. (2003) sum the
emergent factor scores to produce a comprehensive vulnerability score for each
county, which they coin the Social Vulnerability Index (SoVI). Their research
highlights the significance of the relationships between various dimensions of social
vulnerability and disaster-related outcomes, and how a hazards-of-place approach
can facilitate an understanding of the geographic contours of regional disasters. We
use this approach as a springboard for the analysis that follows.
Disasters and migration
In a synthesis of the literature on migration and environmental hazards, Hunter
(2005) notes that while a number of classic theoretical perspectives acknowledge
that environmental considerations can influence migration, such considerations are
rarely emphasized. Theoretical perspectives that do make note of environmental
influences include the ‘‘stress-threshold’’ model (Wolpert 1966) and the notion of a
‘‘threshold of dissatisfaction’’ (Speare 1974), which weigh the influence of
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environmental amenities (and disamenities) on one’s satisfaction in a particular
context and, thus, the likelihood that one chooses to migrate. In a similar vein, the
‘‘value-expectancy’’ model (DeJong and Fawcett 1981) views the value placed on,
and expected likelihood of, individual goals such as safety and comfort in a
residential context as central to migration motivations.
While most migration theories necessarily take a micro-level orientation, the
early work of Petersen (1958, 1975) argues that macro-level factors should be
incorporated into theories of migration to recognize the importance of ecological
‘‘pushes’’ that encourage people to move. Further, Gardner (1981) argues that the
study of migration decisions should account for the broader macro-level context
(social, economic, and geographic) in which individuals are embedded. Because
these perspectives draw attention to the importance of ecological context in
influencing migration, they have proven important for understanding population
redistributions initiated by disasters; in particular, how environmental hazards can
serve as a migration ‘‘push’’ factor (Belcher and Bates 1983; Hugo 1996; Hunter
2005; Hunter et al. 2003). Hugo (1996) extends these ideas by arguing that the
relationship between migration and environment should be understood on a
continuum ranging from migration that is totally voluntary (i.e., entirely the choice
of the migrant) to forced (i.e., migrants must leave their present location or face
death). In the case of the hurricanes under study here, as well as many other
disasters, initial migration is indeed forced, though the decision to return to the
place of origin may become a more individualistic cost–benefit analysis as time
progresses.
As noted earlier, an important contextual consideration in the study of disasters is
social vulnerability. Previous research has identified features of the social fabric that
significantly influence migration in the wake of disaster. Studies in the context of
both developing and developed countries show socially disadvantaged or marginalized groups to be disproportionately susceptible to displacement from disasters
(Hunter 2005). In the United States, Morrow-Jones and Morrow-Jones (1991) used
national-level data to determine how disaster-related migration differs from other
forms of migration. Their work identifies female-headed households, the elderly,
racial minorities, the poor, and the less educated as being especially likely to
migrate following a disaster. Additional research focused on the interrelationships
between hazards, social inequality, and migration has produced similar findings
(Belcher and Bates 1983; Enarson 1998; Fordham 1999; Haas et al. 1977).
Housing damage is an additional reason why those with fewer economic and
social resources are more likely to migrate. Research on hurricane mitigation in
south Florida shows that lower socioeconomic status households are more likely to
reside in housing that is substandard and/or inadequately equipped to withstand a
storm, and are less likely to have undertaken disaster mitigation efforts on their
homes (e.g., installing hurricane-resistant roofing and windows), compared to higher
socioeconomic status households (Peacock and Girard 1997). Additional research
notes that poorer persons are more likely to be renters, mobile home occupants, and/
or to reside in housing with lower-quality construction, thus heightening the threat
from environmental hazards (Fothergill and Peek 2004; Tierney 2006). Research
also suggests that the resources (e.g., assets, insurance) available to those of higher
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socioeconomic status better enables them to maintain their residences and
livelihoods in the wake of a disaster (Morrow-Jones and Morrow-Jones 1991;
Peacock and Girard 1997).
The density of the human and built environment also influences the potential for
disaster-related losses. When highly populated and developed areas are struck by
disasters, the magnitude of residential and commercial loss is magnified (Cutter
et al. 2003), thus increasing the odds of mass displacement. Local economic
conditions are also relevant in shaping disaster vulnerability and, in turn, migration.
For example, when local economies are depressed pre-disaster, such conditions tend
to be exacerbated post-disaster, leaving communities devoid of the fiscal resources
necessary to recover from such an event (Cutter and Emrich 2006). Furthermore,
over-dependence on a single economic sector (versus a more diverse economy)
increases vulnerability, because if the sector is destroyed, so is the local ability to
maintain a livelihood (Gramling and Freudenburg 1990; Freudenburg 1992). In
cases where economies and jobs are devastated by disasters, outmigration is the
rational economic response for workers and their families.
In sum, vulnerability studies have encouraged the scientific community to
recognize that social stratification plays a significant role in shaping disasters. In
what follows, we employ the hazards-of-place based approach to measuring social
vulnerability (Cutter et al. 2003) to examine migration in the Gulf Coast region
following Hurricanes Katrina and Rita. Based upon the literature, we expect that
vulnerability of place is negatively related to net migration. That is, we expect that
more socially vulnerable places will have incurred greater population losses in the
wake of the storms. The purpose of our analysis is to differentiate between various
dimensions of social vulnerability in the region and to test whether the emergent
factors influence migration as anticipated.
Data and methods
Data
We analyze demographic, social, and economic data drawn from a variety of
sources made available by the U.S. Census Bureau, including the Population
Estimates Program; Summary Files 3 and 4 from the 2000 Census; the County and
City Data Book: 2000; and USA Counties. The geography of the study area
comprises counties/parishes (for brevity, from here on ‘‘counties’’) in the Gulf Coast
region that were most affected by Hurricanes Katrina and Rita. More specifically,
counties included in this study are those in which residents were eligible for
Individual and Public Assistance (IPA) following the storms, a status determined by
the Federal Emergency Management Agency (FEMA). Public assistance was
provided to governmental and nonprofit organizations for purposes such as debris
removal, emergency protective measures, and the repair or replacement of disasterdamaged facilities. Individual assistance was granted to individuals and households
for housing, medical, dental, funeral, and transportation expenses related to the
disaster. In total, this region includes 117 counties within four Gulf Coast states:
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Fig 1 Counties designated for individual and public FEMA assistance following Hurricanes Katrina and
Rita
Alabama, Louisiana, Mississippi, and Texas (U.S.Census Bureau 2006). Figure 1
provides an illustration of the hurricane-impacted region, demarcated by the shaded
area. The 117 counties in this region are the units of analysis in this study.
Dependent variable
The dependent variable used for this analysis is percent net migration for the period
from July 1, 2005, to July 1, 2006. More specifically, percent net migration refers to
county-level net migration for the 1-year period (July 1, 2005—July 1, 2006)
expressed as a percentage of the county population at the beginning of the period
(July 1, 2005). Hurricanes Katrina and Rita struck the Gulf Coast at the beginning of
this period, on August 29, 2005, and September 24, 2005, respectively. Thus, the
selected period includes the pre-hurricane and post-hurricane population. Given the
tremendous exodus of people from the storm ravaged areas along the coast where
the hurricanes made landfall (see the storm paths in Fig. 1), the distribution of
percent net migration exhibited substantial negative skew, which is often indicative
of impending problems with unequal error variance and the associated biasing of
standard errors in standard linear regression models. We therefore transformed the
variable by first subtracting all values from the highest value plus 1 (creating an
inverted distribution of all positive values) and then taking the natural logarithm to
normalize its distribution. This inverted distribution is important to note for the
purpose of interpretation. That is, prior to the transformation percent net migration
ranged from -75.1% (St. Bernard Parish, LA) to 8.7% (Pearl River County, MS),
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Fig 2 Percent net migration (ln) for counties in the impacted region, July 1, 2005—July 1, 2006
while following the transformation it ranged from 0 (Pearl River County, MS) to 4.4
(St. Bernard Parish, LA).
The regional distribution of our transformed dependent variable is illustrated in
Fig. 2. Especially notable here are those places one standard deviation above the
mean (substantial outmigration) and one standard deviation below the mean
(substantial inmigration). Those areas that experienced the greatest population
losses due to migration were directly in the paths of the hurricanes. Jefferson,
Orleans, Plaquemines, and St. Bernard Parishes in Louisiana and Hancock and
Harrison Counties in Mississippi were directly impacted by Hurricane Katrina,
while Cameron Parish, Louisiana, on the Texas/Louisiana state border, was directly
impacted by Hurricane Rita. On the other hand, those areas that experienced the
greatest population gains due to migration were just inland from the counties that
took a direct hit from the storms. These places include Pearl River and Stone
Counties in Mississippi and Ascension, East Baton Rouge, Livingston, St. Helena,
St. John the Baptist, St. Tammany, and Tangipahoa Parishes in Louisiana.
Independent variables
The independent variables selected for this analysis are indicators of social
vulnerability that evolve from the literature. Specifically, we used 24 variables that
measure various socioeconomic and demographic characteristics at the county level.
Table 1 provides descriptive statistics for all of the variables employed in the
analysis, while Table 2 lists each of the independent variables and its expected
association with place-based vulnerability. In broad terms, these variables tap
various dimensions of vulnerability, such as the density of the human and built
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Table 1 Descriptive statistics
Variables
Mean
SD
Min
Max
Dependent variable
Percent net migrationa
–0.75
9.20
–75.07
8.65
24,985.00
Independent variables
Per capita income ($)b
15,444.03
2,856.21
9,709.00
Percent household income [ $75,000b
12.89
5.90
5.75
41.13
Percent in poverty
19.58
5.92
7.04
37.92
Percent C 25 years w/o h.s. diploma
28.56
6.63
15.67
46.75
Percent female-headed households
15.24
4.27
7.54
29.37
Percent w/o health insurance
17.07
2.60
9.38
24.24
Percent white, non-Hispanicb
63.54
16.62
13.00
93.70
2,943.00
9,803.00
73.00
71,695.00
185,358.00
674,286.00
9,767.00
6,973,090.00
7.75
2.27
3.80
18.00
51.64
Earnings in all industries/mi2
b
Local government earnings ($)b
Percent unemployed
b
Percent participating in labor force
42.40
4.54
30.65
Number of physicians/1,000 populationb
1.31
1.30
0.10
6.74
Per capita number of hospitalsb
0.0001
0.0005
0.00
0.003
Percent 5 years and younger
7.87
2.10
4.43
15.44
12.56
2.94
5.63
24.87
Per capita living in nursing homes
0.02
0.05
0.00
0.24
Per capita social security recipients
0.18
0.04
0.07
0.38
Percent urban
39.79
30.76
0.00
99.33
Number of housing units/mi2
60.00
145.27
4.00
1,191.00
3.28
8.49
0.13
58.81
Percent of housing units renter-occupied
23.07
8.06
11.83
53.50
Percent of housing units mobile homes
22.06
8.74
0.34
42.41
63,093.16
1,7361.48
39,700.00
116,000.00
297.74
96.12
125.00
614.00
7.48
16.87
0.40
90.20
Percent 65 years and older
Number of commercial establishments/mi2
Median value owner-occup. housing ($)b
Median rent ($)b
Percent occupied housing w/damage
Notes: SD = standard deviation. Max = maximum value. Min = minimum value
a
Original distribution presented, but variable was transformed to its natural log for analysis
b
Original distributions are presented, but variables were rescaled as their inverse (1/x) for the factor
analysis
N = 117
environment, the economic structure, and social inequality, that lead to differential
levels of disaster-susceptibility. All variables were scaled so that higher values
indicate greater social vulnerability and lower values indicate lesser social
vulnerability. This required rescaling ten of the variables as the multiplicative
inverse (1/x) of their original value. For example, per capita income is rescaled to
indicate greater social vulnerability among counties where per capita income is low
and lesser social vulnerability among counties where per capita income is high. In
addition to the variables selected to tap social vulnerability, we also drew data from
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Table 2 Variable associations with social vulnerability
Variables
Expected association
with vulnerability
Per capita income (in dollars)
(-)
Percent household income [ $75,000
(-)
Percent in poverty
(?)
Percent C 25 years w/o h.s. diploma
(?)
Percent female-headed households
(?)
Percent pop. w/o health insurance
(?)
Percent white, non-Hispanic
(-)
Earnings in all industries/mi2
(-)
Local government earnings (in dollars)
(-)
Percent unemployed
(?)
Percent participating in labor force
(-)
Number of physicians/1,000 population
(-)
Per capita # of community hospitals
(-)
Percent 5 years and younger
(?)
Percent 65 years and older
(?)
Per capita living in nursing homes
(?)
Per capita social security recipients
(?)
Percent urban
(?)
Number of housing units/mi2
(?)
Number of commercial establishments/mi2
(?)
Percent of housing units renter-occupied
(?)
Percent of housing units mobile homes
(?)
Median dollar value owner-occup. housing
(-)
Median rent (in dollars)
(-)
Note: See Cutter et al. (2003) for greater elaboration on vulnerability concepts and metrics
the Greater New Orleans Community Data Center (2006) to include the percentage
of occupied housing units that received storm damage. Overall, our expectation is
that areas characterized by greater social vulnerability and housing damage will
have experienced greater outmigration following the storms.
Analytic strategy
In the analysis that follows we employ the analytic approach pioneered by Cutter
et al. (2003). More specifically, we use principal component factor analysis and
varimax rotation to reduce the 24 predictor variables to a smaller set of underlying
and independent dimensions of social vulnerability in the Gulf Coast region. We
then use the resulting dimensions of social vulnerability to develop a cumulative
regional SoVI as well as to estimate Ordinary Least Squares (OLS) and spatial
regression models aimed at parsing out the relationships between various
dimensions of social vulnerability and migration following the storms.
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Table 3 Social Vulnerability Index (SoVI)
Factor
Percent variance
explained
Dominant variable
Disadvantaged populations
26.3
Percent living in poverty
Less development
20.2
Number of physicians/1,000 populationa
Density of built environment
12.5
Number of housing units/mi2
Elderly populations
12.2
Dependent populations
a
8.5
Percent 65 years and older
Percent 5 years and younger
Rescaled as inverse (1/x)
Results
Table 3 presents summary results from the factor analysis. From the original 24
variables, a five-dimension factor structure emerges that explains 79.7% of the
variance among the counties in the hurricane-impacted region. These emergent
dimensions include: (1) disadvantaged populations; (2) less development; (3) density
of the built environment; (4) elderly populations; and (5) dependent populations. We
discuss each of these five dimensions of social vulnerability in further detail below.
Dimensions of social vulnerability
Disadvantaged populations
The first factor clearly identifies socially disadvantaged populations. Specifically,
the following variables load positively on this factor: per capita income (inverse),
median dollar value of owner-occupied housing (inverse), median rent (inverse),
unemployment, households with incomes over $75,000 (inverse), poverty, high
school dropouts, labor force participation (inverse), female-headed households, lack
of health insurance, and the proportion of the population that is non-Hispanic, white
(inverse). This factor accounts for 26.3% of the variance among counties in the
region.
Less development
The second factor clearly illustrates areas characterized by less development. The
number of physicians (inverse), government earnings (inverse), household units that
are mobile homes, and earnings in all industries (inverse) all load positively on this
factor, while renter-occupied housing units, community hospitals, and urban
populations all load negatively. This factor accounts for 20.2% of the variance
among the counties under study.
Density of the built environment
The third factor identifies the density of the built environment and explains 12.5%
of the variance among counties in the region. Specifically, both the number of
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commercial establishments and the number of housing units per square mile load
positively on this construct.
Elderly populations
The percentage of the population over age 65 and per capita Social Security
recipients load positively on the fourth factor. This factor is clearly associated with
elderly populations and accounts for 12.2% of the variance among counties in the
region.
Dependent populations
The fifth and final factor is characterized by variables that represent dependent
populations. Both the percentage of young children (under age 5) and per capita
nursing home residents load positively on this construct. This factor accounts for
8.5% of the variance among the counties examined.
The geography of social vulnerability
Consistent with the method employed by Cutter et al. (2003), we extracted the
factor scores associated with each of the five dimensions of vulnerability and then
summed the five scores to create a cumulative SoVI score for each county. The
geography of social vulnerability is illustrated in Fig. 3. A notable takeaway from
this map is that the greatest cluster of highly vulnerable places is not along the coast,
Fig 3 The geography of social vulnerability for counties in the impacted region
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but further inland in western Alabama. This area is characterized by a high
concentration of rural African Americans and high and persistent poverty, products
of the historical legacy of the slave-based plantation economy. Indeed, the most
socially vulnerable area in the impacted region is Greene County, Alabama, which
has a SoVI score three standard deviations above the regional mean. In contrast, the
least socially vulnerable area in the impacted region is West Feliciana Parish,
Louisiana, which has a SoVI score approximately two standard deviations below the
regional mean. This area is largely rural and serves as a bedroom community to
Baton Rouge, the Louisiana state capital and a regional hub for higher education and
petrochemical industries. (A caveat is that West Feliciana is also home to a
maximum security state penitentiary. While imprisoned populations could arguably
be considered a source of social vulnerability in a disaster context, institutionalized
populations are not included in the data used to create our index). Clearly, the most
socially vulnerable places are clustered in the eastern portion of the impacted
region. However, what is also notable for our purposes is that two of the Louisiana
parishes hit hardest by Katrina also have high SoVI scores, Orleans (New Orleans)
and Jefferson (suburban New Orleans) Parishes near the ‘‘toe’’ of Louisiana.
Spatial effects
Before estimating regression models to parse out the relationship between social
vulnerability and migration in the wake of the hurricanes, we considered the issue of
spatial effects on both substantive and statistical grounds. Substantively, in the wake
of the storms it is logical to expect that people will have been systematically
‘‘pushed’’ from those areas directly impacted by the storms and systematically
‘‘pulled’’ to other areas, likely metro areas further inland. That is, there is reason to
expect that migration patterns will not be spatially random, but rather geographically patterned. Statistically, in spatial analyses it is often the case that
geographically defined units of analysis (e.g., counties) are not fully independent
from one another. Instead it is common to observe spatial ‘‘clustering’’ of variables,
which can lead to the statistical issue of spatial autocorrelation. Failure to detect and
rectify problems presented by spatial autocorrelation can result in inaccurate
statistical inferences when using standard regression techniques (Baller et al. 2001;
Messner and Anselin 2004; Rupasingha and Goetz 2007; Voss et al. 2006). More
specifically, spatial autocorrelation can result from two key types of problems in
regression analysis: (1) spatial error (i.e., correlation across space in the error terms)
and (2) spatial lag (i.e., correlation across space in measured variables and error
terms). When spatial error is present in OLS regression models, the assumption of
uncorrelated residuals is violated, resulting in unreliable estimates. When spatial lag
is present in OLS regression models, the assumptions of independent observations
and independent error terms are violated, resulting in both biased and inefficient
estimates. Therefore, in cases where spatial error or spatial lag creates such
problems, specifications must be made to correct for spatial dependence.
Figure 2 strongly suggests that migration in the wake of the hurricanes was not
randomly distributed over space. Rather population gains and losses were
geographically clustered. As anticipated, those areas hit hardest by the storms
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witnessed great population losses. Conversely, those areas nearby the areas hit
hardest by the storms, but further inland, realized great population gains. This
clustering effect is quantified by the Moran’s I, a statistic which measures the degree
to which a variable is correlated across neighboring spatial units. In the perfect
absence of spatial dependence, the Moran’s I statistic has a mean of zero, while
higher or lower values indicate geographical clustering. As shown in Fig. 4, the
Moran’s I value for logged percent net migration is 0.44, indicating significant
positive spatial autocorrelation (clustering of like values).
Also shown in Fig. 4 is the Moran scatterplot, which plots the log of percent net
migration (horizontal axis) against its spatial lag (vertical axis) for each county. The
data are standardized so that units on the graph represent standard deviations from
the mean. The spatial lag is calculated as the standardized value of the logged
percent net migration averaged across a given county’s neighbors. We used the
‘‘first-order queen’’ convention to define a county’s neighbors, which includes any
counties that share a common border with a given county in any direction. The slope
of the line fitted to these data is the Moran’s I statistic cited above. The quadrants of
the scatterplot correspond to four types of spatial association: (1) the upper right
quadrant shows those counties with above average values on the dependent variable
that are neighbored by counties that also have above average values (high–high); (2)
the lower left quadrant shows those counties with below average values on the
dependent variable that are neighbored by counties that also have below average
values (low–low); (3) the upper left quadrant shows those counties with below
average values on the dependent variable that are neighbored by counties with
Fig 4 Moran scatterplot for percent net migration (ln), July 1, 2005—July 1, 2006
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above average values (low–high); and (4) the lower right quadrant shows those
counties with above average values on the dependent variable that are neighbored
by counties with below average values (high–low). The slope of the line fitted to
these data (the Moran’s I statistic) indicates significant clustering of like values (i.e.,
the line runs through the ‘‘low–low’’ and ‘‘high–high’’ quadrants). Substantively,
what this means is that counties that experienced outmigration following the storms
tended to be clustered with other counties that had the same experience (the data
points in the ‘‘high–high’’ quadrant), while counties characterized by inmigration
tended to be clustered with other counties that saw an influx of people (the data
points in the ‘‘low–low’’ quadrant).
Figure 5 shows a Local Indicators of Spatial Association (LISA) map, which
provides a geographic illustration of the same data presented in the Moran’s I
scatterplot. The LISA map shades significant clusters of counties falling into one of
the four value dimensions displayed in the scatterplot (high–high; low–low; low–
high; and high–low), while counties that are not in significant geographic clusters
are not shaded. This map clearly defines a significant ‘‘high–high’’ cluster including
the four parishes located in the ‘‘toe’’ of Louisiana: Jefferson, Orleans, Plaquemines,
and St. Bernard. These are some of the areas that were hit hardest by Hurricane
Katrina and, in particular, afflicted by widespread flooding due to the subsequent
levee failures, resulting in massive population losses following the storm. In
addition, the LISA map illustrates significant ‘‘low–low’’ clusters in both Louisiana
and Mississippi, a total of 12 counties, including those in the metropolitan areas of
Baton Rouge, Hattiesburg, and the New Orleans suburbs on the ‘‘North Shore’’ of
Lake Pontchartrain. Counties in these clusters witnessed significant population gains
Fig 5 LISA cluster map for percent net migration (ln), July 1, 2005—July 1, 2006
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285
following the hurricanes. These are places just north and inland of the areas hit
hardest by Katrina, primarily metropolitan areas that were nearby the most
devastated communities and could offer shelter and other amenities to the displaced.
In many respects, these places were ‘‘ground zero’’ for the evacuation efforts. Last,
two counties in coastal Mississippi—Hancock and Harrison—are identified as part
of a ‘‘high–low’’ cluster. These are places that experienced significant outmigration,
but were neighbored by areas that witnessed significant inmigration. This reflects
the area that took a direct hit from Katrina, including the cities of Gulfport,
Pascagoula, and Biolxi, and the movement of people from those places to areas just
inland of where the hurricane made landfall.
Regression analysis
In order to assess the relationship between migration and social vulnerability in the
wake of the hurricanes, we estimate models that regress the five dimensions of
social vulnerability and percent housing damage on the log of percent net migration.
Specifically, we begin by estimating an OLS regression model. The results,
presented in Table 4, reveal three significant factors associated with migration in the
wake of the storms. The presence of disadvantaged populations shows a significant
positive effect, indicating that places characterized by larger proportions of
disadvantaged populations were significantly more likely to witness outmigration
following the hurricanes. Further, density of the built environment also shows a
significant positive effect, suggesting that more densely developed places experienced significant population losses. Last, not surprisingly, the percentage of
occupied housing units damaged by the hurricanes shows a significant and positive
effect. That is, those places that experienced greater housing damage also saw
greater outmigration.
Table 4 Unstandardized regression coefficients for the relationship between percent net migration (ln)
and dimensions of social vulnerability
Independent variables
OLS
Disadvantaged populations
Less development
0.095*
-0.008
Spatial lag
(0.041)
(0.040)
0.075*
-0.001
(0.033)
(0.033)
Density of built environment
0.106**
(0.040)
0.063
Elderly populations
0.050
(0.040)
0.023
(0.033)
Dependent populations
0.022
(0.042)
0.002
(0.033)
Percent occupied housing units w/damage
0.007***
(0.002)
0.005**
(0.002)
Constant
1.992***
Moran’s I
Rho (Lag parameter)
0.339***
n/a
R2
0.196
(0.033)
0.786***
n/a
0.575***
0.443
Note: Standard errors are reported in parentheses
p \ .10; * p \ .05; ** p \ .01; *** p \ .001
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The results from the OLS model also show a significant and positive Moran’s I
statistic. This indicates that positive (like-like) spatial clustering is a threat to the
stability of the model, and that specifications to accommodate these spatial effects
are needed. We therefore re-estimate the model using both the spatial error and
spatial lag specifications to determine which provides the best model fit. The
existence of spatial error implies that geographic clustering is due to the influence of
unmeasured and spatially correlated variables, while spatial lag is suggestive of a
spatial diffusion (‘‘spillover’’) process. The results show that the spatial lag model
provides best fit, which supports the notion that the relationship between migration,
social vulnerability, and housing damage at the county level was influenced by a
spatial diffusion process. That is, following the storms the pattern of relationships
among these variables for a given county was significantly influenced by the
variable values among that county’s neighbors. As indicated by the r-square
statistic, the spatial lag model provides superior explanatory power compared to the
OLS model (.443 vs. .196, respectively). With the corrective term (rho) for
autocorrelation added to the model, the effects of disadvantaged populations and
housing damage are slightly ameliorated, but remain positive and significant. The
density of the built environment, however, falls just short of the conventional
threshold for statistical significance (p = .054). Substantively, these findings
demonstrate that following the hurricanes the migration experience of a given
county was not independent of the experiences of neighboring counties. In
particular, there was a significant cluster of counties that experienced substantial
outmigration. These were the areas in the southeastern ‘‘toe’’ of Louisiana that were
devastated following Katrina, largely due to the structural failure of the levee
system constructed to protect the area from flooding. Above and beyond this spatial
effect, places characterized by more disadvantaged populations, housing damage,
and, to a slightly lesser degree, more densely built environments were significantly
more likely to experience outmigration following the hurricanes.
Discussion and implications
The objective of this study was to examine the relationship between place-based
social vulnerability and migration in the U.S. Gulf Coast region following
Hurricanes Katrina and Rita. To achieve this objective we used factor analysis to
identify the underlying dimensions of social vulnerability at the county level in the
hurricane-impacted region. We then employed OLS and spatial regression
techniques to identify significant relationships between the emergent dimensions
of social vulnerability and regional migration patterns following the storms.
The results reveal five distinct dimensions of place-based social vulnerability in
the Gulf Coast region. These dimensions include disadvantaged populations, less
developed areas, density of the built environment, elderly populations, and
dependent populations. Regression analyses show that places characterized by
greater proportions of disadvantaged populations, housing damage, and, to a slightly
lesser extent, more densely built environments were significantly more likely to
experience outmigration in the wake of the storms. These findings are supported by
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287
research and media accounts of the disproportionate displacement of low-income
African American residents (Frey and Singer 2006) and the tremendous problems
posed by housing damage in terms of both stock and affordability (Dewan 2007), as
well as the fact that many of the places most severely impacted by the storms were
dense urban areas.
The regression results also show that significant spatial effects were at play in
shaping the relationship between social vulnerability and post-disaster migration.
Specifically, the results are suggestive of a diffusion process wherein the
relationship between social vulnerability and migration at the county level was
influenced by ‘‘spillover’’ effects from neighboring counties. The counties
experiencing the highest levels of outmigration were clustered around greater
New Orleans, an area devastated by flooding due to the infrastructural failure of its
levee system following Hurricane Katrina and whose problems were compounded
by an inadequate governmental response to the unfolding disaster. Relatedly, the
counties experiencing the greatest levels of inmigration were geographically
clustered further north and inland from the areas people were fleeing in the wake of
the storms, notably the metropolitan areas of Baton Rouge, Hattiesburg, and the
North Shore (suburbs of New Orleans north of Lake Pontchartrain). These were
areas that had not been as severely damaged and could offer refuge and other
amenities needed by the displaced. As stated earlier, these places were ‘‘ground
zero’’ for the evacuation efforts.
This study makes several contributions to the scientific literature. By building on
the hazards-of-place based SoVI approach developed by Cutter et al. (2003) and
linking this method to the migration and environmental hazards literature (see
Hunter 2005), we provide an important bridge between these two bodies of work.
The results show clearly that social vulnerabilities of place—the presence of
disadvantaged populations especially—were at play in shaping county-level
migration following Hurricanes Katrina and Rita. This dovetails with other
vulnerability studies that have brought attention to the nexus between social
inequality and post-disaster migration (Belcher and Bates 1983; Enarson 1998;
Fordham 1999; Fothergill and Peek 2004; Tierney 2006; Haas et al. 1977; MorrowJones and Morrow-Jones 1991; Peacock and Girard 1997). In particular, this
analysis contributes to the growing body of sociological research on Hurricane
Katrina, such as that compiled in the edited volume ‘‘The Sociology of Katrina:
Perspectives on a Modern Catastrophe’’ (Brunsma et al. 2007). This is especially
true of the study contributed by Branshaw and Trainor (2007), which used a mixedmethods approach to demonstrate how the structured inequalities of race, class, and
capital prior to the storm influenced people’s ‘‘choice’’ to evacuate following
Katrina.
Another important contribution of our research is the consideration of spatial
effects. By explicitly testing for, and accommodating, spatial effects in our analysis,
we not only arrive at a better specified statistical model, but also find evidence of
spatial ‘‘spillover’’ effects (i.e., migration patterns in a given county were related
both to dimensions of social vulnerability particular to that county, and to the
migration patterns and social vulnerability of the counties that surround it). This
finding is important on methodological, theoretical, and substantive grounds.
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Methodologically, the implication is that spatial dependence—always a statistical
threat when using geographically defined units of analysis—is an especially
important consideration when modeling post-disaster migration, a process that is
likely to be geographically patterned. Theoretically, the implication is that
geographic space and place are important conceptual considerations in framing an
understanding of the connection between vulnerability and disaster-related migration. And substantively, the implication is that in the wake of a disaster it should be
expected that people will be systematically ‘‘pushed’’ from areas directly impacted
by the disaster and systematically ‘‘pulled’’ to other areas as they leave. These ideas
represent important contributions to the literature aimed at understanding population
redistributions initiated by disasters (Belcher and Bates 1983; Hugo 1996; Hunter
2005; Hunter et al. 2003; Oliver-Smith 2006).
It should be noted that the analytic approach we undertake here is not without
limitations. For example, while the use of aggregate data from secondary sources
allows us to establish trends and generalizations about large-scale population
processes, we necessarily miss out on the context and depth allowed by qualitative
methods. The richness allowed by qualitative approaches could teach us volumes
about the relationship between social vulnerability and migration. Further, the use of
secondary data as opposed to primary data, whether qualitative or quantitative,
necessarily constrains the types of relationships we can assess because we are
limited to what is available from existing data sources. The use of aggregate data at
the county-level also raises important questions related to levels of analysis. For
example, county-level data almost certainly mask important sub-county variation in
both social vulnerability and migration. In addition, while macro-level contexts
undoubtedly create ecological ‘‘pushes’’ and ‘‘pulls’’ that encourage people to move
from one area to another, ultimately migration decisions are made at the microlevel. Our approach also does not parse out differential impacts on demographic
subgroups or the longitudinal nature of disaster and migration processes (we only
have a ‘‘snapshot’’ of a single period).
In light of these limitations, future research on the relationship between social
vulnerability and migration should consider qualitative methodologies and, better
yet, mixed-methods approaches that allow for triangulation. Further, because
migration decisions are made at the micro-level, but by actors embedded in
aggregate contexts, scholars should seek to employ multi-level techniques when
data allow. Future research should also consider how the relationship between social
vulnerability and post-disaster migration differs across demographic subgroups and
various regional and cultural contexts, as well as how social vulnerability influences
disaster-related population processes as they unfold over time (e.g., do these
relationships become more or less pronounced as time goes on?). This latter point is
particularly relevant when disasters are framed as processes rather than events.
In terms of public policy, we believe the SoVI (Cutter et al. 2003) provides an
important diagnostic tool for policymakers interested in identifying the factors that
place communities at differential risk to disasters, and that influence response and
recovery efforts in their aftermath. Policymakers should be cognizant of the fact that
susceptibility to disasters is determined not only by biophysical factors, but by the
social characteristics of communities as well. The SoVI measure—which can be
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applied at local, regional, national, and cross-national levels—provides policymakers with an accessible social science-based tool for assessing risk and targeting
disaster mitigation efforts, as well as anticipating which communities will face
particular obstacles to recovery in the wake of disaster. This approach also
encourages a more holistic conceptualization of disasters as inherently social
phenomena. In the case of the disaster studied here, we believe that many of the
well-documented problems related to the rebuilding and repopulating of New
Orleans in particular should be understood in light of its high level of social
vulnerability prior to the storm.
In the foreword of the ‘‘The Sociology of Katrina’’ Erikson states: ‘‘The only way
for us to ever acquire an understanding of Katrina is to come at it from many
different vantage points—to chip away at it…until all those fragments of
information and insight begin to form a picture’’ (2007, p. xviii). We believe this
study contributes to just that goal.
Acknowledgments We wish to thank the Editor of Population and Environment and the anonymous
reviewers for their helpful insights on earlier drafts of this manuscript. This is a revised version of papers
presented at the annual meetings of the Rural Sociological Society, Santa Clara, CA, 2007, and the
Southern Demographic Association, Birmingham, AL, 2007. This research was supported by funding
from the Minerals Management Service, U.S. Department of the Interior. Special thanks to Huizhen Niu
of the Agricultural Economics Geographic Information Systems (AEGIS) Lab in the Department of
Agricultural Economics and Agribusiness, LSU AgCenter, for her assistance in developing the maps
presented in this paper and used to diagnose spatial effects.
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